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

Düking, Peter; Zinner, Christoph; Trabelsi, Khaled; Reed, Jennifer L.; Holmberg, Hans-Christer; Kunz, Philipp; Sperlich, Billy

doi:10.1016/j.jsams.2021.04.012

Cited by 29 publications

(29 citation statements)

References 38 publications

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“…Such decisions are made based on changes above and below threshold values, constructed at an individual level. Indeed in a recent systematic review and meta-analysis [ 47 ], it was shown that, across eight studies, HRV-guided training had a significant, medium-sized positive effect on the improvement of sub-maximal physiological parameters. Furthermore, there were fewer non-responders to HRV-guided training, meaning that the use of this approach induced more consistent favorable effects.…”

Section: Discussionmentioning

confidence: 99%

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

Altini

Plews

2021

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

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

Altini

Plews

2021

Sensors

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“…In a monitoring purpose the usage of DFA a1 may help inform an athlete about their recovery from previous training sessions. It has become commonplace for individuals to monitor and track resting HRV as a method to direct daily exercise intensity and volume ( Granero-Gallegos et al, 2020 ; Düking et al, 2021 ). Unfortunately, resting HRV requires a regular day-to-day monitoring routine including standardization (e.g., time of day, nutrition; Bellenger et al, 2016 ) and logistically may not fit into an irregular schedule.…”

Section: Can Dfa A1 Be Used As An Index Of Fatigue Daily Training Gui...mentioning

confidence: 99%

“…Preliminary data is suggestive ( Rogers et at., 2021f ), but additional research is needed. Prospective training intervention studies of participants who utilize knowledge of DFA a1 behavior during the early stage of routine exercise sessions for training guidance (as shown in monitoring with time domain HRV values during rest conditions ( Düking et al, 2021 )) would be of great interest as a tool to determine daily “readiness to train”.…”

Section: Future Directionsmentioning

confidence: 99%

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

Rogers

Gronwald

2022

Front. Physiol.

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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.

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“…En pacientes con EPOC ha sido útil a la hora de determinar la actividad física en los programas de rehabilitación respiratoria y en la predicción de exacerbaciones [51]. A nivel cardiaco los dispositivos portátiles comerciales son precisos para medir los pasos y la frecuencia cardíaca [52].…”

Section: Telemonitorizaciónunclassified

Nuevas tecnologías en Medicina

Alonso¹,

Sánchez²

2022

RIECS

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Varios sectores de la sociedad, y de forma especial la medicina, están en un momento de cambio que, apoyado en un gran desarrollo tecnológico, va a revolucionar nuestra forma de entender la gestión, investigación y asistencia clínica. Actualmente la medicina se basa en estudios, frecuentemente con pobre validez externa, cuyos resultados se trasladan a un paciente concreto a pesar de estar basados en simples medias estadísticas. El avance hacia una medicina personalizada solo será posible con nuevos enfoques en los que el Big Data y la inteligencia artificial van a ser de gran ayuda a la hora de trasladar la mejor investigación clínica a nuestro paciente, sin olvidar todo lo que estas herramientas pueden aportar en la investigación básica. A nivel de gestión, el modelo asistencial presencial deberá complementarse con otras formas de trabajo mucho más eficientes que ayuden a consolidar la sostenibilidad del sistema sanitario actual. En este entorno, el desarrollo tecnológico proporcionará las herramientas necesarias para implementar estos cambios. El Big Data como fuente de información, la inteligencia artificial con todas las variantes, que incluyen desde el procesamiento de lenguaje natural hasta el desarrollo de técnicas de imagen, y el desarrollo de nuevas tecnologías en múltiples áreas, van a ser herramientas habituales para el médico, muy posiblemente antes de que finalice esta década. Aunque en esta revisión nos hemos focalizado en patologías respiratorias este plantea-miento es plenamente extrapolable a cualquier otra área de la medicina.

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

Cited by 29 publications

References 38 publications

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

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

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

Nuevas tecnologías en Medicina

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