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Longitudinal Study Design

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

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Saul Mcleod, PhD

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A longitudinal study is a type of observational and correlational study that involves monitoring a population over an extended period of time. It allows researchers to track changes and developments in the subjects over time.

What is a Longitudinal Study?

In longitudinal studies, researchers do not manipulate any variables or interfere with the environment. Instead, they simply conduct observations on the same group of subjects over a period of time.

These research studies can last as short as a week or as long as multiple years or even decades. Unlike cross-sectional studies that measure a moment in time, longitudinal studies last beyond a single moment, enabling researchers to discover cause-and-effect relationships between variables.

They are beneficial for recognizing any changes, developments, or patterns in the characteristics of a target population. Longitudinal studies are often used in clinical and developmental psychology to study shifts in behaviors, thoughts, emotions, and trends throughout a lifetime.

For example, a longitudinal study could be used to examine the progress and well-being of children at critical age periods from birth to adulthood.

The Harvard Study of Adult Development is one of the longest longitudinal studies to date. Researchers in this study have followed the same men group for over 80 years, observing psychosocial variables and biological processes for healthy aging and well-being in late life (see Harvard Second Generation Study).

When designing longitudinal studies, researchers must consider issues like sample selection and generalizability, attrition and selectivity bias, effects of repeated exposure to measures, selection of appropriate statistical models, and coverage of the necessary timespan to capture the phenomena of interest.

Panel Study

• A panel study is a type of longitudinal study design in which the same set of participants are measured repeatedly over time.
• Data is gathered on the same variables of interest at each time point using consistent methods. This allows studying continuity and changes within individuals over time on the key measured constructs.
• Prominent examples include national panel surveys on topics like health, aging, employment, and economics. Panel studies are a type of prospective study .

Cohort Study

• A cohort study is a type of longitudinal study that samples a group of people sharing a common experience or demographic trait within a defined period, such as year of birth.
• Researchers observe a population based on the shared experience of a specific event, such as birth, geographic location, or historical experience. These studies are typically used among medical researchers.
• Cohorts are identified and selected at a starting point (e.g. birth, starting school, entering a job field) and followed forward in time. 
• As they age, data is collected on cohort subgroups to determine their differing trajectories. For example, investigating how health outcomes diverge for groups born in 1950s, 1960s, and 1970s.
• Cohort studies do not require the same individuals to be assessed over time; they just require representation from the cohort.

Retrospective Study

• In a retrospective study , researchers either collect data on events that have already occurred or use existing data that already exists in databases, medical records, or interviews to gain insights about a population.
• Appropriate when prospectively following participants from the past starting point is infeasible or unethical. For example, studying early origins of diseases emerging later in life.
• Retrospective studies efficiently provide a “snapshot summary” of the past in relation to present status. However, quality concerns with retrospective data make careful interpretation necessary when inferring causality. Memory biases and selective retention influence quality of retrospective data.

Allows researchers to look at changes over time

Because longitudinal studies observe variables over extended periods of time, researchers can use their data to study developmental shifts and understand how certain things change as we age.

High validation

Since objectives and rules for long-term studies are established before data collection, these studies are authentic and have high levels of validity.

Eliminates recall bias

Recall bias occurs when participants do not remember past events accurately or omit details from previous experiences.

Flexibility

The variables in longitudinal studies can change throughout the study. Even if the study was created to study a specific pattern or characteristic, the data collection could show new data points or relationships that are unique and worth investigating further.

Limitations

Costly and time-consuming.

Longitudinal studies can take months or years to complete, rendering them expensive and time-consuming. Because of this, researchers tend to have difficulty recruiting participants, leading to smaller sample sizes.

Large sample size needed

Longitudinal studies tend to be challenging to conduct because large samples are needed for any relationships or patterns to be meaningful. Researchers are unable to generate results if there is not enough data.

Participants tend to drop out

Not only is it a struggle to recruit participants, but subjects also tend to leave or drop out of the study due to various reasons such as illness, relocation, or a lack of motivation to complete the full study.

This tendency is known as selective attrition and can threaten the validity of an experiment. For this reason, researchers using this approach typically recruit many participants, expecting a substantial number to drop out before the end.

Report bias is possible

Longitudinal studies will sometimes rely on surveys and questionnaires, which could result in inaccurate reporting as there is no way to verify the information presented.

• Data were collected for each child at three-time points: at 11 months after adoption, at 4.5 years of age and at 10.5 years of age. The first two sets of results showed that the adoptees were behind the non-institutionalised group however by 10.5 years old there was no difference between the two groups. The Romanian orphans had caught up with the children raised in normal Canadian families.
• The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents (Marques Pais-Ribeiro, & Lopez, 2011)
• The correlation between dieting behavior and the development of bulimia nervosa (Stice et al., 1998)
• The stress of educational bottlenecks negatively impacting students’ wellbeing (Cruwys, Greenaway, & Haslam, 2015)
• The effects of job insecurity on psychological health and withdrawal (Sidney & Schaufeli, 1995)
• The relationship between loneliness, health, and mortality in adults aged 50 years and over (Luo et al., 2012)
• The influence of parental attachment and parental control on early onset of alcohol consumption in adolescence (Van der Vorst et al., 2006)
• The relationship between religion and health outcomes in medical rehabilitation patients (Fitchett et al., 1999)

Goals of Longitudinal Data and Longitudinal Research

The objectives of longitudinal data collection and research as outlined by Baltes and Nesselroade (1979):

• Identify intraindividual change : Examine changes at the individual level over time, including long-term trends or short-term fluctuations. Requires multiple measurements and individual-level analysis.
• Identify interindividual differences in intraindividual change : Evaluate whether changes vary across individuals and relate that to other variables. Requires repeated measures for multiple individuals plus relevant covariates.
• Analyze interrelationships in change : Study how two or more processes unfold and influence each other over time. Requires longitudinal data on multiple variables and appropriate statistical models.
• Analyze causes of intraindividual change: This objective refers to identifying factors or mechanisms that explain changes within individuals over time. For example, a researcher might want to understand what drives a person’s mood fluctuations over days or weeks. Or what leads to systematic gains or losses in one’s cognitive abilities across the lifespan.
• Analyze causes of interindividual differences in intraindividual change : Identify mechanisms that explain within-person changes and differences in changes across people. Requires repeated data on outcomes and covariates for multiple individuals plus dynamic statistical models.

How to Perform a Longitudinal Study

When beginning to develop your longitudinal study, you must first decide if you want to collect your own data or use data that has already been gathered.

Using already collected data will save you time, but it will be more restricted and limited than collecting it yourself. When collecting your own data, you can choose to conduct either a retrospective or prospective study .

In a retrospective study, you are collecting data on events that have already occurred. You can examine historical information, such as medical records, in order to understand the past. In a prospective study, on the other hand, you are collecting data in real-time. Prospective studies are more common for psychology research.

Once you determine the type of longitudinal study you will conduct, you then must determine how, when, where, and on whom the data will be collected.

A standardized study design is vital for efficiently measuring a population. Once a study design is created, researchers must maintain the same study procedures over time to uphold the validity of the observation.

A schedule should be maintained, complete results should be recorded with each observation, and observer variability should be minimized.

Researchers must observe each subject under the same conditions to compare them. In this type of study design, each subject is the control.

Methodological Considerations

Important methodological considerations include testing measurement invariance of constructs across time, appropriately handling missing data, and using accelerated longitudinal designs that sample different age cohorts over overlapping time periods.

Testing measurement invariance

Testing measurement invariance involves evaluating whether the same construct is being measured in a consistent, comparable way across multiple time points in longitudinal research.

This includes assessing configural, metric, and scalar invariance through confirmatory factor analytic approaches. Ensuring invariance gives more confidence when drawing inferences about change over time.

Missing data

Missing data can occur during initial sampling if certain groups are underrepresented or fail to respond.

Attrition over time is the main source – participants dropping out for various reasons. The consequences of missing data are reduced statistical power and potential bias if dropout is nonrandom.

Handling missing data appropriately in longitudinal studies is critical to reducing bias and maintaining power.

It is important to minimize attrition by tracking participants, keeping contact info up to date, engaging them, and providing incentives over time.

Techniques like maximum likelihood estimation and multiple imputation are better alternatives to older methods like listwise deletion. Assumptions about missing data mechanisms (e.g., missing at random) shape the analytic approaches taken.

Accelerated longitudinal designs

Accelerated longitudinal designs purposefully create missing data across age groups.

Accelerated longitudinal designs strategically sample different age cohorts at overlapping periods. For example, assessing 6th, 7th, and 8th graders at yearly intervals would cover 6-8th grade development over a 3-year study rather than following a single cohort over that timespan.

This increases the speed and cost-efficiency of longitudinal data collection and enables the examination of age/cohort effects. Appropriate multilevel statistical models are required to analyze the resulting complex data structure.

In addition to those considerations, optimizing the time lags between measurements, maximizing participant retention, and thoughtfully selecting analysis models that align with the research questions and hypotheses are also vital in ensuring robust longitudinal research.

So, careful methodology is key throughout the design and analysis process when working with repeated-measures data.

Cohort effects

A cohort refers to a group born in the same year or time period. Cohort effects occur when different cohorts show differing trajectories over time.

Cohort effects can bias results if not accounted for, especially in accelerated longitudinal designs which assume cohort equivalence.

Detecting cohort effects is important but can be challenging as they are confounded with age and time of measurement effects.

Cohort effects can also interfere with estimating other effects like retest effects. This happens because comparing groups to estimate retest effects relies on cohort equivalence.

Overall, researchers need to test for and control cohort effects which could otherwise lead to invalid conclusions. Careful study design and analysis is required.

Retest effects

Retest effects refer to gains in performance that occur when the same or similar test is administered on multiple occasions.

For example, familiarity with test items and procedures may allow participants to improve their scores over repeated testing above and beyond any true change.

Specific examples include:

• Memory tests – Learning which items tend to be tested can artificially boost performance over time
• Cognitive tests – Becoming familiar with the testing format and particular test demands can inflate scores
• Survey measures – Remembering previous responses can bias future responses over multiple administrations
• Interviews – Comfort with the interviewer and process can lead to increased openness or recall

To estimate retest effects, performance of retested groups is compared to groups taking the test for the first time. Any divergence suggests inflated scores due to retesting rather than true change.

If unchecked in analysis, retest gains can be confused with genuine intraindividual change or interindividual differences.

This undermines the validity of longitudinal findings. Thus, testing and controlling for retest effects are important considerations in longitudinal research.

Data Analysis

Longitudinal data involves repeated assessments of variables over time, allowing researchers to study stability and change. A variety of statistical models can be used to analyze longitudinal data, including latent growth curve models, multilevel models, latent state-trait models, and more.

Latent growth curve models allow researchers to model intraindividual change over time. For example, one could estimate parameters related to individuals’ baseline levels on some measure, linear or nonlinear trajectory of change over time, and variability around those growth parameters. These models require multiple waves of longitudinal data to estimate.

Multilevel models are useful for hierarchically structured longitudinal data, with lower-level observations (e.g., repeated measures) nested within higher-level units (e.g., individuals). They can model variability both within and between individuals over time.

Latent state-trait models decompose the covariance between longitudinal measurements into time-invariant trait factors, time-specific state residuals, and error variance. This allows separating stable between-person differences from within-person fluctuations.

There are many other techniques like latent transition analysis, event history analysis, and time series models that have specialized uses for particular research questions with longitudinal data. The choice of model depends on the hypotheses, timescale of measurements, age range covered, and other factors.

In general, these various statistical models allow investigation of important questions about developmental processes, change and stability over time, causal sequencing, and both between- and within-person sources of variability. However, researchers must carefully consider the assumptions behind the models they choose.

Longitudinal vs. Cross-Sectional Studies

Longitudinal studies and cross-sectional studies are two different observational study designs where researchers analyze a target population without manipulating or altering the natural environment in which the participants exist.

Yet, there are apparent differences between these two forms of study. One key difference is that longitudinal studies follow the same sample of people over an extended period of time, while cross-sectional studies look at the characteristics of different populations at a given moment in time.

Longitudinal studies tend to require more time and resources, but they can be used to detect cause-and-effect relationships and establish patterns among subjects.

On the other hand, cross-sectional studies tend to be cheaper and quicker but can only provide a snapshot of a point in time and thus cannot identify cause-and-effect relationships.

Both studies are valuable for psychologists to observe a given group of subjects. Still, cross-sectional studies are more beneficial for establishing associations between variables, while longitudinal studies are necessary for examining a sequence of events.

1. Are longitudinal studies qualitative or quantitative?

Longitudinal studies are typically quantitative. They collect numerical data from the same subjects to track changes and identify trends or patterns.

However, they can also include qualitative elements, such as interviews or observations, to provide a more in-depth understanding of the studied phenomena.

2. What’s the difference between a longitudinal and case-control study?

Case-control studies compare groups retrospectively and cannot be used to calculate relative risk. Longitudinal studies, though, can compare groups either retrospectively or prospectively.

In case-control studies, researchers study one group of people who have developed a particular condition and compare them to a sample without the disease.

Case-control studies look at a single subject or a single case, whereas longitudinal studies are conducted on a large group of subjects.

3. Does a longitudinal study have a control group?

Yes, a longitudinal study can have a control group . In such a design, one group (the experimental group) would receive treatment or intervention, while the other group (the control group) would not.

Both groups would then be observed over time to see if there are differences in outcomes, which could suggest an effect of the treatment or intervention.

However, not all longitudinal studies have a control group, especially observational ones and not testing a specific intervention.

Baltes, P. B., & Nesselroade, J. R. (1979). History and rationale of longitudinal research. In J. R. Nesselroade & P. B. Baltes (Eds.), (pp. 1–39). Academic Press.

Cook, N. R., & Ware, J. H. (1983). Design and analysis methods for longitudinal research. Annual review of public health , 4, 1–23.

Fitchett, G., Rybarczyk, B., Demarco, G., & Nicholas, J.J. (1999). The role of religion in medical rehabilitation outcomes: A longitudinal study. Rehabilitation Psychology, 44, 333-353.

Harvard Second Generation Study. (n.d.). Harvard Second Generation Grant and Glueck Study. Harvard Study of Adult Development. Retrieved from https://www.adultdevelopmentstudy.org.

Le Mare, L., & Audet, K. (2006). A longitudinal study of the physical growth and health of postinstitutionalized Romanian adoptees. Pediatrics & child health, 11 (2), 85-91.

Luo, Y., Hawkley, L. C., Waite, L. J., & Cacioppo, J. T. (2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social science & medicine (1982), 74 (6), 907–914.

Marques, S. C., Pais-Ribeiro, J. L., & Lopez, S. J. (2011). The role of positive psychology constructs in predicting mental health and academic achievement in children and adolescents: A two-year longitudinal study. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 12( 6), 1049–1062.

Sidney W.A. Dekker & Wilmar B. Schaufeli (1995) The effects of job insecurity on psychological health and withdrawal: A longitudinal study, Australian Psychologist, 30: 1,57-63.

Stice, E., Mazotti, L., Krebs, M., & Martin, S. (1998). Predictors of adolescent dieting behaviors: A longitudinal study. Psychology of Addictive Behaviors, 12 (3), 195–205.

Tegan Cruwys, Katharine H Greenaway & S Alexander Haslam (2015) The Stress of Passing Through an Educational Bottleneck: A Longitudinal Study of Psychology Honours Students, Australian Psychologist, 50:5, 372-381.

Thomas, L. (2020). What is a longitudinal study? Scribbr. Retrieved from https://www.scribbr.com/methodology/longitudinal-study/

Van der Vorst, H., Engels, R. C. M. E., Meeus, W., & Deković, M. (2006). Parental attachment, parental control, and early development of alcohol use: A longitudinal study. Psychology of Addictive Behaviors, 20 (2), 107–116.

Further Information

• Schaie, K. W. (2005). What can we learn from longitudinal studies of adult development?. Research in human development, 2 (3), 133-158.
• Caruana, E. J., Roman, M., Hernández-Sánchez, J., & Solli, P. (2015). Longitudinal studies. Journal of thoracic disease, 7 (11), E537.

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What is a Longitudinal Study?: Definition and Explanation

In this article, we’ll cover all you need to know about longitudinal research. 

Let’s take a closer look at the defining characteristics of longitudinal studies, review the pros and cons of this type of research, and share some useful longitudinal study examples. 

Content Index

What is a longitudinal study?

Types of longitudinal studies, advantages and disadvantages of conducting longitudinal surveys.

• Longitudinal studies vs. cross-sectional studies

Types of surveys that use a longitudinal study

Longitudinal study examples.

A longitudinal study is a research conducted over an extended period of time. It is mostly used in medical research and other areas like psychology or sociology. 

When using this method, a longitudinal survey can pay off with actionable insights when you have the time to engage in a long-term research project.

Longitudinal studies often use surveys to collect data that is either qualitative or quantitative. Additionally, in a longitudinal study, a survey creator does not interfere with survey participants. Instead, the survey creator distributes questionnaires over time to observe changes in participants, behaviors, or attitudes. 

Many medical studies are longitudinal; researchers note and collect data from the same subjects over what can be many years.

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Longitudinal studies are versatile, repeatable, and able to account for quantitative and qualitative data . Consider the three major types of longitudinal studies for future research:

Panel study: A panel survey involves a sample of people from a more significant population and is conducted at specified intervals for a more extended period. 

One of the panel study’s essential features is that researchers collect data from the same sample at different points in time. Most panel studies are designed for quantitative analysis , though they may also be used to collect qualitative data and unit of analysis .

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Cohort Study: A cohort study samples a cohort (a group of people who typically experience the same event at a given point in time). Medical researchers tend to conduct cohort studies. Some might consider clinical trials similar to cohort studies. 

In cohort studies, researchers merely observe participants without intervention, unlike clinical trials in which participants undergo tests.

Retrospective study: A retrospective study uses already existing data, collected during previously conducted research with similar methodology and variables. 

While doing a retrospective study, the researcher uses an administrative database, pre-existing medical records, or one-to-one interviews.

As we’ve demonstrated, a longitudinal study is useful in science, medicine, and many other fields. There are many reasons why a researcher might want to conduct a longitudinal study. One of the essential reasons is, longitudinal studies give unique insights that many other types of research fail to provide. 

Advantages of longitudinal studies

• Greater validation: For a long-term study to be successful, objectives and rules must be established from the beginning. As it is a long-term study, its authenticity is verified in advance, which makes the results have a high level of validity.
• Unique data: Most research studies collect short-term data to determine the cause and effect of what is being investigated. Longitudinal surveys follow the same principles but the data collection period is different. Long-term relationships cannot be discovered in a short-term investigation, but short-term relationships can be monitored in a long-term investigation.
• Allow identifying trends: Whether in medicine, psychology, or sociology, the long-term design of a longitudinal study enables trends and relationships to be found within the data collected in real time. The previous data can be applied to know future results and have great discoveries.
• Longitudinal surveys are flexible: Although a longitudinal study can be created to study a specific data point, the data collected can show unforeseen patterns or relationships that can be significant. Because this is a long-term study, the researchers have a flexibility that is not possible with other research formats.

