Understanding the Needs of a Mobile Phone–Based Telemonitoring Program for Pregnant Women at High Risk for Pre-Eclampsia: Interpretive Qualitative Description Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,007 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».