Self-Report Tools for Assessing Physical Activity in Community-Living Older Adults with Multiple Chronic Conditions: A Systematic Review of Psychometric Properties and Feasibility
Bibliographic record
Abstract
Identifier l'outil d'autodéclaration de l'activité physique (AP) le plus adapté pour l'évaluation de l'AP chez les personnes âgées vivant dans la communauté qui sont atteintes de multiples maladies chroniques (MMC). L'AP peut avoir une influence positive sur la santé physique et psychologique de cette population. Bien qu'il existe des outils d'auto-évaluation de l'AP, les propriétés psychométriques et la faisabilité de l'utilisation de ces outils chez les personnes âgées avec MMC sont peu connues. Une revue systématique des études publiées entre 2000 et 2018 portant sur les propriétés psychométriques et la faisabilité de 18 outils d'auto-évaluation élaborés pour les personnes âgées vivant en communauté (≥ 65 ans) a été réalisée en vue de déterminer leur pertinence pour les personnes âgées atteintes de MMC. L'évaluation des données disponibles sur les propriétés psychométriques et la faisabilité des 18 outils d'auto-évaluation de l'activité physique a permis d'établir que l'Échelle d'évaluation de l'activité physique chez les personnes âgées (Physical Activity Assessment Scale for the Elderly; PASE) est l'outil d'auto-évaluation le plus adapté, qui devrait être recommandé pour la population de personnes âgées avec MMC. The purpose of this study was to identify the self-report physical activity (PA) tool best suited for assessment of PA in community-dwelling older adults with multiple chronic conditions (MCC). PA can positively influence physical and psychological health in this population. Although self-report PA tools exist, little is known about the psychometric properties and feasibility of using these tools in older adults with MCC. A systematic literature review from 2000 to 2018 was conducted of studies reporting on the psychometric properties and feasibility of 18 self-report PA tools for community-dwelling older adults (≥ 65 years) to determine the suitability of these tools for use in older adults with MCC. Based on an assessment of the available evidence for the psychometric properties and feasibility of 18 different self-report PA tools, the Physical Activity Assessment Scale for the Elderly (PASE) is recommended as the best-suited self-report PA tool for older adults with MCC.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".