Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects

Hernando, David; Roca, Surya; Sancho, Jorge; Alesanco, Álvaro; Bailón, Raquel

doi:10.3390/s18082619

Cited by 160 publications

(148 citation statements)

References 36 publications

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“…Daily use of commercial HR monitors has been the subject of validation studies [146][147][148][149][150], most of which involved the subjects performing predetermined activities. However, in the daily living monitor, Fitbit trackers either overestimated or underestimated the counts compared with the standard device.…”

Section: 44)mentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

177

133

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A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

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Section: 44)mentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

177

133

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“…As such, the use of wearables in characterizing the autonomic pattern of a person (or a group of persons) was seen to be highly acceptable by the end-user, providing useful information about the health and well-being status of the subject. Furthermore, they appear to be affordable-from a logistic point of view, featuring easy recharge, high portability and, somewhat, low cost-and reliable, with good stability of the signal acquired and easy data analysis and interpretation, as already seen in other works [7][8][9][10]35].…”

Section: Discussionmentioning

confidence: 74%

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

et al. 2020

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Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A subjects displayed a significant difference in the heart rate variability-related parameter SDNN across the test phases, whereas both groups displayed a different GSR response, albeit at different levels, with Group A displaying greater differences across phases with respect to Group B. Overall, the majority of the volunteers enrolled self-reported an improvement of their well-being status, according to structured questionnaires. The use of neural networks helped in discriminating those with a positive effect of the relaxation protocol from those with a negative/neutral impact based on basal autonomic features with a 79.2% accuracy. The results obtained demonstrated a significant heterogeneity in autonomic effects of the relaxation, highlighting the importance of maintaining a structured, well-defined protocol to produce significant benefits at the ANS level. Machine learning approaches can be useful to predict the outcome of such protocols, therefore providing subjects less prone to positive responses with personalized advice that could improve the effect of such protocols on self-relaxation perception.

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“…19 This is further supported by offthe-shelf, wearable wrist PPG showing good accuracy with HRV metrics and the ability to detect changes when subjects were put through stressful exercises compared to non-stress activities. 20 Further research will need to focus on various time durations of monitoring as this can be significantly impacted by motion artifact with wrist movement.…”

Section: Discussionmentioning

confidence: 99%

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

Sheridan

Dehart

Lin

et al. 2020

Psychiatry Investig

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Objective Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.Methods This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.Results Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.Conclusion Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

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Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects

Cited by 160 publications

References 36 publications

Current progress of photoplethysmography and SPO2 for health monitoring

Current progress of photoplethysmography and SPO2 for health monitoring

Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

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