Understanding the Needs of a Mobile Phone–Based Telemonitoring Program for Pregnant Women at High Risk for Pre-Eclampsia: Interpretive Qualitative Description Study
Bibliographic record
Abstract
BACKGROUND: Lack of early risk detection, diagnosis, and treatment of pregnant women at high risk for pre-eclampsia can result in high maternal mortality and morbidity not only in Pakistan but also in other low- to middle-income countries (LMICs). A potential tool for supporting pregnant women at high risk for pre-eclampsia for early detection is telemonitoring (TM). However, there is a limited body of evidence on end-user needs and preferences to inform the design of the TM programs for pregnant women at high risk for pre-eclampsia, specifically in LMICs such as Pakistan. OBJECTIVE: This study aims to explore the needs of TM for pregnant women at high risk for pre-eclampsia in Karachi, Pakistan, to inform a potential future feasibility trial of a mobile phone-based TM program. METHODS: An interpretive qualitative description approach was used to conduct and analyze 36 semistructured interviews with 15 (42%) pregnant women and 21 (58%) key informants, including clinicians; nurses; maternal, neonatal, and child health specialists; and digital health experts to explore the perspectives, needs, and preferences of a mobile phone-based TM program to support pregnant women at high risk for pre-eclampsia. Pregnant women were identified through heterogeneous sampling, whereas key informants were selected through purposive sampling. The interview transcripts were analyzed using a conventional content analysis technique. RESULTS: The following four themes emerged from the analysis of the transcripts: poor use of antenatal care during pregnancy, the value of a TM program in high-risk pregnancy, barriers influencing the adoption of TM programs and potential strategies, and considerations for implementing TM programs. The pregnant women and health care providers were willing to use a TM program as they perceived many benefits, including early identification of pregnancy complications, prompt treatment, convenience, cost-effectiveness, increased sense of empowerment for one's health care, improved care continuity, and reduced clinical workload. However, some providers and pregnant women mentioned some concerns regarding the adoption of a TM program, including malfunctioning and safety concerns, potential inaccuracy of blood pressure machines, increased clinical workload, and resistance to learning new technology. Our study recommends building the capacity of patients and providers on TM program use, sensitizing the community and family members on the usefulness of the TM program, using an approach incorporating user-centered design and phased implementation to determine the clinical workload and whether additional staff for the TM program is required, and ensuring greater levels of co-design and the engagement of consumer representatives. CONCLUSIONS: Our findings highlight the perceived feasibility of a mobile phone-based TM program for pregnant women at high risk for pre-eclampsia and provide insights that can be directly used for the design of future TM programs with the aim of reducing mortality and morbidity from pre-eclampsia and eclampsia in LMICs.
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".