Poor Sleep Quality and Mental Health in Aging
Bibliographic record
Abstract
Introduction \nThe significant aging of the world's population is a major health problem, so public health strategies focus on the multitude of symptoms that characterize aging, with particular attention to mental and physical health. Previous research has indicated that sleep problems tend to coexist with mental health problems later in life but is relatively little known about the relationships between sleep quality and mental health. \nAims \nThe study aims (a) to examine whether self-reported sleep quality and mental health in individuals with altered sleep were associated with aging; (b) to highlight whether age contributes to this relationship, highlighting different associative patterns. \nMethod \nA total of 143 participants (48 elderly, 70.3±5.6 years; 52 middle-age, 54.3±6.3 years; 43 young adults, 26±4.6 years) with poor sleep quality were selected. Poor sleep quality was defined by a score greater than 5 on the Pittsburgh Sleep Quality Index (PSQI). After the anamnestic data collection, all participants filled in some questionnaires to evaluate depression (Beck Depression Inventory, BDI), alexithymia (Twenty-Items Toronto Alexithymia Scale, TAS-20), trait anxiety (State-Trait Anxiety Inventory, STAI). \nResults \nANOVA comparing Young adults, Middle age adults, and the Elderly with poor sleep quality revealed that the elderly presented higher poor sleep than Young adults (p< .03) and used more sleep medications than both Young adults and Middle age adults (p< .001). Regression analyses revealed that in young adults, medication use is mainly predicted by depression (R2= .36; p< .03) while in older adults by poor sleep quality (R2= .31; p< .03). \nConclusions \nDifferent predictive patterns can be observed between young and older adults. These results could be useful for interventions aimed at improving sleep throughout the lifespan. \nThe present findings are important because previous studies focused on transitions from good to poor sleep quality, whereas no study has identified the phenomena that characterize full-blown poor sleep quality.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".