Monitoring and adapting endurance training on the basis of heart rate variability monitored by wearable technologies: A systematic review with meta-analysis

Advancing Sports Cardiology: Integrating Artificial Intelligence with Wearable Devices for Cardiovascular Health Management

Sports cardiology focuses on athletes' cardiovascular health, yet sudden cardiac death remains a significant concern despite preventative measures. Prolonged physical activity leads to notable cardiovascular adaptations, known as the athlete's heart, which can resemble certain pathological conditions, complicating accurate diagnoses and potentially leading to serious consequences such as unnecessary exclusion from sports or missed treatment opportunities. Wearable devices, including smartwatches and smart glasses, have become prevalent for monitoring health metrics, offering potential clinical applications for sports cardiologists. These gadgets are capable of spotting exercise-induced arrhythmias, uncovering hidden heart problems, and offering crucial information for training and recovery, to minimize exercise-related cardiac incidents and enhance heart health care. However, concerns about data accuracy and the actionable value of the obtained information persist. A major challenge lies in the integration of artificial intelligence with wearables, research gaps remain regarding their ability to provide real-time, reliable, and clinically relevant insights. Combining artificial intelligence with wearable devices can improve how data is managed and used in sports cardiology. Artificial intelligence, particularly machine learning, can classify, predict, and draw inferences from the data collected by wearables, revolutionizing patient data usage. Despite artificial intelligence's proven effectiveness in managing chronic conditions, the limited research on its application in sports cardiology, particularly regarding wearables, creates a critical gap that needs to be addressed. This review examines commercially available wearables and their applications in sports cardiology, exploring how artificial intelligence can be integrated into wearable technology to advance the field.

Fractal correlation properties of heart rate variability and respiratory frequency as measures of endurance exercise durability

PURPOSE

Field-based measures of durability (exercise-related physiologic deterioration over time) for assessing athletic fitness often rely on changes in maximal power profiles or heart rate (HR) drift. This study aimed to determine whether an index of HR variability based on the short-term exponent of Detrended Fluctuation Analysis (DFA a1) along with respiratory frequency (fB) could demonstrate changes in durability during a Time to Task Failure (TTF) Trial.

METHODS

Ten participants performed a cycling TTF at an intensity of 95% of the respiratory compensation point (RCP) on two occasions, Control and a "Reward" where a monetary incentive was offered when task failure was signaled. Metabolic responses including oxygen uptake (V ˙ O 2 ), lactate and glucose along with HR, DFA a1 and fB were measured and compared over each quarter of the TTF up to the time of signaling (Q1, Q2, Q3, and Q4).

RESULTS

The elapsed time of TTF sessions was statistically similar (p = 0.54). After initial equilibration, metabolic responses remained largely stable over Q2-Q4. HR, DFA a1 and fB displayed drift over Q2-Q4 with significant ANOVA. Repeatability of quarterly HR, DFA a1, and fB between Control and Reward sessions was high with ICC between 0.73 and 0.94, Pearson's r was between 0.83 and 0.98 with no difference in mean values by paired t testing.

CONCLUSION

HR, fB and DFA a1 are useful metrics representing alteration in physiologic characteristics demonstrating durability loss during an endurance exercise session. These measures were repeatable across sessions and have the potential to be monitored retrospectively or in real time in the field with low-cost consumer equipment.

Monitoring Sleep and Nightly Recovery with Wrist-Worn Wearables: Links to Training Load and Performance Adaptations

