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Background: Understanding links between behaviour change techniques (BCTs) and mechanisms of action (the processes through which they affect behaviour) helps inform the systematic development of behaviour change interventions., Purpose: This research aims to develop and test a methodology for linking BCTs to their mechanisms of action., Methods: Study 1 (published explicit links): Hypothesised links between 93 BCTs (from the 93-item BCT taxonomy, BCTTv1) and mechanisms of action will be identified from published interventions and their frequency, explicitness and precision documented. Study 2 (expert-agreed explicit links): Behaviour change experts will identify links between 61 BCTs and 26 mechanisms of action in a formal consensus study. Study 3 (integrated matrix of explicit links): Agreement between studies 1 and 2 will be evaluated and a new group of experts will discuss discrepancies. An integrated matrix of BCT-mechanism of action links, annotated to indicate strength of evidence, will be generated. Study 4 (published implicit links): To determine whether groups of co-occurring BCTs can be linked to theories, we will identify groups of BCTs that are used together from the study 1 literature. A consensus exercise will be used to rate strength of links between groups of BCT and theories., Conclusions: A formal methodology for linking BCTs to their hypothesised mechanisms of action can contribute to the development and evaluation of behaviour change interventions. This research is a step towards developing a behaviour change 'ontology', specifying relations between BCTs, mechanisms of action, modes of delivery, populations, settings and types of behaviour.

Background: Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a 'Knowledge System' that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question 'What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?'., Methods: The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility., Discussion: The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

Background: Behaviour change interventions are likely to be reproducible only if reported clearly. We assessed whether the behaviour change technique taxonomy version 1 (BCTTv1), with and without training in identifying BCTs, improves the clarity and replicability of written reports of observed behaviour change interventions., Methods: Three studies assessed effects of using and training in the use of BCTTv1 on the clarity and replicability of intervention descriptions written after observing videos of smoking cessation interventions. Study 1 examined the effects of using and not using BCTTv1. Study 2 examined the effects of using BCTTv1 and training in use of BCTTv1 compared no use and no training. Study 3 employed a within-group design to assess change in descriptions written before and after training. One-hundred and 66 'writers' watched videos of behaviour change interventions and wrote descriptions of the active components delivered. In all studies, the participants' written descriptions were evaluated by (i) 12 'raters' (untrained in BCTTv1) for clarity and replicability and (ii) 12 'coders' (trained in BCTTv1) for reliability of BCT coding. Writers rated the usability and accessibility of using BCTTv1 to write descriptions., Results: Ratings of clarity and replicability did not differ between groups in study 1 (all ps > 0.05), were poorer for trained users in study 2 (all ps < 0.01) and improved following training in study 3 (all ps < 0.05). BCT identification was more reliable from descriptions written by trained BCTTv1 users (p < 0.05; study 2) but not simple use of BCTTv1 (p = 0.93; study 1) or by writers who had written a description without BCTTv1, before training (p = 0.50; study 3). Writers reported that using BCTTv1 was difficult but 'useful', 'good' and 'desirable' and that their descriptions would be clear and replicable (all means above mid-point of the scale)., Conclusions: Effects of training to use BCTTv1 on the quality of written reports of observed interventions were mixed, with some suggestion of improved clarity and replicability of reporting in the within- (study 3) but not the between-group studies (studies 1 and 2). Potential benefits of using BCTTv1 may have been limited by the artificial nature and time constraints of the task.

Purpose: The aim of this paper is to assess the frequency of identification as well as the inter-coder and test-retest reliability of identification of behavior change techniques (BCTs) in written intervention descriptions., Methods: Forty trained coders applied the "Behavior Change Technique Taxonomy version 1" (BCTTv1) to 40 intervention descriptions published in protocols and repeated this 1 month later., Results: Eighty of 93 defined BCTs were identified by at least one trained coder, and 22 BCTs were identified in 16 (40 %) or more of 40 descriptions. Good inter-coder reliability was observed across 80 BCTs identified in the protocols: 66 (80 %) achieved mean prevalence and bias-adjusted kappa (PABAK) scores of 0.70 or greater, and 59 (74 %) achieved mean scores of 0.80 or greater. There was good within-coder agreement between baseline and 1 month, demonstrating good test-retest reliability., Conclusions: BCTTv1 can be used by trained coders to identify BCTs in intervention descriptions reliably. However, some frequently occurring BCT definitions require further clarification.

