Evidence map›Paper›PMID 42712355›Full record

ReviewBMJ digital health & AI2026

Staged, life-cycle approach to evidence generation for digital health interventions in low- and middle-income countries.

Gareth Obery, Eva Weicken, Shubhanan Upadhyay, Max Rath, Bilal A Mateen, Saira Ghafur

Abstract readReview
In one paragraph

Review in BMJ digital health & AI, 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

6 authors.

Gareth OberyProva Health, London, UK.ORCID https://orcid.org/0000-0002-7257-6864
Eva WeickenFraunhofer-Institut für Nachrichtentechnik Heinrich-Hertz-Institut HHI, Berlin, Germany.ORCID https://orcid.org/0009-0007-5185-8145
Shubhanan UpadhyaySandiQ Global, Bordeaux, France.
Max RathAI Diagnostics, Cape Town, South Africa.ORCID https://orcid.org/0009-0009-4997-9233
Bilal A MateenPATH, London, UK.
Saira GhafurProva Health, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital health interventions (DHIs) are rapidly expanding across low- and middle-income countries (LMICs), driven by their potential to address persistent health system constraints, workforce shortages and health inequities. However, the evaluation of these interventions has not kept pace with their adoption. Traditional evidence-generation approaches, particularly those centred on rigid, resource-intensive study designs, are often poorly aligned with the iterative nature of digital technologies and the realities of resource-constrained settings. This misalignment can exacerbate existing system pressures, slow validation and contribute to an enduring evidence-implementation gap that limits sustainable adoption and scale. This narrative review synthesises peer-reviewed biomedical literature, grey literature, institutional reports and applied case studies published between 2015 and 2025 to examine how evidence for DHIs is generated in LMICs. The analysis is organised across three interrelated domains: contextual and stakeholder dynamics shaping evaluations, methodological innovations suited to LMIC realities and top-down policy and regulatory mechanisms influencing adoption and scale. Drawing on this synthesis, the review argues for a staged, life-cycle-based approach to evidence generation, in which evaluation methods and evidence strategies evolve as the intervention matures and the implementation context evolves. This approach emphasises pragmatic, context-sensitive strategies that balance scientific rigour with feasibility. This review aims to provide decision-oriented guidance for innovators, implementers, policy-makers and regulators seeking to generate appropriate, actionable and policy-relevant evidence to support the adoption, integration and scale of DHIs in LMIC health systems.

Indexed as

Artificial intelligenceGlobal HealthImplementation Science

Identifiers

PMID42712355
PMCPMC13548215

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.