ReviewSensors (Basel, Switzerland)2024
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Blood Pressure Phenotypes in Patients Recently Diagnosed With Type 2 Diabetes Mellitus and Matched Controls: A Study in Urban Mozambique.Journal of clinical hypertension (Greenwich, Conn.) · 2026Article
- Rural-Urban Differences in the Hypertension Cascade of Care in Northwestern Tanzania: A Community-Based Cross-Sectional Study.Research square · 2026Article
- Deep learning prediction of nocturnal hypertension for patients intolerant to ambulatory blood pressure monitoring.Communications medicine · 2026Article
- Cascade of care for type 2 diabetes mellitus and hypertension in rural Bangladesh: sub-regional mapping of gaps in care.BMJ public health · 2026Article
- Estimating blood pressure from the electrocardiogram: findings of a large-scale negative results study.Physiological measurement · 2025Article
- Evaluating the Accuracy of Low-Cost Wearable Sensors for Healthcare Monitoring.Micromachines · 2025Article
- Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters.medRxiv : the preprint server for health sciences · 2024Article
- Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.Journal of medical Internet research · 2024Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.