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

2020 ◽  
Vol 54 (11) ◽  
pp. 827-842
Author(s):  
Lauren Connell Bohlen ◽  
Susan Michie ◽  
Marijn de Bruin ◽  
Alexander J Rothman ◽  
Michael P Kelly ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Intervention Development ◽  
Evidence Base ◽  
Behavior Change Techniques ◽  
Multiple Behavior Change ◽  
Behavior Change Interventions ◽  
Theory Use ◽  
Behavior Change Theories

Abstract Background Behavioral 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. Purpose This 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. Methods The 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. Results Five 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. Conclusions It 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.

scholarly journals Do combinations of behaviour change techniques that occur frequently in interventions reflect underlying theory?

2019 ◽  
Author(s):  
Lauren Connell Bohlen ◽  
Susan Michie ◽  
Marijn de Bruin ◽  
Alexander Rothman ◽  
Michael P Kelly ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Behaviour Change ◽  
Change Theory ◽  
Intervention Development ◽  
Evidence Base ◽  
Behaviour Change Techniques ◽  
Behavioural Interventions ◽  
Behavioral Theories ◽  
Theory Use ◽  
Selection Of

Background: Behavioural interventions typically include multiple behaviour 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 behaviour change outcomes. Purpose: This study aimed to identify groups of BCTs commonly occurring together in behaviour change interventions and examine whether behaviour change theories underlying these groups could be identified.Methods: The study involved three phases: 1) a factor analysis to identify groups of co-occurring BCTs from 277 behaviour change intervention reports; 2) an expert consensus exercise (n=25) to examine links between BCT groups and behavioral theories; 3) a comparison of the expert-linked theories with theories explicitly mentioned by authors of the 277 intervention reports.Results: Five 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 behaviour 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.Conclusions: It is possible to identify groups of BCTs frequently used together in interventions. Experts made shared inferences about behaviour change theory underlying these BCT groups, suggesting 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 multi-component interventions and build the evidence base for further understanding theory-based intervention development and evaluation.

scholarly journals Understanding Outcomes in Behavior Change Interventions to Prevent Pediatric Obesity: The Role of Dose and Behavior Change Techniques

2018 ◽  
Vol 46 (2) ◽  
pp. 312-321 ◽  
Author(s):  
Meghan M. JaKa ◽  
Simone A. French ◽  
Julian Wolfson ◽  
Robert W. Jeffery ◽  
Fabianna Lorencatto ◽  
...  
Keyword(s):  
Behavior Change ◽  
Pediatric Obesity ◽  
Behavioral Interventions ◽  
Specific Weight ◽  
Primary Study ◽  
Session Length ◽  
Behavior Change Techniques ◽  
Effective Interventions ◽  
Behavior Change Interventions ◽  
Child Bmi

Background. Behavioral interventions to prevent pediatric obesity have shown inconsistent results across the field. Studying what happens within the “black box” of these interventions and how differences in implementation lead to different outcomes will help researchers develop more effective interventions. Aim. To compare the implementation of three features of a phone-based intervention for parents (time spent discussing weight-related behaviors, behavior change techniques used in sessions, and intervention activities implemented by parents between sessions) with study outcomes. Methods. A random selection of 100 parent–child dyads in the intervention arm of a phone-based obesity prevention trial was included in this analysis. Sessions were coded for overall session length, length of time spent discussing specific weight-related behaviors, number of behavior change techniques used during the sessions, and number of intervention-recommended activities implemented by the parents between sessions (e.g., parent-reported implementation of behavioral practice/rehearsal between sessions). The primary study outcome, prevention of unhealthy increase in child body mass index (BMI) percentile, was measured at baseline and 12 months. Results. Overall session length was associated with decreases in child BMI percentile ( b = −0.02, p = .01). There was no association between the number of behavior change techniques used in the sessions and decreases in child BMI percentile ( b = −0.29, p = .27). The number of activities the parents reported implementing between sessions was associated with decreases in child BMI percentile ( b = −1.25, p = .02). Discussion. To improve future interventions, greater attention should be paid to the intended and delivered session length, and efforts should be made to facilitate parents’ implementation of intervention-recommended activities between sessions (ClinicalTrials.gov, No. NCT01084590).

Training commercial energy users in behavior change: A case study

2020 ◽  
Author(s):  
Sea Rotmann ◽  
Beth Karlin
Keyword(s):  
Behavior Change ◽  
Behavioral Interventions ◽  
Professional Training ◽  
Energy Use ◽  
Behavioral Science ◽  
Research Process ◽  
Commercial Sector ◽  
Great Opportunity ◽  
Behavior Change Interventions ◽  
Commercial Energy

Within the commercial sector, energy managers and building operators have a large impact over their organizations’ energy use. However, they mostly focus on technology solutions and retrofits, rather than human or corporate behaviors, and how to change them. This gap in targeted commercial sector research and behavioral interventions provides a great opportunity which is currently not being addressed. This paper presents a field research pilot where an empirical behavior change research process was applied and taught to commercial energy users in Ontario, Canada. This course served to fill an identified market gap and to improve commercial energy managers’ literacy in behavioral science theory and techniques. A needs assessment identified a clear gap in behavioral training for energy managers, and high interest in the course further proved out the market opportunity for professional training on how to design, implement and evaluate behavior change interventions. Evaluation results identified positive feedback in terms of course reaction, self-reported learning and behavioral outcomes, and tangible results when course participants returned to work to apply their learnings. Evaluation results suggest that such training fills a vital gap in the current Strategic Energy Management (SEM) landscape, and could unlock significant savings in the commercial energy sector.

