Journal articles on the topic 'Behavior change interventions'
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Elwyn, Glyn, Katy Marrin, Dominick L. Frosch, and James White. "Sustainable Change Sequence: A framework for developing behavior change interventions for patients with long-term conditions." European Journal for Person Centered Healthcare 2, no. 2 (April 8, 2014): 212. http://dx.doi.org/10.5750/ejpch.v2i2.736.
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ObjectiveInteractive interventions are increasingly advocated to support behavior change for patients who have long-term conditions. Such interventions are most likely to achieve behavior change when they are based on appropriate theoretical frameworks. Developers of interventions are faced with a diverse set of behavioral theories that do not specifically address intervention development. The aim of our work was to develop a framework to guide the developers of interactive healthcare interventions that was derived from relevant theory, and which guided developers towards appropriate behavior change techniques.MethodsWe reviewed theories that inform behavior change interventions, where relevant to the management of long-term conditions. Theoretical constructs and behavior change techniques were grouped according to similarity in aims.ResultsWe developed a logic model that operationalizes behavior change theories and techniques into five steps likely to lead to sustained behavior change. The steps are: 1) create awareness of need; 2) facilitate learning; 3) enhance motivation; 4) prompt behaviour change; and 5) ensure sustainability of behaviour change.Conclusion and Practice implicationsA framework that sequences behavioural change techniques along a sustainability model provides a practical template for the developers of interactive healthcare applications and interventions.
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Bartholomew, John B. "Environments Change Child Behavior, But Who Changes Environments?" Kinesiology Review 4, no. 1 (February 2015): 71–76. http://dx.doi.org/10.1123/kr.2014-0077.
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Numerous interventions have been designed to modify children's physical activity and eating behaviors. While early research centered on the individual as the target of intervention, more recent work targets change in the environment. These studies have consistently supported the importance of environmental contributors to both physical activity and eating behavior, but little research has considered those who are responsible for implementing environmental change. For example, if we expect school environments to support activity and healthy eating, we must consider the motivation of school administrators to affect change. This review will present examples of an ecological approach to behavior change along with recent data to support this approach.
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JaKa, Meghan M., Simone A. French, Julian Wolfson, Robert W. Jeffery, Fabianna Lorencatto, Susan Michie, Rona L. Levy, Shelby L. Langer, and Nancy E. Sherwood. "Understanding Outcomes in Behavior Change Interventions to Prevent Pediatric Obesity: The Role of Dose and Behavior Change Techniques." Health Education & Behavior 46, no. 2 (September 14, 2018): 312–21. http://dx.doi.org/10.1177/1090198118798679.
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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).
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Kolodko, Julia, Kelly Ann Schmidtke, Daniel Read, and Ivo Vlaev. "#LetsUnlitterUK: A demonstration and evaluation of the Behavior Change Wheel methodology." PLOS ONE 16, no. 11 (November 16, 2021): e0259747. http://dx.doi.org/10.1371/journal.pone.0259747.
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The Behavior Change Wheel is the most comprehensive and practically useful methodology available for developing behavior change interventions. The current article demonstrates how it can be applied to optimize pro-environmental behaviors and, in so doing, give interventionists access to a rigorous set of theories and techniques for systematically developing pro-environmental interventions. Section 1 describes the development of an intervention to increase people’s intentions to post anti-littering messages on social media. Study 2 describes the development and evaluation of an intervention to increase people’s actual anti-littering posts. Both evaluations are randomized controlled trials that compare the effectiveness of the developed intervention with interventions less informed by the Wheel. We found interventions completely informed by the Wheel to be more effective than interventions less (or not at all) informed by the Wheel. The discussion explores how the Behavior Change Wheel methodology can be used to design future pro-environment interventions.
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Burgermaster, Marissa, Isobel Contento, Pamela Koch, and Lena Mamykina. "Behavior change is not one size fits all: psychosocial phenotypes of childhood obesity prevention intervention participants." Translational Behavioral Medicine 8, no. 5 (January 17, 2018): 799–807. http://dx.doi.org/10.1093/tbm/ibx029.
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Abstract Variability in individuals’ responses to interventions may contribute to small average treatment effects of childhood obesity prevention interventions. But, neither the causes of this individual variability nor the mechanism by which it influences behavior are clear. We used qualitative methods to characterize variability in students’ responses to participating in a childhood obesity prevention intervention and psychosocial characteristics related to the behavior change process. We interviewed 18 students participating in a school-based curriculum and policy behavior change intervention. Descriptive coding, summary, and case-ordered descriptive meta-matrices were used to group participants by their psychosocial responses to the intervention and associated behavior changes. Four psychosocial phenotypes of responses emerged: (a) Activated—successful behavior-changers with strong internal supports; (b) Inspired—motivated, but not fully successful behavior-changers with some internal supports, whose taste preferences and food environment overwhelmed their motivation; (c) Reinforced—already practiced target behaviors, were motivated, and had strong family support; and (d) Indifferent—uninterested in behavior change and only did target behaviors if family insisted. Our findings contribute to the field of behavioral medicine by suggesting the presence of specific subgroups of participants who respond differently to behavior change interventions and salient psychosocial characteristics that differentiate among these phenotypes. Future research should examine the utility of prospectively identifying psychosocial phenotypes for improving the tailoring of nutrition behavior change interventions.
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Bluethmann, Shirley M., L. Kay Bartholomew, Caitlin C. Murphy, and Sally W. Vernon. "Use of Theory in Behavior Change Interventions." Health Education & Behavior 44, no. 2 (July 10, 2016): 245–53. http://dx.doi.org/10.1177/1090198116647712.
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Objective. Theory use may enhance effectiveness of behavioral interventions, yet critics question whether theory-based interventions have been sufficiently scrutinized. This study applied a framework to evaluate theory use in physical activity interventions for breast cancer survivors. The aims were to (1) evaluate theory application intensity and (2) assess the association between extensiveness of theory use and intervention effectiveness. Methods. Studies were previously identified through a systematic search, including only randomized controlled trials published from 2005 to 2013, that addressed physical activity behavior change and studied survivors who were <5 years posttreatment. Eight theory items from Michie and Prestwich’s coding framework were selected to calculate theory intensity scores. Studies were classified into three subgroups based on extensiveness of theory use (Level 1 = sparse; Level 2 = moderate; and Level 3 = extensive). Results. Fourteen randomized controlled trials met search criteria. Most trials used the transtheoretical model ( n = 5) or social cognitive theory ( n = 3). For extensiveness of theory use, 5 studies were classified as Level 1, 4 as Level 2, and 5 as Level 3. Studies in the extensive group (Level 3) had the largest overall effect size ( g = 0.76). Effects were more modest in Level 1 and 2 groups with overall effect sizes of g = 0.28 and g = 0.36, respectively. Conclusions. Theory use is often viewed as essential to behavior change, but theory application varies widely. In this study, there was some evidence to suggest that extensiveness of theory use enhanced intervention effectiveness. However, there is more to learn about how theory can improve interventions for breast cancer survivors.
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de Ruijter, Dennis, Enrique Mergelsberg, Matty Crone, Eline Smit, and Ciska Hoving. "Identifying Active Ingredients, Working Mechanisms, and Fidelity Characteristics Reported in Smoking Cessation Interventions in Dutch Primary Care: A Systematic Review." Nicotine & Tobacco Research 24, no. 5 (November 12, 2021): 654–62. http://dx.doi.org/10.1093/ntr/ntab236.
