Implementation barriers for mHealth for non-communicable diseases management in low and middle income countries: a scoping review and field-based views from implementers
Bibliographic record
Abstract
Background : Mobile health (mHealth) has been hailed as a potential gamechanger for non-communicable disease (NCD) management, especially in low- and middle-income countries (LMIC). Individual studies illustrate barriers to implementation and scale-up, but an overview of implementation issues for NCD mHealth interventions in LMIC is lacking. This paper explores implementation issues from two perspectives: information in published papers and field-based knowledge by people working in this field. Methods : Through a scoping review publications on mHealth interventions for NCDs in LMIC were identified and assessed with the WHO mHealth Evidence Reporting and Assessment (mERA) tool. A two-stage web-based survey on implementation barriers was performed within a NCD research network and through two online platforms on mHealth targeting researchers and implementors. Results : 16 studies were included in the scoping review. Short Message Service (SMS) messaging was the main implementation tool. Most studies focused on patient-centered outcomes. Most studies did not report on process measures and on contextual conditions influencing implementation decisions. Few publications reported on implementation barriers. The websurvey included twelve projects and the responses revealed additional information, especially on practical barriers related to the patients’ characteristics, low demand, technical requirements, integration with health services and with the wider context. Many interventions used low-cost software and devices with limited capacity that not allowed linkage with routine data or patient records, which incurred fragmented delivery and increased workload. Conclusion : Text messaging is a dominant mHealth tool for patient-directed of quality improvement interventions in LMIC. Publications report little on implementation barriers, while a questionnaire among implementors reveals significant barriers and strategies to address them. This information is relevant for decisions on scale-up of mHealth in the domain of NCD. Further knowledge should be gathered on implementation issues, and the conditions that allow universal coverage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.180 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".