The Usability and Effect of a Novel Intelligent Rehabilitation Exergame System on Quality of Life in Frail Older Adults: Prospective Cohort Study
Bibliographic record
Abstract
Background: Aging in older adults results in a decline in physical function and quality of daily life. Due to the COVID-19 pandemic, the exercise frequency among older adults decreased, further contributing to frailty. Traditional rehabilitation using repetitive movements tends not to attract older adults to perform independently. Objective: Intelligent Rehabilitation Exergame System (IRES), a novel retro interactive exergame that incorporates real-time surface electromyography, was developed and evaluated. Methods: Frail older adults were invited to use the IRES for rehabilitation using lower limb training twice per week for 4 weeks. Participants were required to have no mobility or communication difficulties and be willing to complete the 4-week study. The enrolled cohort had baseline scores ranging from 1 to 5 on the Clinical Frailty Scale, as described by Rockwood et al. Three major lower limb movements (knee extension, plantar flexion, and dorsiflexion) were performed 20 times for each leg within 30 minutes. The surface electromyography collected and analyzed muscle potential signals for review by health care professionals to customize the protocol for the next training. The System Usability Scale (SUS) and Taiwanese version of the EuroQol-5 Dimensions (EQ-5D) were administered after completing the first (week 1, baseline) and last training (week 4, one-month follow-up) to evaluate the usability of the IRES and its effects on the quality of life of participants. Results: A total of 49 frail older adults (mean age 74.6 years) were included in the analysis. The usability of the IRES improved according to the mean SUS score, from 82.09 (good) at baseline to 87.14 (good+) at 1-month follow-up. The willingness to use (t96=-4.51; P<.001), learnability (t96=-4.83; P<.001), and confidence (t96=-2.27; P=.02) in working with the IRES increased. After using the IRES for 1 month, significant improvements were observed in the ease of use (t47=2.05; P=.04) and confidence (t47=2.68; P<.001) among participants without previous rehabilitation experience. No sex-based differences in the SUS scores were found at baseline or 1-month follow-up. The quality of life, as assessed by the EQ-5D, improved significantly after 1 month of IRES training compared to that at baseline (t96=6.03; P<.001). Conclusions: The novel IRES proposed in this study received positive feedback from frail older adults. Integrating retro-style exergame training into rehabilitation not only improved their rehabilitation motivation but also increased their learning, system operability, and willingness to continue rehabilitation. The IRES provides an essential tool for the new eHealth care service in the post-COVID-19 era.
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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 | 0.002 | 0.003 |
| 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) | 0.000 | 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".