Background: Meeting global health challenges requires effective behaviour change interventions (BCIs). This depends on advancing the science of behaviour change which, in turn, depends on accurate intervention reporting. Current reporting often lacks detail, preventing accurate replication and implementation. Recent developments have specified intervention content into behaviour change techniques (BCTs) - the 'active ingredients', for example goal-setting, self-monitoring of behaviour. BCTs are 'the smallest components compatible with retaining the postulated active ingredients, i.e. the proposed mechanisms of change. They can be used alone or in combination with other BCTs' (Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change. Health Psychol Rev 2012;6:1-6). Domain-specific taxonomies of BCTs have been developed, for example healthy eating and physical activity, smoking cessation and alcohol consumption. We need to build on these to develop an internationally shared language for specifying and developing interventions. This technology can be used for synthesising evidence, implementing effective interventions and testing theory. It has enormous potential added value for science and global health., Objective: (1) To develop a method of specifying content of BCIs in terms of component BCTs; (2) to lay a foundation for a comprehensive methodology applicable to different types of complex interventions; (3) to develop resources to support application of the taxonomy; and (4) to achieve multidisciplinary and international acceptance for future development., Design and Participants: Four hundred participants (systematic reviewers, researchers, practitioners, policy-makers) from 12 countries engaged in investigating, designing and/or delivering BCIs. Development of the taxonomy involved a Delphi procedure, an iterative process of revisions and consultation with 41 international experts; hierarchical structure of the list was developed using inductive 'bottom-up' and theory-driven 'top-down' open-sort procedures (n = 36); training in use of the taxonomy (1-day workshops and distance group tutorials) (n = 161) was evaluated by changes in intercoder reliability and validity (agreement with expert consensus); evaluating the taxonomy for coding interventions was assessed by reliability (intercoder; test-retest) and validity (n = 40 trained coders); and evaluating the taxonomy for writing descriptions was assessed by reliability (intercoder; test-retest) and by experimentally testing its value (n = 190)., Results: Ninety-three distinct, non-overlapping BCTs with clear labels and definitions formed Behaviour Change Technique Taxonomy version 1 (BCTTv1). BCTs clustered into 16 groupings using a 'bottom-up' open-sort procedure; there was overlap between these and groupings produced by a theory-driven, 'top-down' procedure. Both training methods improved validity (both p < 0.05), doubled the proportion of coders achieving competence and improved confidence in identifying BCTs in workshops (both p < 0.001) but did not improve intercoder reliability. Good intercoder reliability was observed for 80 of the 93 BCTs. Good within-coder agreement was observed after 1 month (p < 0.001). Validity was good for 14 of 15 BCTs in the descriptions. The usefulness of BCTTv1 to report descriptions of observed interventions had mixed results., Conclusions: The developed taxonomy (BCTTv1) provides a methodology for identifying content of complex BCIs and a foundation for international cross-disciplinary collaboration for developing more effective interventions to improve health. Further work is needed to examine its usefulness for reporting interventions., Funding: This project was funded by the Medical Research Council Ref: G0901474/1. Funding also came from the Peninsula Collaboration for Leadership in Applied Health Research and Care.

Objectives: Behaviour change technique (BCT) Taxonomy v1 is a hierarchically grouped, consensus-based taxonomy of 93 BCTs for reporting intervention content. To enhance the use and understanding of BCTs, the aims of the present study were to (1) quantitatively examine the 'bottom-up' hierarchical structure of Taxonomy v1, (2) identify whether BCTs can be reliably mapped to theoretical domains using a 'top-down' theoretically driven approach, and (3) identify any overlap between the 'bottom-up' and 'top-down' groupings., Methods and Design: The 'bottom-up' structure was examined for higher-order groupings using a dendrogram derived from hierarchical cluster analysis. For the theory-based 'top-down' structure, 18 experts sorted BCTs into 14 theoretical domains. Discriminant Content Validity was used to identify groupings, and chi-square tests and Pearson's residuals were used to examine the overlap between groupings., Results: Behaviour change techniques relating to 'Reward and Punishment' and 'Cues and Cue Responses' were perceived as markedly different to other BCTs. Fifty-nine of the BCTs were reliably allocated to 12 of the 14 theoretical domains; 47 were significant and 12 were of borderline significance. Thirty-four of 208 'bottom-up' × 'top-down' pairings showed greater overlap than expected by chance. However, only six combinations achieved satisfactory evidence of similarity., Conclusions: The moderate overlap between the groupings indicates some tendency to implicitly conceptualize BCTs in terms of the same theoretical domains. Understanding the nature of the overlap will aid the conceptualization of BCTs in terms of theory and application. Further research into different methods of developing a hierarchical taxonomic structure of BCTs for international, interdisciplinary work is now required. Statement of contribution What is already known on this subject? Behaviour change interventions are effective in improving health care and health outcomes. The 'active' components of these interventions are behaviour change techniques and over 93 have been identified. Taxonomies of behaviour change techniques require structure to enable potential applications. What does this study add? This study identifies groups of BCTs to aid the recall of BCTs for intervention coding and design. It compares two methods of grouping--'bottom-up' and theory-based 'top-down'--and finds a moderate overlap. Building on identified BCT groups, it examines relationships between theoretical domains and BCTs., (© 2014 The British Psychological Society.)

