Combination Of Behavior Change Techniques Research Articles

Desire to get drunk partially mediates effects of a combined text message-based alcohol intervention for young adults

BackgroundThis study aimed to test the causal effect of different text message interventions on reducing alcohol consumption indirectly by altering desire to get drunk. MethodsParticipants were young adults randomized to interventions with different behavior change techniques: self-monitoring alone (TRACK); pre-drinking plan feedback (PLAN); post-drinking alcohol consumption feedback (USE); pre- and post-drinking goal feedback (GOAL); and a combination of techniques (COMBO) who completed at least 2 days of both pre- and post-drinking assessments over 12 weeks of intervention exposure. On the two days per week they planned to drank alcohol, participants were asked to report desire to get drunk (0 "none" to 8 "completely"). The next day, participants reported drinking quantity. Outcomes included binge drinking (defined as 4+ drinks for a woman and 5+ drinks for a man) and drinks per drinking day. Mediation was tested using path models of simultaneous between-person and within-person effects using maximum likelihood estimation. ResultsAt the between-person level, controlling for race and baseline AUDIT-C and within-person associations, 35.9 % of the effects of USE and 34.4 % of the effects of COMBO on reducing binge drinking were mediated through desire to get drunk. 60.8 % of the effects of COMBO on reducing drinks per drinking day were mediated through desire to get drunk. We did not find significant indirect effects for any other text-message intervention. DiscussionFindings support the hypothesized mediation model where desire to get drunk partially mediates the effects of a text message intervention using a combination of behavior change techniques on reducing alcohol consumption.

The Effectiveness of Internet-Guided Self-help Interventions to Promote Physical Activity Among Individuals With Depression: Systematic Review

Depression is a prevalent and debilitating mental disorder and a leading cause of disability worldwide. Physical activity (PA) interventions have been shown to alleviate depressive symptoms. However, not all patients have access to PA programing tailored for depression. Internet-guided self-help (IGSH) interventions may be an effective option for increasing PA among people with depression who cannot or prefer not to access supervised exercise treatment. We aimed to evaluate the effectiveness of IGSH interventions in increasing PA and alleviating depressive symptoms in people with depression. A systematic literature search was conducted for randomized controlled trials and quasiexperimental studies using 9 electronic databases. The review was registered in PROSPERO (2020 CRD42020221713). A total of 4 randomized controlled trials (430 participants) met the inclusion criteria. Of these, 3 were web-based and 1 was app-based. Three studies found IGSH interventions to have medium to large effects on decreasing depressive symptoms but not on increasing PA compared with waitlist or usual care. One study showed increased self-reported PA but no significant difference in depressive symptoms in the intervention group compared with the control group. Goal setting was the most common behavior change technique used in the interventions. Dropout rates within the intervention groups were relatively low (0%-19%). Our findings suggested that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions with different combinations of behavior change techniques, particularly those targeting the emotion domain, are needed to assess the overall effectiveness and feasibility of using IGSH interventions to increase PA among people with depression. PROSPERO CRD42020221713; https://tinyurl.com/ysaua5bu.

Identifying Persuasive Design Principles and Behavior Change Techniques Supporting End User Values and Needs in eHealth Interventions for Long-Term Weight Loss Maintenance: Qualitative Study.

