Feasibility of the Nintendo WiiFit™ for improving walking in individuals with a lower limb amputation
Bibliographic record
Abstract
OBJECTIVES: To evaluate the feasibility of the Nintendo WiiFit™ as an adjunct to usual therapy in individuals with a lower limb amputation. METHODS: The study was a Multiple Baseline (AB) Single Subject Research Design. Subjects were ≥19 years old, had their first unilateral transtibial or transfemoral amputation ≤12 months ago, and were participating in prosthetic training. WiiFit training was provided for 30 min, 5 times a week, for a minimum of 2 and a maximum of 6 weeks in addition to usual therapy. Feasibility indicators were safety, post-intervention fatigue and pain levels, adherence, and subject's acceptability of the program as measured by the Short Feedback Questionnaire-modified (SFQ-M). The primary clinical outcome was walking capacity assessed by the 2 Minute Walk Test (2MWT). The secondary clinical outcomes were the Short Physical Performance Battery, L-test, and Activities-Specific Balance Confidence. RESULTS: Subjects (4 transtibial; 2 transfemoral) had a median age of 48.5 years (range = 45-59 years). No adverse events associated with the intervention occurred. Median pain and fatigue levels were 1.3 (range = 0.5-3.5) and 3.1 (range = 1.4-4.1), respectively. Median adherence was 80%. Subjects found the WiiFit enjoyable and acceptable (median SFQ-M = 35). Five subjects showed statistical improvement on the 2MWT and four on the secondary outcomes (p < 0.05). CONCLUSION: The WiiFit intervention was found to be feasible in individuals with unilateral lower limb amputation. This research provides the foundation for future clinical research investigating the use of the WiiFit as a viable adjunctive therapy to improve outcomes in individuals with unilateral lower limb amputation who are participating in prosthetic training.
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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 | 0.001 |
| 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) | 0.002 | 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".