Additional data points can be collected to study unexpected findings, allowing changes to be made to the survey based on the approach that is detected.

Disadvantages of longitudinal studies

• Research time The main disadvantage of longitudinal surveys is that long-term research is more likely to give unpredictable results. For example, if the same person is not found to update the study, the research cannot be carried out. It may also take several years before the data begins to produce observable patterns or relationships that can be monitored.
• An unpredictability factor is always present It must be taken into account that the initial sample can be lost over time. Because longitudinal studies involve the same subjects over a long period of time, what happens to them outside of data collection times can influence the data that is collected in the future. Some people may decide to stop participating in the research. Others may not be in the correct demographics for research. If these factors are not included in the initial research design, they could affect the findings that are generated.
• Large samples are needed for the investigation to be meaningful To develop relationships or patterns, a large amount of data must be collected and extracted to generate results.
• Higher costs Without a doubt, the longitudinal survey is more complex and expensive. Being a long-term form of research, the costs of the study will span years or decades, compared to other forms of research that can be completed in a smaller fraction of the time.

Longitudinal studies vs. Cross-sectional studies

Longitudinal studies are often confused with cross-sectional studies. Unlike longitudinal studies, where the research variables can change during a study, a cross-sectional study observes a single instance with all variables remaining the same throughout the study. A longitudinal study may follow up on a cross-sectional study to investigate the relationship between the variables more thoroughly.

The design of the study is highly dependent on the nature of the research questions . Whenever a researcher decides to collect data by surveying their participants, what matters most are the questions that are asked in the survey.

Knowing what information a study should gather is the first step in determining how to conduct the rest of the study. 

With a longitudinal study, you can measure and compare various business and branding aspects by deploying surveys. Some of the classic examples of surveys that researchers can use for longitudinal studies are:

Market trends and brand awareness: Use a market research survey and marketing survey to identify market trends and develop brand awareness. Through these surveys, businesses or organizations can learn what customers want and what they will discard. This study can be carried over time to assess market trends repeatedly, as they are volatile and tend to change constantly.

Product feedback: If a business or brand launches a new product and wants to know how it is faring with consumers, product feedback surveys are a great option. Collect feedback from customers about the product over an extended time. Once you’ve collected the data, it’s time to put that feedback into practice and improve your offerings.

Customer satisfaction: Customer satisfaction surveys help an organization get to know the level of satisfaction or dissatisfaction among its customers. A longitudinal survey can gain feedback from new and regular customers for as long as you’d like to collect it, so it’s useful whether you’re starting a business or hoping to make some improvements to an established brand.

Employee engagement: When you check in regularly over time with a longitudinal survey, you’ll get a big-picture perspective of your company culture. Find out whether employees feel comfortable collaborating with colleagues and gauge their level of motivation at work.

Now that you know the basics of how researchers use longitudinal studies across several disciplines let’s review the following examples:

Example 1: Identical twins

Consider a study conducted to understand the similarities or differences between identical twins who are brought up together versus identical twins who were not. The study observes several variables, but the constant is that all the participants have identical twins.

In this case, researchers would want to observe these participants from childhood to adulthood, to understand how growing up in different environments influences traits, habits, and personality.

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Over many years, researchers can see both sets of twins as they experience life without intervention. Because the participants share the same genes, it is assumed that any differences are due to environmental analysis , but only an attentive study can conclude those assumptions.

Example 2: Violence and video games

A group of researchers is studying whether there is a link between violence and video game usage. They collect a large sample of participants for the study. To reduce the amount of interference with their natural habits, these individuals come from a population that already plays video games. The age group is focused on teenagers (13-19 years old).

The researchers record how prone to violence participants in the sample are at the onset. It creates a baseline for later comparisons. Now the researchers will give a log to each participant to keep track of how much and how frequently they play and how much time they spend playing video games. This study can go on for months or years. During this time, the researcher can compare video game-playing behaviors with violent tendencies. Thus, investigating whether there is a link between violence and video games.

Conducting a longitudinal study with surveys is straightforward and applicable to almost any discipline. With our survey software you can easily start your own survey today. 

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Longitudinal studies

Edward joseph caruana, marius roman, jules hernández-sánchez, piergiorgio solli, introduction.

Longitudinal studies employ continuous or repeated measures to follow particular individuals over prolonged periods of time—often years or decades. They are generally observational in nature, with quantitative and/or qualitative data being collected on any combination of exposures and outcomes, without any external influenced being applied. This study type is particularly useful for evaluating the relationship between risk factors and the development of disease, and the outcomes of treatments over different lengths of time. Similarly, because data is collected for given individuals within a predefined group, appropriate statistical testing may be employed to analyse change over time for the group as a whole, or for particular individuals ( 1 ).

In contrast, cross-sectional analysis is another study type that may analyse multiple variables at a given instance, but provides no information with regards to the influence of time on the variables measured—being static by its very nature. It is thus generally less valid for examining cause-and-effect relationships. Nonetheless, cross-sectional studies require less time to be set up, and may be considered for preliminary evaluations of association prior to embarking on cumbersome longitudinal-type studies.

Longitudinal study designs

Longitudinal research may take numerous different forms. They are generally observational, however, may also be experimental. Some of these are briefly discussed below:

• Repeated cross-sectional studies where study participants are largely or entirely different on each sampling occasion;
• Cohort panels wherein some or all individuals in a defined population with similar exposures or outcomes are considered over time;
• Representative panels where data is regularly collected for a random sample of a population;
• Linked panels wherein data collected for other purposes is tapped and linked to form individual-specific datasets.
• Retrospective studies are designed after at least some participants have already experienced events that are of relevance; with data for potential exposures in the identified cohort being collected and examined retrospectively.

Advantages and disadvantages

Longitudinal cohort studies, particularly when conducted prospectively in their pure form, offer numerous benefits. These include:

• The ability to identify and relate events to particular exposures, and to further define these exposures with regards to presence, timing and chronicity;
• Establishing sequence of events;
• Following change over time in particular individuals within the cohort;
• Excluding recall bias in participants, by collecting data prospectively and prior to knowledge of a possible subsequent event occurring, and;
• Ability to correct for the “cohort effect”—that is allowing for analysis of the individual time components of cohort (range of birth dates), period (current time), and age (at point of measurement)—and to account for the impact of each individually.

Disadvantages

Numerous challenges are implicit in the study design; particularly by virtue of this occurring over protracted time periods. We briefly consider the below:

• Incomplete and interrupted follow-up of individuals, and attrition with loss to follow-up over time; with notable threats to the representative nature of the dynamic sample if potentially resulting from a particular exposure or occurrence that is of relevance;
• Difficulty in separation of the reciprocal impact of exposure and outcome, in view of the potentiation of one by the other; and particularly wherein the induction period between exposure and occurrence is prolonged;
• The potential for inaccuracy in conclusion if adopting statistical techniques that fail to account for the intra-individual correlation of measures, and;
• Generally-increased temporal and financial demands associated with this approach.

Embarking on a longitudinal study

Conducting longitudinal research is demanding in that it requires an appropriate infrastructure that is sufficiently robust to withstand the test of time, for the actual duration of the study. It is essential that the methods of data collection and recording are identical across the various study sites, as well as being standardised and consistent over time. Data must be classified according to the interval of measure, with all information pertaining to particular individuals also being linked by means of unique coding systems. Recording is facilitated, and accuracy increased, by adopting recognised classification systems for individual inputs ( 2 ).

Numerous variables are to be considered, and adequately controlled, when embarking on such a project. These include factors related the population being studied, and their environment; wherein stability in terms of geographical mobility and distribution, coupled with an ability to continue follow-up remotely in case of displacement, are key. It is furthermore essential to appropriately weigh the various measures, and classify these accordingly so as to facilitate the allocation effort at the data collection stage, and also guide the use of possibly limited funds ( 3 ). Additionally, the engagement and commitment of organisations contributing to the project is essential; and should be maintained and facilitated by means of regular training, communication and inclusion as possible.

The frequency and degree of sampling should vary according to the specific primary endpoints; and whether these are based primarily on absolute outcome or variation over time. Ethical and consent considerations are also specific to this type of research. All effort should be made to ensure maximal retention of participants; with exit interviews offering useful insight as to the reason for uncontrolled departures ( 3 ).

The Critical Appraisal Skills Programme (CASP) ( 4 ) offers a series of tools and checklists that are designed to facilitate the evaluation of scientific quality of given literature. This may be extrapolated to critically assess a proposed study design. Additional depth of quality assessment is available through the use of various tools developed alongside the Consolidated Standards of Reporting Trials (CONSORT) guidelines, including a structured 33-point checklist proposed by Tooth et al . in 2004 ( 5 ).

Following adequate design, the launch and implementation of longitudinal research projects may itself require a significant amount of time; particularly if being conducted at multiple remote sites. Time invested in this initial period will improve the accuracy of data eventually received, and contribute to the validity of the results. Regular monitoring of outcome measures, and focused review of any areas of concern is essential ( 3 ). These studies are dynamic, and necessitate regular updating of procedures and retraining of contributors, as dictated by events.

Statistical analyses

The statistical testing of longitudinal data necessitates the consideration of numerous factors. Central amongst these are (I) the linked nature of the data for an individual, despite separation in time; (II) the co-existence of fixed and dynamic variables; (III) potential for differences in time intervals between data instances, and (IV) the likely presence of missing data ( 6 ).

Commonly applied approaches ( 7 ) are discussed below: (I) univariate (ANOVA) and multivariate (MANOVA) analysis of variance is often adopted for longitudinal analysis. Note, in both cases, the assumption of equal interval lengths and normal distribution in all groups; and that only means are compared, sacrificing individual-specific data. (II) mixed-effect regression model (MRM) focuses specifically on individual change over time, whilst accounting for variation in the timing of repeated measures, and for missing or unequal data instances, and (III) generalised estimating equation (GEE) models that rely on the independence of individuals within the population to focus primarily on regression data ( 6 ).

With ever-growing computational abilities, the repertoire of statistical tests is ever expanding. In depth understanding and appropriate selection is increasingly more important to ensure meaningful results.

Common errors

Inaccuracies in the analysis of longitudinal research are rampant, and most commonly arise when repeated hypothesis testing is applied to the data, as it would for cross-sectional studies. This leads to an underutilisation of available data, an underestimation of variability, and an increased likelihood of type II statistical error (false negative) ( 8 ).

Example: the Framingham heart study

The mid-20 th century saw a steady increase in cardiovascular-associated morbidity and mortality after efforts in improving sanitation along with the introduction of penicillin in the 1940s resulted in a significant decline in communicable disease. A drive to identify the risk factors for cardiovascular disease gave birth to the Framingham Heart study in 1948 ( 9 ).

Numerous predisposing factors were postulated to align together to produce cardiovascular disease, with increasing age being considered a central determinant. These formed the basis for the hypothesis that underpinned this longitudinal study.

The Framingham study is widely recognised as the quintessential longitudinal study in the history of medical research. An original cohort of 5,209 subjects from Framingham, Massachusetts between the ages of 30 and 62 years of age was recruited and followed up for 20 years. A number of hypothesis were generated and described by Dawber et al . ( 10 ) in 1980 listing various presupposed risk factors such as increasing age, increased weight, tobacco smoking, elevated blood pressure, elevated blood cholesterol and decreased physical activity. It is largely quoted as a successful longitudinal study owing to the fact that a large proportion of the exposures chosen for analysis were indeed found to correlate closely with the development of cardiovascular disease.

A number of biases exist within the Framingham Heart Study. Firstly it was a study carried out in a single population in a single town, bringing into question the generalisability and applicability of this data to different groups. However, Framingham was sufficiently diverse both in ethnicity and socio-economic status to mitigate this bias to a degree. Despite the initial intent of random selection, they needed the addition of over 800 volunteers to reach the pre-defined target of 5,000 subjects thus reducing the randomisation. They also found that their cohort of patients was uncharacteristically healthy.

The Framingham Heart study has given us invaluable data pertaining to the incidence of cardiovascular disease and further confirming a number of risk factors. The success of this study was further potentiated by the absence of treatments or modifiers, such as statin therapy and anti-hypertensives. This has enabled this study to more clearly delineate the natural history of this complex disease process.

Conclusions

Longitudinal methods may provide a more comprehensive approach to research, that allows an understanding of the degree and direction of change over time. One should carefully consider the cost and time implications of embarking on such a project, whilst ensuring complete and proven clarity in design and process, particularly in view of the protracted nature of such an endeavour; and noting the peculiarities for consideration at the interpretation stage.

Acknowledgements

Conflicts of Interest: The authors have no conflicts of interest to declare.

What (Exactly) Is A Longitudinal Study?

A plain-language explanation & definition (with examples).

By: Derek Jansen (MBA) | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably feeling a bit overwhelmed by all the technical lingo that’s hitting you. If you’ve landed here, chances are one of these terms is “longitudinal study”, “longitudinal survey” or “longitudinal research”.

Worry not – in this post, we’ll explain exactly:

• What a longitudinal study is (and what the alternative is)
• What the main advantages of a longitudinal study are
• What the main disadvantages of a longitudinal study are
• Whether to use a longitudinal or cross-sectional study for your research

What is a longitudinal study?

A longitudinal study or a longitudinal survey (both of which make up longitudinal research) is a study where the same data are collected more than once,  at different points in time . The purpose of a longitudinal study is to assess not just  what  the data reveal at a fixed point in time, but to understand  how (and why) things change  over time.

Example: Longitudinal vs Cross-Sectional

Here are two examples – one of a longitudinal study and one of a cross-sectional study – to give you an idea of what these two approaches look like in the real world:

Longitudinal study: a study which assesses how a group of 13-year old children’s attitudes and perspectives towards income inequality evolve over a period of 5 years, with the same group of children surveyed each year, from 2020 (when they are all 13) until 2025 (when they are all 18).

Cross-sectional study: a study which assesses a group of teenagers’ attitudes and perspectives towards income equality at a single point in time. The teenagers are aged 13-18 years and the survey is undertaken in January 2020.

From this example, you can probably see that the topic of both studies is still broadly the same (teenagers’ views on income inequality), but the data produced could potentially be very different . This is because the longitudinal group’s views will be shaped by the events of the next five years, whereas the cross-sectional group all have a “2020 perspective”. 

Additionally, in the cross-sectional group, each age group (i.e. 13, 14, 15, 16, 17 and 18) are all different people (obviously!) with different life experiences – whereas, in the longitudinal group, each the data at each age point is generated by the same group of people (for example, John Doe will complete a survey at age 13, 14, 15, and so on). 

There are, of course, many other factors at play here and many other ways in which these two approaches differ – but we won’t go down that rabbit hole in this post.

What are the advantages of a longitudinal study?

Longitudinal studies and longitudinal surveys offer some major benefits over cross-sectional studies. Some of the main advantages are:

Patterns  – because longitudinal studies involve collecting data at multiple points in time from the same respondents, they allow you to identify emergent patterns across time that you’d never see if you used a cross-sectional approach. 

Order  – longitudinal studies reveal the order in which things happened, which helps a lot when you’re trying to understand causation. For example, if you’re trying to understand whether X causes Y or Y causes X, it’s essential to understand which one comes first (which a cross-sectional study cannot tell you).

Bias  – because longitudinal studies capture current data at multiple points in time, they are at lower risk of recall bias . In other words, there’s a lower chance that people will forget an event, or forget certain details about it, as they are only being asked to discuss current matters.

Need a helping hand?

What are the disadvantages of a longitudinal study?

As you’ve seen, longitudinal studies have some major strengths over cross-sectional studies. So why don’t we just use longitudinal studies for everything? Well, there are (naturally) some disadvantages to longitudinal studies as well.

Cost  – compared to cross-sectional studies, longitudinal studies are typically substantially more expensive to execute, as they require maintained effort over a long period of time.

Slow  – given the nature of a longitudinal study, it takes a lot longer to pull off than a cross-sectional study. This can be months, years or even decades. This makes them impractical for many types of research, especially dissertations and theses at Honours and Masters levels (where students have a predetermined timeline for their research)

Drop out  – because longitudinal studies often take place over many years, there is a very real risk that respondents drop out over the length of the study. This can happen for any number of reasons (for examples, people relocating, starting a family, a new job, etc) and can have a very detrimental effect on the study.

Which one should you use?

Choosing whether to use a longitudinal or cross-sectional study for your dissertation, thesis or research project requires a few considerations. Ultimately, your decision needs to be informed by your overall research aims, objectives and research questions (in other words, the nature of the research determines which approach you should use). But you also need to consider the practicalities. You should ask yourself the following:

• Do you really need a view of how data changes over time, or is a snapshot sufficient?
• Is your university flexible in terms of the timeline for your research?
• Do you have the budget and resources to undertake multiple surveys over time?
• Are you certain you’ll be able to secure respondents over a long period of time?

If your answer to any of these is no, you need to think carefully about the viability of a longitudinal study in your situation. Depending on your research objectives, a cross-sectional design might do the trick. If you’re unsure, speak to your research supervisor or connect with one of our friendly Grad Coaches .

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Cross-Sectional and Longitudinal Studies

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Cohort study ; Observational study ; Panel study ; Prevalence study ; Prospective study ; Retrospective study ; Transverse study

Cross-sectional study refers to a study where researchers observe and analyze data from a sample at a single point in time. In a longitudinal study, researchers conduct multiple observations over a period of time, ranging from weeks to many years.

Time is an important dimension in social scientific research. In other words, researchers are interested in both the prevalence and dynamic changes over time regarding a topic of interest. Cross-sectional studies measure the prevalence of social phenomena in a population at one point in time. Information derived from cross-sectional data is used to examine an association – for example, a relationship between physical activity and psychological well-being. However, cross-sectional studies cannot establish what is cause and what is effect, as several biases may arise due to the selection...

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Table of Content

What is a longitudinal study definition, types, and examples.

Is your long-term research strategy unclear? Learn how longitudinal studies decode complexity. Read on for insights.

Have you ever wondered why certain questions can only be answered by going back in time?

Imagine being able to predict the long-term success of a new marketing campaign—not necessarily just from the first click-through rates, but from tracking the customer journey month after month or even year after year. This might include tracking brand awareness , purchase behavior, and customer satisfaction at appropriate intervals. This is where longitudinal studies, like time travelers, enter the research world to decode change and development across individuals, populations, and entire societies.

In this blog, we not only define what longitudinal studies are but also explore the different types and find real-world examples that prove the advantage of this research approach. 

What is a Longitudinal Study?

A longitudinal study is a type of research where the scope of the study is observed and studied in the same set of people over a long period of time. This could be from a few weeks to many years.

