Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: Longitudinal Observational Study
Bibliographic record
Abstract
BACKGROUND: Morning emotions can significantly affect daily wellness. While many studies have analyzed daily survey responses to identify factors influencing morning emotions, these methods require additional time and effort from individuals for emotional monitoring. OBJECTIVE: This study aims to identify daily alarm usage patterns related to morning emotions. METHODS: We recruited 373 users of the Alarmy app (DelightRoom) in the United States and South Korea and surveyed their demographics and usual behaviors related to morning emotions. Participants described their morning emotions over a 2-week period, during which we collected daily alarm app logs. We used a generalized estimating equation (GEE) method to identify factors affecting morning emotions. RESULTS: The findings indicate that varied alarm usage is related to morning emotions. Alarm set time was positively associated with feelings of peacefulness and refreshment in the morning, while task-based alarms were related to nervousness. The time taken to deactivate the alarm after it rang was negatively correlated with happiness. In addition, usual behaviors and demographic factors were found to be related to morning emotions, consistent with previous studies. CONCLUSIONS: The study reveals that daily alarm usage is related to morning emotions, suggesting that daily alarm logs can supplement survey methods to facilitate daily emotion monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".