{"id":7832,"date":"2021-03-19T06:30:00","date_gmt":"2021-03-19T11:30:00","guid":{"rendered":"https:\/\/thesportjournal.org\/?p=7832"},"modified":"2021-03-09T08:36:49","modified_gmt":"2021-03-09T14:36:49","slug":"monitoring-cardiac-autonomic-function-and-sleep-duration-in-ncaa-division-i-football-players-during-preseason-and-in-season-using-wearable-tracking-devices","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/monitoring-cardiac-autonomic-function-and-sleep-duration-in-ncaa-division-i-football-players-during-preseason-and-in-season-using-wearable-tracking-devices\/","title":{"rendered":"Monitoring cardiac autonomic function and sleep duration in NCAA Division I football players during preseason and in-season using wearable tracking devices"},"content":{"rendered":"\n

Authors: <\/strong>Portia Resnick1<\/sup>, Davis Hale2<\/sup>, Roger Kollock2<\/sup>, Tori Stafford2<\/sup>, Erich Anthony3<\/sup><\/p>\n\n\n\n

1<\/sup>Department of Kinesiology, California State University, Long Beach, Long Beach, CA
2<\/sup>Oxley College of Health Sciences, University of Tulsa, Tulsa, OK
3<\/sup>Department of Athletics, University of Tulsa, Tulsa, OK<\/p>\n\n\n\n

Corresponding Author:<\/strong>
Portia B. Resnick, PhD, ATC, BCTMB
California State University, Long Beach
Department of Kinesiology
1250 Bellflower Boulevard
Long Beach, CA 90840
Portia.resnick@csulb.edu
908-812-9320<\/p>\n\n\n\n

Portia Resnick is an assistant professor at California State University, Long Beach<\/p>\n\n\n\n

Monitoring cardiac autonomic function and sleep duration in NCAA Division I football players during preseason and in-season using wearable tracking devices<\/h3>\n\n\n\n

ABSTRACT<\/strong><\/p>\n\n\n\n

Sleep duration (SD) is critical for exercise recovery, however collegiate student athletes are typically sleep deprived secondary to early morning workouts, class responsibilities, late day competitions, and travel.  As such, cardiovascular autonomic function (CAF), measured via heart rate variability (HRV) and resting heart rate (RHR), can help monitor athlete recovery.  PURPOSE<\/strong>:  The purpose of this study was to compare two six-week periods, preseason and in-season, on HRV, RHR, and SD in college football players.  METHODS:  <\/strong>Eight malecollege football players were fitted with WHOOP\u00ae wearable activity\/recovery tracking devices that use photoplethysmography and accelerometry to determine HRV (RMSSD), RHR (bpm), and SD (hrs\/day).  The devices were worn 24 hours a day over two six-weeks data collection periods during which the athletes participated in their normal day-to-day preseason conditioning and in-season practice sessions.  RESULTS<\/strong>:  A series of three, paired sample t-tests were performed to compare HRV, RHR, and SD between pooled data from preseason and in-season, reflecting the changes of the group and not the change of any individual participant.  Both HRV (preseason =100 \u00b1 35 ms, in-season = 82 \u00b1 34 ms, p <\/em>= 0.002) and SD (preseason = 4.55 \u00b1 1.49 hrs\/day, in-season = 5.33 \u00b1 1.55 hrs\/day, p <\/em>= 0.002) were different between the two six-week periods while RHR was not different (preseason = 56 \u00b1 6 bpm, in-season = 58 \u00b1 6 bpm, p <\/em>= 0.201).  CONCLUSIONS: <\/strong>Athletes had higher HRV during the preseason period, indicative of greater parasympathetic activity, and had increased SD during the in-season period; however, RHR did not differ.  APPLICATIONS IN SPORT<\/strong>:  The examination of HRV, RHR, and SD during various periods of conditioning in collegiate football players found differences that could not be explained and therefore warrants further research.   <\/p>\n\n\n\n\n\n\n\n

Key Words<\/strong>: heart rate variability, sleep, football<\/p>\n\n\n\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