BackgroundAn increasing number of eHealth interventions aim to support healthy behaviors that facilitate weight loss. However, there is limited evidence of the effectiveness of the interventions and little focus on weight loss maintenance. Knowledge about end user values and needs is essential to create meaningful and effective eHealth interventions, and to identify persuasive system design (PSD) principles and behavior change techniques (BCTs) that may contribute to the behavior change required for successful long-term weight loss maintenance.ObjectiveThis study aimed to provide insight into the design of eHealth interventions supporting behavior change for long-term weight maintenance. The study sought to identify the values and needs of people with obesity aiming to maintain weight after weight loss, and to identify PSD principles, BCTs, and design requirements that potentially enable an eHealth intervention to meet end user values and needs.MethodsThis study presents the concept of integrating PSD principles and BCTs into the design process of eHealth interventions to meet user values and needs. In this study, individual interviews and focus groups were conducted with people with obesity (n=23) and other key stakeholders (n=27) to explore end user values and needs related to weight loss maintenance. Design thinking methods were applied during the focus group sessions to identify design elements and to explore how eHealth solutions can support the needs to achieve sustainable weight loss maintenance. The PSD model and behavior change taxonomy by Michie were used to identify PSD principles and BCT clusters to meet end user values and needs.ResultsA total of 8 key end user values were identified, reflecting user needs for weight loss maintenance support: self-management, personalized care, autonomy, feel supported, positive self-image, motivation, happiness, and health. Goals and planning, feedback and monitoring, repetition and substitution, shaping knowledge, social support, identity, and self-belief were some of the BCT clusters identified to address these concepts, together with PSD principles such as personalization, tailoring, self-monitoring, praise, and suggestions.ConclusionsThe process of translating end user values and needs into design elements or features of eHealth technologies is an important part of the design process. To our knowledge, this is the first study to explore how PSD principles and BCTs can be integrated when designing eHealth self-management interventions for long-term weight loss maintenance. End users and other key stakeholders highlighted important factors to be considered in the design of eHealth interventions supporting sustained behavior change. The PSD principles and BCTs identified provide insights and suggestions about design elements and features to include for supporting weight loss maintenance. The findings indicate that a combination of BCTs and PSD principles may be needed in evidence-based eHealth interventions to stimulate motivation and adherence to support healthy behaviors and sustained weight loss maintenance.Trial RegistrationClinicalTrials.gov NCT04537988; https://clinicaltrials.gov/ct2/show/NCT04537988

Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis.

BackgroundDecreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA.ObjectiveThe aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users’ PA behavior.MethodsThe BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin Vívoactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature.ResultsThe most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin Vívoactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA.ConclusionsWearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.

Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention

BackgroundE- and m-health interventions are promising to change health behaviour. Many of these interventions use a large variety of behaviour change techniques (BCTs), but it’s not known which BCTs or which combination of BCTs contribute to their efficacy. Therefore, this experimental study investigated the efficacy of three BCTs (i.e. action planning, coping planning and self-monitoring) and their combinations on physical activity (PA) and sedentary behaviour (SB) against a background set of other BCTs.MethodsIn a 2 (action planning: present vs absent) × 2 (coping planning: present vs absent) × 2 (self-monitoring: present vs absent) factorial trial, 473 adults from the general population used the self-regulation based e- and m-health intervention ‘MyPlan2.0’ for five weeks. All combinations of BCTs were considered, resulting in eight groups. Participants selected their preferred target behaviour, either PA (n = 335, age = 35.8, 28.1% men) or SB (n = 138, age = 37.8, 37.7% men), and were then randomly allocated to the experimental groups. Levels of PA (MVPA in minutes/week) or SB (total sedentary time in hours/day) were assessed at baseline and post-intervention using self-reported questionnaires. Linear mixed-effect models were fitted to assess the impact of the different combinations of the BCTs on PA and SB.ResultsFirst, overall efficacy of each BCT was examined. The delivery of self-monitoring increased PA (t = 2.735, p = 0.007) and reduced SB (t = − 2.573, p = 0.012) compared with no delivery of self-monitoring. Also, the delivery of coping planning increased PA (t = 2.302, p = 0.022) compared with no delivery of coping planning. Second, we investigated to what extent adding BCTs increased efficacy. Using the combination of the three BCTs was most effective to increase PA (x2 = 8849, p = 0.003) whereas the combination of action planning and self-monitoring was most effective to decrease SB (x2 = 3.918, p = 0.048). To increase PA, action planning was always more effective in combination with coping planning (x2 = 5.590, p = 0.014; x2 = 17.722, p < 0.001; x2 = 4.552, p = 0.033) compared with using action planning without coping planning. Of note, the use of action planning alone reduced PA compared with using coping planning alone (x2 = 4.389, p = 0.031) and self-monitoring alone (x2 = 8.858, p = 003), respectively.ConclusionsThis study provides indications that different (combinations of) BCTs may be effective to promote PA and reduce SB. More experimental research to investigate the effectiveness of BCTs is needed, which can contribute to improved design and more effective e- and m-health interventions in the future.Trial registrationThis study was preregistered as a clinical trial (ID number: NCT03274271). Release date: 20 October 2017.

