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Effect of electronic health interventions on metabolic syndrome: a systematic review and meta-analysis

OBJECTIVE: We aimed to examine whether eHealth interventions can effectively improve anthropometric and biochemical indicators of patients with metabolic syndrome (MetS). DESIGN: Systematic review and meta-analysis. METHODS: PubMed, the Web of Science, Embase, Medline, CINAHL, PsycINFO, the Cochrane...

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Detalles Bibliográficos
Autores principales: Chen, Dandan, Ye, Zhihong, Shao, Jing, Tang, Leiwen, Zhang, Hui, Wang, Xiyi, Qiu, Ruolin, Zhang, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545661/
https://www.ncbi.nlm.nih.gov/pubmed/33033085
http://dx.doi.org/10.1136/bmjopen-2020-036927
Descripción
Sumario:OBJECTIVE: We aimed to examine whether eHealth interventions can effectively improve anthropometric and biochemical indicators of patients with metabolic syndrome (MetS). DESIGN: Systematic review and meta-analysis. METHODS: PubMed, the Web of Science, Embase, Medline, CINAHL, PsycINFO, the Cochrane Library, the Chinese National Knowledge Infrastructure, the Wanfang and Weipu databases were comprehensively searched for papers that were published from database inception to May 2019. Articles were included if the participants were metabolic syndrome (MetS) patients, the participants received eHealth interventions, the participants in the control group received usual care or were wait listed, the outcomes included anthropometric and biochemical indicators of MetS, and the study was a randomised controlled trial (RCT) or a controlled clinical trial (CCT). The Quality Assessment Tool for Quantitative Studies was used to assess the methodological quality of the included articles. The meta-analysis was conducted using Review Manager V.5.3 software. RESULTS: In our review, seven RCTs and two CCTs comprising 935 MetS participants met the inclusion criteria. The results of the meta-analysis revealed that eHealth interventions resulted in significant improvements in body mass index (standardised mean difference (SMD)=−0.36, 95% CI (−0.61 to −0.10), p<0.01), waist circumference (SMD=−0.47, 95% CI (−0.84 to −0.09), p=0.01) and systolic blood pressure(SMD=−0.35, 95% CI (−0.66 to −0.04), p=0.03) compared with the respective outcomes associated with the usual care or wait-listed groups. Based on the included studies, we found significant effects of the eHealth interventions on body weight. However, we did not find significant positive effects of the eHealth interventions on other metabolic parameters. CONCLUSIONS: The results indicated that eHealth interventions were beneficial for improving specific anthropometric outcomes, but did not affect biochemical indicators of MetS. Therefore, whether researchers adopt eHealth interventions should be based on the purpose of the study. More rigorous studies are needed to confirm these findings.