Ecological Momentary Assessment of Physical Activity and Wellness Behaviors in College Students Throughout a School Year: Longitudinal Naturalistic Study
Bibliographic record
Abstract
BACKGROUND: The Wellness Environment app study is a longitudinal study focused on promoting health in college students. OBJECTIVE: The two aims of this study were (1) to assess physical activity (PA) variation across the days of the week and throughout the academic year and (2) to explore the correlates that were associated with PA, concurrently and longitudinally. METHODS: The participants were asked to report their wellness and risk behaviors on a 14-item daily survey through a smartphone app. Each student was provided an Apple Watch to track their real time PA. Data were collected from 805 college students from Sept 2017 to early May 2018. PA patterns across the days of the week and throughout the academic year were summarized. Concurrent associations of daily steps with wellness or risk behavior were tested in the general linear mixed-effects model. The longitudinal, reciprocal association between daily steps and health or risk behaviors were tested with cross-lagged analysis. RESULTS: Female college students were significantly more active than male ones. The students were significantly more active during the weekday than weekend. Temporal patterns also revealed that the students were less active during Thanksgiving, winter, and spring breaks. Strong concurrent positive correlations were found between higher PA and self-reported happy mood, 8+ hours of sleep, ≥1 fruit and vegetable consumption, ≥4 bottles of water intake, and ≤2 hours of screen time (P<.001). Similar longitudinal associations found that the previous day's wellness behaviors independently predicted the following day's higher PA except for mood. Conversely, the higher previous-day PA levels were associated with better mood, more fruit and vegetable consumption, and playing less music, but with higher liquor consumption the next day. CONCLUSIONS: This study provides a comprehensive surveillance of longitudinal PA patterns and their independent association with a variety of wellness and risk behaviors in college students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".