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Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review

Rachel davis.

a Department of Clinical, Educational and Health Psychology , University College London , London, UK

Rona Campbell

b School of Social and Community Medicine , University of Bristol , Bristol, UK

Lorna Hobbs

Susan michie.

Interventions to change health-related behaviours typically have modest effects and may be more effective if grounded in appropriate theory. Most theories applied to public health interventions tend to emphasise individual capabilities and motivation, with limited reference to context and social factors. Intervention effectiveness may be increased by drawing on a wider range of theories incorporating social, cultural and economic factors that influence behaviour. The primary aim of this paper is to identify theories of behaviour and behaviour change of potential relevance to public health interventions across four scientific disciplines: psychology, sociology, anthropology and economics. We report in detail the methodology of our scoping review used to identify these theories including which involved a systematic search of electronic databases, consultation with a multidisciplinary advisory group, web searching, searching of reference lists and hand searching of key behavioural science journals. Of secondary interest we developed a list of agreed criteria for judging the quality of the theories. We identified 82 theories and 9 criteria for assessing theory quality. The potential relevance of this wide-ranging number of theories to public health interventions and the ease and usefulness of evaluating the theories in terms of the quality criteria are however yet to be determined.

Introduction

Human behaviours, including tobacco and alcohol consumption, dietary behaviours, physical activity and sexual practices, play a key role in many of the leading causes of death in developing and developed countries (Aveyard & West, 2007 ; Danaei et al., 2009 ; Ezzati et al., 2002 ; Mokdad, Marks, Stroup, & Gerberding, 2004 ; Parkin, Boyd, & Walker, 2011 ; Solomon & Kington, 2002 ). Even small changes in such behaviours can have substantial effects on population health outcomes (Ezzati et al., 2002 ; Mokdad et al., 2004 ; National Institute for Health and Clinical Excellence (NICE), 2010 ; Solomon & Kington, 2002 ). Understanding these behaviours and the contexts in which they occur is essential for developing effective evidence-based health behaviour change interventions and policies and for reducing avoidable mobility and mortality (House of Lords, 2011 ; Office of Behavioural and Social Sciences Research, 2006 ).

Despite the relatively small investment in preventive health and behavioural science (Marteau, Dieppe, Foy, Kinmonth, & Schneiderman, 2006 ), there is evidence for the effectiveness of behaviour change interventions at individual, community and population levels (Abraham, Kelly, West, & Michie, 2009 ; Albarracin et al., 2005 ; Michie & West, 2013 ; National Institute for Health and Care Excellence, 2007 ; Nigg, Allegrante, & Ory, 2002 ). Interventions have been targeted at behavioural risk factors (e.g., smoking; Carr & Ebbert, 2012 ; Rice & Stead, 2008 ), encouraging protective behaviours (e.g., health screening; Brouwers et al., 2011 ; Everett et al., 2011 ), improving adaptation to chronic and acute illness (e.g., adherence to medical advice; Cutrona et al., 2010 ) and changing health professional behaviours to improve the quality and efficiency of services (e.g., hand hygiene compliance; Fuller et al., 2012 ). While there are many examples of successful interventions, there are also examples of ineffective interventions (e.g., Coleman, 2010 ; Summerbell et al., 2005 ); for those that are effective, the effects tend to be modest, with significant heterogeneity of short-term and long-term effects (Michie, Johnston, Francis, Hardeman, & Eccles, 2008 ).

To maximise the potential efficacy of interventions, it is necessary to understand behaviour and behaviour change: in other words, it is necessary to have a theoretical understanding of behaviour change. In this context, theory represents the accumulated knowledge of the mechanisms of action (mediators) and moderators of change as well as the a priori assumptions about what human behaviour is, and what the influences on it are. The application of theory is advocated as an integral step in intervention design and evaluation and in evidence synthesis, for example, by the UK Medical Research Council's guidance for developing and evaluating complex interventions (Campbell et al., 2000 , 2007 ; Craig et al., 2008 ; Glanz & Bishop, 2010 ). This is for several reasons. First, the antecedents of behaviour and the causal determinants of change can be appropriately identified and targeted by the intervention (Hardeman et al., 2005 ; Michie & Abraham, 2004 ; Michie et al., 2008 ) and component behaviour change techniques can be selected and/or refined and tailored (Michie & Prestwich, 2010 ; Michie et al., 2008 ; Rothman, 2004 ). Second, theoretically identified mechanisms of action (i.e., mediators) can be investigated to gain further understanding as to how the intervention brings about its effects (Michie & Abraham, 2004 ; Rothman, 2004 , 2009 ). This allows researchers to determine whether unsuccessful interventions have failed either because the intervention has had no effect upon the hypothesised mediator or because the hypothesised (and successfully influenced) mediator has had no effect upon behaviour (Michie & Abraham, 2004 ; Rothman, 2004 , 2009 ), thus facilitating more efficient refinement of the intervention. Third, theory summarises the cumulative knowledge of how to change behaviour across different populations, behaviours and contexts. Finally, theory-based interventions provide an opportunity in which theory can be tested. This aids development of more useful theories which, in turn, supports intervention optimisation (Michie et al., 2008 ; Rothman, 2004 ).

The question as to whether interventions that are explicitly based on theory are more effective that those that are not is a complex one. Some reviews have found a positive association (Albada, Ausems, Bensing, & van Dulmen, 2009 ; Albarracin et al., 2005 ; Glanz & Bishop, 2010 ; Noar, Benac, & Harris, 2007 ; Swann, Bowe, Kosmin, & McCormick, 2003 ; Taylor, Conner, & Lawton, 2011 ), but others have found no association, or, even a negative association (Gardner, Wardle, Poston, & Croker, 2011 ; Roe, Hunt, Bradshaw, & Rayner, 1997 ; Stephenson, Imrie, & Sutton, 2000 ). Some reviews have reported a mixture depending on the measure of effectiveness (Ammerman, Lindquist, Lohr, & Hersey, 2002 ; Bhattarai et al., 2013 ; Kim, Stanton, Li, Dickersin, & Galbraith, 1997 ).

There are several factors that may explain this mixed picture. Theory is often poorly applied. A review investigating application of theory using the 19-item ‘Theory Coding Scheme’ (Michie & Prestwich, 2010 ), found that only 10% of studies of theory-based interventions reported links between behaviour change techniques and theoretical constructs and only 9% reported that all the constructs had been targeted by behaviour change techniques. Another explanation may be that the choice of theory may not have been appropriate. For example, if a behaviour is heavily influenced by habit or emotional states then a theory that focuses on beliefs and reflective thought processes may not be appropriate when informing intervention design.

The importance of understanding the theoretical underpinnings of behavioural interventions has been highlighted in previous research suggesting theoretical bases for combining behaviour change techniques within interventions to allow synergistic effects and enhance their effectiveness (Dombrowski et al., 2012 ; Michie, Abraham, Whittington, McAteer, & Gupta, 2009 ; Taylor et al., 2011 ; Webb, Joseph, Yardley, & Michie, 2010 ). Despite the advantages of theory, behaviour change interventions are often designed without reference to theory (Davies, Walker, & Grimshaw, 2010 ; Prestwich et al., 2013 ). For instance, a recent meta-analysis found that only 22.5% of 235 implementation studies explicitly used theories of behaviour change (Davies et al., 2010 ). Where theory is used, it is often only loosely referred to rather than rigorously applied to intervention design and evaluation (Painter, Borba, Hynes, Mays, & Glanz, 2008 ; Prestwich et al., 2013 ). In those situations where interventions are based on ‘explicit theory’, theory is often used sub-optimally to develop or evaluate the intervention (e.g., only a few of the theoretical constructs may be targeted and/or theory is not used to appropriately tailor the intervention).

Choosing a relevant theory can be a challenging task for intervention designers, especially given the large number of theories, many of which have the same or overlapping constructs, to choose from (Michie et al., 2005 ). There is a lack of guidance on how to select an appropriate theory for a particular purpose (Michie, 2008 ), with a predominance in published intervention evaluations of a small number of theories that have already gained recognition in the field (Painter et al., 2008 ). By using a ‘common’ or ‘favourite’ theory, rather than one that may be more suited to the particular characteristics of the target population, behaviour and context, the potential benefit of using theory is limited.

One approach to addressing the plethora of different, overlapping theories and lack of guidance as to how to choose between them was the development of the Theoretical Domains Framework (TDF; Cane, O'Connor, & Michie, 2012 ; Michie et al., 2005 ). Developed by psychologists and implementation researchers, the TDF provides a framework of theoretical domains to explain barriers and facilitators of behaviour in any particular situation. Informed by 128 explanatory constructs from 33 theories of behaviour, the TDF has been used in many contexts to understand behaviour and design theoretically informed interventions (Francis, O'Connor, & Curran, 2012 ; French et al., 2012 ). Another resource for theory-informed research is the US National Institute of Health's ‘Grid Enabled Measures’ (GEM) web-based database. GEM provides the descriptions of theoretical constructs and behavioural and social measures to assess these constructs ( https://www.gem-beta.org/Public/Home.aspx ). While both these approaches are of value, neither specifies relationships between theoretical domains and constructs in terms of the effect that one domain or construct may have on another. They deal with theoretical domains and constructs, not theories per se. One previous consensus exercise did generate a list of eight constructs thought to influence HIV-related behaviours, with the resulting framework specifying links between the constructs and behaviour (Fishbein et al., 2001 ). However, it is not clear how this consensus was reached and how relevant the included constructs are to other behaviours, given the focus on HIV-related behaviours. Researchers or interventions designers may want to select specific theories either at the beginning of the intervention design process or after conducting some preliminary research to indicate which theories are likely to be relevant and useful. In these situations there is a need for an accessible source of potentially useful theories, as well as a method for selecting amongst them.

At present, theories used in public health and behaviour change interventions more generally tend to emphasise individual and sometimes interpersonal rather than broader social and environmental variables (Glanz & Bishop, 2010 ). Capabilities and motivation (individual factors) are often targeted, but context (social and environmental variables) is far less likely to be considered. NICE's ( 2007 ) behaviour change guidance concluded that interventions were more effective if they simultaneously targeted variables at different levels (e.g., individual, community and population; National Institute for Health and Care Excellence, 2007 ). Therefore, to maximise effectiveness, intervention designers are likely to benefit from drawing from a wider range of theories than currently used. Current resources on theories of behaviour change tend to reflect specific contexts and disciplines, and are thus inevitably limited in the range of theories considered (Agar, 2008 ; Conner & Norman, 2005 ; Glanz & Bishop, 2010 ; Glanz, Rimer, & Lewis, 2002 ).

To improve the selection and application of theory we need to consider, across relevant disciplines, those theories which may be of potential use in informing public health questions. By identifying a range of theories we can assess which theories may be of value given the behaviour, population and context in question. To this end, we conducted a scoping review and consensus exercise, informed by the disciplines of psychology, sociology, anthropology and economics. The scoping review and consensus exercise primarily aimed to address the question, ‘What theories exist across the disciplines of psychology, sociology, anthropology and economics that could be of value to guiding behaviour change interventions?’

To be as comprehensive as possible we focused on both theories of behaviour and behaviour change. Theories of behaviour tend to be linear, and explain the reasons why behaviour may occur by considering a number of predictors and their associations with one another and how these could influence the likelihood of a particular behaviour (Agar, 2008 ; Conner & Norman, 2005 ; Glanz & Rimer, 1997 ; Head & Noar, 2013 ). Theories of change tend to be more cyclical and identify interactional and dynamic behaviour change processes (Agar, 2008 ; Head & Noar, 2013 ). In practice, it is sometimes difficult to distinguish between the two and some theories could be viewed as both.

Of secondary interest we also addressed ‘What criteria should we consider when evaluating the quality and potential appropriateness of behaviour change theory?’ Finally, we assessed the extent to which the theories we identified had been applied within the behaviour change field.

The scope of the present paper is twofold: (i) to report in detail the methodology employed to identify relevant theories and to produce a compendium of these theories and (ii) to provide the list of agreed criteria for judging the quality of the theories. Ways in which some of the theories have been used to study behaviour change are also briefly summarised, though it is beyond the scope of this paper to discuss this in detail. Research examining how the theories have been operationalised and the quality of their empirical application (as measured by the quality criteria reported here) forms part of the future research programme.

Theories of behaviour and behaviour change were identified through five sources: expert consultation with a multidisciplinary project advisory group, electronic databases, web searching, forward and backward searching of reference lists and hand searching of key behavioural science journals. Empirical application of the theories was identified from electronic databases and searching the reference lists of retrieved articles. These, together with expert consultations with the advisory group, informed the development of the quality assessment criteria.

Expert advisory group

Twenty-four UK experts from the social and behavioural sciences and/or population health research formed the advisory group, which determined the scope, methods and conduct of the review. The group comprised four sociologists, five economists, five psychologists, four health service researchers, three anthropologists, two epidemiologists and one policy researcher.

Definition of key terms

One of the first tasks of the advisory group was to agree definitions of the terms ‘theory’ and ‘behaviour’. A shortlist of potentially relevant definitions of each term was compiled from peer-reviewed journals, reports and books, for example, the American Psychological Association Dictionary . In the first of two rounds of a Delphi process, advisory group members were asked to rate each definition and parts of the definition for potential use. When a definition (or a part of it) was rated as important by at least 50% of the group it was retained as relevant. In the second round, core concepts were extracted and synthesised by the authors and used to create working definitions which were then considered for refinement by the advisory group in order to create the final definitions:

The term theory was defined as: ‘a set of concepts and/or statements with specification of how phenomena relate to each other. Theory provides an organising description of a system that accounts for what is known, and explains and predicts phenomena’.

Behaviour was defined as: ‘anything a person does in response to internal or external events. Actions may be overt (motor or verbal) and directly measurable or, covert (activities not viewable but involving voluntary muscles) and indirectly measurable; behaviours are physical events that occur in the body and are controlled by the brain’.

Identification of relevant theories

To inform the literature search strategy, theories of behaviour and behaviour change were identified through expert consultation with the advisory group and an initial scoping of the literature using generic and discipline-specific terms related to behaviour and behaviour change theories. For example, the term ‘cultural change’ tended to be used by anthropologists, ‘action’ by sociologists and ‘behaviour’ by psychologists.

Literature search strategy

The literature search was conducted primarily to uncover theories of behaviour and behaviour change that were not identified through expert consultation with our advisory group. Secondary to this we identified the ways in which the theories we identified had been empirically applied. While we briefly report this, it was beyond the scope of the study to analyse this comprehensively and in detail. In order to retrieve relevant literature across different disciplines six databases were searched between 1 January 1960 and 11 September 2012: PsycINFO, Econlit, Cochrane Database of Systematic reviews, International Bibliography of Social Sciences, EMBASE and MEDLINE. Databases were chosen based on their coverage of discipline- and content-specific literature and on the volume of public health literature. Databases that did not allow the use of wildcards (to account for variations in spellings) or sets of search terms to be entered and combined through the use of Boolean operators, and/or databases that only retrieved titles of articles but not abstracts were not used (e.g., Anthropology Index Online). The final search was conducted on the 11 September 2012.

The search strategy included four sets of search terms: those that (i) apply theory to behaviour change (e.g., ‘behaviour change theory’); (ii) are relevant to behaviour change and also of relevance in understanding behaviour more generally [e.g., ‘Health Belief Model’ (HBM)]; (iii) are relevant to behaviour change but that do not mention theory (e.g., ‘behaviour modification’); and (iv) discipline-specific terms combined with the term behaviour change (e.g., ‘economics and behaviour change’). A list of the search terms together with how these terms were combined can be found in the online supplemental material ( Supplemental Figure 1 ).

The search strategy was customised to each database. Standard filters were used to capture systematic reviews where applicable. A sensitivity analysis was performed to ensure that the search results included key articles on theories relevant to behaviour change (identified through the initial scoping of the literature). Given the complex body of evidence, in terms of cross-cutting disciplines and sheer breadth and volume of literature, the search was restricted to titles and abstracts to tighten the search specificity.

Additional potentially relevant theories were identified through expert consultation and web searching for key documents from organisations known for their interest in behaviour change. This included, from the USA, the National Institute of Health's Behaviour Change Consortium and, from the UK, the NICE, Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI Centre), Government Social Research Unit, House of Lords Science and Technology Select Committee Report on Behaviour Change and National Institute of Health Research's Health Technology Assessment.

Forward and backward citation searching, and hand searching of key behavioural science journals were performed to minimise the likelihood of relevant theories being missed. The journals hand searched were: Annals of Behavioural Medicine , BMC Health Services Research , British Journal of Health Psychology , Health Psychology , Health Psychology Review , Implementation Science , International Journal of Clinical and Health Psychology , Journal of Applied Behavioural Science and Social Science & Medicine .

Inclusion criteria for theories

Theories were included if they: (i) met our definition of theory and behaviour and (ii) considered individual behaviour as an outcome or part of the process leading to the outcome. Theories that considered group behaviour (e.g., ‘organisational behaviour’), without reference to individual behaviour were excluded. While we acknowledge that such theories are of interest to intervention designers who want to change group behaviour we decided to limit the scope of the review to theories concerned with individual behaviour change to keep it manageable. The inclusion of each theory was considered independently by at least two of the four authors and by members of the advisory group. Inter-rater reliability was assessed.