They are most often found in different fields like health, economics, and medicine. They serve to give knowledge of the passage of events without knowing what is happening.

A company may conduct a study to observe how things change with time without interfering with what's happening. For example, an e-commerce company may ask the same questions to the same people every few months or years to determine whether the advertisement is working or whether more people are falling in love with their products.

Types of Longitudinal Studies

Cohort Studies  

Cohort studies follow specific groups, or cohorts, of individuals over time. These groups usually share a common characteristic, such as being born in the same year or living in the same area. Researchers observe how this group changes and develops, often focusing on the impact of certain exposures or events on their health, behavior, or other outcomes.

Key Points:

Selection: Participants are chosen based on a shared characteristic.

Focus: Studying the effects of exposures or experiences on the group.

Example: The Nurses' Health Study , a large prospective cohort study launched in 1976, has followed over 100,000 female nurses to investigate various risk factors for chronic diseases like heart disease, cancer, and dementia. By observing their health and lifestyle choices over decades, researchers have gained valuable insights into the long-term impact of different factors on health outcomes.

Panel Studies  

Panel studies involve collecting data from the same group of individuals at multiple time points. Unlike cohort studies, panel studies focus on the same people rather than forming groups based on shared characteristics. This allows researchers to examine individual-level changes over time. 

Selection: Representative sample of a larger population.

Focus: Observing general trends and changes within the sample.

Example: The American National Election Studies (ANES) is a long-running panel study that surveys a representative sample of the US population every two years. This allows researchers to track changes in public opinion on various political and social issues over time, revealing trends in voter preferences and societal attitudes.

Retrospective Studies

Retrospective studies look back in time to collect data on past events or behaviors. Researchers gather information from participants about their past experiences and then follow up with them to track outcomes. These studies are useful for investigating long-term effects or rare events. 

Data source: Existing records, medical charts, surveys, etc.

Focus: Analyzing past data to identify trends and associations.

Example: The Danish National Birth Cohort study utilizes existing data from national registries, following all individuals born in Denmark since 1996. Researchers can analyze their health records, educational attainment, and socioeconomic data to identify risk factors for various health conditions. By analyzing historical data over a long period, researchers can investigate the long-term consequences of early-life exposures on health outcomes later in life. This can inform preventative measures and interventions during critical developmental stages. 

Pros & Cons of Longitudinal Studies

Advantages of longitudinal studies, understanding change.

They can give some of the most valuable insights into the way people, population, or phenomena change over time, enabling a researcher to trace trends, patterns, and causal relations that would be invisible in a snapshot view.

Cause-and-effect Insights  

Longitudinal studies—though not conclusive—can add to the understanding of potential cause-and-effect relationships by tracing how changes in one variable herald changes in another.

Rare Events 

They can trace events that are rare and that, in a snapshot study, might not be seen. It provides data about rare occurrences.

Generalizability

Longitudinal studies can yield generalizable results depending on sample size and ways of selecting the sample.

Disadvantages of Longitudinal Studies

Time and resource intensive.

Conducting longitudinal studies can take years and usually requires hundreds of hours of time, resources, and sustained participant engagement. Thus, this is often the major barrier, particularly for long-term studies.

Participants who drop out of a study can also affect generalizability and introduce bias. The researcher will need to develop strategies to minimize attrition and control for possible biases.

Taking repeated data from the same participants can be expensive and will require a substantial amount of funding and logistical planning.

Delayed Results

Due to its extended duration, it may take years to notice meaningful changes and to obtain definitive results. This must be braved with challenges, while the research area requires an immediate solution.

Longitudinal studies have significant advantages in the study of change and development through time. However, there are substantial challenges in the design and conduct of such research that need to be pondered carefully.

Ways to Collect Longitudinal Study Data

When you're planning a study that follows people over time, you have to decide where to get your information from. There are two main options: using data that's already been collected by someone else or collecting your own data.

Using data from other sources means you can access information that's already been gathered by previous studies. This saves you time and money because you don't have to collect it yourself. But the downside is that you're limited to the information that was collected before, and it might not cover everything you're interested in.

If you can't find data that fits your study, you'll have to collect your own. This means gathering information yourself, which can make sure it's exactly what you need. The methods you use to collect data depend on the type of study you're doing. You can use live interviews, surveys, focus group discussions, etc, to collect data.

The key to getting good data is using the right tools to collect it. This helps you get the information quickly and accurately.

Use Cases of Longitudinal Studies

Here are some compelling use cases for longitudinal studies in consumer research:

Tracking Brand Loyalty and Customer Satisfaction

Panel Study: A company can recruit a representative sample of their customers and conduct regular surveys over time. This enables them to track changes in brand awareness, satisfaction levels, and purchase behavior. By seeing how these metrics change, companies can identify trends in customer loyalty, pinpoint areas for improvement, and measure the effectiveness of marketing campaigns.

Understanding Consumer Behavior and Preferences

Cohort Study: A company may focus on a specific customer segment defined by demographics, purchasing habits, or product usage. By following this cohort over time, they are able to observe how preferences, needs, and behaviors change with changing life stages, economic situations, or technological advancements. This helps companies adapt their products, services, and marketing strategies to stay relevant to their target audience.

Measuring the Long-term Impact of Marketing Initiatives

Prospective Cohort Study: A company can introduce a new marketing campaign and recruit a group of customers exposed to it. By following this cohort over time and comparing their behavior to a control group, the company can measure the long-term impact of the campaign on brand awareness, purchase behavior, and customer lifetime value. This allows for evidence-based decisions on future marketing investment and campaign optimization.

Identifying Emerging Trends and Predicting Future Needs

Retrospective and Panel Studies: With the help of historical customer data along with current trends, companies can identify emerging patterns in consumer behavior. This can inform product development, service innovation, and marketing strategies to stay ahead of the curve and anticipate future customer needs.

Personalization and Customer Relationship Management:

Longitudinal Data Gathering: A business that works on continuous data collection on customers' tastes and purchase history and their association with the brand can be better in personalized marketing messages, product recommendations, and other customer service interactions. This facilitates the building of more intimate relationships and, hence, better customer satisfaction and retention.

Monitoring Product Usage and User Engagement

Panel Study: Enlist a sample of users representing the population and monitor usage patterns over some period. This enables users to follow the frequency of use for specific features, how users engage with the product over time, and many other factors. This information can help in the product design, pointing out areas for improvement and tailoring the user experience.

Understanding User Needs and Preferences

Cohort Study: Focus on a specific subset of users defined by demographics, usage patterns, or some other dimension. By following this cohort over time, a company can monitor how their needs, preferences, and expectations are changing as they become experienced users. This information may help in product updates, feature development, and marketing strategies in order to match user needs.

Evaluating the Long-Term Effectiveness of Product Updates

Prospective Cohort Study: Make a specific product update or feature available to one group of users and expose the control group to the product without receiving the update. By monitoring the changes in usage patterns, satisfaction levels, and task completion rates over time, a company may know how effective the update is and what should be improved.

Feature Adoption and User Behavior Trends

Retrospective and Panel Studies: The analysis of historical usage data and current trends brings the emerging patterns of interaction of users with the product. This would help drive future product roadmaps and feature development priorities and even predict emerging user needs.

Personalization and User Experience Optimization

Longitudinal Data Collection: Constant data gathering with respect to user behavior, preferences, and feedback allows for personal product recommendations, feature suggestions, and in-app guidance. This leads to a more engaged and customized user experience; hence, it increases the satisfaction and retention of users.

Longitudinal Studies with Decode Diary Studies

Decode provides a powerful DIY platform that makes it quite easy for researchers to conduct longitudinal studies, providing such rich tools to capture the experiences and behavior of users over long periods. Decode Diary studies allow researchers to conduct longitudinal research using longitudinal surveys, video responses, and image responses, improving the longitudinal research design.

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Frequently Asked Questions

What is a longitudinal study in research.

In a longitudinal study, researchers keep checking the same people over time to see if anything changes. These studies are like watching from the sidelines without trying to change anything, just to see how things naturally evolve.

What is the difference between longitudinal and cross-sectional studies?

The big difference is that cross-sectional studies talk to new groups of people every time they happen, while longitudinal studies stick with the same group of people and watch how they change over time.

What is an example of a longitudinal study?

A company tracking the career progression and performance of employees participating in a leadership development program over several years. By regularly collecting data on metrics like job performance, promotions, and job satisfaction, the company can evaluate the program's long-term impact on grooming future leaders and improving organizational success.

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• Chapter 7. Longitudinal studies

Clinical follow up studies

• Chapter 1. What is epidemiology?
• Chapter 2. Quantifying disease in populations
• Chapter 3. Comparing disease rates
• Chapter 4. Measurement error and bias
• Chapter 5. Planning and conducting a survey
• Chapter 6. Ecological studies
• Chapter 8. Case-control and cross sectional studies
• Chapter 9. Experimental studies
• Chapter 10. Screening
• Chapter 11. Outbreaks of disease
• Chapter 12. Reading epidemiological reports
• Chapter 13. Further reading

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Information

• Open access
• Published: 01 October 2022

Qualitative longitudinal research in health research: a method study

• Åsa Audulv 1 ,
• Elisabeth O. C. Hall 2 , 3 ,
• Åsa Kneck 4 ,
• Thomas Westergren 5 , 6 ,
• Liv Fegran 5 ,
• Mona Kyndi Pedersen 7 , 8 ,
• Hanne Aagaard 9 ,
• Kristianna Lund Dam 3 &
• Mette Spliid Ludvigsen 10 , 11  

BMC Medical Research Methodology volume  22 , Article number:  255 ( 2022 ) Cite this article

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Qualitative longitudinal research (QLR) comprises qualitative studies, with repeated data collection, that focus on the temporality (e.g., time and change) of a phenomenon. The use of QLR is increasing in health research since many topics within health involve change (e.g., progressive illness, rehabilitation). A method study can provide an insightful understanding of the use, trends and variations within this approach. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

This method study used an adapted scoping review design. Articles were eligible if they were written in English, published between 2017 and 2019, and reported results from qualitative data collected at different time points/time waves with the same sample or in the same setting. Articles were identified using EBSCOhost. Two independent reviewers performed the screening, selection and charting.

A total of 299 articles were included. There was great variation among the articles in the use of methodological traditions, type of data, length of data collection, and components of longitudinal data collection. However, the majority of articles represented large studies and were based on individual interview data. Approximately half of the articles self-identified as QLR studies or as following a QLR design, although slightly less than 20% of them included QLR method literature in their method sections.

Conclusions

QLR is often used in large complex studies. Some articles were thoroughly designed to capture time/change throughout the methodology, aim and data collection, while other articles included few elements of QLR. Longitudinal data collection includes several components, such as what entities are followed across time, the tempo of data collection, and to what extent the data collection is preplanned or adapted across time. Therefore, there are several practices and possibilities researchers should consider before starting a QLR project.

Peer Review reports

Health research is focused on areas and topics where time and change are relevant. For example, processes such as recovery or changes in health status. However, relating time and change can be complicated in research, as the representation of reality in research publications is often collected at one point in time and fixed in its presentation, although time and change are always present in human life and experiences. Qualitative longitudinal research (QLR; also called longitudinal qualitative research, LQR) has been developed to focus on subjective experiences of time or change using qualitative data materials (e.g., interviews, observations and/or text documents) collected across a time span with the same participants and/or in the same setting [ 1 , 2 ]. QLR within health research may have many benefits. Firstly, human experiences are not fixed and consistent, but changing and diverse, therefore people’s experiences in relation to a health phenomenon may be more comprehensively described by repeated interviews or observations over time. Secondly, experiences, behaviors, and social norms unfold over time. By using QLR, researchers can collect empirical data that represents not only recalled human conceptions but also serial and instant situations reflecting transitions, trajectories and changes in people’s health experiences, personal development or health care organizations [ 3 , 4 , 5 ].

Key features of QLR

Whether QLR is a methodological approach in its own right or a design element of a particular study within a traditional methodological approach (e.g., ethnography or grounded theory) is debated [ 1 , 6 ]. For example, Bennett et al. [ 7 ] describe QLR as untied to methodology, giving researchers the flexibility to develop a suitable design for each study. McCoy [ 6 ] suggests that epistemological and ontological standpoints from interpretative phenomenological analysis (IPA) align with QLR traditions, thus making longitudinal IPA a suitable methodology. Plano-Clark et al. [ 8 ] described how longitudinal qualitative elements can be used in mixed methods studies, thus creating longitudinal mixed methods. In contrast, several researchers have argued that QLR is an emerging methodology [ 1 , 5 , 9 , 10 ]. For example, Thomson et al. [ 9 ] have stated “What distinguishes longitudinal qualitative research is the deliberate way in which temporality is designed into the research process, making change a central focus of analytic attention” (p. 185). Tuthill et al. [ 5 ] concluded that some of the confusion might have arisen from the diversity of data collection methods and data materials used within QLR research. However, there are no investigations showing to what extent QLR studies use QLR as a distinct methodology versus using a longitudinal data collection as a more flexible design element in combination with other qualitative methodologies.

QLR research should focus on aspects of temporality, time and/or change [ 11 , 12 , 13 ]. The concepts of time and change are seen as inseparable since change is happening with the passing of time [ 13 ]. However, time can be conceptualized in different ways. Time is often understood from a chronological perspective, and is viewed as fixed, objective, continuous and measurable (e.g., clock time, duration of time). However, time can also be understood from within, as the experience of the passing of time and/or the perspective from the current moment into the constructed conception of a history or future. From this perspective, time is seen as fluid, meaning that events, contexts and understandings create a subjective experience of time and change. Both the chronological and fluid understanding of time influence QLR research [ 11 ]. Furthermore, there is a distinction between over-time, which constitutes a comparison of the difference between points in time, often with a focus on the latter point or destination, and through-time, which means following an aspect across time while trying to understand the change that occurs [ 11 ]. In this article, we will mostly use the concept of across time to include both perspectives.

Some authors assert that QLR studies should include a qualitative data collection with the same sample across time [ 11 , 13 ], whereas Thomson et al. [ 9 ] also suggest the possibility of returning to the same data collection site with the same or different participants. When a QLR study involves data collection in shorter engagements, such as serial interviews, these engagements are often referred to as data collection time points. Data collection in time waves relates to longer engagements, such as field work/observation periods. There is no clear-cut definition for the minimum time span of a QLR study; instead, the length of the data collection period must be decided based upon what processes or changes are the focus of the study [ 13 ].

Most literature describing QLR methods originates from the social sciences, where the approach has a long tradition [ 1 , 10 , 14 ]. In health research, one-time-data collection studies have been the norm within qualitative methods [ 15 ], although health research using QLR methods has increased in recent years [ 2 , 5 , 16 , 17 ]. However, collecting and managing longitudinal data has its own sets of challenges, especially regarding how to integrate perspectives of time and/or change in the data collection and subsequent analysis [ 1 ]. Therefore, a study of QLR articles from the health research literature can provide an insightful understanding of the use, trends and variations of how methods are used and how elements of time/change are integrated in QLR studies. This could, in turn, provide inspiration for using different possibilities of collecting data across time when using QLR in health research. The aim of this study was to map how QLR articles within the existing health research literature are designed to capture aspects of time and/or change.

More specifically, the research questions were:

What methodological approaches are described to inform QLR research?

What methodological references are used to inform QLR research?

How are longitudinal perspectives articulated in article aims?

How is longitudinal data collection conducted?

In this method study, we used an adapted scoping review method [ 18 , 19 , 20 ]. Method studies are research conducted on research studies to investigate how research design elements are applied across a field [ 21 ]. However, since there are no clear guidelines for method studies, they often use adapted versions of systematic reviews or scoping review methods [ 21 ]. The adaptations of the scoping review method consisted of 1) using a large subsample of studies (publications from a three-year period) instead of including all QLR articles published, and 2) not including grey literature. The reporting of this study was guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 20 , 22 ] (see Additional file 1 ). A (unpublished) protocol was developed by the research team during the spring of 2019.

Eligibility criteria

In line with method study recommendations [ 21 ], we decided to draw on a manageable subsample of published QLR research. Articles that were eligible for inclusion were health research primary studies written in English, published between 2017 and 2019, and with a longitudinal qualitative data collection. Our operating definition for qualitative longitudinal data collection was data collected at different time points (e.g., repeated interviews) or time waves (e.g., periods of field work) involving the same sample or conducted in the same setting(s). We intentionally selected a broad inclusion criterion for QLR since we wanted a wide variety of articles. The selected time period was chosen because the first QLR method article directed towards health research was published in 2013 [ 1 ] and during the following years the methodological resources for QLR increased [ 3 , 8 , 17 , 23 , 24 , 25 ], thus we could expect that researchers publishing QLR in 2017–2019 should be well-grounded in QLR methods. Further, we found that from 2012 to 2019 the rate of published QLR articles were steady at around 100 publications per year, so including those from a three-year period would give a sufficient number of articles (~ 300 articles) for providing an overview of the field. Published conference abstracts, protocols, articles describing methodological issues, review articles, and non-research articles (e.g., editorials) were excluded.

Search strategy

Relevant articles were identified through systematic searches in EBSCOhost, including biomedical and life science research and nursing and allied health literature. A librarian who specialized in systematic review searches developed and performed the searches, in collaboration with the author team (LF, TW & ÅA). In the search, the term “longitudinal” was combined with terms for qualitative research (for the search strategy see Additional file 2 ). The searches were conducted in the autumn of 2019 (last search 2019-09-10).

Study selection

All identified citations were imported into EndNote X9 ( www.endnote.com ) and further imported into Rayyan QCRI online software [ 26 ], and duplicates were removed. All titles and abstracts were screened against the eligibility criteria by two independent reviewers (ÅA & EH), and conflicting decisions were discussed until resolved. After discussions by the team, we decided to include articles published between 2017 and 2019, that selection alone included 350 records with diverse methods and designs. The full texts of articles that were eligible for inclusion were retrieved. In the next stage, two independent reviewers reviewed each full text article to make final decisions regarding inclusion (ÅA, EH, Julia Andersson). In total, disagreements occurred in 8% of the decisions, and were resolved through discussion. Critical appraisal was not assessed since the study aimed to describe the range of how QLR is applied and not aggregate research findings [ 21 , 22 ].

Data charting and analysis

A standardized charting form was developed in Excel (Excel 2016). The charting form was reviewed by the research team and pretested in two stages. The tests were performed to increase internal consistency and reduce the risk of bias. First, four articles were reviewed by all the reviewers, and modifications were made to the form and charting instructions. In the next stage, all reviewers used the charting form on four other articles, and the convergence in ratings was 88%. Since the convergence was under 90%, charting was performed in duplicate to reduce errors in the data. At the end of the charting process, the convergence among the reviewers was 95%. The charting was examined by the first author, who revised the charting in cases of differences.

