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

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.

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Iterative development and evaluation methods of mHealth behavior change interventions

Current Opinion in Psychology ◽

10.1016/j.copsyc.2015.09.001 ◽

2016 ◽

Vol 9 ◽

pp. 33-37 ◽

Cited By ~ 14

Author(s):

Megan A Jacobs ◽

Amanda L Graham

Keyword(s):

Behavior Change ◽

Evaluation Methods ◽

Iterative Development ◽

Behavior Change Interventions

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The Person-Based Approach to Intervention Development: Application to Digital Health-Related Behavior Change Interventions

Journal of Medical Internet Research ◽

10.2196/jmir.4055 ◽

2015 ◽

Vol 17 (1) ◽

pp. e30 ◽

Cited By ~ 378

Author(s):

Lucy Yardley ◽

Leanne Morrison ◽

Katherine Bradbury ◽

Ingrid Muller

Keyword(s):

Behavior Change ◽

Digital Health ◽

Intervention Development ◽

Behavior Change Interventions ◽

Health Related ◽

Development Application

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Developing Behavior Change Interventions for Self-Management in Chronic Illness

European Psychologist ◽

10.1027/1016-9040/a000330 ◽

2019 ◽

Vol 24 (1) ◽

pp. 7-25 ◽

Cited By ~ 21

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.

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

Annals of Behavioral Medicine ◽

10.1093/abm/kaaa078 ◽

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

Translational Behavioral Medicine ◽

10.1093/tbm/ibaa116 ◽

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.

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Bayesian evaluation of behavior change interventions: A brief introduction and a practical example

10.31234/osf.io/xmgwv ◽

2017 ◽

Author(s):

Matti Toivo Juhani Heino ◽

Matti Vuorre ◽

Nelli Hankonen

Keyword(s):

Health Psychology ◽

Behavior Change ◽

Statistical Methods ◽

Hierarchical Modeling ◽

Impact Behavior ◽

Basic Principles ◽

Bayesian Hierarchical ◽

Software Packages ◽

Behavior Change Interventions ◽

Central Interest

Introduction: Evaluating effects of behavior change interventions is a central interest in health psychology and behavioral medicine. Researchers in these fields routinely use frequentist statistical methods to evaluate the extent to which these interventions impact behavior and the hypothesized mediating processes in the population. However, calls to move beyond exclusive use of frequentist reasoning are now widespread in psychology and allied fields. We suggest adding Bayesian statistical methods to the researcher’s toolbox of statistical methods. Objectives: We first present the basic principles of Bayesian approach to statistics and why they are useful for researchers in health psychology. We then provide a practical example on how to evaluate intervention effects using Bayesian methods, with a focus on Bayesian hierarchical modeling. We provide the necessary materials for introductory level readers to follow the tutorial. Conclusion: Bayesian analytical methods are now available to researchers through easy-to-use software packages, and we recommend using them to evaluate the effectiveness of interventions for their conceptual and practical benefits.

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Sustainable Change Sequence: A framework for developing behavior change interventions for patients with long-term conditions

European Journal for Person Centered Healthcare ◽

10.5750/ejpch.v2i2.736 ◽

2014 ◽

Vol 2 (2) ◽

pp. 212 ◽

Cited By ~ 1

Author(s):

Glyn Elwyn ◽

Katy Marrin ◽

Dominick L Frosch ◽

James White

Keyword(s):

Behavior Change ◽

Behavioural Change ◽

Intervention Development ◽

Healthcare Applications ◽

Theoretical Frameworks ◽

Long Term Conditions ◽

Appropriate Behavior ◽

Behavior Change Interventions ◽

Healthcare Interventions

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

Annals of Behavioral Medicine ◽

10.1093/abm/kaaa095 ◽

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.

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