Theories that focused purely on cognition were not included. Examples of such theories include Social Comparison Theory (Festinger, 1954 ), which aims to explain how people's opinions are influenced within social groups and Cognitive Adaptation Theory (Taylor, 1983 ), which aims to explain how people cognitively adapt to threatening events. While these theories contribute to our understanding of knowledge, beliefs and intentions about behaviour there are often significant gaps between these and behaviour (Sheeran, 2002 ) and this project was about theories of behaviour and behaviour change.

We distinguished frameworks, which provide an organising structure, from theories which, in addition, offer explanations of how phenomena relate to each other and permit outcomes to be predicted. Thus, conceptual frameworks such as the TDF (Cane et al., 2012 ; Michie et al., 2005 ), or the Ecological Model (McLeroy, Bibeau, Steckler, & Glanz, 1988 ) that are commonly used to guide the design, implementation or evaluation of interventions were not included. While these frameworks have value in implementation and in public health research, policy and practice, this review was of specific theories.

Inclusion criteria for articles

Screening of articles was in two stages. The first stage (title and abstract) was intentionally inclusive, retaining articles if they mentioned: (i) theory in relation to behaviour or behaviour change or (ii) changing behaviour but made no reference to theory (the full text of the article was then checked to see if theory was used to inform the research). We considered all behaviour to be of relevance, not just health-related behaviours. At the second stage of screening (full-text) tighter restrictions applied and articles were included if: (i) theory and behaviour was defined as per our study definitions and (ii) they fell into one of four categories of article: descriptive, intervention, evaluative or review:

• Descriptive articles were defined as those that contained the original description of a theory by the author/s who originally conceived of the theory (i.e., primary theory sources) or by an author/s who proposed advances in the theory by re-specification. Secondary theory sources (i.e., those that only provided an overview/description of the theory) were not included.
• Intervention articles were defined as those that stated in their methods that they used theory to inform the development and/or evaluation of an intervention aimed at changing behaviour and that included a measure of behaviour as an outcome. We focused on behaviour as the end-point rather than the consequence of the behaviour (e.g., weight loss) because there are a number of factors further along the causal chain that could affect the link between behaviour and outcome (Hardeman et al., 2005 ).
• Evaluative articles were defined as those reporting studies that empirically tested a theory longitudinally.
• Review articles were defined as those that systematically reviewed a theory in relation to a change in behavioural outcomes. Narrative reviews or selective overviews of the literature (i.e., those without a description of a search strategy and no clear methodology that could be reproduced independently) were not included.

Articles were excluded if they: focused on cognition (e.g., intention to change behaviour) rather than actual behaviour; were restricted to research participation behaviours, animal studies, scale development, measurement or programme development, cost-effectiveness or single case studies; focused on mental health including therapeutic interventions where cognitive or emotional variables were the primary outcome. Dissertations and doctoral theses, books and book reviews, conference posters and presentations, editorials and commentaries were excluded for practical reasons to limit the volume of material to be retrieved and reviewed to manageable proportions. Articles that used multiple theories to inform their methodology were excluded because our review was of the empirical application of individual theories to changing behaviour.

We did not exclude articles based on their quality, since the methodology of applying these criteria has yet to be developed.

Inter-rater reliability

Articles were screened for relevance at abstract and full-text stage by the lead author (Rachel Davis). At both screening stages, 30% of the abstracts were independently screened by two other researchers (each of which screened 15%) and inter-rater reliability (calculated using percentage agreement) was assessed. Since the data constitute unbalanced cells, we have used percentage agreement as it provides a more transparent and more readily interpretable parameter than Cohen's kappa. As kappa corrects for chance agreement among multiple coders, use of kappa is likely to underestimate reliability (Steinijans, Diletti, Bomches, Greis, & Solleder, 1997 ). Differences of views about inclusion were resolved through discussion and consensus with the other authors.

Data extraction

Data were extracted on: (i) country where the research took place, (ii) theory used, (iii) type of article (descriptive, intervention, evaluative or review), (iv) design (quantitative, qualitative or mixed methods), (v) target behaviour (e.g., smoking, physical activity), (vi) target direction of behaviour [i.e., increase (which also included maintaining behaviour) or decrease in uptake] and (vii) measurement of behaviour (self-report, objective or both). Dual data extraction was conducted independently on 60% of the included papers by two researchers and inconsistencies resolved through joint discussion.

Quality assessment criteria

We reviewed key literature which synthesised scientific and philosophical perspectives on what makes a theory scientific and useful for the purpose of effecting healthy behaviour change in a target population (e.g., Glanz & Rimer, 1997 ; West, 2006 ) and used this to draft an initial list of quality criteria. These were considered by the advisory group in both a face-to-face discussion and a subsequent electronic Delphi-like consultation aimed at achieving consensus.

We report the theories of behaviour and behaviour change identified in our review and the agreed criteria for assessing theory quality. A high-level summary of the key characteristics of the review articles is also provided.

A high level of agreement was observed for decisions on inclusion in relation to both the theories and the articles included in the review (>90%).

Theories identified

Eighty-two theories of behaviour and behaviour change were identified. These are listed in Table 1 along with the lead author, date of the paper that originally described the theory and the number of articles that reported using the theory. Fifty-nine (out of the 82 theories) were applied in the articles included in our review. The remaining theories ( N = 23) were identified by the advisory group and/or through abstracts of the articles retrieved in our literature search. In other words, these were theories that met our inclusion criteria but did not have relevant articles retrieved from our search strategy that met our article inclusion criteria, i.e., articles did not fall within one of our four categories (descriptive, intervention, evaluative, review). Theories identified through our search that were excluded, with reasons for exclusion, can be found in the online supplemental material ( Supplemental Table 1 ).

Note: Theories 30–32 were all reported in one paper.

It is important to note here that while our intention was to provide a list of potentially relevant theories across different disciplines, it was not possible to categorise the theories according to disciplines. Many of the theories had influences from more than one discipline and/or authors were from several disciplines or could not be categorised into any one discipline.

Nine defining features were identified as conceptually important for a good theory: (i) clarity of constructs – ‘Has the case been made for the independence of constructs from each other?’ (ii) clarity of relationships between constructs – ‘Are the relationships between constructs clearly specified?’ (iii) measurability – ‘Is an explicit methodology for measuring the constructs given?’ (iv) testability – ‘Has the theory been specified in such a way that it can be tested?’ (v) being explanatory – ‘Has the theory been used to explain/account for a set of observations? (statistically or logically)’; (vi) describing causality – ‘Has the theory been used to describe mechanisms of change?’ (vii) achieving parsimony – ‘Has the case for parsimony been made?’ (viii) generalisablity – ‘Have generalisations been investigated across’: (a) behaviours? (b) populations? (c) contexts?’ and (ix) having an evidence base.

Articles retrieved

In the results sections that follow we briefly summarise the main findings of the articles included in our review. Further examination of the empirical application of these theories using our quality assessment criteria is part of the future research programme.

Of 8680 articles retrieved through the database search, 6620 were excluded at the first stage of screening (title and abstract) and 1804 articles (out of the remaining 2060) were excluded after full-text screening, leaving 256 articles. To these a further 20 articles were added through searching the reference lists of the included articles, resulting in 276 articles. Figure 1 displays a flow chart of the search results.

Article characteristics

Articles were published between 1977 and 2012, with most of the research conducted in Europe and North America. Eighteen categories of behaviours were identified, with three accounting for 50% of the articles: increasing physical activity ( N = 72; 26%), safe sex practices ( N = 36; 13%) and smoking cessation ( N = 30; 11%). Fifty-two (19%) articles addressed multiple health-related behaviours, with 17 (6%) of these targeting healthy eating and physical activity together. The remaining categories comprised behaviours relating to: healthy eating ( N = 13), addictive behaviours including alcohol and drugs ( N = 12), health examinations and tests ( N = 11), environmental conservation ( N = 10), violence and delinquency ( N = 9), sun protection ( N = 9), drug adherence ( N = 5), job- or education-related activities ( N = 4), Internet- or other technology-related behaviours ( N = 4), health care professional adherence to health care guidelines ( N = 3), financial-related activities ( N = 2), speeding ( N = 2) and 2 ‘others’ which were behaviours that did not fall into any of the above categories including pet removal from domestic residence ( N = 1) and repairing mosquito nets ( N = 1).

The majority of articles used quantitative methods ( N = 243; 88%) and most reported interventions ( N = 168; 61%) or were evaluative ( N = 62; 35%). Thirty-one descriptive articles (either primary theory sources or extensions of a theory) were identified. Behaviour was most commonly measured by self-report methods ( N = 194; 70%). For a high-level summary of these key characteristics, please refer to Table 2 ; a more detailed account of each individual article can be found online in Supplemental Table 2 .

Papers published by the same first author and focused on the same theory were assessed to identify cases in which multiple articles based on the same intervention (i.e., intervention protocol and outcomes) or data-set had been published. This was found to be the case for 19 articles in total (covering 9 interventions/data-sets; see Tables 1 and ​ and2 2 ).

Frequency of use

Of the 82 theories identified, just 4 theories accounted for 174 (63%) of articles: the Transtheoretical Model of Change (TTM; N = 91; 33%), the Theory of Planned Behaviour (TPB; N = 36; 13%), Social Cognitive Theory (SCT; N = 29; 11%) and the Information-Motivation-Behavioural-Skills Model (IMB; N = 18; 7%). A further four theories accounted for an additional 32 (12%) of the included articles: the HBM ( N = 9; 3%), Self-determination Theory (SDT; N = 9; 3%), Health Action Process Approach (HAPA; N = 8; 3%) and Social Learning Theory (SLT; N = 6; 2%; SLT is a precursor of SCT). The remaining theories ( N = 70) were applied fewer than 6 times each in the literature that met our inclusion criteria, with most only being applied once or twice (see Table 1 ).

This scoping review of theories of behaviour/behaviour change of potential relevance to designing and evaluating public health interventions was informed by the disciplines of psychology, sociology, anthropology and economics. Eighty-two theories were identified that spanned a myriad of behaviours and could be applied to designing and evaluating interventions to improve public health, as well as tackle other social issues such as environmental sustainability and public safety.

It is important to note that the literature identified in the scoping review reflects the search strategy that aimed to identify theories rather than exhaustively review theoretically informed empirical studies. Therefore, whilst the review identified articles that use the theories in relation to our inclusion criteria, it does not reflect the wider application of these theories to public health-related research.

Scoping reviews are used to map or configure a body of evidence. They therefore tend to focus on breadth, including studies that are representative of the variation within the evidence base, rather than focusing on depth and assembling all the eligible material. It can also mean that establishing what the boundaries of the review are, and therefore what should be included or excluded, may be refined during the course of the review (Shemilt et al., 2013 ). Consensus methods can help with this process. While we intended to conduct this review in a systematic and reproducible way, as it was the first attempt that we were aware of to review a bodies of theory in this way, its purpose seemed more akin to that of a scoping than a systematic review. As Gough, Thomas, and Oliver ( 2012 ) have suggested, there is a clear distinction between aggregative systematic reviews that are ‘about seeking evidence to inform decisions’ and configurative scoping reviews which are about ‘seeking concepts to provide enlightenment through new ways of understanding’. Arguably what we wanted to attempt was a combination of these two things but we have nevertheless labelled what we did a scoping review.

From the theories we identified, only a few were frequently applied in literature. While the purpose of our scoping review was not to uncover all the relevant literature on how these theories have been applied, the finding is of interest because it is consistent with other reviews and publications (e.g., Glanz & Bishop, 2010 ; Painter et al., 2008 ; Prestwich et al., 2013 ). Sixty-three per cent of the articles identified in the review related to just four theories: the TTM, TPB, SCT and the IMB Skills Model. While the literature we uncovered was limited by our inclusion criteria, and includes a small number cases in which authors have published more than one article applying the same theory to the same data-set or intervention, it indicates the very uneven distribution of frequency of theory use. This raises the question as to why many theories are so little used. One explanation may be that how often a theory is used, could in part, be confounded by the year in which the theory was introduced. Knowledge of a theory in terms of how much it is discussed in the public domain is also likely to play a role. Another explanation might be that those that are used more frequently are ‘better’ theories and selected for use because they have a stronger evidence base or meet other quality criteria. However, a couple of examples suggest that frequency does not necessarily follow quality. For example, the theory appearing most frequently in our review, the TTM, has been criticised on several grounds (West, 2005 ) and its empirical support has been questioned by systematic review findings (e.g., Cahill, Lancaster, & Green, 2010 ; Etter & Perneger, 1999 ; Littell & Girvin, 2002 ; Whitelaw, Baldwin, Bunton, & Flynn, 2000 ). On the other hand, recent meta-regression evidence has shown good support for Control Theory (Dombrowski et al., 2012 ; Ivers et al., 2012 ; Michie et al., 2009 ); however, this was identified in only one article in our review. Another explanation is that people are not aware of the full range of theories from which to choose and so instead opt for those most commonly applied in the literature. Frequency of use may not reflect perceived quality of the theory but instead, fashion, familiarity, prior training, exposure or incentivisation. We hope that this review will help to increase awareness among intervention designers and researchers about the range of theories available. We report nine criteria agreed as markers of theory quality that could aid selection of the most appropriate theory or theories.

Our decision to focus on theories of behaviour change at the level of the individual and exclude theories concerned with group behaviour is likely to be part of the explanation for the preponderance of psychological theories identified in the review, although even interventions at the community level tend to be informed by psychological or social–psychological theories (e.g., Bonell, Fletcher, et al., 2013 ; Bonell, Jamal, et al., 2013 ; Glanz & Bishop, 2010 ; National Institute for Health and Care Excellence, 2007 ). This, and the decision not to include books where sociological and anthropological theories are more likely to be found, may go some way to explaining why these types of theory are under-represented. In addition, Kelly et al. ( 2010 ) found that sociological theories were missed in electronic searches, particularly if they were more than 25 years old. Given that interventions may be improved by drawing on theories specifically targeting group behaviours, this would be a useful focus for a future literature review as we are not aware of there being such a review.

This review raised the issues as to what constitutes ‘a theory’ and ‘a behaviour’. Theories, as conceptualised here, ranged from quite specific (e.g., to a particular behavioural domain or type of intervention) to very general, including multiple levels of influence. The cut-offs at either end of this spectrum were agreed by consensus but were inevitably arbitrary. A general observation was that more general theories may have greater face validity but be less useable in guiding research than more specific theories; choice of theory will therefore be partly guided by the purpose it is to be put to. Another observation was that there appeared to be no generally accepted use of terms such as theory, model, framework and orientation, with different uses by different authors. Increasing the precision of, and consensus on, use of terminology would be helpful for the field.

‘Behaviour’ also varies in level of specificity: for example, physical activity includes sports which includes volleyball which includes running. Behaviours are also part of sequences, often dependent on previous behaviours (e.g., carrying gym kit) and sometimes on other people's behaviours (e.g., others turning up for a team game). Just as the relevance of a particular theory may vary across type of behaviour, so it may vary according to the level of specificity.

The review also suggests that there are a large number of theories that are of potential use in designing public health interventions. The cataloguing of 83 theories of behaviour change is an important resource for researchers wishing to draw on theories beyond the few that currently dominate the literature. However, few of these theories have been subjected to wide-scale rigorous empirical evaluation. There have been calls for more operationalization, application, testing and refining of theories over many years (e.g., Michie & Johnston, 2012 ; Noar & Zimmerman, 2005 ; Rothman, 2004 ; Weinstein, 2007 ; Weinstein & Rothman, 2005 ), but advances are slow. We need more investment into methodological and substantive research in this area, for example, the use of fractionated factorial (Collins et al., 2011 ) and n -of-1 (Johnston, Jones, Charles, McCann, & McKee, 2013 ) designs to tease apart complex interventions and the extent to which theories can be generalised across populations, behaviours and contexts.

Identifying the theories in this review is just the first step in a much larger and ongoing programme of work aimed at improving the use of appropriate theory and the scientific rigour with which it is applied. Future work will investigate the ways in which theories have been operationalised and the extent to which different theories share constructs and can be seen as ‘families’ of theory. Transforming the nine quality criteria into forms, such as reliable scales or response options that can be used in evaluating theories is a complex task, and a study in its own right. The evolution of theories over time, including the issue of when a theory is considered a new theory, will also be examined. Many theories contained similar constructs or the same constructs but with slightly different names. Understanding these similarities and working towards a common set of terminology would facilitate the building of a cumulative understanding of mechanisms of action from both primary research and evidence syntheses. It would also further our understanding of the evolution of theories and how theories have been revised and/or integrated with other theories over time. Having said this, it is also important to recognise that not only language varies across and within disciplines but so do epistemological and ontological assumptions and preoccupations.

The next phase of the current research is to (i) investigate the connectedness of theories with each other and (ii) operationalize and demonstrate the application of the agreed quality criteria. These will both inform the understanding of theory and its development, and help guide researchers, policy-makers and interventions on the appropriate selection and application behaviour change theories to developing public health and other behaviour change interventions.

Acknowledgements

We are grateful to the study's advisory group for developing the literature search strategy, key definitions and the quality criteria for evaluating theory: Robert Aunger, Mary Barker, Mick Bloor, Heather Brown, Richard Cookson, Cyrus Cooper, Peter Craig, Paul Dieppe, Anna Dixon, Rachel Gooberman-Hill, Simon Griffin, Graham Hart, Kate Hunt, Susan Jebb, Marie Johnston, Mike Kelly, Steve Morris, Mark Petticrew, Paschal Sheeran, Mark Suhreke, Ivo Vlaev, Robert West, Daniel Wight, Daniel Zizzo. We are also grateful to Kate Sheals for invaluable help in the latter stages of manuscript preparation.

