Mobile Health Technology Using a Wearable Sensorband for Female College Students With Problem Drinking: An Acceptability and Feasibility Study
Bibliographic record
Abstract
BACKGROUND: An increasing number of mobile app interventions have been developed for problem drinking among college students; however, few studies have examined the integration of a mobile app with continuous physiological monitoring and alerting of affective states related to drinking behaviors. OBJECTIVE: The aim of this paper was to evaluate the acceptability and feasibility of Mind the Moment (MtM), a theoretically based intervention for female college students with problem drinking that combines brief, in-person counseling with ecological momentary intervention (EMI) on a mobile app integrated with a wearable sensorband. METHODS: We recruited 10 non-treatment seeking, female undergraduates from a university health clinic who scored a 3 or higher on the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) to participate in this pilot study. Study activities involved an in-person baseline intake and 1 follow-up assessment, 2 in-person alcohol brief intervention counseling sessions, and use of MtM technology components (sensorband and EMI on a mobile app) for approximately 3-4 weeks. The intervention used motivational interviewing (MI) and cognitive behavioral therapy (CBT) strategies for reducing risks associated with drinking. We used both qualitative and quantitative assessments to measure acceptability of the intervention and feasibility of delivery. Use patterns of the sensorband and mobile app were also collected. RESULTS: Quantitative and qualitative data indicated high levels of acceptability for the MtM intervention. Altogether, participants made reports on the app on 26.7% (78/292) the days the technology was available to them and completed a total of 325 reports with wide variation between participants. Qualitative findings indicated that sensorband-elicited alerts promoted an increase in awareness of thoughts, feelings, and behaviors related to current environmental stressors and drinking behaviors in theoretically meaningful ways. Specific challenges related to functionality and form of the sensorband were identified. CONCLUSIONS: Delivering intervention material "just-in-time," at the moment participants need to use behavioral strategies has great potential to individualize behavioral interventions for reducing problem drinking and other health behaviors. These findings provide initial evidence for the promise of wearable sensors for increasing potency of theoretically grounded mobile health interventions and point to directions for future research and uptake of these technologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".