Previous studies on the effects of intensified training on sleep quality/quantity have been somewhat contradictory. Moreover, recreational athletes often track various sleep metrics, and those metrics' actual connections to training adaptations are unknown. This study explored the effects of intensified training on sleep and nightly recovery along with their associations with training adaptations. A total of 24 participants (10 females) performed a 3-week baseline training period (BL), a 2-week overload period (OL), and a 1-week recovery period (REC), which were followed by test days (T1-T3). The endurance performance was assessed with a 3000 m running test. Throughout all of the periods, the nightly recovery information was monitored with a wrist-worn wearable, including sleep quantity and quality, heart rate (HR) and HR variability (HRV), and proprietary parameters combining several parameters and scaling the results individually. In addition, the perceived strain and muscle soreness were evaluated daily. The 3000 m running performance improved from T1 to T2 (-1.2 ± 1.7%, p = 0.006) and from T1 to T3 (-1.7 ± 1.2%, p = 0.002). The perceived strain and muscle soreness increased (p < 0.001) from the final week of the BL to the final week of the OL, but the subjective sleep quality and nightly recovery metrics remained unchanged. The OL average of the proprietary parameter, autonomic nervous system charge ("ANS charge", combining the HR, HRV, and breathing rate), as well as the change in the sleep HR and HRV from the BL to the OL, were associated (p < 0.05) with a change in the 3000 m running time. In conclusion, the subjective recovery metrics were impaired by intensified training, while the sleep and nightly recovery metrics showed no consistent changes. However, there were substantial interindividual differences in nightly recovery, which were also associated with the training adaptations. Therefore, monitoring nightly recovery can help in recognizing individual responses to training and assist in optimizing training prescriptions.

Correlation properties of heart rate variability to assess the first ventilatory threshold and fatigue in runners

PURPOSE

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA-a1) of heart rate variability (HRV) has shown potential to delineate the first ventilatory threshold (VT1). The aims of this study were to investigate the accuracy of this method for VT1 determination in runners using a consumer grade chest belt and to explore the effects of acute fatigue.

METHODS

We compared oxygen uptake (V ˙ O2) and heart rate (HR) at gas exchange VT1 to V ˙ O2 and HR at a DFA-a1 value of 0.75. Gas exchange and HRV data were obtained from 14 individuals during a treadmill run involving two incremental ramps. Agreement was assessed using Bland-Altman analysis and linear regression.

RESULTS

Bland-Altman analysis between gas exchange and HRV V ˙ O2 and HR at VT1 during the first ramp showed a mean (95% limits of agreement) bias of -0.5 (-6.8 to 5.8) ml∙kg-1∙min-1, and -0.9 (-12.2 to 10.5) beats∙min-1, with R2 of 0.83 and 0.56, respectively. During the second ramp, the differences were -7.3 (-18.1 to 3.5) ml∙kg-1∙min-1 and -12.3 (-30.4 to 5.9) beats∙min-1, with R2 of 0.62 and 0.43, respectively.

CONCLUSION

A chest-belt derived DFA-a1 of 0.75 is closely related to gas exchange VT1, with the variability in accuracy at an individual level being similar to gas exchange methods. This suggests this to be a useful method for exercise intensity demarcation. The altered relationship during the second ramp indicates that DFA-a1 is only able to accurately demarcate exercise intensity thresholds in a non-fatigued state, but also opens opportunities for fatigue-based training prescription.

Effects of individualized resistance training prescription with heart rate variability on muscle strength, muscle size and functional performance in older women

Introduction

This study aimed to investigate whether individualizing autonomic recovery periods between resistance training (RT) sessions (IND) using heart rate variability (HRV), measured by the root mean square of successive R-R interval differences (RMSSD), would lead to greater and more consistent improvements in muscle strength, muscle mass, and functional performance in older women compared to a fixed recovery protocol (FIX).

Methods

Twenty-one older women (age 66.0 ± 5.0 years old) were randomized into two different protocols (IND: n = 11; FIX: n = 10) and completed 7 weeks of RT. Measurements of RMSSD were performed within a five-day period to establish baseline values. The RMSSD values determined whether participants were recovered from the previous session. The assessments included muscle cross-sectional area (CSA), one-repetition maximum (1RM), peak torque (PT), rate of force development (RFD), chair stand (CS), timed up and go (TUG), 6-minutes walking (6MW), and maximum gait speed (MGS).

Results

There were no significant (P > 0.05) group vs. time interactions. There were significant main effects of time (P < 0.05) for CSA, 1RM, PT, TUG, CS, 6MW, and MGS, while no significant changes were observed for RFD (P > 0.05).