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Abstract Background Evidence-based smoking cessation interventions provided by healthcare professionals can be successful in helping citizens to quit smoking. Yet, evidence is needed about the active ingredients of these interventions, how these ingredients work and how they are implemented in practice. Such knowledge is required to effectively support healthcare professionals to optimally put evidence-based smoking cessation interventions to (inter)national practice. Objective To identify active ingredients (including behavior change techniques), mechanisms of action and implementation fidelity reported in smoking cessation interventions in Dutch primary care settings and to relate these to intervention effectiveness. Methods A systematic review was conducted by searching nine national intervention or funding databases, five international scientific databases and consulting 17 national smoking cessation experts. Out of 1066 identified manuscripts, 40 interventions were eligible for this review. Based on published protocols, information regarding behavior change techniques and mechanisms of action was systematically abstracted. Additionally, information regarding study characteristics and other active ingredients, effects on smoking behavior and implementation fidelity was abstracted. Comparative effectiveness concerning abstracted intervention characteristics was qualitatively explored. Results Active ingredients, mechanisms of action and implementation fidelity were moderately to poorly reported. Interventions applying behavior change techniques and interventions with a single behavioral target (i.e. smoking-only versus multiple behaviors) seemed to provide stronger evidence for successfully changing smoking behavior. Conclusion Attention to and reporting on interventions’ active ingredients (e.g. behavior change techniques), mechanisms of action and implementation fidelity are prerequisites for developing more effective evidence-based smoking cessation interventions to be successfully implemented in primary healthcare. Implications This systematic review provides an overview of smoking cessation interventions in Dutch primary care settings, identified since the year 2000. Smoking cessation support is offered in various forms, but our qualitative findings show that interventions including more behavior change techniques and interventions targeting only smoking cessation (compared to multiple behaviors) might be more effective. Results also show that—based on available intervention reports—it is difficult to distinguish patterns of active ingredients (such as behavior change techniques), mechanisms of action and fidelity of implementation in relation to interventions’ effectiveness. This means (quality of) reporting on these intervention characteristics should improve.
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Direito, Artur, Deirdre Walsh, Moohamad Hinbarji, Rami Albatal, Mark Tooley, Robyn Whittaker, and Ralph Maddison. "Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change." Health Education & Behavior 45, no. 3 (December 7, 2017): 331–48. http://dx.doi.org/10.1177/1090198117742438.
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Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals’ behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.
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Thomas Craig, Kelly J., Laura C. Morgan, Ching-Hua Chen, Susan Michie, Nicole Fusco, Jane L. Snowdon, Elisabeth Scheufele, Thomas Gagliardi, and Stewart Sill. "Systematic review of context-aware digital behavior change interventions to improve health." Translational Behavioral Medicine 11, no. 5 (October 21, 2020): 1037–48. http://dx.doi.org/10.1093/tbm/ibaa099.
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Abstract Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors.
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Müssener, Ulrika. "Digital encounters: Human interactions in mHealth behavior change interventions." DIGITAL HEALTH 7 (January 2021): 205520762110297. http://dx.doi.org/10.1177/20552076211029776.
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Digitalization and high mobile phone ownership globally have radically changed communication in all areas of society, including health care. Previous research has shown the effectiveness of behavior change interventions delivered by mobile phones and has highlighted advantages, such as that they require fewer resources than traditional face-to-face interventions and can be delivered at any time. One of the foremost questions pertaining to unsupported digital interventions is whether they can ever be comparable to in-person interventions. Little is known about the therapeutic alliance and the specific qualities of encounters in digital interactions for behavior change. Human interactions in digital interventions and their relationship with outcomes require further investigation. This paper aims to encourage critical reflection and further consideration of mHealth behavior change interventions in a digital age, when even the professional is excluded from the intervention. Questions are raised on the feelings associated with digital therapeutic relationships and how such interactions might affect user’s capacity for behavioral change. Some technological features and human-like considerations for enhancing digital encounters in mHealth interventions are given. Finally, suggestions for future research to facilitate the digital encounter in mHealth behavior change interventions is presented.
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Smith, William A., and John Elder. "Applied Behavior Change: A framework for behavior change interventions and research." Drugs: Education, Prevention and Policy 3, no. 1 (January 1996): 91–99. http://dx.doi.org/10.3109/09687639609019314.
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Wilkes, M. S. "Educational interventions can change clinical behavior." Western Journal of Medicine 172, no. 3 (March 1, 2000): 163. http://dx.doi.org/10.1136/ewjm.172.3.163.
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Plow, Matthew, and Marcia Finlayson. "Beyond supervised therapy: Promoting behavioral changes in people with MS." Multiple Sclerosis Journal 25, no. 10 (August 30, 2019): 1379–86. http://dx.doi.org/10.1177/1352458519861267.
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A critical aspect of many rehabilitation interventions for people with multiple sclerosis (MS) is incorporating strategies that support behavior change. The main purpose of this topical review was to summarize recent randomized clinical trials (RCTs) of rehabilitation interventions in which participants learn and apply skills or engage in healthy behaviors. The Capability, Opportunity, Motivation, and Behavior (COM-B) framework was used to broadly classify behavior-change strategies. The included RCTs varied widely in terms of dosing, delivery format, and types of interventionist. Commonly used behavior-change strategies include education, persuasion, and training. We recommend that researchers and clinicians use frameworks like Behavior Change Wheel and Behavior Change Technique Taxonomy to describe and classify intervention strategies used to promote behavior change. We also recommend more sophisticated RCTs be conducted (e.g. sequential multiple assignment randomized trial and three-arm RCTs) to better understand ways of promoting behavior change in rehabilitation interventions.
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Araújo-Soares, Vera, Nelli Hankonen, Justin Presseau, Angela Rodrigues, and Falko F. Sniehotta. "Developing Behavior Change Interventions for Self-Management in Chronic Illness." European Psychologist 24, no. 1 (January 2019): 7–25. http://dx.doi.org/10.1027/1016-9040/a000330.
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Abstract. More people than ever are living longer with chronic conditions such as obesity, type 2 diabetes, and heart disease. Behavior change for effective self-management can improve health outcomes and quality of life in people living with such chronic illnesses. The science of developing behavior change interventions with impact for patients aims to optimize the reach, effectiveness, adoption, implementation, and maintenance of interventions and rigorous evaluation of outcomes and processes of behavior change. The development of new services and technologies offers opportunities to enhance the scope of delivery of interventions to support behavior change and self-management at scale. Herein, we review key contemporary approaches to intervention development, provide a critical overview, and integrate these approaches into a pragmatic, user-friendly framework to rigorously guide decision-making in behavior change intervention development. Moreover, we highlight novel emerging methods for rapid and agile intervention development. On-going progress in the science of intervention development is needed to remain in step with such new developments and to continue to leverage behavioral science’s capacity to contribute to optimizing interventions, modify behavior, and facilitate self-management in individuals living with chronic illness.
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Wongvibulsin, Shannon, Seth S. Martin, Suchi Saria, Scott L. Zeger, and Susan A. Murphy. "An Individualized, Data-Driven Digital Approach for Precision Behavior Change." American Journal of Lifestyle Medicine 14, no. 3 (April 25, 2019): 289–93. http://dx.doi.org/10.1177/1559827619843489.
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Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.
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Plow, Matthew A., and Meghan Golding. "A Qualitative Study of Multiple Health Behaviors in Adults with Multiple Sclerosis." International Journal of MS Care 18, no. 5 (September 1, 2016): 248–56. http://dx.doi.org/10.7224/1537-2073.2015-065.
