Sleep quality and associated factors of patients with mild cognitive impairment at King Chulalongkorn Memorial Hospital (P. 3)
Bibliographic record
Abstract
Background: Thailand has shifted to an aging society.Sleep disturbance is a common problem in the elderly which affects emotion and cognitive levels.Awareness of sleep problems can help implementing elderly care and preventing dementia in the future.Objective: To study sleep quality and determine factors associated with sleep quality among patients with mild cognitive impairment (MCI).Methods: The cross-sectional descriptive study was conducted in MCI patients, aged 50 years and above, from a psychiatric outpatient clinic and cognitive fitness center.The data were collected by questionnaires including demographic data and sleep environment questionnaires; Pittsburgh Sleep Quality Index (PSQI); Sleep Hygiene Index (SHI); STOP-Bang questionnaire; Thai Mental State Examination (TMSE); Montreal Cognitive Assessment (MoCA); Thai Geriatric Depression Scale (TGDS), and Neuropsychiatric Inventory Questionnaire (NPI-Q).The sleep quality was presented as frequency and percentage.The associated factors were analyzed by Chi-square test, Fisher's exact test, and Pearson's correlation coefficient.The predictors of poor sleep quality were analyzed by multiple logistic regression analysis.Results: Of the 100 subjects, 65 were female with a mean age of 71.3 7.5 years old: 64% of them had poor sleep quality.The associated factors of sleep quality were having a history of psychiatric disorders, use of sedating psychotropic drugs, low to moderate sleep hygiene, and anxiety domain of neuropsychiatric symptoms.TGDS and STOP-Bang scores were correlated with PSQI scores (r = 0.215 and 0.230, respectively).The predictors of poor sleep quality were the use of sedating psychotropic drugs (P < 0.01), low to moderate sleep hygiene (P < 0.05), and anxiety domain of neuropsychiatric symptoms (P < 0.05). Conclusion:The prevalence of poor sleep quality in MCI patients was 64%.The associated factors and predicted factors of poor sleep quality were the use of sedating psychotropic drugs, low to moderate sleep hygiene, and anxiety domain of neuropsychiatric symptoms.Therefore, sleep quality should be screened in patients with MCI due to the high prevalence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".