Conclusion

IND does not seem to enhance responses in muscle mass, strength, and functional performance compared FIX in healthy older women.

Morning versus Nocturnal Heart Rate and Heart Rate Variability Responses to Intensified Training in Recreational Runners

BACKGROUND

Resting heart rate (HR) and HR variability (HRV) are widely used parameters to assess cardiac autonomic nervous system function noninvasively. While resting assessments can be performed during sleep or after awakening, it would be relevant to know how interchangeable the results of these measurements are. This study aimed at examining the alignment between nocturnal and morning assessments during regular endurance training and in response to intensive training. A total of 24 recreational runners performed a 3-week baseline period (BL) and a 2-week overload (OL) period (Lucia's training impulse + 80%). Their running performance was assessed with a 3000-m running test after the BL and OL. The participants recorded daily their nocturnal HR and HRV (the natural logarithm of the root mean square of successive differences; LnRMSSD) with a photoplethysmography-based wrist device and performed an orthostatic test (2-min supine, 2-min standing) every morning with a chest-strap HR sensor. The HR and LnRMSSD segments that were analyzed from the nocturnal recordings included start value (SleepStart), end value (SleepEnd), first 4-h segment 30 min after detected sleep onset (Sleep4h), and full sleep time (SleepFull). The morning segments consisted of the last-minute average in both body positions. All segments were compared at BL and in response to the 3000-m test and OL.

RESULTS

All nocturnal HR and LnRMSSD segments correlated with supine and standing segments at BL (r = 0.42 to 0.91, p < 0.05). After the 3000-m test, the HR increased and LnRMSSD decreased only in the SleepStart, Sleep4h, and SleepFull segments (p < 0.05). In response to the OL, the standing HR decreased (p < 0.01), while the LnRMSSD increased (p < 0.05) in all segments except for SleepStart. The Pearson correlations between relative changes in nocturnal and morning segments were - 0.11 to 0.72 (3000-m) and - 0.25 to 0.79 (OL). The OL response in Sleep4h HR and LnRMSSD correlated with the relative change in 3000-m time (r = 0.63, p = 0.001 and r=-0.50, p = 0.013, respectively).

CONCLUSIONS

Nocturnal and morning HR and LnRMSSD correlated moderately or highly in the majority of cases during the BL, but their responses to intensive training were not similarly aligned, especially in LnRMSSD. The nocturnal segments seemed to be sensitive to physical loading, and their responses were associated with the performance-related training responses.

Monitoring fatigue state with heart rate-based and subjective methods during intensified training in recreational runners

The purpose of this study was firstly to examine the sensitivity of heart rate (HR)-based and subjective monitoring markers to intensified endurance training; and secondly, to investigate the validity of these markers to distinguish individuals in different fatigue states. A total of 24 recreational runners performed a 3-week baseline period, a 2-week overload period, and a 1-week recovery period. Performance was assessed before and after each period with a 3000m running test. Recovery was monitored with daily orthostatic tests, nocturnal HR recordings, questionnaires, and exercise data. The participants were divided into subgroups (overreached/OR, n = 8; responders/RESP, n = 12) based on the changes in performance and subjective recovery. The responses to the second week of the overload period were compared between the subgroups. RESP improved their baseline 3000 m time (p < 0.001) after the overload period (-2.5 ± 1.0%), and the change differed (p < 0.001) from OR (0.6 ± 1.2%). The changes in nocturnal HR (OR 3.2 ± 3.1%; RESP -2.8 ± 3.7%, p = 0.002) and HR variability (OR -0.7 ± 1.8%; RESP 2.1 ± 1.6%, p = 0.011) differed between the subgroups. In addition, the decrease in subjective readiness to train (p = 0.009) and increase in soreness of the legs (p = 0.04) were greater in OR compared to RESP. Nocturnal HR, readiness to train, and exercise-derived HR-running power index had ≥85% positive and negative predictive values in the discrimination between OR and RESP individuals. In conclusion, exercise tolerance can vary substantially in recreational runners. The results supported the usefulness of nocturnal HR and subjective recovery assessments in recognizing fatigue states.