Funding Statement

Funding : This project was funded by the Medical Research Council's Population Health Sciences Research Network [grant number PHSRN10 ]. The work was undertaken with the support of The Centre for the Development and Evaluation of Complex Interventions for Public Health Improvement (DECIPHer), a UKCRC Public Health Research: Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council [ RES-590-28-0005 ], Medical Research Council, the Welsh Government and the Wellcome Trust [ WT087640MA ], under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.

This project was funded by the Medical Research Council's Population Health Sciences Research Network [grant number PHSRN10]. The work was undertaken with the support of The Centre for the Development and Evaluation of Complex Interventions for Public Health Improvement (DECIPHer), a UKCRC Public Health Research: Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council [RES-590-28-0005], Medical Research Council, the Welsh Government and the Wellcome Trust [WT087640MA], under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.

Supplemental material

Supplemental material for this article can be accessed here: http://dx.doi.org/10.1080/17437199.2014.941722 .

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Understanding human behavior: theories, patterns and developments

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Articles on Human behavior

Displaying 1 - 20 of 34 articles.

Trust in the shadows: How loyalty fuels illicit economic transactions

Oliver Schilke , University of Arizona

What social robots can teach America’s students

Gerald K. LeTendre , Penn State

Persuading businesses and people to reduce climate emissions is key to slowing climate change – research-based techniques and new approaches from the behavioral sciences can show how to do it

Kira Gunn Barrett , The Conversation

Ancient DNA helps reveal social changes in Africa 50,000 years ago that shaped the human story

Elizabeth Sawchuk , University of Alberta ; Jessica Thompson , Yale University , and Mary Prendergast , Rice University

Making healthy choices for the people we care for may increase our own  self-indulgence

Anika Schumacher , Grenoble École de Management (GEM)

Giving food pantry clients choices – and gently nudging them toward nutritious foods – can lead to healthier diets

Caitlin Caspi , University of Connecticut and Marlene B. Schwartz , University of Connecticut

Ancient eggshells and a hoard of crystals reveal early human innovation and ritual in the Kalahari

Jayne Wilkins , Griffith University and Sechaba Maape , University of the Witwatersrand

Seat belts and smoking rates show people eventually adopt healthy behaviors – but it can take time we don’t have during a pandemic

Randy P. Juhl , University of Pittsburgh

Prehistoric human footprints reveal a rare snapshot of ancient human group behavior

William E.H. Harcourt-Smith , Lehman College, CUNY and Briana Pobiner , Smithsonian Institution

Your brain evolved to hoard supplies and shame others for doing the same

Stephanie Preston , University of Michigan

Americans disagree on how risky the coronavirus is, but most are changing their behavior anyway

Wändi Bruine de Bruin , University of Southern California ; Anya Samek , USC Dornsife College of Letters, Arts and Sciences , and Daniel Bennett , USC Dornsife College of Letters, Arts and Sciences

Think twice before shouting your virtues online – moral grandstanding is toxic

Joshua B. Grubbs , Bowling Green State University

How to use habit science to help you keep your New Year’s resolution

Wendy Wood , USC Dornsife College of Letters, Arts and Sciences

Empathy is the secret ingredient that makes cooperation – and civilization – possible

Arunas L. Radzvilavicius , University of Pennsylvania

Spending too much time on your phone? Behavioral science has an app for that

Ashley Whillans , Harvard University

You can’t characterize human nature if studies overlook 85 percent of people on Earth

Daniel Hruschka , Arizona State University

Speaking with: law professor Cass Sunstein, on why behavioural science is always nudging us

Josh Nicholas, The Conversation

The infantilization of Western culture

Simon Gottschalk , University of Nevada, Las Vegas

The brainwashing myth

Rebecca Moore , San Diego State University

Why proactive leadership is important – or how Congress could have prevented Trump’s Helsinki fiasco

Thomas S. Bateman , University of Virginia and Mike Crant , University of Notre Dame

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OPINION article

Challenges and opportunities for human behavior research in the coronavirus disease (covid-19) pandemic.

• 1 Department of General Psychology, University of Padova, Padua, Italy
• 2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

The COVID-19 pandemic is a serious public health crisis that is causing major worldwide disruption. So far, the most widely deployed interventions have been non-pharmacological (NPI), such as various forms of social distancing, pervasive use of personal protective equipment (PPE), such as facemasks, shields, or gloves, and hand washing and disinfection of fomites. These measures will very likely continue to be mandated in the medium or even long term until an effective treatment or vaccine is found ( Leung et al., 2020 ). Even beyond that time frame, many of these public health recommendations will have become part of individual lifestyles and hence continue to be observed. Moreover, it is implausible that the disruption caused by COVID-19 will dissipate soon. Analysis of transmission dynamics suggests that the disease could persist into 2025, with prolonged or intermittent social distancing in place until 2022 ( Kissler et al., 2020 ).

Human behavior research will be profoundly impacted beyond the stagnation resulting from the closure of laboratories during government-mandated lockdowns. In this viewpoint article, we argue that disruption provides an important opportunity for accelerating structural reforms already underway to reduce waste in planning, conducting, and reporting research ( Cristea and Naudet, 2019 ). We discuss three aspects relevant to human behavior research: (1) unavoidable, extensive changes in data collection and ensuing untoward consequences; (2) the possibility of shifting research priorities to aspects relevant to the pandemic; (3) recommendations to enhance adaptation to the disruption caused by the pandemic.

Data collection is very unlikely to return to the “old” normal for the foreseeable future. For example, neuroimaging studies usually involve placing participants in the confined space of a magnetic resonance imaging scanner. Studies measuring stress hormones, electroencephalography, or psychophysiology also involve close contact to collect saliva and blood samples or to place electrodes. Behavioral studies often involve interaction with persons who administer tasks or require that various surfaces and materials be touched. One immediate solution would be conducting “socially distant” experiments, for instance, by keeping a safe distance and making participants and research personnel wear PPE. Though data collection in this way would resemble pre-COVID times, it would come with a range of unintended consequences ( Table 1 ). First, it would significantly augment costs in terms of resources, training of personnel, and time spent preparing experiments. For laboratories or researchers with scarce resources, these costs could amount to a drastic reduction in the experiments performed, with an ensuing decrease in publication output, which might further affect the capacity to attract new funding and retain researchers. Secondly, even with the use of PPE, some participants might be reluctant or anxious to expose themselves to close and unnecessary physical interaction. Participants with particular vulnerabilities, like neuroticism, social anxiety, or obsessive-compulsive traits, might find the trade-off between risks, and gains unacceptable. Thirdly, some research topics (e.g., face processing, imitation, emotional expression, dyadic interaction) or study populations (e.g., autistic spectrum, social anxiety, obsessive-compulsive) would become difficult to study with the current experimental paradigms ( Table 1 ). New paradigms can be developed, but they will need to first be assessed for reliability and validated, which will undoubtedly take time. Finally, generalized use of PPE by participants and personnel could alter the “usual” experimental setting, introducing additional biases, similarly to the experimenter effect ( Rosenthal, 1976 ).

Table 1 . Possible consequences of non-pharmacological interventions for COVID-19 on human behavior research.

Data collection could also adapt by leveraging technology, such as running experiments remotely via available platforms, like for instance Amazon's Mechanical Turk (MTurk), where any task that programmable with standard browser technology can be used ( Crump et al., 2013 ). Templates of already-programmed and easily customizable experimental tasks, such as the Stroop or Balloon Analog Risk Task, are also available on platforms like Pavlovia. Ecological momentary assessment is another feasible option, since it was conceived from the beginning for remote use, with participants logging in to fill in scales or activity journals in a naturalistic environment ( Shiffman et al., 2008 ). Increasingly affordable wearables can be used for collecting physiological data ( Javelot et al., 2014 ). Web-based research was already expanding before the pandemic, and the quality of the data collected in this way is comparable with that of laboratory studies ( Germine et al., 2012 ). Still, there are lingering issues. For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection ( Crump et al., 2013 ). Further clarifications about quality, such as consistency or interpretability ( Abdolkhani et al., 2020 ), are also needed for data collected using wearables.

Beyond updating data collection practices, a significant portion of human behavior research might change course to focus on the effects of the pandemic. For example, the incidence of mental disorders or of negative effects on psychological and physical well-being, particularly across populations of interest (e.g., recovered patients, caregivers, and healthcare workers), are crucial areas of inquiry. Many researchers might feel hard-pressed to not miss out on studying this critical period and embark on hastily planned and conducted studies. Multiplication and fragmentation of efforts are likely, for instance, by conducting highly overlapping surveys in widely accessible and oversampled populations (e.g., university students). Moreover, rushed planning is bound to lead to taking shortcuts and cutting corners in study design and conduct, e.g., skipping pre-registration or even ethical committee approval or using not validated measurement tools, like ad hoc surveys. Surveys using non-probability and convenience samples, especially for social and mental health problems, frequently produce biased and misleading findings, particularly for estimates of prevalence ( Pierce et al., 2020 ). A significant portion of human behavior research that re-oriented itself to study the pandemic could result in to a heap of non-reproducible, unreliable, or overlapping findings.

Human behavior studies could also aim to inform the planning and enforcement of public health responses in the pandemic. Behavioral scientists might focus on finding and testing ways to increase adherence to NPIs or to lessen the negative effects of isolation, particularly in vulnerable groups, e.g., the elderly or the chronically ill and their caretakers. Studies could also attempt to elucidate factors that make individuals uncollaborative with recommendations from public health authorities. Though all of these topics are important, important caveats must be considered. Psychology and neuroscience have been affected by a crisis in reproducibility and credibility, with several established findings proving unreliable and even non-reproducible ( Button et al., 2013 ; Open Science Collaboration, 2015 ). It is crucial to ensure that only robust and reproducible results are applied or even proposed in the context of a serious public health crisis. For instance, the possible influence of psychological factors on susceptibility to infection and potential psychological interventions to address them could be interesting topics. However, the existing literature is marked by inconsistency, heterogeneity, reverse causality, or other biases ( Falagas et al., 2010 ). Even for robust and reproducible findings, translation is doubtful, particularly when these are based on convenience samples or on simplified and largely artificial experimental contexts. For example, the scarcity of medical resources (e.g., N-95 masks, drugs, or ventilators) in a pandemic with its unavoidable ethical conundrum about allocation principles and triage might appeal to moral reasoning researchers. Even assuming, implausibly, that most of the existent research in this area is robust, translation to dramatic real-life situations and highly specialized contexts, such as intensive care, would be difficult and error-prone. Translation might not even be useful, given that comprehensive ethical guidance and decision rules to support medical professionals already exist ( Emanuel et al., 2020 ).

The COVID-19 pandemic and the corresponding global public health response pose significant and lasting difficulties for human behavior research. In many contexts, such as laboratories with limited resources and uncertain funding, challenges will lead to a reduced research output, which might have further domino effects on securing funding and retaining researchers. As a remedy, modifying data collection practices is useful but insufficient. Conversely, adaptation might require the implementation of radical changes—producing less research but of higher quality and more utility ( Cristea and Naudet, 2019 ). To this purpose, we advocate for the acceleration and generalization of proposed structural reforms (i.e., “open science”) in how research is planned, conducted, and reported ( Munafò et al., 2017 ; Cristea and Naudet, 2019 ) and summarize six key recommendations.

First, a definitive move from atomized and fragmented experimental research to large-scale collaboration should be encouraged through incentives from funders and academic institutions alike. In the current status quo, interdisciplinary research has systematically lower odds of being funded ( Bromham et al., 2016 ). Conversely, funders could favor top-down funding on topics of prominent interest and encourage large consortia with international representativity and interdisciplinarity over bottom-up funding for a select number of excellent individual investigators. Second, particularly for research focused on the pandemic, relevant priorities need to be identified before conducting studies. This can be achieved through assessing the concrete needs of the populations targeted (e.g., healthcare workers, families of victims, individuals suffering from isolation, disabilities, pre-existing physical and mental health issues, and the economically vulnerable) and subsequently conducting systematic reviews so as to avoid fragmentation and overlap. To this purpose, journals could require that some reports of primary research also include rapid reviews ( Tricco et al., 2015 ), a simplified form of systematic reviews. For instance, The Lancet journals require a “Research in context” box, which needs to be based on a systematic search. Study formats like Registered Reports, in which a study is accepted in principle after peer review of its rationale and methods ( Hardwicke and Ioannidis, 2018 ), are uniquely suited for this change. Third, methodological rigor and reproducibility in design, conduct, analysis, and reporting should move to the forefront of the human behavior research agenda ( Cristea and Naudet, 2019 ). For example, preregistration of studies ( Nosek et al., 2019 ) in a public repository should be widely employed to support transparent reporting. Registered reports ( Hardwicke and Ioannidis, 2018 ) and study protocols are formats that ensure rigorous evaluation of the experimental design and statistical analysis plan before commencing data collection, thus making sure shortcuts and methodological shortcomings are eliminated. Fourth, data and code sharing, along with the use of publicly available datasets (e.g., 1000 Functional Connectomes Project, Human Connectome Project), should become the norm. These practices allow the use of already-collected data to be maximized, including in terms of assessing reproducibility, conducting re-analyses using different methods, and exploring new hypotheses on large collections of data ( Cristea and Naudet, 2019 ). Fifth, to reduce publication bias, submission of all unpublished studies, the so-called “file drawer,” should be encouraged and supported. Reporting findings in preprints can aid this desideratum, but stronger incentives are necessary to ensure that preprints also transparently and completely report conducted research. The Preprint Review at eLife ( Elife, 2020 ), in which the journal effectively takes into review manuscripts posted on the preprint server BioRxiv, is a promising initiative in this direction. Journals could also create study formats specifically designed for publishing studies that resulted in inconclusive findings, even when caused by procedural issues, e.g., unclear manipulation checks, insufficient stimulus presentation times, or other technical errors. This would both aid transparency and help other researchers better prepare their own experiments. Sixth, peer review of both articles and preprints should be regarded as on par with the production of new research. Platforms like Publons help track reviewing activity, which could be rewarded by funders and academic institutions involved in hiring, promotion, or tenure ( Moher et al., 2018 ). Researchers who manage to publish less during the pandemic could still be compensated for the onerous activity of peer review, to the benefit of the entire community.

Of course, individual researchers cannot implement such sweeping changes on their own, without decisive action from policymakers like funding bodies, academic institutions, and journals. For instance, decisions related to hiring, promotion, or tenure of academics could reward several of the behaviors described, such as complete and transparent publication regardless of the results, availability of data and code, or contributions to peer review ( Moher et al., 2018 ). Academic institutions and funders should acknowledge the slowdown of experimental research during the pandemic and hence accelerate the move toward more “responsible indicators” that would incentivize best publication practices over productivity and citations ( Moher et al., 2018 ). Funders could encourage submissions leveraging existing datasets or developing tools for data re-use, e.g., to track multiple uses of the same dataset. Journals could stimulate data sharing by assigning priority to manuscripts sharing or re-using data and code, like re-analyses, or individual participant data meta-analyses.

Author Contributions

CG and IC contributed equally to this manuscript in terms of its conceivement and preparation. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was carried out within the scope of the project “use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Ministry of University and Research.

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Keywords: open science, data sharing, social distancing, preprint, preregistration, coronavirus disease, neuroimaging, experimental psychology

Citation: Gentili C and Cristea IA (2020) Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic. Front. Psychol. 11:1786. doi: 10.3389/fpsyg.2020.01786

Received: 29 April 2020; Accepted: 29 June 2020; Published: 10 July 2020.

Reviewed by:

Copyright © 2020 Gentili and Cristea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Claudio Gentili, c.gentili@unipd.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Article Contents

Introduction, what is hbe, a systematic overview of current research, hbe: strengths, weaknesses, opportunities, and open questions, supplementary material, human behavioral ecology: current research and future prospects.

Forum editor: Sue Healy

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Daniel Nettle, Mhairi A. Gibson, David W. Lawson, Rebecca Sear, Human behavioral ecology: current research and future prospects, Behavioral Ecology , Volume 24, Issue 5, September-October 2013, Pages 1031–1040, https://doi.org/10.1093/beheco/ars222

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Human behavioral ecology (HBE) is the study of human behavior from an adaptive perspective. It focuses in particular on how human behavior varies with ecological context. Although HBE is a thriving research area, there has not been a major review published in a journal for over a decade, and much has changed in that time. Here, we describe the main features of HBE as a paradigm and review HBE research published since the millennium. We find that the volume of HBE research is growing rapidly, and its composition is changing in terms of topics, study populations, methodology, and disciplinary affiliations of authors. We identify the major strengths of HBE research as its vitality, clear predictions, empirical fruitfulness, broad scope, conceptual coherence, ecological validity, increasing methodological rigor, and topical innovation. Its weaknesses include a relative isolation from the rest of behavioral ecology and evolutionary biology and a somewhat limited current topic base. As HBE continues to grow, there is a major opportunity for it to serve as a bridge between the natural and social sciences and help unify disparate disciplinary approaches to human behavior. HBE also faces a number of open questions, such as how understanding of proximate mechanisms is to be integrated with behavioral ecology’s traditional focus on optimal behavioral strategies, and the causes and extent of maladaptive behavior in humans.

