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Record W4391025414 · doi:10.2196/preprints.56377

Feasibility and Usability Test of a Developed Cognitive Training System Based on Smart Mirror: Targeting Community-Dwelling Patients with Chronic Stroke (Preprint)

2024· preprint· en· W4391025414 on OpenAlexaboutno aff
Bora Kang, Young-Hyeon Bae, Seong-Hun Park, Hye-Yun Kang

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionCognitive trainingUsabilityPsychologyMontreal Cognitive AssessmentStroke (engine)Physical medicine and rehabilitationMedicineComputer scienceHuman–computer interactionCognitive impairmentEngineeringPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND Cognitive impairment is a prevalent consequence among individuals following a stroke. Numerous stroke survivors reintegrating into the community experience cognitive challenges that restrict their engagement, subsequently contributing to additional cognitive decline and adversely affecting their quality of life. 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. OBJECTIVE The cognitive training system based on smart mirror developed in this study caters specifically to older adults, incorporating training modules for orientation, memory, attention, visual perception, and high cognition. It also integrates kiosk-based activities relevant to daily life and dual-task training, creating a VR environment to stimulate cognitive functions in both community-dwelling older individuals and stroke patients. Thus, the primary objective of this study is to assess the feasibility and usability of the developed cognitive training system based on smart mirror, confirming its utility, acceptability, and consistency as a consumer-centric system leveraging ICT technology. METHODS 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 (MoCA-K) 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 post-cognitive function results. RESULTS 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 MoCA-K (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. CONCLUSIONS 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. CLINICALTRIAL -

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.047
GPT teacher head0.298
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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