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Personalized smart voice-based electronic prescription for remote at-home feedback management in cardiovascular disease rehabilitation

A multi-center randomized controlled trial

Bibliographic Data

ID22088317
AuthorsYin-Hua Zhu, Yinhua Zhu (0009-0002-2803-3579, Zhejiang Hospital), Liping Xia (0000-0001-6198-7644), Li-Ping Xia, Jing Yan (0000-0002-0680-657X, Zhejiang Hospital, corresponding author), Xiao-ling Shou, Xiaoling Shou (0000-0001-8599-8030, Zhejiang Hospital, corresponding author), Liyue Zhu (0000-0002-6116-0644, Zhejiang Hospital, corresponding author), Li-Yue Zhu, Yan Sun (0000-0001-7588-8444), Ju-Fei Wang, Xiaojun Ji (0000-0003-0868-2969, Wenzhou Central Hospital), Xiao-Jun Ji, Mei-Li Zhu, Meili Zhu (Jining First People's Hospital), Bei-Li Feng, Beili Feng (0000-0002-6181-1304, Ningbo No. 2 Hospital), Hua-Xian Chen, Huaxian Chen (0000-0002-0958-3367, Xiang Yang No.1 People's Hospital)
Year2023
Volume11
Pages1113403-1113403
Publication date2023-06-06
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1113403
PMID37346107
OpenAlexW4379538004
LanguageEN
References cited5

Objective To investigate the quality and efficacy of remote at-home rehabilitation for patients with cardiovascular disease (CVD) using personalized smart voice-based electronic prescription, and further explore the standardized health management mode of remote family cardiac rehabilitation. Trial design: A multicenter, randomized (1:1), non-blind, parallel controlled study. Methods A total of 171 patients with CVD who were admitted to 18 medical institutions in China from April 2021 to October 2022 were randomly divided into a treatment group (86 cases) and a control group (85 cases) in a non-blinded experiment, based on the sequence of enrollment. The control group received routine at-home rehabilitation training, and the treatment group received remote feedback-based at-home cardiac rehabilitation management based on routine at-home rehabilitation training. The primary outcome was the difference in VO 2 peak (mL/min/kg) after 12 weeks. A linear mixed model was developed with follow-up as the dependent variable. Age and baseline data were utilized as covariates, whereas hospital and patient characteristics were adjusted as random-effect variables. As the linear mixed model can accommodate missing data under the assumption of random missing data, there was no substitute missing value for quantitative data. Results A total of 171 participants, with 86 in the experimental group and 85 in the control group, were included in the main analysis. The analysis, which used linear mixing model, revealed significant differences in cardiopulmonary function indexes (VO 2 /kg peak, VO 2 peak, AT, METs, and maximum resistance) at different follow-up time (0, 4, and 12 weeks) in the experimental group ( p 0.05). VO 2 /kg peak (LS mean 1.49, 95%CI 0.09–2.89, p = 0.037) and other indicators of cardiopulmonary function ( p < 0.05) were significantly different between the experimental group and the control group at week 12. The results were comparable in the complete case analysis. Conclusion The remote home cardiac rehabilitation management mode using personalized smart voice-based electronic prescription provides several benefits to patients, including improvements in muscle strength, endurance, cardiopulmonary function, and aerobic metabolism. It also helps reduce risk factors for cardiovascular disease and enhances patients’ self-management abilities and treatment compliance. Clinical trial registration: http://www.chictr.org.cn , identifier ChiCTR2100044063

Disease · Disease management · Machine learning · Medical prescription · Missing data · Physical medicine and rehabilitation · Physical therapy · Randomized controlled trial · Cardiac Health and Mental Health · Computer Science · Digital Mental Health Interventions · Medicine · Mobile Health and mHealth Applications · Internal Medicine · Rehabilitation

  • Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017

    Open Access•Gregory A Roth, D Abate et al.•The Lancet•2018

  • European Guidelines on cardiovascular disease prevention in clinical practice (version 2012)

    Authors/Task Force Members, Joep Perk et al.•European Heart Journal•2012

  • Exercise Capacity and Mortality among Men Referred for Exercise Testing

    Jonathan Myers, Manish Prakash et al.•New England Journal of Medicine•2002

  • Global, Regional, and National Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015

    Open Access•Gregory A Roth, Catherine Johnson et al.•Journal of the American College…•2017

  • Mortality, morbidity, and risk factors in China and its provinces, 1990–2017

    Open Access•Maigeng Zhou, Haidong Wang et al.•The Lancet•2019

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