scholarly journals Accuracy of a Smartphone Application to Measure Heart Rate Variability in Adult Females

2018 ◽  
Vol 50 (5S) ◽  
pp. 187
Author(s):  
Clayton Nicks ◽  
Kate Early ◽  
Brian Tyo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Smartphone Application ◽  
Adult Females ◽  
Measure Heart Rate

The Promise of Ultra-Short-Term (UST) Heart Rate Variability Measurements

Biofeedback ◽  
2016 ◽  
Vol 44 (4) ◽  
pp. 229-233 ◽  
Author(s):  
Fredric Shaffer ◽  
Steven Shearman ◽  
Zachary M. Meehan
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Concurrent Validity ◽  
Health Assessment ◽  
Optimal Performance ◽  
State University ◽  
Short Term ◽  
Measure Heart Rate ◽  
Validity Criteria ◽  
University Undergraduates

Researchers have investigated whether ultra-short-term (UST) heart rate variability values can replace traditional 5-minute values in clinical and optimal performance settings. Concurrent validity is the extent to which the results of a measurement correspond to a previously validated assessment of the same construct. Several studies either failed to specify their concurrent validity criteria or used an inappropriate statistical test. The authors proposed a rigorous standard and demonstrated that artifacted resting ultra-short-term heart rate variability values can achieve strong concurrent validity for diverse time-domain, frequency-domain, and nonlinear measurements in healthy undergraduates. Based on these findings, resting baselines as brief as 1 minute should be sufficient to measure heart rate, the standard deviation of the interbeat interval for normal beats (SDNN), and the square root of the mean squared difference of adjacent NN intervals (RMSSD) in clinical, optimal performance, and personal health assessment with individuals who resemble Truman State University undergraduates.

Comparison of Wristband Type Devices to Measure Heart Rate Variability for Mental Stress Assessment

2021 ◽  
Author(s):  
Minjung Kim ◽  
Jungsook Kim ◽  
Kyounghyun Park ◽  
HyunSuk Kim ◽  
Daesub Yoon
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Stress Assessment ◽  
Measure Heart Rate

scholarly journals Evaluation of a technique to measure heart rate variability in anaesthetised cats

2014 ◽  
Vol 199 (2) ◽  
pp. 229-235 ◽  
Author(s):  
Kuan Hua Khor ◽  
Ian A. Shiels ◽  
Fiona E. Campbell ◽  
Ristan M. Greer ◽  
Annie Rose ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Measure Heart Rate

scholarly journals Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: a Comparison to Electrocardiography (ECG)

2020 ◽  
Vol 44 (11) ◽  
Author(s):  
Angela A. T. Schuurmans ◽  
Peter de Looff ◽  
Karin S. Nijhof ◽  
Catarina Rosada ◽  
Ron H. J. Scholte ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Experimental Testing ◽  
Ambulatory Monitoring ◽  
Clinical Population ◽  
Limits Of Agreement ◽  
Significant Difference ◽  
Cross Correlations ◽  
Measure Heart Rate ◽  
Monitoring Devices

Abstract Wearable monitoring devices are an innovative way to measure heart rate (HR) and heart rate variability (HRV), however, there is still debate about the validity of these wearables. This study aimed to validate the accuracy and predictive value of the Empatica E4 wristband against the VU University Ambulatory Monitoring System (VU-AMS) in a clinical population of traumatized adolescents in residential care. A sample of 345 recordings of both the Empatica E4 wristband and the VU-AMS was derived from a feasibility study that included fifteen participants. They wore both devices during two experimental testing and twelve intervention sessions. We used correlations, cross-correlations, Mann-Whitney tests, difference factors, Bland-Altman plots, and Limits of Agreement to evaluate differences in outcomes between devices. Significant correlations were found between Empatica E4 and VU-AMS recordings for HR, SDNN, RMSSD, and HF recordings. There was a significant difference between the devices for all parameters but HR, although effect sizes were small for SDNN, LF, and HF. For all parameters but RMSSD, testing outcomes of the two devices led to the same conclusions regarding significance. The Empatica E4 wristband provides a new opportunity to measure HRV in an unobtrusive way. Results of this study indicate the potential of the Empatica E4 as a practical and valid tool for research on HR and HRV under non-movement conditions. While more research needs to be conducted, this study could be considered as a first step to support the use of HRV recordings provided by wearables.

scholarly journals Validity of the Wrist-Worn Polar Vantage V2 to Measure Heart Rate and Heart Rate Variability at Rest

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 137
Author(s):  
Olli-Pekka Nuuttila ◽  
Elisa Korhonen ◽  
Jari Laukkanen ◽  
Heikki Kyröläinen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Laboratory Data ◽  
Rr Intervals ◽  
Recreational Athletes ◽  
Natural Logarithm ◽  
Intraclass Correlations ◽  
Measure Heart Rate ◽  
Cardiac Parasympathetic Activity ◽  
Rate Sensor

