Effects of Balance-Based Exergame Training With Variable Difficulty on Balance and Spatiotemporal Gait Outcomes in Adults With Mild Cognitive Impairment: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Exergame balance training integrates cognitive and motor challenges, potentially enhancing neuroplasticity, postural control, and gait stability in mild cognitive impairment (MCI). However, whether modulating the task difficulty of a balance-based exergame training may influence posture- and gait-related outcomes remains unknown. OBJECTIVE: We compared balance and gait improvements across exergame training groups performing exercise with different difficulty levels and a Wii Fit group in adults with MCI. METHODS: This 4-armed, parallel design, double-blinded, randomized clinical trial included 97 participants with MCI (Montreal Cognitive Assessment score=18-25). Participants were convenience-sampled from the Railway General Hospital, Rawalpindi, Pakistan, and randomized to one of 4 intervention groups: low-difficulty, moderate-difficulty, high-difficulty exergame training or a Wii Fit training group. Each participant completed 24 sessions (40 min, 3/week) supervised by physical therapists. Gait and balance were assessed using time up and go (TUG), cognitive time up and go (C-TUG), and the Gait & Balance mobile app at baseline and after 4 and 8 weeks. Although the calculated sample size was 80, 97 were recruited to offset attrition. Eighty-seven participants completed the study (94% adherence) (attrition: low-difficulty 1, moderate 3, high 2, Wii Fit 2; 10% total). Data were analyzed using mixed-model analysis of covariance with baseline values as covariates to assess time×group interactions. Bonferroni-adjusted post hoc comparisons revealed between-group differences. RESULTS: High-difficulty training showed the greatest TUG gains (-0.71, SD 0.32; P=.03, anteroposterior (AP) steadiness with eyes open (EO) on firm surface (0.04, SD 0.02; P=.04), step time variability head forward (HF; 0.06, SD 0.09; P=.02), walking speed HF (0.08, SD 0.04; P=.05), step time head turn (HT; -0.04, SD 0.02; P=.04), step time variability HT (-0.35, SD 0.09; P<.001), step length variability (-0.27, SD 0.13; P=.04), and walking speed HT (0.09, SD 0.04; P=.01) versus Wii Fit. Moderate-difficulty training improved AP steadiness EO firm (0.05, SD 0.02; P=.03) and reduced step time variability HT (-0.26, SD 0.09; P=.01). Low-difficulty training improved C-TUG (-1.61, SD 0.63; P=.01), AP steadiness EO firm (0.05, SD 0.02; P=.03), step time variability HF (-0.20, SD 0.09; P=.03), step time variability HT (-0.25, SD 0.09; P=.01), step length variability (-0.31, SD 0.12; P=.014), and walking speed HT (0.11, SD 0.04; P=.03). No significant differences observed between exergame difficulty groups (P>.05). CONCLUSIONS: Balance-based exergame training improves balance and gait in adults with MCI, with no significant differences across difficulty levels, while the high- and low-difficulty training outperformed Wii Fit in several outcomes. High-difficulty training yielded the most consistent improvements in TUG, postural steadiness, gait variability, and walking speed. These results support graded cognitive-motor exergaming as an effective strategy for enhancing postural control and walking stability in MCI, potentially aiding fall prevention and mobility preservation in aging populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04959383; https://clinicaltrials.gov/study/NCT04959383.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".