Background: Resistance exercise, Taichi exercise, and the hybrid exercise program consisting of the two aforementioned methods have been demonstrated to increase the skeletal muscle mass of older individuals with sarcopenia. However, the exercise sequence has not been comprehensively investigated. Therefore, we designed a self-determined sequence exercise program, incorporating resistance exercises, Taichi, and the hybrid exercise program to overcome the decline of skeletal muscle area and reverse sarcopenia in older individuals.
Methods: Ninety-one older patients with sarcopenia between the ages of 60 and 75 completed this three-stage randomized controlled trial for 24 weeks, including the self-determined sequence exercise program group (n = 31), the resistance training group (n = 30), and the control group (n = 30). We used quantitative computed tomography to measure the effects of different intervention protocols on skeletal muscle mass in participants. Participants' demographic variables were analyzed using one-way analysis of variance and chi-square tests, and experimental data were examined using repeated-measures analysis of variance. Furthermore, we utilized the Markov model to explain the effectiveness of the exercise programs among the three-stage intervention and explainable artificial intelligence to predict whether intervention programs can reverse sarcopenia.
Results: Repeated-measures analysis of variance results indicated that there were statistically significant Group × Time interactions detected in the L3 skeletal muscle density, L3 skeletal muscle area, muscle fat infiltration, handgrip strength, and relative skeletal muscle mass index. The stacking model exhibited the best accuracy (84.5%) and the best F1-score (68.8%) compared to other algorithms. In the self-determined sequence exercise program group, strength training contributed most to the reversal of sarcopenia.
Conclusion: One self-determined sequence exercise program can improve skeletal muscle area among sarcopenic older people. Based on our stacking model, we can predict whether sarcopenia in older people can be reversed accurately. The trial was registered in ClinicalTrials.gov. TRN:NCT05694117.
Significance: Our findings indicate that such tailored exercise interventions can substantially benefit sarcopenic patients, and our stacking model provides an accurate predictive tool for assessing the reversibility of sarcopenia in older adults. This approach not only enhances individual health outcomes but also informs future development of targeted exercise programs to mitigate age-related muscle decline.
Keywords: explainable artificial intelligence; older individuals; sarcopenia.