The Person-Based Approach to Intervention Development: Application to Digital Health-Related 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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Maintaining or Losing Intervention-Induced Health-Related Behavior Change. A Mixed Methods Field Study

Frontiers in Psychology ◽

10.3389/fpsyg.2021.688192 ◽

2021 ◽

Vol 12 ◽

Author(s):

Frida Skarin ◽

Erik Wästlund ◽

Henrik Gustafsson

Keyword(s):

Mixed Methods ◽

Behavior Change ◽

Field Study ◽

Quantitative Data ◽

Qualitative Data ◽

Goal Achievement ◽

Body Measurements ◽

Behavior Change Interventions ◽

Health Related

The aim of this mixed methods field study was to gain a better understanding of how psychological factors can contribute to success in intervention-induced behavior change over time. While it can be difficult to change behavior, the use of interventions means that most participants succeed in change during the intervention. However, it is rare for the immediate change to automatically transform into maintained behavior changes. Most research conducted on health-related behavior change interventions contains quantitative studies that investigate key intervention components on a group level. Hence, to bring more knowledge about maintained intervention-induced behavior change, there is need for a study approach that enhances the understanding of individual participants' experiences during and after the intervention. Therefore, the present study, which was conducted in Sweden, used a mixed methods design (triangulation) consisting of pre-, post-, and follow-up quantitative data (questionnaires and body measurements) and qualitative data (interviews), where the individuals' accounts are used to broaden the understanding of the intervention and the behavior change process. All study participants were enrolled in a volitional (fee-based and non-manipulated) intervention given by certified gyms. The quantitative data collection included 22 participants who completed questionnaires and body measurements before and after the intervention, plus 13 complete body measurements 6 months after the intervention. The qualitative data included pre-interviews with 12 participants and six follow-up-interviews. The questions in both questionnaires and interviews related to expectations, efficacy, motivation, goals, achievements, behavior change, and future. Overall, the results show that levels of expectations, efficacy, and motivation cannot be used in isolation to predict maintained intervention-induced behavior change. To successfully extend and maintain immediate change, it was crucial to experience goal achievement (but not BMI change). Furthermore, enabling talk was salient in the pre-interviews with participants reporting successful immediate (and maintained) change. By contrast, pre-interview disabling talk turned out to be evident in interviews, with participants not responding to follow-up. When the qualitative and quantitative results are summarized and integrated, it appears that subjective goal achievement, combined with enabling self-talk, were crucial factors in successful maintained behavior change.

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Antifragile Behavior Change through Digital Behavior Change Interventions (Preprint)

10.2196/preprints.32571 ◽

2021 ◽

Author(s):

Benjamin T Kaveladze ◽

Sean D Young ◽

Stephen M Schueller

Keyword(s):

Mental Health ◽

Decision Making ◽

Behavior Change ◽

Health Outcomes ◽

Real World ◽

Change Process ◽

Digital Health ◽

Health Behavior Change ◽

Behavior Change Interventions ◽

Future Work

UNSTRUCTURED Digital health behavior change interventions (DHBCIs) are popular and widely-accessible tools for helping people to pursue behavior change goals. However, their effectiveness tends to be low in real-world settings. Drawing from Nassim Nicholas Taleb’s concept of antifragility, we introduce antifragile behavior change, a strategy that leverages user-specific characteristics to make the behavior change process more efficient. Next, we propose two principles for designing DHBCIs to support antifragile behavior change: first, DHBCIs should provide personalized guidance that accounts for user-specific circumstances and goals; second, DHBCIs should prioritize user agency by refraining from using nudges that might manipulate user decision-making. We hope this paper will encourage researchers and product developers to reconsider DHBCI design through the lens of antifragility. Future work can examine if DHBCIs that are consistent with our principles of designing for antifragile behavior change lead to better mental health outcomes than other DHBCIs.

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Peer Support: a Human Factor to Enhance Engagement in Digital Health Behavior Change Interventions

Journal of Technology in Behavioral Science ◽

10.1007/s41347-019-00105-x ◽

2019 ◽

Vol 4 (2) ◽

pp. 152-161 ◽

Cited By ~ 9

Author(s):

Karen L. Fortuna ◽

Jessica M. Brooks ◽

Emre Umucu ◽

Robert Walker ◽

Phillip I. Chow

Keyword(s):

Health Behavior ◽

Behavior Change ◽

Peer Support ◽

Human Factor ◽

Digital Health ◽

Health Behavior Change ◽

Behavior Change Interventions

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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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Effort-Optimized-Intervention Model: A Framework for Building and Analyzing Digital Interventions that Require Minimal Effort for Health-Related Gains (Preprint)

10.2196/preprints.24905 ◽

2020 ◽

Author(s):

Amit Baumel ◽

Frederick J. Muench

Keyword(s):

Digital Health ◽

Intervention Development ◽

Ego Depletion ◽

User Engagement ◽

Competing Demands ◽

Therapeutic Mechanism ◽

Health Related ◽

The Face ◽

A Minor ◽

Therapeutic Activities

UNSTRUCTURED The majority of digital health interventions lean on the promise of bringing health and self-care into people’s homes and hands. However, these interventions are delivered while people are in their triggering environment, which places competing demands on their attention. Individuals struggling to change or learn a new behavior have to work hard to achieve even a minor change because of the automatic forces propelling them back to their habitual behaviors. This paper posits that effort and burden should be explored at the outset and throughout the digital intervention development process as a core therapeutic mechanism, beyond the context of design or user experience testing. An effort-focused conceptualization assumes that, even though goals are rational and people want to achieve them, they are overtaken by competing cognitive, emotional and environmental processes. We offer the term “effort-optimized intervention” (EOI) to describe interventions that focus on user engagement in the face of competing demands. We describe design components based on a three-step process in the planning of an EOI sequence: 1) nurturing effortless cognitive and environmental salience to help people keep effort-related goals prominent despite competition; 2) making it as effortless as possible to complete therapeutic activities to avoid ego depletion and self-efficacy reductions; and 3) turning the necessary effortful activities into sustainable assets. We conclude by presenting an example of designing a digital health intervention based on the EOI model.

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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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Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

Frontiers in Digital Health ◽

10.3389/fdgth.2021.620383 ◽

2021 ◽

Vol 3 ◽

Author(s):

Jaime Martín-Martín ◽

Cristina Roldán-Jiménez ◽

Irene De-Torres ◽

Antonio Muro-Culebras ◽

Adrian Escriche-Escuder ◽

...

Keyword(s):

Social Support ◽

Systematic Review ◽

Behavior Change ◽

Sedentary Behavior ◽

Randomized Clinical Trials ◽

Digital Health ◽

Clinical Population ◽

Outcome Variable ◽

Behavior Change Techniques ◽

Behavior Change Interventions

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field.Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681.Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks−3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes.Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

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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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Systematic review of context-aware digital behavior change interventions to improve health

Translational Behavioral Medicine ◽

10.1093/tbm/ibaa099 ◽

2020 ◽

Vol 11 (5) ◽

pp. 1037-1048

Author(s):

Kelly J Thomas Craig ◽

Laura C Morgan ◽

Ching-Hua Chen ◽

Susan Michie ◽

Nicole Fusco ◽

...

Keyword(s):

Systematic Review ◽

Behavior Change ◽

Health Behaviors ◽

Digital Health ◽

Health Improvement ◽

Context Aware ◽

Behavior Change Techniques ◽

Health Technologies ◽

Behavior Change Interventions ◽

Improve Health

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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