Objective: To conduct a systematic review and meta-analysis examining the effectiveness of behavioural interventions targeting diet, physical activity or smoking in low-income adults., Design: Systematic review with random effects meta-analyses. Studies before 2006 were identified from a previously published systematic review (searching 1995-2006) with similar but broader inclusion criteria (including non-randomised controlled trials (RCTs)). Studies from 2006 to 2014 were identified from eight electronic databases using a similar search strategy., Data Sources: MEDLINE, EMBASE, PsycINFO, ASSIA, CINAHL, Cochrane Controlled Trials, Cochrane Systematic Review and DARE., Eligibility Criteria for Selecting Studies: RCTs and cluster RCTs published from 1995 to 2014; interventions targeting dietary, physical activity and smoking; low-income adults; reporting of behavioural outcomes., Main Outcome Measures: Dietary, physical activity and smoking cessation behaviours., Results: 35 studies containing 45 interventions with 17,000 participants met inclusion criteria. At postintervention, effects were positive but small for diet (standardised mean difference (SMD) 0.22, 95% CI 0.14 to 0.29), physical activity (SMD 0.21, 95% CI 0.06 to 0.36) and smoking (relative risk (RR) of 1.59, 95% CI 1.34 to 1.89). Studies reporting follow-up results suggested that effects were maintained over time for diet (SMD 0.16, 95% CI 0.08 to 0.25) but not physical activity (SMD 0.17, 95% CI -0.02 to 0.37) or smoking (RR 1.11, 95% CI 0.93 to 1.34)., Conclusions: Behaviour change interventions for low-income groups had small positive effects on healthy eating, physical activity and smoking. Further work is needed to improve the effectiveness of behaviour change interventions for deprived populations., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: New clinical research findings may require clinicians to change their behaviour to provide high-quality care to people with type 2 diabetes, likely requiring them to change multiple different clinical behaviours. The present study builds on findings from a UK-wide study of theory-based behavioural and organisational factors associated with prescribing, advising, and examining consistent with high-quality diabetes care., Aim: To develop and evaluate the effectiveness and cost of an intervention to improve multiple behaviours in clinicians involved in delivering high-quality care for type 2 diabetes., Design/methods: We will conduct a two-armed cluster randomised controlled trial in 44 general practices in the North East of England to evaluate a theory-based behaviour change intervention. We will target improvement in six underperformed clinical behaviours highlighted in quality standards for type 2 diabetes: prescribing for hypertension; prescribing for glycaemic control; providing physical activity advice; providing nutrition advice; providing on-going education; and ensuring that feet have been examined. The primary outcome will be the proportion of patients appropriately prescribed and examined (using anonymised computer records), and advised (using anonymous patient surveys) at 12 months. We will use behaviour change techniques targeting motivational, volitional, and impulsive factors that we have previously demonstrated to be predictive of multiple health professional behaviours involved in high-quality type 2 diabetes care. We will also investigate whether the intervention was delivered as designed (fidelity) by coding audiotaped workshops and interventionist delivery reports, and operated as hypothesised (process evaluation) by analysing responses to theory-based postal questionnaires. In addition, we will conduct post-trial qualitative interviews with practice teams to further inform the process evaluation, and a post-trial economic analysis to estimate the costs of the intervention and cost of service use., Discussion: Consistent with UK Medical Research Council guidance and building on previous development research, this pragmatic cluster randomised trial will evaluate the effectiveness of a theory-based complex intervention focusing on changing multiple clinical behaviours to improve quality of diabetes care., Trial Registration: ISRCTN66498413.

Background: Theories of action control emphasise the importance of planning, but plans are not universally beneficial., Purpose: The present study investigates whether the effectiveness of plans depends upon the skill of the planner., Methods: Study 1 prospectively predicted changes in unhealthy snacking behaviour over 1 week from intentions, action planning and performance on a standardised cognitive test of planning skill (n = 72). Study 2 experimentally randomised skilled and poor planners to receive (or not) a planning intervention before completing an online food diary (n = 144) RESULTS: Spontaneously generated action plans about snacking explained significantly more variance in subsequent snacking if produced by a skilled rather than a poor planner. The planning intervention (implementation intention) significantly improved goal attainment but only in poor planners., Conclusions: Plans are only as good as the people who make them. Poor planners' plans do not help achieve goals. Planning interventions can compensate for a lack of planning skill.

