ArticleJournal of healthcare engineering2018
Analysis of a Pulse Rate Variability Measurement Using a Smartphone Camera.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Photoplethysmography-Derived Pulse Rate Variability Compared with Electrocardiography-Derived Heart Rate Variability: A Systematic Review and Meta-Analysis.Sensors (Basel, Switzerland) · 2026Pooled it
- Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Smartphone-Based Grading and Rehabilitation in Patients With Facial Palsy Using Computer Vision: Prospective Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- Digital physiological biomarkers predict within-person symptom changes in complex chronic illness.NPJ digital medicine · 2026Article
- Validity of resting heart rate derived from contact-based smartphone photoplethysmography compared with electrocardiography: a scoping review and checklist for optimal acquisition and reporting.Frontiers in digital health · 2024Article
- Observational
- Predicting Depression in Adolescents Using Mobile and Wearable Sensors: Multimodal Machine Learning-Based Exploratory Study.JMIR formative research · 2022Article
- The integration of mHealth technologies in telemedicine during the COVID-19 era: A cross-sectional study.PloS one · 2022Article
- Measurement of Cancer-Related Fatigue Based on Heart Rate Variability: Observational Study.Journal of medical Internet research · 2021Observational
- Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study.JMIR mHealth and uHealth · 2021Article
- Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability.Frontiers in sports and active living · 2021Article
- Enhancing the Robustness of Smartphone Photoplethysmography: A Signal Quality Index Approach.Sensors (Basel, Switzerland) · 2020Article
- Use of Heart Rate Variability Biofeedback to Reduce the Psychological Burden of Frontline Healthcare Professionals Against COVID-19.Frontiers in psychology · 2020Article
- Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective.Journal of healthcare engineering · 2020Review
- A validation study of a smartphone application for heart rate variability assessment in asymptomatic adults.American journal of cardiovascular disease · 2020Article
- Comparison of short-term heart rate variability indexes evaluated through electrocardiographic and continuous blood pressure monitoring.Medical & biological engineering & computing · 2019Article
- Smartphone Sensors for Health Monitoring and Diagnosis.Sensors (Basel, Switzerland) · 2019Review
- Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Heart rate variability (HRV) provides information about the activity of the autonomic nervous system. Because of the small amount of data collected, the importance of HRV has not yet been proven in clinical practice. To collect population-level data, smartphone applications leveraging photoplethysmography (PPG) and some medical knowledge could provide the means for it. Objective: To assess the capabilities of our smartphone application, we compared PPG (pulse rate variability (PRV)) with ECG (HRV). To have a baseline, we also compared the differences among ECG channels. Method: We took fifty parallel measurements using iPhone 6 at a 240 Hz sampling frequency and Cardiax PC-ECG devices. The correspondence between the PRV and HRV indices was investigated using correlation, linear regression, and Bland-Altman analysis. Results: High PPG accuracy: the deviation of PPG-ECG is comparable to that of ECG channels. Mean deviation between PPG-ECG and two ECG channels: RR: 0.01 ms-0.06 ms, SDNN: 0.78 ms-0.46 ms, RMSSD: 1.79 ms-1.21 ms, and pNN50: 2.43%-1.63%. Conclusions: Our iPhone application yielded good results on PPG-based PRV indices compared to ECG-based HRV indices and to differences among ECG channels. We plan to extend our results on the PPG-ECG correspondence with a deeper analysis of the different ECG channels.
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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.