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Background: Evidence regarding inflammatory pathways, elevated cardiovascular risk, and negative effects of secondary conditions on disability progression provide a strong rationale for promoting multiple health behaviors in adults with multiple sclerosis (MS). However, many unanswered questions remain about the best ways to design multiple behavior change interventions for adults with MS. We sought to identify facilitators and barriers to engaging in multiple health behaviors (physical activity, nutrition, and sleep) and to gain further insights into how to develop multiple health behavior change interventions based on preferences of adults with MS. Methods: Focus groups and one-on-one interviews were conducted with 17 participants with MS. Results: Five qualitative themes were identified as either facilitating or hindering engagement in multiple health behaviors: 1) roles, priorities, and preferences; 2) sense of duty; 3) the fatigue and mobility problem; 4) taking control; and 5) resiliency. Participants identified advantages and disadvantages of delivery formats (eg, face-to-face group vs. telephone), frequency of contacts, and intervention strategies based on their individual circumstances and obligations. Participants felt that discussing the benefits of engaging in multiple health behaviors, developing action plans, accommodating preferences, and addressing health problems would be helpful strategies to include in a multiple behavior change intervention. Conclusions: These findings indicate that there may be common facilitators and barriers that can be targeted to promote multiple behavior changes. Future research should explore the best ways to tailor multiple behavior change interventions to preferences, symptoms, psychological traits, and social cognitions.
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Young, Sean D. "The Adaptive Behavioral Components (ABC) Model for Planning Longitudinal Behavioral Technology-Based Health Interventions: A Theoretical Framework." Journal of Medical Internet Research 22, no. 6 (June 26, 2020): e15563. http://dx.doi.org/10.2196/15563.
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A growing number of interventions incorporate digital and social technologies (eg, social media, mobile phone apps, and wearable devices) into their design for behavior change. However, because of a number of factors, including changing trends in the use of technology over time, results on the efficacy of these interventions have been mixed. An updated framework is needed to help researchers better plan behavioral technology interventions by anticipating the needed resources and potential changes in trends that may affect interventions over time. Focusing on the domain of health interventions as a use case, we present the Adaptive Behavioral Components (ABC) model for technology-based behavioral interventions. ABC is composed of five components: basic behavior change; intervention, or problem-focused characteristics; population, social, and behavioral characteristics; individual-level and personality characteristics; and technology characteristics. ABC was designed with the goals of (1) guiding high-level development for digital technology–based interventions; (2) helping interventionists consider, plan for, and adapt to potential barriers that may arise during longitudinal interventions; and (3) providing a framework to potentially help increase the consistency of findings among digital technology intervention studies. We describe the planning of an HIV prevention intervention as a case study for how to implement ABC into intervention design. Using the ABC model to plan future interventions might help to improve the design of and adherence to longitudinal behavior change intervention protocols; allow these interventions to adapt, anticipate, and prepare for changes that may arise over time; and help to potentially improve intervention behavior change outcomes. Additional research is needed on the influence of each of ABC’s components to help improve intervention design and implementation.
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Paul, Sara, and Nancee V. Sneed. "Strategies for Behavior Change in Patients With Heart Failure." American Journal of Critical Care 13, no. 4 (July 1, 2004): 305–13. http://dx.doi.org/10.4037/ajcc2004.13.4.305.
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Appropriate management of chronic heart failure and its signs and symptoms requires a considerable amount of participation by patients. Behavioral changes that prevent or minimize signs and symptoms and disease progression are just as important as the medications prescribed to treat the heart failure. The most difficult lifestyle changes include smoking cessation, weight loss, and restriction of dietary sodium. The Transtheoretical Model is a framework for assessing and addressing the concept of readiness for behavior change, which occurs in a 6-step process. The model consists of 3 dimensions: the stages of change, the processes of change on which interventions are based, and the action criteria for actual behavior. The stages of change are discussed, and interventions are presented to assist patients with heart failure in progressing through those stages toward maintenance of changed lifestyle behaviors. Methods for measuring the level of readiness for change of patients with heart failure are also presented, because correct staging is required before appropriate interventions matched to a patient’s stage can be delivered.
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Stevens, Sally J., and Antonio L. Estrada. "Reducing HIV Risk Behaviors: Perceptions of HIV Risk and Stage of Change." Journal of Drug Issues 26, no. 3 (July 1996): 607–18. http://dx.doi.org/10.1177/002204269602600306.
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The HIV epidemic has had a dramatic impact on the lives of individuals, families, and communities around the world. Originally identified in homosexual men, HIV increasingly affects others, including: (1) those who inject drugs, (2) non-injection drug users who engage in unsafe sex, and (3) non-drug using heterosexuals who engage in high-risk sexual behaviors. The need for effective HIV prevention interventions is critical. All too often interventions have lacked sound theoretical frameworks. However, some attempts have been made to ground HIV risk behavior interventions in behavior theories such as: (1) the health belief model, (2) cognitive social learning theory, (3) the theory of reasoned action, and (4) the transtheoretical model of behavior change (TMBC). This paper describes an HIV prevention intervention that was developed from the TMBC model. The TMBC model hypothesizes stages of change. In this study, injection drug users (IDUs), crack cocaine users (CCUs), and female sexual partners of IDUs and CCUs identified their stage of change and were given an intervention based upon their identified stage. Baseline and post intervention follow-up data were obtained on participants' perceived stage and reported HIV sexual risk behavior. The data indicated that there was little congruence between perceived stage and reported risk. In spite of this incongruence, significant decreases in HIV risk behaviors were evidenced.
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Atkins, Lou, Tim Chadborn, Paulina Bondaronek, Diane Ashiru-Oredope, Elizabeth Beech, Natalie Herd, Victoria de La Morinière, et al. "Content and Mechanism of Action of National Antimicrobial Stewardship Interventions on Management of Respiratory Tract Infections in Primary and Community Care." Antibiotics 9, no. 8 (August 13, 2020): 512. http://dx.doi.org/10.3390/antibiotics9080512.
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A major modifiable factor contributing to antimicrobial resistance (AMR) is inappropriate use and overuse of antimicrobials, such as antibiotics. This study aimed to describe the content and mechanism of action of antimicrobial stewardship (AMS) interventions to improve appropriate antibiotic use for respiratory tract infections (RTI) in primary and community care. This study also aimed to describe who these interventions were aimed at and the specific behaviors targeted for change. Evidence-based guidelines, peer-review publications, and infection experts were consulted to identify behaviors relevant to AMS for RTI in primary care and interventions to target these behaviors. Behavior change tools were used to describe the content of interventions. Theoretical frameworks were used to describe mechanisms of action. A total of 32 behaviors targeting six different groups were identified (patients; prescribers; community pharmacists; providers; commissioners; providers and commissioners). Thirty-nine interventions targeting the behaviors were identified (patients = 15, prescribers = 22, community pharmacy staff = 8, providers = 18, and commissioners = 18). Interventions targeted a mean of 5.8 behaviors (range 1–27). Influences on behavior most frequently targeted by interventions were psychological capability (knowledge and skills); reflective motivation (beliefs about consequences, intentions, social/professional role and identity); and physical opportunity (environmental context and resources). Interventions were most commonly characterized as achieving change by training, enabling, or educating and were delivered mainly through guidelines, service provision, and communications & marketing. Interventions included a mean of four Behavior Change Techniques (BCTs) (range 1–14). We identified little intervention content targeting automatic motivation and social opportunity influences on behavior. The majority of interventions focussed on education and training, which target knowledge and skills though the provision of instructions on how to perform a behavior and information about health consequences. Interventions could be refined with the inclusion of relevant BCTs, such as goal-setting and action planning (identified in only a few interventions), to translate instruction on how to perform a behavior into action. This study provides a platform to refine content and plan evaluation of antimicrobial stewardship interventions.
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Cole-Lewis, Heather, Nnamdi Ezeanochie, and Jennifer Turgiss. "Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions." JMIR Formative Research 3, no. 4 (October 10, 2019): e14052. http://dx.doi.org/10.2196/14052.
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Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term “engagement,” thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as “Big E,” and DBCI engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.
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Steinmetz, Holger, Michael Knappstein, Icek Ajzen, Peter Schmidt, and Rüdiger Kabst. "How Effective are Behavior Change Interventions Based on the Theory of Planned Behavior?" Zeitschrift für Psychologie 224, no. 3 (July 2016): 216–33. http://dx.doi.org/10.1027/2151-2604/a000255.
