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HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application
Abstract
We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.
... Smartphone-based measurements have become popular [3], as smartphoneintegrated sensors could be used, e.g. GPS to track distance and photoplethysmography to track physiological data using the phone's camera and flash light as light source [4], [5]. ...
... Then, we used the HRV4Training app [5] to collect resting HR and HRV data, training summaries (HR during exercise, distance, duration, elevation gain for each workout) and a user anthropometrics (age, weight, height and gender) for a total of 532 runners. We used HRV4Training data to derive running performance over 1 to 8 months as well as to estimate V O 2 max according to the lab-validated CRF estimation models. ...
... PPG is an unobtrusive technique for detecting blood volume changes during a cardiac cycle and is often measured using reflection by illuminating the skin using a LED (e.g. the phone's flash) and detecting the amount of light that is reflected by a photodetector or a camera located next to the light source. Details on this method can be found in [5]. HR was computed as the mean HR over the measurement window. ...
... Smartphone-based measurements have become popular [3], as smartphoneintegrated sensors could be used, e.g. GPS to track distance and photoplethysmography to track physiological data using the phone's camera and flash light as light source [4], [5]. ...
... Then, we used the HRV4Training app [5] to collect resting HR and HRV data, training summaries (HR during exercise, distance, duration, elevation gain for each workout) and a user anthropometrics (age, weight, height and gender) for a total of 532 runners. We used HRV4Training data to derive running performance over 1 to 8 months as well as to estimate V O 2 max according to the lab-validated CRF estimation models. ...
... PPG is an unobtrusive technique for detecting blood volume changes during a cardiac cycle and is often measured using reflection by illuminating the skin using a LED (e.g. the phone's flash) and detecting the amount of light that is reflected by a photodetector or a camera located next to the light source. Details on this method can be found in [5]. HR was computed as the mean HR over the measurement window. ...
In this work, we propose to use anthropometrics and physiological data to estimate cardiorespiratory fitness (CRF) in free-living and analyze the relation between estimated CRF and running performance. In particular, we use the ratio between running speed and heart rate (HR) as predictor for CRF estimation in free-living. The ratio is representative of fitness as lower HR at a given speed is expected for more fit individuals. Then, we analyze the relation between estimated CRF and running performance for 10 km, half marathon and full marathon runs. CRF estimation models were developed using lab-based V O2max measurements. CRF estimates were obtained from data collected in unsupervised free-living in a sample of 532 runners for a period ranging between 1 and 8 months using the HRV4Training app. During the same period, running performance was determined for all runners. We show that the speed to HR ratio provides higher accuracy in CRF estimation compared to resting HR or no-physiological data (15% to 18% reduction in RMSE for person-independent models). Secondly, we found moderate to strong correlations between CRF estimated from free-living data and running performance (Pearson's r = 0.56 − 0.61). We conclude that estimating CRF in free-living using mobile technology and data integration can be a valuable tool to support individualized training plans and to track fitness and performance outside laboratory settings.
... The phone-based PPG has been validated and confirmed to provide reliable HRV recordings [45,46]. HRV4Training application [47] serves as a fast and valid alternative to the electrocardiogram (ECG), the gold-standard method of measurement in research [19,48,49]. Mobile phone PPG through HRV4Training had an almost perfect correlation with the ECGs (r = 0.99) [50]. ...
... HRV4Training implements a peak detection algorithm to determine peak-to-peak intervals from up-sampled PPG data. Peak detection is based on a slope inversion algorithm [48]. In this study, an Honor 8A smart phone was used [(model JAT-L29; main camera: 13 MP, f/1.8, PDAF; OS: Android 9.0, EMUI 9; CPU: Octa-core (4 × 2.3 GHz Cortex-A53 and 4 × 1.8 GHz Cortex-A53)]. ...
Abstract: The purpose of the present cross-sectional study was to examine the relationship between heart rate variability (HRV) and the range of cervical motion, disability, pain intensity, pain catastrophizing, and quality of life in patients with chronic, non-specific neck pain. Thirty-five patients, aged 20–48 years, with chronic non-specific neck pain, completed validated questionnaires regarding
neck pain intensity, pain-associated disability, catastrophic thoughts, and quality of life. The range of cervical motion was assessed using a digital goniometer. HRV indices were recorded in three positions (supine, sitting, and standing) through a smartphone application. Several significant correlations were observed between HRV indices and neck pain disability, the helplessness factor of catastrophizing, neck rotation, and quality of life. These correlations were only observed in the standing position. Pain catastrophizing was positively correlated with disability and pain intensity during active neck movement (Pearson r = 0.544, p < 0.01; Pearson r = 0.605, p < 0.01, respectively). Quality of life was negatively correlated with pain intensity during active movement (Pearson r = −0.347, p < 0.05). HRV indices were correlated with the psychological and physical domains of neck pain. These cardiac indices have been related to neck pain variables in some previous studies.Further research is needed to confirm this relationship in different daily conditions.
... Resting-state photoplethysmography (PPG)-measured HRV data was obtained via an Android App utilized in prior research trials (42)(43)(44) and validated with the Polar H7 device and electrocardiography (ECG) (45) at CVI and CVII. HRV recordings of 1 min were used to assess cardiac-vagal HRV parameters: the root mean square of successive differences (rMSSD), high-frequency (HF) HRV, and the percentage of successive RR intervals that differ by more than 50 ms (pNN50). ...
... Long-term HRV recordings still represent the typical reference standard for predicting health outcomes whereas short-term values are proxies of longterm values with unknown predictive validity; therefore, ultrashort HRV measurements could be considered as "proxies of proxies" (63). Despite the fact that our HRV recording method, instrumentation, and procedure has been validated with both the Polar H7 device and electrocardiography (ECG) (42)(43)(44)(45) evaluating HRV utilizing classic 5-min ECG recording windows during pre and post clinical visitations could have aided in determining whether the observed changes in pain intensity were associated with changes in HRV (64). Based on clinical theories such as the vagal-tank theory, longer vagus nerve innervation treatment methods for those with FM may be needed to detect a significant perturbance of low HRV levels and help understand how the ≪vagal tank≫ sustains self-regulatory efforts to build a higher resting cardiac vagal control over time, yet this is speculative (65). ...
Importance
Vagus nerve innervation via electrical stimulation and meditative-based diaphragmatic breathing may be promising treatment avenues for fibromyalgia.
Objective
Explore and compare the treatment effectiveness of active and sham transcutaneous vagus nerve stimulation (tVNS) and meditative-based diaphragmatic breathing (MDB) for fibromyalgia.
Design
Participants enrolled from March 2019–October 2020 and randomly assigned to active tVNS (n = 28), sham tVNS (n = 29), active MDB (n = 29), or sham MDB (n = 30). Treatments were self-delivered at home for 15 min/morning and 15 min/evening for 14 days. Follow-up was at 2 weeks.
Setting
Outpatient pain clinic in Oslo, Norway.
Participants
116 adults aged 18–65 years with severe fibromyalgia were consecutively enrolled and randomized. 86 participants (74%) had an 80% treatment adherence and 107 (92%) completed the study at 2 weeks; 1 participant dropped out due to adverse effects from active tVNS.
Interventions
Active tVNS is placed on the cymba conchae of the left ear; sham tVNS is placed on the left earlobe. Active MDB trains users in nondirective meditation with deep breathing; sham MDB trains users in open-awareness meditation with paced breathing.
Main outcomes and measures
Primary outcome was change from baseline in ultra short-term photoplethysmography-measured cardiac-vagal heart rate variability at 2 weeks. Prior to trial launch, we hypothesized that (1) those randomized to active MDB or active tVNS would display greater increases in heart rate variability compared to those randomized to sham MDB or sham tVNS after 2-weeks; (2) a change in heart rate variability would be correlated with a change in self-reported average pain intensity; and (3) active treatments would outperform sham treatments on all pain-related secondary outcome measures.
Results
No significant across-group changes in heart rate variability were found. Furthermore, no significant correlations were found between changes in heart rate variability and average pain intensity during treatment. Significant across group differences were found for overall FM severity yet were not found for average pain intensity.
