Feasibility and Usability Test of a Developed Cognitive Training System Based on Smart Mirror: Targeting Community-Dwelling Patients with Chronic Stroke
Bibliographic record
Abstract
The feasibility of the implemented cognitive training system based on smart mirror revealed significant differences in the total score, delayed recall, and orientation items of the MoCAK (P<.05). Additionally, a notable improvement was observed in the accuracy and response time of task performance in the CoSAS (P<.05). Usability test results indicated an SUS mean score of 73.5 (SD 17.25) and an Adapted IMI score of 5.63 (SD 1.55), surpassing suggested thresholds for usability tests. Ten chronic stroke patients aged 60 years or older, each with independent mobility in the community, were involved in this study. The validation process included a 30-minute cognitive training session administered twice a week for eight weeks. The training regimen encompassed a blend of targeted cognitive domain exercises and virtual reality training. The feasibility of cognitive function assessments employed the Korean version of the Montreal Cognitive Assessment (MoCAK) and the Cognitive Assessment System for the Elderly (CoSAS). Pre- and post-test results were compared. Additionally, usability test was performed at the end of the experiment using the System Usability Scale (SUS) and the Adapted Intrinsic Motivation Inventory (IMI). Statistical analyses for cognitive function involved calculating mean and standard deviation values for all variables using the SPSS program. The Wilcoxon signed rank test was then employed to compare pre- and postcognitive function result This study seeks to feasibility a cognitive training system based on smart mirror designed for chronic stroke patients residing in the community and usability test of the associated training equipment. The outcomes of the feasibility and usability test affirm the utility, safety, and motivational aspects of the developed cognitive training system based on smart mirror. Consequently, we advocate for further validation of the training system’s efficacy and usability through clinical trials, specifically targeting groups aiming to enhance cognitive function. Future research should encompass usability and clinical validation of the product across diverse populations, including not only chronic stroke patients but also older adults in the community and children with cognitive impairment. It should incorporate a larger sample size and include a control group. Additionally, research efforts should be directed toward substantiating the effectiveness of cognitive training content and developing tailored programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".