Very soon after behavioral ecology (henceforth BE) emerged as a paradigm in the late 1960s and early 1970s, a tradition of applying behavioral ecological models to human behavior developed. This tradition, henceforth human behavioral ecology (HBE), quickly became an important voice in the human-related sciences, just as BE itself was becoming an established and recognized approach in biology more generally. HBE continues to be an active and innovative area of research. However, it tends not to receive the attention it might, perhaps in part because its adherents are dispersed across a number of different academic disciplines, spanning the life and social sciences. Although there were a number of influential earlier reviews, particularly by Cronk (1991) and Winterhalder and Smith (2000) , there has not been a major review of the HBE literature published in a journal for more than a decade. In this paper, we undertake such a review, with the aim of briefly but systematically characterizing current research activity in HBE, and drawing attention to prospects and issues for the future. The structure of our paper is as follows. In the section “What is HBE?”, we provide a brief overview of the HBE approach to human behavior. The section “A systematic overview of current research” presents our review methodology and briefly describes what we found. We argue that the HBE research published in the period since 2000 represents a distinct phase in the paradigm’s development, with a number of novel trends that require comment. Finally, the section “HBE: strengths, weaknesses, opportunities, and open questions” presents our reflections on the current state and future prospects of HBE, which we structure in terms of strengths, weaknesses, opportunities, and open questions.

BE is the investigation of how behavior evolves in relation to ecological conditions ( Davies et al. 2012 ). Empirically, there are 2 arms to this endeavor. One arm is the study of how measurable variation in ecological conditions predicts variation in the behavioral strategies that individuals display, be it at the between-species, between-population, between-individual, or even within-individual level. (Throughout this paper, “ecological conditions” is to be interpreted in its broadest sense, to include the physical and social aspects of the environment, as well as the state of the individual within that environment.). The other arm concerns the fitness consequences of the behavioral strategies that individuals adopt. Because fitness—the number of descendants left by individuals following a strategy at a point many generations in the future—cannot usually be measured within a study, this generally means measuring the consequences of behavioral strategies in some more immediate proxy currency related to fitness, such as survival, mating success, or energetic return. The 2 arms of BE are tightly linked to one another; the fitness consequences of some behavioral strategy will differ according to the prevailing ecological conditions. Moreover, central to BE is the adaptationist stance. That is, we expect to see, in the natural world, organisms whose behavior is close to optimal in terms of maximizing their fitness given the ecological conditions that they face. This expectation is used as a hypothesis-generating engine about which behaviors we will see under which ecological conditions. The justification for the adaptationist stance is the power of natural selection. Selection, other things being equal, favors genes that contribute to the development of individuals who are prone to behaving optimally across the kinds of environments in which they have to live ( Grafen 2006 ). Note that this does not imply that behavioral strategies are under direct genetic control. On the contrary, selection favors various mechanisms for plasticity, such as individual and social learning, exactly because they allow individuals to acquire locally adaptive behavioral strategies over a range of environments ( Scheiner 1993 ; Pigliucci 2005 ), and it is these plastic mechanisms that are often in immediate control of behavioral decisions. However, the capacity for plasticity is ultimately dependent on genotype, and plasticity is deployed in the service of genetic fitness maximization.

BE is also characterized by a typical approach, to which actual exemplars of research projects conform to varying degrees. This approach is to formulate simple a priori models of what the individual would gain, in fitness terms, by doing A rather than B, and using these models to make predictions either about how variation in ecological conditions will affect the prevalence of behaviors A and B, or about what the payoffs to individuals doing A and B will be, in some currency related to fitness. These models are usually characterized by the assumption that there are no important phylogenetic or developmental constraints on the range of strategies that individuals are able to adopt and also by a relative agnosticism about exactly how individuals arrive at particular behavioral strategies (i.e., about questions of proximate mechanism as opposed to ultimate function; Mayr 1961 ; Tinbergen 1963 ). The assumptions of no mechanistic constraints coming from the genetic architecture or the neural mechanisms are known, respectively, as the phenotypic gambit ( Grafen 1984 ) and the behavioral gambit ( Fawcett et al. 2012 ). To paraphrase Krebs and Davies (1981 ), “think of the strategies and let the mechanisms look after themselves.” We return to the issue of the validity of the behavioral gambit in particular in section “Open questions.” However, one of the remarkable features of early research in BE (what Owens 2006 calls “the romantic period of BE”) was just how well the observed behavior of animals of many different species was explained by very simple optimality models based on the gambits.

HBE is the study of human behavior from an adaptive perspective. Humans are remarkable for their ability to adapt to new niches much faster than the time required for genetic change ( Laland and Brown 2006 ; Wells and Stock 2007 ; Nettle 2009b ). HBE has been particularly concerned with explaining this rapid adaptation and diversity, and thus, the concept of adaptive phenotypic plasticity has been even more central to HBE than it is to BE in general. HBE represents a rejection of the notion that fundamentally different explanatory approaches are necessary for the study of human behavior as opposed to that of any other animal. Note that this does not imply that humans have no unique cognitive and behavioral mechanisms. On the contrary, they clearly do. Rather, it implies that the general scientific strategy for explaining behavior instantiated in BE remains similar for the human case: understand the fitness costs and benefits given the ecological context, make predictions based on the hypothesis of fitness maximization, and test them. There is a pleasing cyclicity to the development of HBE. BE showed that microeconomic models based on maximization, which had come from the human discipline of economics, could be used at least as a first approximation to predict the behavior of nonhuman animals. HBE imported these principles, enriched from their sojourn in biology by a focus on fitness as the relevant currency, back to humans again.

The first recognizably HBE papers appeared in the 1970s (e.g., Wilmsen 1973 ; Dyson-Hudson and Smith 1978 ). The pioneers were anthropologists, and to a lesser extent archaeologists. A major focus was on explaining foraging patterns in hunting and gathering populations ( Smith 1983 ), though other topics were also represented from the outset ( Cronk 1991 ). The focus on foragers was due to the evolutionary antiquity of this mode of subsistence, as well as these being the populations in which optimal foraging theory was most straightforwardly applicable. However, there is no reason in principle for HBE research to be restricted to such populations. The emphasis in HBE is on human adaptability; humans have mechanisms of adaptive learning and plasticity by virtue of which they can rapidly find adaptive solutions to living in many kinds of environments. Thus, we might expect their behavior to be adaptively patterned in societies of all kinds, not just the types of human society, which have existed for many millennia.

The first phase of HBE lasted through the 1980s ( Borgerhoff Mulder 1988 ). In the second phase, the 1990s, HBE grew rapidly, with Winterhalder and Smith (2000) estimating that there were nearly 300 studies published during the decade. Its focus broadened to encompass more studies from nonforaging subsistence populations, such as horticulturalists and pastoralists (e.g., Borgerhoff Mulder 1990 ), and the use of historical demographic data (e.g., Voland 2000 ; Clarke and Low 2001 ). There were also some pioneering forays into the BE of industrialized populations ( Kaplan 1996 ; Wilson and Daly 1997 ). The 1990s were characterized by an increasing emphasis on topics which fall under the general headings of distribution (cooperation and social structure) and particularly reproduction (mate choice, mating systems, reproductive decisions, parental investment), rather than production (foraging). Anthropologists continued to dominate HBE, and the methodologies of the studies reflect this: many of the studies represented the field observations of a single field researcher from a single population, usually a single site. Having briefly outlined what HBE is and where it came from, we now turn to reviewing the HBE research that has appeared in the years since the publication of Winterhalder and Smith (2000) .

Our objective was to ascertain what empirical research has been done within the HBE paradigm since 2000, and characterize its key features, quantitatively where possible. We thus conducted a systematic search of 17 key journals for papers published between the beginning of 2000 and late 2011, which clearly belong in the HBE tradition (see Supplementary material for full methodology). This involved some contentious decisions about how to draw the boundaries of HBE and in the end, we drew it narrowly, including only papers containing quantitative data on naturally occurring behavior in human populations and employing a clearly adaptive perspective. This excludes a large number of studies that take an adaptive perspective but measure hypothetical preferences or decisions in experimental scenarios. It also excludes many studies that focus on nonbehavioral traits such as stature or physical maturation. The sample is not exhaustive even of our chosen subset of HBE, given that some HBE research is published in edited volumes, books, or journals other than those we searched. However, we feel that our strategy provides a good transect through current research, which is prototypically HBE, and the sampling method is at least repeatable and self-consistent over time.

We used the full text of the papers identified to code a number of key variables relevant to our review, including year of publication, journal, first author country of affiliation, and first author academic discipline. We also adopted Winterhalder and Smith’s (2000) ternary classification of topics into production (foraging and other productive activity), distribution (resource sharing, cooperation, social structure), and reproduction (mate choice decisions, sexual selection, life-history decisions, parental and alloparental investment). Finally, we coded the presence of some key features we wished to examine: the presence of any data from foraging populations, the presence of any data from industrialized populations, the use of secondary data, and the use of comparative data from more than one population.

The search resulted in a database of 369 papers (see Supplementary material for reference list and formal statistical analysis; an endnote library of the references of the papers in the database is also available from the corresponding author). The distribution of papers across journals is shown in Table 1 , which also shows the median year of publication of a paper in that journal. The overall median year of publication for the full sample was 2007; thus, the table can be used to identify those journals that carried HBE papers disproportionately earlier in the study interval (e.g., American Anthropologist , median 2004), and those which carried them disproportionately more recently (e.g., American Journal of Human Biology , median 2009). The total number of papers found per year increased significantly over the 12 years sampled, from around 20 at the beginning to nearly 50 in 2011 ( Figure 1a ; regression analysis suggests an average increase of 2.4 papers per year). In the Supplementary material , we show that HBE papers also increased as a proportion of all papers published in our target journals. First authors were affiliated with institutions in 28 different countries, with 57.5% based in the United States and 20.1% in the United Kingdom. In terms of discipline, anthropology (including archaeology) was strongly represented (49.9% of papers), followed by psychology (19.5%) and biology (12.7%). The remaining papers came from demography (3.3%), medicine and public health (3.0%), sociology and social policy (2.4%), economics and political science (2.2%), or were for various reasons unclassifiable (7.0%). However, the growth in number of papers over time was due to increasing HBE activity outside anthropology ( Figure 1a ). In 2000–2003, 64.0% of papers were from anthropology departments, whereas by 2009–2011, this figure was 47.4%. Our search strategy may, if anything, have underestimated the growth in HBE research from outside anthropology, because our search strategy was based on the journals that had carried important BE or HBE research prior to 2000 and did not include any specialist journals from disciplines such as demography or public health.

Numbers and percentages of papers in the database by journal. Also shown is the median year of publication of an HBE paper in the sample in that journal

a Formerly Journal of Cultural and Evolutionary Psychology .

b Targeted search only; for all other journals, all abstracts read.

Number of published papers identified by year over the study period (a) by disciplinary affiliation of first author; (b) by type of study population (other = agriculturalist, pastoralist, horticulturalist, or multiple types); (c) by tripartite classification of topic.

In terms of type of population studied, 80 papers (21.7%) contained some data from foragers, broadly defined to include any subsistence population for whom foraging forms a substantial part of the diet. One hundred and forty-five papers (39.3%) contained data from industrialized populations. The remainder of papers studied either contemporary or historical agricultural, horticultural, and pastoral populations. As Figure 1b shows, the amount of work on industrialized populations has tended to increase over time, with 22 such papers in 2000–2002 (29.3% of total) and 58 in 2009–2011 (43.0%). By contrast, the amount of work on forager populations is much more stable (20 papers [26.7%] in 2000–2002, 27 papers [20.0%] in 2009–2011). As for topic, we classified 64.8% of our papers as concerning reproduction, with 9.5% concerning production and 13.3% distribution. The remaining 12.5% either spanned several topics or fit none of the 3 categories. Table 2 gives some examples of popular research questions addressed in each of the 3 topic areas. The preponderance of reproduction has increased over time ( Figure 1c ); in 2000–2002, 53.3% of the papers fell into this category, whereas by 2009–2011, it was 68.9%. In fact, the growth of HBE papers during the study period has been completely driven by an increase in papers on reproductive topics (see Supplementary material ). We classified papers according to whether they involved analysis of secondary data sets gathered for other purposes. The number of papers involving such secondary analysis increased sharply through the study period, whereas those involving primary data did not (see Supplementary material ). Comparative analyses also increased significantly over time, but not faster than the overall growth in paper numbers.

Some examples of popular research questions in our database of recent HBE papers

To summarize, the data suggest that HBE has changed measurably in the period since 2000. Some of the changes in this period represent continuations of trends already incipient before, such as the expansion away from foraging and foragers toward reproduction and other types of population ( Winterhalder and Smith 2000 ). Our analysis suggests that it is primarily research into the BE of industrialized societies, which has expanded in the subsequent years, such that over 40% of HBE research published in the most recent 3-year period was conducted on such populations. More “traditional” HBE studies of foraging and small-scale food producing societies have continued, but only at a modestly increased rate compared with the 1990s. An unexpected feature of HBE post-2000 is the expansion of HBE in disciplines outside anthropology. Much of the growth has come from the adoption of HBE ideas by researchers based in departments of psychology, and, to a modest extent, other social sciences such as demography, public health, economics, and sociology. This is concomitant with the increasing focus on large-scale industrialized societies, as well as changes in methodology. Anthropologists often work alone or in small teams to gather special-purpose, opportunistic data sets from a particular field site, and many of the pioneering HBE studies were done in this way. In demography, public health, and sociology, by contrast, research tends to be based on very large, systematically collected, representative data sets, such as censuses, cohort, and panel studies, which are designed with multiple purposes in mind. Particular researchers can then interrogate them secondarily to address their particular questions. As HBE has welcomed more researchers from these other social sciences, it has also adopted these secondary methods more strongly (see section “Strengths” for further discussion). We also note the increase in the number of comparative studies. Comparative methods (albeit usually comparing related species rather than populations of the same species) have been a strong feature of BE since the outset (or before, Cullen 1957 ), and thus this is a natural development for HBE. HBE comparative studies use existing cross-cultural databases ( Quinlan 2007 ), integrate multiple ethnographic or historical sources ( Brown et al. 2009 ), or, increasingly, coordinate researchers to collect or derive standardized measures across multiple populations ( Walker et al. 2006 ; Borgerhoff Mulder et al. 2009 ). Comparative studies have become more powerful in their analytical strategies (see section “Strengths”).

The literature review in section “A systematic overview of current research” allowed us to characterize current HBE research and show some of the ways it has changed in the last decade. In this section, we discuss what we see as the strengths, weaknesses, opportunities, and open questions for HBE as a paradigm. This is inevitably more of a personal assessment than the preceding sections, and we appreciate that not everyone in the field will share our views.

The first obvious strength of HBE is vitality . As Darwinians, it comes naturally to us to assume that something that is increasing in frequency has some beneficial features. Thus, the fact that the number of recognizably HBE papers per year found by our search strategy has doubled in a decade, and that there are more and more adopters outside of anthropology, indicates that a range of people find an HBE approach useful. Where does this utility spring from? In part, it is that HBE models tend to make very clear, a priori predictions motivated by theory. The same cannot be said of all other approaches in the human sciences, and, arguably, the more we complicate behavioral ecological models by including details about how proximate mechanisms work, the more this clarity tends to disappear. We return in section “Open questions” to the issue of whether agnosticism about mechanism can be justified, but we note here that a great strength of (and defense for) simple HBE models is that they so often turn out to be empirically fruitful, despite their simplicity. Whether we are considering when to have a first baby ( Nettle 2011 ), what the effects of having an extra child will be in different ecologies ( Lawson and Mace 2011 ), whether to marry polygynously, polyandrously, or monogamously ( Fortunato and Archetti 2010 ; Starkweather and Hames 2012 ), or which relatives to invest time and resources in ( Fox et al. 2010 ), predictions using simple behavioral ecological principles turn out to be useful in making sense of empirically observed diversity in behavior. HBE has also demonstrated the generality of certain principles, such as the fact that male culturally defined social success is positively associated with reproductive success in many different types of society, albeit that the slope of the relationship differs according to features of the social system ( Irons 1979 ; Kaplan and Hill 1985 ; Borgerhoff Mulder 1987 ; Hopcroft 2006 ; Fieder and Huber 2007 ; Nettle and Pollet 2008 ).

A related strength of HBE is its broad scope . HBE models can apply to many kinds of behavioral decision (in principle, all kinds) and in all kinds of society. It is relatively rare in the human sciences for the same set of predictive principles to apply to variation both within and between societies and to societies ranging from small-scale subsistence populations to large-scale industrial states, but HBE thinking about, for example, reproductive decisions has exactly this scope ( Nettle 2011 ; Sear and Coall 2011 ). This would be a strength indeed, even without the crucial additional feature that the explanatory principles invoked are closely related to those that can be applied to species other than our own. Thus, HBE brings a relative conceptual coherence to the study of human behavior, a study that has traditionally been spread across a number of different disciplines each with different conceptual starting points.

Another strength of HBE as we have defined it here is its relatively high ecological validity . Much psychological research into human behavior relies on hypothetical self-reports and self-descriptions, or contrived experimental situations ( Baumeister et al. 2007 ), and much of behavioral economics consists of artificial games whose relevance to actual allocation decisions outwith the laboratory has been questioned ( Levitt and List 2007 ; Bardsley 2008 ; Gurven and Winking 2008 ). Although human behavioral ecologists use such techniques as their purposes require, at the heart of HBE is still a commitment to looking at what people really do, in the environments in which they really live, as a central component of the endeavor. Furthermore, HBE’s focus on behavioral diversity means that it has studied a much wider range of populations than other approaches in the human sciences (see Henrich et al. 2010 ), and this has led to a healthy skepticism of simple generalizations about human universal preferences or motivations ( Brown et al. 2009 ). Measuring relationships between behavior and fitness-relevant outcomes across a broad range of environments, HBE has now amassed considerable evidence in favor of its core assumptions that context matters when studying the adaptive consequences of human behavior and that behavioral diversity arises because the payoffs to alternative behavioral strategies are ecologically contingent.

