Auricular acupressure combined with auricular acupoint massage enhances cognitive function in night shift nurses: a P300 wave analysis
Bibliographic record
Abstract
Objectives: Night-shift work is associated with cognitive impairments, but convenient, effective, and acceptable traditional Chinese medicine-based interventions remain limited. This study aimed to evaluate the effects of auricular acupressure combined with auricular acupoint massage on cognitive function in night-shift nurses, using P300 wave parameters from electroencephalography analysis as objective metrics. Methods: Eighty nurses (40 days-shift, 40 night-shift) participated. The intervention included auricular acupressure and massage targeting six points, performed daily for 4 weeks. Cognitive function was assessed using the Insomnia Severity Index (ISI), Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). P300 amplitude and latency were measured. Results: < 0.05). Before the intervention, After FDR correction for multiple comparisons, P300 amplitude was significantly lower at the T4 electrode site (q = 0.020) in the night-shift group. P300 latency remained significantly prolonged at sites Fz (q = 0.020), F3 (q < 0.001), F4 (q = 0.035), and T5 (q = 0.033). Post-intervention, the night-shift group demonstrated significant increases in P300 amplitude at F3, F4, T3, T4, T5, and T6 (all q < 0.05) and significant reductions in P300 latency at Fz, F4, F7, T5, and T6 (all q < 0.05). Notably, several sites with affected P300 amplitude and latency before the intervention showed significant improvement following intervention. Conclusion: Auricular acupressure and massage significantly improved cognitive function in night-shift nurses, evidenced by enhanced P300 parameters. This non-invasive, cost-effective intervention shows promise for alleviating cognitive impairments from shift work.
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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 | 0.000 | 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) | 0.001 | 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".