Objective: To test and optimize the feasibility and acceptability of a physical activity (PA) and healthy eating behavior change intervention for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities., Methods: Open-pilot intervention study using an uncontrolled pre and post design with ongoing measures on intervention acceptability and feasibility. Participants received 5 weekly nurse-led one-hour long group sessions. Acceptability and feasibility were assessed throughout. PA, dietary behavior and weight were measured before and after the intervention., Results: Of 74 consenting participants, 61 (82%) received and 47 (64%) completed the intervention. Average ratings of intervention materials and components by participants ranged between 4.1 and 4.9 out of 5. Average facilitator satisfaction rating was 90% (range 75-100%). The intervention delivery was feasible as indicated by ratings and comments from participants and the facilitator. Participants lost -0.86 kg of weight t(45)=3.84, p=0.0001, and increased PA by an additional 1.6 (SD=2.7) sessions/week, t(31)=-3.3, p=0.002. No significant dietary differences emerged., Conclusion: The intervention was acceptable to the facilitator and participants and feasible for delivery. Several intervention aspects were further optimized., Practice Implications: The current study outlines a PA and dietary behavior change pilot intervention coupled with a systematic and transparent process of intervention optimization., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

Failing to achieve healthy intentions can have a direct impact on subsequent health. The extent of this impact is partially determined by the size of the discrepancy between intentions and behaviour, that is, on whether an unachieved behavioural target is missed by an inch or a mile. Over two studies, measures of 'executive control' ability were used to predict the size of the intention-behaviour gap for two dietary behaviours - eating fruits and vegetables and snacking. In Study 1, participants (n=50) reported intended dietary intake, completed objective and self-report measures of executive control ability and recorded actual dietary intake over 3 days with computerised diaries. Using multiple regression, general executive control ability was found to account for 16-23% of the variance in the size of intention-behaviour gap for both the dietary behaviours. In Study 2 (n=52), deviation from intentions about snacking was significantly related to individual differences in prepotent response inhibition. Overall, individuals with weak executive control ate less fruits and vegetables and more snacks than intended. Intention-behaviour 'failures' are not homogenous, but instead vary predictably with the availability of executive control resources. This suggests that individuals with large intention-behaviour shortfalls may benefit from interventions designed to reduce the demands on executive control., (© 2011 Taylor & Francis)

Background: The importance of behaviour change in improving health is illustrated by the increasing investment by funding bodies in the development and evaluation of complex interventions to change population, patient, and practitioner behaviours. The development of effective interventions is hampered by the absence of a nomenclature to specify and report their content. This limits the possibility of replicating effective interventions, synthesising evidence, and understanding the causal mechanisms underlying behaviour change. In contrast, biomedical interventions are precisely specified (e.g., the pharmacological 'ingredients' of prescribed drugs, their dose and frequency of administration). For most complex interventions, the precise 'ingredients' are unknown; descriptions (e.g., 'behavioural counseling') can mean different things to different researchers or implementers. The lack of a method for specifying complex interventions undermines the precision of evidence syntheses of effectiveness, posing a problem for secondary, as well as primary, research.We aim to develop a reliable method of specifying intervention components ('techniques') aimed at changing behaviour., Methods/design: The research will be conducted in three phases. The first phase will develop the nomenclature. We will refine a preliminary list of techniques and definitions. Using a formal consensus method, experts will then define the key attributes of each technique and how it relates to, and differs from, others. They will evaluate the techniques and their definitions until they achieve an agreed-upon list of clearly defined, nonredundant techniques. The second phase will test the nomenclature. Trained experts (primary researchers and systematic reviewers), equipped with a coding manual and guidance, will use the nomenclature to code published descriptions of complex interventions. Reliability between experts, over time, and across types of users will be assessed. We will assess whether using the nomenclature to write intervention descriptions enhances the clarity and replicability of interventions. The third phase will develop a web-based users' resource of clearly specified and nonredundant techniques, which will aid the scientific understanding of, and development of, effective complex interventions. Dissemination throughout the project will be through stakeholder meetings, targeted multidisciplinary workshops, conference presentation, journal publication, and publication in an interactive web-based platform (a Wiki)., Discussion: The development of a reliable method of specifying intervention components aimed at changing behaviour will strengthen the scientific basis for developing, evaluating, and reporting complex interventions. It will improve the precision of evidence syntheses of effectiveness, thus enhancing secondary, as well as primary, research.

