Cited by 126Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsTelemedicine and Telehealth Implementation

The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index; 13 of the 115 sources its claims cite were checked for support.

Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries

Open access

Abstract

Current evidence on digital health interventions is disproportionately concerned with high-income countries and hospital settings. This scoping review evaluates the extent of use and effectiveness of digital health interventions for non-communicable disease (NCD) management in primary healthcare settings of low- and middle-income countries (LMICs) and identifies factors influencing digital health interventions’ uptake. We use PubMed, Embase, and Web of Science search results from January 2010 to 2021. Of 8866 results, 52 met eligibility criteria (31 reviews, 21 trials). Benchmarked against World Health Organization’s digital health classifications, only 14 out of 28 digital health intervention categories are found, suggesting critical under-use and lagging innovation. Digital health interventions’ effectiveness vary across outcomes: clinical (mixed), behavioral (positively inclined), and service implementation outcomes (clear effectiveness). We further identify multiple factors influencing digital health intervention uptake, including political commitment, interactivity, user-centered design, and integration with existing systems, which points to future research and practices to invigorate digital health interventions for NCD management in primary health care of LMICs.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This scoping review synthesizes evidence on digital health interventions for non-communicable disease (NCD) management in primary health care in low- and middle-income countries (LMICs), finding under-use relative to WHO classifications and mixed effectiveness across outcomes. It identifies political commitment, interactivity, user-centered design, and integration with existing systems as key factors influencing uptake.

Key findings

  • Of 8,866 records, 52 studies met eligibility criteria: 31 reviews and 21 trials; only 14 of 28 WHO digital health intervention categories were found, indicating “critical under-use and lagging innovation.”
  • Effectiveness varied by outcome: clinical outcomes were “highly mixed,” behavioral outcomes were “more positively inclined,” and service implementation outcomes showed “clear effectiveness” or “consistently positive” results.
  • Identified interventions were mainly used by primary healthcare providers (8 types) and clients (5 types), with 2 used by both; no interventions were found for health system managers.
  • Factors influencing uptake were grouped into policymakers (n = 2), technological industry (n = 4), digital health designers (n = 7), and digital health users (n = 6), including political commitment, interoperability, co-design, training, and incentives.

Methods

  • Scoping review guided by PRISMA-ScR, searching PubMed, Embase, and Web of Science from January 1, 2010, to April 30, 2021, for reviews and experimental studies on digital health for NCD management in primary healthcare settings in LMICs.
  • Three rounds of screening by title, abstract, and full text were conducted by trained reviewers, with at least two independent reviewers per article; 52 papers were included.
  • Data extraction covered study characteristics, digital health interventions, effectiveness by outcomes, and factors influencing uptake; analysis used WHO digital health classifications, WHO quality of care dimensions, and inductive content analysis. Trial quality was assessed with National Heart, Lung, and Blood Institute tools.

Limitations

  • The paper states it focused only on academic publications in English and could not include grey literature, especially from non-English-speaking settings.
  • The paper states high heterogeneity prevented rigorous quantitative synthesis such as meta-analysis, so it used narrative synthesis instead.
  • Evident from the text: more than half of included trials were of suboptimal quality (n = 12), commonly due to lack of randomization (n = 11), lack of evidence on sample size sufficiency (n = 10), and absence of pre-specification for study outcomes (n = 7).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41746-023-00764-4

Cites

87 of 87 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1 Flowchart for study search and screening. The initial results were 8866 items. After duplicate removal and screening by title, abstract, and full-text, a total of 52 papers were included for final analysis.
Fig. 1 Flowchart for study search and screening. The initial results were 8866 items. After duplicate removal and screening by title, abstract, and full-text, a total of 52 papers were included for final analysis.CC BY · PubMed Central
Fig. 2 The effectiveness of digital health for non-communicable disease management in primary health care in included trials. We found three major types of outcomes from all included trials, and digital health interventions’ effectiveness varied among clinical (mixed), behavioral (positively inclined), and service implementation outcomes (clear effectiveness).
Fig. 2 The effectiveness of digital health for non-communicable disease management in primary health care in included trials. We found three major types of outcomes from all included trials, and digital health interventions’ effectiveness varied among clinical (mixed), behavioral (positively inclined), and service implementation outcomes (clear effectiveness).CC BY · PubMed Central
Fig. 3 The effectiveness of digital health for non-communicable disease management in primary health care in included reviews. We found three major types of outcomes from all included reviews, and digital health interventions’ effectiveness varied among clinical (mixed), behavioral (positively inclined), and service implementation outcomes (clear effectiveness).
Fig. 3 The effectiveness of digital health for non-communicable disease management in primary health care in included reviews. We found three major types of outcomes from all included reviews, and digital health interventions’ effectiveness varied among clinical (mixed), behavioral (positively inclined), and service implementation outcomes (clear effectiveness).CC BY · PubMed Central
Fig. 4 WHO classification of digital health interventions, adapted from WHO 20184. The WHO classification categorized digital health interventions into four groups by different users: clients, healthcare providers, health system managers, and data services.
Fig. 4 WHO classification of digital health interventions, adapted from WHO 20184. The WHO classification categorized digital health interventions into four groups by different users: clients, healthcare providers, health system managers, and data services.CC BY · PubMed Central

Verification

Claims the paper makes

40 Not checked yet · 22 Own result · 8 Own interpretation · 7 Own method · 4 Partly supported · 4 No basis given · 1 Supported · accuracy not yet measured

Verify all 102 references — free account
  • It may state two separate claims as one. 40
  • Probably not a substantial claim. 16
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Self-check

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Each contradiction between two passages listed here was confirmed by an independent check.

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Citations

Cites

Show all 87 references

Research works printed but not linked: 2

  1. World Health Organization. Global Conference on Primary Health Care, 25–26 October 2018—Astana, Kazakhstan. Accessed March 10th, 2021. https://www.who.int/teams/primary-health-care/conference.
  2. Ajay, V. S. et al. Development of a smartphone‐enabled hypertension and diabetes mellitus management package to facilitate evidence‐based care delivery in primary healthcare facilities in India: the mPower Heart Project. J. Am. Heart Assoc. 5, e004343 (2016). · DOI · Paper page

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Metadata

Authors
Xiong, S., Lu, H., Peoples, N., Duman, E. K., Najarro, A., Ni, Z., Gong, E., Yin, R., Ostbye, T., Palileo-Villanueva, L. M., Doma, R., Kafle, S., Tian, M., & Yan, L. L.
Affiliations
  • UNSW Sydney (AU)
  • Wake Forest University Health Sciences (US)
  • Baylor College of Medicine (US)
  • University of Oxford (GB)
  • Peking University (CN)
  • Yale University (US)
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • Nanyang Technological University (SG)
  • Duke University (US)
  • Harbin Medical University (CN)
  • Northwestern University (US)
Corresponding author
  • Xiong, Shangzhi
  • Yan, Lijing L.
Venue
npj Digital Medicine
Published
2023-02-01
Volume
6
Issue
1
Pages
12-12
ISSN
2398-6352 (ISSN-L)
OpenAlex
W4318816231
Cited by
126
References
87
Citations by year
2026: 45 · 2025: 51 · 2024: 25 · 2023: 5
FWCI
6.37
Type
journal article
Language
en
Open access
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • The George Institute for Global Health, University of New South Wales, Sydney, Australia
  • WHO | Asia Pacific Observatory on Health Systems and Policies (201963885)
  • Duke Kunshan University, Jiangsu Province, China
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
Keywords
digital health interventions, non-communicable disease management, primary care, low- and middle-income countries, user-centered design, political commitment, health system innovation

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