Evidence map›Paper›PMID 39621937›Full record

Trial reportJMIR aging2024

Quantifying the Enhancement of Sarcopenic Skeletal Muscle Preservation Through a Hybrid Exercise Program: Randomized Controlled Trial.

Hongzhi Guo, Jianwei Cao, Shichun He, Meiqi Wei, Deyu Meng, Ichen Yu, Ziyi Wang, Xinyi Chang, Guang Yang, Ziheng Wang

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in JMIR aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 5 pooled it
–field-weighted citation impact
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

11 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  6. Review
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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

10 authors.

Hongzhi Guo *Graduate School of Human Sciences, Waseda University, Tokorozawa, Japan.ORCID 0000-0002-3454-9929
Jianwei Cao *AI group, Intelligent Lancet LLC, Sacramento, CA, United States.ORCID 0009-0005-7114-6263
Shichun HeChinese Center of Exercise Epidemiology, Northeast Normal University, 5502 Renmin Ave, Nanguan District, Changchun, 130024, China, 81 90-6747-0562.ORCID 0000-0002-0522-2644
Meiqi WeiChinese Center of Exercise Epidemiology, Northeast Normal University, 5502 Renmin Ave, Nanguan District, Changchun, 130024, China, 81 90-6747-0562.ORCID 0009-0003-8299-6242
Deyu MengAI group, Intelligent Lancet LLC, Sacramento, CA, United States.ORCID 0000-0002-3270-9829
Ichen YuAI group, Intelligent Lancet LLC, Sacramento, CA, United States.ORCID 0009-0001-5132-7850
Ziyi WangChinese Center of Exercise Epidemiology, Northeast Normal University, 5502 Renmin Ave, Nanguan District, Changchun, 130024, China, 81 90-6747-0562.ORCID 0009-0006-8253-8614
Xinyi ChangDepartment of Industrial Engineering and Economics, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan.ORCID 0000-0002-8546-9494
Guang YangChinese Center of Exercise Epidemiology, Northeast Normal University, 5502 Renmin Ave, Nanguan District, Changchun, 130024, China, 81 90-6747-0562.ORCID 0000-0001-7236-7483
Ziheng WangAI group, Intelligent Lancet LLC, Sacramento, CA, United States.ORCID 0000-0002-3953-0493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sarcopenia is characterized by the loss of skeletal muscle mass and muscle function with increasing age. The skeletal muscle mass of older people who endure sarcopenia may be improved via the practice of strength training and tai chi. However, it remains unclear if the hybridization of strength exercise training and traditional Chinese exercise will have a better effect. Objective: We designed a strength training and tai chi exercise hybrid program to improve sarcopenia in older people. Moreover, explainable artificial intelligence was used to predict postintervention sarcopenic status and quantify the feature contribution. Methods: To assess the influence of sarcopenia in the older people group, 93 participated as experimental participants in a 24-week randomized controlled trial and were randomized into 3 intervention groups, namely the tai chi exercise and strength training hybrid group (TCSG; n=33), the strength training group (STG; n=30), and the control group (n=30). Abdominal computed tomography was used to evaluate the skeletal muscle mass at the third lumbar (L3) vertebra. Analysis of demographic characteristics of participants at baseline used 1-way ANOVA and χ2 tests, and repeated-measures ANOVA was used to analyze experimental data. In addition, 10 machine-learning classification models were used to calculate if these participants could reverse the degree of sarcopenia after the intervention. Results: A significant interaction effect was found in skeletal muscle density at the L3 vertebra, skeletal muscle area at the L3 vertebra (L3 SMA), grip strength, muscle fat infiltration, and relative skeletal muscle mass index (all P values were <.05). Grip strength, relative skeletal muscle mass index, and L3 SMA were significantly improved after the intervention for participants in the TCSG and STG (all P values were <.05). After post hoc tests, we found that participants in the TCSG experienced a better effect on L3 SMA than those in the STG and participants in the control group. The LightGBM classification model had the greatest performance in accuracy (88.4%), recall score (74%), and F1-score (76.1%). Conclusions: The skeletal muscle area of older adults with sarcopenia may be improved by a hybrid exercise program composed of strength training and tai chi. In addition, we identified that the LightGBM classification model had the best performance to predict the reversion of sarcopenia.

Indexed as

Muscle, SkeletalResistance TrainingSarcopeniaTai JiAgedExercise TherapyFemaleHumansMaleMiddle Agedexplainable artificial intelligenceolder adultsphysical exercise programsarcopeniatai chi

Identifiers

PMID39621937
PMCPMC11587998

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.