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Abstract. The theory of planned behavior (TPB) is a prominent framework for predicting and explaining behavior in a variety of domains. The theory is also increasingly being used as a framework for conducting behavior change interventions. In this meta-analysis, we identified 82 papers reporting results of 123 interventions in a variety of disciplines. Our analysis confirmed the effectiveness of TPB-based interventions, with a mean effect size of .50 for changes in behavior and effect sizes ranging from .14 to .68 for changes in antecedent variables (behavioral, normative, and control beliefs, attitude, subjective norm, perceived behavioral control, and intention). Further analyses revealed that the interventions’ effectiveness varied for the diverse behavior change methods. In addition, interventions conducted in public and with groups were more successful than interventions in private locations or focusing on individuals. Finally, we identified gender and education as well as behavioral domain as moderators of the interventions’ effectiveness.
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Hunter, Ruth F., Helen McAneney, Michael Davis, Mark A. Tully, Thomas W. Valente, and Frank Kee. "“Hidden” Social Networks in Behavior Change Interventions." American Journal of Public Health 105, no. 3 (March 2015): 513–16. http://dx.doi.org/10.2105/ajph.2014.302399.
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Lathia, Neal, Veljko Pejovic, Kiran K. Rachuri, Cecilia Mascolo, Mirco Musolesi, and Peter J. Rentfrow. "Smartphones for Large-Scale Behavior Change Interventions." IEEE Pervasive Computing 12, no. 3 (July 2013): 66–73. http://dx.doi.org/10.1109/mprv.2013.56.
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Ritterband, Lee M., Frances P. Thorndike, Daniel J. Cox, Boris P. Kovatchev, and Linda A. Gonder-Frederick. "A Behavior Change Model for Internet Interventions." Annals of Behavioral Medicine 38, no. 1 (August 2009): 18–27. http://dx.doi.org/10.1007/s12160-009-9133-4.
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Nigg, Claudio R., and Camonia R. Long. "A systematic review of single health behavior change interventions vs. multiple health behavior change interventions among older adults." Translational Behavioral Medicine 2, no. 2 (April 18, 2012): 163–79. http://dx.doi.org/10.1007/s13142-012-0130-y.
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Rad, Dana, and Gavril Rad. "Theory of Change in Digital Behavior Change Interventions (Dbcis) And Community-Based Change Initiatives – A General Framework." Technium Social Sciences Journal 21 (July 9, 2021): 554–69. http://dx.doi.org/10.47577/tssj.v21i1.3950.
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A theory of change is a purposeful model of how an initiative, such as a policy, a strategy, a program, a project or an intervention contributes through a chain of early and intermediate outcomes to the intended result. Theories of change help navigate the complexity of social change. Digital behavior change interventions (DBCIs) and Community-based change initiatives represent complex designable systems. The goal of the DCBI is to provide an effective theoretical framework for behavioral change to practitioners that offer different forms of psychological intervention based on scientifically validated practices. Applying theory of change when designing digital individual and community interventions for optimizing digital wellbeing helps practitioners to achieve results in practice, as this strategic approach is generally considered an evidence-based framework. Theory of change is useful to guide the strategic thinking and action, as most of DCBI/ Community-based change initiatives research endeavors are active in a complex situation, often unplanned events happening. Conclusions and implications are discussed.
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Laranjo, Liliana, Amaël Arguel, Ana L. Neves, Aideen M. Gallagher, Ruth Kaplan, Nathan Mortimer, Guilherme A. Mendes, and Annie Y. S. Lau. "The influence of social networking sites on health behavior change: a systematic review and meta-analysis." Journal of the American Medical Informatics Association 22, no. 1 (July 8, 2014): 243–56. http://dx.doi.org/10.1136/amiajnl-2014-002841.
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Abstract Objective Our aim was to evaluate the use and effectiveness of interventions using social networking sites (SNSs) to change health behaviors. Materials and methods Five databases were scanned using a predefined search strategy. Studies were included if they focused on patients/consumers, involved an SNS intervention, had an outcome related to health behavior change, and were prospective. Studies were screened by independent investigators, and assessed using Cochrane's ‘risk of bias’ tool. Randomized controlled trials were pooled in a meta-analysis. Results The database search retrieved 4656 citations; 12 studies (7411 participants) met the inclusion criteria. Facebook was the most utilized SNS, followed by health-specific SNSs, and Twitter. Eight randomized controlled trials were combined in a meta-analysis. A positive effect of SNS interventions on health behavior outcomes was found (Hedges’ g 0.24; 95% CI 0.04 to 0.43). There was considerable heterogeneity (I2 = 84.0%; T2 = 0.058) and no evidence of publication bias. Discussion To the best of our knowledge, this is the first meta-analysis evaluating the effectiveness of SNS interventions in changing health-related behaviors. Most studies evaluated multi-component interventions, posing problems in isolating the specific effect of the SNS. Health behavior change theories were seldom mentioned in the included articles, but two particularly innovative studies used ‘network alteration’, showing a positive effect. Overall, SNS interventions appeared to be effective in promoting changes in health-related behaviors, and further research regarding the application of these promising tools is warranted. Conclusions Our study showed a positive effect of SNS interventions on health behavior-related outcomes, but there was considerable heterogeneity. Protocol registration The protocol for this systematic review is registered at http://www.crd.york.ac.uk/PROSPERO with the number CRD42013004140.
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Yamin, Fei, Lahlou, and Levy. "Using Social Norms to Change Behavior and Increase Sustainability in the Real World: A Systematic Review of the Literature." Sustainability 11, no. 20 (October 21, 2019): 5847. http://dx.doi.org/10.3390/su11205847.
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Behavioral change interventions based on social norms have proven to be a popular and cost-effective way in which both researchers and practitioners attempt to transform behavior in order to increase environmental and social sustainability in real-world contexts. In this paper, we present a systematic review of over 90 empirical studies that have applied behavioral change interventions based on social norms in field settings. Building on previous research about the sources of information that people use to understand social norms and other local determinants of behavior, we propose a framework organized along two axes that describe intervention context (situated interventions applied in the same context where the target behavior happens versus remote interventions that are applied away from that context) and type of normative information leveraged (interventions that provide summary information about a group versus interventions that expose participants to the opinions and behaviors of others). We also illustrate successful applications for each dimension, as well as the social, psychological and physical determinants of behavior that were leveraged to support change. Finally, based on our results, we discuss some of the elements and practical mechanisms that can be used by both researchers and practitioners to design more integral, effective and sustainable social norm intervention in the real world.
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Bohlen, Lauren Connell, Susan Michie, Marijn de Bruin, Alexander J. Rothman, Michael P. Kelly, Hilary N. K. Groarke, Rachel N. Carey, Joanna Hale, and Marie Johnston. "Do Combinations of Behavior Change Techniques That Occur Frequently in Interventions Reflect Underlying Theory?" Annals of Behavioral Medicine 54, no. 11 (September 22, 2020): 827–42. http://dx.doi.org/10.1093/abm/kaaa078.
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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.
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Amireault, Steve, Angela J. Fong, and Catherine M. Sabiston. "Promoting Healthy Eating and Physical Activity Behaviors: A Systematic Review of Multiple Health Behavior Change Interventions Among Cancer Survivors." American Journal of Lifestyle Medicine 12, no. 3 (August 4, 2016): 184–99. http://dx.doi.org/10.1177/1559827616661490.
