This post explains what the July 24, 2026 scoping review in PLOS Global Public Health found about mHealth for task-sharing in Africa, and what qualitative researchers, program designers, and health system evaluators should do next. The primary keyword is mHealth for task-sharing in Africa and the payoff is a clear set of evidence-backed functions, barriers, and methods you can apply to qualitative studies and program evaluations.
Key Takeaways
According to the PLOS Global Public Health scoping review by Mhlanga et al. (published July 24, 2026) PLOS Global Public Health, mHealth is the dominant digital health approach used by non-physician health workers to support task-sharing for non-communicable disease care in Africa.
According to Mhlanga et al. (PLOS Global Public Health, July 24, 2026), the review screened 4, 857 citations through August 3, 2025, included 15 studies from eight countries, and reported mHealth in 46.7% (7/15) of included studies.
- 4, 857 citations were identified and screened up to August 3, 2025, with 15 studies ultimately included, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
- Community health workers were primary end users in 46.7% (7/15) of studies, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
- The largest country share was South Africa (26.7%, 4/15 studies), and most included studies were published by 2023, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
- Mhlanga et al. concluded that "Digital health, particularly mHealth, supports NPHWs in task-sharing for NCD prevention and management in Africa" (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
- Programs used simple modalities such as SMS and calls; Chibanda et al. described using "up to six SMSs and phone calls" to support psychosocial interventions (Chibanda et al., cited in Mhlanga et al., PLOS Global Public Health, July 24, 2026).
What happened and how the review was done
Answer: The PLOS Global Public Health scoping review mapped how digital health supports task-sharing by non-physician health workers (NPHWs) for NCD prevention and management across Africa, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Method detail: The authors searched PubMed, Scopus, and Google Scholar from 2014 through August 3, 2025, screened 4, 857 records, retrieved 71 full texts, and included 15 studies from eight countries, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Scope detail: The review focused on technology types, user cadres, functions (prevention, screening, treatment delivery, supervision), effectiveness, and enablers/barriers, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Limitations: The review excluded grey literature and non-English reports, which may narrow geographic and programmatic representation, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Findings Snapshot
| Date / Period | Metric | Value | Implication (from Mhlanga et al.) |
|---|---|---|---|
| Search period (to) | Database search end | 3 August 2025 | Defines the literature cut-off for included evidence (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
| Search results | Citations identified | 4, 857 | Large initial yield but narrow final sample signals limited empirical studies on digital health task-sharing in Africa (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
| Included studies | Studies included | 15 studies from 8 countries | Evidence base is small and geographically scattered, limiting generalizability (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
| Technology split | mHealth usage | 46.7% (7/15 studies) | mHealth dominates the evidence; other digital innovations (AI, sensors) are underreported (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
| Primary users | Community health workers (CHWs) | 46.7% (7/15 studies) | CHWs are the main NPHW cadre using mHealth in the included studies (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
| Country concentration | South Africa share | 26.7% (4/15 studies) | A single country contributes a quarter of evidence, suggesting regional research gaps (Mhlanga et al., PLOS Global Public Health, Jul 24, 2026). |
Implications for researchers and program teams
Answer: Researchers and program teams should prioritize mixed-methods evaluations of mHealth task-sharing pilots and document context-sensitive implementation factors, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Design implication: Because the review found only 15 studies from eight countries, program teams should embed rigorous qualitative data collection in pilots to surface local barriers such as digital literacy, electricity, and connectivity, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Measurement implication: Because only four studies reported patient outcomes, researchers should include both process measures and patient-level outcomes (e.g., linkage to care, symptom change) in evaluations, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Technology implication: Because mHealth apps and SMS/call features were predominant, implementers should test context-adapted, low-bandwidth solutions and consider integrating decision support or AI only after infrastructure and governance gaps are addressed, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Equity implication: Because CHWs were the main users in nearly half the studies, programs must co-design tools with CHWs and include practical training (some studies used 2 to 9 days of training) to address low digital literacy, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
How Evidano helps researchers studying mHealth for task-sharing
Problem: scattered qualitative evidence and slow synthesis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution: Use Evidano to ingest interview transcripts, program reports, and the included papers to generate rapid thematic syntheses and frequency analyses that surface barriers like "low digital literacy" and "poor connectivity" identified by Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Tool link: Learn how thematic analysis and AI chat over documents work on our features page.
Problem: transcription/translation gaps in multi-site studies
Answer: Evidano provides automated transcription and translation with custom dictionaries and PII redaction to standardize multi-site qualitative datasets.
Practical use: Upload audio from CHW focus groups or training sessions to Evidano's speech-to-text pipeline to get timestamped transcripts that can be coded and compared across sites, helping teams follow Mhlanga et al.'s recommendation to document training and contextual differences (PLOS Global Public Health, July 24, 2026).
Problem: aligning qualitative themes to program metrics
Answer: Evidano links thematic codes to quantitative segments so teams can compare findings across cadres, regions, and intervention arms.
Practical use: Use Evidano to cross-tabulate themes like "connectivity" or "acceptability" against participant characteristics, yielding the kind of cross-segment analyses that program evaluators need to interpret why some mHealth pilots reported neutral outcomes in blood pressure or HbA1c, as reported by Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
FAQ: mHealth for task-sharing in Africa
How common is mHealth in task-sharing interventions in Africa?
Answer: mHealth was the most frequently used digital health technology in the 15 studies included in the review, appearing in 46.7% (7/15) of studies, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Support: The review reports that mHealth modalities ranged from SMS and voice calls to customized smartphone applications used by CHWs, nurses, and lay counsellors (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
Do mHealth-supported task-sharing interventions improve patient outcomes?
Answer: Evidence is mixed; four studies reported patient outcomes and two reported positive mental health outcomes while two reported neutral biomedical outcomes, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Support: Improvements were reported for common mental health symptoms in two studies, whereas trials of diabetes prevention and hypertension management showed no significant change in some biomedical measures like HbA1c and systolic blood pressure (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
What are the main barriers to effective mHealth task-sharing in Africa?
Answer: The main barriers are low digital literacy among NPHWs, poor internet connectivity, and inadequate electricity for charging devices, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Support: The review includes examples where nurses struggled with smartphone imaging for cervical screening and CHWs found some app forms complicated, underscoring the need for user-centered design and training (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
How should researchers design qualitative components for mHealth task-sharing studies?
Answer: Researchers should embed theory-driven qualitative questions, document training and supervision, and capture contextual enablers and barriers, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
Support: Because the evidence base included only 15 studies and varied reporting on training durations (2 to 9 days where reported), qualitative methods that capture implementation details will strengthen future syntheses and scale-up recommendations (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
Conclusion & Next Steps
Mhlanga et al.'s July 24, 2026 scoping review in PLOS Global Public Health shows that mHealth is the primary digital approach used to support NPHW task-sharing for NCD care in Africa, but the evidence is limited to 15 studies from eight countries and mixed on patient outcomes (Mhlanga et al., PLOS Global Public Health, July 24, 2026).
Researchers and implementers should prioritize rigorous mixed-methods pilots, user-centered design for low-literacy CHWs, and documentation of training, supervision, and infrastructure constraints, according to Mhlanga et al. (PLOS Global Public Health, July 24, 2026).
If you are planning a qualitative or mixed-methods evaluation of an mHealth task-sharing pilot, use AI-enabled tools to accelerate transcription, thematic synthesis, and cross-segment analysis; learn about Evidano's capabilities on our features page.
Next step: Try Evidano for free to import transcripts, run thematic and frequency analyses, and produce implementation-ready evidence for funders and ministries of health.