Analysis of Sleep, Nocturnal Physiology, and Physical Demands of NCAA Women's Ice Hockey Across a Championship Season

ABSTRACT

Merrigan, JJ, Stone, JD, Kraemer, WJ, Friend, C, Lennon, K, Vatne, EA, and Hagen, JA. Analysis of sleep, nocturnal physiology, and physical demands of NCAA women's ice hockey across a championship season. J Strength Cond Res 38(4): 694-703, 2024-The aims of this study were to evaluate the (a) relationships between daily physical demands and nighttime sleep, heart rate (HR), and heart rate variability (HRV); (b) weekly changes in physical demands and sleep; and (c) differences among positions and between training and competition during a competitive season in National Collegiate Athletic Association (NCAA) women's ice hockey. Twenty-five NCAA Division I women's ice hockey athletes wore a sensor at night to monitor sleep quantity or quality (e.g., time asleep and sleep efficiency) and physiology (e.g., HR and HRV). During training and competitions (31 regular season and 7 postseason), athletes wore performance monitoring systems to assess workload demands (e.g., training impulse and TRIMP). As internal workload (TRIMP, Time >80% of HRmax, Average HR) during training or competition increased, nocturnal HRV decreased, HR increased, and Sleep Duration, Sleep Score, and Readiness Score decreased that night. Across the season, athletes experienced lower HRV, but exhibited longer sleep durations. Training Distance, Duration, Time >80% HRmax, Average HR, and TRIMP decreased, whereas competition Total Distance, Duration, and TRIMP increased across weeks throughout the season. There were differences across positions and season blocks when evaluating these data at the mesocycle level. Athletes slept longer before competition compared with training, but physiological data did not differ. Competitions had greater physiological demands than training. We speculate that the increased focus on sleep hygiene, as evidenced by the increase in sleep over the season, may have served as a recovery aid to combat physiological stress of accumulated demands of competitions that increased over time into postseason tournaments.

Vertebral Subluxation and Systems Biology: An Integrative Review Exploring the Salutogenic Influence of Chiropractic Care on the Neuroendocrine-Immune System

In this paper we synthesize an expansive body of literature examining the multifaceted influence of chiropractic care on processes within and modulators of the neuroendocrine-immune (NEI) system, for the purpose of generating an inductive hypothesis regarding the potential impacts of chiropractic care on integrated physiology. Taking a broad, interdisciplinary, and integrative view of two decades of research-documented outcomes of chiropractic care, inclusive of reports ranging from systematic and meta-analysis and randomized and observational trials to case and cohort studies, this review encapsulates a rigorous analysis of research and suggests the appropriateness of a more integrative perspective on the impact of chiropractic care on systemic physiology. A novel perspective on the salutogenic, health-promoting effects of chiropractic adjustment is presented, focused on the improvement of physical indicators of well-being and adaptability such as blood pressure, heart rate variability, and sleep, potential benefits that may be facilitated through multiple neurologically mediated pathways. Our findings support the biological plausibility of complex benefits from chiropractic intervention that is not limited to simple neuromusculoskeletal outcomes and open new avenues for future research, specifically the exploration and mapping of the precise neural pathways and networks influenced by chiropractic adjustment.

Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.

The Between-Day Reliability of Correlation Properties of Heart Rate Variability During Running

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO2) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39-0.96) and 5.5% for DFA-a1, 0.85 (0.38-0.96) and 2.2% for heart rate, and 0.84 (0.31-0.96) and 3.1% for VO2. Post fatiguing ramp, the ICC and SWC were 0.55 (0.00-0.89) and 7.9% for DFA-a1, 0.91 (0.62-0.98) and 1.6% for heart rate, and 0.80 (0.17-0.95) and 3.0% for VO2. DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5-7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness.

