Evidence map›Paper›PMID 41878107›Full record

SynthesisFrontiers in public health2026

Instant messaging-based digital health interventions for diabetes management: a domain-structured systematic review and meta-analysis of randomized controlled trials.

Shan Chen, Emma Mirza Wati Mohamad, Arina Anis Azlan, Xixi Zhao

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Shan ChenFaculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
Emma Mirza Wati MohamadFaculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
Arina Anis AzlanFaculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
Xixi ZhaoFaculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Instant messaging delivered through social platforms is increasingly used to support diabetes self-management. However, evidence remains difficult to interpret because trials vary widely in platform choice, intervention design, outcome constructs, and measurement instruments. Objective: To synthesize the effects of instant-messaging interventions for diabetes across pre-specified outcome domains, and to explore whether platform type, follow-up duration, and study size help explain variation in effect estimates. Methods: We searched seven databases (2010-2025) for randomized controlled trials in which diabetes interventions were primarily delivered via instant-messaging/social platforms. Outcomes were organized a priori into six domains: health behaviors, diabetes knowledge, attitudes/self-efficacy, glycemic outcomes, other clinical outcomes, and diabetes-related complications. Continuous outcomes were pooled as standardized mean differences (SMDs) and binary outcomes as risk ratios (RRs) using random-effects models (REML). To improve interpretability, we prioritized domain-level synthesis and performed platform-stratified pooling only when at least three effect sizes were available within a given domain. Heterogeneity was summarized using τ Results: Twenty-three trials contributed 236 effect estimates. Overall pooled effects across all continuous and binary outcomes were close to null and statistically non-significant, with substantial heterogeneity. Domain-specific synthesis showed clearer patterns: diabetes knowledge demonstrated the largest pooled improvement (SMD = 1.065, 95% CI 0.185-1.944), glycemic outcomes improved on continuous measures (SMD = -0.519, 95% CI -0.719 to -0.319), and behavioral outcomes showed a small but significant benefit (SMD = 0.359, 95% CI 0.010-0.709). Attitudes/self-efficacy and other clinical outcomes were more heterogeneous and did not show clear pooled benefits. For complications (binary outcomes), the pooled estimate suggested a potential reduction in risk (RR = 0.67, 95% CI 0.44-1.00) based on three studies and should be interpreted cautiously. Platform-overview pooling of continuous outcomes suggested variability across platforms, with more consistently positive pooled effects for Facebook Messenger-based interventions than for WhatsApp or WeChat; however, platform-by-domain pooling was often not estimable because many platform-domain combinations contributed fewer than three effect sizes. Meta-regression did not identify a clear linear association of follow-up duration or ln(sample size) with effect size, and explained little heterogeneity. Conclusions: Instant-messaging interventions for diabetes do not yield a clearly favorable overall pooled effect, but they show credible benefits for behavioral outcomes and selected clinical endpoints. Variation in effects appears more consistent with differences in intervention design and implementation than with platform labels alone. Future trials should report intervention components and maintenance strategies in greater detail and evaluate interactive, care-integrated messaging models. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251079157, identifier: CRD420251079157.

Indexed as

Diabetes MellitusSelf-ManagementText MessagingDigital HealthDigital MediaHumansRandomized Controlled Trials as Topicdiabetesdigital healthinstant messagingmeta-analysisrandomized controlled trialsself-managementsocial media

Identifiers

PMID41878107
PMCPMC13006411

What Socratic holds

Textmetadata
LicenceCC BY
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.