scholarly journals Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured with a Wearable and Smartphone App: a Within-Subject Design Using Continuous Monitoring (Preprint)

JMIR Cardio ◽  
10.2196/28731 ◽  
2021 ◽  
Author(s):  
Herman de Vries ◽  
Wim Kamphuis ◽  
Hilbrand Oldenhuis ◽  
Cees van der Schans ◽  
Robbert Sanderman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Continuous Monitoring ◽  
Smartphone App ◽  
Subject Design ◽  
Strain Process

scholarly journals Moderation of the Stressor-Strain Process in Interns by Heart Rate Variability Measured With a Wearable and Smartphone App: Within-Subject Design Using Continuous Monitoring (Preprint)

2021 ◽  
Author(s):  
Herman de Vries ◽  
Wim Kamphuis ◽  
Hilbrand Oldenhuis ◽  
Cees van der Schans ◽  
Robbert Sanderman
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Negative Feedback ◽  
Feedback Loop ◽  
Linear Regression Analysis ◽  
Real Life ◽  
Smartphone App ◽  
Negative Feedback Loop ◽  
Negative Consequences ◽  
Subject Design

BACKGROUND The emergence of smartphones and wearable sensor technologies enables easy and unobtrusive monitoring of physiological and psychological data related to an individual’s resilience. Heart rate variability (HRV) is a promising biomarker for resilience based on between-subject population studies, but observational studies that apply a within-subject design and use wearable sensors in order to observe HRV in a naturalistic real-life context are needed. OBJECTIVE This study aims to explore whether resting HRV and total sleep time (TST) are indicative and predictive of the within-day accumulation of the negative consequences of stress and mental exhaustion. The tested hypotheses are that demands are positively associated with stress and resting HRV buffers against this association, stress is positively associated with mental exhaustion and resting HRV buffers against this association, stress negatively impacts subsequent-night TST, and previous-evening mental exhaustion negatively impacts resting HRV, while previous-night TST buffers against this association. METHODS In total, 26 interns used consumer-available wearables (Fitbit Charge 2 and Polar H7), a consumer-available smartphone app (Elite HRV), and an ecological momentary assessment smartphone app to collect resilience-related data on resting HRV, TST, and perceived demands, stress, and mental exhaustion on a daily basis for 15 weeks. RESULTS Multiple linear regression analysis of within-subject standardized data collected on 2379 unique person-days showed that having a high resting HRV buffered against the positive association between demands and stress (hypothesis 1) and between stress and mental exhaustion (hypothesis 2). Stress did not affect TST (hypothesis 3). Finally, mental exhaustion negatively predicted resting HRV in the subsequent morning but TST did not buffer against this (hypothesis 4). CONCLUSIONS To our knowledge, this study provides first evidence that having a low within-subject resting HRV may be both indicative and predictive of the short-term accumulation of the negative effects of stress and mental exhaustion, potentially forming a negative feedback loop. If these findings can be replicated and expanded upon in future studies, they may contribute to the development of automated resilience interventions that monitor daily resting HRV and aim to provide users with an early warning signal when a negative feedback loop forms, to prevent the negative impact of stress on long-term health outcomes. CLINICALTRIAL

scholarly journals Clinical Applications of Heart Rate Variability in the Triage and Assessment of Traumatically Injured Patients

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Mark L. Ryan ◽  
Chad M. Thorson ◽  
Christian A. Otero ◽  
Thai Vu ◽  
Kenneth G. Proctor
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Continuous Monitoring ◽  
Trauma Patients ◽  
Clinical Implementation ◽  
Patient Assessment ◽  
Prehospital Triage ◽  
Key Issues ◽  
Medical Disciplines ◽  
Injured Patients

Heart rate variability (HRV) is a method of physiologic assessment which uses fluctuations in the RR intervals to evaluate modulation of the heart rate by the autonomic nervous system (ANS). Decreased variability has been studied as a marker of increased pathology and a predictor of morbidity and mortality in multiple medical disciplines. HRV is potentially useful in trauma as a tool for prehospital triage, initial patient assessment, and continuous monitoring of critically injured patients. However, several technical limitations and a lack of standardized values have inhibited its clinical implementation in trauma. The purpose of this paper is to describe the three analytical methods (time domain, frequency domain, and entropy) and specific clinical populations that have been evaluated in trauma patients and to identify key issues regarding HRV that must be explored if it is to be widely adopted for the assessment of trauma patients.

Emotion Detection in Aging Adults Through Continuous Monitoring of Electro-Dermal Activity and Heart-Rate Variability

2019 ◽  
pp. 252-261
Author(s):  
Luz Fernández-Aguilar ◽  
Arturo Martínez-Rodrigo ◽  
José Moncho-Bogani ◽  
Antonio Fernández-Caballero ◽  
José Miguel Latorre
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Continuous Monitoring ◽  
Emotion Detection ◽  
Aging Adults

scholarly journals The Effects of Neurosculpting® Meditation on Stress, Anxiety and Sleep on one group of college students

2021 ◽  
Vol 4 (3) ◽  
pp. 01-06
Author(s):  
Kathleen Kelley
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Undergraduate Students ◽  
Quality Index ◽  
Average Score ◽  
Smartphone App ◽  
Depression Anxiety Stress Scale ◽  
Stress Scale ◽  
Pittsburg Sleep Quality Index ◽  
College Aged Students