Data items that were charted included 1) the article characteristics (e.g., authors, publication year, journal, country), 2) the aim and scope (e.g., phenomenon of interest, population, contexts), 3) the stated methodology and analysis method, 4) text describing the data collection (e.g., type of data material, number of participants, time frame of data collection, total amount of data material), and 5) the qualitative methodological references used in the methods section. Extracted text describing data collection could consist of a few sentences or several sections from the articles (and sometimes figures) concerning data collection practices, rational for time periods and research engagement in the field. This was later used to analyze how the longitudinal data collection was conducted and elements of longitudinal design. To categorize the qualitative methodology approaches, a framework from Cresswell [ 27 ] was used (including the categories for grounded theory, phenomenology, ethnography, case study and narrative research). Overall, data items needed to be explicitly stated in the articles in order to be charted. For example, an article was categorized as grounded theory if it explicitly stated “in this grounded theory study” but not if it referred to the literature by Glaser and Strauss without situating itself as a grounded theory study (See Additional file 3 for the full instructions for charting).

All charting forms were compiled into a single Microsoft Excel spreadsheet (see Supplementary files for an overview of the articles). Descriptive statistics with frequencies and percentages were calculated to summarize the data. Furthermore, an iterative coding process was used to group the articles and investigate patterns of, for example, research topics, words in the aims, or data collection practices. Alternative ways of grouping and presenting the data were discussed by the research team.

Search and selection

A total of 2179 titles and abstracts were screened against the eligibility criteria (see Fig.  1 ). The full text of one article could not be found and the article was excluded [ 28 ]. Fifty full text articles were excluded. Finally, 299 articles, representing 271 individual studies, were included in this study (see additional files 4 and 5 respectively for tables of excluded and included articles).

PRISMA diagram of study selection]

General characteristics and research areas of the included articles

The articles were published in many journals ( n  = 193), and 138 of these journals were represented with one article each. BMJ Open was the most prevalent journal ( n  = 11), followed by the Journal of Clinical Nursing ( n  = 8). Similarly, the articles represented many countries ( n  = 41) and all the continents; however, a large part of the studies originated from the US or UK ( n  = 71, 23.7% and n  = 70, 23.4%, respectively). The articles focused on the following types of populations: patients, families−/caregivers, health care providers, students, community members, or policy makers. Approximately 20% ( n  = 63, 21.1%) of the articles collected data from two or more of these types of population(s) (see Table  1 ).

Approximately half of the articles ( n  = 158, 52.8%) articulated being part of a larger research project. Of them, 95 described a project with both quantitative and qualitative methods. They represented either 1) a qualitative study embedded in an intervention, evaluation or implementation study ( n  = 66, 22.1%), 2) a longitudinal cohort study collecting both quantitative and qualitative material ( n  = 23, 7.7%), or 3) qualitative longitudinal material collected together with a cross sectional survey (n = 6, 2.0%). Forty-eight articles (16.1%) described belonging to a larger qualitative project presented in several research articles.

Methodological traditions

Approximately one-third ( n  = 109, 36.5%) of the included articles self-identified with one of the qualitative traditions recognized by Cresswell [ 27 ] (case study: n  = 36, 12.0%; phenomenology: n  = 35, 11.7%; grounded theory: n  = 22, 7.4%; ethnography: n  = 13, 4.3%; narrative method: n = 3, 1.0%). In nine articles, the authors described using a mix of two or more of these qualitative traditions. In addition, 19 articles (6.4%) self-identified as mixed methods research.

Every second article self-identified as having a qualitative longitudinal design ( n  = 156, 52.2%); either they self-identified as “a longitudinal qualitative study” or “using a longitudinal qualitative research design”. However, in some articles, this was stated in the title and/or abstract and nowhere else in the article. Fifty-two articles (17.4%) self-identified both as having a QLR design and following one of the methodological approaches (case study: n  = 8; phenomenology: n  = 23; grounded theory: n  = 9; ethnography: n  = 6; narrative method: n  = 2; mixed methods: n  = 4).

The other 143 articles used various terms to situate themselves in relation to a longitudinal design. Twenty-seven articles described themselves as a longitudinal study (9.0%) or a longitudinal study within a specific qualitative tradition (e.g., a longitudinal grounded theory study or a longitudinal mixed method study) ( n  = 64, 21.4%). Furthermore, 36 articles (12.0%) referred to using longitudinal data materials (e.g., longitudinal data or longitudinal interviews). Nine of the articles (3.0%) used the term longitudinal in relation to the data analysis or aim (e.g., the aim was to longitudinally describe), used terms such as serial or repeated in relation to the data collection design ( n  = 2, 0.7%), or did not use any term to address the longitudinal nature of their design ( n  = 5, 1.7%).

Use of methodological references

The mean number of qualitative method references in the methods sections was 3.7 (range 0 to 16), and 20 articles did not have any qualitative method reference in their methods sections. Footnote 1 Commonly used method references were generic books on qualitative methods, seminal works within qualitative traditions, and references specializing in qualitative analysis methods (see Table  2 ). It should be noted that some references were comprehensive books and thus could include sections about QLR without being focused on the QLR method. For example, Miles et al. [ 31 ] is all about analysis and coding and includes a chapter regarding analyzing change.

Only approximately 20% ( n  = 58) of the articles referred to the QLR method literature in their methods sections. Footnote 2 The mean number of QLR method references (counted for articles using such sources) was 1.7 (range 1 to 6). Most articles using the QLR method literature also used other qualitative methods literature (except two articles using one QLR literature reference each [ 39 , 40 ]). In total, 37 QLR method references were used, and 24 of the QLR method references were only referred to by one article each.

Longitudinal perspectives in article aims

In total, 231 (77.3%) articles had one or several terms related to time or change in their aims, whereas 68 articles (22.7%) had none. Over one hundred different words related to time or change were identified. Longitudinally oriented terms could focus on changes across time (process, trajectory, transition, pathway or journey), patterns of how something changed (maintenance, continuity, stability, shifts), or phenomena that by nature included change (learning or implementation). Other types of terms emphasized the data collection time period (e.g., over 6 months) or a specific changing situation (e.g., during pregnancy, through the intervention period, or moving into a nursing home). The most common terms used for the longitudinal perspective were change ( n  = 63), over time ( n  = 52), process ( n  = 36), transition ( n  = 24), implementation ( n  = 14), development ( n  = 13), and longitudinal (n = 13). Footnote 3

Furthermore, the articles varied in what ways their aims focused on time/change, e.g., the longitudinal perspectives in the aims (see Table  3 ). In 71 articles, the change across time was the phenomenon of interest of the article : for example, articles investigating the process of learning or trajectories of diseases. In contrast, 46 articles investigated change or factors impacting change in relation to a defined outcome : for example, articles investigating factors influencing participants continuing in a physical activity trial. The longitudinal perspective could also be embedded in an article’s context . In such cases, the focus of the article was on experiences that happened during a certain time frame or in a time-related context (e.g., described experiences of the patient-provider relationship during 6 months of rehabilitation).

Types of data and length of data collection

The QLR articles were often large and complex in their data collection methods. The median number of participants was 20 (range from one to 1366, the latter being an article with open-ended questions in questionnaires [ 46 ]). Most articles used individual interviews as the data material ( n  = 167, 55.9%) or a combination of data materials ( n  = 98, 32.8%) (e.g., interviews and observations, individual interviews and focus group interviews, or interviews and questionnaires). Forty-five articles (15.1%) presented quantitative and qualitative results. The median number of interviews was 46 (range three to 507), which is large in comparison to many qualitative studies. The observation materials were also comprehensive and could include several hundred hours of observations. Documents were often used as complementary material and included official documents, newspaper articles, diaries, and/or patient records.

The articles’ time spans Footnote 4 for data collection varied between a few days and over 20 years, with 60% of the articles’ time spans being 1 year or shorter ( n  = 180) (see Fig.  2 ). The variation in time spans might be explained by the different kinds of phenomena that were investigated. For example, Jensen et al. [ 47 ] investigated hospital care delivery and followed each participant, with observations lasting between four and 14 days. Smithbattle [ 48 ] described the housing trajectories of teen mothers, and collected data in seven waves over 28 years.

Number of articles in relation to the time span of data collection. The time span of data collection is given in months

Three components of longitudinal data collection

In the articles, the data collection was conducted in relation to three different longitudinal data collection components (see Table  4 ).

Entities followed across time

Four different types of entities were followed across time: 1) individuals, 2) individual cases or dyads, 3) groups, and 4) settings. Every second article ( n  = 170, 56.9%) followed individuals across time, thus following the same participants through the whole data collection period. In contrast, when individual cases were followed across time, the data collection was centered on the primary participants (e.g., people with progressive neurological conditions) who were followed over time, and secondary participants (e.g., family caregivers) might provide complementary data at several time points or only at one-time point. When settings were followed over time, the participating individuals were sometimes the same, and sometimes changed across the data collection period. Typical settings were hospital wards, hospitals, smaller communities or intervention trials. The type of collected data corresponded with what kind of entities were followed longitudinally. Individuals were often followed with serial interviews, whereas groups were commonly followed with focus group interviews complemented with individual interviews, observations and/or questionnaires. Overall, the lengths of data collection periods seemed to be chosen based upon expected changes in the chosen entities. For example, the articles following an intervention setting were structured around the intervention timeline, collecting data before, after and sometimes during the intervention.

Tempo of data collection

The data collection tempo differed among the articles (e.g., the frequency and mode of the data collection). Approximately half ( n  = 154, 51.5%) of the articles used serial time points, collecting data at several reoccurring but shorter sequences (e.g., through serial interviews or open-ended questions in questionnaires). When data were collected in time waves ( n  = 50, 16.7%), the periods of data collection were longer, usually including both interviews and observations; often, time waves included observations of a setting and/or interviews at the same location over several days or weeks.

When comparing the tempo with the type of entities, some patterns were detected (see Fig.  3 ). When individuals were followed, data were often collected at time points, mirroring the use of individual interviews and/or short observations. For research in settings, data were commonly collected in time waves (e.g., observation periods over a few weeks or months). In studies exploring settings across time, time waves were commonly used and combined several types of data, particularly from interviews and observations. Groups were the least common studied entity ( n  = 9, 3.0%), so the numbers should be interpreted with caution, but continuous data collection was used in five of the nine studies. The continuous data collection mode was, for example, collecting electronic diaries [ 62 ] or minutes from committee meetings during a time period [ 63 ].

Tempo of data collection in relation to entities followed over time

Preplanned or adapted data collection

A large majority ( n  = 224, 74.9%) of the articles used preplanned data collection (e.g., in preplanned data collection, all participants were followed across time according to the same data collection plan). For example, all participants were interviewed one, six and twelve months’ post-diagnosis. In contrast to the preplanned data collection approach, 44 articles had a participant-adapted data collection (14.7%), and participants were followed at different frequencies and/or over various lengths of time depending on each participant’s situation. Participant-adapted data collection was more common among articles following individuals or individual cases (see Fig.  4 ). To adapt the data collection to the participants, the researchers created strategies to reach participants when crucial events were happening. Eleven articles used a participant entry approach to data collection ( n  = 11, 6.7%), and the whole or parts of the data were independently sent in by participants in the form of diaries, questionnaires, or blogs. Another approach to data collection was using theoretical or analysis-driven ideas to guide the data collection ( n  = 19, 6.4%). In these articles, the analysis and data collection were conducted simultaneously, and ideas arising in the analysis could be followed up, for example, returning to some participants, recruiting participants with specific experiences, or collecting complementary types of data materials. This approach was most common in the articles following settings across time, which often included observations and interviews with different types of populations. Articles using theoretical or analysis driven data collection were not associated with grounded theory to a greater extent than the other articles in the sample (e.g., did not self-identify as grounded theory or referred to methodological literature within grounded theory traditions to a greater proportion).

Preplanned or adapted data collection in relation to entities followed over time

According to our results, some researchers used QLR as a methodological approach and other researchers used a longitudinal qualitative data collection without aiming to investigate change. Adding to the debate on whether QLR is a methodological approach in its own right or a design element in a particular study we suggest that the use of QLR can be described as layered (see Fig.  5 ). Namely, articles must fulfill several criteria in order to use QLR as a methodological approach, and that is done in some articles. In those articles QLR method references were used, the aim was to investigate change of a phenomenon and the longitudinal elements of the data collection were thoroughly integrated into the method section. On the other hand, some articles using a longitudinal qualitative data collection were just collecting data over time, without addressing time and/or change in the aim. These articles can still be interesting research studies with valuable results, but they are not using the full potential of QLR as a methodological approach. In all, around 40% of the articles had an aim that focused on describing or understanding change (either as phenomenon or outcome); but only about 24% of the articles set out to investigate change across time as their phenomenon of interest.

The QLR onion. The use of QLR design can be described as layered, where researchers use more or less elements of a QLR design. The two inmost layers represents articles using QLR as a methodological approach

Regarding methodological influences, about one-third of the articles self-identify with any of the traditional qualitative methodologies. Using a longitudinal qualitative data collection as an element integrated with another methodological tradition can therefore be seen as one way of working with longitudinal qualitative materials. In our results, the articles referring to methodologies other than QLR preferably used case study, phenomenology and grounded theory methodologies. This was surprising since Neale [ 10 ] identified ethnography, case studies and narrative methods as the main methodological influences on QLR. Our findings might mirror the profound impacts that phenomenology and grounded theory have had on the qualitative field of health research. Regarding phenomenology, the findings can also be influenced by more recent discussions of combining interpretative phenomenological analysis with QLR [ 6 ].

Half of the articles self-identified as QLR studies, but QLR method references were used in less than 20% of the identified articles. This is both surprising and troublesome since use of appropriate method literature might have supported researchers who were struggling with for example a large quantity of materials and complex analysis. A possible explanation for the lack of use of QLR method literature is that QLR as a methodological approach is not well known, and authors might not be aware that method literature exists. It is quite understandable that researchers can describe a qualitative project with longitudinal data collection as a qualitative longitudinal study, without being aware that QLR is a specific form of study. Balmer [ 64 ] described how their group conducted serial interviews with medical students over several years before they became aware of QLR as a method of study. Within our networks, we have met researchers with similar experiences. Likewise, peer reviewers and editorial boards might not be accustomed to evaluating QLR manuscripts. In our results, 138 journals published one article between 2017 and 2019, and that might not be enough for editorial boards and peer reviewers to develop knowledge to enable them to closely evaluate manuscripts with a QLR method.

In 2007, Holland and colleagues [ 65 ] mapped QLR in the UK and described the following four categories of QLR: 1) mixed methods approaches with a QLR component; 2) planned prospective longitudinal studies; 3) follow-up studies complementing a previous data collection with follow-up; and 4) evaluation studies. Examples of all these categories can be found among the articles in this method study; however, our results do paint a more complex picture. According to our results, Holland’s categories are not multi-exclusive. For example, studies with intentions to evaluate or implement practices often used a mixed methods design and were therefore eligible for both categories one and four described above. Additionally, regarding the follow-up studies, it was seldom clearly described if they were planned as a two-time-point study or if researchers had gained an opportunity to follow up on previous data collection. When we tried to categorize QLR articles according to the data collection design, we could not identify multi-exclusive categories. Instead, we identified the following three components of longitudinal data collection: 1) entities followed across time; 2) tempo; and 3) preplanned or adapted data collection approaches. However, the most common combination was preplanned studies that followed individuals longitudinally with three or more time points.

The use of QLR differs between disciplines [ 14 ]. Our results show some patterns for QLR within health research. Firstly, the QLR projects were large and complex; they often included several types of populations and various data materials, and were presented in several articles. Secondly, most studies focused upon the individual perspective, following individuals across time, and using individual interviews. Thirdly, the data collection periods varied, but 53% of the articles had a data collection period of 1 year or shorter. Finally, patients were the most prevalent population, even though topics varied greatly. Previously, two other reviews that focused on QLR in different parts of health research (e.g., nursing [ 4 ] and gerontology [ 66 ]) pointed in the same direction. For example, individual interviews or a combination of data materials were commonly used, and most studies were shorter than 1 year but a wide range existed [ 4 , 66 ].

Considerations when planning a QLR project

Based on our results, we argue that when health researchers plan a QLR study, they should reflect upon their perspective of time/change and decide what part change should play in their QLR study. If researchers decide that change should play the main role in their project, then they should aim to focus on change as the phenomenon of interest. However, in some research, change might be an important part of the plot, without having the main role, and change in relation to the outcomes might be a better perspective. In such studies, participants with change, no change or different kinds of change are compared to explore possible explanations for the change. In our results, change in relation to the outcomes was often used in relation to intervention studies where participants who reached a desired outcome were compared to individuals who did not. Furthermore, for some research studies, change is part of the context in which the research takes place. This can be the case when certain experiences happen during a period of change; for example, when the aim is to explore the experience of everyday life during rehabilitation after stroke. In such cases a longitudinal data collection could be advisable (e.g., repeated interviews often give a deep relationship between interviewer and participants as well as the possibility of gaining greater depth in interview answers during follow-up interviews [ 15 ]), but the study might not be called a QLR study since it does not focus upon change [ 13 ]. We suggest that researchers make informed decisions of what kind of longitudinal perspective they set out to investigate and are transparent with their sources of methodological inspiration.

We would argue that length of data collection period, type of entities, and data materials should be in accordance with the type of change/changing processes that a study focuses on. Individual change is important in health research, but researchers should also remember the possibility of investigating changes in families, working groups, organizations and wider communities. Using these types of entities were less common in our material and could probably grant new perspectives to many research topics within health. Similarly, using several types of data materials can complement the insights that individual interviews can give. A large majority of the articles in our results had a preplanned data collection. Participant-adapted data collection can be a way to work in alignment with a “time-as-fluid” conceptualization of time because the events of subjective importance to participants can be more in focus and participants (or other entities) change processes can differ substantially across cases. In studies with lengthy and spaced-out data collection periods and/or uncertainty in trajectories, researchers should consider participant-adapted or participant entry data collection. For example, some participants can be followed for longer periods and/or with more frequency.

Finally, researchers should consider how to best publish and disseminate their results. Many QLR projects are large, and the results are divided across several articles when they are published. In our results, 21 papers self-identified as a mixed methods project or as part of a larger mixed methods project, but most of these did not include quantitative data in the article. This raises the question of how to best divide a large research project into suitable pieces for publication. It is an evident risk that the more interesting aspects of a mixed methods project are lost when the qualitative and quantitative parts are analyzed and published separately. Similar risks occur, for example, when data have been collected from several types of populations but are then presented per population type (e.g., one article with patient data and another with caregiver data). During the work with our study, we also came across studies where data were collected longitudinally, but the results were divided into publications per time point. We do not argue that these examples are always wrong, there are situations when these practices are appropriate. However, it often appears that data have been divided without much consideration. Instead, we suggest a thematic approach to dividing projects into publications, crafting the individual publications around certain ideas or themes and thus using the data that is most suitable for the particular research question. Combining several types of data and/or several populations in an analysis across time is in fact what makes QLR an interesting approach.