Maximizing Strength: The Stimuli and Mediators of Strength Gains and Their Application to Training and Rehabilitation

ABSTRACT

Spiering, BA, Clark, BC, Schoenfeld, BJ, Foulis, SA, and Pasiakos, SM. Maximizing strength: the stimuli and mediators of strength gains and their application to training and rehabilitation. J Strength Cond Res 37(4): 919-929, 2023-Traditional heavy resistance exercise (RE) training increases maximal strength, a valuable adaptation in many situations. That stated, some populations seek new opportunities for pushing the upper limits of strength gains (e.g., athletes and military personnel). Alternatively, other populations strive to increase or maintain strength but cannot perform heavy RE (e.g., during at-home exercise, during deployment, or after injury or illness). Therefore, the purpose of this narrative review is to (a) identify the known stimuli that trigger gains in strength; (b) identify the known factors that mediate the long-term effectiveness of these stimuli; (c) discuss (and in some cases, speculate on) potential opportunities for maximizing strength gains beyond current limits; and (d) discuss practical applications for increasing or maintaining strength when traditional heavy RE cannot be performed. First, by conceptually deconstructing traditional heavy RE, we identify that strength gains are stimulated through a sequence of events, namely: giving maximal mental effort, leading to maximal neural activation of muscle to produce forceful contractions, involving lifting and lowering movements, training through a full range of motion, and (potentially) inducing muscular metabolic stress. Second, we identify factors that mediate the long-term effectiveness of these RE stimuli, namely: optimizing the dose of RE within a session, beginning each set of RE in a minimally fatigued state, optimizing recovery between training sessions, and (potentially) periodizing the training stimulus over time. Equipped with these insights, we identify potential opportunities for further maximizing strength gains. Finally, we identify opportunities for increasing or maintaining strength when traditional heavy RE cannot be performed.

Does Wearable-Measured Heart Rate Variability During Sleep Predict Perceived Morning Mental and Physical Fitness?

The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R² = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.

Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

Fractal correlation properties of HRV as a noninvasive biomarker to assess the physiological status of triathletes during simulated warm-up sessions at low exercise intensity: a pilot study

BACKGROUND

The non-linear index alpha 1 of Detrended Fluctuation Analysis (DFA a1) of heart rate variability, has been shown to be a marker of fatigue during endurance exercise. This report aims to explore its ability to assess the physiological status as a surrogate metric for "readiness to train" while performing simulated warm-up sessions the day after two different exercise sessions.

METHODS

11 triathletes were recruited to determine the first ventilatory threshold (VT1) during a baseline assessment and to perform 10-min of cycling at 90% of VT1 (simulating a warm-up bout) before (PRE) and within 36 h after (POST) light and heavy running exercise. RR intervals were recorded for DFA a1 analysis along with neuromuscular testing to verify the effects of the performed exercise sessions. In addition to common statistical methods, magnitude-based inferences (MBI) were applied to assess the changes in true score and thus also the practical relevance of the magnitude.

RESULTS

Rating of perceived exertion for the heavy exercise session showed a significant higher rating as opposed to the light exercise session (p < 0.001, d = 0.89). In regard of MBIs, PRE versus POST comparisons revealed a significant reduced DFA a1 with large effect size after the heavy exercise session (p = 0.001, d = - 1.44) and a 99% chance that this negative change was clinically relevant.

CONCLUSIONS

Despite inter-individual differences, DFA a1 offers potential to assess physiological status and guide athletes in their training as an easy-to-apply monitoring procedure during a standardized warm-up. A regular assessment including individual data history and statistical references for identification of response is recommended. Further data are necessary to confirm the results in a larger and more homogeneous population.

Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming

Heart rate at rest and exercise may predict cardiovascular risk. Heart rate variability is a measure of variation in time between each heartbeat, representing the balance between the parasympathetic and sympathetic nervous system and may predict adverse cardiovascular events. With advances in technology and increasing commercial interest, the scope of remote monitoring health systems has expanded. In this review, we discuss the concepts behind cardiac signal generation and recording, wearable devices, pros and cons focusing on accuracy, ease of application of commercial and medical grade diagnostic devices, which showed promising results in terms of reliability and value. Incorporation of artificial intelligence and cloud based remote monitoring have been evolving to facilitate timely data processing, improve patient convenience and ensure data security.

