The temporal relationship between physical activity, mood, and sleep in older adults via a lead‐lag analysis
Bibliographic record
Abstract
Abstract Background Physical Activity, sleep, and mood are key modifiable lifestyle behaviours that are individually associated with lower cognitive decline [1]. Prior research suggests there may be a tridirectionally relationship among these three behaviours. The temporal relationship among these behaviours is unknown. Using an Ecological Momentary Assessment (ECA) and a time series analysis we aim to understand the temporal and longitudinal relationship among these factors. Method We performed a 10‐day lead‐lag analysis using Moderate‐to‐Vigorous Physical Activity (MVPA) and sleep as measured by a 24‐hour wrist‐worn accelerometer, and a self‐assessed daily mood scale on 30 community dwelling older adults who participated in a behavioural study to enhance physical activity in sedentary individuals (Clinicaltrials.gov identifier: NCT04315363). Participants were included if they did not exercise more than 150 minutes of MVPA per week (International Physical Activity Questionnaire) and were sitting more than 8 hours per day (Marshall Sitting Questionnaire). We examine each variable separately for auto‐correlative relationships for different timepoints of the same variable. The temporal dynamics are calculated from the rank‐order of the lead‐lag coefficients. The relationships between different timepoints of mood, MVPA, and sleep are examined through cross‐correlations. Results Autocorrelations showed that mood presented seasonality with baseline mood positively correlated to the subsequent day’s mood (r = +.571, p = .037) and negatively correlated with mood at day 7 (r = ‐.330, p = 0.27) and day 8 (r = ‐.248, p = .013). The temporal dynamics identified mood (B = +.602), preceding MVPA time (B = ‐.001), and sleep time (B = ‐.276) (Figure 1). Furthermore, cross‐correlations demonstrated that mood was positively correlated to MVPA at the same time point (r = ‐.605) and also that previous day mood predicted next day’s MVPA (r = ‐.629). Cross‐correlations confirmed that MVPA (r = ‐.507) and mood (r = ‐.504) predicted next day’s sleep time. Conclusion Improved mood precedes the increase in MVPA and sleep time in sedentary older adults. Understanding temporal dynamics between mood, sleep time, and MVPA, can help develop better interventions targeting increase in physical activity in older adults. Reference: [1] 2020 Report of the Lancet Commission. https://doi.org/10.1016/S0140‐6736(20)30367‐6.
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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 | 0.002 | 0.004 |
| 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".