Strengths and limitations

This method study intended to paint a broad picture regarding how longitudinal qualitative methods are used within the health research field by investigating 299 published articles. Method research is an emerging field, currently with limited methodological guidelines [ 21 ], therefore we used scoping review method to support this study. In accordance with scoping review method we did not use quality assessment as a criterion for inclusion [ 18 , 19 , 20 ]. This can be seen as a limitation because we made conclusions based upon a set of articles with varying quality. However, we believe that learning can be achieved by looking at both good and bad examples, and innovation may appear when looking beyond established knowledge, or assessing methods from different angles. It should also be noted that the results given in percentages hold no value for what procedures that are better or more in accordance with QLR, the percentages simply state how common a particular procedure was among the articles.

As described, the included articles showed much variation in the method descriptions. As the basis for our results, we have only charted explicitly written text from the articles, which might have led to an underestimation of some results. The researchers might have had a clearer rationale than described in the reports. Issues, such as word restrictions or the journal’s scope, could also have influenced the amount of detail that was provided. Similarly, when charting how articles drew on a traditional methodology, only data from the articles that clearly stated the methodologies they used (e.g., phenomenology) were charted. In some articles, literature choices or particular research strategies could implicitly indicate that the researchers had been inspired by certain methodologies (e.g., referring to grounded theory literature and describing the use of simultaneous data collection and analysis could indicate that the researchers were influenced by grounded theory), but these were not charted as using a particular methodological tradition. We used the articles’ aims and objectives/research questions to investigate their longitudinal perspectives. However, as researchers have different writing styles, information regarding the longitudinal perspectives could have been described in surrounding text rather than in the aim, which might have led to an underestimation of the longitudinal perspectives.

The experience and diversity of the research team in our study was a strength. The nine authors on the team represent ten universities and three countries, and have extensive experience in different types of qualitative research, QLR and review methods. The different level of experiences with QLR within the team (some authors have worked with QLR in several projects and others have qualitative experience but no experience in QLR) resulted in interesting discussions that helped drive the project forward. These experiences have been useful for understanding the field.

Based on a method study of 299 articles, we can conclude that QLR in health research articles published between 2017 and 2019 often contain comprehensive complex studies with a large variation in topics. Some research was thoroughly designed to capture time/change throughout the methodology, focus and data collection, while other articles included a few elements of QLR. Longitudinal data collection included several components, such as what entities were followed across time, the tempo of data collection, and to what extent the data collection was preplanned or adapted across time. In sum, health researchers need to be considerate and make informed choices when designing QLR projects. Further research should delve deeper into what kind of research questions go well with QLR and investigate the best practice examples of presenting QLR findings.

Availability of data and materials

The datasets used and analyzed in this current study are available in supplementary file  6 .

Qualitative method references were defined as a journal article or book with a title that indicated an aim to guide researchers in qualitative research methods and/or research theories. Primary studies, theoretical works related to the articles’ research topics, protocols, and quantitative method literature were excluded. References written in a language other than English was also excluded since the authors could not evaluate their content.

QLR method references were defined as a journal article or book that 1) focused on qualitative methodological questions, 2) used terms such as ‘longitudinal’ or ‘time’ in the title so it was evident that the focus was on longitudinal qualitative research. Referring to another original QLR study was not counted as using QLR method literature.

Words were charted depending on their word stem, e.g., change, changes and changing were all charted as change.

It should be noted that here time span refers to the data collection related to each participant or case. Researchers could collect data for 2 years but follow each participant for 6 months.

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Acknowledgments

The authors wish to acknowledge Ellen Sejersted, librarian at the University of Agder, Kristiansand, Norway, who conducted the literature searches and Julia Andersson, research assistant at the Department of Nursing, Umeå University, Sweden, who supported the data management and took part in the initial screening phases of the project.

Open access funding provided by Umea University. This project was conducted within the authors’ positions and did not receive any specific funding.

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ÅA conceived the study. ÅA, EH, TW, LF, MKP, HA, and MSL designed the study. ÅA, TW, and LF were involved in literature searches together with the librarian. ÅA and EH performed the screening of the articles. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) took part in the data charting. ÅA performed the data analysis and discussed the preliminary results with the rest of the team. ÅA wrote the 1st manuscript draft, and ÅK, MSL and EH edited. All authors (ÅA, EH, TW, LF, ÅK, MKP, KLD, HA, MSL) contributed to editing the 2nd draft. MSL and LF provided overall supervision. All authors read and approved the final manuscript.

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All authors represent the nursing discipline, but their research topics differ. ÅA and ÅK have previously worked together with QLR method development. ÅA, EH, TW, LF, MKP, HA, KLD and MSL work together in the Nordic research group PRANSIT, focusing on nursing topics connected to transition theory using a systematic review method, preferably meta synthesis. All authors have extensive experience with qualitative research but various experience with QLR.

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Additional file 1..

PRISMA-ScR checklist.

Additional file 2.

Data base searches.

Additional file 3.

 Guidelines for data charting

Additional file 4.

List of excluded articles

Additional file 5.

Table of included articles (author(s), year of publication, reference, country, aims and research questions, methodology, type of data material, length of data collection period, number of participants)

Additional file 6.

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Audulv, Å., Hall, E.O.C., Kneck, Å. et al. Qualitative longitudinal research in health research: a method study. BMC Med Res Methodol 22 , 255 (2022). https://doi.org/10.1186/s12874-022-01732-4

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The value of longitudinal studies during the early years

Longitudinal studies — research that tracks large numbers of people over time (see Box 1) — are an essential element of putting data to work for the early years [1] .

Tracking children through early childhood and beyond presents three key opportunities for strengthening our understanding of early childhood, providing evidence of:

Why the early years matter: Longitudinal studies allow us to understand the lifelong effects of early childhood experiences and development. 

What matters during the early years: Longitudinal studies allow us to understand the different factors — including educational, emotional, material and genetic — associated with healthy development and the origins of inequalities.

What works in supporting families during the early years: Longitudinal studies allow us to evaluate the longer-term impacts of support provided to families. 

What are longitudinal studies? 

A longitudinal study follows the same people repeatedly over time. 

One form of longitudinal study of particular importance for the early years is birth cohort studies , which follow a group of people born at the same point in time at the very beginning of life and follows them throughout their lives. 

Longitudinal studies can collect a broad range of information about participants’ lives, including information about physical and mental health, social and emotional development, cognitive development and academic achievement, behaviour and attitudes, and employment, income and poverty. 

By collecting information from the same people over time, longitudinal studies provide uniquely rich evidence bases for understanding how people develop and why differences (and inequalities) between people exist. They help us understand how different aspects of our lives interact with each other to affect outcomes. For example, the association between poverty and mental health. 

For further information on longitudinal studies, such as data collection methods and the strengths and weaknesses of longitudinal data, see: https://learning.closer.ac.uk/learning-modules/introduction/

The UK has the largest and longest-running collection of longitudinal studies in the world [2]. These include nationally representative birth cohort studies, including the Millennium Cohort Study, locality-focussed studies, such as Born in Bradford, and longitudinal studies that look at specific aspects of the early years, for example the effects of early childhood education and care (see Figure 1).

These studies have provided a unique window into the importance of the early years, identifying associations between early childhood experiences and development, and outcomes through adulthood. The Dunedin Study, which has followed over 1,000 people since birth for over 50 years, provides perhaps the clearest evidence of the lifelong opportunities presented by promoting healthy development in early childhood [3]. The study also highlights the long-run risks of adverse early experiences, such as socioeconomic disadvantage or maltreatment, which are associated with an elevated risk of mental and physical ill health during adulthood [4]. Evidence from UK birth cohort studies supports these findings, demonstrating the important influence early childhood development has on later outcomes, including educational attainment at the end of secondary school and economic and social outcomes in midlife, such as obesity [5]. 

This evidence of lifelong associations should not be cause for fatalism. We know that associations between early skills and later outcomes decrease in magnitude as people grow older. Most important is learning from the studies about what factors are associated with good outcomes and providing timely support that helps address the challenges families with young children face. 

Longitudinal studies have promoted an increasingly sophisticated understanding of what matters in supporting healthy development in the early years. For example, they have shown us:

Positive early social and emotional development is critical for life-chances: The Dunedin study found that children who had developed strong self-control at age three were more likely to have better health, to be financially secure and less likely to be convicted of a criminal offence—even with childhood IQ and family social class taken into account [6]. Recent analysis in the UK has found a clear correlation between cognitive and socio-emotional development, suggesting a ‘double disadvantage’ for children who have high emotional and behavioural problems [7]. 

Inequalities emerge early: The Millennium Cohort Study shows large gaps in cognitive and socio-emotional development among children at age 3 [8]. Inequalities in development are visible by sex, ethnicity, family socioeconomic circumstances, household structure and maternal mental health. A significant proportion of inequalities in development at age 3 can be traced back to inequalities in the educational, emotional and material environments young children are raised in, explaining over 45% of the inequalities in socio-emotional development [9]. Comparing the Millennium Cohort Study (2000–02) and Study or Early Education and Development (SEED, 2010–12) cohorts, socio-economic inequalities in early cognition and socio-emotional development have not changed significantly [10]. One of the strongest associations between early circumstances and inequalities in development remains family income. Children in the poorest 10% of families rank 31 percentiles lower in cognition and 24 percentiles higher in emotional and behavioural difficulties [11]. While the direct and indirect effects of family income on children’s outcomes are complex, the current context of increasing rates of poverty among families with young children is of concern [12]. 

‘What parents do is more important than who parents are’ [13]: The Effective Provision of Pre-School Education (EPPE) study found that, for all children, the quality of the home learning environment was more important for young children’s development than parental occupation, education or income. Analysis of the Millennium Cohort Study has found that the quality of relationships between parents and children are a significant source of variation explaining differences in emotional and behavioural difficulties in children at age 3 [14]. The most recent SEED wave found that higher scores for the home learning environment – which includes activities like reading and play – and warmth of the parent-child relationship were associated with better outcomes on all Early Years Foundation Stage Profile measures [15].   

Parental mental health is closely linked to early childhood inequalities: Analysis of Millennium Cohort Study data shows that children whose mothers have high levels of psychological distress score significantly lower on cognitive tests at age three and are more likely to report socio-emotional and behavioural difficulties [16]. In analysis to understand inequalities in the parent-child relationship, the largest differences are observed in relation to maternal psychological distress, with ‘far lower’ levels of closeness and higher levels of conflict among mothers with high levels of psychological distress. The Born in Bradford study found that while up to 40% of pregnant mums report low mood, very few cases are reported in health data systems. The study also found significant inequalities among ethnic groups, with Pakistani women at greater risk of mental ill health, but half as likely to have a diagnosis recorded with their GP than White British women [17].  

The transformative potential of early childhood education and care: The Perry Preschool Study in the US, which began in the 1960s, identified short- and long-term effects of high-quality preschool education for young children living in poverty, with better high school education outcomes and better rates of employment at age 40 [18]. In the UK, the EPPE study found that pre-school experience enhanced children’s all-round development, with high-quality provision combined with longer duration having the strongest effect on development [19]. Subsequent research of the EPPE cohort found sustained effects on educational outcomes, with those who attended early years education having a greater likelihood of achieving more than 5 GCSEs at grade A-C – with this effect twice as large for children whose mothers had low educational qualifications compared with the whole sample [20]. Recent evidence from SEED has painted a more mixed picture, with some poorer social and emotional outcomes associated with formal early education, especially for young children in group provision for a high number of hours from the age of two [21], providing evidence that the quality and quantity of formal early education are important considerations [22].

The insights provided by longitudinal studies have led to tangible changes in policy and practice to support families with young children. For example, Born In Bradford data found that less than 10% of eligible pregnant women were taking a Vitamin D supplement, with many unaware of the supplement’s importance. Born in Bradford worked with clinicians to promote awareness of and access to Vitamin D supplements during ultrasound appointments, resulting in 97% of women remembering being offered supplements and 87% taking up the offer [23]. Data from the Millennium Cohort Study demonstrated the substantial educational disadvantage of being born at the end of the academic year. This insight led to changes in admissions policy so that parents of children born in late summer could decide which year their child should enter school, depending on the child’s needs.

Challenges of longitudinal studies

While longitudinal studies provide rich insights into the early years, they do face particular challenges and limitations [24]. Longitudinal studies — particularly nationally representative birth cohort studies — are complex and costly. Available administrative data places limitations on sampling of families with young children: identification of nationally representative samples is not currently practicable before children reach nine months of age, limiting our understanding of pregnancy and the earliest months of a baby’s life. Likewise, available data and approaches to sampling and data collection have resulted in “less often heard” populations being under-represented, including babies born into disadvantaged and ethnic minority families — limiting our understanding of these groups’ experiences. 

One specific limitation of existing studies has been a comparatively high focus on mothers rather than fathers, with particular challenges in including non-resident parents. Ensuring longitudinal studies reflect the diversity and complexity of early childhood experiences and development is an ongoing project. The Early Life Cohort Feasibility Study is testing the feasibility of a new nationally representative UK-wide study of babies, with a specific focus on maximising participation of traditionally ‘less often heard’ populations [25].    

Understanding early childhood today:

The children of the 2020s study.

2022 marks an important milestone in longitudinal studies and the early years, with the launch of the first new nationally representative birth cohort study in England for more than two decades: the Children of the 2020s study [26].

What is the Children of the 2020s study?

The study is a nationally representative birth cohort study of babies born in England. Drawing from HMRC Child Benefit records, approximately 8,500 families have been invited to take part, comprising babies born in September, October and November 2021. The sample will include boosted representation of babies from the most disadvantaged backgrounds. 

The study will include five waves of data collection starting when the cohort of children are 9 months and completing when they are age 5, with the potential for the survey to be extended beyond the age of five, subject to funding. Face-to-face interviews will be part of this, taking place when the cohort children are nine months old and three years old. 

Data collected will look at child development, neighbourhood and family context, family structure, health and mental health, the home learning environment, and formal and informal childcare provision and preschool education. The study also links to both parent and baby Department for Education and NHS digital health records enabling researchers to draw on official information, with parents’ consent.

The study is housed in the Centre for Longitudinal Studies (CLS) at University College London (UCL) and will be led by UCL researchers in partnership with Ipsos and the universities of Cambridge and Oxford and Birkbeck, University of London. The study has been commissioned and funded by the Department for Education (DfE).

Her Royal Highness, The Duchess of Cambridge meets with Professor Alissa Goodman, CLS director, Professor Pasco Fearon, director of the Children of the 2020s study, on a visit to the CLS, 5 October 2021.

What makes the study so exciting?

Part of what makes the Children of the 2020s study so exciting is its timing: coming over 20 years since the last nationally representative study in England, it will provide evidence about early childhood following a period of extraordinary societal and technological change. Since the Millennium Cohort Study, digital technology has become a ubiquitous part of life, with most young children today having access to internet-connected devices [27]. 

The study also comes following what has been an incredibly difficult time for many families with young children, with the study providing invaluable evidence following the COVID-19 pandemic and through the cost of living crisis. As such, the study will provide insights into what may become the “new normal” following the pandemic, such as increased parental working from home, reduced attendance in formal pre-school education, and greater reliance on digital service delivery [28]. The critical need to better understand the experiences of today’s young children is underscored by emerging evidence that the proportion of children achieving a ‘good level of development’ in 2020/21 has fallen 13 percentage points since 2018/19 [29].

“The landmark Children of the 2020s study will illustrate the importance of the first five years and provide insights into the most critical aspects of early childhood, as well as the factors which support or hinder positive lifelong outcomes.”

What new questions will the study seek to answer?

Critical to the study’s usefulness is its comparability with previous birth cohort studies. Questions have been aligned with past and existing studies, enabling researchers to understand, for example, how inequalities in young children’s development are changing over time.

“This will be an in-depth study of the wide range of factors that affect children’s development and education in the early years, including the home environment, nurseries and preschool, the neighbourhood, early years services and the broader social and economic circumstances of the family. We want to understand how these factors impact children’s social, cognitive, and early language development, their mental health and readiness for school.”

But the Children of the 2020s will also explore new topics for research. The study will collect information on how parents and young children use technology, with the potential to strengthen the evidence base on how technology affects children’s development and experiences, including parent-child relationships. It will collect more information about fathers and different family forms than previous studies. And it will also collect more information from families about their use of services, with the potential to better inform policymakers about the accessibility and effectiveness of services in meeting the needs of families with young children.

What new methods will the study employ?

One novel aspect of the Children of the 2020s study is its use of an innovative smartphone app called BabySteps. Whereas many previous birth cohort studies have only been able to collect data on an annual basis, BabySteps will allow, at low cost, the research team to collect data more frequently. Participating families will be asked to complete a monthly questionnaire using the app, and have the option to record a range of information — including photos and videos — about their child’s day-to-day experiences. It is hoped that this approach will allow for a more detailed and nuanced understanding of early childhood development, such as language acquisition, and the impacts of aspects of their home environments.

The study also plans to include early years professionals who work with children in the cohort to record their experiences through an app called Teacher Tapp. This has the potential to provide new insights into what quality early education and care looks like.

You can learn more about the Children of the 2020s study at the study’s webpage: https://cls.ucl.ac.uk/cls-studies/children-of-the-2020s-study/

[1] Putting data to work for the early years is one of the six areas of opportunity in the early years identified in our Big Change Starts Small report.

[2] Park, A. and Rainsberry, M. (2020). Introduction to longitudinal Studies. CLOSER.

[3] Belsky, J., Caspi, A., Moffitt, T. E., and Poulton, R. (2020). The Origins of You: How Childhood Shapes Later Life. Harvard University Press.

[4] Centre for Early Childhood. (2021). Big Change Starts Small. Royal Foundation.

[5] Drawing on the Millennium Cohort Study and 1970 British Cohort Survey: Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[6] Belsky, J., Caspi, A., Moffitt, T. E., and Poulton, R. (2020). The Origins of You: How Childhood Shapes Later Life. Harvard University Press.

[7] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[8] Drawing on the Millennium Cohort Study: Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[11] Sylva, K., Melhuish, E., Sammon, P., Siraj-Blatchford, I. and Taggart, B. (2004). Technical Paper 12. The Final Report: Effective Pre-School Education. London: UCL Institute of Education, p14.

[12] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[13] Melhuish, E. C. and Gardiner, J. (2020). Study of early education and development (SEED): Impact study on early education use and child outcomes up to age five years. Department for Education.

[14] Cattan, S., Fitzsimons, E., Goodman, A., Phimister, A., Ploubidis, G. B., and Wertz, J. (2022). Early childhood inequalities. IFS Deaton Review of Inequalities. IFS.