Objective: The purpose of this study was to examine if 11 weeks of Neurosculpting® meditation improved sleep and other variables in college aged students. Participants: Fifteen undergraduate students. Methods: Subjects were evaluated at the beginning and end of the semester using two tools: The Pittsburg Sleep Quality Index and the Depression Anxiety Stress Scale. During each session, heart rate and heart rate variability were measured using a smartphone app, fingertip sensor, and HRV monitor. Subjects received 60 minutes of Neurosculpting® Meditation, one time per week, for 11 weeks. Results: The average score of both the DASS and PSQI decreased (p = .54) and (p = .08) respectively. Within each session, average HR decreased and average HRV increased. However, neither variable showed significant changes from the beginning to the end of the semester. Conclusions: This study demonstrated that consistent Neurosculpting® meditation sessions may improve heart rate variability and sleep in college aged students.

scholarly journals Wearable Epileptic Seizure Prediction System with Machine-Learning-Based Anomaly Detection of Heart Rate Variability

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3987 ◽  
Author(s):  
Toshitaka Yamakawa ◽  
Miho Miyajima ◽  
Koichi Fujiwara ◽  
Manabu Kano ◽  
Yoko Suzuki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Anomaly Detection ◽  
Real Time ◽  
False Positive Rate ◽  
Smartphone App ◽  
Heart Rate Variability Analysis ◽  
Statistical Process ◽  
Time Operation

A warning prior to seizure onset can help improve the quality of life for epilepsy patients. The feasibility of a wearable system for predicting epileptic seizures using anomaly detection based on machine learning is evaluated. An original telemeter is developed for continuous measurement of R-R intervals derived from an electrocardiogram. A bespoke smartphone app calculates the indices of heart rate variability in real time from the R-R intervals, and the indices are monitored using multivariate statistical process control by the smartphone app. The proposed system was evaluated on seven epilepsy patients. The accuracy and reliability of the R-R interval measurement, which was examined in comparison with the reference electrocardiogram, showed sufficient performance for heart rate variability analysis. The results obtained using the proposed system were compared with those obtained using the existing video and electroencephalogram assessments; it was noted that the proposed method has a sensitivity of 85.7% in detecting heart rate variability change prior to seizures. The false positive rate of 0.62 times/h was not significantly different from the healthy controls. The prediction performance and practical advantages of portability and real-time operation are demonstrated in this study.

Continuous monitoring of heart rate variability in preterm infants

1994 ◽  
Vol 37 (2) ◽  
pp. 117-131 ◽  
Author(s):  
Viola Prietsch ◽  
Uwe Knoepke ◽  
Michael Obladen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Preterm Infants ◽  
Continuous Monitoring

Validity of a Smartphone Application and Chest Strap for Recording RR Intervals at Rest in Athletes

2020 ◽  
Vol 15 (6) ◽  
pp. 896-899
Author(s):  
Reabias de A. Pereira ◽  
José Luiz de B. Alves ◽  
João Henrique da C. Silva ◽  
Matheus da S. Costa ◽  
Alexandre S. Silva
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
High Frequency ◽  
Low Frequency ◽  
Smartphone Application ◽  
Smartphone App ◽  
Heart Rate Variability Analysis ◽  
Rr Intervals

Objective: To evaluate the accuracy of the smartphone application (app) HRV Expert (CardioMood) and a chest strap (H10 Polar) for recording R-R intervals compared with electrocardiogram (ECG). Methods: A total of 31 male recreational runners (age 36.1 [6.3] y) volunteered for this study. R-R intervals were recorded simultaneously by the smartphone app and ECG for 5 minutes to analyze heart-rate variability in both the supine and sitting positions. Time-domain indexes (heart rate, mean R-R, SD of RR intervals, count of successive normal R-R intervals differing by more than 50 ms, percentage of successive normal R-R intervals differing by more than 50 ms, and root mean square of successive differences between normal R-R intervals), frequency-domain indexes (low frequency, normalized low frequency, high frequency, normalized high frequency, low-frequency to high-frequency ratio, and very low frequency), and nonlinear indexes (SD of instantaneous beat-to-beat variability and long-term SD of continuous R-R intervals) were compared by unpaired t test, Pearson correlation, simple linear regression, and Bland–Altman plot to evaluate the agreement between the devices. Results: High similarity with P value varying between .97 and 1.0 in both positions was found. The correlation coefficient of the heart-rate-variability indexes was perfect (r = 1.0; P = .00) for all variables. The constant error, standard error of estimation, and limits of agreement between ECG and the smartphone app were considered small. Conclusion: The smartphone app and chest strap provide excellent ECG compliance for all variables in the time domain, frequency domain, and nonlinear indexes, regardless of the assessed position. Therefore, the smartphone app replaces ECG for any heart-rate-variability analysis in runners.

Expectancy and Heart Rate as Predictors of the Speech Performance of Stutterers

1983 ◽  
Vol 26 (3) ◽  
pp. 383-388 ◽  
Author(s):  
John M. Baumgartner ◽  
Gene J. Brutten
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Single Subject ◽  
Single Subject Design ◽  
Regression Analyses ◽  
The Third ◽  
Subject Design ◽  
Speech Performance ◽  
The Relationship ◽  
Mean Heart Rate

Three adult stutterers who displayed a preexperimental pattern of consistent expectation and occurence of stuttering were studied in a single-subject design. Multiple linear regression analyses led to the conclusion that cognitive (signalled) expectancy was predictive of stuttering for two of the subjects. The third subject evidenced essentially no relationship between signalled expectancy and disfluent performance. For two subjects, neither mean heart rate nor heart rate variability was predictive of speech performance. For the third subject, mean heart rate was predictive but heart rate variablity was not. For two subjects, there was essentially no relationship between the measured physiologic variables and cognitive expectancy. However, for the third subject both mean heart rate and heart rate variability were significantly predictive of cognitive expectancy. These results suggest that adult stutterers should not be viewed as a homogeneous group with respect to preutterance activity that is either cognitive or physiologic. The relationship between preutterance heart rate, heart rate variability, and expectancy responses and between these preutterance variables and subsequent stuttering appears to be individualistic.

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