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    HRV-Guided Training for Professional Endurance Athletes: A Protocol for a Cluster-Randomized Controlled Trial. Carrasco-Poyatos María,González-Quílez Alberto,Martínez-González-Moro Ignacio,Granero-Gallegos Antonio International journal of environmental research and public health Physiological training responses depend on sympathetic (SNS) and parasympathetic nervous system (PNS) balance. This activity can be measured using heart rate variability (HRV). Such a measurement method can favor individualized training planning to improve athletes' performance. Recently, HRV-guided training has been implemented both on professional and amateur sportsmen and sportswomen with varied results. There is a dearth of studies involving professional endurance athletes following a defined HRV-guided training protocol. The objectives of the proposed protocol are: (i) to determine changes in the performance of high-level athletes after following an HRV-guided or a traditional training period and (ii) to determine differences in the athletes' performance after following both training protocols. This will be a 12-week cluster-randomized controlled protocol in which professional athletes will be assigned to an HRV-based training group (HRV-G) or a traditional-based training group (TRAD-G). TRAD-G will train according to a predefined training program. HRV-G training will depend on the athletes' daily HRV. The maximal oxygen uptake (VO) attained in an incremental treadmill test will be considered as the primary outcome. It is expected that this HRV-guided training protocol will improve functional performance in the high-level athletes, achieving better results than a traditional training method, and thus providing a good strategy for coaches of high-level athletes. 10.3390/ijerph17155465
    Integrating Breathing Techniques Into Psychotherapy to Improve HRV: Which Approach Is Best? Steffen Patrick R,Bartlett Derek,Channell Rachel Marie,Jackman Katelyn,Cressman Mikel,Bills John,Pescatello Meredith Frontiers in psychology Introduction:Approaches to improve heart rate variability and reduce stress such as breathing retraining are more frequently being integrated into psychotherapy but little research on their effectiveness has been done to date. Specifically, no studies to date have directly compared using a breathing pacer at 6 breaths per minute with compassion focused soothing rhythm breathing. Current Study:In this randomized controlled experiment, 6 breaths per minute breathing using a pacer was compared with compassion focused soothing rhythm breathing, with a nature video being used as a control group condition. Methods:Heart rate variability (HRV) measures were assessed via electrocardiogram (ECG) and respiration belt, and an automated blood pressure machine was used to measure systolic diastolic blood pressure, and heart rate (HR). A total of 96 participants were randomized into the three conditions. Following a 5-min baseline, participants engaged in either 6 breath per minute breathing, soothing rhythm breathing, or watched a nature video for 10 min. To induce a stressful state, participants then wrote for 5 min about a time they felt intensely self-critical. Participants then wrote for 5 min about a time they felt self-compassionate, and the experiment ended with a 10-min recovery period. Results:Conditions did not significantly differ at baseline. Overall, HRV, as measured by standard deviation of NN intervals (SDNN), low frequency HRV (LF HRV), and LF/HF ratio, increased during the intervention period, decreased during self-critical writing, and then returned to baseline levels during the recovery period. High frequency HRV (HF HRV) was not impacted by any of the interventions. The participants in the 6 breath per minute pacer condition were unable to consistently breathe at that rate and averaged about 12 breaths per minute. Time by Condition analyses revealed that both the 6 breaths per minute pacer and soothing breathing rhythm conditions lead to significantly higher SDNN than the nature video condition during breathing practice but there were no significant differences between conditions in response to the self-critical and self-compassionate writing or recovery periods. The 6 breath per minute pacer condition demonstrated a higher LF HRV and LF/HF ratio than the soothing rhythm breathing condition, and both intervention conditions had a higher LF HRV and LF/HF ratio than the nature video. Conclusions:Although the 6 breath per minute pacer condition participants were not able to breath consistently at the low pace, both the participants attempting to breathe at 6 breaths per minute as well as those in the soothing rhythm breathing condition effectively increased HR variability as measured by SDNN, and attempting to breathe at 6 breaths per minute led to the highest LF HRV and LF/HF ratio. Both breathing approaches impacted HRV more than watching a relaxing nature video and can potentially be used as key adjuncts in psychotherapy to aid in regulating physiological functioning, although it appears that consistent breathing practice would be needed. 10.3389/fpsyg.2021.624254
    Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Pham Tam,Lau Zen Juen,Chen S H Annabel,Makowski Dominique Sensors (Basel, Switzerland) The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and affordability of HRV collection, coupled with its clinical relevance and significant relationships with psychophysiological constructs and psychopathological disorders. Despite the wide use of electrocardiograms (ECG) in research and advancements in sensor technology, the analytical approach and steps applied to obtain HRV measures can be seen as complex. Thus, this poses a challenge to users who may not have the adequate background knowledge to obtain the HRV indices reliably. To maximize the impact of HRV-related research and its reproducibility, parallel advances in users' understanding of the indices and the standardization of analysis pipelines in its utility will be crucial. This paper addresses this gap and aims to provide an overview of the most up-to-date and commonly used HRV indices, as well as common research areas in which these indices have proven to be very useful, particularly in psychology. In addition, we also provide a step-by-step guide on how to perform HRV analysis using an integrative neurophysiological toolkit, NeuroKit2. 10.3390/s21123998
    Estimating Running Performance Combining Non-invasive Physiological Measurements and Training Patterns in Free-Living. Altini Marco,Amft Oliver Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 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), 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. 10.1109/EMBC.2018.8512924
    Heart rate variability-guided training in professional runners: Effects on performance and vagal modulation. Carrasco-Poyatos María,González-Quílez Alberto,Altini Marco,Granero-Gallegos Antonio Physiology & behavior PURPOSE:To analyze the training structure following a heart rate variability (HRV) -guided training or traditional training protocol, determining their effects on the cardiovascular performance of professional endurance runners, and describing the vagal modulation interaction. METHODS:This was an 8-week cluster-randomized controlled trial. Twelve professional endurance runners were randomly assigned to an HRV-guided training group (HRV-G; n = 6) or a traditional training group (TRAD-G; n = 6). The training methodology followed by the HRV-G was determined by their daily HRV scores. Training intensities were recorded daily. HRV4Training was used to register the rMSSD every morning and during a 60-second period. Cardiovascular outcomes were obtained through an incremental treadmill test. The primary outcome was the maximal oxygen uptake (VO). RESULTS:Total training volume was significantly higher in TRAD-G, but moderate intensity training was significantly higher in HRV-G (X ± SD=1.98 ± 0.1%; P = 0.006; d = 1.22) and low intensity training in TRAD-G (X ± SD=2.03 ± 0.74%; P = 0.004; d = 1.36). The maximal velocity increased significantly in HRV-G (P = 0.027, d = 0.66), while the respiratory exchange ratio increased in TRAD-G (P = 0.017, d = 1). There was a small effect on the LnRMSSD increment (P = 0.365, d = 0.4) in HRV-G. There were statistical inter-group differences in the ∆maximal heart rate when ∆LnrMSSD was considered as a covariable (F = 7.58; P = 0.025; d = 0.487). There were significant and indirect correlations of LnRMSSD with VO (r =-0.656, P = 0.02), ∆LnrMSSD with ∆VO (r = -0.606, P = 0.037), and ∆LnrMSSD with ∆VENT (r = -0.790, P = 0.002). CONCLUSIONS:higher HRV scores suggest better cardiovascular adaptations due to higher training intensities, favoring HRV as a measure to optimize individualized training in professional runners. 10.1016/j.physbeh.2021.113654
    Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability. Stone Jason D,Ulman Hana K,Tran Kaylee,Thompson Andrew G,Halter Manuel D,Ramadan Jad H,Stephenson Mark,Finomore Victor S,Galster Scott M,Rezai Ali R,Hagen Joshua A Frontiers in sports and active living Commercial off-the shelf (COTS) wearable devices continue development at unprecedented rates. An unfortunate consequence of their rapid commercialization is the lack of independent, third-party accuracy verification for reported physiological metrics of interest, such as heart rate (HR) and heart rate variability (HRV). To address these shortcomings, the present study examined the accuracy of seven COTS devices in assessing resting-state HR and root mean square of successive differences (rMSSD). Five healthy young adults generated 148 total trials, each of which compared COTS devices against a validation standard, multi-lead electrocardiogram (mECG). All devices accurately reported mean HR, according to absolute percent error summary statistics, although the highest mean absolute percent error (MAPE) was observed for CameraHRV (17.26%). The next highest MAPE for HR was nearly 15% less (HRV4Training, 2.34%). When measuring rMSSD, MAPE was again the highest for CameraHRV [112.36%, concordance correlation coefficient (CCC): 0.04], while the lowest MAPEs observed were from HRV4Training (4.10%; CCC: 0.98) and OURA (6.84%; CCC: 0.91). Our findings support extant literature that exposes varying degrees of veracity among COTS devices. To thoroughly address questionable claims from manufacturers, elucidate the accuracy of data parameters, and maximize the real-world applicative value of emerging devices, future research must continually evaluate COTS devices. 10.3389/fspor.2021.585870
    HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. Altini Marco,Amft Oliver Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 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. 10.1109/EMBC.2016.7591265