Observational studyJMIR mHealth and uHealth2023
Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study.
Observational study in JMIR mHealth and uHealth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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Who cites it
13 citing papers in PubMed, 11 citations in OpenAlex.
- Joint effects of real-world cue exposure and affective states on momentary alcohol craving in adults with alcohol use disorder.Drug and alcohol dependence reports · 2026Article
- Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.Health science reports · 2026Review
- The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study.Journal of medical Internet research · 2026Observational
- Preferences for an Experience Sampling Method-Based Tool as an Adjunct to Usual Treatment in Patients With Problem Substance Use: Qualitative Study.JMIR formative research · 2026Article
- Observational
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Forecasting alcohol lapse risk up to two weeks in advance using time-lagged machine learning models.PloS one · 2026Article
- 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
- Current challenges and opportunities in active and passive data collection for mobile health sensing: a scoping review.JAMIA open · 2025Review
- Idiographic Lapse Prediction With State Space Modeling: Algorithm Development and Validation Study.JMIR formative research · 2025Observational
- Current approaches using remote monitoring technology in alcohol use disorder (AUD): an integrative review.Alcohol and alcoholism (Oxford, Oxfordshire) · 2025Review
- Machine learning models for temporally precise lapse prediction in alcohol use disorder.Journal of psychopathology and clinical science · 2024Article
- Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study.JMIR mHealth and uHealth · 2023Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPersonal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications.
objectiveWe aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses.
methodsParticipants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method.
resultsOf the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04).
conclusionsThese results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.
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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.