A study of the efficacy of 500nm blue‐green light therapy on cognition, mood, and sleep in prodromal Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: The study aimed to evaluate the effects of four weeks of 500 nm blue-green light visual stimulation on cognition, mood, and sleep in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHOD: Eighty patients were recruited from the Memory Clinic. The experimental group comprised 42 cases (22 SCD and 20 MCI), while the control group comprised 38 cases (27 SCD and 11 MCI). The experimental group was treated with a 500nm blue-green light (light spectrum intensity of 506 lux lm/m² and 230 µW/cm²) for 50 minutes in the morning for four weeks, while the control group received no treatment. General cognitive function was evaluated using the Mini-mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-B), and Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog), and mood was evaluated using the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). The Pittsburgh Sleep Quotient Index (PSQI) was used to assess the sleep quality. Paired t-test and Wilcoxon signed-rank test were used to compare the differences in cognition, sleep, and mood between the experimental and the control group before and after the intervention. RESULT: There were no significant differences in baseline demographic information, cognition, mood, and sleep between the experimental and control groups (P>0.05). As shown in Table 1. After the intervention, the experimental group showed a significant reduction in the ADAS-cog (P = 0.034), HAMA (P = 0.044), HAMD (P<0.001) and PSQI scores (P = 0.035). The control group showed a significant reduction in HAMA (P = 0.002) and HAMD (P = 0.018) scores. As shown in Table 2. CONCLUSION: Four weeks of 500 nm blue-green light therapy significantly improved overall cognitive function and sleep quality in SCD and MCI patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".