Assessing the Effects of Polychromatic Light Exposure on Mood in Adults: A Systematic Review Contrasting α-opic Equivalent Daylight Illuminances
Bibliographic record
Abstract
This is a systematic review of studies assessing the effects of polychromatic (white) ambient light on mood in healthy adults. We hypothesized that higher melanopic equivalent daylight illuminances (EDI) would be associated with better mood. The search identified 2,994 publications, of which 14 met inclusion criteria. The resulting database included the spectral power distributions, illuminances, mood measures, and methods. We used the CIE S026 toolbox to calculate the five α-opic EDI values to characterize the light exposures in each study. Regression models tested the associations between predictors α-opic EDI, duration, and timing, and the outcome, mood. The results showed that none of the five α-opic values were significantly associated with mood (all estimates <0.044, p > .490). A longer duration of light exposure (all estimates <0.154, p < .018) and timing of light exposure in the morning (all estimates <0.233, p < .001) were associated with better mood, but these effects did not persist after adjusting for the data being nested within studies. Average mood scores remained reasonably consistent across different α-opic EDI profiles, suggesting that exposure profiles might not influence mood. Notably, none of the studies included had an experimental condition with a peak for melanopic EDI greater than the other α-opic EDI values, therefore the hypothesized association between melanopic EDI and mood could not be tested. Future research designs should contrast selected light parameters to identify the photoreceptors involved in the processes of interest.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".