ArticleJournal of the American Medical Informatics Association : JAMIA2019
An expandable approach for design and personalization of digital, just-in-time adaptive interventions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07574463 (Development and Evaluation of a Just-in-Time Adaptive Digital Exercise Rehabilitation Program for Children After Congenital Heart Disease Surgery), which is not on this map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
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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.
Development and Evaluation of a Just-in-Time Adaptive Digital Exercise Rehabilitation Program for Children After Congenital Heart Disease Surgery
Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects - an umbrella review.BMC public health · 2020Pooled it
- Impact of the EILA application on smoking cessation among hospital workers: a randomised controlled trial.Occupational and environmental medicine · 2026Trial
- A Refined Mobile Health Intervention (SMARTFAMILY2.0) to Promote Physical Activity and Healthy Eating in a Family Setting: Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- Efficacy of an mHealth Behavior Change Intervention for Promoting Physical Activity in the Workplace: Randomized Controlled Trial.Journal of medical Internet research · 2023Trial
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Reducing alcohol consumption in UK armed forces veterans: Feasibility of using personalized push notifications with AI.PLOS digital health · 2026Article
- Just-In-Time Adaptive Interventions for Weight Management Among Adults With Excess Body Weight: Scoping Review.Journal of medical Internet research · 2025Article
- Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts.NPJ digital medicine · 2025Article
- Physical activity interventions: an update on advancing sedentary time, technology, and dissemination and implementation research.Journal of behavioral medicine · 2025Review
- Predicting Adherence to Computer-Based Cognitive Training Programs Among Older Adults: Study of Domain Adaptation and Deep Learning.JMIR aging · 2024Article
- Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.PLOS digital health · 2024Article
- Just-in-time adaptive interventions for adolescent and young adult health and well-being: protocol for a systematic review.BMJ open · 2024Article
- Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.JMIR formative research · 2023Article
- The interplay between social media use and problematic internet usage: Four behavioral patterns.Heliyon · 2023Article
- Investigating the Role of Context in the Delivery of Text Messages for Supporting Psychological Wellbeing.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- Adaptive interventions for opioid prescription management and consumption monitoring.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- The Patient Experience of the Future is Personalized: Using Technology to Scale an N of 1 Approach.Journal of patient experience · 2023Article
- Developing the "Choosing Health" Digital Weight Loss and Maintenance Intervention: Intervention Mapping Study.Journal of medical Internet research · 2022Article
- The role of socio-demographic factors and physical functioning in the intra- and interpersonal variability of older adults' sedentary time: an observational two-country study.BMC geriatrics · 2022Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables. Materials and Methods: We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions. Results: We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns. Conclusion: While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.