ArticleTranslational behavioral medicine2020
An iterative, interdisciplinary, collaborative framework for developing and evaluating digital behavior change interventions.
Article in Translational behavioral medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Behavior Change Interventions to Promote Physical Activity and Reduce Sedentary Behavior Among Survivors of Breast Cancer: Systematic Review and Meta-Analysis of Randomized Controlled Trials.Journal of medical Internet research · 2025Pooled it
- Black American and Latinx Parent/Caregiver Participation in Digital Health Obesity Interventions for Children: A Systematic Review.Frontiers in digital health · 2021Pooled it
- A mobile healthy lifestyle intervention to promote mental health in adolescence: a mixed-methods evaluation.BMC public health · 2024Trial
- A mobile app to promote alcohol and drug SBIRT skill translation among multi-disciplinary health care trainees: Results of a randomized controlled trial.Substance abuse · 2022Trial
- Co-Creating in Close Collaboration with Community Stakeholders: Transferable Lessons Learned from the Development of a Group-Based Intervention for Intimate Partner Violence (IPV) Survivors in Sweden.International journal of environmental research and public health · 2026Article
- Engagement of Users in Digital Health Applications: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Reference and Solution Architecture for GenAI- and GIS-Enhanced Physical Activity Interventions: Towards Implementing the AI4Motion Platform.Journal of medical systems · 2025Article
- "A one-stop shop": Real-world use and app-users' experiences of a psychoeducational smartphone app for adults with ADHD.Internet interventions · 2025Article
- Fine-tuning Large Language Models in Behavioral Psychology for Scalable Physical Activity Coaching.medRxiv : the preprint server for health sciences · 2025Article
- Progressing implementation of behavior change frameworks for digital health interventions: challenges and ways forward.Translational behavioral medicine · 2025Article
- Fine-tuning LLMs in behavioral psychology for scalable health coaching.NPJ cardiovascular health · 2025Article
- Design and Development of a Multimodal Digital Intervention (SHIFT App) to Address Sexual Dysfunction in Hematopoietic Stem Cell Transplant (HSCT) Survivors.Transplantation and cellular therapy · 2024Article
- Applying Meaning and Self-Determination Theory to the Development of a Web-Based mHealth Physical Activity Intervention: Proof-of-Concept Pilot Study.JMIR formative research · 2024Article
- Novel behaviour change frameworks for digital health interventions: A critical review.Journal of health psychology · 2023Review
- Challenges and opportunities in mobile e-coaching.Frontiers in digital health · 2023Article
- Omnichannel Communication to Boost Patient Engagement and Behavioral Change With Digital Health Interventions.Journal of medical Internet research · 2022Article
- Parents' Experience With a Mobile Health Intervention to Influence Human Papillomavirus Vaccination Decision Making: Mixed Methods Study.JMIR pediatrics and parenting · 2022Article
- Expanding access to cancer peer support: development of a website to deliver written peer support that meets patients' individual emotional and informational needs while reducing potential harms.Procedia computer science · 2022Article
- Consensus on Recommended Functions of a Smart Home System to Improve Self-Management Behaviors in People With Heart Failure: A Modified Delphi Approach.Frontiers in cardiovascular medicine · 2022Article
- Personalized Digital Health Communications to Increase COVID-19 Vaccination in Underserved Populations: A Double Diamond Approach to Behavioral Design.Frontiers in digital health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.