Conclusions and relevance
These findings suggest that changes in cardiac-vagal heart rate variability when recorded with ultra short-term photoplethysmography in those with fibromyalgia may not be associated with treatment-specific changes in pain intensity. Further research should be conducted to evaluate potential changes in long-term cardiac-vagal heart rate variability in response to noninvasive vagus nerve innervation in those with fibromyalgia.
Clinical trial registration
https://clinicaltrials.gov/ct2/show/NCT03180554, Identifier: NCT03180554.
... However, the frame-rate of smartphones usually operates around 30Hz, which is a major limitation identified by Bolkhovsky et al. [71]. It was proposed that using cubic interpolation, the second derivative and the zero crossing algorithm instead of minima detection would overcome this limitation and allow for better HR detection [99][100][101][102]. A filter is required to remove artefacts without compromising the original signal when conducting time domain analysis to evaluate small variations occurring in N-N intervals [103]. ...
... A filter is required to remove artefacts without compromising the original signal when conducting time domain analysis to evaluate small variations occurring in N-N intervals [103]. Examples of filters are independent component analysis using accelerometer data to remove artefacts [104] or employing a 4 th order bandpass filter [99,101]. A heating problem was also noticed by Garcia-Agundez et al. when testing their algorithm, which resulted in complaints about the discomfort of holding the smartphone [102]. ...
Background
Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.
Objective
This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research.
Methods
We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV).
Results
The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF.
Conclusions
A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.
... In particular, we used the HRV4Training app [8], [9] to collect data from 2113 individuals of different fitness levels, over a time period of 2 years, and developed multiple linear regression models to highlight the relative impact of different predictors on running performance estimation. We show that running performance (10 km time) can be estimated accurately from data acquired in free-living, without supervision or specific laboratory tests. ...
... • Rest: resting physiological data (HR and HRV). HR was computed as the mean HR during the daily morning measurement, while as HRV feature we used the square root of the mean squared RR intervals difference (rMSSD), a marker of parasympathetic activity [3], [9]. • Vol: training volume and speed. ...
In this work, we use data acquired longitudinally, in free-living, to provide accurate estimates of running performance. In particular, we used the HRV4Training app and integrated APIs (e.g. Strava and TrainingPeaks) to acquire different sets of parameters, either via user input, morning measurements of resting physiology, or running workouts to estimate running 10 km running time. Our unique dataset comprises data on 2113 individuals, from world class triathletes to individuals just getting started with running, and it spans over 2 years. Analyzed predictors of running performance include anthropometrics, resting heart rate (HR) and heart rate variability (HRV), training physiology (heart rate during exercise), training volume, training patterns (training intensity distribution over multiple workouts, or training polarization) and previous performance. We build multiple linear regression models and highlight the relative impact of different predictors as well as trade-offs between the amount of data required for features extraction and the models accuracy in estimating running performance (10 km time). Cross-validated root mean square error (RMSE) for 10 km running time estimation was 2.6 minutes (4% mean average error, MAE, 0.87 R^2), an improvement of 58% with respect to estimation models using anthropometrics data only as predictors. Finally, we provide insights on the relationship between training and performance, including further evidence of the importance of training volume and a polarized training approach to improve performance.
... Before introducing the game, a Polar H7 heart rate sensor was attached to the participant's chest and paired with the HRVLogger [17] app that recorded the subject's heart activity. Once participants relaxed and the Heart Rate (HR) baseline was recorded, the experimenter explained the game mechanics and interaction control. ...
... No pre-processing was needed on the HR or HRV features as they were computed by the HRVLogger app [17]. Different HRV features such as the average of normalto-normal intervals (AVNN), root mean square of successive differences (rMSSD) and the low-high frequency ratio (LFHF) were selected for the analysis. ...
The role of affective states in cognitive performance has long been an area of interest in cognitive science. Recent research in game-based cognitive training suggest that cognitive games should incorporate real-time adaptive mechanisms. These adaptive mechanisms would change the game’s difficulty according to the player’s performance in order to provide appropriate challenges and thus, achieve a real cognitive improvement. However, these mechanisms currently ignore the effects of valence and arousal on the player’s cognitive skills. In this paper we investigate how working memory (WM) performance is affected when playing a VR game, and the effects of valence and arousal in this context. To this aim, a custom video game was created for Desktop and VR. Three difficulty levels were designed to evoke different levels of arousal while maintaining the same memory load for each difficulty level. We found an improvement in WM performance when playing in VR compared to Desktop. This effect was particularly pronounced in those with a low WM capacity. Significantly higher levels of valence and arousal were self-reported when playing in VR.We explore the impact that reported affective states could have in the player’s WM performance. We suggest that high levels of arousal and positive valence can lead players to a flow state [1] that may have a positive impact on the player’s WM performance.
... This proves to be the greatest limitation of smartphone PPG, since smartphone cameras currently record usually at around 30 Hz. This limitation, however, can be partially overcome by using cubic spline interpolation (Altini and Amft 2016) and focusing on the second derivative of the PPG signal (Elgendi et al 2010, Ferrer-Mileo et al 2015. ...
... Therefore, it becomes necessary to employ a filter to remove as much noise as possible, while maintaining the original signal intact. Different approaches have been designed to address this issue, such as using independent component analysis (Peng et al 2014), Using accelerometer data to remove artifacts (Fukushima et al 2012), or employing a 4th order bandpass filter (Ferrer-Mileo et al 2015, Altini andAmft 2016). ...
Photoplethysmography (PPG) is an optical technique used to measure the heart rate (HR) and other cardiovascular variables by analyzing volume changes in the microvascular bed of tissue. At the moment, smartphone users can already measure their HR using PPG applications that use the smartphone's built-in camera. However, available applications are unreliable when artifacts are present, such as those caused by movement, finger pressure, or ambient light changes. This contribution aims to analyze the limitations of a smartphone-based PPG algorithm capable of measuring N-N intervals when such artifacts are present by comparing it to a 2-lead electrocardiography (ECG). By using a Bandpass filter and a zero-crossing detection algorithm on a PPG signal captured at 800 × 600 pixels and 30 Hz, we have designed an approach capable of assessing N-N intervals when movement artifacts are present. An evaluation performed on n = 31 users shows our algorithm is capable of measuring N-N intervals with an average relative error of 9.23 ms, when compared to a 2-lead ECG. Our approach proves the reliability of smartphone-based photoplethysmography to measure N-N intervals, even under the presence of movement artifacts, and opens the door for its future use in remote diagnosis scenarios.
... To collect heart rate variability (HRV) data, we used the H10 Polar device to make recordings through the validated HRV4Training app on an Android device; we followed the standard recommended settings [31][32][33]. The process involved first ensuring proper placement of the H10 Polar device on the chest, securely fastening it for accurate readings. ...
Background: Evidence suggests that vagus nerve stimulation can modulate heart rate variability (HRV). However, there is a lack of mechanistic studies in healthy subjects assessing the effects of bilateral transcutaneous auricular vagus nerve stimulation (taVNS) on HRV. Our study aims to investigate how taVNS can influence the HRV response, including the influence of demographic variables in this response. Methods: Therefore, we conducted a randomized controlled study with 44 subjects, 22 allocated to active and 22 to sham taVNS. Results: Our results showed a significant difference between groups in the high-frequency (HF) metric. Active taVNS increased the HF metric significantly as compared to sham taVNS. Also, we found that age was a significant effect modifier of the relationship between taVNS and HF-HRV, as a larger increase in HF-HRV was seen in the older subjects. Importantly, there was a decrease in HF-HRV in the sham group. Conclusions: These findings suggest that younger subjects can adapt and maintain a constant level of HF-HRV regardless of the type of stimulation, but in the older subjects, only the active taVNS recipients were able to maintain and increase their HF-HRV. These results are important because they indicate that taVNS can enhance physiological regulation processes in response to external events.
