Evidence map›Paper›PMID 35742237›Full record

ArticleInternational journal of environmental research and public health2022

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

Deyu Meng, Hongzhi Guo, Siyu Liang, Zhibo Tian, Ran Wang, Guang Yang, Ziheng Wang

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 4 pooled it
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 26 citations in OpenAlex.

  1. Pooled it
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  14. The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly.International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Deyu MengChinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.ORCID 0000-0002-3270-9829
Hongzhi GuoGraduate School of Human Sciences, Waseda University, Tokorozawa 169-8050, Japan.
Siyu LiangChinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.
Zhibo TianSports Marketing Research Group, Sports College, Dankook University, Gyeonggi-do, Yongin-si 16890, Korea.
Ran WangChinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.
Guang YangChinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.
Ziheng WangChinese Center of Exercise Epidemiology, Northeast Normal University, Changchun 130024, China.
Northeast Normal University · CNWaseda University · JPDankook University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Frail ElderlyFrailtyAgedExercise TherapyHumansIntelligencePhysical FitnessPostural BalanceTime and Motion Studiesendurance trainingfrailmachine learningstrength trainingTai Chi

Identifiers

PMID35742237
PMCPMC9223164
OpenAlexW4281702027

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.