[15] https://borninbradford.nhs.uk/wp-content/uploads/Key-Findings_FINAL_Designed.pdf

[16] https://image.highscope.org/wp-content/uploads/2018/11/16053615/perry-preschool-summary-40.pdf

[17] Sylva, K., Melhuish, E., Sammon, P., Siraj-Blatchford, I. and Taggart, B. (2004). Technical Paper 12. The Final Report: Effective Pre-School Education. London: UCL Institute of Education.

[18] Cattan, S., Crawford, C., Dearden, L. (2014). The economic effects of pre-school education and quality. London: IFS.

[19] Melhuish, E. C. and Gardiner, J. (2020). Study of early education and development (SEED): Impact study on early education use and child outcomes up to age five years. Department for Education.

[20] Archer, N. and Oppenheim, C. (2021). The role of early childhood education and care in shaping life chances. Nuffield Foundation.

[21] See https://learning.closer.ac.uk/learning-modules/introduction/what-can-longitudinal-studies-show-us/weaknesses-of-longitudinal-studies/

[22] See https://cls.ucl.ac.uk/cls-studies/early-life-cohort-feasibility-study/

[23] The author would like to thank Pasco Fearon for his time in speaking about the study, which forms the basis of this section.

[24] Batcheler, R., Ireland, E., Oppenheim, C., Rehill, J. (2022). Time for parents. Nuffield Foundation.

[25] Oppenheim, C. (Forthcoming). Bringing up the next generation: from research to policy. Nuffield Foundation.

[26] Tracey, L., Bowyer-Crane, C., Bonetti, S., Nielsen, D., D’Apice, K. and Compton, S. (2022). The impact of the Covid-19 pandemic on children’s socio-emotional wellbeing and attainment during the reception year. Education Endowment Foundation.

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What is a Longitudinal Study? Definition, Types & Examples

Kate williams.

16 February 2024

Table Of Contents

What is a Longitudinal Study?

• Types of Longitudinal Studies?

Pros and Cons of Longitudinal Research Design

Examples of longitudinal surveys.

Sonia was conflicted. A few months ago, a survey from a grocery delivery app had asked her if she preferred normal eggs or the free-range ones.

She was financially stressed and couldn’t afford to pay more for free-range eggs, so she picked the normal ones.

But last night, she had watched a popular documentary on Netflix about how hens were treated in cages and now felt much more strongly about wanting to buy free-range eggs.

There was no way for Sonia to communicate this new preference to her grocery delivery app.

But that’s the thing about consumer trends. They are constantly shifting, and one survey taken years ago is not going to give you an accurate picture of the shifts in trends.

That’s why your business needs to understand what a longitudinal study is.

At times, a one-off survey simply isn’t enough to give you the data you need. If you need to observe certain trends, behaviors, or preferences over time, you can use a longitudinal study.

The simplest way to understand what is a longitudinal study is to think of it as a survey taken over time. The passing of time could influence the responses of the same person to the same question. Like Sonia, her preferences for eggs changed since she watched the documentary. That’s the kind of thing that longitudinal research design measures.

As for a formal definition, a longitudinal study is a research method that involves repeated observations of the same variable (e.g. a set of people) over some time. The observations over a period of time might be undertaken in the form of an online survey. It can be tremendously useful in a variety of fields to be able to observe behavior or trends over time.

Longitudinal studies are used in fields like:

• Clinical psychology to measure a patient’s thoughts over time
• Market research to observe consumer trends
• Political polling and sociology, observing life events and societal shifts over time
• Longitudinal research design is also used in medicine to discover predictors of certain diseases

We are dealing with nuanced changes over time here, and surveys excel at capturing these shifts in attitudes, behaviors, and experiences. Unlike one-time snapshots, surveys repeated over time enable you to track trends and understand how variables evolve. Plus, it is cost-effective and flexible in terms of reach!

For instance, SurveySparrow’s Recurring Surveys let you schedule and automate the entire process.

With this feature, you can share periodic surveys at any frequency that you set. Also, give a slight nudge to those silent respondents over a friendly reminder via email. The best part? The platform’s conversational surveys reap a higher response rate.

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Types of Longitudinal Studies

When talking about what is a longitudinal study, we cannot go without also discussing the types of longitudinal research design. There are different studies based on your needs. When you understand all three types of longitudinal studies, you’ll be able to pick out the one that’s best suited to your needs.

Panel Study

When we want to find out trends in a larger population, we often use a sample size to survey. A panel study simply observes that sample size over time. By doing so, panel studies can identify cultural shifts and new trends in a larger population.

Panel studies are designed for quantitative analysis. Through the data from online surveys, you can identify common patterns in the responses from your sample (which remain the same over time). A comprehensive dashboard will help you make informed decisions.

But what’s the need to visualize?

In panel studies, the same set of people must be studied over time. If you pick a different sample, variations in individual preferences could skew your results.

Observing the same set of people can make sure that what you’re observing is a change over time. Visualizing the change over time will give you a clear idea of the trends and patterns, resulting in informed and effective decision-making.

Cohort Study

A longitudinal cohort study is one in which we study people who share a single characteristic over a period of time. Cohort studies are regularly conducted by medical researchers to ascertain the effects of a new drug or the symptoms of a disease.

In cohort studies, the behaviors of the selected group of people are observed over time to find patterns and trends. Often, these studies can go on for years. They can also be particularly useful for ascertaining consumer trends if you’re trying to research consumers with a specific common characteristic. An example of such a study would be observing the choice of cereal for kids who go to Sunshine Elementary School over time.

If you’re confused between panel studies and cohort studies, don’t worry. The one key difference between cohort studies and panel studies is that the same set of people has to be observed in the latter. In cohort studies, you can pick a different sample of the same demographic to study over time.

Retrospective Study

A retrospective longitudinal study is when you take pre-existing data from previous online surveys and other research. The objective here is to put your results in a larger timeline and observe the variation in results over time. What makes retrospective studies longitudinal is simply the fact that they’re aimed at revealing trends over time.

When understanding what is a longitudinal study, it’ll be well worth your while to look into retrospective studies. For your company, retrospective longitudinal studies can reveal crucial insights without you having to spend a single dime. Since these studies depend on existing data, they not only don’t cost much themselves but also improve the returns from your earlier research efforts.

How can retrospective longitudinal studies be useful to you? Let’s assume, for example, that you conduct an employee engagement survey every year. If your organization has done these surveys for the past 10 years, you now have more than enough material to conduct a retrospective study. You can then find out how employee engagement at your company has varied over time.

Like with every research method , longitudinal studies have their advantages and disadvantages. While trying to understand what is a longitudinal study, it is important to get the particular ways in which they’re useful, and situations in which they’re not.  Let’s go over some of the major pros and cons of longitudinal surveys.

Advantages of Longitudinal Studies

• Rigorous Insights : A one-off online survey, no matter how well designed, is only so rigorous. Even though the results are often useful, sometimes you need more rigor in your surveys. A longitudinal survey, by observing respondents over time, can offer more rigorous results.
• Long-term Data : When thinking about what is a longitudinal study, it is crucial to understand that it is best used for a specific type of data collection. When you need to understand trends over the longer term, longitudinal studies are best suited to that task.
• Discover Trends : Most companies, in one way or another, rely on trends they estimate will be relevant in the future. Longitudinal studies can be great at finding out those trends and capitalizing on them before the competition.
• Open To Surprises : When designing an online survey, it is very tough to allow for surprises. Mostly, you get what you ask for. With longitudinal surveys, you’re allowing for the possibility that you might spot patterns you didn’t imagine could exist. Longitudinal studies are more flexible in that regard and allow us to discover the unexpected.

Disadvantages of Longitudinal Studies

• Higher Costs : Because longitudinal research needs to be conducted over time, and in some cases with the same set of people, they end up being costlier than one-off surveys. From conducting the observations to analyzing the data, it can add up financially. Using a cost-effective online survey tool like Surveysparrow can be one way to reduce costs.
• More Demanding : One of the biggest challenges you can face while conducting a survey is to get enough respondents. Even for normal online surveys, it can be tough to get people to take your survey. Longitudinal surveys are far more demanding, so it is unlikely that anyone will participate without strong incentives.
• Unpredictability : While unpredictability can sometimes be a good thing, at times it can also lead the whole exercise astray. The success of a longitudinal study depends not just on the resources you invest in it, but also on the respondents who have to participate in a long-term commitment. Things can go wrong when respondents are suddenly unavailable. That’s why there’s always an element of unpredictability with longitudinal surveys.
• Time-Consuming : Unlike simple online surveys, you don’t get the results instantly with longitudinal surveys. They require a certain vision, and you have to be patient enough to see it through to get your desired results.

Longitudinal surveys have been used by researchers and businesses for a long time now, so there is no dearth of examples. Let’s walk through a few of them so you can better understand what is a longitudinal survey.

Australia’s ‘45 and Up’ Survey

There is no better example to understand what longitudinal research is than the 45 and Up study being conducted in Australia. It aims to understand healthy aging and has 250,000 participants who are aged 45 or older. The idea is to get a better idea of Australians’ health as they age.

Such a study needed to be a longitudinal survey since you can only understand the effects of aging en masse by considering the results over time. The results from this study are being used in areas like cardiovascular research and preventable hospitalizations.

Smoking and Lung Cancer

To understand the effects of smoking, you need to be able to assess its consequences over time. The British Doctors Study, which ran from 1951 to 2001, yielded results that strongly indicated the link between smoking and lung cancer. If not for longitudinal research methods, we might never have known.

Even though the research was first published in 1956, the study went on for almost half a century after that. When thinking about what is a longitudinal study, we must also consider that these studies give results while they’re ongoing. Conclusively proving the link between smoking and cancer required a robust, longitudinal survey.

Growing Up In Ireland

Started in 2006, Growing Up In Ireland is a longitudinal study conducted by the Irish government to understand what children’s life looks like in different age brackets. One cohort that the study started following at 9 years of age is now 23. The long-term study can yield interesting results by following a set of children throughout their childhood.

The thing to remember when thinking about what a longitudinal study is is that they can have broad objectives. You can go in without really knowing what you’re trying to find and what that might lead to. You can then use the surprises along the way to generate actionable insights.

Wrapping Up

If you started out wondering what is a longitudinal study, we hope that we’ve addressed that question and more in this article. If you want to create a longitudinal survey, don’t forget to first plan out your survey. A retrospective study, like we just talked about, can also be a great solution to your problems.

Here at SurveySparrow, we love surveys of all kinds. For certain types of questions, you need to conduct longitudinal surveys, and we’re here to support you through the process. With our online templates and intuitive UI, conducting a longitudinal survey will be much easier.

What we love about recurring surveys is the surprising results they can yield. That is really what drives us at Surveysparrow, that you might find something in the results you didn’t expect, and it might change the course of your company for the better.

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What Is a Case Study?

Weighing the pros and cons of this method of research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Cara Lustik is a fact-checker and copywriter.

Verywell / Colleen Tighe

• Pros and Cons

What Types of Case Studies Are Out There?

Where do you find data for a case study, how do i write a psychology case study.

A case study is an in-depth study of one person, group, or event. In a case study, nearly every aspect of the subject's life and history is analyzed to seek patterns and causes of behavior. Case studies can be used in many different fields, including psychology, medicine, education, anthropology, political science, and social work.

The point of a case study is to learn as much as possible about an individual or group so that the information can be generalized to many others. Unfortunately, case studies tend to be highly subjective, and it is sometimes difficult to generalize results to a larger population.

While case studies focus on a single individual or group, they follow a format similar to other types of psychology writing. If you are writing a case study, we got you—here are some rules of APA format to reference.  

At a Glance

A case study, or an in-depth study of a person, group, or event, can be a useful research tool when used wisely. In many cases, case studies are best used in situations where it would be difficult or impossible for you to conduct an experiment. They are helpful for looking at unique situations and allow researchers to gather a lot of˜ information about a specific individual or group of people. However, it's important to be cautious of any bias we draw from them as they are highly subjective.

What Are the Benefits and Limitations of Case Studies?

A case study can have its strengths and weaknesses. Researchers must consider these pros and cons before deciding if this type of study is appropriate for their needs.

One of the greatest advantages of a case study is that it allows researchers to investigate things that are often difficult or impossible to replicate in a lab. Some other benefits of a case study:

• Allows researchers to capture information on the 'how,' 'what,' and 'why,' of something that's implemented
• Gives researchers the chance to collect information on why one strategy might be chosen over another
• Permits researchers to develop hypotheses that can be explored in experimental research

On the other hand, a case study can have some drawbacks:

• It cannot necessarily be generalized to the larger population
• Cannot demonstrate cause and effect
• It may not be scientifically rigorous
• It can lead to bias

Researchers may choose to perform a case study if they want to explore a unique or recently discovered phenomenon. Through their insights, researchers develop additional ideas and study questions that might be explored in future studies.

It's important to remember that the insights from case studies cannot be used to determine cause-and-effect relationships between variables. However, case studies may be used to develop hypotheses that can then be addressed in experimental research.

Case Study Examples

There have been a number of notable case studies in the history of psychology. Much of  Freud's work and theories were developed through individual case studies. Some great examples of case studies in psychology include:

• Anna O : Anna O. was a pseudonym of a woman named Bertha Pappenheim, a patient of a physician named Josef Breuer. While she was never a patient of Freud's, Freud and Breuer discussed her case extensively. The woman was experiencing symptoms of a condition that was then known as hysteria and found that talking about her problems helped relieve her symptoms. Her case played an important part in the development of talk therapy as an approach to mental health treatment.
• Phineas Gage : Phineas Gage was a railroad employee who experienced a terrible accident in which an explosion sent a metal rod through his skull, damaging important portions of his brain. Gage recovered from his accident but was left with serious changes in both personality and behavior.
• Genie : Genie was a young girl subjected to horrific abuse and isolation. The case study of Genie allowed researchers to study whether language learning was possible, even after missing critical periods for language development. Her case also served as an example of how scientific research may interfere with treatment and lead to further abuse of vulnerable individuals.

Such cases demonstrate how case research can be used to study things that researchers could not replicate in experimental settings. In Genie's case, her horrific abuse denied her the opportunity to learn a language at critical points in her development.

This is clearly not something researchers could ethically replicate, but conducting a case study on Genie allowed researchers to study phenomena that are otherwise impossible to reproduce.

There are a few different types of case studies that psychologists and other researchers might use:

• Collective case studies : These involve studying a group of individuals. Researchers might study a group of people in a certain setting or look at an entire community. For example, psychologists might explore how access to resources in a community has affected the collective mental well-being of those who live there.
• Descriptive case studies : These involve starting with a descriptive theory. The subjects are then observed, and the information gathered is compared to the pre-existing theory.
• Explanatory case studies : These   are often used to do causal investigations. In other words, researchers are interested in looking at factors that may have caused certain things to occur.
• Exploratory case studies : These are sometimes used as a prelude to further, more in-depth research. This allows researchers to gather more information before developing their research questions and hypotheses .
• Instrumental case studies : These occur when the individual or group allows researchers to understand more than what is initially obvious to observers.
• Intrinsic case studies : This type of case study is when the researcher has a personal interest in the case. Jean Piaget's observations of his own children are good examples of how an intrinsic case study can contribute to the development of a psychological theory.

The three main case study types often used are intrinsic, instrumental, and collective. Intrinsic case studies are useful for learning about unique cases. Instrumental case studies help look at an individual to learn more about a broader issue. A collective case study can be useful for looking at several cases simultaneously.

The type of case study that psychology researchers use depends on the unique characteristics of the situation and the case itself.

There are a number of different sources and methods that researchers can use to gather information about an individual or group. Six major sources that have been identified by researchers are:

• Archival records : Census records, survey records, and name lists are examples of archival records.
• Direct observation : This strategy involves observing the subject, often in a natural setting . While an individual observer is sometimes used, it is more common to utilize a group of observers.
• Documents : Letters, newspaper articles, administrative records, etc., are the types of documents often used as sources.
• Interviews : Interviews are one of the most important methods for gathering information in case studies. An interview can involve structured survey questions or more open-ended questions.
• Participant observation : When the researcher serves as a participant in events and observes the actions and outcomes, it is called participant observation.
• Physical artifacts : Tools, objects, instruments, and other artifacts are often observed during a direct observation of the subject.

If you have been directed to write a case study for a psychology course, be sure to check with your instructor for any specific guidelines you need to follow. If you are writing your case study for a professional publication, check with the publisher for their specific guidelines for submitting a case study.

Here is a general outline of what should be included in a case study.

Section 1: A Case History

This section will have the following structure and content:

Background information : The first section of your paper will present your client's background. Include factors such as age, gender, work, health status, family mental health history, family and social relationships, drug and alcohol history, life difficulties, goals, and coping skills and weaknesses.

Description of the presenting problem : In the next section of your case study, you will describe the problem or symptoms that the client presented with.

Describe any physical, emotional, or sensory symptoms reported by the client. Thoughts, feelings, and perceptions related to the symptoms should also be noted. Any screening or diagnostic assessments that are used should also be described in detail and all scores reported.

Your diagnosis : Provide your diagnosis and give the appropriate Diagnostic and Statistical Manual code. Explain how you reached your diagnosis, how the client's symptoms fit the diagnostic criteria for the disorder(s), or any possible difficulties in reaching a diagnosis.

Section 2: Treatment Plan

This portion of the paper will address the chosen treatment for the condition. This might also include the theoretical basis for the chosen treatment or any other evidence that might exist to support why this approach was chosen.

• Cognitive behavioral approach : Explain how a cognitive behavioral therapist would approach treatment. Offer background information on cognitive behavioral therapy and describe the treatment sessions, client response, and outcome of this type of treatment. Make note of any difficulties or successes encountered by your client during treatment.
• Humanistic approach : Describe a humanistic approach that could be used to treat your client, such as client-centered therapy . Provide information on the type of treatment you chose, the client's reaction to the treatment, and the end result of this approach. Explain why the treatment was successful or unsuccessful.
• Psychoanalytic approach : Describe how a psychoanalytic therapist would view the client's problem. Provide some background on the psychoanalytic approach and cite relevant references. Explain how psychoanalytic therapy would be used to treat the client, how the client would respond to therapy, and the effectiveness of this treatment approach.
• Pharmacological approach : If treatment primarily involves the use of medications, explain which medications were used and why. Provide background on the effectiveness of these medications and how monotherapy may compare with an approach that combines medications with therapy or other treatments.

This section of a case study should also include information about the treatment goals, process, and outcomes.

When you are writing a case study, you should also include a section where you discuss the case study itself, including the strengths and limitiations of the study. You should note how the findings of your case study might support previous research. 

In your discussion section, you should also describe some of the implications of your case study. What ideas or findings might require further exploration? How might researchers go about exploring some of these questions in additional studies?

Need More Tips?

Here are a few additional pointers to keep in mind when formatting your case study:

• Never refer to the subject of your case study as "the client." Instead, use their name or a pseudonym.
• Read examples of case studies to gain an idea about the style and format.
• Remember to use APA format when citing references .

