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Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD

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The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability (HRV) in three different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated and standing positions. lnRMSSD was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 via ECG and 81.6 ± 11.3 via SPFS (p = 0.63, Cohen's d = 0.01) in the supine position, 76.5 ± 8.2 via ECG and 77.5 ± 8.2 via SPFS (p = 0.007, Cohen's d = 0.11) in the seated position, and 66.5 ± 9.2 via ECG and 67.8 ± 9.1 via SPFS (p < 0.001, Cohen's d = 0.15) in the standing positions. The SPFS showed a possibly strong correlation to the ECG in all three positions (r values from 0.98 to 0.99). In addition, the limits of agreement (CE ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Though significant differences were noted between the two methods in the seated and standing positions, the effect sizes were trivial.
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... The pulse rate variability (PRV) of the PPG signal has been highly correlated with both time and frequency-domain metrics from ECG-derived HRV indices [9,10]. Several PPG-based HRV smartphone applications have been validated in the literature [7,[11][12][13] for acquiring the parasympathetically derived marker of the root mean square of successive R-R interval differences (RMSSD) [11,14,15]. Previous research has suggested that the RMSSD is the preferred HRV metric for field recordings, primarily because it can be accurately measured with an ultra-shortened recording time of only one min [14] following a 1 min stabilization period [16]. ...
... Agreement between the ultrashort-term LnRMSSD values was evaluated using Bland-Altman analysis. The agreement was quantified as the calculated ratio of half the 95% confidence interval (CI) and the mean of the average values, where a "good" agreement was considered if the ratio was less than 0.1, "moderate" agreement was considered if the ratio was 0.1-0.2, and "insufficient" agreement, if the ratio was >0.2 [10,15]. The validation statistics also involved calculating the standard error of the estimate (SEE) for the PPG values against ECG. ...
... Though the exact mechanisms behind the possible differences seen in some of the literature between ECG-and PPG-derived HRV metrics have not been fully agreed upon, it was suggested that different positions may cause variations in the accuracy of PRV [10]. The slight deterioration in mean accuracy in seated and standing positions compared to supine may be due to an orthostatic stress-induced increase in sympathetic activation leading to heightened arterial stiffness and decreased pulse wave velocity [15]. While investigating the simultaneous measurements of PPG and ECG, Lu et al. (2009) found good agreement between PRV and HRV in supine and upright positions but saw slight deteriorations with upright posture [35]. ...
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The aim was to examine the validity of heart rate variability (HRV) measurements from photoplethysmography (PPG) via a smartphone application pre-and post-resistance exercise (RE) and to examine the intraday and interday reliability of the smartphone PPG method. Thirty-one adults underwent two simultaneous ultrashort-term electrocardiograph (ECG) and PPG measurements followed by 1-repetition maximum testing for back squats, bench presses, and bent-over rows. The participants then performed RE, where simultaneous ultrashort-term ECG and PPG measurements were taken: two pre-and one post-exercise. The natural logarithm of the root mean square of successive normal-to-normal (R-R) differences (LnRMSSD) values were compared with paired-sample t-tests, Pearson product correlations, Cohen's d effect sizes (ESs), and Bland-Altman analysis. Intra-class correlations (ICC) were determined between PPG LnRMSSDs. Significant, small-moderate differences were found for all measurements between ECG and PPG: BasePre1 (ES = 0.42), BasePre2 (0.30), REPre1 (0.26), REPre2 (0.36), and REPost (1.14). The correlations ranged from moderate to very large: BasePre1 (r = 0.59), BasePre2 (r = 0.63), REPre1 (r = 0.63), REPre2 (r = 0.76), and REPost (r = 0.41)-all p < 0.05. The agreement for all the measurements was "moderate" (0.10-0.16). The PPG LnRMSSD exhibited "nearly-perfect" intraday reliability (ICC = 0.91) and "very large" interday reliability (0.88). The smartphone PPG was comparable to the ECG for measuring HRV at rest, but with larger error after resistance exercise.
