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

ABSTRACT: 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.

SUBMITTER: Chen D

PROVIDER: S-EPMC7545661 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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

Chen Dandan D Ye Zhihong Z Shao Jing J Tang Leiwen L Zhang Hui H Wang Xiyi X Qiu Ruolin R Zhang Qi Q

BMJ open 20201008 10

<h4>Objective</h4>We aimed to examine whether eHealth interventions can effectively improve anthropometric and biochemical indicators of patients with metabolic syndrome (MetS).<h4>Design</h4>Systematic review and meta-analysis.<h4>Methods</h4>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 ...[more]

PMID: 33033085

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Project description:BackgroundIntimate partner violence (IPV) is a major public health concern. eHealth interventions may reduce exposure to violence and health-related consequences as the technology provides a safe and flexible space for the target population. However, the evidence is unclear.ObjectiveThe goal of the review is to examine the effect of eHealth interventions compared with standard care on reducing IPV, depression, and posttraumatic stress disorder (PTSD) among women exposed to IPV.MethodsWe searched EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, PsycInfo, Scopus, Global Health Library, ClinicalTrials.gov, and International Clinical Trials Registry Platform for published and unpublished trials from inception until April 2019. Trials with an eHealth intervention targeting women exposed to violence were included. We assessed risk of bias using the Cochrane Risk of Bias Tool. Trials that reported effect estimates on overall IPV; physical, sexual, and psychological violence; depression; or posttraumatic stress disorder were included in meta-analyses.ResultsA total of 14 trials were included in the review; 8 published trials, 3 unpublished trials and 3 ongoing trials. Of the 8 published trials, 2 were judged as overall low risk of bias trials. The trials reported 23 types of outcomes, and 7 of the trials had outcomes that were eligible for meta-analyses. Our pooled analyses found no effect of eHealth interventions on any of our prespecified outcomes: overall IPV (SMD -0.01; 95% CI -0.11 to 0.08; I2=0%; 5 trials, 1668 women); physical violence (SMD 0.01; 95% CI -0.22 to 0.24; I2=58%; 4 trials, 1128 women); psychological violence (SMD 0.07; 95% CI -0.12 to 0.25; I2=40%; 4 trials, 1129 women); sexual violence (MD 0.36; 95% CI -0.18 to 0.91; I2=0%; 2 trials, 1029 women); depression (SMD -0.13; 95% CI -0.37 to 0.11; I2=78%; 5 trials, 1600 women); and PTSD (MD -0.11; 95% CI -1.04 to 0.82; I2=0%; 5 trials, 1267 women).ConclusionsThere is no evidence from randomized trials of a beneficial effect of eHealth interventions on IPV. More high-quality trials are needed, and we recommend harmonizing outcome reporting in IPV trials by establishing core outcome sets.Trial registrationPROSPERO International Prospective Register of Systematic Reviews CRD42019130124; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130124.

Project description:BACKGROUND:Electronic health (eHealth) is increasingly used for self-management and service delivery of HIV-related diseases. With the publication of studies increasingly focusing on antiretroviral therapy (ART) adherence, this makes it possible to quantitatively and systematically assess the effectiveness and feasibility of eHealth interventions. OBJECTIVE:The purpose of this review was to explore the effectiveness of eHealth interventions on improving ART adherence in people living with HIV. The effects of different intervention characteristics, participant characteristics, and study characteristics were also assessed. METHODS:We systematically searched MEDLINE (via PubMed), EMBASE, the Cochrane Central Register of Controlled Trials, and 3 conference abstract databases using search terms related to HIV, ART, adherence, and eHealth interventions. We independently screened the studies, extracted the data, and assessed the study quality and then compared the information in pairs. Articles published in English that used randomized controlled trials to assess eHealth interventions to improve ART adherence of people living with HIV were identified. We extracted the data including study characteristics, participant characteristics, intervention characteristics, and outcome measures. The Cochrane risk-of-bias tool was used to assess the risk of bias and study overall quality. Odds ratios, Cohen d, and their 95% CIs were estimated using random-effects models. We also performed multiple subgroup analyses and sensitivity analyses to define any sources of heterogeneity. RESULTS:Among 3941 articles identified, a total of 19 studies (including 21 trials) met the inclusion criteria. We found 8 trials from high-income countries and 13 trials from low- and middle-income countries. Furthermore, at baseline, the health status of participants in 14 trials was healthy. Of the trials included, 7 of 21 used personality content, 12 of 21 used a 2-way communication strategy, and 7 of 21 used medical content. In the pooled analysis of 3937 participants (mean age: 35 years; 47.16%, 1857/3937 females), eHealth interventions significantly improved the ART adherence of people living with HIV (pooled Cohen d=0.25; 95% CI 0.05 to 0.46; P=.01). The interventions were also correlated with improved biochemical outcomes reported by 11 trials (pooled Cohen d=0.25; 95% CI 0.11 to 0.38; P<.001). The effect was sensitive to sample size (Q=5.56; P=.02) and study duration (Q=8.89; P=.003), but it could not be explained by other moderators. The primary meta-analysis result was stable in the 3 sensitivity analyses. CONCLUSIONS:Some of the eHealth interventions may be used as an effective method to increase the ART adherence of people living with HIV. Considering that most of the trials included a small sample size and were conducted for a short duration, these results should be interpreted with caution. Future studies need to determine the features of eHealth interventions to better improve ART adherence along with long-term effectiveness of interventions, effectiveness of real-time adherence monitoring, enhancement of study design, and influences on biochemical outcomes.