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach .  BMC Med Res Methodol . 2011;11:100.

Crowe S, Cresswell K, Robertson A, Huby G, Avery A, Sheikh A. The case study approach . BMC Med Res Methodol . 2011 Jun 27;11:100. doi:10.1186/1471-2288-11-100

Gagnon, Yves-Chantal.  The Case Study as Research Method: A Practical Handbook . Canada, Chicago Review Press Incorporated DBA Independent Pub Group, 2010.

Yin, Robert K. Case Study Research and Applications: Design and Methods . United States, SAGE Publications, 2017.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts

• 1 Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
• 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
• 3 School of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
• 4 School of Medical Sciences, Örebro University, Örebro, Sweden
• 5 Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom
• 6 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom

Question   Could multitrait polygenic risk scores be used to strengthen genetic prediction of longitudinal depression across adolescence?

Findings   In this longitudinal cohort replication study of 14 112 adolescents, stronger effect sizes of multitrait polygenic risk association with adverse depression trajectories were found compared with unitrait genetic risk.

Meaning   Longitudinal depression has a robust genetic underpinning, and leveraging shared genetic information across multiple psychiatric traits may strengthen prediction models of depression in adolescence.

Importance   Adolescent depression is characterized by diverse symptom trajectories over time and has a strong genetic influence. Research has determined genetic overlap between depression and other psychiatric conditions; investigating the shared genetic architecture of heterogeneous depression trajectories is crucial for understanding disease etiology, prediction, and early intervention.

Objective   To investigate univariate and multivariate genetic risk for adolescent depression trajectories and assess generalizability across ancestries.

Design, Setting, and Participants   This cohort study entailed longitudinal growth modeling followed by polygenic risk score (PRS) association testing for individual and multitrait genetic models. Two longitudinal cohorts from the US and UK were used: the Adolescent Brain and Cognitive Development (ABCD; N = 11 876) study and the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8787) study. Included were adolescents with genetic information and depression measures at up to 8 and 4 occasions, respectively. Study data were analyzed January to July 2023.

Main Outcomes and Measures   Trajectories were derived from growth mixture modeling of longitudinal depression symptoms. PRSs were computed for depression, anxiety, neuroticism, bipolar disorder, schizophrenia, attention-deficit/hyperactivity disorder, and autism in European ancestry. Genomic structural equation modeling was used to build multitrait genetic models of psychopathology followed by multitrait PRS. Depression PRSs were computed in African, East Asian, and Hispanic ancestries in the ABCD cohort only. Association testing was performed between all PRSs and trajectories for both cohorts.

Results   A total sample size of 14 112 adolescents (at baseline: mean [SD] age, 10.5 [0.5] years; 7269 male sex [52%]) from both cohorts were included in this analysis. Distinct depression trajectories (stable low, adolescent persistent, increasing, and decreasing) were replicated in the ALSPAC cohort (6096 participants; 3091 female [51%]) and ABCD cohort (8016 participants; 4274 male [53%]) between ages 10 and 17 years. Most univariate PRSs showed significant uniform associations with persistent trajectories, but fewer were significantly associated with intermediate (increasing and decreasing) trajectories. Multitrait PRSs—derived from a hierarchical factor model—showed the strongest associations for persistent trajectories (ABCD cohort: OR, 1.46; 95% CI, 1.26-1.68; ALSPAC cohort: OR, 1.34; 95% CI, 1.20-1.49), surpassing the effect size of univariate PRS in both cohorts. Multitrait PRSs were associated with intermediate trajectories but to a lesser extent (ABCD cohort: hierarchical increasing, OR, 1.27; 95% CI, 1.13-1.43; decreasing, OR, 1.23; 95% CI, 1.09-1.40; ALSPAC cohort: hierarchical increasing, OR, 1.16; 95% CI, 1.04-1.28; decreasing, OR, 1.32; 95% CI, 1.18-1.47). Transancestral genetic risk for depression showed no evidence for association with trajectories.

Conclusions and Relevance   Results of this cohort study revealed a high multitrait genetic loading of persistent symptom trajectories, consistent across traits and cohorts. Variability in univariate genetic association with intermediate trajectories may stem from environmental factors. Multitrait genetics may strengthen depression prediction models, but more diverse data are needed for generalizability.

Read More About

Grimes PZ , Adams MJ , Thng G, et al. Genetic Architectures of Adolescent Depression Trajectories in 2 Longitudinal Population Cohorts. JAMA Psychiatry. Published online May 15, 2024. doi:10.1001/jamapsychiatry.2024.0983

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• Open access
• Published: 16 May 2024

Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress

• Anna Jochmann 1 ,
• Burkhard Gusy 1 ,
• Tino Lesener 1 &
• Christine Wolter 1  

BMC Psychology volume  12 , Article number:  276 ( 2024 ) Cite this article

148 Accesses

Metrics details

It is generally assumed that procrastination leads to negative consequences. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. Therefore, the aim of our study was to examine the harmful consequences of procrastination on students’ stress and mental health. We selected the procrastination-health model as our theoretical foundation and tried to evaluate the model’s assumption that trait procrastination leads to (chronic) disease via (chronic) stress in a temporal perspective. We chose depression and anxiety symptoms as indicators for (chronic) disease and hypothesized that procrastination leads to perceived stress over time, that perceived stress leads to depression and anxiety symptoms over time, and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

To examine these relationships properly, we collected longitudinal data from 392 university students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models.

Procrastination did lead to depression and anxiety symptoms over time. However, perceived stress was not a mediator of this effect. Procrastination did not lead to perceived stress over time, nor did perceived stress lead to depression and anxiety symptoms over time.

Conclusions

We could not confirm that trait procrastination leads to (chronic) disease via (chronic) stress, as assumed in the procrastination-health model. Nonetheless, our study demonstrated that procrastination can have a detrimental effect on mental health. Further health outcomes and possible mediators should be explored in future studies.

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Introduction

“Due tomorrow? Do tomorrow.”, might be said by someone who has a tendency to postpone tasks until the last minute. But can we enjoy today knowing about the unfinished task and tomorrow’s deadline? Or do we feel guilty for postponing a task yet again? Do we get stressed out because we have little time left to complete it? Almost everyone has procrastinated at some point when it came to completing unpleasant tasks, such as mowing the lawn, doing the taxes, or preparing for exams. Some tend to procrastinate more frequently and in all areas of life, while others are less inclined to do so. Procrastination is common across a wide range of nationalities, as well as socioeconomic and educational backgrounds [ 1 ]. Over the last fifteen years, there has been a massive increase in research on procrastination [ 2 ]. Oftentimes, research focuses on better understanding the phenomenon of procrastination and finding out why someone procrastinates in order to be able to intervene. Similarly, the internet is filled with self-help guides that promise a way to overcome procrastination. But why do people seek help for their procrastination? Until now, not much research has been conducted on the negative consequences procrastination could have on health and well-being. Therefore, in the following article we examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship on the basis of the procrastination-health model by Sirois et al. [ 3 ].

Procrastination and its negative consequences

Procrastination can be defined as the tendency to voluntarily and irrationally delay intended activities despite expecting negative consequences as a result of the delay [ 4 , 5 ]. It has been observed in a variety of groups across the lifespan, such as students, teachers, and workers [ 1 ]. For example, some students tend to regularly delay preparing for exams and writing essays until the last minute, even if this results in time pressure or lower grades. Procrastination must be distinguished from strategic delay [ 4 , 6 ]. Delaying a task is considered strategic when other tasks are more important or when more resources are needed before the task can be completed. While strategic delay is viewed as functional and adaptive, procrastination is classified as dysfunctional. Procrastination is predominantly viewed as the result of a self-regulatory failure [ 7 ]. It can be understood as a trait, that is, as a cross-situational and time-stable behavioral disposition [ 8 ]. Thus, it is assumed that procrastinators chronically delay tasks that they experience as unpleasant or difficult [ 9 ]. Approximately 20 to 30% of adults have been found to procrastinate chronically [ 10 , 11 , 12 ]. Prevalence estimates for students are similar [ 13 ]. It is believed that students do not procrastinate more often than other groups. However, it is easy to examine procrastination in students because working on study tasks requires a high degree of self-organization and time management [ 14 ].

It is generally assumed that procrastination leads to negative consequences [ 4 ]. Negative consequences are even part of the definition of procrastination. Research indicates that procrastination is linked to lower academic performance [ 15 ], health impairment (e.g., stress [ 16 ], physical symptoms [ 17 ], depression and anxiety symptoms [ 18 ]), and poor health-related behavior (e.g., heavier alcohol consumption [ 19 ]). However, most studies targeting consequences of procrastination are cross-sectional [ 4 ]. For that reason, it often remains unclear whether an examined outcome is a consequence or an antecedent of procrastination, or whether a reciprocal relationship between procrastination and the examined outcome can be assumed. Additionally, regarding negative consequences of procrastination on health, it is still largely unknown by which mechanisms they are mediated. Uncovering such mediators would be helpful in developing interventions that can prevent negative health consequences of procrastination.

The procrastination-health model

The first and only model that exclusively focuses on the effect of procrastination on health and the mediators of this effect is the procrastination-health model [ 3 , 9 , 17 ]. Sirois [ 9 ] postulates three pathways: An immediate effect of trait procrastination on (chronic) disease and two mediated pathways (see Fig.  1 ).

Adopted from the procrastination-health model by Sirois [ 9 ]

The immediate effect is not further explained. Research suggests that procrastination creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 ]. The described feelings could have a detrimental effect on mental health [ 23 , 24 , 25 ].

The first mediated pathway leads from trait procrastination to (chronic) disease via (chronic) stress. Sirois [ 9 ] assumes that procrastination creates stress because procrastinators are constantly aware of the fact that they still have many tasks to complete. Stress activates the hypothalamic-pituitary-adrenocortical (HPA) system, increases autonomic nervous system arousal, and weakens the immune system, which in turn contributes to the development of diseases. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress. She believes that, in the short term, single incidents of procrastination cause acute stress, which leads to acute health problems, such as infections or headaches. In the long term, chronic procrastination, as you would expect with trait procrastination, causes chronic stress, which leads to chronic diseases over time. There is some evidence in support of the stress-related pathway, particularly regarding short-term effects [ 3 , 17 , 26 , 27 , 28 ]. However, as we mentioned above, most of these studies are cross-sectional. Therefore, the causal direction of these effects remains unclear. To our knowledge, long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress have not yet been investigated.

The second mediated pathway leads from trait procrastination to (chronic) disease via poor health-related behavior. According to Sirois [ 9 ], procrastinators form lower intentions to carry out health-promoting behavior or to refrain from health-damaging behavior because they have a low self-efficacy of being able to care for their own health. In addition, they lack the far-sighted view that the effects of health-related behavior only become apparent in the long term. For the same reason, Sirois [ 9 ] believes that there are no short-term, but only long-term effects of procrastination on health mediated by poor health-related behavior. For example, an unhealthy diet leads to diabetes over time. The findings of studies examining the behavioral pathway are inconclusive [ 3 , 17 , 26 , 28 ]. Furthermore, since most of these studies are cross-sectional, they are not suitable for uncovering long-term effects of trait procrastination on (chronic) disease mediated by poor health-related behavior.

In summary, previous research on the two mediated pathways of the procrastination-health model mainly found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. However, only short-term effects have been investigated so far. Moreover, longitudinal studies are needed to be able to assess the causal direction of the relationship between trait procrastination, (chronic) stress, and (chronic) disease. Consequently, our study is the first to examine long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, using a longitudinal design. (Chronic) disease could be measured by a variety of different indicators (e.g., physical symptoms, diabetes, or coronary heart disease). We choose depression and anxiety symptoms as indicators for (chronic) disease because they signal mental health complaints before they manifest as (chronic) diseases. Additionally, depression and anxiety symptoms are two of the most common mental health complaints among students [ 29 , 30 ] and procrastination has been shown to be a significant predictor of depression and anxiety symptoms [ 18 , 31 , 32 , 33 , 34 ]. Until now, the stress-related pathway of the procrastination-health model with depression and anxiety symptoms as the health outcome has only been analyzed in one cross-sectional study that confirmed the predictions of the model [ 35 ].

The aim of our study is to evaluate some of the key assumptions of the procrastination-health model, particularly the relationships between trait procrastination, (chronic) stress, and (chronic) disease over time, surveyed in the following analysis using depression and anxiety symptoms.

In line with the key assumptions of the procrastination-health model, we postulate (see Fig.  2 ):

Procrastination leads to perceived stress over time.

Perceived stress leads to depression and anxiety symptoms over time.

Procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

The section of the procrastination-health model we examined

Materials and methods

Our study was part of a health monitoring at a large German university Footnote 1 . Ethical approval for our study was granted by the Ethics Committee of the university’s Department of Education and Psychology. We collected the initial data in 2019. Two occasions followed, each at an interval of six months. In January 2019, we sent out 33,267 invitations to student e-mail addresses. Before beginning the survey, students provided their written informed consent to participate in our study. 3,420 students took part at the first occasion (T1; 10% response rate). Of these, 862 participated at the second (T2) and 392 at the third occasion (T3). In order to test whether dropout was selective, we compared sociodemographic and study specific characteristics (age, gender, academic semester, number of assessments/exams) as well as behavior and health-related variables (procrastination, perceived stress, depression and anxiety symptoms) between the participants of the first wave ( n  = 3,420) and those who participated three times ( n  = 392). Results from independent-samples t-tests and chi-square analysis showed no significant differences regarding sociodemographic and study specific characteristics (see Additional file 1: Table S1 and S2 ). Regarding behavior and health-related variables, independent-samples t-tests revealed a significant difference in procrastination between the two groups ( t (3,409) = 2.08, p  < .05). The mean score of procrastination was lower in the group that participated in all three waves.

The mean age of the longitudinal respondents was 24.1 years ( SD  = 5.5 years), the youngest participants were 17 years old, the oldest one was 59 years old. The majority of participants was female (74.0%), 7 participants identified neither as male nor as female (1.8%). The respondents were on average enrolled in the third year of studying ( M  = 3.9; SD  = 2.3). On average, the students worked about 31.2 h ( SD  = 14.1) per week for their studies, and an additional 8.5 h ( SD  = 8.5) for their (part-time) jobs. The average income was €851 ( SD  = 406), and 4.9% of the students had at least one child. The students were mostly enrolled in philosophy and humanities (16.5%), education and psychology (15.8%), biology, chemistry, and pharmacy (12.5%), political and social sciences (10.6%), veterinary medicine (8.9%), and mathematics and computer science (7.7%).

We only used established and well evaluated instruments for our analyses.

• Procrastination

We adopted the short form of the Procrastination Questionnaire for Students (PFS-4) [ 36 ] to measure procrastination. The PFS-4 assesses procrastination at university as a largely stable behavioral disposition across situations, that is, as a trait. The questionnaire consists of four items (e.g., I put off starting tasks until the last moment.). Each item was rated on a 5-point scale ((almost) never = 1 to (almost) always = 5) for the last two weeks. All items were averaged, with higher scores indicating a greater tendency to procrastinate. The PFS-4 has been proven to be reliable and valid, showing very high correlations with other established trait procrastination scales, for example, with the German short form of the General Procrastination Scale [ 37 , 38 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.90.

Perceived stress

The Heidelberger Stress Index (HEI-STRESS) [ 39 ] is a three-item measure of current perceived stress due to studying as well as in life in general. For the first item, respondents enter a number between 0 (not stressed at all) and 100 (completely stressed) to indicate how stressed their studies have made them feel over the last four weeks. For the second and third item, respondents rate on a 5-point scale how often they feel “stressed and tense” and as how stressful they would describe their life at the moment. We transformed the second and third item to match the range of the first item before we averaged all items into a single score with higher values indicating greater perceived stress. We proved the scale to be one-dimensional and Cronbach’s alpha for our study was 0.86.

Depression and anxiety symptoms

We used the Patient Health Questionnaire-4 (PHQ-4) [ 40 ], a short form of the Patient Health Questionnaire [ 41 ] with four items, to measure depression and anxiety symptoms. The PHQ-4 contains two items from the Patient Health Questionnaire-2 (PHQ-2) [ 42 ] and the Generalized Anxiety Disorder Scale-2 (GAD-2) [ 43 ], respectively. It is a well-established screening scale designed to assess the core criteria of major depressive disorder (PHQ-2) and generalized anxiety disorder (GAD-2) according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, it was shown that the GAD-2 is also appropriate for screening other anxiety disorders. According to Kroenke et al. [ 40 ], the PHQ-4 can be used to assess a person’s symptom burden and impairment. We asked the participants to rate how often they have been bothered over the last two weeks by problems, such as “Little interest or pleasure in doing things”. Response options were 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. Calculated as the sum of the four items, the total scores range from 0 to 12 with higher scores indicating more frequent depression and anxiety symptoms. The total scores can be categorized as none-to-minimal (0–2), mild (3–5), moderate (6–8), and severe (9–12) depression and anxiety symptoms. The PHQ-4 was shown to be reliable and valid [ 40 , 44 , 45 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.86.

Data analysis

To test our hypotheses, we performed structural equation modelling (SEM) using R (Version 4.1.1) with the package lavaan. All items were standardized ( M  = 0, SD  = 1). Due to the non-normality of some study variables and a sufficiently large sample size of N near to 400 [ 46 ], we used robust maximum likelihood estimation (MLR) for all model estimations. As recommended by Hu and Bentler [ 47 ], we assessed the models’ goodness of fit by chi-square test statistic, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Tucker-Lewis index (TLI), and comparative fit index (CFI). A non-significant chi-square indicates good model fit. Since chi-square is sensitive to sample size, we also evaluated fit indices less sensitive to the number of observations. RMSEA and SRMR values of 0.05 or lower as well as TLI and CFI values of 0.97 or higher indicate good model fit. RMSEA values of 0.08 or lower, SRMR values of 0.10 or lower, as well as TLI and CFI values of 0.95 or higher indicate acceptable model fit [ 48 , 49 ]. First, we conducted confirmatory factor analysis for the first occasion, defining three factors that correspond to the measures of procrastination, perceived stress, and depression and anxiety symptoms. Next, we tested for measurements invariance over time and specified the measurement model, before testing our hypotheses.