... For applied settings, different portable high-resolution systems have been validated such as chest strap sensor systems for ECG-accurate electrophysiological recordings coupled via Bluetooth connection with receiving devices (smartwatches, smartphone via apps) for acquisition during rest and physical exercise conditions (18). Finally, PPG short-term measurements (metric: RMSSD, see figure 1) with the index finger and smartphone camera (light signal: flashing light) can currently also be recommended depending on the specific solution under resting conditions (3,52,66). Other PPG technique form factors like finger rings present promising user-friendly applications for frequent measurements in standardized measuring intervals, e.g., during the night (34). ...
English:
Heart rate variability (HRV) operationalizes the successive beat-to-beat fluctuations over a defined period of time, is derived from the time series of successive R-R intervals using various context-dependent metrics, and reflects the complex dynamic modulation of the heart’s chronotropic response to physiological and/or pathological perturbations. HRV metrics are used as markers of human cardiovascular health and risk stratification, or as measures of load quantification, exercise response and performance, respectively. However, a valid use of HRV in the fields of sports medicine and exercise science requires careful consideration of the specific measurement principle of the recording device, standardized assessment, preprocessing, analysis, and context-sensitive interpretation.
German:
Die Herzfrequenzvariabilität (HRV) operationalisiert die aufeinanderfolgenden Schlag-zu-Schlag-Schwankungen über einen bestimmten Zeitraum, wird aus der Zeitreihe aufeinanderfolgender R-R-Intervalle unter Verwendung verschiedener kontextabhängiger Metriken abgeleitet und spiegelt die komplexe dynamische Modulation der chronotropen Reaktion des Herzens auf physiologische und/oder pathologische Störungen wider. HRV-Metriken werden als Marker für die kardiovaskuläre Gesundheit des Menschen und zur Risikostratifizierung bzw. als Maß für die Quantifizierung von Beanspruchung und Leistungsfähigkeit verwendet. Eine sinnvolle Verwendung der HRV in den Bereichen Sportmedizin und Trainingswissenschaft erfordert jedoch eine sorgfältige Berücksichtigung des spezifischen Messprinzips des Aufzeichnungsgeräts, eine standardisierte Erhebung, Vorverarbeitung, Analyse und kontext-sensitive Interpretation.
... Heart rate has produced the majority of high-quality research, especially in the context of contact methods. There have been several experimental studies and randomised controlled trials measuring heart rate and cardiac arrhythmias in large populations using other methods of PPG [19,20]. which use index finger PPG data for heart rate and HRV measurements and these appear to have been more robustly assessed in clinical settings [23,24]. ...
Contactless photoplethysmography (cPPG) is a method of physiological monitoring. It differs from conventional monitoring methods (e.g., saturation probe) by ensuring no contact with the subject by use of a camera. The majority of research on cPPG is conducted in a laboratory setting or in healthy populations. This review aims to evaluate the current literature on monitoring using cPPG in adults within a clinical setting. Adhering to the Preferred Items for Systematic Reviews and Meta-analysis (PRISMA, 2020) guidelines, OVID, Webofscience, Cochrane library, and clinicaltrials.org were systematically searched by two researchers. Research articles using cPPG for monitoring purposes in adults within a clinical setting were selected. Twelve studies with a total of 654 individuals were included. Heart rate (HR) was the most investigated vital sign ( n = 8) followed by respiratory rate (( n = 2), Sp02 ( n = 2), and HR variability ( n = 2). Four studies were included in a meta-analysis of HR compared to ECG data which demonstrated a mean bias of –0.13 (95% CI, –1.22–0.96). This study demonstrates cPPG can be a useful tool in the remote monitoring of patients and has demonstrated accuracy for HR. However, further research is needed into the clinical applications of this method.
... Several algorithms were developed for peak-to-peak interval detection in PPG using slope analysis [16,17], automatic multiscale peak detection [18,19], neural networks [20], adaptive threshold peak detection [21,22]. Existing algorithms filter out erroneous intervals using outlier detection based solely on the lengths of detected intervals. ...
Peak-to-peak intervals in Photoplethysmography (PPG) can be used for heart rate variability (HRV) estimation if the PPG is collected from a healthy person at rest. Many factors, such as a person’s movements or hardware issues, can affect the signal quality and make some parts of the PPG signal unsuitable for reliable peak detection. Therefore, a robust HRV estimation algorithm should not only detect peaks, but also identify corrupted signal parts. We introduce such an algorithm in this paper. It uses continuous wavelet transform (CWT) for peak detection and a combination of features derived from CWT and metrics based on PPG signals’ self-similarity to identify corrupted parts. We tested the algorithm on three different datasets: a newly introduced Welltory-PPG-dataset containing PPG signals collected with smartphones using the Welltory app, and two publicly available PPG datasets: TROIKA and PPG-DaLiA. The algorithm demonstrated good accuracy in peak-to-peak intervals detection and HRV metric estimation.
... It would appear that this created an additional barrier in some players uploading their data, with several suggesting a smartphone application being a more appropriate forum for the system. Training load monitoring using a smartphone application has been concluded to be a feasible and practical tool to be used outside of a controlled laboratory setting (Altini and Amft, 2016). Furthermore, five players suggested the addition of automated reminders before and after training would be beneficial, which would again lend support to using a smartphone application. ...
Introduction: Amateur Rugby Union has an inherent risk of injury that is associated with detrimental effects on player welfare and team performance. The monitoring of players’ preparedness for, and response to, training has become an integral tool for coaches in injury risk management as it may aid in the prescription and design of training. A training monitoring system (TMS) should be both attainable and scientifically grounded, however, there is a paucity of information in relation to monitoring training at the amateur level and the inherent challenges this presents.
Aim: The aim of this doctoral research was to explore the associations between subjective measures of training load (TL) and wellness with injury occurrence in match-play and training sessions in amateur rugby in Ireland. Fundamentally, this programme of research aimed to offer practical methods of monitoring training that has the potential to mitigate injury risk and, in turn, benefit the health and wellbeing of players.
Methods: Five studies were conducted in this programme of research which: (1) systematically reviewed and critically appraised the existing relevant literature regarding associations between the acute:chronic workload ratio (ACWR), and injury in team sports (Chapter Three), (2) established the current training monitoring practices of practitioners working with in amateur Rugby Union clubs (Chapter Four), (3) developed and evaluated an online TMS (Chapter Five), examined methods of addressing missing TL using missing value imputation (MVI) (Chapter Six), and (5) explored possible associations between subjective self-reported measures of wellness, various training load metrics, and injury in amateur Rugby Union.
Results: The findings of the systematic review support the association between the ACWR and non-contact injuries and its use as a valuable tool for monitoring TL as part of a larger scale multifaceted monitoring system that includes other proven methods. 72.7% of practitioners working with amateur Rugby Union clubs monitored training with the most common method being the session rate of perceived exertion (sRPE), used in 83.3% of monitoring systems. The 3 most prominent challenges to motoring training were found to be lack of player compliance, data inconsistency and match-day challenges. Practitioners should strive to keep missing TL data at a minimum, however imputing missing data with the Daily Team Mean (DTMean) was the most accurate MVI method of the twelve MWI methods examined. Lastly, logistic regression found significant, strong associations (odds ratio (OR) = 6.172, 95% CI = 0.254 – 0.473, p < 0.001) between the occurrence of injury and the summative score of overall wellness (0-day lag). Significant weak associations were found between the occurrence of injury and the majority of ACWR calculations when 3-day and 7-day injury lag periods were applied.
Conclusion: The findings of this programme of research support the positive association between injury and both subjective wellness and TL. Monitoring training of amateur athletes has its own unique challenges and confounders (e.g., limited time with players, occupation of players, resources available). Practitioners must accept that due to the complexity of injury, a risk will always be present and instead focus on prescribing training that they deem will promote positive adaptations in a safe manner. However, a TMS consisting of subjective measures may mitigate injury risk in amateur Rugby Union by supporting decisions around training prescription.
... HRV data was collected each morning through the HRV4Training smartphone app, validated in several peer reviewed studies, using photoplethysmography (PPG). [26][27][28][29] Following standard procedures, upon waking, the participants were to sit up comfortably in bed and take their morning HRV. Subjects were instructed to cover the camera lens and camera light of their smartphone device with their finger. ...
