A Nature study of 196 participants found AI applied to smartwatch photoplethysmography and accelerometry detected hypertension with 65.8% sensitivity and 90.0% specificity against 24-hour ambulatory blood pressure monitoring.
Nature has published a study on opportunistically detecting signs of hypertension on a consumer smartwatch. The article, titled "Opportunistically Detecting Signs of Hypertension on a Consumer Smartwatch," was published on 05 November 2026 and is available at
The researchers validated an AI-based method called AI-PPG-ACC-HTN in a prospective, multicenter study of 196 diverse participants free from known cardiovascular disease and antihypertensive medication. The validation was conducted against gold-standard 24-hour ambulatory blood pressure monitoring. Monitoring took place over seven days of real-world monitoring using a widely available consumer smartwatch. The method applied artificial intelligence to multimodal waveforms, specifically photoplethysmography and accelerometry, without any cuff calibration.
Against the 24-hour ambulatory blood pressure monitoring reference, AI-PPG-ACC-HTN detected hypertension with a sensitivity of 65.8% (95% CI, 54.0%-76.3%), specificity of 90.0% (95% CI, 83.2%-94.7%), and positive predictive value (PPV) of 80.6% (95% CI, 68.6%-89.6%).
For comparison, initial office blood pressure screening achieved a sensitivity of 55.3% (95% CI, 43.4%-66.7%), specificity of 90.0% (95% CI, 83.2%-94.7%) and PPV of 77.8% (95% CI, 64.4%-88.0%).
The abstract states that hypertension is a silent killer, with over half of affected adults unaware of their condition, and that this lack of awareness is a major concern because early intervention is critical for preventing major adverse cardiovascular events. The results highlight an opportunity for consumer smartwatches to facilitate population-level opportunistic hypertension screening by prompting at-risk individuals toward clinical assessment and confirmatory diagnosis using established cuff-based methodologies.
The authors are Paolo Di Achille, Lawrence Cai, Jiang Wu, Mingwu Gao, Bhavna Daryani, Jonathan Wang, Utkarsh Khanna, Ho Ko, Anupam Pathak, Mark Malhotra, Shwetak Patel, Jacqueline B. Shreibati, Stephen P. Juraschek, Pramod Rudrapatna, Matthew Thompson and Ming-Zher Poh. Author affiliations listed include Google Research in Cambridge, MA, USA; Google Research in Mountain View, CA, USA; Google Research in Seattle, WA, USA; the University of Washington in Seattle, WA, USA; and the Division of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School. The corresponding author is Ming-Zher Poh. The article was received on 01 December 2025 and accepted on 20 October 2026.
The study suggests that consumer smartwatches could offer an accessible tool for population-level opportunistic hypertension screening, potentially addressing a major public health challenge by identifying individuals who may benefit from clinical assessment and confirmatory diagnosis with cuff-based methods.
Sources
This article was drafted with AI assistance and checked against the sources above. Company claims are reported as claims. Cover image is AI-generated.
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