Do Combinations of Behavior Change Techniques That Occur Frequently in Interventions Reflect Underlying Theory?

BackgroundBehavioral interventions typically include multiple behavior change techniques (BCTs). The theory informing the selection of BCTs for an intervention may be stated explicitly or remain unreported, thus impeding the identification of links between theory and behavior change outcomes.PurposeThis study aimed to identify groups of BCTs commonly occurring together in behavior change interventions and examine whether behavior change theories underlying these groups could be identified.MethodsThe study involved three phases: (a) a factor analysis to identify groups of co-occurring BCTs from 277 behavior change intervention reports; (b) examining expert consensus (n = 25) about links between BCT groups and behavioral theories; (c) a comparison of the expert-linked theories with theories explicitly mentioned by authors of the 277 intervention reports.ResultsFive groups of co-occurring BCTs (range: 3–13 BCTs per group) were identified through factor analysis. Experts agreed on five links (≥80% of experts), comprising three BCT groups and five behavior change theories. Four of the five BCT group–theory links agreed by experts were also stated by study authors in intervention reports using similar groups of BCTs.ConclusionsIt is possible to identify groups of BCTs frequently used together in interventions. Experts made shared inferences about behavior change theory underlying these BCT groups, suggesting that it may be possible to propose a theoretical basis for interventions where authors do not explicitly put forward a theory. These results advance our understanding of theory use in multicomponent interventions and build the evidence base for further understanding theory-based intervention development and evaluation.

The effect of SMS behavior change techniques on event-level desire to get drunk in young adults.

Text messaging (SMS) interventions incorporating a combination of behavior change techniques can assist reductions in alcohol consumption among young adult hazardous drinkers, but mechanisms of action remain unknown. In this secondary analysis, we test the hypothesis that desire to get drunk (DD) recorded prior to drinking episodes would mediate SMS intervention effects on the likelihood of event-level heavy drinking (4 +/5 + drinks for women/men). We recruited young adult hazardous drinkers to a trial where they were randomized to 1 of 5 SMS interventions: TRACK (self-monitoring of alcohol use), PLAN (feedback on drinking plans and DD), USE (postdrinking feedback on alcohol consumed), GOAL (goal prompts/support), and COMBO (i.e., 4 interventions combined). Up to 3 days per week for 14 weeks, when participants reported a plan to drink, they were asked to report DD on a scale from 0 (none) to 8 (completely) and next day asked to recall drink quantity. Multilevel structural equation models showed that DD mediated the treatment effect of GOAL on heavy drinking. This work illustrates the importance of goal support features in digital alcohol interventions and the utility of measuring desire to get drunk as a key mediator in alcohol studies. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

MHealth App recommendation based on the prediction of suitable behavior change techniques

In light of individuals' increasing concern regarding their physical health, mobile health applications (mHealth Apps) have gained popularity in recent years as important tools for addressing health problems. However, users find it challenging to choose appropriate mHealth Apps, as these Apps incorporate diverse behavior change techniques (BCTs), and their individual behavioral intervention effects on users vary. This study proposes a novel BCT-based mHealth App recommendation method to suggest suitable mHealth Apps to users. Specifically, we encode mHealth Apps to obtain information on the BCT adopted by the Apps. Based on the combination of BCTs in each mHealth App and its usage information, we construct a User-BCT matrix to represent users' preferences concerning BCTs. We also construct a user profile for each user, which considers their characteristics related to BCTs. Next, we build a prediction model that links each user's profile to BCTs, and use the AdaBoost algorithm to predict suitable BCTs for a target user. Finally, we recommend mHealth Apps with the highest BCT-matching levels to a target user. We also investigate the performance of the proposed method using a real dataset. The experimental results demonstrate the advantages of the proposed method.