Introduction: Research has increasingly looked at the effects of sleep on athletic performance. Although there is currently a plethora of data expressing the detrimental effects of sleep deprivation on athletic performance, fewer studies have assessed the effects of sleep extension. These studies have all been done with field or team sport athletes and all have been conducted with athletes who traditionally have practice times later in the day. Rowing is a sport with traditionally early practice times and represents an under examined population at a high risk of sleep deprivation. The purpose of the present study was to determine what sport specific performance benefits would be gained from extending the athlete’s sleep. Methods: Nineteen members of the Temple University’s men’s rowing team were asked to increase their sleep to nine to ten hours a night for four weeks, following a two-week baseline period. A two-week post-intervention phase followed the sleep extension period. Three sport specific assessments (Open rate 1-minute, Rate-capped 1-minute, and Interval tests) and daily HRV recordings were captured each week. Results: Subjects were unable to extend their sleep from baseline during the intervention, 392.07 ± 53.69 minutes and 374.11 ± 41.53 minutes, respectively (p = .137). Significant variation was found in the week to week comparison of the Interval test and OR1-Min test. Conclusion: Athletes failed to increase their time asleep, limiting our ability to assess the impact of sleep on performance. Performance did suffer over the course of the study, suggesting participants were below he minimal amount of sleep necessary to maintain performance. Better athlete education by coaches might prove beneficial for athletes to develop the habits necessary for sufficient sleep and improved performance.
... In this regard, as the pre-planned loads were adapted on a daily basis, with consideration of all these factors (including HRV morning data [18]), we do not know if a fixed periodization would result in similar outcomes. In addition, these associations may be different when using other HRV protocols [19], parameters [20], and Apps with different correction algorithms [21][22][23]. ...
Background: The association between heart rate variability (HRV), training load (TL),
and performance is poorly understood. Methods: A middle-aged recreational female runner was monitored during a competitive 20-wk macrocycle divided into first (M1) and second mesocycle (M2) in which best performances over 10 km and 21 km were recorded. Volume (km), session rating of perceived exertion (sRPE), TL, and monotony (mean TL/SD TL) were the workload parameters recorded. The root mean square of the successive differences in R-R intervals (RMSSD), its coefficient of variation (RMSSDcv), and the RMSSD:RR ratio were the HRV parameters monitored. Results: During M2, RMSSD (p = 0.006) and RMSSD:RR (p = 0.002) were significantly increased, while RR was significantly reduced (p = 0.017). Significant correlations were identified between monotony and volume (r = 0.552; p = 0.012), RR (r = 0.447; p = 0.048), and RMSSD:RR (r = -0.458; p = 0.042). A sudden reduction in RMSSD (from 40.31 to 24.34 ms) was observed the day before the first symptoms of an influenza. Conclusions: The current results confirm the practicality of concurrent HRV and sRPE monitoring in recreational runners, with the RMSSD:RR ratio indicative of specific adaptations. Excessive training volume may be associated to both elevated monotony and reduced RMSSD:RR. Identification of mesocycle patterns is recommended for better individualization of the periodization used.
... It would appear that this created an additional barrier in some players uploading their data, with several suggesting a smartphone application being a more appropriate forum for the system. Training load monitoring using a smartphone application has been concluded to be a feasible and practical tool to be used outside of a controlled laboratory setting [50]. Furthermore, five players suggested the addition of automated reminders before and after training would be beneficial, which would again lend support to using a smartphone application. ...
A training monitoring system (TMS) should be both attainable and scientifically grounded; however, the optimal method of monitoring training is not yet fully understood. The purpose of this study was to develop and evaluate an online TMS for amateur rugby union. The experimental approach to the problem consisted of five phases: (1) establishing the current training and training load (TL) monitoring practices of amateur rugby union teams, (2) designing and developing the TMS, (3) recruiting teams and subsequently introducing the TMS, (4) supporting the strength and conditioning (S&C) coaches using the TMS, and (5) evaluating the TMS. The findings of this study support the use of an online TMS as a useful and effective method of facilitating training prescription and design in an effort to reduce injury risk and enhance performance. The main barriers impeding player compliance are the lack of feedback on their data and evidence of its use in training design, coaching, and prescription. The effectiveness of the system is dependent on the extent to which the associated challenges are mitigated to ensure quality and consistent data. However, this study offers a method of monitoring training that can be effective while also establishing pitfalls to avoid for both practitioners and researchers alike.
... However, lower correlation values for HF (ICC = 0.71) and LF (ICC = 0.54) were reported compared to those in the present study (Nussinovitch et al., 2011). The discrepancy with the frequency domain parameters cannot be fully explained, but both studies suggest that RMSSD may be accurately acquired within a 1-min time segment, which agree with the findings from similar research in athletes (Esco et al., 2018;Esco & Flatt, 2014), healthy individuals (Altini & Amft, 2016) and clinical patients (Nussinovitch, Cohen, Kaminer, Ilani, & Nussinovitch, 2012). Because of these findings, in addition to being uninfluenced by respiratory rate (Saboul, Pialoux, & Hautier, 2013) and relatively easy to calculate and interpret Plews, Laursen, Stanley, Kilding, & Buchheit, 2013), RMSSD has been considered the preferred index for monitoring HRV in practical settings (Buchheit, 2014). ...
Purpose
With the increasing popularity of ultra-short heart rate variability (HRV) measurements being utilized with mobile devices outside of controlled, research settings, it is important to determine the proper methodology to ensure accuracy. Therefore, the purpose of this study was to examine the validity of ultra-short-term HRV metrics across three different body positions in recreationally active individuals.
Methods
Twenty-six subjects (12 males: 24.1 ± 3.6 yrs., 178.6 ± 6.4 cm, 82.9 ± 8.7 kg; 15 females: 21.3 ± 1.2 yrs., 170.7 ± 10.5 cm, 71.6 ± 18.9 kg) participated in 10-min electrocardiogram recordings in the supine, seated, and standing positions. HRV analysis using a variety of time, frequency, and non-linear parameters were performed following traditional recommendations (i.e., last 5 min of each 10-min recording) and ultra-short-term recordings (i.e., 1-min epoch following a 1-min stabilization period).
Results
Slight decreases (e.g., “near perfect” to “very large”) in intraclass correlations (ICC) and increases in the limits of agreement (LOA) were noted for most of the HRV metrics as position changed to sitting and then standing. However, throughout all three positions, the highest ICC values (0.88 to 0.92) and tightest LOA (CE ± 1.96 SD) were displayed in RMSSD.
Conclusions
This study supports the use of RMSSD and SD1 for assessing HRV under ultra-short-term recordings of 1 min regardless of position. However, practitioners should be consistent with the preferred position for measurements and not use them interchangeably to reduce potential errors during long-term monitoring.
... The emergence of Smartphones with finger-tip PPG capability offers an alternative. This alternative has been validated and confirmed to provide reliable HRV when participants are in the supine position (Altini & Amft, 2016;Charlot, Cornolo, Brugniaux, Richalet, & Pichon, 2009). Through Bland-Altman analysis the agreement between ECG and finger-tip PPG using the iPhone 6 while seated has been reported as moderate for rMSSD (BA ratio: 0.106), good agreement for SDNN (BA ratio: 0.035), and LF (BA ratio: 0.089), and poor agreement for HF (BA ratio 0.249; Banhalmi et al., 2018). ...