Reliability and Sensitivity of Nocturnal Heart Rate and Heart-Rate Variability in Monitoring Individual Responses to Training Load

PURPOSE

To assess the reliability of nocturnal heart rate (HR) and HR variability (HRV) and to analyze the sensitivity of these markers to maximal endurance exercise.

METHODS

Recreational runners recorded nocturnal HR and HRV on nights after 2 identical low-intensity training sessions (n = 15) and on nights before and after a 3000-m running test (n = 23). Average HR, the natural logarithm of the root mean square of successive differences (LnRMSSD), and the natural logarithm of the high-frequency power (LnHF) were analyzed from a full night (FULL), a 4-hour (4H) segment starting 30 minutes after going to sleep, and morning value (MOR) based on the endpoint of the linear fit through all 5-minute averages during the night. Differences between the nights were analyzed with a general linear model, and intraclass correlation coefficient (ICC) was used for internight reliability assessments.

RESULTS

All indices were similar between the nights followed by low-intensity training sessions. A very high ICC (P < .001) was observed in all analysis segments with a range of .97 to .98 for HR, .92 to .97 for LnRMSSD, and .91 to .96 for LnHF. HR increased (P < .001), whereas LnRMSSD (P < .01) and LnHF (P < .05) decreased after the 3000-m test compared with previous night only in 4H and FULL. Increments in HR (P < .01) and decrements in LnRMSSD (P < .05) were greater in 4H compared with FULL and MOR.

CONCLUSIONS

Nocturnal HR and HRV indices are highly reliable. Demanding maximal exercise increases HR and decreases HRV most systematically in 4H and FULL segments.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

Differences between Elite Male and Female Badminton Athletes Regarding Heart Rate Variability, Arterial Stiffness, and Aerobic Capacity

Cardiovascular health and aerobic capacity play crucial roles in determining the performance of athletes in the highly competitive sport of badminton. Few studies have directly compared heart rate variability (HRV), arterial stiffness, and aerobic capacity between male and female athletes, especially among badminton athletes. This study investigated sex differences in HRV, arterial stiffness, and aerobic capacity in badminton athletes. Elite badminton athletes were recruited and divided into male (n = 20, 21.0 ± 1.8 years old) and female (n = 16, 21.2 ± 2.3 years old) groups. Both groups performed an incremental treadmill running test for the evaluation of maximal oxygen consumption (V.O2max), anaerobic threshold, and time to exhaustion. They started exercising at a treadmill speed of 2.7 km/h and an inclination of 10% gradient for 3 min, and the speed and inclination were gradually increased every 3 min until they were exhausted or fatigued volitionally. HRV was examined using the Polar heart rate monitor over a period of 5 min at rest in the supine position. Subsequently, the index of arterial stiffness was examined under the same condition. Our results revealed significant differences between the male and female athletes in V.O2max (men: 60.38 ± 8.98 mL/kg/min, women: 48.13 ± 7.72 mL/kg/min, p < 0.05), anaerobic threshold (men: 41.50 ± 7.26 mL/kg/min, women: 32.51 ± 6.19 mL/kg/min, p < 0.05), time to exhaustion (men: 902.15 ± 120.15 s, women: 780.56 ± 67.63 s, p < 0.05), systolic blood pressure (men: 125.27 ± 7.76 mmHg, women: 107.16 ± 11.09 mmHg, p < 0.05), and arterial stiffness index (men: 63.56 ± 12.55, women: 53.83 ± 8.03, p < 0.05). However, no significant differences in HRV measures were observed between the two groups. These findings suggested that the male badminton athletes demonstrated significantly higher aerobic capacity than did the female athletes, but there were no significant differences in HRV measures. The female athletes exhibited superior arterial function, compared with their male counterparts.

What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living

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.