Which behavior change techniques help young adults reduce binge drinking? A pilot randomized clinical trial of 5 text message interventions

Text message (SMS) interventions can reduce binge drinking in young adults, but optimal behavior change techniques (BCTs) remain unknown. The present study tests the acceptability and preliminary efficacy of different combinations of SMS-delivered BCTs. 149 young adults who screened positive for hazardous drinking completed a baseline survey in the Emergency Department. For the following 2-weeks, on days they typically drank (1 to 3 days per week), participants received ecological momentary assessments (EMA) of drinking plans and desire to get drunk; the next day they were prompted to report recall of number of drinks consumed the prior day. Participants who responded to at least 50% these EMA (N = 127) were randomized to one of five 12-week interventions: Cued Self-Monitoring (TRACK); Drinking Intentions Feedback (PLAN); Drinking Performance Feedback (USE); Adaptive Goal Support (GOAL); and a combination of BCTs (COMBO). 79% of all EMA were completed over 12 weeks, which decreased from around 93% on week 1 to 65% by week 12. Using EMA data, relative to TRACK, only COMBO showed significant reductions in binge drinking and max drinks per drinking episode over time. Using TLFB data, there were no significant differences between groups from baseline to 14- and 28-weeks follow-up. Results lay the groundwork for a larger trial testing the effects of BCTs on binge drinking for young adults.

Development and piloting of a highly tailored digital intervention to support adherence to antihypertensive medications as an adjunct to primary care consultations

ObjectivesThis paper describes the systematic development and piloting of a highly tailored text and voice message intervention to increase adherence to medication in primary care.MethodsFollowing the Medical Research Council guidance,...

Behavior Change Techniques in mHealth Apps for the Mental and Physical Health of Employees: Systematic Assessment

BackgroundEmployees remain at risk of developing physical and mental health problems. To improve the lifestyle, health, and productivity many workplace interventions have been developed. However, not all of these interventions are effective. Mobile and wireless technology to support health behavior change (mobile health [mHealth] apps) is a promising, but relatively new domain for the occupational setting. Research on mHealth apps for the mental and physical health of employees is scarce. Interventions are more likely to be useful if they are rooted in health behavior change theory. Evaluating the presence of specific combinations of behavior change techniques (BCTs) in mHealth apps might be used as an indicator of potential quality and effectiveness.ObjectiveThe aim of this study was to assess whether mHealth apps for the mental and physical health of employees incorporate BCTs and, if so, which BCTs can be identified and which combinations of BCTs are present.MethodsAn assessment was made of apps aiming to reduce the risk of physical and psychosocial work demands and to promote a healthy lifestyle for employees. A systematic search was performed in iTunes and Google Play. Forty-five apps were screened and downloaded. BCTs were identified using a taxonomy applied in similar reviews. The mean and ranges were calculated.ResultsOn average, the apps included 7 of the 26 BCTs (range 2-18). Techniques such as “provide feedback on performance,” “provide information about behavior-health link,” and “provide instruction” were used most frequently. Techniques that were used least were “relapse prevention,” “prompt self-talk,” “use follow-up prompts,” and “provide information about others’ approval.” “Stress management,” “prompt identification as a role model,” and “agree on behavioral contract” were not used by any of the apps. The combination “provide information about behavior-health link” with “prompt intention formation” was found in 7/45 (16%) apps. The combination “provide information about behavior-health link” with “provide information on consequences,” and “use follow-up prompts” was found in 2 (4%) apps. These combinations indicated potential effectiveness. The least potentially effective combination “provide feedback on performance” without “provide instruction” was found in 13 (29%) apps.ConclusionsApps for the occupational setting might be substantially improved to increase potential since results showed a limited presence of BCTs in general, limited use of potentially successful combinations of BCTs in apps, and use of potentially unsuccessful combinations of BCTs. Increasing knowledge on the effectiveness of BCTs in apps might be used to develop guidelines for app developers and selection criteria for companies and individuals. Also, this might contribute to decreasing the burden of work-related diseases. To achieve this, app developers, health behavior change professionals, experts on physical and mental health, and end-users should collaborate when developing apps for the working context.

