Association of Qi-stagnation and Subjective Sleep Characteristics with Mild Cognitive Impairment Among Non-depressed Elderly in Community: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Objective: Depression and sleep disturbance is commonly reported in patients with mild cognitive impairment (MCI). However, it remains unclear whether Qi-stagnation is still a risk factor for MCI before the older adults suffer from depression. The purpose of this study was to examine the association between Qi-stagnation and subjective sleep quality with MCI among non-depressed elderly in the Chinese community.Methods: A simple random sampling method was used to abstract research subjects from 34 community elderly day care centers in Fuzhou city based on their electronic health records from March 2019 to December 2020. Intensive face-to-face interviews were conducted using tools such as Montreal cognitive function assessment, AD8 dementia screening questionnaire, Pittsburgh Sleep Quality Index, and TCM constitution assessment scale, among others to analyze the proportion of older adults with MCI who suffer from sleep disturbance and Qi-stagnation in the community. Multi-factor logistical regression was employed to analyze the association among subjective sleep quality, TCM constitution, and MCI.Results: A total of 1,268 subjects were investigated and 1,071 cases were included in this study, among which 314 cases were of MCI patients, with a morbidity of 29.3%. The proportion of individuals having Qi-deficiency (12.4%) and Qi-stagnation (11.1%) was higher in MCI patients than in the controls with normal cognitive function (P<0.05). After adjusting for age, gender, and years of education, the probability of the old with Qi-deficiency and Qi-stagnation suffering from MCI was 1.559 times [95% confidence interval (CI): 1.009–2.407] and 1.706 times (95% CI: 1.078–2.700) higher than that of the older adults without Qi-deficiency and Qi-stagnation, respectively. In the Pittsburgh sleep quality index (PSQI) scale, individuals with MCI had poorer subjective sleep quality (Z=-3.404, P=0.001), longer sleep latency (Z=-3.398, P=0.001), shorter sleep duration (Z=-2.237, P=0.025), and aggravated daytime dysfunction (Z=-3.723, P<0.001) compared with those without MCI. The intergroup differences showed no statistical significance in the three dimensions including habitual sleep efficiency, sleep disturbance, and hypnotics between groups. The results of multi-factor logistical regression showed that sleep latency [odds ratio (OR)=1.168, 95% CI: 1.016–1.342], daytime dysfunction (OR=1.261, 95% CI: 1.087–1.463), and Qi-stagnation (OR=1.449, 95% CI: 1.022–2.055) were the risk factors for MCI; the OR for older adults with sleep disturbance and Qi-stagnation suffering from MCI was 2.581 (95% CI 1.706–3.907).Conclusion: MCI patients have a higher incidence of sleep disorders and Qi-stagnation, and may show specific changes in their daytime and nighttime sleep characteristics, with the specific manifestations such as difficulty in falling asleep, easily waking up at night/ early morning, and daytime dysfunction, among others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".