scholarly journals What the guide does not tell you: reflections on and lessons learned from applying the COM-B behavior model for designing real life interventions

2020 ◽  
Author(s):  
Amanda Whittal ◽  
Lou Atkins ◽  
Oliver Rudolf Herber
Keyword(s):  
Behavior Change ◽  
Design Theory ◽  
Real Life ◽  
Self Care ◽  
Intervention Development ◽  
Lessons Learned ◽  
Effective Theory ◽  
Behavior Model ◽  
Management Interventions ◽  
Behavior Change Interventions

Abstract Substantial inconsistency exists in the effectiveness of existing interventions to improve heart failure (HF) self-care, which can be partially explained by the fact that self-management interventions often lack theoretical models that underpin intervention development. The COM-B behavior model is a comprehensive theoretical framework that can be used to develop effective, theory-based interventions. The aim of this article is to highlight the challenges and practical solutions when applying the COM-B model to HF self-care, in order to provide useful support for researchers intending to use the model for designing behavior change interventions. “The Behaviour Change Wheel” handbook provides a step-by-step guide to understand and change behavior. When following the guide, some practical and methodological challenges were encountered. Lessons learnt to overcome these challenges are reported. Although the handbook is a comprehensive guide for designing behavior change interventions, a number of challenges arose. For example, the descriptions provided in the guide were not always sufficient to make solid judgments on how to categorize determinants; narrowing down intervention possibilities to a manageable number and prioritizing potential behavior change techniques over others involved a certain amount of subjectivity in an otherwise highly systematic and structured approach. For the encountered challenges, solutions are provided to illustrate how the model was applied practically to design theory-based behavior change interventions. This article provides a useful reference for researchers’ use of the COM-B behavior model, as it outlines challenges that may occur and potential solutions to overcome them.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

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.

Applying the COM-B behaviour model to overcome barriers to heart failure self-care: A practical application of a conceptual framework for the development of complex interventions (ACHIEVE study)

2020 ◽  
pp. 147451512095729
Author(s):  
Amanda Whittal ◽  
Stefan Störk ◽  
Barbara Riegel ◽  
Oliver Rudolf Herber
Keyword(s):  
Heart Failure ◽  
Behaviour Change ◽  
Complex Interventions ◽  
Self Care ◽  
Intervention Development ◽  
Heart Association ◽  
Barriers And Facilitators ◽  
Behaviour Change Techniques ◽  
Anticipated Regret ◽  
Management Interventions

Background: Effective interventions to enhance adherence to self-care recommendations in patients with heart failure have immense potential to improve health and wellbeing. However, there is substantial inconsistency in the effectiveness of existing self-management interventions, partly because they lack theoretical models underpinning intervention development. Aim: To outline how the capability, opportunity and motivation behaviour model has been applied to guide the development of a theory-based intervention aiming to improve adherence to heart failure self-care recommendations. Methods: The application of the capability, opportunity and motivation behaviour model involved three steps: (a) identification of barriers and facilitators to heart failure self-care from two comprehensive meta-studies; (b) identification of appropriate behaviour change techniques to improve heart failure self-care; and (c) involvement of experts to reduce and refine potential behaviour change techniques further. Results: A total of 119 barriers and facilitators were identified. Fifty-six behaviour change techniques remained after applying three steps of the behaviour model for designing interventions. Expert involvement ( n=39, of which 31 were patients (67% men; 45% New York Heart Association II)) further reduced and refined potential behaviour change techniques. Experts disliked some behaviour change techniques such as ‘anticipated regret’ and ‘salience of consequences’. This process resulted in a final comprehensive list consisting of 28 barriers and 49 appropriate behaviour change techniques potentially enhancing self-care that was put forward for further use. Conclusion: The application of the capability, opportunity and motivation behaviour model facilitated identifying important factors influencing adherence to heart failure self-care recommendations. The model served as a comprehensive guide for the selection and design of interventions for improving heart failure self-care adherence. The capability, opportunity and motivation behaviour model enabled the connection of heart failure self-care barriers to particular behaviour change techniques to be used in practice.

scholarly journals Let’s build an app for that

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Manuel Ester ◽  
Julianna Dreger ◽  
Utkarsh Subnis ◽  
Shaneel Pathak ◽  
S.Nicole Culos-Reed
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Physical Activity Behavior ◽  
Mobile App ◽  
Lessons Learned ◽  
Behavior Change Interventions ◽  
Improvement Process ◽  
Activity Behavior ◽  
Exercise Oncology ◽  
Physical Activity Behavior Change

The promotion of physical activity behavior change among adults with cancer is a research priority. Within this field, increasing attention is being devoted to the use of health technology, which includes mobile phones and applications, or apps, to support and deliver physical activity behavior change interventions. While building a mobile app is a popular proposal among exercise oncology researchers, little practical information exists on how this process should be done or what considerations researchers should take in collaboration with participants and industry. The present article provides an overview of recent experiences with app development in exercise oncology and outlines several recommendations for future research. Methods and Results: After forming an interdisciplinary team of researchers, industry partners, and exercise oncology program participants, an iterative, user-centered app improvement process was followed to collect feedback and make meaningful changes to an existing mobile health app for its use in exercise oncology. Participant feedback was summarized and addressed collaboratively via open discussion and detailed action plans. Changes made include enhanced introductory materials for the app and improvements to usability and personalization. Some requests remain to be addressed in future updates. Two challenges identified during the app improvement process were balancing the unique needs and priorities of all parties, as well as addressing the variable feedback from a variable population of adults with cancer. Conclusions and significance: A multidisciplinary participant-oriented app improvement process led to meaningful updates to the mobile application of interest, preparing researchers to carry out an evaluation of its effectiveness within exercise oncology. Furthermore, based on lessons learned, the research team present key recommendations to consider in future mobile app research before, during, and after the development process.

scholarly journals Developing Behavior Change Interventions for Self-Management in Chronic Illness

2019 ◽  
Vol 24 (1) ◽  
pp. 7-25 ◽  
Author(s):  
Vera Araújo-Soares ◽  
Nelli Hankonen ◽  
Justin Presseau ◽  
Angela Rodrigues ◽  
Falko F. Sniehotta
Keyword(s):  
Chronic Illness ◽  
Behavior Change ◽  
Intervention Development ◽  
Chronic Illnesses ◽  
Self Management ◽  
Behavior Change Interventions ◽  
Disease Behavior ◽  
User Friendly

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.

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.

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