ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2019
Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Quantitative assessment of baseline imbalances in evolocumab and alirocumab trials: a meta-epidemiological study.BMC medical research methodology · 2024Pooled it
- Efficacy and Safety of a Fixed-Dose Combination of Cinnarizine 20 mg and Dimenhydrinate 40 mg in the Treatment of Patients with Vestibular Vertigo: An Individual Patient Data Meta-Analysis of Randomised, Double-Blind, Controlled Clinical Trials.Clinical drug investigation · 2022Pooled it
- Electronic Health Interventions for Type 2 Diabetes and Obesity in Hispanic or Latino Adults: A Systematic Review of English and Spanish Studies.Diabetes spectrum : a publication of the American Diabetes Association · 2024Article
- Outcomes of Surgical Treatment of Migraines: A Systematic Review & Meta-Analysis.Plastic surgery (Oakville, Ont.) · 2023Article
- Digital Behavior Change Interventions for the Prevention and Management of Type 2 Diabetes: Systematic Market Analysis.Journal of medical Internet research · 2022Review
- E-Health Practices and Technologies: A Systematic Review from 2014 to 2019.Healthcare (Basel, Switzerland) · 2021Review
- The Role of Continuous Glucose Monitoring, Diabetes Smartphone Applications, and Self-Care Behavior in Glycemic Control: Results of a Multi-National Online Survey.Journal of clinical medicine · 2019Article
- Popular Diabetes Apps and the Impact of Diabetes App Use on Self-Care Behaviour: A Survey Among the Digital Community of Persons With Diabetes on Social Media.Frontiers in endocrinology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsPooling the effect sizes of randomized controlled trials (RCTs) from continuous outcomes, such as glycated hemoglobin level (HbA1c), is an important method in evidence syntheses. However, due to challenges related to baseline imbalances and pre/post correlations, simple analysis of change scores (SACS) and simple analysis of final values (SAFV) meta-analyses result in under- or overestimation of effect estimates. This study was aimed to compare pooled effect sizes estimated by Analysis of Covariance (ANCOVA), SACS, and SAFV meta-analyses, using the example of RCTs of digital interventions with HbA1c as the main outcome. MATERIALS AND
methodsThree databases were systematically searched for RCTs published from 1993 through June 2017. Two reviewers independently assessed titles and abstracts using predefined eligibility criteria, assessed study quality, and extracted data, with disagreements resolved by arbitration from a third reviewer.
resultsANCOVA, SACS, and SAFV resulted in pooled HbA1c mean differences of -0.39% (95% CI: [-0.51, -0.26]), -0.39% (95% CI: [-0.51, -0.26]), and -0.34% (95% CI: [-0.48-0.19]), respectively. Removing studies with both high baseline imbalance (≥±0.2%) and pre/post correlation of ≥±0.6 resulted in a mean difference of -0.39% (95% CI: [-0.53, -0.26]), -0.40% (95% CI: [-0.54, -0.26]), and -0.33% (95% CI: [-0.48, -0.18]) with ANCOVA, SACS, and SAFV meta-analyses, respectively. Substantial heterogeneity was noted. Egger's test for funnel plot symmetry did not indicate evidence of publication bias for all methods.
conclusionBy all meta-analytic methods, digital interventions appear effective in reducing HbA1c in type 2 diabetes. The effort to adjust for baseline imbalance and pre/post correlation using ANCOVA relies on the level of detail reported from individual studies. Reporting detailed summary data and, ideally, access to individual patient data of intervention trials are essential.
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