Polypharmacy, physical activity, and sedentary time in older adults: A scoping review
Bibliographic record
Abstract
OBJECTIVE: To map out the studies that have investigated the associations of polypharmacy and/or potentially inappropriate medication (PIM) use with physical activity and sedentary time in older adults. METHODS: We conducted a literature search from inception to December 2022 in PubMed, Embase, Web of Science, and Scopus. INCLUSION CRITERIA: observational studies including older adults (≥60 years); English, Portuguese, and Spanish languages; any definition of polypharmacy; implicit and explicit criteria of PIM use; physical activity and/or sedentary time data. RESULTS: Fourteen cross-sectional studies were included; 11 defined polypharmacy as ≥5 medications (prevalence ranging from 9.5 % to 57 %). No study reported information on PIM use. Most studies included participants aged <80 years. Twelve studies included self-reported measures of physical activity, while two studies used accelerometer-measured physical activity. Ten studies included analyses adjusted for confounders, and nine considered polypharmacy as an outcome. All of them demonstrated an inverse association between physical activity and polypharmacy, irrespective of the definition of polypharmacy and the assessment method employed (self-reported or accelerometry). One study reported an inverse association between polypharmacy (as the exposure) and physical activity (as the outcome). None of the studies investigated the association between sedentary time and polypharmacy. CONCLUSIONS: Limited evidence suggests an inverse association between physical activity and polypharmacy in older adults. However, the relationship between PIM use, physical activity, and sedentary time remains unknown. Longitudinal studies utilizing objectively-measured physical activity and sedentary time are needed to better clarify the relationship between these movement behaviors and polypharmacy and/or PIM use in 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".