Identifying configurations of behavior change techniques in effective medication adherence interventions: a qualitative comparative analysis.

BackgroundInterventions to improve medication adherence are diverse and complex. Consequently, synthesizing this evidence is challenging. We aimed to extend the results from an existing systematic review of interventions to improve medication adherence by using qualitative comparative analysis (QCA) to identify necessary or sufficient configurations of behavior change techniques among effective interventions.MethodsWe used data from 60 studies in a completed systematic review to examine the combinations of nine behavior change techniques (increasing knowledge, increasing awareness, changing attitude, increasing self-efficacy, increasing intention formation, increasing action control, facilitation, increasing maintenance support, and motivational interviewing) among studies demonstrating improvements in adherence.ResultsAmong the 60 studies, 34 demonstrated improved medication adherence. Among effective studies, increasing patient knowledge was a necessary but not sufficient technique. We identified seven configurations of behavior change techniques sufficient for improving adherence, which together accounted for 26 (76 %) of the effective studies. The intervention configuration that included increasing knowledge and self-efficacy was the most empirically relevant, accounting for 17 studies (50 %) and uniquely accounting for 15 (44 %).ConclusionsThis analysis extends the completed review findings by identifying multiple combinations of behavior change techniques that improve adherence. Our findings offer direction for policy makers, practitioners, and future comparative effectiveness research on improving adherence.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0255-z) contains supplementary material, which is available to authorized users.

Machine Learning for Digital Behaviour Change Interventions (DCBIs)

Event Abstract Back to Event Machine Learning for Digital Behaviour Change Interventions (DCBIs) Alex Matei1* 1 BUPA, UK This presentation discusses the latest advances and opportunities that arise from applying Machine Learning (ML) techniques to develop and improve Digital Behaviour Change Interventions (DBCIs). Machine learning can enable us to develop intervention programs that can predict user behaviour, identify the optimal time to intervene (aka “just-in-time adaptive interventions”, or JITAIs), and automatically select a combination of behaviour change techniques and content to be used considering each user’s individual context, in real time. The presentation sets out to discuss best practices and practical examples of using machine learning for DBCIs, focusing on: · Novel algorithms successfully used in DBCIs · New types of User Experience (UX) made possible by machine learning · Insight into the challenges and opportunities for data collection · Privacy and ethical concerns Adaptation for specific behaviours Keywords: machine learning, algorithm, User Experience, Behaviour Change, personalisation Conference: 2nd Behaviour Change Conference: Digital Health and Wellbeing, London, United Kingdom, 24 Feb - 25 Feb, 2016. Presentation Type: Oral presentation Topic: Academic Citation: Matei A (2016). Machine Learning for Digital Behaviour Change Interventions (DCBIs). Front. Public Health. Conference Abstract: 2nd Behaviour Change Conference: Digital Health and Wellbeing. doi: 10.3389/conf.FPUBH.2016.01.00123 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Feb 2016; Published Online: 24 Feb 2016. * Correspondence: Mr. Alex Matei, BUPA, London, UK, alex.matei@bupa.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alex Matei Google Alex Matei Google Scholar Alex Matei PubMed Alex Matei Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Behaviour change techniques and contraceptive use in low and middle income countries: a review.