Cognitions influence recovery from activity limitations. In this study we aimed to independently test, compare and combine the common sense self-regulation model (CS-SRM) and social cognitive theory (SCT) in predicting recovery from activity limitations due to acute injury. Measures were gathered at two time points 5-6 weeks apart. The sample consisted of 146 university students (Mean age: 21.86, SD: 4.83, 62% female) with a heterogeneous range of injuries that limited their participation in physical activity. The dependent variable was recovery from activity limitations (Physical Functioning - Short Form-36). The predictor variables were measured using the Brief Illness Perception Questionnaire and SCT items designed according to theoretical recommendations. Time-line (TL) and self-efficacy (SE) were significant predictors of recovery in a multivariate analysis, controlling for reported pain at Time 1. A combined model including the best predictors from both models, TL (beta = -0.25, p < 0.05, R(2) change = 0.17, p < 0.01) and SE (beta =0.31, p < 0.05, R(2) change = 0.05, p < 0.05), accounted for a significant amount of the variance in recovery from activity limitations. A combination of key variables from both models may be particularly useful for understanding the cognitive factors that influence recovery from activity limitations.

Perceived behavioral control (PBC) and intention, the proximal predictors from the theory of planned behavior (TPB), were used to predict cardiovascular risk behaviors in 597 patients 1 year after diagnosis with coronary heart disease. The outcome measures were self-report measures of exercise plus objective measures of fitness (distance walked in 6 min) and cotinine-confirmed smoking cessation. In multivariate analyses incorporating both PBC and intention, PBC predicted exercise, distance walked, and smoking cessation, but intention was not a reliable independent predictor of any health behavior measured. Thus, the effective theoretical component of the TPB was PBC. Similar predictions could derive from social-cognitive theory. In coronary patients, behavioral change needs to address issues of action implementation rather than motivational factors alone., (((c) 2004 APA, all rights reserved))

Background : Governments have relied on their citizens to adhere to a variety of transmission-reducing behaviours (TRBs) to suppress the Covid-19 pandemic. Understanding the psychological and sociodemographic predictors of adherence to TRBs will be heavily influenced by the particular theories used by researchers. This review aims to identify the theories and theoretical constructs used to understand adherence to TRBs during the pandemic within the UK social and legislative context. Methods: A systematic review identified studies to understand TRBs of adults in the UK during the pandemic. Identified theoretical constructs were coded to the Theoretical Domains Framework. Data are presented as a narrative summary. Results: Thirty-five studies (n = 211,209) investigated 123 TRBs, applied 13 theoretical frameworks and reported 50 sociodemographic characteristics and 129 psychological constructs. Most studies used social cognition theories to understand TRBs and employed cross-sectional designs. Risk of sampling bias was high. Relationships between constructs and TRBs varied, but in general, beliefs about the disease (e.g. severity and risk perception) and about TRBs (e.g. behavioural norms) influenced behavioural intentions and self-reported adherence. More studies than not found that older people and females were more adherent. Conclusions: Behavioural scientists in the UK generated a significant and varied body of work to understand TRBs during the pandemic. However, more use of theories that do not rely on deliberative processes to effect behaviour change and study designs better able to support causal inferences should be used in future to inform public health policy and practice. Prospero Registration: CRD42021282699. [ABSTRACT FROM AUTHOR]

There is a need for a measure to monitor adherence to transmission-reducing behaviours (TRBs) during pandemics. An adherence measure can monitor current TRBs, assess change over time and, potentially, predict later behaviours. The TRB adherence measure (scale consisting of seven items) includes questions based on government behavioural directives in Scotland that were common internationally, i.e., physical distancing, face covering and hand hygiene. Data were collected weekly for 6 weeks at the beginning of the pandemic, including a later follow-up repeated measure of some participants, in 20-minute structured telephone surveys with a nationally representative random sample of adults in Scotland. A total of 2969 people completed the adherence items and were highly adherent. Confirmatory factor analysis supported a unidimensional scale (CFI =.95; TLI =.93; RMSEA =.08; SRMR =.08), although internal consistency was low (Cronbach's alpha =.49). The adherence score significantly predicted adherence to a validity test item (ΔR2 =.114, F(1,2964) = 379.76, p <.001). It also predicted adherence to TRBs later over and above personal habitual styles (Creature of Habit Scale: COHS). The adherence score has been developed for routine monitoring of adherence to TRBs during the COVID-19 pandemic. It can be used to predict future similar behaviours and adherence to other behaviours, although it may be necessary to explore adherence to the specific behaviours occasionally. Adherent behaviour for one TRB is likely to be associated with adherence to government directives to other TRBs. Importantly, these TRBs are likely to be crucial in reducing COVID-19 case numbers, as well as protecting against other infectious diseases including influenza and the common cold. [ABSTRACT FROM AUTHOR]

