Evidence map›Paper›PMID 39764147›Full record

ArticleFrontiers in bioengineering and biotechnology2024

Musculoskeletal model predicted paraspinal loading may quick estimate the effect of exercise on spine BMD.

Shizhong Liu, Xiaoyu Xia, Yinxia Nie, Mengen Huang, Lin Meng, Juan Du

Abstract read
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Article in Frontiers in bioengineering and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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

6 authors.

Shizhong Liu *Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Xiaoyu Xia *Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Yinxia NieCollege of Sports Health, Tianjin University of Sport, Tianjin, China.
Mengen HuangAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Lin MengAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Juan DuAcademy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Spine is the most commonly found fracture site due to osteoporosis. Combined exercise including high-impact and resistance exercise shows the potential to improve bone mineral density (BMD) in the spine. However, the mechanical loading introduced by exercise, which is the mechanism of BMD changes, has not been investigated. The purpose of this study is to provide a new insight to investigate the mechanical stimuli of exercise induced bone remodelling. Methods: Ten postmenopausal women with osteopenia who finalized a 6-month combined exercise of high-impact and resistance intervention were included. The changes in BMD were analyzed based on QCT images obtained from pre and post intervention. A modified full-body musculoskeletal model was built to estimate paravertebral muscle force (MF) and intervertebral compression force (ICF) during daily activities (walking and heel drop) and combined exercise including high-impact (jumping) and resistance exercise (pulling elastic bands). Results: The paravertebral MF and ICF during jumping and pulling elastic bands exercise were all significantly greater than walking and heel drop exercise with up to 1.22-8.18 times. Spine BMD remained at the same level with no significant decline observed, especially at L1 (pre 247.95 ± 26.77 mg/cm Conclusion: It has been proved that paravertebral muscle forces and intervertebral compression forces generated by the combined exercise, rather than daily exercise, were the main reasons for the improvement of spine BMD. This study contributes into the understanding of exercise induced spine adaptation as well as a potential in fast prediction to evaluate the effect of physical exercise therapy.

Indexed as

bone remodellingexercisemusculoskeletal modelosteoporosisspine biomechanics

Identifiers

PMID39764147
PMCPMC11701238

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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.