HBE is also characterized by increasing methodological rigor. The early phases of HBE were defined by exciting theoretical developments, as evolutionary hypotheses for human behavioral variation were first formulated and presented in the literature. However, conducting empirical studies capable of rigorously testing hypotheses derived from HBE theory presents a number of methodological challenges, not least because the human species is relatively long lived and rarely amenable to experimental manipulation. These challenges are now being increasingly overcome, as HBE expands its tool kit to include new sources of data, statistical methods, and study designs. As noted in the section “A systematic overview of current research,” recent years have witnessed an increased use of secondary demographic and social survey data sets, which often provide larger, more representative samples and a broader range of variables than afforded by field research. Some sources of secondary data have also enabled lineages to be tracked beyond the life span of any individual researcher, providing valuable new data on the correlates of long-term fitness (e.g., Lahdenpera et al. 2004 ; Goodman and Koupil 2009 ).

Statistical methods have also become more advanced. Multilevel analyses are now routinely used in HBE research to deal with hierarchically structured data and accurately partition sources of behavioral variance at different levels (e.g., within and between villages; Lamba and Mace 2011 ). Phylogenetic comparative methods, which utilize information on historical relationships between populations, have become popular for testing coevolutionary hypotheses since they were first applied to human populations in the early 1990s ( Mace and Pagel 1994 ; Mace and Holden 2005 ), though debate remains about their suitability for modeling behavioral transmission in humans ( Borgerhoff Mulder et al. 2006 ). Issues of causal inference are also being addressed with more sophisticated analytical techniques. For example, structural equation modeling and longitudinal methods such as event history analysis have enabled researchers to achieve greater confidence when controlling for potential cofounding relationships (e.g., Sear et al. 2002 ; Lawson and Mace 2009 ; Nettle et al. 2011 ). HBE researchers are also following wider trends in the social and natural sciences by exploring alternatives to classic significance testing, such as information-theoretic and Bayesian approaches for considering competing hypotheses ( Towner and Luttbeg 2007 ). Some researchers have also been able to harness “natural experiments” in situations where comparable populations or individuals are selectively exposed to socioecological change. For example, Gibson and Gurmu (2011) examined the effect of changes in land tenure (from family inheritance to government redistribution) on a population in rural Ethiopia, demonstrating that competition between siblings for marital and reproductive success only occurs when land is inherited across generations. These advancements represent an exciting and necessary step forward, as empirical methods “catch up” with the powerful theoretical framework set out in the early days of HBE.

Finally, HBE has shown itself capable of topical innovation. A pertinent recent example is cooperative breeding (typically loosely defined in HBE as the system whereby women receive help from other individuals in raising their offspring). The idea that human females might breed cooperatively had been around for several decades ( Williams 1957 ), and began to be tested empirically in the late 1980s and 1990s (e.g., Hill and Hurtado 1991 ), but it was the 21st century that saw a real upsurge in interest in this topic, leading to a revitalization of the study of kinship in humans ( Shenk and Mattison 2011 ). HBE has now mined many of the rich demographic databases available for our species to test empirically the hypothesis that the presence of other kin members is associated with reproductive outcomes such as child survival rates and fertility rates. These analyses typically find support for the hypothesis that women adopt a flexible cooperative breeding strategy where they corral help variously from the fathers of their children, other men, and pre- and postreproductive women ( Hrdy 2009 ).

Though we see HBE as a strong paradigm, there are some important weaknesses of its current research to be noted. The first is HBE’s relative isolation from the rest of BE. The core journals of BE are Behavioral Ecology and Behavioral Ecology and Sociobiology . Our search revealed only 8 HBE papers in these journals (2.2% of the sample). The vast majority of papers in our sample appeared in journals which never carry studies of species other than humans, and we know of rather few human behavioral ecologists who also work on other systems. West et al. (2011) have recently argued that evolutionary concepts are widely misapplied (or outdated understandings are applied, a phenomenon colloquially dubbed “the disco problem”) in human research, due to insufficient active integration between HBE and the rest of evolutionary biology.

HBE is clearly not completely decoupled from the rest of BE (see Machery and Cohen 2012 for quantitative evidence on this point). For example, within BE, there has been a decline in interest in foraging theory and a rise in interest in sexual selection ( Owens 2006 ), which are mirrored in the changes in HBE described in section “A systematic overview of current research.” Behavioral ecologists have also become less concerned with simply showing that animals make adaptive decisions, and more concerned with the nature of the neurobiological and genetic mechanisms underlying this ( Owens 2006 ). Parallel developments have occurred in the human literature, with the rise of adaptive studies of psychological mechanisms (see e.g., Buss 1995 ). Our search strategy did not include these studies, because their methodologies are different from those of “classical” HBE, but there is no doubt that they have increased in number. Finally, we note that there has been a recent increase in interest in measuring natural selection directly in contemporary human populations ( Nettle and Pollet 2008 ; Byars et al. 2010 ; Stearns et al. 2010 ; Milot et al. 2011 ; Courtiol et al. 2012 ). This anchors HBE much more strongly to evolutionary biology in general. Despite these developments, we see the isolation of HBE from the rest of biology as a potential risk. We hope to see more behavioral ecologists start to work on humans, and more projects across taxonomic boundaries, in the future.

Finally, we note the rather restricted topic base. HBE has had a great deal to say recently about mating strategies, reproductive decisions, fertility, and reproductive success, but much less about diet, resource extraction, resource storage, navigation, spatial patterns of habitat use, hygiene, social coordination, or the many other elements involved in staying alive. In part, this is because, as HBE expands to focus more on large-scale populations, it discovers that there are already disciplines (economics, sociology, human geography, public health) that deal extensively with these topics. It is in the general area of reproduction that it is easiest to come up with predictions that are obviously Darwinian and differentiate HBE from existing social science approaches. Nonetheless, the explanatory strategy of HBE is of potential use for any topic where behavioral effort has to be allocated in one way rather than another, and thus we would hope to see a broadening of the range of questions addressed as HBE continues to grow.

Opportunities

As HBE continues to expand, we see a major opportunity for HBE to build bridges to the social sciences. At the moment, most HBE papers are published in journals that only carry papers that take an adaptive evolutionary perspective, not general social science journals. Thus, HBE is possibly as separated from other approaches to human behavior as it is from parallel approaches to the behavior of other species. This may be because early proponents of HBE saw it as radically different from existing social science approaches to the same problems, by virtue of its generalizing hypothetico-deductive framework and commitment to quantitative hypothesis testing ( Winterhalder and Smith 2000 ). However, the social science those authors came into closest contact with was sociocultural anthropology, which is perhaps not a very typical social science (see Irons 2000 for an account of the hostile reception of HBE within sociocultural anthropology). As HBE’s expansion brings it into closer proximity with disciplines like economics, sociology, demography, public health, development studies, and political science, there may be more common ground than was previously thought. Social scientists are united in the notion that human behavior is very variable and that context is extremely important in giving rise to this variation. These are commitments that HBE obviously shares. Indeed, although it is still common in the human sciences for authors to rhetorically oppose “evolutionary” to “nonevolutionary” (or “social” and “biological”) explanations of the same problem as if these were mutually exclusive endeavors ( Nettle 2009a ), HBE defies such dichotomies adeptly.

Much of social science is highly quantitative and, generally lacking the ability to perform true experiments, relies on multivariate statistical approaches applied to observational data sets to test between competing explanations for behavior patterns. HBE is just the same, and indeed, since the millennium, has become much more closely allied to other social sciences, adopting the large-scale data resources they provide, as well as methodological tools like multilevel modeling, which they have developed to deal with these. HBE employs a priori models based on the individual as maximizer, a position not shared explicitly by all social sciences. However, this approach is widespread in economics and political science. Indeed, it was economics that gave it to BE. The big difference between HBE and much of social science is the explicit invocation of inclusive fitness (or its proxies) as the end to which behavior is deployed. This does not necessarily make it a competing endeavor, especially because what is measured in HBE is not usually fitness itself, but more immediate proxies. Rather, HBE models can often be seen as adding an explicitly ultimate layer of explanation, giving rise to new predictions and unifying diverse empirical observations, without being incompatible with existing, more proximate theories.

Indeed, our perception is that a number of social science theories make assumptions about the ends of behavior, which are quite similar to those of HBE, just not explicitly expressed in Darwinian terms; basically, people’s sets of choices are constrained by the environment in which they have to live, and they make the best choices they can given these constraints, often with knock-on effects that behavioral ecologists would describe as trade-offs. Examples include the work of Geronimus on how African American women adjust their patterns of childbearing to the prevailing rates of mortality and morbidity in their neighborhoods ( Geronimus et al. 1999 ), the work of Drewnowski and colleagues on how people adjust the type of foodstuffs they consume to the budgets they have to spend ( Drewnowski and Specter 2004 ; Drewnowski et al. 2007 ), or Downey’s work on the effects of increasing family size on socioeconomic outcomes of the children ( Downey 2001 ). If the introductory sections of any of these papers were written from a more explicitly Darwinian perspective, they would look perfectly at home in a BE journal. The breaking down of the social science–natural science divide has long been held as desirable, but is not easy to achieve in practice. HBE’s boundary with the social sciences may be one frontier where some progress can occur. Social scientists have long lamented the fragmentation of their field into multiple disciplinary areas with little common ground (e.g., Davis 1994 ). Given HBE’s broad scope and general principles, it has the potential to serve as something of a lingua franca across social scientists working on different kinds of problems.

A related opportunity for HBE is the potential for applied impact . HBE models have the potential to provide new and practical insights into contemporary world issues, from natural resource management ( Tucker 2007 ) to the consequences of inequality within developed populations ( Nettle 2010 ). The causes and consequences of recent human behavioral and environmental changes (including urbanization, economic development, and population growth) are recurring themes in recent studies in HBE. The utility of an ecological approach is clearly demonstrated in studies exploring the effectiveness of public policies or intervention schemes seeking to change human behavior or environments. HBE models clarify that human behavior tends to be deployed in the service of reproductive success, not financial prudence, health, personal or societal wellbeing ( Hill 1993 ), an important insight that differs from some economic or psychological theories. By providing insights into ultimate motivations and proximate pathways to human behavioral change, HBE studies can sometimes offer direct recommendations for the design and implementation of future initiatives ( Gibson and Mace 2006 ; Shenk 2007 ; Gibson and Gurmu 2011 ). Addressing contemporary world issues does, however, present methodological and theoretical challenges for HBE, requiring more explicit consideration of how research insights may be translated into interventions and communicated to policymakers and users ( Tucker and Taylor 2007 ).

Open questions

An open question for HBE is how the study of mechanism can be integrated into functional enquiry. This is an issue for BE generally, not just the human case. As mentioned in the section “What is HBE?”, BE has tended to proceed by the behavioral gambit—the assumption that the nature of the proximate mechanisms underlying behavioral decisions is not important in theorizing about the functions of behavior. It is important to understand the status of the behavioral gambit because it has sometimes been unfairly criticized (see Parker and Maynard Smith 1990 ). In the natural world, individuals do not always behave optimally with respect to any particular decision because there are phylogenetic or mechanistic constraints on their ability to reach adaptive solutions. However, in general terms, the only way to discover the existence of such departures from optimality is to have a theoretical model that shows what the optimal behavior would be and to test empirically whether individual behavior shows the predicted pattern. Where it does not, this may point to unappreciated constraints or trade-offs and thus shed light on the biology of the organism under study. Thus, the use of the term gambit is entirely apt; the behavioral gambit is a way of opening the enquiry designed to gain some advantage in the quest to understand. It is not the end game.

Where there is no sizable departure from predicted optimality, the ultimate adaptive explanation does not depend critically on understanding the mechanisms. This does not mean the question of mechanism is unimportant, of course; mechanistic explanations must still be sought and integrated with functional ones. This is beginning to occur in some cases. In the field of human reproductive ecology, the physiological mechanisms involved in adaptive strategies are beginning to be understood ( Kuzawa et al. 2009 ; Flinn et al. 2011 ), and there is also increasing interchange between HBE researchers and experimentalists studying psychological mechanisms ( Sear et al. 2007 ), which is clearly a development to be welcomed.

Where there is a patterned departure from optimality, understanding the mechanism becomes more critical. Aspects of mechanism can then be modeled as additional constraints, which may explain the strategies individuals pursue. For example, Kacelnik and Bateson (1996) showed that the pattern of risk aversion for variability in food amount and risk proneness for variability in food delay is not predicted by optimal foraging theory, except when Weber’s law (the principle that perceptions of stimulus magnitude are logarithmically, not linearly, related to actual stimulus magnitude) is incorporated into models as a mechanistic constraint. At a deeper level, though, this just raises further questions. Why should Weber’s law have evolved, and once it has evolved, can selection relax it for any particular task? These are what McNamara and Houston call “evo-mecho” questions ( McNamara and Houston 2009 ). Departures from optimality in one particular context raise such questions pervasively. Issues such as the robustness, neural instantiability, efficiency, and developmental cost of different kinds of mechanisms become salient here, and many apparently irrational quirks of behavior become interpretable as side effects of evolved mechanisms whose overall benefits have exceeded their costs over evolutionary time ( Fawcett et al. 2012 ). However, we would still argue that the best first approximation in understanding a question is to employ the behavioral gambit to generate and test simple optimality predictions, even though an understanding of mechanism will be essential for explaining why these may fail.

Although the issue of how incorporation of mechanism changes the predictions of BE models is a general one, in the human case, it has been discussed in particular with reference to transmitted culture because this is a class of mechanism on which humans are reliant to a unique extent ( Richerson and Boyd 2005 ). Transmitted culture refers to the behavioral traditions that arise from repeated social learning. Social learning can be an evolutionarily adaptive strategy, and the equilibrium solutions reached by it will often be the fitness-maximizing ones under reasonable assumptions ( Henrich and McElreath 2003 ). After all, if reliance on culture on average led to maladaptive outcomes, there would be strong selection on humans to rely on it less. Indeed, there is evidence that humans tend to forage efficiently for socially acquired information, using it when it is adaptive to do so ( Morgan et al. 2012 ). Thus, we would argue that culture can be treated, to a first approximation, just like any other proximate mechanism: that is, it can be set aside in the initial formulation of functional explanations ( Scott-Phillips et al. 2011 , though see Laland et al. 2011 for a different view). As an example, we could take Henrich and Henrich’s (2010) data on food taboos for pregnant and lactating women in Fiji. These authors show that the taboos reduce women’s chances of fish poisoning by 30% during pregnancy and 60% during breastfeeding and thus are plausibly adaptive. The fact that in this case it is culture by which women acquire them, rather than genes or individual learning, does not affect this conclusion or the data needed to test it. However, the quirks of how human social learning works may well explain some nonadaptive taboos that are found alongside the adaptive ones, which are in effect carried along by the generally adaptive reliance on social learning. Thus, although the behavioral gambit can be used to explain the major adaptive features of these taboos, an understanding of the cultural mechanisms is required to explain the details of how the observed behavior departs in subtle ways from the optimal pattern. Culture may often lead to maladaptive side effects in this way ( Richerson and Boyd 2005 ). Although its general effect is to allow humans to rapidly reach adaptive equilibria, nonadaptive traits can be carried along by it, and, compared with other proximate mechanisms, it produces very different dynamics of adaptive change.

A final open question is the extent of human maladaptation. Humans have increased their absolute numbers by orders of magnitude and colonized all major habitats of the planet, so they are clearly adept at finding adaptive solutions to the problem of living. However, there are also some clear cases of quite systematic departures from adaptive behavior. Perhaps most pertinently, the low fertility rate typical of industrial populations still defies a convincing adaptive explanation, despite being a longstanding topic for HBE research (see Borgerhoff Mulder 1998 ; Kaplan et al. 2002 ; Shenk 2009 ). There are patterns in the fertility of modernizing populations, which can be readily understood from an HBE perspective: parents in industrialized populations who have large families suffer a cost to the quality of their offspring, particularly with regard to educational achievement and adult socioeconomic success, so there is a quality–quantity trade-off ( Lawson and Mace 2011 ). Moreover, the reduction in fertility rate is closely associated with improvement in the survival of offspring to breed themselves, so that, as the transition to small families proceeds, the probability of having at least one grandchild may remain roughly constant ( Liu and Lummaa 2011 ). However, despite all this, it remains the case that people in affluent societies could still have many more grandchildren and great-grandchildren by having more children, and yet they do not ( Goodman et al. 2012 ). Any explanation of the demographic transition must, therefore, invoke some kind of maladaptation or mismatch between the conditions under which decision-making mechanisms evolved and those under which they are now operating.

Our review has shown that HBE is a growing and rapidly developing research area. The weaknesses of HBE mostly amount to a need for more research activity, and the unresolved questions, though important, do not in our view undermine HBE’s core strengths of theoretical coherence and empirical utility. HBE is being applied to more questions in more human populations with better methods than ever before. Our hope is that HBE will inspire more behavioral biologists to work on humans, for whom a wealth of data is available, and more social scientists to adopt an adaptive, ecological perspective on their behavioral questions, thus adding a layer of deeper explanations, as well as generating new insights.

Supplementary material can be found at Supplementary Data

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Article contents

Habit formation and behavior change.

