The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly
Author: Ziyi Wang1, Deyu Meng1, Shichun He1, Hongzhi Guo2,3, Zhibo Tian4, Meiqi Wei1, Guang Yang1, Ziheng Wang1,3,5
Affiliation:
1 Chinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.
2 Graduate School of Human Sciences, Waseda University, Tokorozawa 169-8050, Japan.
3 AI Group, Intelligent Lancet LLC, Sacramento, CA 95816, USA.
4 College of Physical Education and Health, Guangxi Normal University, Guilin 541006, China.
5 Advanced Research Center for Human Sciences, Waseda University, Tokorozawa 169-8050, Japan.
Conference/Journal: Int J Environ Res Public Health
Date published: 2022 Sep 4
Other:
Volume ID: 19 , Issue ID: 17 , Pages: 11063 , Special Notes: doi: 10.3390/ijerph191711063. , Word Count: 293
Background:
Frailty is a serious physical disorder affecting the elderly all over the world. However, the frail elderly have low physical fitness, which limits the effectiveness of current exercise programs. Inspired by this, we attempted to integrate Baduanjin and strength and endurance exercises into an exercise program to improve the physical fitness and alleviate frailty among the elderly. Additionally, to achieve the goals of personalized medicine, machine learning simulations were performed to predict post-intervention frailty.
Methods:
A total of 171 frail elderly individuals completed the experiment, including a Baduanjin group (BDJ), a strength and endurance training group (SE), and a combination of Baduanjin and strength and endurance training group (BDJSE), which lasted for 24 weeks. Physical fitness was evaluated by 10-meter maximum walk speed (10 m MWS), grip strength, the timed up-and-go test (TUGT), and the 6 min walk test (6 min WT). A one-way analysis of variance (ANOVA), chi-square test, and two-way repeated-measures ANOVA were carried out to analyze the experimental data. In addition, nine machine learning models were utilized to predict the frailty status after the intervention.
Results:
In 10 m MWS and TUGT, there was a significant interactive influence between group and time. When comparing the BDJ group and the SE group, participants in the BDJSE group demonstrated the maximum gains in 10 m MWS and TUGT after 24 weeks of intervention. The stacking model surpassed other algorithms in performance. The accuracy and precision rates were 75.5% and 77.1%, respectively.
Conclusion:
The hybrid exercise program that combined Baduanjin with strength and endurance training proved more effective at improving fitness and reversing frailty in elderly individuals. Based on the stacking model, it is possible to predict whether an elderly person will exhibit reversed frailty following an exercise program.
Keywords: Baduanjin; Explainable Artificial Intelligence; endurance training; frail; strength training.
PMID: 36078781 DOI: 10.3390/ijerph191711063
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