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Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance

Background: Due to the low physical fitness of the frail elderly, current exercise program strategies have a limited impact. Eight-form Tai Chi has a low intensity, but high effectiveness in the elderly. Inspired by it, we designed an exercise program that incorporates eight-form Tai Chi, strength,...

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Autores principales: Meng, Deyu, Guo, Hongzhi, Liang, Siyu, Tian, Zhibo, Wang, Ran, Yang, Guang, Wang, Ziheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223164/
https://www.ncbi.nlm.nih.gov/pubmed/35742237
http://dx.doi.org/10.3390/ijerph19126988
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author Meng, Deyu
Guo, Hongzhi
Liang, Siyu
Tian, Zhibo
Wang, Ran
Yang, Guang
Wang, Ziheng
author_facet Meng, Deyu
Guo, Hongzhi
Liang, Siyu
Tian, Zhibo
Wang, Ran
Yang, Guang
Wang, Ziheng
author_sort Meng, Deyu
collection PubMed
description Background: Due to the low physical fitness of the frail elderly, current exercise program strategies have a limited impact. Eight-form Tai Chi has a low intensity, but high effectiveness in the elderly. Inspired by it, we designed an exercise program that incorporates eight-form Tai Chi, strength, and endurance exercises, to improve physical fitness and reverse frailty in the elderly. Additionally, for the ease of use in clinical practice, machine learning simulations were used to predict the frailty status after the intervention. Methods: For 24 weeks, 150 frail elderly people completed the experiment, which comprised the eight-form Tai Chi group (TC), the strength and endurance training group (SE), and a comprehensive intervention combining both TC and SE (TCSE). The comparison of the demographic variables used one-way ANOVA for continuous data and the chi-squared test for categorical data. Two-way repeated measures analysis of variance (ANOVA) was performed to determine significant main effects and interaction effects. Eleven machine learning models were used to predict the frailty status of the elderly following the intervention. Results: Two-way repeated measures ANOVA results before the intervention, group effects of ten-meter maximum walking speed (10 m MWS), grip strength (GS), timed up and go test (TUGT), and the six-minute walk test (6 min WT) were not significant. There was a significant interaction effect of group × time in ten-meter maximum walking speed, grip strength, and the six-minute walk test. Post hoc tests showed that after 24 weeks of intervention, subjects in the TCSE group showed the greatest significant improvements in ten-meter maximum walking speed (p < 0.05) and the six-minute walk test (p < 0.05) compared to the TC group and SE group. The improvement in grip strength in the TCSE group (4.29 kg) was slightly less than that in the SE group (5.16 kg). There was neither a significant main effect nor a significant interaction effect for TUGT in subjects. The stacking model outperformed other algorithms. Accuracy and the F1-score were 67.8% and 71.3%, respectively. Conclusion: A hybrid exercise program consisting of eight-form Tai Chi and strength and endurance exercises can more effectively improve physical fitness and reduce frailty among the elderly. It is possible to predict whether an elderly person will reverse frailty following an exercise program based on the stacking model.
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spelling pubmed-92231642022-06-24 Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance Meng, Deyu Guo, Hongzhi Liang, Siyu Tian, Zhibo Wang, Ran Yang, Guang Wang, Ziheng Int J Environ Res Public Health Article Background: Due to the low physical fitness of the frail elderly, current exercise program strategies have a limited impact. Eight-form Tai Chi has a low intensity, but high effectiveness in the elderly. Inspired by it, we designed an exercise program that incorporates eight-form Tai Chi, strength, and endurance exercises, to improve physical fitness and reverse frailty in the elderly. Additionally, for the ease of use in clinical practice, machine learning simulations were used to predict the frailty status after the intervention. Methods: For 24 weeks, 150 frail elderly people completed the experiment, which comprised the eight-form Tai Chi group (TC), the strength and endurance training group (SE), and a comprehensive intervention combining both TC and SE (TCSE). The comparison of the demographic variables used one-way ANOVA for continuous data and the chi-squared test for categorical data. Two-way repeated measures analysis of variance (ANOVA) was performed to determine significant main effects and interaction effects. Eleven machine learning models were used to predict the frailty status of the elderly following the intervention. Results: Two-way repeated measures ANOVA results before the intervention, group effects of ten-meter maximum walking speed (10 m MWS), grip strength (GS), timed up and go test (TUGT), and the six-minute walk test (6 min WT) were not significant. There was a significant interaction effect of group × time in ten-meter maximum walking speed, grip strength, and the six-minute walk test. Post hoc tests showed that after 24 weeks of intervention, subjects in the TCSE group showed the greatest significant improvements in ten-meter maximum walking speed (p < 0.05) and the six-minute walk test (p < 0.05) compared to the TC group and SE group. The improvement in grip strength in the TCSE group (4.29 kg) was slightly less than that in the SE group (5.16 kg). There was neither a significant main effect nor a significant interaction effect for TUGT in subjects. The stacking model outperformed other algorithms. Accuracy and the F1-score were 67.8% and 71.3%, respectively. Conclusion: A hybrid exercise program consisting of eight-form Tai Chi and strength and endurance exercises can more effectively improve physical fitness and reduce frailty among the elderly. It is possible to predict whether an elderly person will reverse frailty following an exercise program based on the stacking model. MDPI 2022-06-07 /pmc/articles/PMC9223164/ /pubmed/35742237 http://dx.doi.org/10.3390/ijerph19126988 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Meng, Deyu
Guo, Hongzhi
Liang, Siyu
Tian, Zhibo
Wang, Ran
Yang, Guang
Wang, Ziheng
Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title_full Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title_fullStr Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title_full_unstemmed Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title_short Effectiveness of a Hybrid Exercise Program on the Physical Abilities of Frail Elderly and Explainable Artificial-Intelligence-Based Clinical Assistance
title_sort effectiveness of a hybrid exercise program on the physical abilities of frail elderly and explainable artificial-intelligence-based clinical assistance
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223164/
https://www.ncbi.nlm.nih.gov/pubmed/35742237
http://dx.doi.org/10.3390/ijerph19126988
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