Exploring the Use of Telemonitoring for Patients at High Risk for Hypertensive Disorders of Pregnancy in the Antepartum and Postpartum Periods: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: High blood pressure complicates 2% to 8% of pregnancies, and its complications are present in the antepartum and postpartum periods. Blood pressure during and after pregnancy is routinely monitored during clinic visits. Some guidelines recommend using home blood pressure measurements for the management and treatment of hypertension, with increased frequency of monitoring for high-risk pregnancies. Blood pressure self-monitoring may have a role in identifying those in this high-risk group. Therefore, this high-risk pregnancy group may be well suited for telemonitoring interventions. OBJECTIVE: The aim of this study was to explore the use of telemonitoring in patients at high risk for hypertensive disorders of pregnancy (HDP) during the antepartum and postpartum periods. This paper aims to answer the following question: What is the current knowledge base related to the use of telemonitoring interventions for the management of patients at high risk for HDP? METHODS: A literature review following the methodological framework described by Arksey et al and Levac et al was conducted to analyze studies describing the telemonitoring of patients at high risk for HDP. A qualitative study, observational studies, and randomized controlled trials were included in this scoping review. RESULTS: Of the 3904 articles initially identified, 20 met the inclusion criteria. Most of the studies (13/20, 65%) were published between 2017 and 2018. In total, there were 16 unique interventions described in the 20 articles, all of which provide clinical decision support and 12 of which are also used to facilitate the self-management of HDP. Each intervention's design and process of implementation varied. Overall, telemonitoring interventions for the management of HDP were found to be feasible and convenient, and they were used to facilitate access to health services. Two unique studies reported significant findings for the telemonitoring group, namely, spontaneous deliveries were more likely, and one study, reported in two papers, described inductions as being less likely to occur compared with the control group. However, the small study sample sizes, nonrandomized groups, and short study durations limit the findings from the included articles. CONCLUSIONS: Although current evidence suggests that telemonitoring could provide benefits for managing patients at high risk for HDP, more research is needed to prove its safety and effectiveness. This review proposes four recommendations for future research: (1) the implementation of large prospective studies to establish the safety and effectiveness of telemonitoring interventions; (2) additional research to determine the context-specific requirements and patient suitability to enhance accessibility to healthcare services for remote regions and underserved populations; (3) the inclusion of privacy and security considerations for telemonitoring interventions to better comply with healthcare information regulations and guidelines; and (4) the implementation of studies to better understand the effective components of telemonitoring interventions.
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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 | 0,012 | 0,073 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,016 | 0,015 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».