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Book Chapter: Quantitative Methods for User-Centered Sarcopenia Identification and Management
| Title | Quantitative Methods for User-Centered Sarcopenia Identification and Management |
|---|---|
| Authors | |
| Issue Date | 26-Sep-2024 |
| Abstract | This study aimed to develop a faster and simpler user-centered approach for sarcopenia identification and management using a novel balance sensor system and wearable robots. The study design was a cross-sectional study. The research was conducted based on a community-based study in Hong Kong. A total of 144 community-dwelling older adults were included. Sarcopenia was defined according to the guidelines published by the Asian Working Group for Sarcopenia 2019. Appendicular skeletal muscle mass was calculated using the Lee equation. Among the 46 features extracted from the balance sensor system, 15 displayed a sensitivity >0.8 through a machine-learning approach. The area under the receiver operating characteristics curve of the logistic model in discriminating sarcopenia was 0.68. This study demonstrated that a novel balance sensor system proved useful in sarcopenia identification in older adults. Furthermore, the balance sensor data were valuable in informing the development of wearable robots for sarcopenia management. |
| Persistent Identifier | http://hdl.handle.net/10722/362060 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Cheng, Clio Yuen Man | - |
| dc.contributor.author | Lou, Vivian W.Q. | - |
| dc.contributor.author | Ma, Xin | - |
| dc.contributor.author | Chen, Jiaming | - |
| dc.contributor.author | Xi, Ning | - |
| dc.date.accessioned | 2025-09-19T00:31:29Z | - |
| dc.date.available | 2025-09-19T00:31:29Z | - |
| dc.date.issued | 2024-09-26 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/362060 | - |
| dc.description.abstract | <p>This study aimed to develop a faster and simpler user-centered approach for sarcopenia identification and management using a novel balance sensor system and wearable robots. The study design was a cross-sectional study. The research was conducted based on a community-based study in Hong Kong. A total of 144 community-dwelling older adults were included. Sarcopenia was defined according to the guidelines published by the Asian Working Group for Sarcopenia 2019. Appendicular skeletal muscle mass was calculated using the Lee equation. Among the 46 features extracted from the balance sensor system, 15 displayed a sensitivity >0.8 through a machine-learning approach. The area under the receiver operating characteristics curve of the logistic model in discriminating sarcopenia was 0.68. This study demonstrated that a novel balance sensor system proved useful in sarcopenia identification in older adults. Furthermore, the balance sensor data were valuable in informing the development of wearable robots for sarcopenia management.<br></p> | - |
| dc.language | eng | - |
| dc.relation.ispartof | Updates on Sarcopenia - Advances in the Prevention, Diagnosis, and Management | - |
| dc.title | Quantitative Methods for User-Centered Sarcopenia Identification and Management | - |
| dc.type | Book_Chapter | - |
| dc.identifier.doi | 10.5772/intechopen.1005848 | - |