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Record W4387383763 · doi:10.2196/48845

A Newly Developed Exergame-Based Telerehabilitation System for Older Adults: Usability and Technology Acceptance Study

2023· article· en· W4387383763 on OpenAlexvenueno aff
Julia Seinsche, Eling D. de Bruin, Enrico Saibene, Francesco Rizzo, Ilaria Carpinella, Maurizio Ferrarin, Sotiria Moza, T.A. Ritter, Eleftheria Giannouli

Bibliographic record

VenueJMIR Human Factors · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsnot available
FundersResearch and Innovation FoundationInnosuisse - Schweizerische Agentur für InnovationsförderungMinistero della SaluteEidgenössische Technische Hochschule ZürichEuropean Commission
KeywordsUsabilityTelerehabilitationFocus groupApplied psychologyTechnology acceptance modelSystem usability scalePsychologyMedical educationMedicineTelemedicineHealth careHeuristic evaluationComputer scienceHuman–computer interaction

Abstract

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BACKGROUND: Telerehabilitation has gained significance as a tool to deliver and supervise therapy and training as effective as traditional rehabilitation methods yet more accessible and affordable. An exergame-based telerehabilitation system has recently been developed within the scope of the international Continuum-of-Care (COCARE) project. The system comprises training devices for use in clinics (Dividat Senso) and at home (Dividat Senso Flex), an assessment system, and a rehabilitation cockpit, and its focus lies on home-based motor-cognitive training, which is remotely managed by health care professionals (HPs). OBJECTIVE: This study aims to analyze the usability, acceptance, and enjoyment of the COCARE system from the perspective of primary (older adults [OAs]) and secondary (HPs) end users. METHODS: At 3 trial sites (located in Switzerland, Italy, and Cyprus), participants engaged in a single-session trial of the COCARE system, including testing of exergames and assessments. Mixed methods encompassing qualitative approaches (eg, think aloud) and quantitative measures (eg, Exergame Enjoyment Questionnaire [EEQ], System Usability Scale [SUS], and Unified Theory of Acceptance and Use of Technology [UTAUT] questionnaire) were used to analyze participants' perceptions of the system and identify potential barriers to its implementation in a home setting. In addition, the associations of performance during gameplay and assessments, demographics, and training motivation (Behavioral Regulation in Exercise Questionnaire-3 [BREQ-3]) with usability, acceptance, and enjoyment were explored. RESULTS: A total of 45 OAs and 15 HPs participated in this study. The COCARE system achieved good acceptance ratings (OAs: 83%, range 36%-100% and HPs: 81%, range 63.8%-93.3% of the maximum score), and OAs indicated high enjoyment (mean 73.3, SD 12.7 out of 100 points in the EEQ) during the exergame session. The system's usability, assessed with the SUS, received scores of 68.1 (SD 18.8; OAs) and 70.7 (SD 12.3; HPs) out of 100 points, with substantial differences observed between the trial sites. Several requirements for improvement were identified. Commonly mentioned barriers to adoption included the movement-recognition sensitivity of the Senso Flex, its limited markings, and difficulties in understanding certain instructions for assessments and games. Performance in games and assessments showed the highest significant correlations with the SUS (Spearman ρ=0.35, P=.02 to ρ=0.52, P<.001). The BREQ-3 had significant correlations with all usability measures, thereby even large significant correlations with enjoyment (Spearman ρ=0.58; P<.001). Age had moderately significant correlations with the SUS (Spearman ρ=-0.35; P=.02) and the UTAUT total score (ρ=-0.35; P=.02) but no significant correlation with the EEQ. Concerning sex and years of education, no significant correlations were found. CONCLUSIONS: The study's findings will inform the further development of the COCARE system toward a user-friendly and widely accepted version, enhancing cognitive and physical functions in OAs. Future randomized controlled trials should evaluate the system's feasibility and effectiveness.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.442
Teacher spread0.366 · 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 teacher head, not a consensus.

Study designObservational
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

Citations18
Published2023
Admission routes1
Has abstractyes

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