What are relevant predictors of physical activity in older adults with lower limb loss (LLL)? Results of a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: People with lower limb loss (LLL) have reduced physical activity (PA). There is evidence of physical and psychosocial predictors of PA in older adults with limb loss. However, these 2 areas (physical/psychosocial) have not been evaluated in the same analysis. OBJECTIVES: To describe and identify predictors of PA in individuals with LLL. STUDY DESIGN: Cross-sectional study. METHODS: Secondary analysis of data from a multisite Canadian randomized control trial involving community-dwelling prosthetic ambulators with unilateral transtibial or transfemoral amputation (N = 72). The dependent variable was the Physical Activity Scale for the Elderly. Potential predictors were four step square test, 2-minute walk test, Short Physical Performance Battery, Life Space Assessment, walking while talking test, and Activities-specific Balance Confidence scale. RESULTS: Seventy-two community-dwelling lower limb prosthesis users were enrolled. The sample included 62 male participants (86%), and 58 participants (81%) had transtibial amputation. The average age of participants was 65 (8.9) years, and for 49 participants (70%), the amputation was over 24 months ago. The total mean Standard Deviation (SD) Physical Activity Scale for the Elderly score was 153.2 (88.3), with scores of 148.1 (11.4) and 184.5 (24.7) for male and female participants, respectively. Regression analysis identified Life Space Assessment (β = 1.15, p = 0.007) and Short Physical Performance Battery (β = 3.51, p = 0.043) as statistically significant predictors accounting for 25% of the variance in PA. CONCLUSIONS: Community mobility and physical performance are the most meaningful predictors of PA. Future research should examine additional factors (e.g., environment, motivation). Understanding the predictors for PA after LLL would improve clinical practice as clinicians would have increased knowledge to modify and improve training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".