• Benjamin Gardner Benjamin Gardner Department of Psychology, King's College London
• , and  Amanda L. Rebar Amanda L. Rebar Department of Human, Health, and Social Sciences, Central Queensland University
• https://doi.org/10.1093/acrefore/9780190236557.013.129
• Published online: 26 April 2019

Within psychology, the term habit refers to a process whereby contexts prompt action automatically, through activation of mental context–action associations learned through prior performances. Habitual behavior is regulated by an impulsive process, and so can be elicited with minimal cognitive effort, awareness, control, or intention. When an initially goal-directed behavior becomes habitual, action initiation transfers from conscious motivational processes to context-cued impulse-driven mechanisms. Regulation of action becomes detached from motivational or volitional control. Upon encountering the associated context, the urge to enact the habitual behavior is spontaneously triggered and alternative behavioral responses become less cognitively accessible.

By virtue of its cue-dependent automatic nature, theory proposes that habit strength will predict the likelihood of enactment of habitual behavior, and that strong habitual tendencies will tend to dominate over motivational tendencies. Support for these effects has been found for many health-related behaviors, such as healthy eating, physical activity, and medication adherence. This has stimulated interest in habit formation as a behavior change mechanism: It has been argued that adding habit formation components into behavior change interventions should shield new behaviors against motivational lapses, making them more sustainable in the long-term. Interventions based on the habit-formation model differ from non-habit-based interventions in that they include elements that promote reliable context-dependent repetition of the target behavior, with the aim of establishing learned context–action associations that manifest in automatically cued behavioral responses. Interventions may also seek to harness these processes to displace an existing “bad” habit with a “good” habit.

Research around the application of habit formation to health behavior change interventions is reviewed, drawn from two sources: extant theory and evidence regarding how habit forms, and previous interventions that have used habit formation principles and techniques to change behavior. Behavior change techniques that may facilitate movement through discrete phases in the habit formation trajectory are highlighted, and techniques that have been used in previous interventions are explored based on a habit formation framework. Although these interventions have mostly shown promising effects on behavior, the unique impact on behavior of habit-focused components and the longevity of such effects are not yet known. As an intervention strategy, habit formation has been shown to be acceptable to intervention recipients, who report that through repetition, behaviors gradually become routinized. Whether habit formation interventions truly offer a route to long-lasting behavior change, however, remains unclear.

• automaticity
• behavior change
• dual process

What Are Habits and Habitual Behaviors ?

Everyday behaviors shape human health. Many of the dominant causes of death, including heart disease, diabetes, cancer, chronic lower respiratory diseases, and stroke, are preventable (World Health Organization, 2017 ). Adopting health-promoting behaviors such as eating more healthily or increasing physical activity may improve quality of life, physical and mental health, and extend lives (Aune et al., 2017 ; Centers for Disease Control and Prevention, 2014 ; Rebar et al., 2015 ; World Health Organization, 2015 ). For some behaviors, one performance is sufficient to attain desired health outcomes; a single vaccination, for example, can yield immunity to disease (e.g., Harper et al., 2004 ). For many behaviors, however, achieving meaningful health outcomes depends on repeated performance: Going for a run once, for example, will not achieve the same health benefits as regular activity over a prolonged period (Erikssen et al., 1998 ). In such instances, behavior change must be viewed as a long-term process, which can be conceptually separated into stages of initiation and maintenance (Prochaska & DiClemente, 1986 ; Rothman, 2000 ). This distinction is important from a practical perspective because while people may possess the capability, opportunity, and motivation to initiate behavior change (Michie, van Stralen, & West, 2011 ), they often fail to maintain it over time, lapsing back into old patterns of behavior (Dombrowski, Knittle, Avenell, Araujo-Soares, & Sniehotta, 2014 ). Some have attributed this to changes in motivation after initial experiences of action (Armitage, 2005 ; Rothman, 2000 ). People may overestimate the likelihood of positive outcomes or the valence of such outcomes, or they may fail to anticipate negative outcomes (Rothman, 2000 ). Alternatively, a newly adopted behavior may lose value and so become deprioritized over time. Motivation losses threaten to derail initially successful behavior change attempts.

Habit formation has attracted special attention as a potential mechanism for behavior change maintenance (Rothman, Sheeran, & Wood, 2009 ; Verplanken & Wood, 2006 ) because habitual behaviors are thought to be protected against any dips in conscious motivation. Viewing habit as a means to maintenance may seem truistic; in everyday discourse, a habit is an action done repetitively and frequently, and so making action habitual will necessarily entail maintenance. Within psychology, however, the term habit denotes a process whereby exposure to a cue automatically triggers a non-conscious impulse to act due to the activation of a learned association between the cue and the action (Gardner, 2015 ). Habit is learned through “context-dependent repetition” (Lally, van Jaarsveld, Potts, & Wardle, 2010 ): Repeated performance following exposure to a reliably co-occurring cue reinforces mental cue-action associations. As these associations develop, the habitual response gradually becomes the default, with alternative actions becoming less cognitively accessible (Danner, Aarts, & de Vries, 2008 ). Habit is formed when exposure to the cue is sufficient to arouse the impulse to enact the associated behavior without conscious oversight (Gardner, 2015 ; Neal, Wood, Labrecque, & Lally, 2012 ; Wood, Labrecque, Lin, & Rünger, 2014 ). In the absence of stronger influences favoring alternative actions, the habit impulse will translate smoothly and non-consciously into action, and the actor will experience behavior as directly cued by the context (Wood & Neal, 2007 ).

Defining habit as a process that generates behavior breaks with earlier definitions, which depicted habit as a form of behavior (see Gardner, 2015 ). This definition of habit as a process resolves a logical inconsistency that arises from portraying habit as a determinant of behavior (e.g., Hall & Fong, 2007 ; Triandis, 1980 ); as Maddux ( 1997 , pp. 335–336) noted, “a habit cannot be both the behavior and the cause of the behavior.” It also allows for the habit process to manifest in multiple ways for any behavior. A distinction has been drawn between habitually instigated and habitually executed behavior (Gardner, Phillips, & Judah, 2016 ; Phillips & Gardner, 2016 ). Habitual instigation refers to habitual triggering of the selection of an action and a non-conscious commitment to performing it upon encountering a cue that has consistently been paired with the action in the past. Habitual execution refers to habit facilitating completion of the sub-actions that comprise any given action such that the cessation of one action in a sequence automatically triggers the next. Take, for example, “eating a bag of chips.” While people typically mentally represent this activity as a single unit of action (Wegner, Connally, Shearer, & Vallacher, 1983 , cited in Vallacher & Wegner, 1987 ), it can be deconstructed into a series of discrete sub-actions (e.g., “opening bag,” “putting hand in bag,” “putting food in mouth,” “chewing,” “swallowing”; Cooper & Shallice, 2000 ). “Eating a bag of chips” is habitually instigated to the extent that the actor is automatically cued to select “eating chips” from available behavioral options. This may also activate the first sub-action in the sequence (“opening bag”). “Eating a bag of chips” is habitually executed to the extent that the cessation of, for example, “putting my hand in the bag” habitually cues “putting food in mouth,” the cessation of which habitually cues “chewing,” and so on, until the perceptually unitary action (“eating a bag of chips”) is complete. 1 The term habitual behavior describes any action that is either instigated or executed habitually. This includes actions that are habitually instigated but non-habitually executed (e.g., habitually triggered to begin eating a bag of chips, but deliberates about how many chips to put in mouth), non-habitually instigated but habitually executed (e.g., consciously decides to eat a bag of chips, but habitually puts the chips in mouth, chews, and swallows), or both habitually instigated and habitually executed (e.g., habitually starts eating chips, and habitually puts them in mouth, chews, and swallows; Gardner, 2015 ). This description allows for a behavior to be habitual, yet not fully automated (see Aarts, Paulussen, & Schaalma, 1997 ; Marien, Custers, & Aarts, 2019 ) and better resonates with everyday experiences of complex health behaviors such as physical activity, which may be partly habit-driven, yet also require conscious oversight to be successfully completed (Rhodes & Rebar, 2019 ).

Habit has been implicated in behaviors across a range of domains, including media consumption (LaRose, 2010 ), purchasing patterns (Ji & Wood, 2007 ), environmentally relevant actions (Kurz, Gardner, Verplanken, & Abraham, 2014 ), and health behaviors. Studies have pointed to a multitude of health-related actions that may potentially be performed habitually, including dietary consumption (Adriaanse, Kroese, Gillebaart, & De Ridder, 2014 ), physical activity (Rebar, Elavsky, Maher, Doerksen, & Conroy, 2014 ), medication adherence (Hoo, Boote, Wildman, Campbell, & Gardner, 2017 ), handwashing (Aunger et al., 2010 ), and dental hygiene (Wind, Kremers, Thijs, & Brug, 2005 ). Habit strength is consistently found to correlate positively with behavioral frequency (Gardner, de Bruijn, & Lally, 2011 ; Rebar et al., 2016 ) and may bridge the “gap” between intention and behavior, though there are varying accounts regarding interplay between habits and intentions in regulating behavior. Some have argued that people are more likely to act on intentions when they have habits for doing so (Rhodes & de Bruijn, 2013 ). When motivation is momentarily low upon encountering associated contexts, habit may translate into performance despite motivational lapses. In this way, habit has been proposed to represent a form of self-control, protecting regularly performed behaviors that are desired in the longer-term against shorter-term motivation losses (Galla & Duckworth, 2015 ). Other studies have suggested that habit can direct action despite intentions not to act (Neal, Wood, Wu, & Kurlander, 2011 ; Orbell & Verplanken, 2010 ; but see Rebar et al., 2014 ). For example, one study showed that United Kingdom smokers with habits for smoking while drinking alcohol reported “action slips” after the introduction of a smoking ban in public houses; despite intending to adhere to the ban, several reporting “finding themselves” beginning to light up cigarettes while consuming alcohol (Orbell & Verplanken, 2010 ). These two perspectives concur in highlighting the potential for habit to override conscious motivational tendencies. Such effects may be attributable to habitual instigation rather than execution (Gardner et al., 2016 ); someone who is habitually prompted to act is more likely to frequently perform those actions and to do so without relying on intention.

The effects of habit—or more specifically, instigation habit (Gardner et al., 2016 )—have important implications for behavior maintenance. By virtue of their cue-dependent, automatic nature (Orbell & Verplanken, 2010 ), habitually instigated behaviors should, in theory, persist even when they no longer serve the goal that initially motivated performance, or where motivation has eroded (Wood & Neal, 2007 ). For example, a person starting a new job out of town may consistently decide to commute by bicycle, which will likely create a habit for bicycle commuting whereby the workday morning context automatically prompts bicycle use without any deliberation over available alternatives (Verplanken, Aarts, Knippenberg, & Moonen, 1998 ). This may, however, lead to instances whereby the commuter “accidentally” uses the bicycle out of habit, despite, for example, knowing of road closures that will slow the journey and which would render alternative transport modes preferable (see Verplanken, Aarts, & Van Knippenberg, 1997 ). This example demonstrates several key features of habitual responses: learning via consistent pairing of cues (e.g., 8 a.m. on a workday) and action (selecting the bicycle); cue-dependent automaticity (using the bicycle at 8 a.m. on a workday without deliberation); and goal-independence, persisting even where an actor no longer has the motivation to act or is motivated to act in another way (e.g., when roads are closed). It also demonstrates how habit formation can maintain behavior by “locking in” new behaviors, protecting them against losses in conscious motivation. Habit development may also play a useful role in cessation of unwanted behaviors. Many ingrained behaviors—for example, eating high-calorie snacks—persist because they have become habitual and so are difficult to change. The lack of reliance on conscious intentions that is characteristic of habitual behavior, and which is thought to protect new behaviors against motivation losses, makes it difficult to break unwanted habits despite strong intentions to do so (Webb & Sheeran, 2006 ). While habit formation per se is not a sufficient strategy for “giving up” an unwanted behavior, behavior change can be made easier by seeking to form a new (“good”) habit in place of the old (“bad”) habit, rather than attempting only to inhibit the unwanted action (Adriaanse, van Oosten, de Ridder, de Wit, & Evers, 2011 ). Indeed, in the real world, habit development often involves displacing existing actions with more desirable alternatives such as eating healthy snacks in place of higher-calorie foods (Lally, Wardle, & Gardner, 2011 ; McGowan et al., 2013 ). Such “habit substitution” can take one of two basic forms, involving either avoidance of cues to the unwanted action or the development of new responses that compete with the unwanted habitual response. The “habit discontinuity hypothesis” speaks to the former of these, arguing that naturally occurring disruption of contexts—such as a residential relocation, for example—discontinues exposure to old habit cues (Walker, Thomas, & Verplanken, 2015 ). This represents an opportunity for people to act on their conscious motivation in response to newly encountered cues, and so to develop new, potentially more desirable habitual responses such as using active travel modes in place of more sedentary travel options like driving (Verplanken & Roy, 2016 ). Bad habits offer established cue-response structures that can hasten learning of new, good habits. Thus, where discontinued cue exposure is not feasible, people may seek to develop new cue-behavior associations to compete with and ultimately override old associations (Bouton, 2000 ; Walker et al., 2015 ). For example, people wishing to reduce habitual unhealthy snacking may form plans that dictate that when they are watching television and wish to snack (cue), they will eat fruit (new, desired behavior) instead of high-calorie foods (undesired, habitual behavior; e.g., Adriaanse, Gollwitzer, De Ridder, De Wit, & Kroese, 2011 ). In both instances of discontinued cue exposure and the adoption of competing responses to existing cues, the development of new habit associations and the decaying (or deprioritizing) of old habit associations are thought to occur concurrently (Adriaanse et al., 2011 ; Walker et al., 2015 ; Wood & Neal, 2007 ).

How Does Habit Form?

There have been calls for habit formation, whether focused solely on establishing new actions or displacing unwanted actions, to be adopted as an explicit goal for behavior change interventions (Rothman et al., 2009 ; Verplanken & Wood, 2006 ). Developing effective habit formation interventions requires an understanding of how habit forms.

The concept of behavior as an automatic response to covarying contextual cues, directed by learned cue-action associations, is rooted in behaviorist principles and studies of animal learning (e.g., Hull, 1943 ; Skinner, 1938 ; Thorndike, 1911 ). For example, in his maze-learning studies, Tolman ( 1932 ) noted that his rats, having repeatedly run down the route at the end of which was a food reward, continued to pursue that route even when the reward was removed. Adams ( 1982 ) trained rats to press a lever in a cage so as to receive intermittently delivered sucrose pellets. After receiving a lithium chloride injection that caused ingestion of the sucrose to induce nausea, those rats that were more highly trained (i.e., had pressed and received the sucrose reward a greater number of times in the training phase) were likely to persist longer in pressing the lever. Of course, unlike rats, humans possess the cognitive capacity to anticipate and reflect on their actions, and health-related behaviors among humans are inherently more complex than selecting maze routes or pressing levers. Yet, homologous neural processes are implicated in the acquisition and practice of habitual responses in rats and humans (Balleine & O’Doherty, 2010 ), and, like rats, people can acquire habitual behavioral responses despite a lack of insight into those behaviors or the associations that govern their performance (Bayley, Frascino, & Squire, 2005 ).

The route to human habit formation is conceptually simple: A behavior must be repeatedly performed in the presence of a cue or set of cues (i.e., context) so that cue-behavior associations may develop. For behaviors that are initially purposeful and goal-directed, the habit-formation process represents a period of transition whereby behavioral regulation transfers from a reflective and deliberative processing system to an impulsive system, which generates action rapidly and automatically based solely on activation of associative stores of knowledge (Strack & Deutsch, 2004 ). While there has been much lab-based research into the learning of relatively simple habitual responses in humans (e.g., button pressing; Webb, Sheeran, & Luszczynska, 2009 ), only relatively recently have studies focused on formation of real-world health-related habits (Fournier et al., 2017 ; Judah, Gardner, & Aunger, 2013 ; Lally et al., 2010 ). This work has largely been facilitated by the development of the Self-Report Habit Index (SRHI; Verplanken & Orbell, 2003 ), which affords reflections on the “symptoms” of habit, such as repetitive performance, mental efficiency, and lack of awareness.

Lally et al.’s ( 2010 ) seminal habit formation study used an SRHI sub-scale to assess the trajectory of the relationship between repetition and habit development among 96 participants for a 12-week period. They were instructed to perform a self-chosen physical activity or diet-related behavior (e.g., “going for a walk”) in response to a naturally occurring once-daily cue (e.g., “after breakfast”). Each day, they reported whether they had performed the action on the previous day, and if so, rated the experienced automaticity of its performance. Habit development within individuals was found to be most accurately depicted by an asymptotic curve, with early repetitions achieving sharpest habit gains, which later slowed to a plateau. The level at which habit peaked differed across participants, with some reportedly attaining scores at the high end of the automaticity index and others peaking below the scale mean. This plateau was reached at a median of 66 days post-baseline, though there was considerable between-person variation in the time taken to reach the plateau (18–254 days, the latter a statistical forecast assuming continued performance beyond the study period). These findings were echoed in a study of adoption of a novel stretching behavior (Fournier et al., 2017 ). Once-daily performance was found to yield asymptotic increases in self-reported habit strength. Habit plateaued at a median of 106 days for a group that performed the stretch every morning upon waking, and 154 days for those who stretched in the evening before bed, which the authors interpreted as evidence of the role of cortisol (which naturally peaks in the morning) in habit learning.

