Exploring digital health interventions for pregnant women at high risk for pre-eclampsia and eclampsia in low-income and-middle-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To explore digital health interventions that have been used to support pregnant women at high risk for pre-eclampsia/eclampsia (HRPE/E) in low-income and middle-income countries (LMICs). DESIGN: Scoping review. DATA SOURCE: EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews and CINAHL were searched between 1 January 2000 and 20 October 2020. ELIGIBILITY CRITERIA: The review included original research studies that were published in English, involved pregnant women at HRPE/E and implemented digital health interventions for PE/E in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently completed the data extraction for each of the 19 final articles. An inductive approach was used to thematically organise and summarise the results from the included articles. RESULTS: A total of 19 publications describing 7 unique studies and 9 different digital health interventions were included. Most studies were conducted in South Asia and sub-Saharan Africa (n=16). Of nine unique digital health interventions, two served the purpose of predicting risk for adverse maternal health outcomes while seven focused on monitoring high-risk pregnant women for PE/E. Both of these purposes used mobile phone applications as interface to facilitate data collection, decision making, and communication between health workers and pregnant women. The review identified key functions of interventions including data collection, prediction of adverse maternal outcomes, integrated diagnostic and clinical decision support, and personal health tracking. The review reported three major outcomes: maternal health outcomes including maternal and neonatal morbidity and mortality (n=4); usability and acceptability including ease-of-use, and perceived usefulness, (n=5); and intervention feasibility and fidelity including accuracy of device, and intervention implementation (n=7). CONCLUSION: Although the current evidence base shows some potential for the use of digital health interventions for PE/E, more prospective experimental and longitudinal studies are needed prior to recommending the use of digital health interventions for PE/E.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".