ArticleFrontiers in digital health2022
Using apple watch ECG data for heart rate variability monitoring and stress prediction: A pilot study.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review.Biosensors · 2025Pooled it
- Cutting weight, gaining stress: the hidden battle that outweighs fight day tension in kickboxing.Frontiers in psychology · 2026Article
- Digital phenotyping for assessment and prediction of interoception, chronic stress, and self-regulation in adults: a scoping review.Frontiers in digital health · 2026Review
- Predicting experiences of paranoia and auditory verbal hallucinations in daily life with ambulatory sensor data - A feasibility study.Psychological medicine · 2025Article
- Using Apple Watches to Monitor Health and Behaviors of Individuals With Cognitive Impairment: A Case Series Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Remote Monitoring of Cardiac Devices and Clinical Outcomes in Patients with Structural Heart Diseases: Rationale and Design of the ReVe Study.Journal of clinical medicine · 2025Article
- Diagnostic Accuracy of Apple Watch Electrocardiogram for Atrial Fibrillation: A Systematic Review and Meta-Analysis.JACC. Advances · 2025Article
- Leveraging mHealth Technologies for Public Health.JMIR public health and surveillance · 2024Review
- Real-time monitoring to predict depressive symptoms: study protocol.Frontiers in psychiatry · 2024Article
- Can peripheral psychophysiological markers predict response to exposure-based cognitive behavioral therapy in youth with severely impairing irritability? A study protocol.BMC psychiatry · 2023Article
- Can heart rate variability data from the Apple Watch electrocardiogram quantify stress?Frontiers in public health · 2023Article
- Patient Outcomes Associated with the First Remote Monitoring Experience of Cardiac Implantable Electronic Devices in South Korea.Clinical interventions in aging · 2023Article
- Application of a mobile health data platform for public health surveillance: A case study in stress monitoring and prediction.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stress is an increasingly prevalent mental health condition that can have serious effects on human health. The development of stress prediction tools would greatly benefit public health by allowing policy initiatives and early stress-reducing interventions. The advent of mobile health technologies including smartphones and smartwatches has made it possible to collect objective, real-time, and continuous health data. We sought to pilot the collection of heart rate variability data from the Apple Watch electrocardiograph (ECG) sensor and apply machine learning techniques to develop a stress prediction tool. Random Forest (RF) and Support Vector Machines (SVM) were used to model stress based on ECG measurements and stress questionnaire data collected from 33 study participants. Data were stratified into socio-demographic classes to further explore our prediction model. Overall, the RF model performed slightly better than SVM, with results having an accuracy within the low end of state-of-the-art. Our models showed specificity in their capacity to assess "no stress" states but were less successful at capturing "stress" states. Overall, the results presented here suggest that, with further development and refinement, Apple Watch ECG sensor data could be used to develop a stress prediction tool. A wearable device capable of continuous, real-time stress monitoring would enable individuals to respond early to changes in their mental health. Furthermore, large-scale data collection from such devices would inform public health initiatives and policies.
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Registered trials
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