Trial reportContemporary clinical trials2021
Sense2Stop: A micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention.
Trial report in Contemporary clinical trials, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 39 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.
Use of Noninvasive Wearables Biomonitoring to Detect Pre-Smoking, Smoking, And Post-Smoking Stages: An Observational Laboratory Study
A Digital Real-Time Support for Stress-Induced Smoking and Unhealthy Eating Among Gender and Sexual Minorities in Dhaka
Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.Frontiers in digital health · 2025Pooled it
- Effect of Physical Exercise Programs Based on Mobile Health and Ecological Momentary Assessment on the Physical and Mental Health, Cognitive Functions, and Social Environment of Adults in Developing Countries: A Systematic Review.Medicina (Kaunas, Lithuania) · 2024Pooled it
- Smartband-based smoking detection and real-time brief mindfulness intervention: findings from a feasibility clinical trial.Annals of medicine · 2024Trial
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Effective monitoring of online AI decision-making algorithms in just-in-time adaptive interventions.NPJ digital medicine · 2026Article
- Article
- Just-in-Time Adaptive Interventions: Where Are We Now and What Is Next?Annual review of psychology · 2026Review
- Missing data in microrandomized trials: Challenges and opportunities.Behavior research methods · 2025Article
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Predicting Engagement Patterns With Connected Wearable Devices in a Health System: Survival Analysis.Journal of medical Internet research · 2025Article
- Article
- Reinforcement Learning on Dyads to Enhance Medication Adherence.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2025Article
- Exploring perspectives on digital smoking cessation just-in-time adaptive interventions: A focus group study with adult smokers and smoking cessation professionals.PLOS digital health · 2025Article
- Designing digital health interventions with causal inference and multi-armed bandits: a review.Frontiers in digital health · 2025Review
- How stress influences e-cigarette health message perceptions and intentions to abstain from vaping among young adults who vape.Addictive behaviors · 2025Article
- A feature-based qualitative assessment of smoking cessation mobile applications.PLOS digital health · 2024Article
- Sensors for Smoking Detection in Epidemiological Research: Scoping Review.JMIR mHealth and uHealth · 2024Article
- From lab to life: Evaluating the reliability and validity of psychophysiological data from wearable devices in laboratory and ambulatory settings.Behavior research methods · 2024Article
- HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundRelapse to smoking is commonly triggered by stress, but behavioral interventions have shown only modest efficacy in preventing stress-related relapse. Continuous digital sensing to detect states of smoking risk and intervention receptivity may make it feasible to increase treatment efficacy by adapting intervention timing.
objectiveAims are to investigate whether the delivery of a prompt to perform stress management behavior, as compared to no prompt, reduces the likelihood of (a) being stressed and (b) smoking in the subsequent two hours, and (c) whether current stress moderates these effects. STUDY
designA micro-randomized trial will be implemented with 75 adult smokers who wear Autosense chest and wrist sensors and use the mCerebrum suite of smartphone apps to report and respond to ecological momentary assessment (EMA) questions about smoking and mood for 4 days before and 10 days after a quit attempt and to access a set of stress-management apps. Sensor data will be processed on the smartphone in real time using the cStress algorithm to classify minutes as probably stressed or probably not stressed. Stressed and non-stressed minutes will be micro-randomized to deliver either a prompt to perform a stress management exercise via one of the apps or no prompt (2.5-3 stress management prompts will be delivered daily). Sensor and self-report assessments of stress and smoking will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to prevent smoking relapse. SIGNIFICANCE: Sense2Stop will be the first digital trial using wearable sensors and micro-randomization to optimize a just-in-time adaptive stress management intervention for smoking relapse prevention.
Indexed as
Identifiers
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.