Autonomic Function Following Concussion in Youth Athletes: An Exploration of Heart Rate Variability Using 24-hour Recording Methodology

Abstract

Participation in organized sports makes a significant contribution to youth development, but places youth at a higher risk for sustaining a concussion. To date, return-to-activity decision-making has been anchored in the monitoring of self-reported concussion symptoms and neurocognitive testing. However, multi-modal assessments that corroborate objective physiological measures with traditional subjective symptom reporting are needed and can be valuable. Heart rate variability (HRV) is a non-invasive physiological indicator of the autonomic nervous system, capturing the reciprocal interplay between the sympathetic and parasympathetic nervous systems. There is a dearth of literature exploring the effect of concussion on HRV in youth athletes, and developmental differences preclude the application of adult findings to a pediatric population. Further, the current state of HRV methodology has primarily included short-term (5-15 min) recordings, by using resting state or short-term physical exertion testing to elucidate changes following concussion. The novelty in utilizing a 24 h recording methodology is that it has the potential to capture natural variation in autonomic function, directly related to the activities a youth athlete performs on a regular basis. Within a prospective, longitudinal research setting, this novel approach to quantifying autonomic function can provide important information regarding the recovery trajectory, alongside traditional self-report symptom measures. Our objectives regarding a 24 h recording methodology were to (1) evaluate the physiological effects of a concussion in youth athletes, and (2) describe the trajectory of physiological change, while considering the resolution of self-reported post-concussion symptoms. To achieve these objectives, non-invasive sensor technology was implemented. The raw beat-to-beat time intervals captured can be transformed to derive time domain and frequency domain measures, which reflect an individual's ability to adapt and be flexible to their ever-changing environment. By using non-invasive heart rate technology, autonomic function can be quantified outside of a traditional controlled research setting.

O uso da análise da variabilidade da frequência cardíaca no monitoramento de lesões esportivas e sua influência sobre o balanço autonômico: uma revisão sistemática

“…In the second phase, there is an increase in parasympathetic modulation until about the 75th day post-concussion for the male sex and 90th day for the female sex, in which there is a decrease in symptoms. The third phase goes up to, on average, the 120th day for the male sex which is when normally no more symptoms are presented and there is an autonomic balance, and in women this aspect may take even longer 19 . The change in parameters of female athletes was more sensitive after concussion, hypothesizing parasympathetic dysregulation (lower HF and higher risk of injury).…”

Monitoring and Visualization of Physical Exercise Physiological Indicators Driven by Discrete Data

“…If there is such a relationship, then when visualizing, all samples will be in a certain, and if this relationship does not exist, the visualization effect is a group of discrete points [1]. Cluster analysis is to divide the samples in the data set into several groups according to a certain relationship, the similarity in the same group is large, and the similarity between different groups is small [2][3][4][5], so this paper first adopts the principal component analysis (MFPCA) of multivariate functional data to reduce the dimension, and then uses the multivariate functional data clustering through. e BIC criterion presets the number of clusters, and the EM algorithm estimates the Gaussian mixture model density function parameter R and then uses R software to visually realize the clustering results of physiological indicators in various regions of the country in the text, and obtain different changing laws in different regions, and add the amount of data to assist in the analysis of the clustering results [6][7][8].…”