Associations between light at night and mental health: A systematic review and meta-analysis
Bibliographic record
Abstract
Light at night (LAN) disrupts the circadian rhythm by altering the natural light-dark cycle. With increasing interest in the relationship between LAN and mental health, there is value in an updated systematic review and meta-analysis considering prominent mental health conditions that also critically appraises LAN exposure assessment. We conducted a systematic review of six databases, two registries, and Google Scholar to identify studies examining the associations between indoor and outdoor LAN exposure (high vs. low exposure) and mental health outcomes (depression, bipolar disorder, anxiety, and schizophrenia), completing inverse-variance random-effects meta-analyses. Nineteen studies with a total population of 556,861 were included in the review. LAN exposure was associated with increased odds of depression prevalence (odds ratio [OR]: 1.18; 95 % confidence interval [CI]: 1.09 to 1.28), with stronger associations for bedside (OR: 1.45; 95 % CI: 1.03 to 2.04) or wrist-measured indoor LAN (OR: 1.30; 95 % CI: 1.22 to 1.38) than satellite-measured outdoor LAN exposure (OR: 1.10; 95 % CI: 1.04 to 1.17) and in older adults (OR: 1.56; 95 % CI: 1.24 to 1.96) compared to general adults (OR: 1.16; 95 % CI: 1.06 to 1.28) or youth (OR: 1.07; 95 % CI: 0.99 to 1.16). LAN exposure was also associated with an increased prevalence of bipolar disorder (OR: 1.19; 95 % CI: 1.08 to 1.31) and anxiety (OR: 1.10; 95 % CI: 1.02 to 1.20) and increased incidence of anxiety (hazard ratio [HR]: 1.08; 95 % CI: 1.02 to 1.15) and schizophrenia (HR: 1.55; 95 % CI: 1.01 to 2.39); however, the incidence of depression did not achieve statistical significance (HR: 1.53; 95 % CI: 0.98 to 2.38). Overall, our results suggest that LAN exposure influences adverse mental health conditions, with differences in results appreciated for depression prevalence depending on how LAN exposure was measured. Additional research is needed on the effects of LAN given its undoubted interest in the prevention and treatment of mental disorders.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".