Associations of Electronic Device Use Before and After Sleep With Psychological Distress Among Chinese Adults in Hong Kong: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Hong Kong has a high rate of electronic device (e-device; computer, smartphone, and tablet) use. However, little is known about the associations of the duration of e-device use before and after sleep with psychological symptoms. OBJECTIVE: This study aimed to investigate the associations of the duration of e-device use before and after sleep with psychological distress. METHODS: A probability-based telephone survey was conducted on 3162 Hong Kong adults (54.6% female; mean age 47.4 years, SD 18.3 years) in 2016. Multivariate linear and Poisson regressions were used to calculate adjusted regression coefficients (aBs) and prevalence ratios (aPRs) of anxiety and depressive symptoms (measured by Patient Health Questionnaire-4) for the duration from waking to the first e-device use (≥61, 31-60, 6-30, and ≤5 minutes) and the duration of e-device use before sleeping (≤5, 6-30, 31-60, and ≥61 minutes). RESULTS: The first e-device use in ≤5 (vs ≥61) minutes after waking was associated with anxiety (aB 0.35, 95% CI 0.24-0.46; aPR 1.74, 95% CI 1.34-2.25) and depressive symptoms (aB 0.27, 95% CI 0.18-0.37; aPR 1.84, 95% CI 1.33-2.54). Using e-devices for ≥61 (vs ≤5) minutes before sleeping was also associated with anxiety (aB 0.17, 95% CI 0.04-0.31; aPR 1.32, 95% CI 1.01-1.73) and depressive symptoms (aB 0.17, 95% CI 0.05-0.28; aPR 1.47, 95% CI 1.07-2.02). E-device use both ≤5 minutes after waking and for ≥61 minutes before sleeping was strongly associated with anxiety (aB 0.68, 95% CI 0.47-0.90; aPR 2.64, 95% CI 1.90-3.67) and depressive symptoms (aB 0.55, 95% CI 0.36-0.74; aPR 2.56, 95% CI 1.69-3.88). CONCLUSIONS: E-device use immediately (≤5 minutes) after waking and use for a long duration (≥61 minutes) before sleeping were associated with anxiety and depressive symptoms among Chinese adults in Hong Kong.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".