1198 IS TOTAL SLEEP TIME ASSOCIATED WITH APP BEHAVIORAL CONSTRUCT SCORE?
Bibliographic record
Abstract
Little is known about how well sleep apps are grounded in behavioral theory. Additionally, limited research exists on whether apps that reflect behavioral constructs influence sleep behavior. A validated instrument was used to assign a behavioral construct score to a commercially available sleep app. The score was based on features of the app that encouraged realistic goal setting, self-monitoring, and other behavioral constructs related to sleep behavior. Data from approximately 4000 users across 32 countries were made available to analyze total daily sleep time. However, users without 5 days of sleep data, and less than 5 users per country were eliminated for a final sample of 213 users and 4600 unique daily entries on wake and sleep times. The sleep app was assigned a behavioral construct score of 40 out of 100 possible points. The final sample consisted of users from 10 countries: Australia, Belarus, Canada, France, Germany, Great Britain, Russia, Spain, Ukraine, and the United States. The average sleep time across all countries was 6.9 hours (95% CI, 3.5, 10.9), and users in four countries reported less than the recommended 7 hours of sleep per night. Users in France reported the least amount of sleep (6.02 hours, 95% CI, 4.3, 8.2) while users in Belarus reported the most sleep (7.3 hours, 95% CI, 6.3, 8.3). Sleep apps with low behavioral construct scores may not encourage healthy sleep behaviors. However, additional comparisons with high scoring apps are needed to determine whether integrating behavioral constructs in sleep apps influence sleep time among adults. This work was supported by research funds from the Department of Kinesiology and Community Health at the University of Illinois at Urbana Champaign.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".