CONTACTLESS AND CONTACT-BASED SMARTPHONE PHOTOPLETHYSMOGRAPHY AS A MULTI-PARAMETER CARDIOMETABOLIC HOME SCREEN: BEYOND ATRIAL FIBRILLATION TOWARD BLOOD PRESSURE AND DYSGLYCEMIA ESTIMATION
DOI:
https://doi.org/10.31435/ijitss.3(51).2026.6042Keywords:
Photoplethysmography, Smartphone Health, Atrial Fibrillation Screening, Cuffless Blood Pressure, Digital Biomarkers, Remote MonitoringAbstract
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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Copyright (c) 2026 Jerzy Buszko, Michał Dyś, Adrianna Buż, Damian Danilczuk, Marta Bajkowska-Piterak, Monika Szlachta-Gubernat, Nicol Baran, Przemysław Piterak, Wiktor Adamiec
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