ArticleNPJ digital medicine2020
Prediction of stress and drug craving ninety minutes in the future with passively collected GPS data.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2023Pooled it
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Week-Ahead Prediction of High-Risk Drinking Episodes Among Young Adults Using Wearable Biosignals and Psychological Vulnerabilities: Prospective Observational Machine Learning Study.JMIR mHealth and uHealth · 2026Observational
- Smartphone movement data can reliably predict smoking lapses and cravings to enable timely smoking cessation support.Scientific reports · 2026Article
- Passive data do not improve prediction or detection of alcohol consumption beyond temporal patterns in major depression: A 90-day cross-validated study.Addictive behaviors · 2026Article
- Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Daily Life Assessment of Seven Alcohol-Use-Disorder Symptoms.Clinical psychological science : a journal of the Association for Psychological Science · 2026Article
- Computational network models for forecasting and control of mental health trajectories in digital applications.NPJ digital medicine · 2025Article
- Maximizing Engagement, Trust, and Clinical Benefit of AI-Generated Recovery Support Messages for Alcohol Use Disorder: Protocol for an Optimization Study.JMIR research protocols · 2025Article
- Predicting Craving-Related Emotions among Opioid Use Disorder Patients: Preliminary Results.... International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks · 2025Article
- Ambulatory assessment to advance the science of nondrug reward in addiction and recovery.Addiction research & theory · 2025Article
- Proximal Effects of a Just-in-Time Adaptive Intervention for Smoking Cessation With Wearable Sensors: Microrandomized Trial.JMIR mHealth and uHealth · 2025Article
- Momentary mindfulness versus distraction coping messages to reduce cannabis craving among young adults: A microrandomized trial.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2025Article
- Article
- Machine learning models for temporally precise lapse prediction in alcohol use disorder.Journal of psychopathology and clinical science · 2024Article
- Daily Time-Use Patterns and Quality of Life in Parents: Protocol for a Pilot Quasi-Experimental, Nonrandomized Controlled Trial Using Ecological Momentary Assessment.JMIR research protocols · 2024Article
- Age moderates the association of optimism on craving during substance use disorder treatment.Journal of substance use and addiction treatment · 2024Article
- Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2024Article
- Pharmacokinetics-Informed Neural Network for Predicting Opioid Administration Moments with Wearable Sensors.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- Time-varying effect modeling with intensive longitudinal data: Examining dynamic links among craving, affect, self-efficacy and substance use during addiction recovery.Addiction (Abingdon, England) · 2023Article
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
Just-in-time adaptive interventions (JITAIs), typically smartphone apps, learn to deliver therapeutic content when users need it. The challenge is to "push" content at algorithmically chosen moments without making users trigger it with effortful input. We trained a randomForest algorithm to predict heroin craving, cocaine craving, or stress (reported via smartphone app 3x/day) 90 min into the future, using 16 weeks of field data from 189 outpatients being treated for opioid-use disorder. We used only one form of continuous input (along with person-level demographic data), collected passively: an indicator of environmental exposures along the past 5 h of movement, as assessed by GPS. Our models achieved excellent overall accuracy-as high as 0.93 by the end of 16 weeks of tailoring-but this was driven mostly by correct predictions of absence. For predictions of presence, "believability" (positive predictive value, PPV) usually peaked in the high 0.70s toward the end of the 16 weeks. When the prediction target was more rare, PPV was lower. Our findings complement those of other investigators who use machine learning with more broadly based "digital phenotyping" inputs to predict or detect mental and behavioral events. When target events are comparatively subtle, like stress or drug craving, accurate detection or prediction probably needs effortful input from users, not passive monitoring alone. We discuss ways in which accuracy is difficult to achieve or even assess, and warn that high overall accuracy (including high specificity) can mask the abundance of false alarms that low PPV reveals.
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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.