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Multiple health behavior change (MHBC) interventions have great potential for enhancing health and well-being following cancer diagnosis and treatment. However, the characteristics and effects of MHBC interventions remain elusive for cancer survivors. The main purpose of this study was to evaluate the effectiveness of MHBC interventions on healthy eating and physical activity behaviors among cancer survivors. A secondary aim was to examine the effect of using a simultaneous and sequential design approach to MHBC (ie, changing both behaviors at the same time or one after the other). Randomized controlled trials reporting the impact of a MHBC intervention on both healthy eating and physical activity behaviors among cancer survivors were retrieved from MEDLINE, Cochrane Library, and PsycINFO. A total of 27 MHBC interventions were identified; most (92.6%) were designed to promote simultaneous change in both behaviors and assessed end-of-treatment effect among breast cancer survivors. MHBC interventions led by nurses or multidisciplinary teams showed the most compelling evidence for small to moderate improvement in both behaviors, with interventions that lasted ≥17 weeks more likely to improve both behaviors. This study identifies research priorities and provides preliminary evidence for clinical decision making and advancements in MHBC intervention design and delivery for clinical oncology.
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Tate, Deborah F., Leslie A. Lytle, Nancy E. Sherwood, Debra Haire-Joshu, Donna Matheson, Shirley M. Moore, Catherine M. Loria, et al. "Deconstructing interventions: approaches to studying behavior change techniques across obesity interventions." Translational Behavioral Medicine 6, no. 2 (January 6, 2016): 236–43. http://dx.doi.org/10.1007/s13142-015-0369-1.
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Bryant, Ashley Leak, Rachel Hirschey, Courtney Caiola, Ya-Ning Chan, Brenda Plassman, Bei Wu, Donald Bailey, and Ruth Anderson. "Refining an Oral Health Care Partner Intervention Using Behavior Change Techniques." Innovation in Aging 5, Supplement_1 (December 1, 2021): 413. http://dx.doi.org/10.1093/geroni/igab046.1599.
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Abstract Following a pilot, we refined an oral health carepartner intervention for individuals with mild dementia (IMD). In this intervention, we use behavior change techniques (BCTs) to foster changes by carepartners including using new oral-care techniques and developing skills for using cueing and communications approaches to support behavior changes by IMD (duration and frequency of toothbrushing and oral-hygiene skills); thus, improving plaque and gingival indices. We describe our approach to refining the intervention manual including a) completing the self-paced BCT taxonomy course, b) developing a coding schema, c) coding the original manual for evidence of BCTs, and d) refining the manual to improve use of BCTS in the refined intervention. Our results detail how BCTs can be applied to refine and improve interventions. This research demonstrates the value in using BCTs for interventions to address how carepartners and IMD can collaborate to improve oral hygiene care.
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Hutchison, Andrew J., Jeff D. Breckon, and Lynne H. Johnston. "Physical Activity Behavior Change Interventions Based on the Transtheoretical Model: A Systematic Review." Health Education & Behavior 36, no. 5 (July 7, 2008): 829–45. http://dx.doi.org/10.1177/1090198108318491.
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This review critically examines Transtheoretical Model (TTM)—based interventions for physical activity (PA) behavior change. It has been suggested that the TTM may not be the most appropriate theoretical model for applications to PA behavior change. However, previous reviews have paid little or no attention to how accurately each intervention represents the TTM. Findings comprise two sections: sample characteristics of each intervention reviewed and a summary outlining the use of the TTM to develop the interventions. Results reveal numerous inconsistencies regarding the development and implementation/application of TTM-based interventions. Specifically, the majority of interventions reported to be based on the TTM fail to accurately represent all dimensions of the model. Therefore, until interventions are developed to accurately represent the TTM, the efficacy of these approaches and the appropriateness of the underpinning theoretical model cannot be determined.
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Costello, Nessan, Jim McKenna, Louise Sutton, Kevin Deighton, and Ben Jones. "Using Contemporary Behavior Change Science to Design and Implement an Effective Nutritional Intervention Within Professional Rugby League." International Journal of Sport Nutrition and Exercise Metabolism 28, no. 5 (September 1, 2018): 553–57. http://dx.doi.org/10.1123/ijsnem.2017-0298.
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Designing and implementing successful dietary intervention is integral to the role of sport nutrition professionals as they attempt to positively change the dietary behavior of athletes. High-performance sport is a time-pressured environment where immediate results can often supersede pursuit of the most effective evidence-based practice. However, efficacious dietary intervention necessitates comprehensive, systematic, and theoretical behavioral design and implementation, if the habitual dietary behaviors of athletes are to be positively changed. Therefore, this case study demonstrates how the Behaviour Change Wheel was used to design and implement an effective nutritional intervention within a professional rugby league. The eight-step intervention targeted athlete consumption of a high-quality dietary intake of 25.1 MJ each day to achieve an overall body mass increase of 5 kg across a 12-week intervention period. The capability, opportunity, motivation, and behavior model and affordability, practicability, effectiveness/cost-effectiveness, acceptability, safety, and equity criteria were used to identify population-specific intervention functions, policy categories, behavior change techniques, and modes of intervention delivery. The resulting intervention was successful, increasing the average daily energy intake of the athlete to 24.5 MJ, which corresponded in a 6.2 kg body mass gain. Despite consuming 0.6 MJ less per day than targeted, secondary outcome measures of diet quality, strength, body composition, and immune function all substantially improved, supporting sufficient energy intake and the overall efficacy of a behavioral approach. Ultimately, the Behaviour Change Wheel provides sport nutrition professionals with an effective and practical stepwise method to design and implement effective nutritional interventions for use within high-performance sport.
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Huang, Yitong, Steve Benford, Dominic Price, Roma Patel, Benqian Li, Alex Ivanov, and Holly Blake. "Using Internet of Things to Reduce Office Workers’ Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach." JMIR mHealth and uHealth 8, no. 7 (July 29, 2020): e17914. http://dx.doi.org/10.2196/17914.
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Background Sedentary behavior (SB) is associated with various adverse health outcomes. The prevalence of prolonged sitting at work among office workers makes a case for SB interventions to target this setting and population. Everyday mundane objects with embedded microelectronics and ubiquitous computing represent a novel mode of delivering health behavior change interventions enabled by internet of things (IoTs). However, little is known about how to develop interventions involving IoT technologies. Objective This paper reports the design and development of an IoT-enabled SB intervention targeting office workers. Methods The process was guided by the behavior change wheel (BCW), a systematic framework for theory-informed and evidence-based development of behavior change interventions, complemented by the human-centered design (HCD) approach. Intervention design was shaped by findings from a diary-probed interview study (n=20), a stakeholder design workshop (n=8), and a series of theoretical mapping and collaborative technical design activities. Results The resulting intervention named WorkMyWay targets a reduction in office workers’ prolonged stationary behaviors at work and an increase in regular breaks by modifying behavioral determinants in 11 theoretical domains with 17 behavior change techniques. The delivery technology consists of a wearable activity tracker, a light-emitting diode reminder device attached to a vessel (ie, water bottle or cup), and a companion Android app connected to both devices over Bluetooth. The delivery plan consists of a 2-week baseline assessment, a 30-min face-to-face action planning session, and 6-week self-directed use of the delivery technology. Conclusions This is the first study to demonstrate that it is possible to develop a complex IoT-enabled intervention by applying a combination of the BCW and HCD approaches. The next step is to assess the feasibility of WorkMyWay prior to testing intervention efficacy in a full-scale trial. The intervention mapping table that links individual intervention components with hypothesized mechanisms of action can serve as the basis for testing and clarifying theory-based mechanisms of action in future studies on WorkMyWay.
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Asher, Arash, Celina Shirazipour, Jeffrey Wertheimer, and Jamie S. Myers. "Emerging from the haze: Feasibility pilot of a virtual multi-dimensional psycho-educational cognitive rehabilitation intervention for cancer survivors with decreased perceived cognitive function after cancer therapy." Journal of Clinical Oncology 39, no. 28_suppl (October 1, 2021): 276. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.276.
