RESUMEN
In this paper, we propose an artificial intelligence (AI)-based sarcopenia diagnostic technique for stroke patients utilizing bio-signals from the neuromuscular system. Handgrip, skeletal muscle mass index, and gait speed are prerequisite components for sarcopenia diagnoses. However, measurement of these parameters is often challenging for most hemiplegic stroke patients. For these reasons, there is an imperative need to develop a sarcopenia diagnostic technique that requires minimal volitional participation but nevertheless still assesses the muscle changes related to sarcopenia. The proposed AI diagnostic technique collects motor unit responses from stroke patients in a resting state via stimulated muscle contraction signals (SMCSs) recorded from surface electromyography while applying electrical stimulation to the muscle. For this study, we extracted features from SMCS collected from stroke patients and trained our AI model for sarcopenia diagnosis. We validated the performance of the trained AI models for each gender against other diagnostic parameters. The accuracy of the AI sarcopenia model was 96%, and 95% for male and females, respectively. Through these results, we were able to provide preliminary proof that SMCS could be a potential surrogate biomarker to reflect sarcopenia in stroke patients.
RESUMEN
Sarcopenia, a condition characterized by muscle weakness and mass loss, poses significant risks of accidents and complications. Traditional diagnostic methods often rely on physical function measurements like handgrip strength which can be challenging for affected patients, including those with stroke. To address these challenges, we propose a novel sarcopenia diagnosis model utilizing stimulated muscle contraction signals captured via wearable devices. Our approach achieved impressive results, with an accuracy of 93% and 100% in sarcopenia classification for male and female stroke patients, respectively. These findings underscore the significance of our method in diagnosing sarcopenia among stroke patients, offering a non-invasive and accessible solution.
RESUMEN
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.