Do measures of physical capacity and walking self-efficacy relate to frailty in older adults with difficulty walking outdoors? A secondary data analysis
Bibliographic record
Abstract
Purpose Measures of physical capacity and self-efficacy may help rehabilitation professionals better understand and detect frailty in older adults. We aimed to characterize frailty, walking self-efficacy, physical capacity, and their inter-relationships in older adults with difficulty walking outdoors.Materials and methods A secondary analysis of baseline data from 187 older adults (age ≥ 65 years) with mobility limitations was performed. Frailty was evaluated using the cardiovascular health study frailty index. Physical capacity was measured using the 10-meter walk test (10mWT), 6-min walk test (6MWT), 30-second sit-to-stand test (30STST), mini balance evaluation systems test (mini-BESTest), and hand dynamometry. Self-efficacy was assessed using the ambulatory self-confidence questionnaire (ASCQ). We evaluated associations between scores on physical capacity and walking self-efficacy measures and frailty level using an ANOVA or the Kruskal Wallis H-test.Results The percentage of participants not frail, pre-frail, and frail was 33.7%, 57.2%, and 9.1%, respectively. Median score on the 10mWT-comfortable pace, 10mWT-fast pace, 6MWT, 30STST, mini-BESTest, grip strength, and ASCQ was 1.06 m/s, 1.42 m/s, 354.0 m, 9.0 repetitions, 22 points, 23.0 kg, and 8.1 points, respectively. Scores on physical capacity and walking self-efficacy measures were associated with frailty level (p < 0.01).Conclusions Findings provide insight into the utility of rehabilitation measures to indicate frailty among older adults with mobility limitations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".