Evidence map›Paper›PMID 37066946›Full record

ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2023

Analysis of biomechanical characterization of the thumb rubbing method.

Wenjie Wu, Juncheng Xie, Tangyi Liu, Jie Chen, Shaoxiong Li, Gang Xu

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Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Wenjie WuSchool of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Juncheng XieSchool of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Tangyi LiuSchool of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Jie ChenXiangya Boai Renabilitation Hospital, Changsha, Hunan, China.
Shaoxiong LiSchool of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Gang XuSchool of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThumb rubbing is one of the widely accepted massage techniques, owing to its simple and effective operation. Exploring the biomechanical characteristics of the thumb rubbing method can assist the understanding of the operating characteristics of manipulation, thereby improvising the therapeutic role of manipulation.

objectiveTo study the kinematic and kinetic characteristics of the thumb kneading method from the biomechanical point of view, and to quantitatively analyze the key points of thumb kneading operation.

methodsWe explored the biomechanical characteristics of the thumb kneading operation by an analysis of the parameters scored by the experts and students using the "thumb kneading data glove and data collection system".

results(1) Force trajectory: The expert group showed a regular force trajectory compared to the student group, with a stable thumb suction position, small drift and concentrated force. (2) Force value: The average force value of the expert group was concentrated in the range 0.614 ± 0.041 kg, while the average force value of the student group was concentrated in the range 0.650 ± 0.146 kg and the difference was not statistically significant. (3) Frequency: The frequency of the expert group was mainly concentrated in the range 134.280 ± 39.106 times/min, while that of the student group was 66.04 ± 23.651 times/min, (P< 0.05). (4) Period: The operation cycle during the thumb kneading of the expert and student groups was mainly concentrated in the range of 0.476 ± 0.117 s and (0.990 ± 0.259) s, respectively, and the difference was statistically significant (P< 0.05).

conclusionThe present study revealed that the technical operation of the expert group was more stable and standardized than that of the student group. It was found that the force value was inversely proportional to the frequency of the operation. In the "circular rotation" operation of the thumb rubbing method, the force value conversion degree of different parts of the thumb reflected the motion trajectory. Furthermore, the "circular rotation" operation performed by the expert group was better than the student group. The study of the parameters, including the angle of frequency, period and force value can reflect the biomechanical characteristics of thumb rubbing method to a significant extent.

Indexed as

ThumbBiomechanical PhenomenaHumansbiomechanicsmechanical characteristicsthumb rubbing

Identifiers

PMID37066946
PMCPMC10200190

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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.