Hybrid Exercise Program for Sarcopenia in Older Adults: The Effectiveness of Explainable Artificial Intelligence-Based Clinical Assistance in Assessing Skeletal Muscle Area

Meiqi Wei, Deyu Meng, Hongzhi Guo, Shichun He, Zhibo Tian, Ziyi Wang, Guang Yang () and Ziheng Wang ()
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Meiqi Wei: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Deyu Meng: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Hongzhi Guo: Graduate School of Human Sciences, Waseda University, Tokorozawa 169-8050, Japan
Shichun He: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Zhibo Tian: College of Physical Education and Health, Guangxi Normal University, Guilin 541006, China
Ziyi Wang: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Guang Yang: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China
Ziheng Wang: Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China

IJERPH, 2022, vol. 19, issue 16, 1-17

Abstract: Background: Sarcopenia is a geriatric syndrome characterized by decreased skeletal muscle mass and function with age. It is well-established that resistance exercise and Yi Jin Jing improve the skeletal muscle mass of older adults with sarcopenia. Accordingly, we designed an exercise program incorporating resistance exercise and Yi Jin Jing to increase skeletal muscle mass and reverse sarcopenia in older adults. Additionally, machine learning simulations were used to predict the sarcopenia status after the intervention. Method: This randomized controlled trial assessed the effects of sarcopenia in older adults. For 24 weeks, 90 older adults with sarcopenia were divided into intervention groups, including the Yi Jin Jing and resistance training group (YR, n = 30), the resistance training group (RT, n = 30), and the control group (CG, n = 30). Computed tomography (CT) scans of the abdomen were used to quantify the skeletal muscle cross-sectional area at the third lumbar vertebra (L3 SMA). Participants’ age, body mass, stature, and BMI characteristics were analyzed by one-way ANOVA and the chi-squared test for categorical data. This study explored the improvement effect of three interventions on participants’ L3 SMA, skeletal muscle density at the third lumbar vertebra (L3 SMD), skeletal muscle interstitial fat area at the third lumbar vertebra region of interest (L3 SMFA), skeletal muscle interstitial fat density at the third lumbar vertebra (L3 SMFD), relative skeletal muscle mass index (RSMI), muscle fat infiltration (MFI), and handgrip strength. Experimental data were analyzed using two-way repeated-measures ANOVA. Eleven machine learning models were trained and tested 100 times to assess the model’s performance in predicting whether sarcopenia could be reversed following the intervention. Results: There was a significant interaction in L3 SMA ( p < 0.05), RSMI ( p < 0.05), MFI ( p < 0.05), and handgrip strength ( p < 0.05). After the intervention, participants in the YR and RT groups showed significant improvements in L3 SMA, RSMI, and handgrip strength. Post hoc tests showed that the YR group ( p < 0.05) yielded significantly better L3 SMA and RSMI than the RT group ( p < 0.05) and CG group ( p < 0.05) after the intervention. Compared with other models, the stacking model exhibits the best performance in terms of accuracy (85.7%) and F1 (75.3%). Conclusion: One hybrid exercise program with Yi Jin Jing and resistance exercise training can improve skeletal muscle area among older adults with sarcopenia. Accordingly, it is possible to predict whether sarcopenia can be reversed in older adults based on our stacking model.

Keywords: sarcopenia; older adults; exercise program; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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