The effects of weather on physical activity and sedentary behaviour in older adults
Bibliographic record
Abstract
Background: Many older adults do not meet current physical activity (PA) guidelines, and this might be influenced by environmental factors (e.g., temperature). The purpose of this study was to determine how various weather variables influence light PA (LPA), moderate-vigorous PA (MVPA), and sedentary behaviour (SB), steps, and time spent walking outdoors. Methods: Fifty community-dwelling older adults completed the 10-day assessment period, using a Fitbit fitness tracker, Global Position System (GPS), and completing self-report questionnaires. Forty participants were included in the analysis. LPA, MVPA, and SB were calculated using heart rate and multilevel models were used to assess their association with weather variables. Results: < 0.01). Increased age was associated with less time spent engaging in LPA (total and outdoors), reduced steps, increased time spent sedentary, and less time spent walking outdoors. LPA outdoors was positively associated with mean temperature. Freeze thaw cycles were associated with time spent walking outdoors. Females spent more time in total LPA and LPA indoors, and as age increased total LPA and LPA outdoors decreased. Conclusions: Older adults located in a dry (∼690 mm precipitation annually) region with warm winters and hot summers (-5°C-30°C) seemingly choose indoor activities. Females tend to participate in indoor LPA, and as age increases the time spent in outdoor LPA decreases. Implications: Consideration needs to be given to designing indoor PA interventions, with a focus on increasing MVPA for older adults.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".