Evidence map›Paper›PMID 35959298›Full record

SynthesisFrontiers in aging neuroscience2022

Effect of Wearable Sensor-Based Exercise on Musculoskeletal Disorders in Individuals With Neurodegenerative Diseases: A Systematic Review and Meta-Analysis.

Xin Li, Zhengquan Chen, Yiming Yue, Xuan Zhou, Shuangyu Gu, Jing Tao, Haibin Guo, Meiwen Zhu, Qing Du

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
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

9 authors at 3 institutions in 1 country.

Xin LiDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhengquan ChenDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yiming YueInstitute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China.
Xuan ZhouDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shuangyu GuDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jing TaoDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Haibin GuoDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Meiwen ZhuChongming Branch of Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Qing DuDepartment of Rehabilitation, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Jiao Tong University · CNXinHua Hospital · CNUniversity of Shanghai for Science and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The application of wearable sensor technology in an exercise intervention provides a new method for the standardization and accuracy of intervention. Considering that the deterioration of musculoskeletal conditions is of serious concern in patients with neurodegenerative diseases, it is worthwhile to clarify the effect of wearable sensor-based exercise on musculoskeletal disorders in such patients compared with traditional exercise. Methods: Five health science-related databases, including PubMed, Cochrane Library, Embase, Web of Science, and Ebsco Cumulative Index to Nursing and Allied Health, were systematically searched. The protocol number of the study is PROSPERO CRD42022319763. Randomized controlled trials (RCTs) that were published up to March 2022 and written in English were included. Balance was the primary outcome measure, comprising questionnaires on postural stability and computerized dynamic posturography. The secondary outcome measures are motor symptoms, mobility ability, functional gait abilities, fall-associated self-efficacy, and adverse events. Stata version 16.0 was used for statistical analysis, and the weighted mean difference (WMD) was selected as the effect size with a 95% confidence interval (CI). Results: Fifteen RCTs involving 488 participants with mean ages ranging from 58.6 to 81.6 years were included in this review, with 14 of them being pooled in a quantitative meta-analysis. Only five included studies showed a low risk of bias. The Berg balance scale (BBS) was used in nine studies, and the pooled data showed a significant improvement in the wearable sensor-based exercise group compared with the traditional exercise group after 3-12-week intervention (WMD = 1.43; 95% CI, 0.50 to 2.36, Conclusion: The wearable sensor-based exercise had advantages in improving balance in patients with neurodegenerative diseases, while there was a lack of evidence in motor symptoms, mobility, and functional gait ability enhancement. Future studies are recommended to construct a comprehensive rehabilitation treatment system for the improvement in both postural control and quality of life. Systematic Review Registration: http://www.crd.york.ac.uk/prospero/, identifier CRD42022319763.

Indexed as

Alzheimer’s diseasebalancedynamic postural controlmusculoskeletal disordersneurodegenerative diseasesParkinson’s diseasewearable sensor-based exercise

Identifiers

PMID35959298
PMCPMC9360755
OpenAlexW4287921819

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.