Evidence map›Paper›PMID 35853936›Full record

ReviewNPJ digital medicine2022

A systematic review of healthcare provider-targeted mobile applications for non-communicable diseases in low- and middle-income countries.

Pascal Geldsetzer, Sergio Flores, Grace Wang, Blanca Flores, Abu Bakarr Rogers, Aditi Bunker, Andrew Y Chang, Rebecca Tisdale

Open access · goldAbstract readReview
In one paragraph

Review in NPJ digital medicine, 2022. 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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
5.3field-weighted citation impact, top 4% 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.

  1. Pooled it
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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

8 authors at 5 institutions in 3 countries.

Pascal Geldsetzer *Division of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA.
Sergio Flores *Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden.ORCID http://orcid.org/0000-0001-8700-1089
Grace WangDepartment of Biology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-1044-9381
Blanca FloresHeidelberg University Hospital, Heidelberg, Germany.ORCID http://orcid.org/0000-0002-9978-9552
Abu Bakarr RogersStanford University School of Medicine, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-6552-1750
Aditi BunkerHeidelberg Institute of Global Health, Heidelberg University, Heidelberg, Germany.
Andrew Y ChangCenter for Innovation in Global Health, Stanford University, Stanford, CA, USA.
Rebecca TisdaleVeterans Affairs Palo Alto Healthcare System, Center for Innovation to Implementation, Menlo Park, CA, USA. rtisdale@stanford.edu.ORCID http://orcid.org/0000-0003-2749-6889
Heidelberg University · DEStanford University · USCardiovascular Institute of the South · USUmeå University · SEVA Palo Alto Health Care System · US

Funding

Institutional Career Development Core (KL2)KL2TR003143 · NCATS · STANFORD UNIVERSITY · PI ASCH, STEVEN M. · 2019 to 2023
$8.7M
NCATS NIH HHS KL2 TR003143U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (NCATS) KL2TR003143
6 · The paper itself

Abstract

Mobile health (mHealth) interventions hold promise for addressing the epidemic of noncommunicable diseases (NCDs) in low- and middle-income countries (LMICs) by assisting healthcare providers managing these disorders in low-resource settings. We aimed to systematically identify and assess provider-facing mHealth applications used to screen for, diagnose, or monitor NCDs in LMICs. In this systematic review, we searched the indexing databases of PubMed, Web of Science, and Cochrane Central for studies published between January 2007 and October 2019. We included studies of technologies that were: (i) mobile phone- or tablet-based, (ii) able to screen for, diagnose, or monitor an NCD of public health importance in LMICs, and (iii) targeting health professionals as users. We extracted disease type, intervention purpose, target population, study population, sample size, study methodology, technology stage, country of development, operating system, and cost. Our initial search retrieved 13,262 studies, 315 of which met inclusion criteria and were analyzed. Cardiology was the most common clinical domain of the technologies evaluated, with 89 publications. mHealth innovations were predominantly developed using Apple's iOS operating system. Cost data were provided in only 50 studies, but most technologies for which this information was available cost less than 20 USD. Only 24 innovations targeted the ten NCDs responsible for the greatest number of disability-adjusted life years lost globally. Most publications evaluated products created in high-income countries. Reported mHealth technologies are well-developed, but their implementation in LMICs faces operating system incompatibility and a relative neglect of NCDs causing the greatest disease burden.

Identifiers

PMID35853936
PMCPMC9296618
OpenAlexW4285793733

What OpenQuestion 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 OpenQuestion graph.