Evidence mapPaperPMID 37542200Full record

SynthesisActa diabetologica2023

The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials.

Mozhgan Tanhapour, Maryam Peimani, Sharareh Rostam Niakan Kalhori, Ensieh Nasli Esfahani, Hadi Shakibian, Niloofar Mohammadzadeh, Mostafa Qorbani

Erratum issuedAbstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Acta diabetologica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
7.4field-weighted citation impact, top 3% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 2 countries.

Mozhgan Tanhapour *Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0003-4691-4740
Maryam Peimani *Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Science, Tehran, Iran.ORCID http://orcid.org/0000-0003-4602-5468
Sharareh Rostam Niakan KalhoriDepartment of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0002-7577-1200
Ensieh Nasli EsfahaniDiabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Science, Tehran, Iran.ORCID http://orcid.org/0000-0001-6334-5469
Hadi ShakibianDepartment of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran.ORCID http://orcid.org/0000-0002-6828-2361
Niloofar MohammadzadehDepartment of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran. nmohammadzadeh@sina.tums.ac.ir.ORCID http://orcid.org/0000-0001-7586-9227
Mostafa QorbaniNon-communicable Disease Research Center, Alborz University of Medical Sciences, Karaj, Iran.ORCID http://orcid.org/0000-0001-9465-7588
Tehran University of Medical Sciences · IRAlzahra University · IRJahrom University of Medical Sciences · IRMedizinische Hochschule Hannover · DESina Hospital · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsType 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care. Intelligent digital health (IDH) solutions have a promising role in training self-care behaviors based on patients' needs. This study reviews the effects of RCTs offering individualized self-care training systems for T2D patients.

methodsPubMed, Web of Science, Scopus, Cochrane Library, and Science Direct databases were searched. The included RCTs provided data-driven, individualized self-care training advice for T2D patients. Due to the repeated studies measurements, an all-time-points meta-analysis was conducted to analyze the trends over time. The revised Cochrane risk-of-bias tool (RoB 2.0) was used for quality assessment.

resultsIn total, 22 trials met the inclusion criteria, and 19 studies with 3071 participants were included in the meta-analysis. IDH interventions led to a significant reduction of HbA1c level in the intervention group at short-term (in the third month: SMD = - 0.224 with 95% CI - 0.319 to - 0.129, p value < 0.0; in the sixth month: SMD = - 0.548 with 95% CI - 0.860 to - 0.237, p value < 0.05). The difference in HbA1c reduction between groups varied based on patients' age and technological forms of IDH services delivery. The descriptive results confirmed the impact of M-Health technologies in improving HbA1c levels.

conclusionsIDH systems had significant and small effects on HbA1c reduction in T2D patients. IDH interventions' impact needs long-term RCTs. This review will help diabetic clinicians, self-care training system developers, and researchers interested in using IDH solutions to empower T2D patients.

Indexed as

Diabetes Mellitus, Type 2Glycated HemoglobinHumansSelf CareGlycated HemoglobinArtificial intelligenceDigital technologyPrecision medicineSelf-careType 2 diabetes

Identifiers

PMID37542200
OpenAlexW4385565631

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.