Malnutrition, Depression, Poor Sleep Quality, and Difficulty Falling Asleep at Night Are Associated with a Higher Risk of Cognitive Frailty in Older Adults during the COVID-19 Restrictions
Bibliographic record
Abstract
The COVID-19 restrictions, such as social isolation and disruption of daily routines, can have detrimental effects, including increased stress, anxiety, sleep disturbance, and physical and cognitive decline among older adults. This study aimed to examine the association between nutritional status, depression, sleep quality, falling asleep at night, and cognitive frailty (CF) among older Thai adults during the COVID-19 pandemic. This cross-sectional study included 408 older adults with an average age of 70.54 (5.49) years. CF was determined using Fried's frailty phenotype and the Montreal Cognitive Assessment Basic. The Mini Nutritional Assessment-Short Form, Pittsburgh Sleep Quality Index, and geriatric depression assessment were used for assessment. Multiple logistic regression analysis demonstrated that participants who were malnourished (OR 3.786; 95%CI 1.719-8.335), depressed (OR 5.003; 95%CI 2.399-10.434), had poor sleep quality (OR 1.613; 95%CI 1.041-2.500), and engaged in difficulty falling asleep (OR 1.809; 95%CI 1.022-3.203) had a higher risk of CF compared to those who did not exhibit these factors. Therefore, malnutrition, depression, poor sleep quality, and difficulty falling asleep were identified as risk factors for CF among older adults in Thailand linked to the impact of the COVID-19 pandemic. It is crucial to develop interventions to prevent CF resulting from the mentioned variables.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".