These studies reveal that habit development is not linear; if this were so, the fourth repetition of a behavior would have the same reinforcing impact on habit as would, say, the 444th. Rather, the asymptotic growth curve demonstrates that initial repetitions have the greatest impact on habit development. This in turn demands that the habit formation process be broken down into discrete phases and that the early phase, characterized by the sharpest gains in automaticity, may be a critical period during which people require most support to sustain motivation before the action becomes automatic (Gardner, Lally, & Wardle, 2012 ). Lally and Gardner ( 2013 ) have proposed a framework that organizes habit formation (and substitution) into four interlinked phases (see also Gardner & Lally, 2019 ). It argues that, for new behaviors initially driven by conscious motivation, habit forms when a person (1) makes a decision to act and (2) acts on his or her decision (3) repeatedly, (4) in a manner conducive to the development of cue-behavior associations. Phases 1 and 2 may be taken together to represent pre-initiation, occurring before the first enactment of the new behavior, whereas phases 3 and 4 are post-initiation phases, addressing the motivational and volitional elements needed to sustain behavior after initial performance (phase 3) and the effect of repetition on habit associations (phase 4) (see also Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ; Rothman, 2000 ). Phase 3 captures the critical period after initiation but before habit strength has peaked (Fournier et al., 2017 ; Lally et al., 2010 ).

The framework is not intended as a theory or model of the habit formation process, but rather as a means to conceptually organize the processes and mechanisms that underpin habit development. According to the framework, any variable can promote habit formation in one or more of four ways: It may enhance motivation (phase 1) or action control (i.e., the enactment of intentions into behavior; Kuhl, 1984 ; Rhodes & de Bruijn, 2013 ) (phase 2) so as to initiate the behavior; it may modify motivation and other action control processes to continue to perform the behavior (phase 3); or it may strengthen cue-behavior associations (phase 4). One variable may operate through multiple processes: For example, anticipating pleasure from action can motivate people to perform it for the first time (phase 1) and to continue to perform it (phase 3) (Radel, Pelletier, Pjevac, & Cheval, 2017 ; Rothman et al., 2009 ). The experience of pleasure can also quicken learning of cue-behavior associations (phase 4) (de Wit & Dickinson, 2009 ). By extension, Lally and Gardner’s ( 2013 ) framework categorizes techniques that promote habit formation according to their likely mechanism (or mechanisms) of action; techniques may enhance motivation (phase 1) or action control (phase 2) to initiate change, sustain motivation and action control over time (phase 3), or reinforce cue-behavior associations (phase 4).

Which Behavior Change Techniques Should Be Used to Form Habit?

The most comprehensive taxonomy of behavior change techniques currently available defines habit formation as a discrete technique, which it defines as any effort to “prompt rehearsal and repetition of the behavior in the same context repeatedly so that the context elicits the behaviour” (Michie et al., 2013 , Suppl. Table 3 , p. 10). Yet, this definition incorporates only context-dependent repetition and not any other technique that may promote habit by increasing the likelihood of context-dependent repetition (i.e., promoting motivation or action control; phases 1–3 of Lally and Gardner’s framework) or enhancing the contribution of each repetition to the learning of habit associations (phase 4). Although context-dependent repetition is necessary for habit to form, it realistically requires supplementation with techniques targeting pre- and post-initiation phases en route to habit formation (Gardner Lally, & Wardle, 2012 ). While Michie et al. ( 2013 ) treat habit formation as a unitary technique, habit formation may perhaps be more realistically seen as an intervention approach that comprises a broader suite of techniques, which marry context-dependent repetition with strategies that: reinforce motivation; boost action control capacity, opportunity, or skills; facilitate post-initiation repetition; or quicken the learning of associations arising from repetition.

Theory points to techniques that may facilitate progression through these phases. Intention formation (phase 1 of Lally & Gardner’s [ 2013 ] framework) is likely when people anticipate that the action or its likely consequences will be positive and believe that they have a realistic opportunity and capability to perform the behavior (Ajzen, 1991 ; Bandura, 2001 ; Michie et al., 2011 ; Rogers, 1983 ; Schwarzer, Lippke, & Luszczynska, 2011 ). Providing information on the likely positive consequences of action, or choosing to pursue actions that are already most highly valued, may therefore aid habit development by enhancing motivation. Action control skills are required to initiate intention enactment (phase 2) and to maintain the behavior by consistently prioritizing the intention over competing alternatives (phase 3). This will likely be facilitated by self-regulatory techniques such as planning, setting reminders, self-monitoring, and reviewing goals to ensure they remain realistic and attractive, and receiving (intrinsic) rewards contingent on successful performance (Gardner et al., 2012 ; Lally & Gardner, 2013 ). People are most likely to engage in context-dependent repetition in response to highly salient cues (e.g., event- rather than time-based cues, which likely require conscious monitoring; McDaniel & Einstein, 1993 ). Pairing the action with more frequently and consistently encountered cues may quicken habit learning at phase 4 (Gardner & Lally, 2019 ). Highly specific action plans detailing exactly what will be done and in exactly which situation (i.e., implementation intentions; Gollwitzer, 1999 ) should therefore be conducive to the acquisition of associations (but see Webb et al., 2009 ). Implementation intentions can also facilitate habit substitution: By consistently enacting new, pre-specified cue responses that directly compete with existing habitual responses, such as feeding children water instead of sugary drinks (McGowan et al., 2013 ), new responses may acquire the potential to override and erode old habitual responses (Adriaanse et al., 2011 ). The reinforcing value of repetition may also be strengthened where intrinsic reward is delivered or attention is drawn to an undervalued intrinsic reward arising from action (Radel et al., 2017 ).

Which Behavior Change Techniques Have Been Used to Form Habit, and with What Effect?

While theory can recommend techniques that should be used to promote habit formation, evaluations of habit-based interventions are needed to show which techniques have been used, and with what effect, in real-world behavior change contexts. To this end, a systematic literature search was run to identify habit-based health-promotion interventions and to document the behavior change methods used.

Four psychology and health databases (Embase, Medline, PsycInfo, Web of Science) were searched in March 2018 to identify sources that had cited one of nine key papers about habit and health. These sources were selected to capture topics of habit measurement (Gardner, Abraham, Lally, & de Bruijn, 2012 ; Ouellette & Wood, 1998 ; Verplanken & Orbell, 2003 ), principles and processes of habit formation (Gardner, Lally, & Wardle, 2012 ; Lally & Gardner, 2013 ; Lally et al., 2010 ; Lally et al., 2011 ), and conceptual commentaries (Gardner, 2015 ; Wood & Rünger, 2016 ). Papers were eligible for review if they (a) were published in English, (b) were peer-reviewed, (c) reported primary quantitative or qualitative data, (d) had tested efficacy or effectiveness for changing behavior or habit, (e) used interventions designed to promote habit formation for health behaviors, (f) targeted context-dependent repetition, and (g) were informed by theory or evidence around habit, operationalized as a learned automatic response to contextual cues or a process that generates such responses. Interventions adopted primarily to elucidate the habit formation process (rather than to develop or assess intervention effectiveness; e.g., Judah et al., 2013 ; Lally et al., 2010 ) and any that focused exclusively on breaking existing habits (e.g., Armitage, 2016 ) were excluded. For each eligible intervention, all available material was coded, including linked publications (e.g., protocols), to identify component techniques using the Behavior Change Technique Taxonomy v1 (Michie et al, 2013 ).

Twenty papers, reporting evaluations of 19 interventions, were identified. Four of the 19 interventions represented variants of interventions used elsewhere in the 20 papers. For example, one trial evaluated the same habit-based intervention component in two conditions, which varied only in the frequency of supplementary motivational interviews and booster phone calls (Simpson et al., 2015 ). Thus, the 19 could be reduced to 15 unique habit-based interventions, of which four focused on both dietary and physical activity habits, six on physical activity (or sedentary behavior) only, two on dietary consumption only, two on dental hygiene, and one on food safety. In all of the studies, habit measures were self-reported.

Diet and Physical Activity Interventions

One randomized controlled trial (RCT) compared, in overweight and obese adults, an intervention that included advice on forming and substituting healthy for unhealthy habits, with a non-habit-based intervention that emphasized relationships with food, body image, and weight biases (Carels et al., 2014 ; see also Carels et al., 2011 ). Those in the habit-based intervention received training on changing old routines and developing new ones, including advice on using cues and forming implementation intentions. Both intervention groups received weekly weight assessments and monitored their physical activity, calorie intake, and output. At a 6-month follow-up, both the habit-based ( n = 30) and non-habit intervention groups ( n = 29) were eating a healthier diet, exercising more regularly, and had lost weight. Physical activity habit strengthened and sitting habit weakened in both groups, though no between-group differences were found in weight loss or habit strength.

Lally et al.’s ( 2008 ) “Ten Top Tips” weight loss intervention centered on a leaflet outlining recommendations for forming healthy eating and physical activity habits, as supplemented by a daily adherence monitoring diary. The leaflet included advice on routinization, identifying effective cues, and habit substitution. A small non-randomized trial compared the intervention, augmented with monthly ( n = 35) or weekly weighing ( n = 34), against a no-treatment control. The intervention group lost more weight than the control group at 8 weeks and maintained weight loss at 32 weeks. Scores at 32 weeks suggested the tips had become habitual, and habit change correlated positively with weight loss (Lally et al., 2008 ; see also Lally et al., 2011 ). In a subsequent RCT (Beeken et al., 2012 , 2017 ), intervention recipients ( n = 267) lost more weight at 3 months than did a usual-care group ( n = 270). At 24 months, the intervention group had maintained weight loss, though the usual care group had lost a similar amount of weight. Habit strength, measured only at baseline and 3 months, increased more in the intervention than in the control group (Beeken et al., 2017 ). Weight loss at 3 months was attributable to gains in both habit and self-regulatory skill (Kliemann et al., 2017 ).

Simpson et al.’s ( 2015 ) weight-loss intervention provided participants with motivational advice designed to prompt intention formation, with information about how to form dietary and activity habits, and social support. Two intervention variants, differing according to the frequency of sessions, were evaluated against a minimal-treatment control, which did not feature habit-based advice, in a feasibility RCT among obese patients. Recipients of the more intensive intervention variant ( n = 55) showed greater BMI reduction at a 12-month follow-up than did the less intensive intervention ( n = 55) or control groups ( n = 60). There were no between-group differences at 12 months in physical activity or overall healthy eating, nor were there differences in activity or diet habit scores.

One RCT compared an 8-week computer-tailored intervention designed to reduce cardiovascular risk against a no-treatment control among cardiac and diabetes rehabilitation patients who already intended to increase their activity and fruit and vegetable consumption (Storm et al., 2016 ). The intervention provided information about health risks of inactivity and unhealthy diet and enhancing self-regulatory skills. Immediately following intervention cessation, fruit and vegetable consumption and physical activity habit and behavior scores were greater among the intervention ( n = 403) than control group ( n = 387), but no differences were observed 3 months post-baseline.

Physical Activity and Sedentary Behavior Interventions

An intervention for new gym members promoted habits for both physical activity and preparatory actions for gym attendance (e.g., packing a gym bag; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). Members received advice on how to form habits, including selecting time cues, setting action plans, and using accessories to increase enjoyment and so support cue-consistent performance and foster intrinsic motivation, which theory suggests can strengthen the impact of repetition on habit development (Lally & Gardner, 2013 ). Moderate-to-vigorous physical activity gains, objectively observed at an 8-week follow-up, were greater among intervention recipients ( n = 47) than the no-treatment control group ( n = 47). Habit strength was not assessed.

All 49 participants in Fournier et al.’s ( 2017 ) RCT were given access to twice-weekly, 1-hour tailored physical activity sessions for 28 weeks, with one group ( n = 23) also sent SMS reminders targeting intrinsic motivation and consistent performance to the intervention group to foster habitual attendance. Although physical activity habit strength (assessed using a subscale of the SRHI) increased for both groups immediately post-intervention, the SMS group experienced quicker habit gains. Marginally greater activity was observed in the SMS group at 12 months.

One 4-month intervention for middle- to older-aged adults comprised seven 2-hour group sessions and sought to create new balance and strength exercise habits by recommending small modifications to everyday routines (e.g., placing frequently used items on high shelves to promote stretching to reach them) (Fleig et al., 2016 ; see also Clemson et al., 2012 ). An uncontrolled trial among 13 participants showed that, while there were no apparent changes in objectively measured physical performance, there were considerable habit strength gains for the recommended actions over 6 months. Notably, participants reported in interviews that the exercises had become automatically triggered, yet they performed them consciously, suggesting that the intervention promoted habitual instigation rather than execution.

Another intervention promoting small activity changes in older adulthood was evaluated in two papers (Matei et al., 2015 ; White et al., 2017 ). Drawing on Lally et al.’s ( 2008 ) “Ten Top Tips,” it comprised a leaflet offering recommendations for integrating and substituting light-intensity physical activities into everyday routines, with supplementary self-monitoring record sheets (Gardner, Thune-Boyle, et al., 2014 ). An 8-week uncontrolled trial was undertaken among two discrete samples (Matei et al., 2015 ). No changes were found in sitting time, physical activity, or sitting or physical activity habit among one sample ( n = 16), but a second sample ( n = 27) reported decreased sitting time and increased walking. Qualitative data suggested both groups experienced automaticity gains and some health benefits. A subsequent pilot RCT showed that intervention recipients ( n = 45) experienced no greater change than did a control group ( n = 46) who received a pre-existing fact sheet promoting activity and reducing sitting, but with no habit-based advice (White et al., 2017 ). Both groups reduced sitting time and sitting habit and increased activity and activity habit.

Using an experience sampling design, Luo et al. ( 2018 ) tracked change in standing or moving breaks from sedentary behavior in office workers given 3 weeks of access to automated computer-based reminders to break up sitting, timed to occur based on daily self-selected work and break durations. Although sitting behavior was not monitored, habit strength and self-regulation for taking “moving breaks” during work hours both increased significantly across the study.

Similarly, Pedersen et al. ( 2014 ) evaluated a software package that automatically deactivated desk-based employees’ computer screens every 45 minutes to substitute new physical activity habits for existing prolonged sitting habits. Although all participants received information on the detrimental health impact of sitting and benefits of activity, self-report activity data suggested that those who used the software for 13 weeks ( n = 17) expended greater energy per day than did those not given the software ( n = 17).

Dietary Interventions

One intervention promoted habitual healthy child-feeding practices among parents of children aged 2–6 years (McGowan et al., 2013 ). On each of four occasions over 8 weeks, parents chose to pursue one of four families of habit formation targets (increased feeding of fruit, vegetables, water, and healthy snacks). They received advice on the importance of child dietary consumption and on self-regulatory strategies, including action planning, goal setting, and context-dependent repetition. An RCT showed that intervention parents ( n = 58) reported greater child intake of vegetables, water, and healthy snacks but a waiting-list control group ( n = 68) did not. Habit strength increased for all three behaviors, and a habit score averaged across behaviors correlated with behavior change (McGowan et al., 2013 ; see also Gardner, Sheals, Wardle, & McGowan, 2014 ).

In one RCT, fruit and vegetable consumption changes were compared between participants who received habit-based messages, and those receiving general, non-habit-based tips for increasing consumption or messages about healthy eating more broadly (Rompotis et al., 2014 ). Notably, habit-based messages focused on anticipating stimulus control and environmental modification and on eating the same fruits and vegetables at the same time each day, so targeting both habitual instigation and execution (see Phillips & Gardner, 2016 ). The intervention was delivered via SMS in one set of conditions and email in the other. At 8-weeks post-intervention, both intervention groups (SMS n = 26, email n = 30) had increased fruit consumption and fruit habit strength, but those in all other conditions had not (SMS fruit and vegetable tips, n = 24, SMS healthy eating tips, n = 23; email fruit and vegetable tips, n = 29, email healthy eating n = 29). No effects were found on vegetable consumption or habit.

Oral Hygiene

Two school-based interventions aimed to increase tooth brushing in primary school children. One involved weekly dental hygiene lessons and daily tooth brushing practice time (Gaeta, Cavazos, Cabrera, & Rosário, 2018 ). School visits were also made by health promoters, and a seminar was held for teachers. One control group ( n = 52) received the visits and seminar only, and a second control group ( n = 52) received the seminar only. A quasi-experiment showed that children in the habit-based intervention ( n = 106) and visits-and-seminar control group had less dental plaque, and a stronger tooth brushing habit at 12-week follow-up than did the seminar-only control group. The habit-based intervention group had the lowest plaque.

Wind et al.’s ( 2005 ) intervention also involved allocation of a designated tooth brushing time during the school day and encouragement from teachers. Tooth brushing rates increased in the intervention group ( n = 141) during treatment but not in the control group (the nature of which could not be identified from the published report; n = 155). There were no differences in behavior at 12-months post-intervention nor in habit at any follow-up.

Food Safety

An intervention promoted the microwaving of dishcloths or sponges, for hygiene reasons (Mullan, Allom, Fayn, & Johnston, 2014 ). Recipients received emails and a poster providing instructions on how and why to microwave the dishcloths and sponges, designed to be placed in kitchens to act as a cue to the action. In an RCT, one intervention group was instructed to self-monitor their action, for intervention purposes, every 3 days ( n = 15) and another every 5 days ( n = 17). Relative to those who received an unrelated control treatment ( n = 13), frequency and habit strength increased in the two intervention groups at 3 weeks and was sustained to the final 6-week follow-up.