Heart rate variability (HRV) is a biomarker used to reflect both healthy and pathological state(s). The effect of the menstrual cycle and menstrual cycle phases (follicular, luteal) on HRV remains unclear. Active eumenorrheic women free from exogenous hormones completed five consecutive weeks of daily, oral basal body temperature (BBT) and HRV measurements upon waking. Descriptive statistics were used to characterize shifts in the HRV measures: Standard deviation of NN intervals (SDNN), root mean square of successive difference (rMSSD), high (HF) and low frequency (LF) across the menstrual cycle and between phases. All HRV measures were assessed by medians (Mdn), median difference of consecutive days (Mdnâ†) and variance. Seven participants (M ± SD; age: 28.60 ± 8.40 year) completed the study with regular menstrual cycles (28.40 ± 2.30 days; ovulation day 14.57 ± 0.98 day). Median rMSSD displayed a nonlinear decrease across the menstrual cycle and plateau around the day of ovulation. A negative shift before ovulation in Mdnâ†, rMSSD, SDNN, and LF as well as peak on luteal phase Day 4 in rMSSD and SDNN was observed. Median variance increased in rMSSD (150.06 ms ² ) SDNN (271.12 ms ² ), and LF variance (0.001 sec ² /Hz) from follicular to luteal phase. Daily HRV associated with the parasympathetic nervous system was observed to decrease nonlinearly across the menstrual cycle.
Purpose: The purpose of this study was to analyze whether the autonomic response of female soccer players, measured by daily heart rate variability (HRV), is related to the player's training load of the previous day and, therefore, could be used as a valid indicator in women's soccer. Methods: Seventeen professional female soccer players participated in the study. Over a 6-week period, players were instructed to measure their daily HRV, as well as other self-reported indicators of fatigue, recovery and sleep quality, using the HRV4Training-app. Player's training load was measured in arbitrary units. The phases of the menstrual cycle were monitored using a self-reported calendar. For the statistical analysis, a multilevel analysis was performed for the entire sample based on linear mixed models and a second multilevel analysis for the sample segmented by phase of the menstrual cycle. Results: Player's autonomic response, measured by HRV, was not associated with the training load of the previous day (p=0.557), regardless of the phase of the menstrual cycle (menstrual p=0.566; follicular p=0.189; luteal p=0.699). However, HRV was significantly associated with other self-reported indicators of fatigue, recovery, and sleep quality (p<0.001). Being altered some of these relationships according to the phase of the menstrual cycle. Conclusions: Overall, the findings suggested that HRV alone cannot be used as a valid indicator of training load in women's soccer. Nevertheless, HRV is related to important indicators of adaptation to training load. In addition, this relationship is altered depending on the phases of the player's menstrual cycle. Abstract • There is no association between the perception of training load and HRV (p=0.557). The absence of association is maintained regardless of the phase of the menstrual cycle (menstrual phase p=0.566; follicular phase p=0.189; luteal phase p=0.699). • HRV was statistically associated, with a low to moderate effect size, with self-reported indicators of fatigue (p=0.000), muscle pain (p=0.000), training motivation (p=0.000), mental energy (p=0.000) and sleep quality (p=0.000). The menstrual cycle influenced these results. Specifically, this association seems to be consolidated in the luteal phase and less consistent in the menstrual and follicular phases.
Heart rate variability (HRV) is a proxy of physiological stress, capturing autonomic nervous system responses to training and other stressors. As such, a vast body of literature has investigated the impact of acute and chronic stressors on HRV. Technological advancements such as mobile apps and wearables able to capture HRV data in more practical settings have further pushed the adoption and use of HRV analysis. In this chapter, we cover the physiological underpinnings of HRV analysis and discuss metrics and their relation to acute and longer-term stressors and in the context of training periodization and planning. Finally, we provide practical advice on validated technologies and best practices for practitioners interested in using HRV analysis to capture individual responses to training and lifestyle stressors.
Purpose: Stretching exercise and gymnastics both have beneficial effects, such as improvement of autonomic nervous system activity and mood. Additionally, studies on the effects of exercise on cognitive function have been conducted covering a wide range of age groups and have attracted much attention. However, conventional studies have set up programs with implementation times of 20 to 30 minutes. Therefore, shorter stretching programs are needed in order to fit them more easily into one’s free time. We examined the effects of a short 7-minute stretching gymnastics regime on the autonomic nervous system activity and cognitive function in 21 healthy participants. Methods: In this study, the participants performed a 10-minute cognitive task, followed by either Stretch Well Gymnastics, Stretch Band Gymnastics, or Radio Gymnastics sessions on different days. The participants then performed the cognitive task again. Heart rate was measured continuously throughout the experiment and we analyzed the heart rate variability. The cognitive tasks completed by all of the participants were evaluated for inhibitory control and cognitive flexibility. Results: A significant increase was shown in the sympathetic nerve activity during the Stretch Well Gymnastics, compared to the Radio Gymnastics and Stretch Band Gymnastics. Parasympathetic nerve levels were significantly increased after the gymnastics, compared to during the gymnastics, although there were no significant differences between any of the tasks. Additionally, in both the Stroop task and the number-Letter task, reaction times were faster in all of the sessions. In particular, the Stroop task showed the highest values for the Radio Gymnastics sessions, with marginally significantly lower scores for the Stretch Well Gymnastics sessions. Conclusion: The results showed that these heart rate variability responses supported the effects of autonomic activity associated with conventional low-intensity exercise. Additionally, stretching gymnastics for less than 10 minutes showed a positive effect on inhibitory function and cognitive flexibility.
The aim of this study was to investigate the relationship between heart rate and heart rate variability (HRV) with respect to individual characteristics and acute stressors. In particular, the relationship between heart rate, HRV, age, sex, body mass index (BMI), and physical activity level was analyzed cross-sectionally in a large sample of 28,175 individuals. Additionally, the change in heart rate and HRV in response to common acute stressors such as training of different intensities, alcohol intake, the menstrual cycle, and sickness was analyzed longitudinally. Acute stressors were analyzed over a period of 5 years for a total of 9 million measurements (320±374 measurements per person). HRV at the population level reduced with age (p < 0.05, r = −0.35, effect size = moderate) and was weakly associated with physical activity level (p < 0.05, r = 0.21, effect size = small) and not associated with sex (p = 0.35, d = 0.02, effect size = negligible). Heart rate was moderately associated with physical activity level (p < 0.05, r = 0.30, effect size = moderate) and sex (p < 0.05, d = 0.63, effect size = moderate) but not with age (p = 0.35, r = −0.01). Similar relationships between BMI, resting heart rate (p < 0.05, r = 0.19, effect size = small), and HRV (p < 0.05, r = −0.10, effect size = small) are shown. In response to acute stressors, we report a 4.6% change in HRV (p < 0.05, d = 0.36, effect size = small) and a 1.3% change in heart rate (p < 0.05, d = 0.38, effect size = small) in response to training, a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and a 12% reduction in HRV (p < 0.05, d = 0.55, effect size = moderate) after high alcohol intake, a 1.6% change in heart rate (p < 0.05, d = 1.41, effect size = large) and a 3.2% change in HRV (p < 0.05, d = 0.80, effect size = large) between the follicular and luteal phases of the menstrual cycle, and a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and 10% reduction in HRV (p < 0.05, d = 0.47, effect size = moderate) during sickness. Acute stressors analysis revealed how HRV is a more sensitive but not specific marker of stress. In conclusion, a short resting heart rate and HRV measurement upon waking using a smartphone app can effectively be used in free-living to quantify individual stress responses across a large range of individuals and stressors.
This study aims to evaluate whether the photoplethysmography technique (PPG) can be used as an instrument to assess heart rate variability (HRV) parameters in individuals with spinal cord injury (SCI). Twenty-one subjects (2 tetraplegic, 18 paraplegic and 1 diplegic) participated in the study (37.5 ± 12.3 years, 58.8 ± 15.7 kg and 160.0 ± 21.4 cm). HRV parameters were obtained simultaneously for 5 min with PPG (HRV-4 training mobile app) and Polar H7. Although significant correlations between PPG and Polar H7 have been found for SDNN (r = 0.508, p = 0.019), RMSSD (r = 0.456, p = 0.038), PNN50 (r = 0.620, p = 0.003) and LF (r = 0.456, p = 0.038) parameters, Bland-Altman analyses of agreement showed that PPG overestimates all HRV parameters (SDNN, Bias = 47.61 ms; RMSSD, Bias = 17.91 ms; PNN50, Bias = 11.52%; LF, Bias = 0.22 Hz; and HF, Bias = 0.18 Hz). These results indicate that the HRV parameters have a correlation between the methods evaluated by PPG and PolarH7. However, caution is recommended when using PPG for HRV analysis in people with spinal cord injury, as the results indicate an overestimation of HRV parameters.
Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.
The athlete gut microbiome differs from that of non‐athletes in its composition and metabolic function. Short‐term fitness improvement in sedentary adults does not replicate the microbiome characteristics of athletes. The objective of this study was to investigate whether sustained fitness improvement leads to pronounced alterations in the gut microbiome. This was achieved using a repeated‐measures, case‐study approach that examined the gut microbiome of two initially unfit volunteers undertaking progressive exercise training over a 6‐month period. Samples were collected every two weeks, and microbiome, metabolome, diet, body composition, and cardiorespiratory fitness data were recorded. Training culminated in both participants completing their respective goals (a marathon or Olympic‐distance triathlon) with improved body composition and fitness parameters. Increases in gut microbiota α‐diversity occurred with sustained training and fluctuations occurred in response to training events (eg, injury, illness, and training peaks). Participants’ BMI reduced during the study and was significantly associated with increased urinary measurements of N‐methyl nicotinate and hippurate, and decreased phenylacetylglutamine. These results suggest that sustained fitness improvements support alterations to gut microbiota and physiologically‐relevant metabolites. This study provides longitudinal analysis of the gut microbiome response to real‐world events during progressive fitness training, including intercurrent illness and injury.
Background:
Chronic widespread pain (CWP), including fibromyalgia (FM), affects one in every ten adults and is one of the leading causes of sick leave and emotional distress. Due to an unclear etiology and a complex pathophysiology, FM is a condition with few, if any, effective and safe treatments. However, current research within the field of vagal nerve innervation suggests psychophysiological and electrical means by which FM may be treated. This study will investigate the efficacy of two different noninvasive vagal nerve stimulation techniques for the treatment of FM.
Methods:
The study will use a randomized, single-blind, sham-controlled design to investigate the treatment efficacy of motivational nondirective resonance breathing (MNRB™) and transcutaneous vagus nerve stimulation (Nemos® tVNS) on patients diagnosed with FM. Consenting FM patients (N = 112) who are referred to the Department of Pain Management and Research at Oslo University Hospital, in Oslo, Norway, will be randomized into one of four independent groups. Half of these participants (N = 56) will be randomized to either an experimental tVNS group or a sham tVNS group. The other half (N = 56) will be randomized to either an experimental MNRB group or a sham MNRB group. Both active and sham treatment interventions will be delivered twice per day at home, 15 min/morning and 15 min/evening, for a total duration of 2 weeks (14 days). Participants are invited to the clinic twice, once for pre- and once for post-intervention data collection. The primary outcome is changes in photoplethysmography-measured heart rate variability. Secondary outcomes include self-reported pain intensity on a numeric rating scale, changes in pain detection threshold, pain tolerance threshold, and pressure pain limit determined by computerized pressure cuff algometry, blood pressure, and health-related quality of life.
Discussion:
The described randomized controlled trial aims to compare the efficacy of two vagal nerve innervation interventions, MNRB and tVNS, on heart rate variability and pain intensity in patients suffering from FM. This project tests a new and potentially effective means of treating a major public and global health concern where prevalence is high, disability is severe, and treatment options are limited.
Trial registration:
ClinicalTrials.gov NCT03180554 . Registered on August 06, 2017.
Endurance running has become an immensely popular sporting activity, with millions of recreational runners around the world. Despite the great popularity of endurance running as a recreational activity during leisure time, there is no consensus on the best practice for recreational runners to effectively train to reach their individual objectives and improve physical performance in a healthy manner. Moreover, there are lots of anecdotal data without scientific support, while most scientific evidence on endurance running was developed from studies observing both recreational and professional athletes of different levels. Further, the transference of all this information to only recreational runners is difficult due to differences in the genetic predisposition for endurance running, the time available for training, and physical, psychological, and physiological characteristics. Therefore, the aim of this review is to present a selection of scientific evidence regarding endurance running to provide training guidelines to be used by recreational runners and their coaches. The review will focus on some key aspects of the training process, such as periodization, training methods and monitoring, performance prediction, running technique, and prevention and management of injuries associated with endurance running.
A measurement method of heart rate and heart rate variability (HRV) based on smartphone has been developed and validated. The method is based on photoplethysmography (PPG) acquired with the smartphone camera (SPPG). SPPG was compared with the electrocardiogram (ECG), used as the gold standard, and with an external PPG sensor. Twenty-three healthy subjects were measured using two different smartphone models in three different breathing conditions. The error of the first differentiation between SPPG and ECG series is minimized with the fiducial point at maximum first derivative of the SPPG. The obtained standard deviation of error (SDE) between SPPG and ECG is around 5.4 ms and it is similar to SDE between PPG and ECG. Good agreement between SPPG and ECG for NN, SDNN and RMSSD have been found but it is insufficient agreement for LF/HF. Similar levels of agreement for SPPG-ECG and PPG-ECG have been obtained for the HRV indices. Finally, the differences between smartphone models for HRV indices are slight. Therefore, the smartphone can be used for measuring accurately the following HRV indices: NN, SDNN and RMSSD.
Background
Advancements in wearable technology have provided practitioners and researchers with the ability to conveniently measure various health and/or fitness indices. Specifically, portable devices have been devised for convenient recordings of heart rate variability (HRV). Yet, their accuracies remain questionable.
Objective
The aim was to quantify the accuracy of portable devices compared to electrocardiography (ECG) for measuring a multitude of HRV metrics and to identify potential moderators of this effect.
Methods
This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Articles published before July 29, 2017 were located via four electronic databases using a combination of the terms related to HRV and validity. Separate effect sizes (ESs), defined as the absolute standardized difference between the HRV value recorded using the portable device compared to ECG, were generated for each HRV metric (ten metrics analyzed in total). A multivariate, multi-level model, incorporating random-effects assumptions, was utilized to quantify the mean ES and 95% confidence interval (CI) and explore potential moderators.
Results
Twenty-three studies yielded 301 effects and revealed that HRV measurements acquired from portable devices differed from those obtained from ECG (ES = 0.23, 95% CI 0.05–0.42), although this effect was small and highly heterogeneous (I² = 78.6%, 95% CI 76.2–80.7). Moderator analysis revealed that HRV metric (p <0.001), position (p = 0.033), and biological sex (β = 0.45, 95% CI 0.30–0.61; p <0.001), but not portable device, modulated the degree of absolute error. Within metric, absolute error was significantly higher when expressed as standard deviation of all normal–normal (R–R) intervals (SDNN) (ES = 0.44) compared to any other metric, but was no longer significantly different after a sensitivity analysis removed outliers. Likewise, the error associated with the tilt/recovery position was significantly higher than any other position and remained significantly different without outliers in the model.
Conclusions
Our results suggest that HRV measurements acquired using portable devices demonstrate a small amount of absolute error when compared to ECG. However, this small error is acceptable when considering the improved practicality and compliance of HRV measurements acquired through portable devices in the field setting. Practitioners and researchers should consider the cost–benefit along with the simplicity of the measurement when attempting to increase compliance in acquiring HRV measurements.
The aim of the present work was to propose a Smartphone algorithm to analyze, in real time, the evolution of Heart Rate Variability (HRV) in order to individualize and reduce the recording time according to the specificities of each user. During HRV recording, a new RMSSD value is calculated each time a new RR is captured. The recording process stops once an acceptable stability of HRV is reached. This new method was tested on 3 groups of 15 subjects (cardiac patients, sedentary employees and national-level athletes) and compared with the gold standard method (5 min HRV recording time). The RMSSD indices provided by the short method and by the gold standard method (respectively 62.1 ± 43.7 ms vs. 62.7 ± 44.1 ms) showed no significant differences. In addition, a very strong correlation was observed between RMSSD values obtained by the 2 methods (n = 45; R = 0.998; p < 0.001). Routine duration of the new method was significantly shorter with a time-savings of 2 min (178 ± 51 s vs. 300 s; p < 0.05). This new algorithm seems to adapt perfectly to each subject, and it can detect the stability phase for HRV measurements during the recording process. Algorithm provides an adapted and personal routine duration that can evolve each day depending on parameters such as fatigue or stress level that are known to influence HRV. This solution can be easily implemented in a smartphone application and seems particularly suitable for performing daily HRV monitoring in field conditions.
