Key result
Smartphone PPG detects AFib with ~96% sensitivity but remains unreliable for blood pressure and dysglycemia.
Why the study?
Does smartphone photoplethysmography accurately detect atrial fibrillation, blood pressure, and dysglycemia compared to reference standards?
Does smartphone photoplethysmography accurately detect atrial fibrillation, blood pressure, and dysglycemia compared to reference standards?
Smartphone PPG is a clinically ready tool for AF detection and monitoring, but BP estimation requires individual calibration and dysglycemia screening remains investigational.
Supports smartphone PPG for AF detection in workflows; leaves open reliable BP and dysglycemia use pending prospective validation.
Smartphone photoplethysmography (PPG) demonstrates a stark maturity gradient across cardiometabolic parameters. For atrial fibrillation (AF) detection, meta-analytic evidence from 14 studies (n = 5,090) shows pooled sensitivity of 96% (95% CI 93 to 97%) and specificity of 97% (95% CI 95 to 98%), with individual validation studies reporting sensitivity up to 99.7% and specificity up to 99.7% using machine learning algorithms in ambulatory settings. AF detection performance is consistent across multiple smartphone devices, robust in unsupervised home use, and clinically impactful: population screening increased oral anticoagulation uptake from 56% to 74%, and digital post-ablation monitoring detected arrhythmia recurrence at more than double the rate of conventional ECG follow-up (38.5% versus 17.7%, OR 3.4). Signal quality rates for PPG (89 to 97%) were comparable to or exceeded those for single-lead ECG. Blood pressure (BP) estimation, by contrast, remains unreliable for population-level screening. Of 18 studies evaluated, only two clearly passed established AAMI/ESH/ISO validation protocols, both relying on per-individual calibration against a reference measurement. The two largest prospective trials (n = 965 and n = 62) explicitly failed validation standards, and contactless remote PPG showed critically low sensitivity for hypertension detection (systolic BP sensitivity 0.04) in a diverse-skinned field population. Diastolic BP was consistently estimated more accurately than systolic BP across approaches. Dysglycemia detection rests on a single large study demonstrating moderate screening discrimination (AUC 0.74 to 0.77) using a deep neural network applied to PPG waveforms, with the score independently associated with HbA1c. The evidence supports smartphone PPG as a clinically ready tool for AF detection and monitoring, particularly in populations aged over 65, but BP estimation requires individual calibration and cannot yet substitute for cuff-based measurement, and dysglycemia screening remains investigational.
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Buszko et al. (2026) conducted a review in Cardiometabolic disease (atrial fibrillation, hypertension, dysglycemia). Smartphone photoplethysmography (PPG) vs. Reference standard measurements was evaluated on Diagnostic accuracy for cardiometabolic parameters. Smartphone photoplethysmography is clinically validated for atrial fibrillation detection with roughly 96% sensitivity and 97% specificity, whereas blood pressure and dysglycemia estimation remain unreliable or investigational.
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