ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2018
Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis 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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Smartphone-Based Interventions to Reduce Sedentary Behavior and Promote Physical Activity Using Integrated Dynamic Models: Systematic Review.Journal of medical Internet research · 2021Pooled it
- How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.JMIR mHealth and uHealth · 2023Trial
- Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.Contemporary clinical trials · 2021Trial
- A Mobile Health Intervention in Caregivers of Patients Undergoing Hematopoietic Cell Transplantation: A Randomized Controlled Trial to Examine Health-Related Quality of Life.BMC digital health · 2025Article
- Realizing the potential of mobile interventions for education.NPJ science of learning · 2024Review
- Toward Tailoring Just-in-Time Adaptive Intervention Systems for Workplace Stress Reduction: Exploratory Analysis of Intervention Implementation.JMIR mental health · 2024Article
- A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health.Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Review
- Microrandomized Trials: Developing Just-in-Time Adaptive Interventions for Better Public Health.American journal of public health · 2023Article
- Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation.JMIR mHealth and uHealth · 2022Article
- Data-driven Interpretable Policy Construction for Personalized Mobile Health.IEEE International Conference on Digital Health. IEEE International Conference on Digital Health · 2022Article
- Advancing Behavioral Intervention and Theory Development for Mobile Health: The HeartSteps II Protocol.International journal of environmental research and public health · 2022Article
- Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial.JMIR research protocols · 2021Article
- Development of a Digital Lifestyle Modification Intervention for Use after Transient Ischaemic Attack or Minor Stroke: A Person-Based Approach.International journal of environmental research and public health · 2021Article
- The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.Sensors (Basel, Switzerland) · 2021Article
- Microrandomized Trial Design for Evaluating Just-in-Time Adaptive Interventions Through Mobile Health Technologies for Cardiovascular Disease.Circulation. Cardiovascular quality and outcomes · 2021Article
- Off-Policy Estimation of Long-Term Average Outcomes with Applications to Mobile Health.Journal of the American Statistical Association · 2021Article
- Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science.Current addiction reports · 2020Article
- Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study.JMIR formative research · 2020Article
- Digital Health and Addiction.Current opinion in systems biology · 2020Article
- Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
There is a growing scientific interest in the use and development of just-in-time adaptive interventions in mobile health. These mobile interventions typically involve treatments, such as reminders, activity suggestions and motivational messages, delivered via notifications on a smartphone or a wearable to help users make healthy decisions in the moment. To be effective in influencing health, the combination of the right treatment and right delivery time is likely critical. A variety of prediction/detection algorithms have been developed with the goal of pinpointing the best delivery times. The best delivery times might be times of greatest risk and/or times at which the user might be most receptive to the treatment notifications. In addition, to avoid over burdening users, there is often a constraint on the number of treatments that should be provided per time interval (e.g., day or week). Yet there may be many more times at which the user is predicted or detected to be at risk and/or receptive. The goal then is to spread treatment uniformly across all of these times. In this paper, we introduce a method that spreads the treatment uniformly across the delivery times. This method can also be used to provide data for learning whether the treatments are effective at the delivery times. This work is motivated by our work on two mobile health studies, a smoking cessation study and a physical activity study.
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What OpenQuestion 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 OpenQuestion graph.