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276 Background: Standardized, effective cognitive rehabilitation interventions that can be widely disseminated are urgently needed for cancer-related cognitive impairment. The purpose of this single-arm pilot study was to test the feasibility/acceptability of virtual delivery of a cognitive rehabilitation intervention for participants in virtual groups. Study aims included: (1) Recruit 30 participants and achieve a 70% retention rate; (2) Demonstrate feasibility/acceptability of measures assessing determinants of behavior change (missing data <25%; reliability >.70); and (3) Explore interventional impact on perceived cognitive function (PCF), determinants of behavior change, and loneliness. Methods: Adult cancer survivors reporting impaired cognitive function following primary treatment were enrolled from two cancer centers and affiliates. Two cohorts (N=37) attended six weekly sessions and completed pre-/post- patient reported outcome questionnaires designed to measure PCF, loneliness, and determinants of behavior change for exercise, sleep, and mindfulness. Results: Participant retention rate was 85%. Measures of determinants of behavior change were reliable ( r >.70) across all three behaviors. Post-intervention scores for PCF, determinants of behavior change, and loneliness ratings significantly improved (Table). Inverse correlation between changes in loneliness, PCF ( r= -.376 to -.452, p <.05) and exercise intention ( r = -0.544, p =.001) were noted. Conclusions: Virtual delivery of cognitive rehabilitation interventions may be feasible and acceptable to cancer survivors reporting impairment in cognitive function after primary treatment. Loneliness may be an important predictor of both issues with cognitive function and intention to change behavior. [Table: see text]
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Biber, David D., and Rebecca Ellis. "The effect of self-compassion on the self-regulation of health behaviors: A systematic review." Journal of Health Psychology 24, no. 14 (June 15, 2017): 2060–71. http://dx.doi.org/10.1177/1359105317713361.
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The purpose of this review was to systematically review the published research on the effect of self-compassion interventions on health behaviors. A self-regulation intervention was defined as participants engaged in goal-setting behavior, goal-directed behavior, monitoring, and/or adjusting health behavior. Seven studies met the inclusion criteria and were analyzed in this review. Self-compassion interventions were just as effective as other behavior change techniques at improving self-regulation of health behavior. The review discusses sample characteristics, study design, health behavior measures, self-compassion intervention implementation, and the theoretical frameworks of the studies, along with limitations of the research and suggestions for future researchers.
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Tewodros, Tsedenia, Liris Berra, Carolina Escobar, and Amy Webb Girard. "Systematic Review of Social Behavior Change Strategies and Behavior Change Techniques Used in Nutrition Sensitive Agriculture Interventions." Current Developments in Nutrition 6, Supplement_1 (June 2022): 867. http://dx.doi.org/10.1093/cdn/nzac065.051.
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Abstract Objectives While social behavior change (SBC) is an important component of nutrition sensitive agriculture interventions (NSeAI), there is limited research on what specific behavior change techniques (BCT) are effective. This systematic review aims to 1) describe SBC design and implementation in the context of NSeAI 2) examine the BCTs used in NSeAI and 3) quantify associations between BCTs and improvements in diet diversity. Methods Literature on NSeAI with nutrition SBC was systematically reviewed. Papers published in 2000–2021, aimed to improve diet quality, and described nutrition SBC activities were selected. Agriculture-nutrition pathways and SBC approaches used by interventions were mapped. BCTs were coded using a validated taxonomy. Effectiveness ratios (ERs) were calculated to assess BCTs in relation to program success. Trials were considered effective if there was significant improvement (P < 0.05) in household, women's, and child diet diversity. Results Of the 56 interventions included in the study, most were in Sub-Saharan Africa (n = 30) and Asia (n = 21). Thirty-three interventions used formative research to guide SBC design; only six discussed applying a specific behavioral theory. Interventions aimed to increase home production for home consumption (n = 54); increase women's empowerment (n = 30) and increase agricultural income (n = 25) as a pathway to improved nutrition. Fifty-two projects used interpersonal communication, 36 projects used community engagement and 12 used mass media as an approach to behavior change. The most frequently used BCTs included “instructions on how to perform the behavior” (n = 56), “social support (unspecified)” (n = 39), and using a “credible source” for information dissemination (n = 35). On average, projects used 7 BCTs. The BCT “behavioral practice” had the highest effectiveness ratio (ER = 1.0). Conclusions Interventions focused predominantly on the home production for home consumption pathway. Women's empowerment and agriculture for income pathways were less frequent. All interventions provided instructions on how to perform the behavior but relatively few provided opportunities for ‘behavioral practice’; however, this BCT had the highest effectiveness ratio with all interventions using this BCT demonstrating significant improvements in diet quality. Funding Sources Bill & Melinda Gates Foundation.
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Simeon, Rosiane, Omar Dewidar, Jessica Trawin, Stephanie Duench, Heather Manson, Jordi Pardo Pardo, Jennifer Petkovic, et al. "Behavior Change Techniques Included in Reports of Social Media Interventions for Promoting Health Behaviors in Adults: Content Analysis Within a Systematic Review." Journal of Medical Internet Research 22, no. 6 (June 11, 2020): e16002. http://dx.doi.org/10.2196/16002.
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Background Social media are an increasingly commonly used platform for delivering health promotion interventions. Although recent research has focused on the effectiveness of social media interventions for health promotion, very little is known about the optimal content within such interventions, and the active ingredients to promote health behavior change using social media are not clear. Identifying which behavior change techniques (BCTs) are reported may help to clarify the content of interventions using a generalizable terminology that may facilitate future intervention development. Objective This study aimed to identify which BCTs are reported in social media interventions for promoting health behavior change in adults. Methods We included 71 studies conducted with adult participants (aged ≥18 years) and for which social media intervention was considered interactive in a Cochrane review of the effectiveness of such interventions. We developed a coding manual informed by the Behavior Change Technique Taxonomy version 1 (BCTTv1) to identify BCTs in the included studies. We identified BCTs in all study arms (including control) and described BCTs in the group and self-directed components of studies. We characterized the dose of delivery for each BCT by low and high intensity. We used descriptive analyses to characterize the reported BCTs. Results Our data consisted of 71 studies published from 2001 to 2017, mainly conducted in high-income countries (n=65). Most studies (n=31) used tailored, interactive websites to deliver the intervention; Facebook was the most used mainstream platform. In developing our coding manual, we adapted some BCTTv1 instructions to better capture unique nuances of how BCTs were operationalized in social media with respect to likes, retweets, smiles, congratulations, and badges. Social support (unspecified), instruction on how to perform the behavior, and credible source were most frequently identified BCTs in intervention arms of studies and group-delivery settings, whereas instruction on how to perform the behavior was most commonly applied in self-directed components of studies, control arms, and individual participant settings. Instruction on how to perform the behavior was also the most frequently reported BCT in both intervention and control arms simultaneously. Instruction on how to perform the behavior, social support (unspecified), self-monitoring of behavior, information about health consequences, and credible source were identified in the top 5 BCTs delivered with the highest intensity. Conclusions This study within a review provides a detailed description of the BCTs and their dose to promote behavior change in web-based, interactive social media interventions. Clarifying active ingredients in social media interventions and the intensity of their delivery may help to develop future interventions that can more clearly build upon the existing evidence.
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Tighe, Sarah A., Kylie Ball, Finn Kensing, Lars Kayser, Jonathan C. Rawstorn, and Ralph Maddison. "Toward a Digital Platform for the Self-Management of Noncommunicable Disease: Systematic Review of Platform-Like Interventions." Journal of Medical Internet Research 22, no. 10 (October 28, 2020): e16774. http://dx.doi.org/10.2196/16774.