Behavior Change Techniques Used in Previous Interventions

A total of 32 discrete behavior change techniques were each identified in at least one of the 15 interventions (see Table 1 and Table 2 ). Aside from context-dependent repetition itself—which, as an inclusion criterion, was necessarily present in all interventions—the most commonly used were “use prompts and cues” (present in 11 interventions; 73%), “action planning” (8 interventions; 53%), “provide instruction on how to perform the behavior” (8 interventions; 53%), “set behavioral goals” (8 interventions; 53%), and “self-monitor behavior” (7 interventions; 47%). Also common were “behavioral practice or rehearsal” (6 interventions; 40%), “provide information on health consequences” (6 interventions; 40%), and “problem solving” (5 interventions; 33%). “Behavioral substitution” and habit substitution (labeled “habit reversal” in the taxonomy) were each used in 4 interventions (27%).

Table 1. Behavior Change Techniques Identified in 15 Habit Formation Interventions

Note . With the exception of “context-dependent repetition,” all technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

* This technique is labeled “habit formation” in the BCT Taxonomy v1 (Michie et al., 2013 ). Rephrasing this as “context-dependent repetition” more clearly delineates the underlying technique (i.e., to consistently repeat behavior in an unvarying context) from the outcome that it is designed to serve (i.e., to form habit). It also better acknowledges the possibility that such repetition may not lead to the formation of habit. For example, Lally et al. ( 2010 ) observed some participants who failed to attain peak habit strength in an 84-day study period, and some who experienced gains that peaked at low levels, suggesting that while repetition had rendered the behavior more habitual, the action remained predominantly regulated by conscious motivation rather than habit.

Table 2. Behavior Change Techniques Documented in 15 Habit Formation Interventions

Note . All technique labels are taken from the BCT Taxonomy v1 (Michie et al., 2013 ).

While all 15 interventions were based on the principle of habit formation, none used context-dependent repetition as a standalone technique. 2 The use of techniques additional to repetition echoes the view that in the real world, habit is best promoted by embedding context-dependent repetition into a broader package of techniques that also target motivation and action control, which are prerequisites for repetition (Lally & Gardner, 2013 ). Techniques most commonly adopted in past interventions have focused predominantly on action control (e.g., planning, goal-setting, identifying cues, rehearsing action, problem solving). The relative paucity of techniques targeting motivation may reflect an assumption that, for most of the behaviors targeted, intervention recipients generally recognize the value of behavior change, but lack the volitional skills, opportunities, or resources to change. Whether motivation should be targeted as part of a habit-formation intervention will depend on whether target populations understand the need for change and prioritize the target behavior above alternatives.

Fewer than half of the 15 interventions appear to have addressed factors that may moderate the relationship between repetition and habit development. Theory and evidence suggest that the mental associations that underlie habit will develop most strongly or quickly where actions are more simple or intrinsically rewarding and in response to cues that are salient and consistently encountered (Lally & Gardner, 2013 ; McDaniel & Einstein, 1993 ; Radel et al., 2017 ). Several of the reviewed interventions purposively promoted habit formation for simple behaviors (Beeken et al., 2017 ; Fleig et al., 2016 ; Lally et al., 2010 , 2011 ; Matei et al., 2015 ; Mullan et al., 2014 ; White et al., 2017 ). Kaushal et al. ( 2017 ) emphasized the importance of intrinsic reward in their physical activity promotion intervention, and Fournier et al. ( 2017 ) targeted intrinsic motivation. These studies highlight how interventions may move beyond simply promoting repetition toward targeting factors that may reduce the number of repetitions required for a target behavior to become habitual.

How Should Habit-Based Interventions Be Evaluated?

Previous interventions attest to the potential for habit-based approaches to change behavior. Although many intervention studies were not designed to test effectiveness, 13 of the 15 interventions were associated with positive change on at least one index of behavior or behavior-contingent outcomes (e.g., weight loss) at one or more follow-ups. Process evaluations pointed to the strengthening of habit as a key mechanism underpinning behavioral change based on increases in self-reported automaticity scores or qualitative reflections on the subjective experience of automaticity (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Kliemann et al., 2017 ; Lally et al., 2011 ; Matei et al., 2015 ). Additionally, acceptability studies have suggested that recipients find the concept of context-dependent repetition—which distinguishes habit-based and non-habit-based interventions—easy to understand and follow (Fleig et al., 2016 ; Gardner, Sheals, et al., 2014 ; Lally et al., 2011 ; Matei et al., 2015 ).

Limitations of evaluation methods preclude understanding of how best to support habit formation. It is not yet clear whether promotion of context-dependent repetition is necessary for habit to develop or, indeed, whether it represents the most “active” ingredient of a habit formation intervention. One study found that a control group that did not receive habit-based advice reported similar physical activity habit gains to those among a group that received habit guidance (White et al., 2017 ). Conversely, another study showed that intervention recipients deviated from habit-based advice (e.g., by setting goals that were not specific, measurable, or achievable), yet habit strengthened (Gardner, Sheals, et al., 2014 ). Habit formation may therefore arise as a byproduct of interventions that do not explicitly target habit development. The unique contribution of context-dependent repetition to behavior change remains unclear because none of the reviewed studies compared a habit-based intervention with an otherwise identical non-habit-based equivalent. Indeed, most studies have evaluated habit formation interventions against minimal-treatment control groups or used uncontrolled designs. Future research should seek to compare matched habit- and non-habit-based interventions or otherwise use factorial designs, which allow testing for isolated effects within a multicomponent intervention, or mediation analyses, which can assess whether habit change underpins intervention effects.

Intervention evaluations have also been limited by short follow-up periods, which is ironic given that the key purported benefit of incorporating habit formation into interventions is the potential to increase longevity of behavior change. Few studies evaluated outcomes over 12 months or longer, with the longest observed follow-up being 24 months (Beeken et al., 2017 ). Beeken et al.’s ( 2017 ) “Ten Top Tips” intervention showed greater impact than did a non-habit-based usual-care treatment on dietary and physical activity habits, and weight loss, at the 3-month follow-up, which the authors found to be due in part to habit development (Kliemann et al., 2017 ). Yet, while weight loss was maintained at 24 months, the advantage conferred by the habit-based intervention over usual care was lost, suggesting that any habit gains may have dissipated, or alternatively, that for those who were successful in maintaining the behaviors over the 2-year period, habit formation had occurred regardless of condition. These possibilities cannot be investigated because habit strength was not evaluated at 24 months. Elsewhere, however, a small exploratory (non-intervention) study suggested that habit gains may erode over time: Among a group of participants forming dental flossing habits over 8 weeks, habit strength had considerably eroded in the subgroup of participants who provided data at a 6-month follow-up (Judah et al., 2013 ). Until more is done to assess the longevity of habit-based intervention effects, the hypothesis that habit persists over time, and so supports behavior maintenance, remains insufficiently tested.

Theory proposes that, through consistent performance, behaviors become habitual such that they are initiated automatically upon encountering cues via the activation of learned context-behavior associations. Habitual behaviors are thought to be self-sustaining, and so forming a habit has been proposed as a means to promote long-term maintenance of behavior. Interventions that seek to promote habit formation should include not only advice on context-dependent repetition, but also techniques that support the motivation and action control needed to repeat the action and that may enhance the reinforcing value of repetition on habit development. Fifteen interventions were found to have used habit formation principles to encourage engagement in health-promoting behaviors, and these have tended to supplement advice on repetition with action control techniques. Previous research suggests a habit-based approach has much to offer to behavior change initiatives; habit formation offers an acceptable, easily understood intervention strategy, with the potential to change behavior and yield favorable health outcomes. Yet, the unique effects of habit-specific techniques, and the longevity of effects, have not been adequately explored. The central assumption of the habit-based approach—that habit gains translate into long-term behavior maintenance—remains largely untested.

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1. Rhodes and colleagues have extended this line of thinking by incorporating preparatory actions into the process, showing that habitual preparation for an activity (e.g., packing a gym bag) can influence frequency of engagement in the focal behavior (in this case, exercise; Kaushal, Rhodes, Meldrum, & Spence, 2017 ). However, this differs from the instigation–execution distinction in that it focuses on the role of habit in different behaviors (preparatory actions vs. focal actions) rather than different roles of habit in the same behavior.

2. This is perhaps inevitable given the present review criteria, which excluded studies that used context-dependent repetition to study the habit formation process itself. However, real-world studies of the formation of health habits have not been based on context-dependent repetition alone; both Lally et al. ( 2010 ) and Fournier et al. ( 2017 ) instructed participants to use prompts and cues and set action plans or implementation intentions (see also Judah et al., 2013 ).

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Awe is characterized as an ambivalent experience in the human behavior and cortex: integrated virtual reality-electroencephalogram study

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Ambivalent feelings are a defining feature of awe, which has been understood as a source of its psychosocial benefits. However, due to the conventional unidimensional model of affective valence, behavior and neural representation of ambivalent feelings during awe remain elusive. To address this gap, we combined awe-inducing virtual reality clips, electroencephalogram, and a deep learning-based dimensionality reduction ( N = 43). Behaviorally, awe ratings were predicted by the duration and intensity of ambivalent feelings, not by single valence-related metrics. In the electrophysiological analysis, we identified latent neural space for each participant sharing valence representation structures across individuals and stimuli. In these spaces, ambivalent feelings during awe were distinctly represented from positive and negative ones, and the variability in their distinctiveness specifically predicted awe ratings. Additionally, frontal delta oscillations mainly engaged in differentiating valence representations. Our findings demonstrate that awe is fundamentally an ambivalent experience reflected in both behavior and electrophysiological activities. This work provides a new framework for understanding complex emotions and their neural underpinnings, with potential implications for affective neuroscience and relevant fields.

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Incorporating Choice: Examining the Beliefs and Practices of Behavior Analysts Working with Individuals with Disabilities

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• Published: 10 September 2024

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• Yev Veverka   ORCID: orcid.org/0000-0003-4947-1543 1 ,
• Adriana Luna 1 ,
• Ashley Penney 2 ,
• Katherine Bateman 1 ,
• Malika Pritchett 3 ,
• Ilene Schwartz 1 &
• Zeyad Zaino 1  

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Choice-making for individuals with disabilities is an important topic in the field of Applied Behavior Analysis (ABA). Choice is a fundamental human right, and opportunities to make decisions about an individual's own life honors and respects dignity and autonomy. This study explores the beliefs and practices of behavior analysts in relation to choice-making for individuals with disabilities. A total of 81 practicing behavior analysts participated in an online survey that assessed their training experience, beliefs about choice, and reported practices regarding choice in ABA service delivery. The survey responses were analyzed using descriptive statistics and Wilcoxon Signed-Rank Test to compare beliefs and practices. Results showed that while most behavior analysts strongly agreed that choice should be incorporated into ABA services, discrepancies were observed between beliefs and actual practice regarding various factors that influence opportunities to make choices. Multiple barriers to providing choice-making opportunities were identified. The findings underscore the need for increased training and coursework on the subject of choice as well as changes in practice.

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Data described in this article are openly available in the Open Science Framework at https://osf.io/wmgpq/?view_only=dc11f5344a03469f934713f645600a13 .

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• Published: 16 November 2022

Climate change and human behaviour

Nature Human Behaviour volume  6 ,  pages 1441–1442 ( 2022 ) Cite this article

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Climate change is an immense challenge. Human behaviour is crucial in climate change mitigation, and in tackling the arising consequences. In this joint Focus issue between Nature Climate Change and Nature Human Behaviour , we take a closer look at the role of human behaviour in the climate crisis.

In the late 19th century, the scientist (and suffragette) Eunice Newton Foote published a paper suggesting that a build-up of carbon dioxide in the Earth’s atmosphere could cause increased surface temperatures 1 . In the mid-20th century, the British engineer Guy Callendar was the first to concretize the link between carbon dioxide levels and global warming 2 . Now, a century and a half after Foote’s work, there is overwhelming scientific evidence that human behaviour is the main driver of climatic changes and global warming.

The negative effects of rising temperatures on the environment, biodiversity and human health are becoming increasingly noticeable. The years 2020 and 2016 were among the hottest since the record keeping of annual surface temperatures began in 1880 (ref. 3 ). Throughout 2022, the globe was plagued by record-breaking heatwaves. Even regions with a naturally warm climate, such as Pakistan or India, experienced some of their hottest days much earlier in the year — very probably a consequence of climate change 4 . According to the National Centers for Environmental Information of the United States, the surface global temperature during the decade leading up to 2020 was +0.82 °C (+1.48 °F) above the 20th-century average 5 . It is clear that we are facing a global crisis that requires urgent action.

During the Climate Change Conference (COP21) of the United Nations in Paris 2015, 196 parties adopted a legally binding treaty with the aim to limit global warming to ideally 1.5 °C and a maximum of 2 °C, compared to pre-industrial levels. A recent report issued by the UN suggests that we are very unlikely to meet the targets of the Paris Agreement. Instead, current policies are likely to cause temperatures to increase up to 2.8 °C this century 6 . The report suggests that to get on track to 2 °C, new pledges would need to be four times higher — and seven times higher to get on track to 1.5 °C. This November, world leaders will meet for the 27th time to coordinate efforts in facing the climate crisis and mitigating the effects during COP27 in Sharm El-Sheikh, Egypt.

This Focus issue

Human behaviour is not only one of the primary drivers of climate change but also is equally crucial for mitigating the impact of the Anthropocene. In 2022, this was also explicitly acknowledged in the report of the Intergovernmental Panel on Climate Change (IPCC). For the first time, the IPCC directly discussed behavioural, social and cultural dynamics in climate change mitigation 7 . This joint Focus highlights some of the aspects of the human factor that are central in the adaptation to and prevention of a warming climate, and the mitigation of negative consequences. It features original pieces, and also includes a curated collection of already published content from across journals in the Nature Portfolio.

Human behaviour is a neglected factor in climate science

In the light of the empirical evidence for the role of human behaviour in climatic changes, it is curious that the ‘human factor’ has not always received much attention in key research areas, such as climate modelling. For a long time, climate models to predict global warming and emissions did not account for it. This oversight meant that predictions made by these models have differed greatly in their projected rise in temperatures 8 , 9 .

Human behaviour is complex and multidimensional, making it difficult — but crucial — to account for it in climate models. In a Review , Brian Beckage and colleagues thus look at existing social climate models and make recommendations for how these models can better embed human behaviour in their forecasting.

The psychology of climate change

The complexity of humans is also reflected in their psychology. Despite an overwhelming scientific consensus on anthropogenic climate change, research suggests that many people underestimate the effects of it, are sceptical of it or deny its existence altogether. In a Review , Matthew Hornsey and Stephan Lewandowsky look at the psychological origins of such beliefs, as well as the roles of think tanks and political affiliation.

Psychologists are not only concerned with understanding and addressing climate scepticism but are also increasingly worried about mental health consequences. Two narrative Reviews address this topic. Neil Adger et al. discuss the direct and indirect pathways by which climate change affects well-being, and Fiona Charlson et al. adopt a clinical perspective in their piece. They review the literature on the clinical implications of climate change and provide practical suggestions for mental health practitioners.

Individual- and system-level behaviour change

To limit global warming to a minimum, system-level and individual-level behaviour change is necessary. Several pieces in this Focus discuss how such change can be facilitated.

Many interventions for individual behaviour change and for motivating environmental behaviour have been proposed. In a Review , Anne van Valkengoed and colleagues introduce a classification system that links different interventions to the determinants of individual environmental behaviour. Practitioners can use the system to design targeted interventions for behaviour change.

Ideally, interventions are scalable and result in system-level change. Scalability requires an understanding of public perceptions and behaviours, as Mirjam Jenny and Cornelia Betsch explain in a Comment . They draw on the experiences of the COVID-19 pandemic and discuss crucial structures, such as data observatories, for the collection of reliable large-scale data.

Such knowledge is also key for designing robust climate policies. Three Comments in Nature Climate Change look at how insights from behavioural science can inform policy making in areas such as natural-disaster insurance markets , carbon taxing and the assignment of responsibility for supply chain emissions .

Time to act

To buck the trend of rising temperatures, immediate and significant climate action is needed.

Natural disasters have become more frequent and occur at ever-closer intervals. The changing climate is driving biodiversity loss, and affecting human physical and mental health. Unfortunately, the conversations about climate change mitigation are often dominated by Global North and ‘WEIRD’ (Western, educated, industrialized, rich and democratic) perspectives, neglecting the views of countries in the Global South. In a Correspondence , Charles Ogunbode reminds us that climate justice is social justice in the Global South and that, while being a minor contributor to emissions and global warming, this region has to bear many of the consequences.

The fight against climate change is a collective endeavour and requires large-scale solutions. Collective action, however, usually starts with individuals who raise awareness and drive change. In two Q&As, Nature Human Behaviour entered into conversation with people who recognized the power of individual behaviour and took action.

Licypriya Kangujam is a 10-year-old climate activist based in India. She tells us how she hopes to raise the voices of the children of the world in the fight against climate change and connect individuals who want to take action.

Wolfgang Knorr is a former academic who co-founded Faculty for a Future to help academics to transform their careers and address pressing societal issues. In a Q&A , he describes his motivations to leave academia and offers advice on how academics can create impact.

Mitigation of climate change (as well as adaptation to its existing effects) is not possible without human behaviour change, be it on the individual, collective or policy level. The contents of this Focus shed light on the complexities that human behaviour bears, but also point towards future directions. It is the duty of us all to turn this knowledge into action.

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Climate change and human behaviour. Nat Hum Behav 6 , 1441–1442 (2022). https://doi.org/10.1038/s41562-022-01490-9

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