ArticleJMIR mental health2020
Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic.
Article in JMIR mental health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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
20 citing papers in PubMed.
- Digital Health Technology Adoption Among Chinese Physicians: Latent Profile Analysis and Cross-Sectional Study.Journal of medical Internet research · 2025Article
- Personalization of AI Using Personal Foundation Models Can Lead to More Precise Digital Therapeutics.JMIR AI · 2025Article
- App-Based Ecological Momentary Assessment of Problematic Smartphone Use During Examination Weeks in University Students: 6-Week Observational Study.Journal of medical Internet research · 2025Observational
- Sounds like gambling: detection of gambling venue visitation from sounds in gamblers' environments using a transformer.Scientific reports · 2025Article
- Feasibility and acceptability of collecting passive phone usage and sensor data via Apple SensorKit.PloS one · 2025Article
- 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
- Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.Journal of medical Internet research · 2023Article
- Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model.Journal of medical Internet research · 2023Article
- Overdose Detection Technologies to Reduce Solitary Overdose Deaths: A Literature Review.International journal of environmental research and public health · 2023Review
- Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study.Frontiers in psychiatry · 2023Article
- The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design.JMIR research protocols · 2022Article
- Smartphone Ownership, Smartphone Utilization, and Interest in Using Mental Health Apps to Address Substance Use Disorders: Literature Review and Cross-sectional Survey Study Across Two Sites.JMIR formative research · 2022Article
- Opportunities and Challenges for Professionals in Psychiatry and Mental Health Care Using Digital Technologies During the COVID-19 Pandemic: Systematic Review.JMIR human factors · 2022Review
- The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations.Frontiers in psychiatry · 2022Article
- Application of Digital Medicine in Addiction.Journal of Shanghai Jiaotong University (science) · 2022Article
- Emerging digital technologies in cancer treatment, prevention, and control.Translational behavioral medicine · 2021Review
- Opportunities to Integrate Mobile App-Based Interventions Into Mental Health and Substance Use Disorder Treatment Services in the Wake of COVID-19.American journal of health promotion : AJHP · 2021Review
- Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study.JMIR mHealth and uHealth · 2021Article
- Pathways between COVID-19 public health responses and increasing overdose risks: A rapid review and conceptual framework.The International journal on drug policy · 2021Review
- Non-pharmaceutical interventions during the COVID-19 pandemic: A review.Physics reports · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.
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.