... Recently, ultra-short-term recordings for HRV assessment have received notable attention in cardiovascular medicine [13][14][15], metabolic disease [16], cognitive function [8,9], exercise testing [17][18][19], and sports training [11,20] studies due to the time efficiency it offers to both patients and practitioners. Ultra-short-term recording only requires R-R intervals of less than 60 seconds. ...
... Today, several HRV smartphone apps have been developed to evaluate autonomic health by using photoplethysmography [19,22,23]. ...
... It is suggested that the RMSSD is independent of respiratory sinus arrhythmia and is associated with high-frequency changes of HR modulation in response to respiratory patterns due to its strength of mathematical calculation [33]. The RMSSD has been widely accepted to evaluate cardiac-related parasympathetic activation [8,11,13,18,19,34]. Additionally, the RMSSD is recognized as a sensitive parameter to detect autonomic adaptations in response to mental stress [8,35,36] and psychophysiological strain after exercise as well as recovery status during the training period [10,37]. ...
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Background: Smartphone apps for heart rate variability (HRV) measurement have been extensively developed in the last decade. However, ultra–short-term HRV recordings taken by wearable devices have not been examined. Objective: The aims of this study were the following: (1) to compare the validity and reliability of ultra–short-term and short-term HRV time-domain and frequency-domain variables in a novel smartphone app, Pulse Express Pro (PEP), and (2) to determine the agreement of HRV assessments between an electrocardiogram (ECG) and PEP. Methods: In total, 60 healthy adults were recruited to participate in this study (mean age 22.3 years [SD 3.0 years], mean height 168.4 cm [SD 8.0 cm], mean body weight 64.2 kg [SD 11.5 kg]). A 5-minute resting HRV measurement was recorded via ECG and PEP in a sitting position. Standard deviation of normal R-R interval (SDNN), root mean square of successive R-R interval (RMSSD), proportion of NN50 divided by the total number of RR intervals (pNN50), normalized very-low–frequency power (nVLF), normalized low-frequency power (nLF), and normalized high-frequency power (nHF) were analyzed within 9 time segments of HRV recordings: 0-1 minute, 1-2 minutes, 2-3 minutes, 3-4 minutes, 4-5 minutes, 0-2 minutes, 0-3 minutes, 0-4 minutes, and 0-5 minutes (standard). Standardized differences (ES), intraclass correlation coefficients (ICC), and the Spearman product-moment correlation were used to compare the validity and reliability of each time segment to the standard measurement (0-5 minutes). Limits of agreement were assessed by using Bland-Altman plot analysis. Results: Compared to standard measures in both ECG and PEP, pNN50, SDNN, and RMSSD variables showed trivial ES (<0.2) and very large to nearly perfect ICC and Spearman correlation coefficient values in all time segments (>0.8). The nVLF, nLF, and nHF demonstrated a variation of ES (from trivial to small effects, 0.01-0.40), ICC (from moderate to nearly perfect, 0.39-0.96), and Spearman correlation coefficient values (from moderate to nearly perfect, 0.40-0.96). Furthermore, the Bland-Altman plots showed relatively narrow values of mean difference between the ECG and PEP after consecutive 1-minute recordings for SDNN, RMSSD, and pNN50. Acceptable limits of agreement were found after consecutive 3-minute recordings for nLF and nHF. Conclusions: Using the PEP app to facilitate a 1-minute ultra–short-term recording is suggested for time-domain HRV indices (SDNN, RMSSD, and pNN50) to interpret autonomic functions during stabilization. When using frequency-domain HRV indices (nLF and nHF) via the PEP app, a recording of at least 3 minutes is needed for accurate measurement.
... 6 Increasing support for the applied utility of HRV has given way to more affordable and time-efficient acquisition methodologies. 10 These advancements provide a practical means of tracking autonomic status in a large roster of players that is feasible for season-long implementation. ...
... This tool has shown acceptable agreement with simultaneous electrocardiographic comparisons for determining HRV parameters. 10 Five tablet devices (iPad; Apple Inc, Cupertino, CA) with finger sensors inserted into headphone slots were distributed to players seated comfortably on an athletic training table with their backs supported against the wall. Players would insert their left index finger into the cuff, select their identification from the application, and perform a supervised measurement. ...