Background: To investigate the effect of in-person delivered behavioural interventions in people with multimorbidity and which behaviour change techniques (BCTs), targeting lifestyle behaviours, are associated with better outcomes. Methods: Systematic review of randomised controlled trials. We searched MEDLINE, EMBASE, CENTRAL, and CINAHL and screened reference list of reviews including people with multimorbidity, registries, and citation tracking of included studies. Meta-analyses using random-effects model to assess the effect of behavioural interventions and meta-regression analyses and effectiveness ratios to investigate the impact of mediators on effect estimates. Cochrane 'Risk of Bias Tool' 2.0 and the GRADE assessment to evaluate the overall quality of evidence. Results: Fourteen studies involving 1,378 people. Behavioural interventions had little to no effect on physical activity (standardised mean difference 0.38, 95% CI −0.12–0.87) and the effect on weight loss was uncertain (BMI mean difference −0.17, 95% CI −1.1–0.83) at the end-treatment follow-up. Small improvements were seen in health-related quality of life (SMD 0.29, 95% CI 0.17–0.42) and physical function (SMD 0.42, 95% CI 0.12–0.73), and moderate improvements were seen for depression symptoms (SMD −0.70, 95% CI −0.97–0.42). Studies using the BCTs 'action planning' and 'social support (practical)' reported greater physical activity and weight loss. Conclusions: Behavioural interventions targeting lifestyle behaviours may improve health-related quality of life and physical function, and reduce depression, whereas little to no effect was achieved on physical activity and weight loss in people with multimorbidity. However, the evidence for physical activity and weight loss were of low quality and the end-treatment benefits diminished over time. [ABSTRACT FROM AUTHOR]

Background: The competence of the person delivering person-to-person behaviour change interventions may influence the effectiveness of the intervention. However, we lack a framework for describing the range of competences involved. The objective of the current work was to develop a competency framework for health behaviour change interventions. Method: A preliminary framework was developed by two judges rating the relevance of items in the competency framework for cognitive behaviour therapies; adding relevant items from reviews and other competency frameworks; and obtaining feedback from potential users on a draft framework. The Health Behaviour Change Competency Framework (HBCCF) was used to analyse the competency content of smoking cessation manuals. Results: Judges identified 194 competency items as relevant, which were organised into two domains: foundation (12 competency topics comprising 56 competencies) and behaviour change (12 topics, 54 competencies); several of the 54 and 56 competencies were composed of sub-competencies (84 subcompetencies in total). Smoking cessation manuals included 14 competency topics from the foundation and behaviour change competency domains. Conclusion: The HBCCF provides a structured method for assessing and reporting competency to deliver behaviour change interventions. It can be applied to assess a practitioner's competency and training needs and to identify the competencies needed for a particular intervention. To date, it has been used in self-assessments and in developing training programmes. We propose the HBCCF as a practical tool for researchers, employers, and those who design and provide training. We envisage the HBCFF maturing and adapting as evidence that identifies the essential elements required for the effective delivery of behaviour change interventions emerges. [ABSTRACT FROM AUTHOR]

Purpose: Healthy eating, physical activity and smoking interventions for low-income groups may have small, positive effects. Identifying effective intervention components could guide intervention development. This study investigated which content and delivery components of interventions were associated with increased healthy behavior in randomised controlled trials (RCTs) for low-income adults.Method: Data from a review showing intervention effects in 35 RCTs containing 45 interventions with 17,000 participants were analysed to assess associations with behavior change techniques (BCTs) and delivery/context components from the template for intervention description and replication (TIDieR) checklist. The associations of 46 BCTs and 14 delivery/context components with behavior change (measures of healthy eating, physical activity and smoking cessation) were examined using random effects subgroup meta-analyses. Synergistic effects of components were examined using classification and regression trees (meta-CART) analyses based on both fixed and random effects assumptions.Results: For healthy eating, self-monitoring, delivery through personal contact, and targeting multiple behaviors were associated with increased effectiveness. Providing feedback, information about emotional consequences, or using prompts and cues were associated with reduced effectiveness. In synergistic analyses, interventions were most effective without feedback, or with self-monitoring excluding feedback. More effective physical activity interventions included behavioral practice/rehearsal or instruction, focussed solely on physical activity or took place in home/community settings. Information about antecedents was associated with reduced effectiveness. In synergistic analyses, interventions were most effective in home/community settings with instruction. No associations were identified for smoking.Conclusion: This study identified BCTs and delivery/context components, individually and synergistically, linked to increased and reduced effectiveness of healthy eating and physical activity interventions. The identified components should be subject to further experimental study to help inform the development effective behavior change interventions for low-income groups to reduce health inequalities. [ABSTRACT FROM AUTHOR]

