Evidence mapPaperPMID 33033085Full record

SynthesisBMJ open2020

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

Dandan Chen, Zhihong Ye, Jing Shao, Leiwen Tang, Hui Zhang, Xiyi Wang, Ruolin Qiu, Qi Zhang

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
field-weighted citation impact
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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

8 authors.

Dandan ChenAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zhihong YeAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China yezh@zju.edu.cn.ORCID 0000-0001-6947-3330
Jing ShaoSchool of Nursing, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Leiwen TangAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Hui ZhangGuizhou Provincial People's Hospital, Guiyang, China.
Xiyi WangAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID 0000-0002-6470-8556
Ruolin QiuAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Qi ZhangAffiliated Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveWe aimed to examine whether eHealth interventions can effectively improve anthropometric and biochemical indicators of patients with metabolic syndrome (MetS).

designSystematic review and meta-analysis.

methodsPubMed, 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.

resultsIn 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.

conclusionsThe 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.

Indexed as

Metabolic SyndromeTelemedicineBlood PressureElectronicsHumansRandomized Controlled Trials as Topicgeneral diabeteshealth informaticshypertension

Identifiers

PMID33033085
PMCPMC7545661

What Socratic holds

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Registered trials

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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 Socratic graph.