Fit-Frailty App as an Evaluative Measure in Rehabilitation
Bibliographic record
Abstract
Introduction: Frailty is a common indicator of health, but more evidence is needed to establish its use as an evaluative measure. Understanding frailty responsiveness is important when selecting tools to monitor treatment effect. This pre-post observational study investigated the feasibility of measuring meaningful change in frailty following rehabilitation using the Fit-Frailty App, a validated and interactive mobile health application. Methods: The Fit-Frailty App multidimensional assessment considers medical history, physical performance, cognition, nutrition, daily function, and psychosocial domains to calculate a total frailty index score. We recruited a convenience sample of 52 adults age 65+ admitted to a slow stream rehabilitation unit in Hamilton, Ontario. Research assistants administered the App within 3 days after admission (baseline 1), after initial assessment (baseline 2), and before discharge (follow-up). Feasibility outcomes (e.g., recruitment, safety, acceptability) were evaluated based on Thabane et al.’s framework. Baseline 1 and follow-up scores were compared using paired t-tests with ≥0.03 change considered clinically meaningful and p≤0.007 statistically significant. An ICC and 95%CI were calculated to evaluate test-retest reliability between baseline 1 and 2 scores. Results: Between Mar-Sep 2024, n=125 were admitted, n=62 were screened, and n=52 consented (58% female, mean age 80.1, SD=8.9 years). Mean Fit-Frailty scores were 0.45 (SD=0.08) at baseline 1 (n=52), 0.44 (SD=0.09) at baseline 2 (n=50), and 0.35 (SD=0.11) at follow-up (n=40). All feasibility criteria were met except retention rate (<80%). There was clinically meaningful mean improvement between baseline 1 and follow-up scores of 0.10 (95%CI=0.07-0.12, p<0.001) over mean 55.7 (SD=24.9) days length of stay. The App demonstrated good-to-excellent reliability over mean 3.7 (SD=1.4) days (ICC=0.896, 95%CI=0.823-0.939). Conclusions: The Fit-Frailty App was feasibly implemented on a slow stream rehabilitation unit with good-to-excellent short-term test-retest reliability and large long-term responsiveness. The Fit-Frailty App results summary of frailty contributors can support intervention and discharge planning.
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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 | 0.001 |
| 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) | 0.040 | 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".