scholarly journals An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions

2019 ◽  
Author(s):  
Madalina Sucala ◽  
Nnamdi Peter Ezeanochie ◽  
Heather Cole-Lewis ◽  
Jennifer Turgiss
Keyword(s):  
Behavior Change ◽  
Data Science ◽  
Intervention Development ◽  
Evaluation Methods ◽  
Rapid Expansion ◽  
Impact Behavior ◽  
Science Field ◽  
Technology Industry ◽  
Behavior Change Interventions ◽  
Behavior Science

Abstract The rapid expansion of technology promises to transform the behavior science field by revolutionizing the ways in which individuals can monitor and improve their health behaviors. To fully live into this promise, the behavior science field must address distinct challenges, including: building interventions that are not only scientifically sound but also engaging; using evaluation methods to precisely assess intervention components for intervention optimization; and building personalized interventions that acknowledge and adapt to the dynamic ecosystem of individual and contextual variables that impact behavior change. The purpose of this paper is to provide a framework to address these challenges by leveraging behavior science, human-centered design, and data science expertise throughout the cycle of developing and evaluating digital behavior change interventions (DBCIs). To define this framework, we reviewed current models and practices for intervention development and evaluation, as well as technology industry models for product development. The framework promotes an iterative process, aiming to maximize outcomes by incorporating faster and more frequent testing cycles into the lifecycle of a DBCI. Within the framework provided, we describe each phase, from development to evaluation, to discuss the optimal practices, necessary stakeholders, and proposed evaluation methods. The proposed framework may inform practices in both academia and industry, as well as highlight the need to offer collaborative platforms to ensure successful partnerships that can lead to more effective DBCIs that reach broad and diverse populations.

scholarly journals Behavioral Intervention Components Associated With Cost-effectiveness: A Comparison of Six Domains

2021 ◽  
Author(s):  
E Beard ◽  
F Lorencatto ◽  
B Gardner ◽  
S Michie ◽  
L Owen ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Behavior Change ◽  
Lower Cost ◽  
Self Regulation ◽  
Cost Effective ◽  
Cost Effectiveness Ratio ◽  
Behavior Change Techniques ◽  
Multiple Health Behaviors ◽  
Behavior Change Interventions ◽  
Multiple Health

Abstract Background To help implement behavior change interventions (BCIs) it is important to be able to characterize their key components and determine their effectiveness. Purpose This study assessed and compared the components of BCIs in terms of intervention functions identified using the Behaviour Change Wheel Framework (BCW) and in terms of their specific behavior change techniques (BCTs) identified using the BCT TaxonomyV1, across six behavioral domains and the association of these with cost-effectiveness. Methods BCIs in 251 studies targeting smoking, diet, exercise, sexual health, alcohol and multiple health behaviors, were specified in terms of their intervention functions and their BCTs, grouped into 16 categories. Associations with cost-effectiveness measured in terms of incremental cost-effectiveness ratio (ICER) upper and lower estimates were determined using regression analysis. Results The most prevalent functions were increasing knowledge through education (72.1%) and imparting skills through training (74.9%). The most prevalent BCT groupings were shaping knowledge (86.5%), changing behavioral antecedents (53.0%), supporting self-regulation (47.7%), and providing social support (44.6%). Intervention functions associated with better cost-effectiveness were those based on training (βlow = −15044.3; p = .002), persuasion (βlow = −19384.9; p = .001; βupp = −25947.6; p < .001) and restriction (βupp = −32286.1; p = .019), and with lower cost-effectiveness were those based on environmental restructuring (β = 15023.9low; p = .033). BCT groupings associated with better cost-effectiveness were goals and planning (βlow = −8537.3; p = .019 and βupp = −12416.9; p = .037) and comparison of behavior (βlow = −13561.9, p = .047 and βupp = −30650.2; p = .006). Those associated with lower cost-effectiveness were natural consequences (βlow = 7729.4; p = .033) and reward and threat (βlow = 20106.7; p = .004). Conclusions BCIs that focused on training, persuasion and restriction may be more cost-effective, as may those that encourage goal setting and comparison of behaviors with others.

scholarly journals Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review (Preprint)

2017 ◽  
Author(s):  
Sheik Mohammad Roushdat Ally Elaheebocus ◽  
Mark Weal ◽  
Leanne Morrison ◽  
Lucy Yardley
Keyword(s):  
Systematic Review ◽  
Social Media ◽  
Behavior Change ◽  
Behavioral Interventions ◽  
Selection Process ◽  
Future Research ◽  
Behavioral Risk ◽  
Lack Of Information ◽  
Behavior Change Interventions ◽  
High Level

BACKGROUND Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. OBJECTIVE The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. METHODS Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. RESULTS A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. CONCLUSIONS Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.

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