ArticleJMIR mHealth and uHealth2019
Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 4 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
47 citing papers in PubMed, 4 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis.Heart (British Cardiac Society) · 2022Pooled it
- Mobile health solutions for atrial fibrillation detection and management: a systematic review.Clinical research in cardiology : official journal of the German Cardiac Society · 2022Pooled it
- Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis.JMIR mHealth and uHealth · 2021Pooled it
- Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review.JMIR mHealth and uHealth · 2019Pooled it
- Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters.World journal of cardiology · 2025Trial
- Beyond burden metrics: Wearable photoplethysmography-derived spatiotemporal progression of atrial fibrillation linked to clinical outcomes.Heart rhythm O2 · 2026Article
- Photoplethysmography-Based Machine Learning Approaches for Atrial Fibrillation Burden: Algorithm Development and Validation.JMIR cardio · 2025Article
- Photoplethysmography in recent-onset atrial fibrillation: automatic detection of rhythm change and burden.European heart journal. Digital health · 2025Article
- Atrial Fibrillation and Atrial Flutter Detection Using Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation.Biophysics reviews · 2025Review
- Screening OSA in Chinese Smart Device Consumers: A Real-World Arrhythmia-Related Study.Nature and science of sleep · 2025Article
- Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study.Cardiovascular engineering and technology · 2024Article
- [Application of photoplethysmography for atrial fibrillation in early warning, diagnosis and integrated management].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Detection and Diagnostic Accuracy of Cardiac Arrhythmias Using Wearable Health Devices: A Systematic Review.Cureus · 2023Review
- Smartphone-based atrial fibrillation screening in the general population: feasibility and impact on medical treatment.European heart journal. Digital health · 2023Article
- ECG Smart Monitoring versus Implantable Loop Recorders for Atrial Fibrillation Detection after Cryptogenic Stroke-An Overview for Decision Making.Journal of cardiovascular development and disease · 2023Review
- Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset.NPJ digital medicine · 2023Article
- Improving the management of patients with chronic cardiac and respiratory diseases by extending pulse-oximeter uses: the dynamic pulse-oximetry.Multidisciplinary respiratory medicine · 2023Article
- Wrist-worn device combining PPG and ECG can be reliably used for atrial fibrillation detection in an outpatient setting.Frontiers in cardiovascular medicine · 2023Article
- Consumer-Led Screening for Atrial Fibrillation: A Report From the mAFA-II Trial Long-Term Extension Cohort.JACC. Asia · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG).
objectiveWe investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG.
methodsA total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes.
resultsIn all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99).
conclusionsThe algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population.
trial registrationChinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6).
Indexed as
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.