The Impact of Sleep Quality on Subjective Wellbeing Among Older Adults With Multimorbidity: A Moderated Mediation Model
Bibliographic record
Abstract
Background Studies have found that poor sleep quality is negatively associated with subjective wellbeing in older adults, but the mechanisms underlying are unclear. In this study, we aimed to examine the mediating role of negative emotions and the moderating role of perceived social support in the relationship between sleep quality and subjective wellbeing in older adults with multimorbidity. Methods A multi-stage random sampling method was used to select a sample of 3,266 older adults aged 60 years and older. The Memorial University of Newfoundland Scale of Happiness (MUNSH), Pittsburgh Sleep Quality Index (PSQI), Depression Anxiety Stress Scales-21 (DASS-21), and Perceived Social Support Scale (PSSS) were used to assess subjective wellbeing, sleep quality, negative emotional states, and perceived social support, respectively. The moderated mediation models were examined using SPSS PROCESS Version 3.3 software. Results Sleep quality had a significant direct effect on subjective wellbeing in older adults ( β = −0.997, t = −11.783, p < 0.001). Negative emotions partially mediated the effect of sleep quality on subjective wellbeing ( ab = −0.608, 95%CI: −0.728, −0.497). The indirect effect was moderated by perceived social support ( β = −0.038, 95% CI : −0.062, −0.014, p < 0.001; β = −0.002, 95% CI : −0.004, −0.01, p = 0.008). Conclusion Negative emotions increased the negative association between sleep quality and the subjective wellbeing of older adults with multimorbidity, and perceived social support played a moderating role. Psychological and behavioral interventions should be implemented as early as possible to promote mental health and enhance social support level of older adults with multimorbidity, and ultimately improve the subjective wellbeing of older adults.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".