A Novel Gamified Exercise Program Incorporating Stampede Training for Enhancing Functional Fitness, Physical Activity Levels, and Quality of Life in Community-Dwelling Older Adults: Randomized Parallel Exploratory Trial
Bibliographic record
Abstract
Background: With the growing prevalence of aging populations, improving the health and quality of life of older adults has become a critical concern globally. In this context, sports technology presents promising applications. The exergame-based training mat-an electronic exercise technology-integrates gamification with diverse training designs, offering a safe and engaging approach to promoting health and well-being in older adults. Objective: This study aims to examine the impact of a novel exergame-based mat training program on community-dwelling older adults, particularly evaluating its effectiveness in enhancing physical activity levels, quality of life, and functional fitness. The primary end point was exploratory, focusing on the feasibility and effectiveness of the exergame-based training program on physical fitness, physical activity, and quality of life. Methods: This randomized parallel-designed study enrolled 32 older adults aged 60-80 years from Taipei City. Participants were randomly assigned to either the experimental or control group. The experimental group underwent a 10-week exergame-based mat training program, consisting of 2 sessions per week (70 minutes per session), using gamified group-based exercise training. The control group maintained their usual daily activities. Pre- and postintervention assessments were conducted using the International Physical Activity Questionnaire (IPAQ), the World Health Organization Quality of Life Brief Version (WHOQOL-BREF), the Senior Fitness Test, and the AFAscan fitness assessment. Results: The experimental group demonstrated significantly increased overall and high-intensity physical activity levels (P=.04; mean difference [MD] 439, 95% CI 28-914; d=0.72). Quality of life significantly enhanced across the physical (P=.01; r=0.53), psychological (P=.02; r=0.52), and social (P=.02; r=0.50) domains of the WHOQOL-BREF. Furthermore, functional fitness parameters, including upper limb muscular strength (P=.007; MD 5.33, 95% CI 1.59-9.07; d=1.06), lower limb muscular strength (P=.01; MD 4.73, 95% CI 1.15-8.32; d=0.98), core strength (P<.001; MD 13.1, 95% CI 7.90-18.2; d=1.89), lower limb flexibility (P=.008; MD 6.47, 95% CI 1.82-11.1; d=1.04), dynamic balance (P=.03; MD -0.72, 95% CI -1.36 to -0.07; d=0.84), static balance (P=.005; MD 15.1, 95% CI 5.01-25.3; d=1.12), agility (P=.001; MD 32.6, 95% CI 15.6-49.6; d=1.44), and cardiorespiratory endurance (P=.04; MD 9.73, 95% CI 0.37-19.1; d=0.78), showed significant enhancements with the exergame-based mat training program. There were no adverse events observed during the study. Conclusions: In this exploratory trial, the exergame-based mat training program produced medium-to-large improvements (Cohen d ranging from 0.72 to 1.89) across physical activity, quality of life, and functional fitness domains. Although the precision of the CIs varied, the consistent direction of effects supports a meaningful impact of the intervention. These findings suggest that exergame-based mat training programs may serve as a practical community health promotion strategy, potentially preventing age-related frailty and enhancing independence and well-being among older adults.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".