Measurement invariance over time

To test for measurement invariance over time, we defined one latent variable for each of the three occasions, corresponding to the measures of procrastination, perceived stress, and depression and anxiety symptoms, respectively. As recommended by Geiser and colleagues [ 50 ], the links between indicators and factors (i.e., factor loadings and intercepts) should be equal over measurement occasions; therefore, we added indicator specific factors. A first and least stringent step of testing measurement invariance is configural invariance (M CI ). It was examined whether the included constructs (procrastination, perceived stress, depression and anxiety symptoms) have the same pattern of free and fixed loadings over time. This means that the assignment of the indicators to the three latent factors over time is supported by the underlying data. If configural invariance was supported, restrictions for the next step of testing measurement invariance (metric or weak invariance; M MI ) were added. This means that each item contributes to the latent construct to a similar degree over time. Metric invariance was tested by constraining the factor loadings of the constructs over time. The next step of testing measurement invariance (scalar or strong invariance; M SI ) consisted of checking whether mean differences in the latent construct capture all mean differences in the shared variance of the items. Scalar invariance was tested by constraining the item intercepts over time. The constraints applied in the metric invariance model were retained [ 51 ]. For the last step of testing measurement invariance (residual or strict invariance; M RI ), the residual variables were also set equal over time. If residual invariance is supported, differences in the observed variables can exclusively be attributed to differences in the variances of the latent variables.

We used the Satorra-Bentler chi-square difference test to evaluate the superiority of a more stringent model [ 52 ]. We assumed the model with the largest number of invariance restrictions – which still has an acceptable fit and no substantial deterioration of the chi-square value – to be the final model [ 53 ]. Following previous recommendations, we considered a decrease in CFI of 0.01 and an increase in RMSEA of 0.015 as unacceptable to establish measurement invariance [ 54 ]. If a more stringent model had a significant worse chi-square value, but the model fit was still acceptable and the deterioration in model fit fell within the change criteria recommended for CFI and RMSEA values, we still considered the more stringent model to be superior.

Hypotheses testing

As recommended by Dormann et al. [ 55 ], we applied autoregressive time-lagged panel models to test our hypotheses. In the first step, we specified a model (M 0 ) that only included the stabilities of the three variables (procrastination, perceived stress, depression and anxiety symptoms) over time. In the next step (M 1 ), we added the time-lagged effects from procrastination (T1) to perceived stress (T2) and from procrastination (T2) to perceived stress (T3) as well as from perceived stress (T1) to depression and anxiety symptoms (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3). Additionally, we included a direct path from procrastination (T1) to depression and anxiety symptoms (T3). If this path becomes significant, we can assume a partial mediation [ 55 ]. Otherwise, we can assume a full mediation. We compared these nested models using the Satorra-Bentler chi-square difference test and the Akaike information criterion (AIC). The chi-square difference value should either be non-significant, indicating that the proposed model including our hypotheses (M 1 ) does not have a significant worse model fit than the model including only stabilities (M 0 ), or, if significant, it should be in the direction that M 1 fits the data better than M 0 . Regarding the AIC, M 1 should have a lower value than M 0 .

Table  1 displays the means, standard deviations, internal consistencies (Cronbach’s alpha), and stabilities (correlations) of all study variables. The alpha values of procrastination, perceived stress, and depression and anxiety symptoms are classified as good (> 0.80) [ 56 ]. The correlation matrix of the manifest variables used for the analyses can be found in the Additional file 1: Table  S3 .

We observed the highest test-retest reliabilities for procrastination ( r  ≥ .74). The test-retest reliabilities for depression and anxiety symptoms ( r  ≥ .64) and for perceived stress ( r  ≥ .54) were a bit lower (see Table  1 ). The pattern of correlations shows a medium to large but positive relationship between procrastination and depression and anxiety symptoms [ 57 , 58 ]. The association between procrastination and perceived stress was small, the one between perceived stress and depression and anxiety symptoms very large (see Table  1 ).

Confirmatory factor analysis showed an acceptable to good fit (x 2 (41) = 118.618, p  < .001; SRMR = 0.042; RMSEA = 0.071; TLI = 0.95; CFI = 0.97). When testing for measurement invariance over time for each construct, the residual invariance models with indicator specific factors provided good fit to the data (M RI ; see Table  2 ), suggesting that differences in the observed variables can exclusively be attributed to differences of the latent variables. We then specified and tested the measurement model of the latent constructs prior to model testing based on the items of procrastination, perceived stress, and depression and anxiety symptoms. The measurement model fitted the data well (M M ; see Table  3 ). All items loaded solidly on their respective factors (0.791 ≤ β ≤ 0.987; p  < .001).

To test our hypotheses, we analyzed the two models described in the methods section.

The fit of the stability model (M 0 ) was acceptable (see Table  3 ). Procrastination was stable over time, with stabilities above 0.82. The stabilities of perceived stress as well as depression and anxiety symptoms were somewhat lower, ranging from 0.559 (T1 -> T2) to 0.696 (T2 -> T3) for perceived stress and from 0.713 (T2 -> T3) to 0.770 (T1 -> T2) for depression and anxiety symptoms, respectively.

The autoregressive mediation model (M 1 ) fitted the data significantly better than M 0 . The direct path from procrastination (T1) to depression and anxiety symptoms (T3) was significant (β = 0.16; p  < .001), however, none of the mediated paths (from procrastination (T1) to perceived stress (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3)) proved to be substantial. Also, the time-lagged paths from perceived stress (T1) to depression and anxiety symptoms (T2) and from procrastination (T2) to perceived stress (T3) were not substantial either (see Fig.  3 ).

To examine whether the hypothesized effects would occur over a one-year period rather than a six-months period, we specified an additional model with paths from procrastination (T1) to perceived stress (T3) and from perceived stress (T1) to depression and anxiety symptoms (T3), also including the stabilities of the three constructs as in the stability model M 0 . The model showed an acceptable fit (χ 2 (486) = 831.281, p  < .001; RMSEA = 0.048; SRMR = 0.091; TLI = 0.95; CFI = 0.95), but neither of the two paths were significant.

Therefore, our hypotheses, that procrastination leads to perceived stress over time (H1) and that perceived stress leads to depression and anxiety symptoms over time (H2) must be rejected. We could only partially confirm our third hypothesis, that procrastination leads to depression and anxiety over time, mediated by perceived stress (H3), since procrastination did lead to depression and anxiety symptoms over time. However, this effect was not mediated by perceived stress.

Results of the estimated model including all hypotheses (M 1 ). Note Non-significant paths are dotted. T1 = time 1; T2 = time 2; T3 = time 3. *** p  < .001

To sum up, we tried to examine the harmful consequences of procrastination on students’ stress and mental health. Hence, we selected the procrastination-health model by Sirois [ 9 ] as a theoretical foundation and tried to evaluate some of its key assumptions in a temporal perspective. The author assumes that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and postulated, in line with the key assumptions of the procrastination-health model, that procrastination leads to perceived stress over time (H1), that perceived stress leads to depression and anxiety symptoms over time (H2), and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress (H3). To examine these relationships properly, we collected longitudinal data from students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models. Our first and second hypotheses had to be rejected: Procrastination did not lead to perceived stress over time, and perceived stress did not lead to depression and anxiety symptoms over time. However, procrastination did lead to depression and anxiety symptoms over time – which is in line with our third hypothesis – but perceived stress was not a mediator of this effect. Therefore, we could only partially confirm our third hypothesis.

Our results contradict previous studies on the stress-related pathway of the procrastination-health model, which consistently found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. Since most of these studies were cross-sectional, though, the causal direction of these effects remained uncertain. There are two longitudinal studies that confirm the stress-related pathway of the procrastination-health model [ 27 , 28 ], but both studies examined short-term effects (≤ 3 months), whereas we focused on more long-term effects. Therefore, the divergent findings may indicate that there are short-term, but no long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress.

Our results especially raise the question whether trait procrastination leads to (chronic) stress in the long term. Looking at previous longitudinal studies on the effect of procrastination on stress, the following stands out: At shorter study periods of two weeks [ 27 ] and four weeks [ 28 ], the effect of procrastination on stress appears to be present. At longer study periods of seven weeks [ 59 ], three months [ 28 ], six months, and twelve months, as in our study, the effect of procrastination on stress does not appear to be present. There is one longitudinal study in which procrastination was a significant predictor of stress symptoms nine months later [ 34 ]. The results of this study should be interpreted with caution, though, because the outbreak of the COVID-19 pandemic fell within the study period, which could have contributed to increased stress symptoms [ 60 ]. Unfortunately, Johansson et al. [ 34 ] did not report whether average stress symptoms increased during their study. In one of the two studies conducted by Fincham and May [ 59 ], the COVID-19 pandemic outbreak also fell within their seven-week study period. However, they reported that in their study, average stress symptoms did not increase from baseline to follow-up. Taken together, the findings suggest that procrastination can cause acute stress in the short term, for example during times when many tasks need to be completed, such as at the end of a semester, but that procrastination does not lead to chronic stress over time. It seems possible that students are able to recover during the semester from the stress their procrastination caused at the end of the previous semester. Because of their procrastination, they may also have more time to engage in relaxing activities, which could further mitigate the effect of procrastination on stress. Our conclusions are supported by an early and well-known longitudinal study by Tice and Baumeister [ 61 ], which compared procrastinating and non-procrastinating students with regard to their health. They found that procrastinators experienced less stress than their non-procrastinating peers at the beginning of the semester, but more at the end of the semester. Additionally, our conclusions are in line with an interview study in which university students were asked about the consequences of their procrastination [ 62 ]. The students reported that, due to their procrastination, they experience high levels of stress during periods with heavy workloads (e.g., before deadlines or exams). However, the stress does not last, instead, it is relieved immediately after these periods.

Even though research indicates, in line with the assumptions of the procrastination-health model, that stress is a risk factor for physical and mental disorders [ 63 , 64 , 65 , 66 ], perceived stress did not have a significant effect on depression and anxiety symptoms in our study. The relationship between stress and mental health is complex, as people respond to stress in many different ways. While some develop stress-related mental disorders, others experience mild psychological symptoms or no symptoms at all [ 67 ]. This can be explained with the help of vulnerability-stress models. According to vulnerability-stress models, mental illnesses emerge from an interaction of vulnerabilities (e.g., genetic factors, difficult family backgrounds, or weak coping abilities) and stress (e.g., minor or major life events or daily hassles) [ 68 , 69 ]. The stress perceived by the students in our sample may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. However, since we did not assess individual vulnerability and stress factors in our study, these considerations are mere speculation.

In our study, procrastination led to depression and anxiety symptoms over time, which is consistent with the procrastination-health model as well as previous cross-sectional and longitudinal evidence [ 18 , 21 , 31 , 32 , 33 , 34 ]. However, it is still unclear by which mechanisms this effect is mediated, as perceived stress did not prove to be a substantial mediator in our study. One possible mechanism would be that procrastination impairs affective well-being [ 70 ] and creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 , 62 , 71 ], which in turn could lead to depression and anxiety symptoms [ 23 , 24 , 25 ]. Other potential mediators of the relationship between procrastination and depression and anxiety symptoms emerge from the behavioral pathway of the procrastination-health model, suggesting that poor health-related behaviors mediate the effect of trait procrastination on (chronic) disease. Although evidence for this is still scarce, the results of one cross-sectional study, for example, indicate that poor sleep quality might mediate the effect of procrastination on depression and anxiety symptoms [ 35 ].

In summary, we found that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. For the most part, the relationships between procrastination, perceived stress, and depression and anxiety symptoms did not match the relationships between trait procrastination, (chronic) stress, and (chronic) disease as assumed in the procrastination-health model. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. In conclusion, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model.

Limitations and suggestions for future research

In our study, we tried to draw causal conclusions about the harmful consequences of procrastination on students’ stress and mental health. However, since procrastination is a trait that cannot be manipulated experimentally, we have conducted an observational rather than an experimental study, which makes causal inferences more difficult. Nonetheless, a major strength of our study is that we used a longitudinal design with three waves. This made it possible to draw conclusions about the causal direction of the effects, as in hardly any other study targeting consequences of procrastination on health before [ 4 , 28 , 55 ]. Therefore, we strongly recommend using a similar longitudinal design in future studies on the procrastination-health model or on consequences of procrastination on health in general.

We chose a time lag of six months between each of the three measurement occasions to examine long-term effects of procrastination on depression and anxiety symptoms mediated by perceived stress. However, more than six months may be necessary for the hypothesized effects to occur [ 72 ]. The fact that the temporal stabilities of the examined constructs were moderate or high (0.559 ≤ β ≤ 0.854) [ 73 , 74 ] also suggests that the time lags may have been too short. The larger the time lag, the lower the temporal stabilities, as shown for depression and anxiety symptoms, for example [ 75 ]. High temporal stabilities make it more difficult to detect an effect that actually exists [ 76 ]. Nonetheless, Dormann and Griffin [ 77 ] recommend using shorter time lags of less than one year, even with high stabilities, because of other influential factors, such as unmeasured third variables. Therefore, our time lags of six months seem appropriate.

It should be discussed, though, whether it is possible to detect long-term effects of the stress-related pathway of the procrastination-health model within a total study period of one year. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress, but does not address how long it might take for long-term effects to occur or when effects can be considered long-term instead of short-term. The fact that an effect of procrastination on stress is evident at shorter study periods of four weeks or less but in most cases not at longer study periods of seven weeks or more, as we mentioned earlier, could indicate that short-term effects occur within the time frame of one to three months, considering the entire stress-related pathway. Hence, it seems appropriate to assume that we have examined rather long-term effects, given our study period of six and twelve months. Nevertheless, it would be beneficial to use varying study periods in future studies, in order to be able to determine when effects can be considered long-term.

Concerning long-term effects of the stress-related pathway, Sirois [ 9 ] assumes that chronic procrastination causes chronic stress, which leads to chronic diseases over time. The term “chronic stress” refers to prolonged stress episodes associated with permanent tension. The instrument we used captures perceived stress over the last four weeks. Even though the perceived stress of the students in our sample was relatively stable (0.559 ≤ β ≤ 0.696), we do not know how much fluctuation occurred between each of the three occasions. However, there is some evidence suggesting that perceived stress is strongly associated with chronic stress [ 78 ]. Thus, it seems acceptable that we used perceived stress as an indicator for chronic stress in our study. For future studies, we still suggest the use of an instrument that can more accurately reflect chronic stress, for example, the Trier Inventory for Chronic Stress (TICS) [ 79 ].

It is also possible that the occasions were inconveniently chosen, as they all took place in a critical academic period near the end of the semester, just before the examination period began. We chose a similar period in the semester for each occasion for the sake of comparability. However, it is possible that, during this preparation periods, stress levels peaked and procrastinators procrastinated less because they had to catch up after delaying their work. This could have introduced bias to the data. Therefore, in future studies, investigation periods should be chosen that are closer to the beginning or in the middle of a semester.

Furthermore, Sirois [ 9 ] did not really explain her understanding of “chronic disease”. However, it seems clear that physical illnesses, such as diabetes or cardiovascular diseases, are meant. Depression and anxiety symptoms, which we chose as indicators for chronic disease, represent mental health complaints that do not have to be at the level of a major depressive disorder or an anxiety disorder, in terms of their quantity, intensity, or duration [ 40 ]. But they can be viewed as precursors to a major depressive disorder or an anxiety disorder. Therefore, given our study period of one year, it seems appropriate to use depression and anxiety symptoms as indicators for chronic disease. At longer study periods, we would expect these mental health complaints to manifest as mental disorders. Moreover, the procrastination-health model was originally designed to be applied to physical diseases [ 3 ]. Perhaps, the model assumptions are more applicable to physical diseases than to mental disorders. By applying parts of the model to mental health complaints, we have taken an important step towards finding out whether the model is applicable to mental disorders as well. Future studies should examine additional long-term health outcomes, both physical and psychological. This would help to determine whether trait procrastination has varying effects on different diseases over time. Furthermore, we suggest including individual vulnerability and stress factors in future studies in order to be able to analyze the effect of (chronic) stress on (chronic) diseases in a more differentiated way.

Regarding our sample, 3,420 students took part at the first occasion, but only 392 participated three times, which results in a dropout rate of 88.5%. At the second and third occasion, invitation e-mails were only sent to participants who had indicated at the previous occasion that they would be willing to participate in a repeat survey and provided their e-mail address. This is probably one of the main reasons for our high dropout rate. Other reasons could be that the students did not receive any incentives for participating in our study and that some may have graduated between the occasions. Selective dropout analysis revealed that the mean score of procrastination was lower in the group that participated in all three waves ( n  = 392) compared to the group that participated in the first wave ( n  = 3,420). One reason for this could be that those who have a higher tendency to procrastinate were more likely to procrastinate on filling out our survey at the second and third occasion. The findings of our dropout analysis should be kept in mind when interpreting our results, as lower levels of procrastination may have eliminated an effect on perceived stress or on depression and anxiety symptoms. Additionally, across all age groups in population-representative samples, the student age group reports having the best subjective health [ 80 ]. Therefore, it is possible that they are more resilient to stress and experience less impairment of well-being than other age groups. Hence, we recommend that future studies focus on other age groups as well.

It is generally assumed that procrastination leads to lower academic performance, health impairment, and poor health-related behavior. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. In consequence, the aim of our study was to examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship. We selected the procrastination-health model as a theoretical foundation and used the stress-related pathway of the model, assuming that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and collected longitudinal data from students at three occasions over a one-year period. This allowed us to draw conclusions about the causal direction of the effects, as in hardly any other study examining consequences of procrastination on (mental) health before. Our results indicate that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient on its own, that is, without the presence of other risk factors, to cause depression and anxiety symptoms. Overall, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model. Our study emphasizes the importance of identifying the consequences procrastination can have on health and well-being and determining by which mechanisms they are mediated. Only then will it be possible to develop interventions that can prevent negative health consequences of procrastination. Further health outcomes and possible mediators should be explored in future studies, using a similar longitudinal design.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

University Health Report at Freie Universität Berlin.

Abbreviations

Comparative fit index

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Generalized Anxiety Disorder Scale-2

Heidelberger Stress Index

Hypothalamic-pituitary-adrenocortical

Robust maximum likelihood estimation

Short form of the Procrastination Questionnaire for Students

Patient Health Questionnaire-2

Patient Health Questionnaire-4

Root mean square error of approximation

Structural equation modeling

Standardized root mean square residual

Tucker-Lewis index

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Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

• Monika Teuber 1 ,
• Daniel Leyhr 1 , 2 &
• Gorden Sudeck 1 , 3  

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Institute of Sports Science, Faculty of Economics and Social Sciences, University of Tübingen, Tübingen, Germany

Monika Teuber, Daniel Leyhr & Gorden Sudeck

Methods Center, Faculty of Economics and Social Sciences, University of Tübingen, Tübingen, Germany

Daniel Leyhr

Interfaculty Research Institute for Sports and Physical Activity, University of Tübingen, Tübingen, Germany

Gorden Sudeck

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Contributions

M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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The study involves human participants and was reviewed and approved by the Ethics Committee of the Faculty of Social Sciences and Economics, University of Tübingen (ref. A2.54-127_kr). The participants provided their written informed consent to participate in this study. All methods were carried out in accordance with relevant guidelines and regulations.

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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Received : 30 June 2023

Accepted : 12 February 2024

Published : 24 February 2024

DOI : https://doi.org/10.1186/s12889-024-18082-z

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