Conference Paper

HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application

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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.

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... 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. ...
Article
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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.
... 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. ...
... 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). ...
Article
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.
... 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. ...
Conference Paper
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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.
... 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. ...
Conference Paper
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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.
... 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]. ...
Article
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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.
... 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). ...
Article
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.
... 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. ...
Article
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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.
... 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). ...
Article
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.
... 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]. ...
Article
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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. ...
Article
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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.
... 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. ...
Article
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.
... 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. ...
Thesis
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.
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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.
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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.
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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.
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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.
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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.
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