Self-directed usage of an in-home exergame after a supervised telerehabilitation training program for older adults with lower-limb amputation
Bibliographic record
Abstract
Background: While home-based exergames help overcome accessibility barriers to rehabilitation, it is unclear what constitutes effective intervention design in using exergames to support self-efficacy and engagement. Objective: Examine usage of an in-home exergame, compared to control, unsupervised after supervised training by older persons with lower-limb amputation. Study design: Secondary analysis of a multi-site parallel evaluator-masked randomized control trial. Methods: WiiNWalk uses the WiiFit and teleconferencing for in-home group-based exergame therapy with clinical supervision. Participants engaged in a 4-week supervised training phase followed by a 4-week unsupervised phase in experimental (WiiNWalk) and attention control groups. Usage between phases and between groups was compared using unsupervised/supervised ratio of session count (over 4 weeks) and session time (mean min/session over 4 weeks) for each phase. Results: Participants: n=36 experimental, n=28 control, unilateral lower-limb amputation, age > 50 years, prosthesis usage ≥ 2 hours/day. Session count ratio unsupervised/supervised, median and interquartile range (IQR), was less than parity (p<0.01) for experimental (0.25, IQR 0.00 -0.68) and control (0.18, IQR 0.00 -0.67) groups, with no different between groups (p=0.92). Experimental session time unsupervised/supervised showed consistency (1.12, IQR 0.80 -1.41) between phases (p=0.24); control showed lower (0.76, IQR 0.57 -1.08) ratios compared to experimental (p=0.027). Conclusions: Unsupervised exercise duration remained consistent with supervised, but frequency was reduced. Social and clinical guidance features may remain necessary for sustained lower-limb amputation exergame engagement at home. Clinical relevance This study provides context regarding when prosthesis users are more likely to use exergames such as Wii Fit for exercise therapy. Clinicians may consider our results when applying exergames in their practice or when developing new exergame intervention strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".