Exploring the Use of Telemonitoring for Patients at High Risk for Hypertensive Disorders of Pregnancy in the Antepartum and Postpartum Periods: Scoping Review
Bibliographic record
Abstract
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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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 | 0.012 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".