Physical mobility determinants among older adults: a scoping review of self-reported and performance-based measures
Bibliographic record
Abstract
Objective To synthesise the available evidence on physical factors, such as muscle strength and power, body mass index and their association with older adults’ self-reported and performance-based mobility outcomes.Method This review followed the Askey and O'Malley framework. We systematically searched PubMed, EMBASE, PsychINFO, Web of Science, AgeLine, Allied and Complementary Medicine Database, and Cumulative Index to Nursing and Allied Health Literature databases, from Jan. 2000 to Jan. 2022. Teams of two reviewers independently conducted title, abstract, full-text screening, and data extraction using predefined inclusion and exclusion criteria.Result A total of 239 quantitative articles, mostly cross-sectional design, conducted in 32 countries were included in this review. We identified 18 physical factors significantly associated with mobility outcomes in the expected direction. Muscle strength, body composition, falls (number and history of), and chronic conditions (number of and type) were the most studied physical factors.Conclusion Older adults with muscle weakness, weight concerns, history of falls, and chronic conditions had poorer mobility outcomes, such as slower gait speed, poor balance, limited community mobility and poor driving outcomes compared to their counterparts. Studies exploring the role of physical factors on the use of an assisted device, transportation, or driving, are limited.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".