Literature DB >> 36078781

The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly.

Ziyi Wang1, Deyu Meng1, Shichun He1, Hongzhi Guo2,3, Zhibo Tian4, Meiqi Wei1, Guang Yang1, Ziheng Wang1,3,5.   

Abstract

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.

Entities:  

Keywords:  Baduanjin; Explainable Artificial Intelligence; endurance training; frail; strength training

Mesh:

Year:  2022        PMID: 36078781      PMCID: PMC9517902          DOI: 10.3390/ijerph191711063

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   4.614


  55 in total

1.  Validation and reliability of the Physical Activity Scale for the Elderly in Chinese population.

Authors:  Shirley P C Ngai; Roy T H Cheung; Priscillia L Lam; Joseph K W Chiu; Eric Y H Fung
Journal:  J Rehabil Med       Date:  2012-05       Impact factor: 2.912

2.  Multicomponent exercises including muscle power training enhance muscle mass, power output, and functional outcomes in institutionalized frail nonagenarians.

Authors:  Eduardo L Cadore; Alvaro Casas-Herrero; Fabricio Zambom-Ferraresi; Fernando Idoate; Nora Millor; Marisol Gómez; Leocadio Rodriguez-Mañas; Mikel Izquierdo
Journal:  Age (Dordr)       Date:  2013-09-13

3.  Prevalence and Risk Factors for Frailty among Community-Dwelling Older People in China: A Systematic Review and Meta-Analysis.

Authors:  B He; Y Ma; C Wang; M Jiang; C Geng; X Chang; B Ma; L Han
Journal:  J Nutr Health Aging       Date:  2019       Impact factor: 4.075

Review 4.  Frailty in elderly people.

Authors:  Andrew Clegg; John Young; Steve Iliffe; Marcel Olde Rikkert; Kenneth Rockwood
Journal:  Lancet       Date:  2013-02-08       Impact factor: 79.321

5.  Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Authors:  Rudresh Deepak Shirwaikar; Dinesh Acharya U; Krishnamoorthi Makkithaya; Surulivelrajan M; Shikhar Srivastava; Leslie Edward S Lewis U
Journal:  Artif Intell Med       Date:  2019-07-25       Impact factor: 5.326

6.  Effects of Kinect-based exergaming on frailty status and physical performance in prefrail and frail elderly: A randomized controlled trial.

Authors:  Ying-Yi Liao; I-Hsuan Chen; Ray-Yau Wang
Journal:  Sci Rep       Date:  2019-06-27       Impact factor: 4.379

7.  Multi-System Physical Exercise Intervention for Fall Prevention and Quality of Life in Pre-Frail Older Adults: A Randomized Controlled Trial.

Authors:  Jiraporn Chittrakul; Penprapa Siviroj; Somporn Sungkarat; Ratana Sapbamrer
Journal:  Int J Environ Res Public Health       Date:  2020-04-29       Impact factor: 3.390

8.  Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare.

Authors:  Somya D Mohanty; Deborah Lekan; Thomas P McCoy; Marjorie Jenkins; Prashanti Manda
Journal:  Patterns (N Y)       Date:  2021-12-03

9.  Developing an AI-Enabled Integrated Care Platform for Frailty.

Authors:  Angelina Kouroubali; Haridimos Kondylakis; Fokion Logothetidis; Dimitrios G Katehakis
Journal:  Healthcare (Basel)       Date:  2022-02-26

10.  Frailty as a Predictor of Death or New Disability After Surgery: A Prospective Cohort Study.

Authors:  Daniel I McIsaac; Monica Taljaard; Gregory L Bryson; Paul E Beaulé; Sylvain Gagné; Gavin Hamilton; Emily Hladkowicz; Allen Huang; John A Joanisse; Luke T Lavallée; David MacDonald; Husein Moloo; Kednapa Thavorn; Carl van Walraven; Homer Yang; Alan J Forster
Journal:  Ann Surg       Date:  2020-02       Impact factor: 12.969

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