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Purpose: To track cardiac-autonomic functioning, indexed by heart-rate variability, in American college football players throughout a competitive period. Methods: Resting heart rate (RHR) and the natural logarithm root mean square of successive differences (LnRMSSD) were obtained throughout preseason and ∼3 times weekly leading up to the national championship among 8 linemen and 12 nonlinemen. Seated 1-minute recordings were performed via mobile device and standardized for time of day and proximity to training. Results: Relative to preseason, linemen exhibited suppressed LnRMSSD during camp-style preparation for the playoffs (P = .041, effect size [ES] = -1.01), the week of the national semifinal (P < .001, ES = -1.27), and the week of the national championship (P = .005, ES = -1.16). As a combined group, increases in RHR (P < .001) were observed at the same time points (nonlinemen ES = 0.48-0.59, linemen ES = 1.03-1.10). For all linemen, RHR trended upward (positive slopes, R2 = .02-.77) while LnRMSSD trended downward (negative slopes, R2 = .02-.62) throughout the season. Preseason to postseason changes in RHR (r = .50, P = .025) and LnRMSSD (r = -.68, P < .001) were associated with body mass. Conclusions: Heart-rate variability tracking revealed progressive autonomic imbalance in the lineman position group, with individual players showing suppressed values by midseason. Attenuated parasympathetic activation is a hallmark of impaired recovery and may contribute to cardiovascular maladaptations reported to occur in linemen following a competitive season. Thus, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.
... (1 June 2021)). The HRV4Training software utilizes photoplethysmography to determine the variability in R-R intervals from continuous heart rate data [19,20]. To maintain HRV reliability, participants were instructed to use the application in the morning upon waking, after excretion from the urinary bladder and resting for five minutes. ...
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Heart rate variability (HRV) may be useful for prescribing high-intensity functional training (HIFT) exercise programs. This study aimed to compare effects of HRV-guided and predetermined HIFT on cardiovascular function, body composition, and performance. Methods: Recreationally-active adults (n = 55) were randomly assigned to predetermined HIFT (n = 29, age = 24.1 ± 4.1 years) or HRV-guided HIFT (n = 26, age = 23.7 ± 4.5) groups. Both groups completed 11 weeks of daily HRV recordings, 6 weeks of HIFT (5 d·week-1), and pre- and post-test body composition and fitness assessments. Meaningful changes in resting HRV were used to modulate (i.e., reduce) HRV-guided participants' exercise intensity. Linear mixed models were used with Bonferroni post hoc adjustment for analysis. Results: All participants significantly improved resting heart rate, lean mass, fat mass, strength, and work capacity. However, no significant between-groups differences were observed for cardiovascular function, body composition, or fitness changes. The HRV-guided group spent significantly fewer training days at high intensity (mean difference = -13.56 ± 0.83 days; p < 0.001). Conclusion: HRV-guided HIFT produced similar improvements in cardiovascular function, body composition, and fitness as predetermined HIFT, despite fewer days at high intensity. HRV shows promise for prescribing individualized exercise intensity during HIFT.
... For all recordings, the athletes placed their left index finger directly on the posterior camera sensor of their mobile device. The RMSSD recordings were performed in the upright seated position, with their back supported comfortably by a stable, backed chair, in order to reduce potential parasympathetic saturation commonly observed among highly fit individuals with low resting HR. 20 The athletes were instructed to limit any bodily movement and practice spontaneous breathing 11,19 before opening the HRV4Training application. The rowers then initiated a 1-minute stabilization period, followed by a 1-minute data acquisition period. ...
