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• W4383111107 abstract "Objective To facilitate the older adults with knee pain to perform exercises and improve knee health, we proposed the design of a machine learning-based system for lower-limb exercise training that features three main components: video demonstration of exercises, real-time movement feedback, and tracking of exercise progress. At this early stage of design, we aimed to examine the perceptions of a paper-based prototype among older adults with knee pain and investigate the factors that may influence their perceptions of the system. Methods A cross-sectional survey of the participants’ ( N = 94) perceptions of the system was conducted using a questionnaire, which assessed their perceived effects of the system, perceived ease of use of the system, attitude toward the system, and intention to use the system. Ordinal logistic regression was conducted to examine whether the participants’ perceptions of the system were influenced by their demographic and clinical characteristics, physical activity level, and exercise experience. Results The participants’ responses to the perception statements exhibited consensus agreement (≥ 75%). Age, gender, duration of knee pain, knee pain intensity, experience with exercise therapy, and experience with technology-supported exercise programs were significantly associated with the participants’ perceptions of the system. Conclusions Our results demonstrate that the system appears promising for use by older adults to manage their knee pain. Therefore, it is needed to develop a computer-based system and further investigate its usability, acceptance, and clinical effectiveness." @default.
• W4383111107 created "2023-07-05" @default.
• W4383111107 creator A5037354576 @default.
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• W4383111107 date "2023-01-01" @default.
• W4383111107 modified "2023-10-06" @default.
• W4383111107 title "Perceptions of a machine learning-based lower-limb exercise training system among older adults with knee pain" @default.
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• W4383111107 doi "https://doi.org/10.1177/20552076231186069" @default.
• W4383111107 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37426581" @default.
• W4383111107 hasPublicationYear "2023" @default.
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