Purpose:
To establish the validity of smartphone photoplethysmography (PPG) and heart rate sensor in the measurement of heart rate variability (HRV).
Methods:
29 healthy subjects were measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The root mean sum of the squared differences between R-R intervals (rMSSD) was determined from each device.
Results:
Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% (90% CI) = 6.35 (5.13; 8.5)). When assessed as a standardised difference, all differences were deemed "Trivial" (average std. diff (90% CI) = 0.10 (0.08; 0.13). Both PPG and HR sensor derived measures had almost perfect correlations with ECG (R = 1.00 (0.99; 1:00).
Conclusion:
Both PPG and heart rate sensor provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.
This study evaluated the 7-day mean and CV of supine and standing ultra-short log transformed root mean square of successive R-R intervals multiplied by 20 (lnRMSSDx20) obtained with a smartphone application (app) in response to varying weekly training load (TL). Additionally, we aimed to determine if these values could be accurately assessed in as few as 5 or 3 days per week.
9 females from a collegiate soccer team performed daily HRV measures with an app in supine and standing positions over 3 weeks of moderate, high and low TL. The mean and CV over 7, 5, and 3 days were compared within and between each week.
The 5 and 3-day measures within each week provided very good to near perfect intraclass correlations (ICCs ranging from 0.74 - 0.99) with typical errors ranging from 0.64 - 5.65 when compared with the 7-day criteria. The 7, 5, and 3-day supine CV and the 7-day standing CV were moderately lower during the low load compared to the high load week (p values ranged from 0.003 - 0.045 and effect sizes ranged from 0.86 - 0.92), with no significant changes occurring in the other measures.
This study supports the use of the mean and CV of lnRMSSD measured across at least 5 days for reflecting weekly values. The supine lnRMSSDx20 CV as measured across 7, 5, and 3 days was the most sensitive marker to the changes in TL within the 3-week period.
Purpose: The purpose of this investigation was to cross-validate the ithleteTM heart rate variability smart phone application with an electrocardiograph for determining ultra-short-term root mean square of successive R-R intervals.
Methods: The root mean square of successive R-R intervals was simultaneously determined via electrocardiograph and ithleteTM at rest in twenty five healthy participants.
Results: There were no significant differences between the electrocardiograph and ithleteTM derived root mean square of successive R-R interval values (p > 0.05) and the correlation was near perfect (r = 0.99, p < 0.001). In addition, the ithleteTM revealed a Standard Error of the Estimate of 1.47 and Bland Altman plot showed that the limits of agreement ranged from 2.57 below to 2.63 above the constant error of -0.03.
Conclusions: In conclusion, the ithleteTM appeared to provide a suitably accurate measure of root mean square of successive R-R intervals when compared to the electrocardiograph measure obtained in the laboratory within the current sample of healthy adult participants.
Future Directions: The current study lays groundwork for future research determining the efficacy of ithleteTM for reflecting athletic training status over a chronic conditioning period.
Keywords: HRV, Athlete Monitoring, Parasympathetic, Mobile Device
The purpose of this study was to evaluate the agreement of the vagal-related heart rate variability index, log-transformed root mean square of successive R-R intervals (lnRMSSD), measured under ultra-short-term conditions (< 60 seconds) with conven-tional longer term recordings of 5 minutes in collegiate athletes under resting and post-exercise conditions. Electrocardio-graphic readings were collected from twenty-three athletes within 5-minute segments at rest and at 25-30 minutes of supine recovery following a maximal exercise test. From each 5-minute segment, lnRMSSD was recorded as the criterion meas-ure. Within each 5-minute segment, lnRMSSD was also deter-mined from randomly selected ultra-short-term segments of 10-, 30-, and 60-seconds in length, which were compared to the criterion. When compared to the criterion measures, the signifi-cant intraclass correlation (from 0.98 to 0.81, p < 0.05) and typical error (from 0.11 to 0.34) increased as ultra-short-term measurement duration decreased (i.e., from 60 seconds to 10 seconds). In addition, the limits of agreement (Bias ± 1.98 SD) increased as ultra-short-term lnRMSSD duration decreased as follows: 0.00 ± 0.22 ms, -0.07 ± 0.41 ms, -0.20 ± 0.94 ms for the 60-, 30-, and 10-second pre-exercise segments, respectively, and -0.15 ± 0.39 ms, -0.14 ± 0.53 ms, -0.12 ± 0.76 ms for the 60-, 30-, and 10-second post-exercise segments, respectively. This study demonstrated that as ultra-short-term measurement dura-tion decreased from 60 seconds to 10 seconds, the agreement to the criterion decreased. Therefore, 60 seconds appears to be an acceptable recording time for lnRMSSD data collection in col-legiate athletes.
Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.
The purpose of this investigation was to cross-validate the ithlete™ heart rate variability smart phone application with an electrocardiograph for determining ultra-short-term root mean square of successive R-R intervals. The root mean square of successive R-R intervals was simultaneously determined via electrocardiograph and ithlete™ at rest in twenty five healthy participants. There were no significant differences between the electrocardiograph and ithlete™ derived root mean square of successive R-R interval values (p > 0.05) and the correlation was near perfect (r = 0.99, p < 0.001). In addition, the ithlete™ revealed a Standard Error of the Estimate of 1.47 and Bland Altman plot showed that the limits of agreement ranged from 2.57 below to 2.63 above the constant error of -0.03. In conclusion, the ithlete™ appeared to provide a suitably accurate measure of root mean square of successive R-R intervals when compared to the electrocardiograph measures obtained in the laboratory within the current sample of healthy adult participants. The current study lays groundwork for future research determining the efficacy of ithlete™ for reflecting athletic training status over a chronic conditioning period.
In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.
The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0-90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1-48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24-48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.
The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
Competitive swimming as a physical activity results in changes to the activity level of the autonomic nervous system (ANS). However, the precise relationship between ANS activity, fatigue and sports performance remains contentious. To address this problem and build a model to support a consistent relationship, data were gathered from national and regional swimmers during two 30 consecutive-week training periods. Nocturnal ANS activity was measured weekly and quantified through wavelet transform analysis of the recorded heart rate variability. Performance was then measured through a subsequent morning 400 meters freestyle time-trial. A model was proposed where indices of fatigue were computed using Banister's two antagonistic component model of fatigue and adaptation applied to both the ANS activity and the performance. This demonstrated that a logarithmic relationship existed between performance and ANS activity for each subject. There was a high degree of model fit between the measured and calculated performance (R(2) = 0.84±0.14,p<0.01) and the measured and calculated High Frequency (HF) power of the ANS activity (R(2) = 0.79±0.07, p<0.01). During the taper periods, improvements in measured performance and measured HF were strongly related. In the model, variations in performance were related to significant reductions in the level of 'Negative Influences' rather than increases in 'Positive Influences'. Furthermore, the delay needed to return to the initial performance level was highly correlated to the delay required to return to the initial HF power level (p<0.01). The delay required to reach peak performance was highly correlated to the delay required to reach the maximal level of HF power (p = 0.02). Building the ANS/performance identity of a subject, including the time to peak HF, may help predict the maximal performance that could be obtained at a given time.
Advancements in technology have always had major impacts in medicine. The smartphone is one of the most ubiquitous and dynamic trends in communication, in which one's mobile phone can also be used for communicating via email, performing Internet searches, and using specific applications. The smartphone is one of the fastest growing sectors in the technology industry, and its impact in medicine has already been significant.
To provide a comprehensive and up-to-date summary of the role of the smartphone in medicine by highlighting the ways in which it can enhance continuing medical education, patient care, and communication. We also examine the evidence base for this technology.