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Resting heart rate variability (HRV) is a potentially useful marker to consider for monitoring training status in athletes. However, traditional HRV data collection methodology requires a 5-min recording period preceded by a 5-min stabilization period. This lengthy process may limit HRV monitoring in the field due to time constraints and high compliance demands of athletes. Investigation into more practical methodology for HRV data acquisitions is required. The aim of this study was to determine the time course for stabilization of ECG-derived lnRMSSD from traditional HRV recordings. Ten-minute supine ECG measures were obtained in ten male and ten female collegiate cross-country athletes. The first 5 min for each ECG was separately analysed in successive 1-min intervals as follows: minutes 0-1 (lnRMSSD0-1 ), 1-2 (lnRMSSD1-2 ), 2-3 (lnRMSSD2-3 ), 3-4 (lnRMSSD3-4 ) and 4-5 (lnRMSSD4-5 ). Each 1-min lnRMSSD segment was then sequentially compared to lnRMSSD of the 5- to 10-min ECG segment, which was considered the criterion (lnRMSSDC riterion ). There were no significant differences between each 1-min lnRMSSD segment and lnRMSSDC riterion , and the effect sizes were considered trivial (ES ranged from 0·07 to 0·12). In addition, the ICC for each 1-min segment compared to the criterion was near perfect (ICC values ranged from 0·92 to 0·97). The limits of agreement between the prerecording values and lnRMSSDC riterion ranged from ±0·28 to ±0·45 ms. These results lend support to shorter, more convenient ECG recording procedures for lnRMSSD assessment in athletes by reducing the prerecording stabilization period to 1 min. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.
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Background Regular exercise promotes functional and structural changes in the central and peripheral mechanisms of the cardiovascular system. Heart rate variability (HRV) measurement provides a sensitive indicator of the autonomic balance. However, because of the diversity of methods and variables used, the results are difficult to compare in the sports sciences. Since the protocol (supine, sitting, or standing position) and measure (time or frequency domain) are not well defined. The aim of this study is to investigate the HRV measures that better indicates the chronic adaptations of physical exercise in athletes.Method PubMed (MEDLINE), Web of Science, SciELO (Scientific Electronic Library), and Scopus databases were consulted. Original complete articles in English with short-term signals evaluating young and adult athletes, between 17 and 40 years old, with a control group, published up to 2013 were included.ResultsSelected 19 of 1369 studies, for a total sample pool of 333 male and female athletes who practice different sports. The main protocols observed were the supine or standing positions in free or controlled breathing conditions. The main statistical results found in this study were the higher mean RR, standard deviation of RR intervals, and high frequency in athletes group. In addition, the analyses of Cohen's effect size showed that factors as modality of sport, protocol used and unit of measure selected could influence this expect results.Conclusion Our findings indicate that time domain measures are more consistent than frequency domain to describe the chronic cardiovascular autonomic adaptations in athletes.
Article
Purpose This study examined the sensitivity of maximal (Yo-Yo Intermittent Recovery [IR] 1 and 2) and submaximal (5’-5’) tests to identify training adaptations in futsal players along with the suitability of heart-rate (HR) and HR-variability (HRV) measures to identify these adaptations. Methods Eleven male professional futsal players were assessed before (pretraining) and after (posttraining) a 5-wk period. Assessments included 5’-5’ and Yo-Yo IR1 and IR2 performances and HR and HRV at rest and during the IR and 5’-5’ tests. Magnitude-based-inference analyses examined the differences between pre- and posttraining, while relationships between changes in variables were determined via correlation. Results Posttraining, Yo-Yo IR1 performance likely increased while Yo-Yo IR2 performance almost certainly increased. Submaximal HR during the Yo-Yo IR1 and Yo-Yo IR2 almost certainly and likely, respectively, decreased with training. HR during the 5’-5’ was very likely decreased, while HRV at rest and during the 5’-5’ was likely increased after training. Changes in both Yo-Yo IR performances were negatively correlated with changes in HR during the Yo-Yo IR1 test and positively correlated with the change in HRV during the 5’-5’. Conclusions The current study has identified the Yo-Yo IR2 as more responsive for monitoring training-induced changes of futsal players than the Yo-Yo IR1. Changes in submaximal HR during the Yo-Yo IR and HRV during the 5’-5’ were highly sensitive to changes in maximal performance and are recommended for monitoring training. The 5’-5’ was recommended as a time-efficient method to assess training adaptations for futsal players.