Background: Clinicians' behaviours require deliberate decision-making in complex contexts and may involve both impulsive (automatic) and reflective (motivational and volitional) processes. Purpose: The purpose of this study was to test a dual process model applied to clinician behaviours in their management of type 2 diabetes. Methods: The design used six nested prospective correlational studies. Questionnaires were sent to general practitioners and nurses in 99 UK primary care practices, measuring reflective (intention, action planning and coping planning) and impulsive (automaticity) predictors for six guideline-recommended behaviours: blood pressure prescribing ( N = 335), prescribing for glycemic control ( N = 288), providing diabetes-related education ( N = 346), providing weight advice ( N = 417), providing self-management advice ( N = 332) and examining the feet ( N = 218). Results: Respondent retention was high. A dual process model was supported for prescribing behaviours, weight advice, and examining the feet. A sequential reflective process was supported for blood pressure prescribing, self-management and weight advice, and diabetes-related education. Conclusions: Reflective and impulsive processes predict behaviour. Quality improvement interventions should consider both reflective and impulsive approaches to behaviour change. [ABSTRACT FROM AUTHOR]

The Theory of Planned Behaviour (TPB) can identify cognitions that predict differences in behaviour between individuals. However, it is not clear whether the TPB can predict the behaviour of an individual person. This study employs a series ofn-of-1 studies and time series analyses to examine the ability of the TPB to predict physical activity (PA) behaviours of six individuals. Sixn-of-1 studies were conducted, in which TPB cognitions and up to three PA behaviours (walking, gym workout and a personally defined PA) were measured twice daily for six weeks. Walking was measured by pedometer step count, gym attendance by self-report with objective validation of gym entry and the personally defined PA behaviour by self-report. Intra-individual variability in TPB cognitions and PA behaviour was observed in all participants. The TPB showed variable predictive utility within individuals and across behaviours. The TPB predicted at least one PA behaviour for five participants but had no predictive utility for one participant. Thus,n-of-1 designs and time series analyses can be used to test theory in an individual. [ABSTRACT FROM AUTHOR]

Objective: Disability from chronic illness is a major problem for society, yet the study of its determinants lacks an overall theoretical paradigm. Johnston (1996) has proposed conceptualizing disability as behavior and integrating biomedical and behavioral predictors. Dixon, Johnston, Rowley, and Pollard (2008) tested a model including constructs from the International Classification of Functioning, Disability and Health (ICF) and the theory of planned behavior (TPB) using structural equation modeling; it fitted better and explained more variance than the ICF or TPB alone. We replicated their study with a new sample from the same population (orthopedic patients awaiting joint replacement) and also tested the model after the patients had surgery. Methods: Two weeks before surgery, 342 orthopedic patients who had joint pain (most with arthritis) completed a questionnaire, with 228 completing it again 1 year after surgery. The authors tested Dixon et al.'s best-fit models cross-sectionally (before and after surgery) and assessed the goodness of fit of these imposed models to our data using structural equation modeling. Results: Findings strongly supported those of Dixon et al. Before surgery, results were very similar to Dixon et al. with all models accounting for significant variance and fitting well, but the integrated model fitted better and accounted for more variance. One year after surgery, Dixon et al.'s models showed even stronger fit to the data. Conclusions: Although behavioral and biomedical (ICF) models were supported, the integrated model provided a better explanation of disability in this population than either of these models alone and suggests biopsychosocial interventions to reduce disability. [ABSTRACT FROM AUTHOR]

Aims: Patient beliefs about heart failure (HF) may be important determinants of adherence. This study assessed levels of adherence to angiotensin-converting-enzyme inhibitors (ACEI) and examined whether beliefs about HF were associated with adherence to ACEI. [ABSTRACT FROM PUBLISHER]

Theory-based intervention programmes to support health-related behaviour change aim to increase health impact and improve understanding of mechanisms of behaviour change. However, the science of intervention development remains at an early stage. We present a causal modelling approach to developing complex interventions for evaluation in randomized trials. In this approach a generic model links behavioural determinants, causally through behaviour, to physiological and biochemical variables, and health outcomes. It is tailored to context, target population, behaviours and health outcomes. The development of a specific causal model based on theory and evidence is illustrated by the ProActive programme, supporting increased physical activity among individuals at risk of Type 2 diabetes. The model provides a rational guide to appropriate measures, intervention points and intervention techniques, and can be tested quantitatively. Causal modelling is critically compared to other approaches to intervention development and evaluation, and research directions are indicated. [ABSTRACT FROM AUTHOR]

