Evidence map›Paper›PMID 42871285›Full record

ReviewFrontiers in public health2026

Three pillars for digital health assessment in LMICs: a mini review of technology benchmarking, digital health literacy, and social media listening.

Anggi Septia Irawan, Bence Döbrössy, Edmond Girasek

Abstract readReview
In one paragraph

Review 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

3 authors.

Anggi Septia IrawanInstitute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
Bence DöbrössyInstitute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.
Edmond GirasekInstitute of Behavioural Sciences, Semmelweis University, Budapest, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health has emerged as a critical component of healthcare delivery in low- and middle-income countries (LMICs), offering opportunities to strengthen access, monitoring, and engagement across diverse populations. However, existing evaluations often focus narrowly on technological functionality while overlooking broader contextual factors such as digital literacy and public discourse. This mini-review proposes an integrative framework for digital health assessment built around three complementary pillars: technology benchmarking, digital health literacy, and social media listening. A structured literature search of PubMed, supplemented by Web of Science, was conducted to identify representative studies published between 2020 and 2026 relevant to digital health implementation in LMICs. Fifteen studies were selected to provide illustrative evidence across the three domains. The proposed framework highlights the need for multidimensional evaluation approaches that move beyond conventional app-based metrics. While broadly applicable across healthcare sectors, the framework offers particular relevance for population groups requiring sustained engagement, including maternal, child, and adolescent health contexts. This review contributes a practical conceptual model for researchers, policymakers, and developers seeking to strengthen digital health strategies in resource-constrained settings.

Indexed as

BenchmarkingDeveloping CountriesDigital HealthHealth LiteracySocial MediaDigital MediaHumansResource-Limited Settingsdigital healthdigital health literacyhealth technology assessmentmHealthsocial media listeningtechnology benchmarking

Identifiers

PMID42871285
PMCPMC13659100

What OpenQuestion 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 OpenQuestion graph.