Latent profile analysis of sleep characteristics of patients with mild cognitive impairment and influencing factors
Bibliographic record
Abstract
ObjectiveTo identify potential sleep typing in MCI patients and to explore the characteristics and influencing factors of different sleep typing.MethodsFrom March to December in 2023，community⁃based MCI older adults in Nanjing，Jiangsu Province，China，were enrolled in the study using convenience sampling method.Neuropsychological tests were conducted using the General Information Questionnaire，Montreal Cognitive Assessment Scale（MoCA），Brief Mental State Exercise（MMSE），Ability to Perform Daily Living（ADL），and Clinical Dementia Rating Scale（CDR），and their sleep conditions were assessed and monitored using the Pittsburgh Sleep Quality Index（PSQI） and ActiGraphy.Potential sleep subtypes of MCI patients were identified by latent profile analysis based on objective sleep characteristics monitored by the somatic ActiGraphy as observational variables.Univariate analysis and unordered multicategorical Logistic regression analysis were used to explore the characteristics and influencing factors of different sleep typologies.ResultsA total of 217 MCI patients were recruited in this study，and the results of the potential profiling analysis showed that the sleep of MCI patients could be categorized into four potential categories：sleep deprivation⁃ineffective（31.3%），good sleep（30.0%），sleep fragmentation（27.6%） and difficulty falling asleep（11.1%）.Significant differences were found in gender，family history of dementia，smoking，re⁃employment after retirement，social activities，physical activity，hypertension，and FRAIL and MoCA scores among MCI patients with different sleep typologies（P<0.05）.The results of unordered multicategorical Logistic regression analysis，The results of multicategorical Logistic regression analysis showed that gender，smoking，re⁃employment after retirement，frailty status，and cognitive function were the influencing factors of sleep typing in MCI patients.ConclusionMCI patients' sleep is characterized by significant group heterogeneity.Healthcare professionals should conduct a comprehensive sleep assessment of MCI patients，identify their sleep typing，and adopt targeted sleep management and intervention strategies for different sleep typing in order to improve the sleep status of MCI patients and delay the progression of cognitive decline.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".