Background: Advancements in mobile phone technology offer huge potential for enhancing the timely delivery of health behavior change interventions. The development of smartphone-based health interventions (apps) is a rapidly growing field of research, yet there have been few longitudinal examinations of how people experience and use these apps within their day-to-day routines, particularly within the context of a hybrid Web- and app-based intervention. Objective: This study used an in-depth mixed-methods design to examine individual variation in (1) impact on self-reported goal engagement (ie, motivation, self-efficacy, awareness, effort, achievement) of access to a weight management app (POWeR Tracker) when provided alongside a Web-based weight management intervention (POWeR) and (2) usage and views of POWeR Tracker. Methods: Thirteen adults were provided access to POWeR and were monitored over a 4-week period. Access to POWeR Tracker was provided in 2 alternate weeks (ie, weeks 1 and 3 or weeks 2 and 4). Participants' goal engagement was measured daily via self-report. Mixed effects models were used to examine change in goal engagement between the weeks when POWeR Tracker was and was not available and whether the extent of change in goal engagement varied between individual participants. Usage of POWeR and POWeR Tracker was automatically recorded for each participant. Telephone interviews were conducted and analyzed using inductive thematic analysis to further explore participants' experiences using POWeR and POWeR Tracker. Results: Access to POWeR Tracker was associated with a significant increase in participants' awareness of their eating (β1=0.31, P=.04) and physical activity goals (β1=0.28, P=.03). The level of increase varied between individual participants. Usage data showed that participants used the POWeR website for similar amounts of time during the weeks when POWeR Tracker was (mean 29 minutes, SD 31 minutes) and was not available (mean 27 minutes, SD 33 minutes). POWeR Tracker was mostly accessed in short bursts (mean 3 minutes, SD 2 minutes) during convenient moments or moments when participants deemed the intervention content most relevant. The qualitative data indicated that nearly all participants agreed that it was more convenient to access information on-the-go via their mobiles compared to a computer. However, participants varied in their views and usage of the Web- versus app-based components and the informational versus tracking tools provided by POWeR Tracker. Conclusions: This study provides evidence that smartphones have the potential to improve individuals' engagement with their health-related goals when used as a supplement to an existing online intervention. The perceived convenience of mobile access to information does not appear to deter use of Web-based interventions or strengthen the impact of app access on goal engagement. A mixed-methods design enabled exploration of individual variation in daily usage of the app-based tools. [ABSTRACT FROM AUTHOR]

Objectives: To gain a better understanding of the influence of the health condition on emotions and control cognitions by using the International Classification of Functioning, Disability and Health (ICF) framework. The relations between health condition, impairment, activity limitations, and emotions or control cognitions were investigated in people with 1 of 2 different disabling conditions, chronic idiopathic axonal polyneuropathy (CIAP) or stroke. Method: Two existing databases were used: cross-sectional data from 56 participants with CIAP; longitudinal data of 100 participants with stroke. Participants were assessed on measures of the health condition, impairment, activity limitations, emotions, and control cognitions. Results: In participants with CIAP, impairment explained variance in concurrent control cognitions (9%-25%), activity limitation in concurrent depression (8%), and control cognitions (26%-36%). In stepwise regression activity, limitation was more powerful in explaining variance in emotions and control cognitions than impairment. In people with stroke, only activity limitations explained variance in emotions (4%-9%) and control cognitions (4%-7%). Conclusions: These results suggest that targeting activity limitations in rehabilitation efforts is likely to relate to control cognitions, which in themselves have been shown to benefit recovery of physical consequences in chronic illness. [ABSTRACT FROM AUTHOR]

Three studies were conducted to examine the effect of group identification and normative content of social identities on healthy eating intentions and behaviour. In Study 1 (N = 87) Australian participants were shown images that portrayed a norm of healthy vs. unhealthy behaviour among Australians. Participants' choices from an online restaurant menu were used to calculate energy content as the dependent variable. In Study 2 (N = 117), female participants were assigned to a healthy or unhealthy norm condition. The dependent variable was the amount of food eaten in a taste test. Social group identification was measured in both studies. In Study 3 (N = 117), both American identification and healthiness norm were experimentally manipulated, and participants' choices from an online restaurant menu constituted the dependent variable. In all three studies, the healthiness norm presented interacted with participants' group identification to predict eating behaviour. Contrary to what would be predicted under the traditional normative social influence account, higher identifiers chose higher energy food from an online menu and ate more food in a taste test when presented with information about their in-group members behaving healthily. The exact psychological mechanism responsible for these results remains unclear, but the pattern of means can be interpreted as evidence of vicarious licensing, whereby participants feel less motivated to make healthy food choices after being presented with content suggesting that other in-group members are engaging in healthy behaviour. These results suggest a more complex interplay between group membership and norms than has previously been proposed. [ABSTRACT FROM AUTHOR]