We aimed to identify effective behaviour change techniques to increase modern contraceptive use in low and middle income countries (LMICs). Literature was identified in Global Health, Web of Science, MEDLINE, PsycINFO and Popline, as well as peer reviewed journals. Articles were included if they were written in English, had an outcome evaluation of contraceptive use, modern contraceptive use, contraceptive initiation/uptake, contraceptive adherence or continuation of contraception, were a systematic review or randomised controlled trial, and were conducted in a low or middle income country. We assessed the behaviour change techniques used in each intervention and included a new category of male partner involvement. We identified six studies meeting the inclusion criteria. The most effective interventions were those that involve male partner involvement in the decision to initiate contraceptive use. The findings also suggest that providing access to contraceptives in the community promotes their use. The interventions that had positive effects on contraceptive use used a combination of behaviour change techniques. Performance techniques were not used in any of the interventions. The use of social support techniques, which are meant to improve wider social acceptability, did not appear except in two of the interventions. Our findings suggest that when information and contraceptives are provided, contraceptive use improves. Recommendations include reporting of behaviour change studies to include more details of the intervention and techniques employed. There is also a need for further research to understand which techniques are especially effective.

Preliminary outcomes from the PULSE randomised controlled trial—A multi-component type 2 diabetes prevention program for men

Limiting gestational weight gain (GWG) to recommended levels is important to optimize health outcomes for both mother and baby, including the prevention of maternal obesity and the development of childhood overweight and obesity. Currently, interventions designed to promote healthy GWG have reported limited effectiveness; addressing psychological factors and identifying useful behavior change techniques (BCTs) may help improve the effectivenes of such interventions. We extend current research by: (i) using a generalized health psychology perspective to systematically evaluate the theories of behavior change informing GWG interventions, and (ii) meta-analyzing BCTs reported in the interventions to identify areas of greater potential for successful intervention. Interventions designed to limit GWG were searched for using health, nursing, and psychology databases. Papers reporting an underpinning theory were identified and the CALO-RE taxonomy developed by Michie et al. was used to determine individual BCTs. Nineteen studies were identified for inclusion. Of these 19 studies, 8 were informed by a behavior change theory and 6 of these reported favorable effects on GWG. Overall, studies based on theory were found to be as effective as non-theory-based studies at limiting GWG. An average of 5.5 (range 1—12) BCTs were reported per intervention. In general, more techniques were reported in effective interventions. There was also a trend for interventions with a larger number of techniques to report greater differences in GWG between the intervention and control groups (r(n = 20) = .30, p = .10). There were 18 techniques that were not utilized in any intervention. Meta-analysis revealed the following BCTs to be key strategies for promoting healthy GWG in the reviewed studies: provision of information, motivational interviewing, behavioral self-monitoring, and providing rewards contingent on successful behavior. Future research should focus on determining the exact combination of BCTs and which underpinning theories are most useful for limiting GWG to healthy levels.

Interventions designed to limit gestational weight gain: a systematic review of theory and meta‐analysis of intervention components

Limiting gestational weight gain (GWG) to recommended levels is important to optimize health outcomes for mother and baby. Surprisingly, a recent review revealed that theory-based interventions to limit GWG were less effective than interventions that did not report a theory-base; however, strict criteria were used to identify theory-informed studies. We extended this review and others by systematically evaluating the theories of behaviour change informing GWG interventions using a generalized health psychology perspective, and meta-analysing behaviour change techniques reported in the interventions. Interventions designed to limit GWG were searched for using health, nursing and psychology databases. Papers reporting an underpinning theory were identified and the CALO-RE taxonomy was used to determine individual behaviour change techniques. Nineteen studies were identified for inclusion. Eight studies were informed by a behaviour change theory; six reported favourable effects on GWG. Overall, studies based on theory were as effective as non-theory-based studies at limiting GWG. Furthermore, the provision of information, motivational interviewing, behavioural self-monitoring and providing rewards contingent on successful behaviour appear to be key strategies when intervening in GWG. Combining these behaviour change techniques with dietary interventions may be most effective. Future research should focus on determining the exact combination of behaviour change techniques, or which underpinning theories, are most useful for limiting GWG.