Promoting Wellness Through Mobile Health Technology in a College Student Population: Protocol Development and Pilot Study
Bibliographic record
Abstract
BACKGROUND: The health and well-being of college students has garnered widespread attention and concern in recent years. At the same time, the expansion and evaluation of digital technologies has grown in recent years for different target populations. OBJECTIVE: This protocol aims to describe a pilot feasibility study on wearables to assess student interest and to gather baseline data from college freshmen, for the academic year 2019 to 2020. METHODS: All full-time college freshmen residing in a single residence hall were eligible to participate. Study invitations were sent by post and email 5 weeks prior to move-in. Web-based enrollment and in-person attendance at study orientation sessions were mandatory. We provided the incoming freshmen with a wearable and study app. Wearable data and weekly survey data will be collected through the study app and analyzed. We have collected demographic, enrollment, and attrition data and the number and type of support requests from students. RESULTS: The planning phase of the WearDuke initiative was completed in 2018 to 2019, and the pilot study was launched in July 2019. Of the 175 students invited, 120 enrolled and 114 started the study; 107 students remained active participants till the end of the fall semester. For Apple Watch participants (the majority of study population), weekly survey completion rates ranged from 70% (74/106) to 96% (95/99). CONCLUSIONS: Halfway through the pilot, we noticed that the initiative has been received positively by the students with minimal attrition. The short- and long-term benefits may be substantial for students, the campus, the utilization of health services, and long-term health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/16474.
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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 | 0.100 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".