Project description:Introduction:The objectives of this review paper were to synthesize the data from randomized controlled trials in the literature to come to a conclusion on the effects of e-health interventions on promoting physical activity in older people. Methods:The Medline, CINAHL, Embase, PsycINFO, and SportDiscus databases were searched for articles about studies that 1) recruited subjects with a mean age of >?50?years, 2) tested e-health interventions, 3) employed control groups with no or less advanced e-health strategies, 4) measured physical activity as an outcome, 5) were published between 1st January 2008 and 31st May 2019, and 6) employed randomized controlled trials. The risk of bias in individual studies was assessed using the Physiotherapy Evidence Database scale. To examine the effects of the interventions, variables quantifying the amount of physical activity were extracted. The within-group effects of individual studies were summarized using Hedges g and 95% confidence intervals. Between-group effects were summarized by meta-analyses using RevMan 5.0 with a random effect model. Results:Of the 2810 identified studies, 38 were eligible, 25 were included in the meta-analyses. The within-group effect sizes (Hedges g) of physical activity in the intervention group at T1 ranged from small to large: physical activity time (0.12 to 0.84), step counts (-?0.01 to 11.19), energy expenditure (-?0.05 to 0.86), walking time (0.13 to 3.33), and sedentary time (-?0.12 to -?0.28). The delayed effects as observed in T2 and T3 also ranged from small to large: physical activity time (0.24 to 1.24) and energy expenditure (0.15 to 1.32). In the meta-analysis, the between-group effect of the e-health intervention on physical activity time measured by questionnaires, physical activity time measured by objective wearable devices, energy expenditure, and step counts were all significant with minimal heterogeneity. Conclusion:E-health interventions are effective at increasing the time spent on physical activity, energy expenditure in physical activity, and the number of walking steps. It is recommended that e-health interventions be included in guidelines to enhance physical activity in older people. Further studies should be conducted to determine the most effective e-health strategies.

Project description:BackgroundElectronic health (eHealth) has the potential to improve health outcomes. However, eHealth systems need to match the eHealth literacy needs of users to be equitably adopted. Socially disadvantaged groups have lower access and skills to use technologies and are at risk of being digitally marginalized, leading to the potential widening of health disparities.ObjectiveThis systematic review aims to explore the role of eHealth literacy and user involvement in developing eHealth interventions targeted at socially disadvantaged groups.MethodsA systematic search was conducted across 10 databases for eHealth interventions targeted at older adults, ethnic minority groups, low-income groups, low-literacy groups, and rural communities. The eHealth Literacy Framework was used to examine the eHealth literacy components of reviewed interventions. The results were analyzed using narrative synthesis.ResultsA total of 51 studies reporting on the results of 48 interventions were evaluated. Most studies were targeted at older adults and ethnic minorities, with only 2 studies focusing on low-literacy groups. eHealth literacy was not considered in the development of any of the studies, and no eHealth literacy assessment was conducted. User involvement in designing interventions was limited, and eHealth intervention developmental frameworks were rarely used. Strategies to assist users in engaging with technical systems were seldom included in the interventions, and accessibility features were limited. The results of the included studies also provided inconclusive evidence on the effectiveness of eHealth interventions.ConclusionsThe findings highlight that eHealth literacy is generally overlooked in developing eHealth interventions targeted at socially disadvantaged groups, whereas evidence about the effectiveness of such interventions is limited. To ensure equal access and inclusiveness in the age of eHealth, eHealth literacy of disadvantaged groups needs to be addressed to help avoid a digital divide. This will assist the realization of recent technological advancements and, importantly, improve health equity.

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

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

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