Heart rate (HR) and heart rate variability (HRV) can be monitored with wearable devices throughout the day. Resting HRV in particular, reflecting cardiac parasympathetic activity, has been proposed to be a useful marker in the monitoring of health and recovery from training. This study examined the validity of the wrist-based photoplethysmography (PPG) method to measure HR and HRV at rest. Recreationally endurance-trained participants recorded pulse-to-pulse (PP) and RR intervals simultaneously with a PPG-based watch and reference heart rate sensor (HRS) at a laboratory in a supine position (n = 39; 5-min recording) and at home during sleep (n = 29; 4-h recording). In addition, analyses were performed from pooled laboratory data (n = 11340 PP and RR intervals). Differences and correlations were analyzed between the HRS- and PPG-derived HR and LnRMSSD (the natural logarithm of the root mean square of successive differences). A very good agreement was found between pooled PP and RR intervals with a mean bias of 0.17 ms and a correlation coefficient of 0.993 (p < 0.001). In the laboratory, HR did not differ between the devices (mean bias 0.0 bpm), but PPG slightly underestimated the nocturnal recordings (bias −0.7 bpm, p < 0.001). PPG overestimated LnRMSSD both in the laboratory (bias 0.20 ms, p < 0.001) and nocturnal recordings (bias 0.17 ms, p < 0.001). However, very strong intraclass correlations in the nocturnal recordings were found between the devices (HR: 0.998, p < 0.001; LnRMSSD: 0.931, p < 0.001). In conclusion, PPG was able to measure HR and HRV with adequate accuracy in recreational athletes. However, when strict absolute values are of importance, systematic overestimation, which seemed to especially concern participants with low LnRMSSD, should be acknowledged.

scholarly journals The Use of a Smartphone Application in Monitoring HRV during an Altitude Training Camp in Professional Female Cyclists: A Preliminary Study

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5497
Author(s):  
Alejandro Javaloyes ◽  
Manuel Mateo-March ◽  
Agustín Manresa-Rocamora ◽  
Santiago Sanz-Quinto ◽  
Manuel Moya-Ramón
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Smartphone Application ◽  
Individual Response ◽  
Altitude Training ◽  
Training Volume ◽  
Training Camp ◽  
Common Strategy ◽  
Preliminary Study ◽  
The Individual

Altitude training is a common strategy to improve performance in endurance athletes. In this context, the monitoring of training and the athletes’ response is essential to ensure positive adaptations. Heart rate variability (HRV) has been proposed as a tool to evaluate stress and the response to training. In this regard, many smartphone applications have emerged allowing a wide access to recording HRV easily. The purpose of this study was to describe the changes of HRV using a validated smartphone application before (Pre-TC), during (TC), and after (Post-TC) an altitude training camp in female professional cyclists. Training load (TL) and vagal markers of heart rate variability (LnRMSSD, LnRMSSDcv) of seven professional female cyclists before, during, and after and altitude training camp were monitored. Training volume (SMD = 0.80), LnRMSSD (SMD = 1.06), and LnRMSSDcv (SMD = −0.98) showed moderate changes from Pre-TC to TC. Training volume (SMD = 0.74), TL (SMD = 0.75), LnRMSSD (SMD = −1.11) and LnRMSSDcv (SMD = 0.83) showed moderate changes from TC to Post-TC. Individual analysis showed that heart rate variability responded differently among subjects. The use of a smartphone application to measure HRV is a useful tool to evaluate the individual response to training in female cyclists.

scholarly journals Validity Of A Finger Sensor Device And Smartphone Application To Collect Heart Rate Variability Data

2021 ◽  
Vol 53 (8S) ◽  
pp. 70-70
Author(s):  
David J. Cornell ◽  
Stephanie L. Amico ◽  
Thomas J. Sherriff ◽  
Andreas T. Himariotis ◽  
Kevin Ha ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Smartphone Application ◽  
Sensor Device ◽  
Variability Data

Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone Application and the Banister Impulse-Response Model

2020 ◽  
pp. 1-9
Author(s):  
Eva Piatrikova ◽  
Nicholas J. Willsmer ◽  
Marco Altini ◽  
Mladen Jovanović ◽  
Lachlan J.G. Mitchell ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Impulse Response ◽  
Coefficient Of Variation ◽  
Seasonal Changes ◽  
Well Being ◽  
Smartphone Application ◽  
Response Model ◽  
Subjective Well Being ◽  
Competitive Swimmers

Purpose: First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14. Results: The level of agreement between predicted and actual HRV data was R2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21–1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = −.68, P = .03). Conclusion: The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.

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.

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