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Background Digital interventions are effective for health behavior change, as they enable the self-management of chronic, noncommunicable diseases (NCDs). However, they often fail to facilitate the specific or current needs and preferences of the individual. A proposed alternative is a digital platform that hosts a suite of discrete, already existing digital health interventions. A platform architecture would allow users to explore a range of evidence-based solutions over time to optimize their self-management and health behavior change. Objective This review aims to identify digital platform-like interventions and examine their potential for supporting self-management of NCDs and health behavior change. Methods A literature search was conducted in January 2020 using EBSCOhost, PubMed, Scopus, and EMBASE. No digital platforms were identified, so criteria were broadened to include digital platform-like interventions. Eligible platform-like interventions offered a suite of discrete, evidence-based health behavior change features to optimize self-management of NCDs in an adult population and provided digitally supported guidance for the user toward the features best suited to their needs and preferences. Data collected on interventions were guided by the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist, including evaluation data on effectiveness and process outcomes. The quality of the included literature was assessed using the Mixed Methods Appraisal Tool. Results A total of 7 studies were included for review. Targeted NCDs included cardiovascular diseases (CVD; n=3), diabetes (n=3), and chronic obstructive pulmonary disease (n=1). The mean adherence (based on the number of follow-up responders) was 69% (SD 20%). Of the 7 studies, 4 with the highest adherence rates (80%) were also guided by behavior change theories and took an iterative, user-centered approach to development, optimizing intervention relevance. All 7 interventions presented algorithm-supported user guidance tools, including electronic decision support, smart features that interact with patterns of use, and behavior change stage-matching tools. Of the 7 studies, 6 assessed changes in behavior. Significant effects in moderate-to-vigorous physical activity were reported, but for no other specific health behaviors. However, positive behavior change was observed in studies that focused on comprehensive behavior change measures, such as self-care and self-management, each of which addresses several key lifestyle risk factors (eg, medication adherence). No significant difference was found for psychosocial outcomes (eg, quality of life). Significant changes in clinical outcomes were predominately related to disease-specific, multifaceted measures such as clinical disease control and cardiovascular risk score. Conclusions Iterative, user-centered development of digital platform structures could optimize user engagement with self-management support through existing, evidence-based digital interventions. Offering a palette of interventions with an appropriate degree of guidance has the potential to facilitate disease-specific health behavior change and effective self-management among a myriad of users, conditions, or stages of care.
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Baker, John, Kathryn Berzins, Krysia Canvin, Sarah Kendal, Stella Branthonne-Foster, Judy Wright, Tim McDougall, Barry Goldson, Ian Kellar, and Joy Duxbury. "Components of interventions to reduce restrictive practices with children and young people in institutional settings: the Contrast systematic mapping review." Health and Social Care Delivery Research 10, no. 8 (May 2022): 1–180. http://dx.doi.org/10.3310/yvkt5692.
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Background Incidents in which children or young people experience severe distress or harm or cause distress or harm to others occur frequently in children and young people’s institutional settings. These incidents are often managed using restrictive practices, such as restraint, seclusion, sedation or constant observation; however, these also present significant risks of physical and psychological harm to children and young people as well as staff. Numerous interventions aim to reduce the use of restrictive techniques, but research is hampered by limited attention to specific intervention components. The behavior change technique taxonomy may improve reporting by providing a common language for specifying the content and mechanisms of behaviour change. This study aimed to identify, standardise and report the effectiveness of components of interventions to reduce restrictive practices in children and young people’s institutional settings. Objectives To map interventions aimed at reducing restrictive practices in children and young people’s institutional settings internationally, to conduct behaviour change technique analysis of intervention components, to identify process elements, and to explore effectiveness evidence to identify promising behaviour change techniques and compare the results with those found in adult psychiatric inpatient settings in a companion review. Design Systematic mapping review with programme content coding using the behavior change technique taxonomy. Review methods Eleven relevant English-language health and social care research databases 1989–2019 [including Applied Social Sciences Index (ASSIA), Criminal Justice Abstracts, Educational Resources Information Center (ERIC), MEDLINE and PsycInfo®], grey literature and social media were searched during 2019 (updated January 2020). Data extraction, guided by Workgroup for Intervention Development and Evaluation Research (WIDER), Cochrane Library and theory coding scheme recommendations, included intervention characteristics and study design and reporting. Screening and quality appraisal used the Mixed Methods Appraisal Tool. The behavior change technique taxonomy was applied systematically, and interventions were coded for behaviour change technique components. Outcomes data were then related back to these components. Results There were 121 records, including 76 evaluations. Eighty-two interventions, mostly multicomponent, were identified. Evaluation approaches commonly used a non-randomised design. There were no randomised controlled trials. Behaviour change techniques from 14 out of a possible 16 clusters were detected. Four clusters (i.e. goals and planning, antecedents, shaping knowledge, and feedback and monitoring) contained the majority of identified behaviour change techniques and were detected in over half of all interventions. Two clusters (i.e. self-belief and covert learning) contained no identified behaviour change techniques. The most common setting in which behaviour change techniques were found was ‘mental health’. The most common procedure focused on staff training. The two most common behaviour change techniques were instruction on how to perform the behaviour and restructuring the social environment. Promising behaviour change techniques included instruction on how to perform the behaviour, restructuring the social environment, feedback on outcomes of behaviour and problem-solving. Compared with the companion review, service user perspectives were more sparse and there was more interest in trauma-informed approaches. Effectiveness evidence, range of interventions and reporting were broadly similar. Limitations Poor reporting may have prevented detection of some behaviour change techniques. The finding that the evidence was weak restricted the feasibility of examining behaviour change technique effectiveness. Literature searches were restricted to English-language sources. Conclusions This study generated, to our knowledge, the first review of evidence on the content and effectiveness of interventions to reduce restrictive practices in children and young people’s institutional settings. Interventions tend to be complex, reporting is inconsistent and robust evaluation data are limited, but some behaviour change techniques seem promising. Future work Promising behaviour change techniques could be further explored. Better evidence could help address the urgent need for effective strategies. Study registration This study is registered as PROSPERO CRD42019124730. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 8. See the NIHR Journals Library website for further project information.
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Taj, Fawad, Michel C. A. Klein, and Aart Van Halteren. "Motivating Machines: The Potential of Modeling Motivation as MoA for Behavior Change Systems." Information 13, no. 5 (May 17, 2022): 258. http://dx.doi.org/10.3390/info13050258.
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The pathway through which behavior change techniques have an effect on the behavior of an individual is referred to as the Mechanism of Action (MoA). Digitally enabled behavior change interventions could potentially benefit from explicitly modelling the MoA to achieve more effective, adaptive, and personalized interventions. For example, if ‘motivation’ is proposed as the targeted construct in any behavior change intervention, how can a model of this construct be used to act as a mechanism of action, mediating the intervention effect using various behavior change techniques? This article discusses a computational model for motivation based on the neural reward pathway with the aim to make it act as a mediator between behavior change techniques and target behavior. This model’s formal description and parametrization are described from a neurocomputational sciences prospect and elaborated with the help of a sub-question, i.e., what parameters/processes of the model are crucial for the generation and maintenance of motivation. An intervention scenario is simulated to show how an explicit model of ‘motivation’ and its parameters can be used to achieve personalization and adaptivity. A computational representation of motivation as a mechanism of action may also further advance the design, evaluation, and effectiveness of personalized and adaptive digital behavior change interventions.
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Greenwell, Kate, Debbie Featherstone, and Derek J. Hoare. "The Application of Intervention Coding Methodology to Describe the Tinnitus E-Programme, an Internet-Delivered Self-Help Intervention for Tinnitus." American Journal of Audiology 24, no. 3 (September 2015): 311–15. http://dx.doi.org/10.1044/2015_aja-14-0089.
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Purpose This article describes the Tinnitus E-Programme, a previously untested Internet-delivered self-help intervention for tinnitus. Method Intervention coding methodology was applied to describe the intervention components, techniques, and mode of delivery. Results The intervention consists of 5 self-management intervention components, 5 behavior change techniques, and 3 modes of Internet delivery, which aim to promote relaxation behavior and reduce tinnitus distress. Conclusions The intervention coding provided a reliable method for reporting Internet-delivered self-help interventions. It will be used to facilitate our understanding of the intervention's potential mechanisms of change and will guide future evaluation work.
