MétaCan
Menu
Back to cohort
Record W3112761458 · doi:10.2196/23548

WeChat as a Platform for Baduanjin Intervention in Patients With Stable Chronic Obstructive Pulmonary Disease in China: Retrospective Randomized Controlled Trial

2020· article· en· W3112761458 on OpenAlexvenueno aff
Junjie Bi, Wei Yang, Ping Hao, Zhao Yong-mei, Dan Wei, Yipeng Sun, Yuhua Lin, Meng Sun, Xuan Chen, Xuming Luo, Shanqun Li, Wei Zhang, Xiongbiao Wang

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCOPDPulmonary rehabilitationPhysical therapyRandomized controlled trialRehabilitationPulmonary diseasePulmonary function testingQuality of life (healthcare)Internal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Pulmonary rehabilitation is a crucial part of the nonpharmacological treatment of stable chronic obstructive pulmonary disease (COPD), but management remains problematic. WeChat could serve as a useful tool in patient management. Baduanjin is a popular exercise in China that is usually applied in pulmonary rehabilitation, which has been confirmed to be effective in improving lung function and life quality. OBJECTIVE: This study aimed to explore the efficiency of WeChat in the management of Baduanjin exercise in COPD patients. METHODS: A total of 200 patients from the respiratory department of Putuo Hospital participated in the Baduanjin rehabilitation project from September 2018 to October 2019, and were randomly assigned to the WeChat and control groups and followed up using the WeChat platform or telephone for 12 weeks. The frequency of Baduanjin exercise, lung function (percentage of forced expiratory volume in 1 second predicted, FEV1% predicted), and COPD assessment test (CAT) scores were collected and compared between the two groups. The number of message exchanges and a satisfaction survey on the WeChat platform were used to assess the feasibility of WeChat management outside the hospital. RESULTS: The Baduanjin exercise frequency significantly differed between the control group and WeChat group (F=33.82, P<.001) and across various time points (F=214.87, P<.001). After the follow-up on WeChat, there were fewer patients not performing Baduanjin exercise. The FEV1% predicted value significantly differed before and after Baduanjin exercise in the control group (Z=-3.686, P<.001) and the WeChat group (Z=-6.985, P<.001). A significant difference in the FEV1% predicted value was observed after Baduanjin exercise between the two groups (Z=-3.679, P<.001). The CAT score significantly differed before and after Baduanjin exercise in the control group (Z=-4.937, P<.001) and the WeChat group (Z=-5.246, P<.001). A significant difference in the CAT score was observed after Baduanjin exercise between the two groups (Z=-5.246, P<.001). The number of completed Baduanjin exercises, lung function, and CAT scores in active patients were higher than those in nonactive patients. All satisfaction survey items were scored with more than 4 points. Among the items, the highest score (mean 4.54, SD 0.77) was for continued WeChat management, followed by the effective management of Baduanjin exercise (mean 4.46, SD 0.87). The patients in the WeChat group showed much higher enthusiasm for and compliance with Baduanjin exercise, resulting in better life quality and lung function. The patients were very satisfied with the WeChat management because of the obvious curative effect and home feeling. CONCLUSIONS: The WeChat platform provided a feasible, effective, and sustainable management plan for Baduanjin rehabilitation. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900028248; http://www.chictr.org.cn/showprojen.aspx?proj=46995.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.304
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJMIR mhealth and uhealthSame topicBiofield Effects and BiophysicsFrench-language works237,207