We conducted a review of all published uses of the smartphone that could be applicable to the field of medicine and medical education with the exclusion of only surgical-related uses.
In the 60 studies that were identified, we found many uses for the smartphone in medicine; however, we also found that very few high-quality studies exist to help us understand how best to use this technology.
While the smartphone's role in medicine and education appears promising and exciting, more high-quality studies are needed to better understand the role it will have in this field. We recommend popular smartphone applications for physicians that are lacking in evidence and discuss future studies to support their use.
The aim of this study was to examine how sampling rates below 1000
Hz influence the precision of heart rate variability analysis. Simulated
tachograms as well as tachograms from healthy persons were investigated.
With parameters from time, frequency domain and non-linear dynamics
these tachograms were classified. The results were compared with
theoretical assumptions and special error measures were derived. New
developed measures from non-linear dynamics showed an insensitive
behavior to a reduced sampling frequency down to 128 Hz. The major
results of this study lead to the conclusion to use sampling rates only
above 120 Hz which implies a total error in the range of approximately
1.8±1.5%. However only the use of sampling rates above 200 Hz
provides nearly error free results
We show that a mobile phone can serve as an accurate monitor for several physiological variables, based on its ability to record and analyze the varying color signals of a fingertip placed in contact with its optical sensor. We confirm the accuracy of measurements of breathing rate, cardiac R-R intervals, and blood oxygen saturation, by comparisons to standard methods for making such measurements (respiration belts, ECGs, and pulse-oximeters, respectively). Measurement of respiratory rate uses a previously reported algorithm developed for use with a pulse-oximeter, based on amplitude and frequency modulation sequences within the light signal. We note that this technology can also be used with recently developed algorithms for detection of atrial fibrillation or blood loss.
Photoplethysmography variability (PPGV) is currently considered to be a good surrogate to heart rate variability (HRV) measurements using the time between two pulse waves instead of RR intervals. Nevertheless, the interchangeability between HRV and PPGV has never been evaluated in situations with severe alterations in the autonomic nervous system (ANS). We aimed to identify the conditions for a correct utilization of PPGV in evaluating the consequences of sympathetic stimulations. Nine subjects performed three tests: active orthostatic test, slow walk and moderate and exhaustive cycling exercises in the supine position. Pulse waves at the fingertip and RR intervals were recorded at the same time. We used correlations and the Bland and Altman method to compare and evaluate interchangeability between several HRV indices. Bland and Altman analysis highlighted small discrepancies between PPGV and HRV for all HRV indices in the supine position and for LF(ms)(2), HF(ms)(2), LF(peak) and RMSSD in the upright position. During the slow walk, it was impossible to detect properly PPG peaks. We observed large differences between the two methods during the cycling exercise. In conclusion, PPGV can be used instead of HRV without reserve in the supine position but only for some HRV indices in the upright position and not during slow walk and cycling exercise.
Monitoring physical performance is of major importance in competitive sports. Indices commonly used, like resting heart rate, VO2max, and hormones, cannot be easily used because of difficulties in routine use, of variations too small to be reliable, or of technical challenges in acquiring the data.
We chose to assess autonomic nervous system activity using heart rate variability in seven middle-distance runners, aged 24.6 +/- 4.8 yr, during their usual training cycle composed of 3 wk of heavy training periods, followed by a relative resting week. The electrocardiogram was recorded overnight twice a week and temporal and frequency indices of heart rate variability, using Fourier and Wavelet transforms, were calculated. Daily training loads and fatigue sensations were estimated with a questionnaire. Similar recordings were performed in a sedentary control group.
The results demonstrated a significant and progressive decrease in parasympathetic indices of up to -41% (P < 0.05) during the 3 wk of heavy training, followed by a significant increase during the relative resting week of up to +46% (P < 0.05). The indices of sympathetic activity followed the opposite trend, first up to +31% and then -24% (P < 0.05), respectively. The percentage increasing mean nocturnal heart rate variation remained below 12% (P < 0.05). There was no significant variation in the control group.
This study confirmed that heavy training shifted the cardiac autonomic balance toward a predominance of the sympathetic over the parasympathetic drive. When recorded during the night, heart rate variability appeared to be a better tool than resting heart rate to evaluate cumulated physical fatigue, as it magnified the induced changes in autonomic nervous system activity. These results could be of interest for optimizing individual training profiles.
In brief: An elevated resting pulse rate is generally considered a marker of overtraining in endurance athletes who greatly increase their workout distance. This study supports that assumption, demonstrating increased morning heart rates in 12 men who ran twice their regular training mileage during a 500-km (312-mile) road race over 20 days. After the first week of running, morning pulse rates were slightly reduced, but thereafter they progressively increased, becoming 10 beats min−1 higher (p <.01) as the race ended. Blood pressure, oral temperature, body weight, sweat loss, and blood glucose, lactate, insulin, and Cortisol levels were not related to the increase in morning heart rate.
We hypothesized that an iPhone 4s can be used to detect atrial fibrillation (AF) based on its ability to record a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4s for AF detection, 25 prospective subjects with AF pre- and post-electrical cardioversion were recruited. Using an iPhone 4s, we collected 2-minute pulsatile time series. We investigated 3 statistical methods consisting of the Root Mean Square of Successive Differences (RMSSD), the Shannon entropy (ShE) and the Sample entropy (SampE), which have been shown to be useful tools for AF assessment. The beat-to-beat accuracy for RMSSD, ShE and SampE was found to be 0.9844, 0.8494 and 0.9552, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF or normal sinus rhythm (NSR) in the data. Using this criterion, we achieved an accuracy of 100% for both detecting the presence of either AF or NSR.
To study the physiological response to heavy training, seven male competitive cyclists intensified their normal training program for two weeks (IIT) in order to achieve a state of short-term overtraining. The subjects underwent a graded cycle ergometer test to exhaustion, an outdoor 8.5 km time trial and a computerized test to study reaction time and visual perception, before, during and after the two weeks of intensified training and after two weeks of recovery. Furthermore subjects kept a daily log in the form of a questionnaire. After two weeks of IIT all subjects showed symptoms of overtraining: the general state of well being declined as indicated by the questionnaire while performances on time trial (mean +/- SEM: 830 +/- 14 sec-871 +/- 19 sec), contests and maximal power output (mean +/- SEM: 336 7 watt-310 +/- 5 watt) declined significantly. Maximal (mean +/- SEM 11.8 +/- 1.1 mmol.l-1-5.9 +/- 0.5 mmol.l-1) and submaximal lactate values were significantly lowered during ergometer test after the IIT, while the workload at the 4 mmol point increased significantly (mean +/- SEM 234 +/- 10 watt-267 +/- 13 watt). Sleeping heart rate increased significantly (mean +/- SEM 49.5 +/- 9.3 BPM-54.3 +/- 8.8 BPM). Maximal heart rate (mean +/- SEM 185 +/- 3 BPM-178 +/- 2 BPM, mean heart rate during the time trial (mean +/- SEM 178 +/- 2 BPM-169 +/- 2 BPM) and VO2max (mean +/- SEM 4801 +/- 121 ml.min-1-4409 +/- 101 ml.min-1) were all significantly lowered by the IIT.(ABSTRACT TRUNCATED AT 250 WORDS)
In this study respiratory rates of 3, 4, 6, 8, 10, 12, and 14 breaths per minute were employed to investigate the effects of these rates on heart rate variability (HRV). Data were collected 16 times at each respiratory rate on 3 female volunteers, and 12 times on 2 female volunteers. Although mean heart rates did not differ among these respiratory rates, respiratory-induced trough heart rates at 4 and 6 breaths per minute were significantly lower than those at 14 breaths per minute. Slower respiratory rates usually produced higher amplitudes of HRV than did faster respiratory rates. However, the highest amplitudes were at 4 breaths per minute. HRV amplitude decreased at 3 breaths per minute. The results are interpreted as reflecting the possible effects of the slow rate of acetylcholine metabolism and the effect of negative resonance at 3 cycles per minute.