An Exercise Prescription for Patients with Stroke and Sarcopenia Based on the Modified Delphi Study.

BACKGROUND: We aimed to develop a consensus on the need for and priorities of exercise to treat preexisting sarcopenia with hemiplegic stroke. METHODS: A modified three-round Delphi study was conducted. The panelists responded to the questionnaire on a 7-point Likert scale. Responses were returned with descriptive statistics in the next round. Consensus was defined as >75% agreement (score of 5-7) with a median > 5. The percentage of strong agreement (score of 6-7) and Kendall's coefficient of concordance were calculated to demonstrate a more refined interpretation of the consensus. RESULTS: Fifteen panelists contributed to all rounds. The need for exercise was demonstrated. The consensus was reached on 53 of 58 items in the first round and all items in the second and final rounds. The percentage of strong agreement was high for all but eight items. CONCLUSIONS: This study is the first Delphi study to investigate the need for and priorities of exercise for treating preexisting sarcopenia in stroke hemiplegia. We present a standard recommendation including 57 priorities and a strong recommendation including 49 priorities. The eight items that were excluded reflected factors that are less important to hemiplegic patients with poor balance, cognitive decline, or mental vulnerability.

Digital Biomarkers for Diagnosis of Muscle Disorders Using Stimulated Muscle Contraction Signal.

We propose a digital biomarker related to muscle strength and muscle endurance (DB/MS and DB/ME) for the diagnosis of muscle disorders based on a multi-layer perceptron (MLP) using stimulated muscle contraction. When muscle mass is reduced in patients with muscle-related diseases or disorders, measurement of DBs that are related to muscle strength and endurance is needed to suitably recover damaged muscles through rehabilitation training. Furthermore, it is difficult to measure DBs using traditional methods at home without an expert; moreover, the measuring equipment is expensive. Additionally, because traditional measurements depend on the subject's volition, we propose a DB measurement technique that is unaffected by the subject's volition. To achieve this, we employed an impact response signal (IRS) based on multi-frequency electrical stimulation (MFES) using an electromyography sensor. The feature vector was then extracted using the signal. Because the IRS is obtained from stimulated muscle contraction, which is caused by electrical stimulation, it provides biomedical information about the muscle. Finally, to estimate the strength and endurance of the muscle, the feature vector was passed through the DB estimation model learned through the MLP. To evaluate the performance of the DB measurement algorithm, we collected the MFES-based IRS database for 50 subjects and tested the model with quantitative evaluation methods using the reference for the DB. The reference was measured using torque equipment. The results were compared with the reference, indicating that it is possible to check for muscle disorders which cause decreased physical performance using the proposed algorithm.