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McDonald, Gavin, Molly Wilson, Diogo Veríssimo, Rebecca Twohey, Michaela Clemence, Dean Apistar, Stephen Box, et al. "Catalyzing sustainable fisheries management through behavior change interventions." Conservation Biology 34, no. 5 (April 15, 2020): 1176–89. http://dx.doi.org/10.1111/cobi.13475.
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Roberts, Hannah, Rosemary McEachan, Tamsin Margary, Mark Conner, and Ian Kellar. "Identifying Effective Behavior Change Techniques in Built Environment Interventions to Increase Use of Green Space: A Systematic Review." Environment and Behavior 50, no. 1 (December 19, 2016): 28–55. http://dx.doi.org/10.1177/0013916516681391.
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Green space has beneficial impacts on health, and there is increasing interest in how to modify green space to promote use. We identified effective behavior change techniques in environmental interventions that aimed to encourage use of green space. Fifteen studies met the inclusion criteria. Interventions were coded by reviewers using the Behavior Change Technique taxonomy (BCTTv1). Eleven studies reported an increase in green space use post-intervention. Techniques involving physical environment changes (“adding objects to the environment” or “restructuring the physical environment”) were commonly delivered alongside additional techniques such as “restructuring the social environment,” introducing “prompts or cues” and “demonstration of the behavior.” Risk of bias was high or unclear for all, and the quality of evidence was very low. Intervention content was poorly described according to current reporting guidelines. More rigorous evaluations of green space interventions are needed, coupled with full descriptions of intervention content, to allow replication.
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Klusmann, Verena, Alan J. Gow, Philippe Robert, and Gabriele Oettingen. "Using Theories of Behavior Change to Develop Interventions for Healthy Aging." Journals of Gerontology: Series B 76, Supplement_2 (July 6, 2021): S191—S205. http://dx.doi.org/10.1093/geronb/gbab111.
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Abstract Healthy aging requires people to adopt and maintain beneficial behaviors in all stages of the life span. Supporting behavior change, including via the motivation to make and maintain those changes, is therefore important for the promotion of healthy aging. The aim of this overview is to introduce theoretical frameworks from the psychology of motivation that lend themselves to the development of effective interventions promoting behavior change conducive to healthy aging. We discuss theoretical frameworks referring to the determinants, properties, and functionality of goals aimed at behavior change, and consider the implications of the various theories for designing interventions to support healthy aging. We first consider theories that focus on beliefs and attitudes as determinants of goals, then we address theories that focus on the structure and content as important properties of goals, and, finally, we examine theories drawing on conscious and nonconscious processes underlying the functionality of these goals. We will present if–then planning and mental contrasting, as well as nudging and boosting, that is, novel strategies of behavior change that support the creation of scalable interventions for healthy aging across the life span. Against this background, new perspectives emerge for modern, state-of-the-art, and individually tailored interventions with the aim of enhancing older people’s healthy living.
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Hutchison, Andrew J., and Lynne H. Johnston. "Exploring the Potential of Case Formulation Within Exercise Psychology." Journal of Clinical Sport Psychology 7, no. 1 (March 2013): 60–76. http://dx.doi.org/10.1123/jcsp.7.1.60.
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The purpose of this article is to expand the literature on case formulation as a clinical tool for use within exercise psychology, generally and lifestyle behavior change interventions, specifically. Existing research offers limited support for the efficacy of current physical activity behavior change intervention strategies, particularly in the long-term. The present paper argues that intervention strategies need to pay greater attention to the complex and individualistic nature of exercise and health related behaviors. It has been suggested that existing intervention designs tend to conform to a medical model approach, which can at times potentially neglect the complex array of personal and situational factors that impact on human motivation and behavior. Case formulation is presented as a means of encouraging a dynamic and comprehensive approach to the development and implementation of practical interventions within the health behavior change field. The adoption of these clinical techniques may facilitate the careful consideration of variations in the development, manifestation, and maintaining mechanisms of problematic behaviors (e.g., inactivity). An overview of case formulation in its different forms is presented alongside a justification for its use within exercise psychology.
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Frey, Erin, and Todd Rogers. "Persistence." Policy Insights from the Behavioral and Brain Sciences 1, no. 1 (October 2014): 172–79. http://dx.doi.org/10.1177/2372732214550405.
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Interventions intended to change people’s behavior are ubiquitous in modern society. Some interventions produce changes in behavior that persist even after the interventions are discontinued, while other interventions generate only short-term behavior changes that disappear once the interventions stop. The framework presented here guides understanding of why and how behavior changes (treatment effects) persist after interventions (treatments) are discontinued. Four persistence pathways explain how persistent treatment effects may arise: building psychological habits, changing what and how people think, changing future costs, and harnessing external reinforcement. Each pathway is illustrated by describing how the pathway may have contributed to the persistent treatment effects produced by a widely used energy-efficiency intervention conducted by the energy-efficiency company OPOWER. Different conditions may make each pathway more or less likely to generate persistent treatment effects in the world. Finally, policymakers might develop more persistent interventions by leveraging each pathway.
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Brigden, Amberly, Emma Anderson, Catherine Linney, Richard Morris, Roxanne Parslow, Teona Serafimova, Lucie Smith, Emily Briggs, Maria Loades, and Esther Crawley. "Digital Behavior Change Interventions for Younger Children With Chronic Health Conditions: Systematic Review." Journal of Medical Internet Research 22, no. 7 (July 31, 2020): e16924. http://dx.doi.org/10.2196/16924.
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Background The prevalence of chronic health conditions in childhood is increasing, and behavioral interventions can support the management of these conditions. Compared with face-to-face treatment, the use of digital interventions may be more cost-effective, appealing, and accessible, but there has been inadequate attention to their use with younger populations (children aged 5-12 years). Objective This systematic review aims to (1) identify effective digital interventions, (2) report the characteristics of promising interventions, and (3) describe the user’s experience of the digital intervention. Methods A total of 4 databases were searched (Excerpta Medica Database [EMBASE], PsycINFO, Medical Literature Analysis and Retrieval System Online [MEDLINE], and the Cochrane Library) between January 2014 and January 2019. The inclusion criteria for studies were as follows: (1) children aged between 5 and 12 years, (2) interventions for behavior change, (3) randomized controlled trials, (4) digital interventions, and (5) chronic health conditions. Two researchers independently double reviewed papers to assess eligibility, extract data, and assess quality. Results Searches run in the databases identified 2643 papers. We identified 17 eligible interventions. The most promising interventions (having a beneficial effect and low risk of bias) were 3 targeting overweight or obesity, using exergaming or social media, and 2 for anxiety, using web-based cognitive behavioral therapy (CBT). Characteristics of promising interventions included gaming features, therapist support, and parental involvement. Most were purely behavioral interventions (rather than CBT or third wave), typically using the behavior change techniques (BCTs) feedback and monitoring, shaping knowledge, repetition and substitution, and reward. Three papers included qualitative data on the user’s experience. We developed the following themes: parental involvement, connection with a health professional is important for engagement, technological affordances and barriers, and child-centered design. Conclusions Of the 17 eligible interventions, digital interventions for anxiety and overweight or obesity had the greatest promise. Using qualitative methods during digital intervention development and evaluation may lead to more meaningful, usable, feasible, and engaging interventions, especially for this underresearched younger population. The following characteristics could be considered when developing digital interventions for younger children: involvement of parents, gaming features, additional therapist support, behavioral (rather than cognitive) approaches, and particular BCTs (feedback and monitoring, shaping knowledge, repetition and substitution, and reward). This review suggests a model for improving the conceptualization and reporting of behavioral interventions involving children and parents.