ReviewNPJ digital medicine2023
Emerging sensing and modeling technologies for wearable and cuffless blood pressure monitoring.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in carotid research: a 25-year bibliometric analysis of global trends and future directions.Frontiers in artificial intelligence · 2026Pooled it
- Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation.IEEE transactions on bio-medical engineering · 2024Pooled it
- Clinical Readiness of Hemodynamic Estimation from Cardiac Vibration Signals: An Endpoint-Specific Critical Review.Annals of biomedical engineering · 2026Review
- From electrocardiography to energetics: methods, constraints, and practical considerations for heart rate biologging in small animals.Journal of comparative physiology. B, Biochemical, systemic, and environmental physiology · 2026Review
- The role of dataset integrity, calibration and signal quality in neural network-based blood pressure estimation.Scientific reports · 2026Article
- HEART-Watch: A multimodal physiological dataset from a Google Pixel Watch across different physical states.PLOS digital health · 2026Article
- Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.Journal of personalized medicine · 2026Review
- Wearable ultrasound: a review of core technologies and clinical applications in cardiovascular monitoring.Microsystems & nanoengineering · 2026Review
- Flexible Pressure Sensor With Multi-Stage Microdome Structure Enabling Ultra-Wide Linear Range and High Sensitivity for Wearable Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- Cuffless hemodynamic monitoring with physics-informed machine learning models.Nature communications · 2026Article
- Transforming healthcare through in-body bioelectronic systems.Nature communications · 2026Review
- Sensors Fusion in Digital Healthcare Applications.Sensors (Basel, Switzerland) · 2026Article
- Optimized stress transfer interfaces enabled wearable nano-electronics for fatigue driving monitoring.Microsystems & nanoengineering · 2026Article
- Feasibility and performance evaluation of PPG on a Galaxy Watch in continuous central blood pressure monitoring.European heart journal. Digital health · 2026Article
- Review
- Artificial Intelligence-Enhanced Wearable Blood Pressure Monitoring in Resource-Limited Settings: A Co-Design of Sensors, Model, and Deployment.Nano-micro letters · 2026Review
- Accuracy of wearable smartwatch for measuring blood pressure and oxygen saturation across a wide altitudinal gradient: a comparative study in Migrant and Resident populations.Frontiers in physiology · 2026Article
- Wrist-based wearable oscillometric blood pressure monitoring during Ramadan: a comparative study with standard devices.Frontiers in cardiovascular medicine · 2026Article
- Arterial stiffness and vascular aging: mechanisms, prevention, and therapy.Signal transduction and targeted therapy · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVDs) are a leading cause of death worldwide. For early diagnosis, intervention and management of CVDs, it is highly desirable to frequently monitor blood pressure (BP), a vital sign closely related to CVDs, during people's daily life, including sleep time. Towards this end, wearable and cuffless BP extraction methods have been extensively researched in recent years as part of the mobile healthcare initiative. This review focuses on the enabling technologies for wearable and cuffless BP monitoring platforms, covering both the emerging flexible sensor designs and BP extraction algorithms. Based on the signal type, the sensing devices are classified into electrical, optical, and mechanical sensors, and the state-of-the-art material choices, fabrication methods, and performances of each type of sensor are briefly reviewed. In the model part of the review, contemporary algorithmic BP estimation methods for beat-to-beat BP measurements and continuous BP waveform extraction are introduced. Mainstream approaches, such as pulse transit time-based analytical models and machine learning methods, are compared in terms of their input modalities, features, implementation algorithms, and performances. The review sheds light on the interdisciplinary research opportunities to combine the latest innovations in the sensor and signal processing research fields to achieve a new generation of cuffless BP measurement devices with improved wearability, reliability, and accuracy.
Identifiers
What OpenQuestion 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 OpenQuestion graph.