The Temporal Relationship Between Physical Activity, Mood, and Sleep in Older Adults: Lead‐Lag Discovery and Replication Analyses
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) affects about 416 million individuals across the disease continuum. An estimated 40% of dementia cases can be prevented or delayed in onset by addressing modifiable risk factors like sleep time, physical activity (PA), and mood. These three behaviors (sleep time, physical inactivity, and mood) are linked to cognitive decline, and their tridirectional link has been shown by prior research work. However, their longitudinal and potential temporal interrelatedness remain unclear. Method Thirty cognitively unimpaired (CU) sedentary older adults participating in the Daily Activity Study of Health (DASH) (Clinicaltrials.gov:NCT04315363) underwent objective PA measurement using a wrist‐worn accelerometer to monitor 24‐hour daily activity (discovery sample). Participants also completed a daily morning mood scale; through an Ecological Momentary Assessment and daily activity data, we performed a 10‐day lead‐lag analysis using moderate‐to‐vigorous PA (MVPA) time, mood, and sleep time. The temporal patterns were derived by assessing the rank order of the lead‐lag coefficients. The relationship between different time points of the variables were examined through cross‐correlations. We replicated the results in a sample of 25 CU, physically inactive older adults from the Healthy Aging Brain Study (HABS) (Clinical trials.gov:NCT06038643) with familial risk of AD. An identical methodological and analytic approach was applied to this replication sample. Result The lead‐lag analysis in the DASH cohort (mean age in years: 69.39±5.14, 26 female, mean education years 16.60 ± 3.03) determined positive mood change preceded increase in sleep time, and was then followed by an increase in MVPA. The relationship between positive mood change and increased MVPA was replicated in the HABS lead‐lag analysis (mean age = 69.71±3.54 years; 20 females; mean education years = 16.12±2.99). Conclusion In this study, we found that (1) improved mood precedes the increase in physical activity and sleep time in CU sedentary older adults. We confirmed that MVPA and mood precede sleep time. We also found that (2) mood precedes MVPA time, which in turn precedes sleep time. Understanding temporal dynamics between mood, sleep time, and MVPA, can help develop better interventions targeting an increase in physical inactivity in older adults, a major risk factor of AD.
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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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".