Sleep is a crucial part of recovery from training and competition (7).  While sleep studies often focus on performance factors, sleep deprivation also leads to an increase in circulatory cortisol, and an increase in the sympathetic activity of the autonomic nervous system (ANS) (13, 16).  Sleep deprivation has the potential to impair the recovery process thereby inhibiting training adaptations which, when combined with heightened psychological stress, can lead to further increases in sympathetic activity (11).  For optimal recovery it is recommended that athletes get 7 to 9 hours of sleep per night; however, student athletes are often sleep deprived secondary to early morning practices, late night competitions, and travel, along with the requirements of maintaining a full-time academic load (9, 16).  Male athletes, in particular male field sport athletes, have a lower sleep efficiency than any other group (16). <\/p>\n\n\n\n

The time domain measure of heart rate variability (HRV), expressed as the root mean squared of the standard deviation (RMSSD), is the most reliable measure of cardiac autonomic function (CAF) (5).  Athletes with a resting heart rate (RHR) below 60 bpm, with higher HRV values may have greater parasympathetic activity (5).  While there are no established HRV norms, continuous monitoring allows for athletes and coaches to track the effects of training on the autonomic nervous system, which may relate to levels of recovery or preparedness for competition (5).  Devices worn on the wrist involve photoplethysmography to track pulse rate and pulse rate variability, which has been shown to be an effective measure of CAF in healthy subjects at rest (18).  The use of continuous monitoring through a wearable device allows for the most accurate data of HRV, RHR and sleep duration (SD) (7, 8). <\/p>\n\n\n\n

Investigating HRV in American football players allows for unique analyses as there is a mixture of player positions that rely on slightly different energy pathways.  Skill position players rely on both of the anaerobic energy pathways, ATP-PC and glycolytic, with more potential for disturbing the muscle\u2019s buffering capacity than linemen, who focus more on the repeated bouts of peak anaerobic power with limited time for recovery between efforts.  These two types of athletes may experience varied HRV patterns based on these position differences although the reactivation of parasympathetic activity following a training session is unique to each individual (6, 12, 13, 19).  Regardless of position played, each athlete underwent a similar training protocol of early morning conditioning in the summer and late afternoon team practices and games during the fall seasons. <\/p>\n\n\n\n

Therefore, the purpose of this study was to compare two six-week periods, preseason and in-season, on HRV, RHR, and SD in college football players.  It was hypothesized that the in-season schedule would lead to an increase in SD and HRV and a decrease in RHR, indicative of increased parasympathetic activity.<\/p>\n\n\n\n

METHODS<\/strong><\/p>\n\n\n\n

This study used a quasi-experimental interrupted time-series design.\u00a0 Following IRB approval, football players identified to be significant contributors to the season were recruited to participate in data collection.\u00a0 Significant contributors were those athletes identified by the coaching staff as being a starter or having a potential role that related to substantial playing time during games.\u00a0 None of the athletes had any known cardiac conditions such as premature ventricular contractions nor were diagnosed with a sleep condition such as sleep apnea.\u00a0 All athletes underwent informed consent, yearly pre-participation physical exams, and were cleared by the Sports Medicine staff for participation in all activities.\u00a0 Following prescreening, a total of 22 athletes were selected for study participation.\u00a0 For statistical comparisons, athlete data was only retained if the athlete wore the device for the study duration (15).\u00a0 A final number of eight athletes participating in both time periods completed data collection for six-weeks of preseason conditioning and six weeks of in-season practices and were included for data analyses.\u00a0 Completion criteria were defined as wearing the device for a minimum of five days during each of the six-week time frames.\u00a0 The athletes participated in their normal day-to-day conditioning and practice sessions during the preseason and in-season data collection periods.\u00a0 The two six-week periods compared were preseason early morning (6:00 am) summer conditioning and in-season fall afternoon (3:30 pm) practices.\u00a0 No alterations were made to conditioning and\/or practice sessions based on the data collection process.\u00a0<\/p>\n\n\n\n

Athletes were instructed to wear the WHOOP\u00ae wearable activity tracking device system 24 hours a day, seven days a week during both six-week periods.  The WHOOP\u00ae device utilizes three techniques to assess HRV, HR, and SD: a tri-axial accelerometer, an optical sensor, and a touch\/ambient temperature sensor at a sampling rate of 100 Hz.  Wrist based resting assessment of HRV via photoplethysmography is a valid method of measuring R-R intervals and wrist based accelerometry has been accepted as a reliable and valid measure of sleep time, staging, and wakefulness (2, 10, 14, 17, 18). A recent study by Berryhill and colleagues (2020) reported strong evidence of the WHOOP\u00ae device\u2019s ability to consistently measure sleep and cardiac autonomic function when compared to ECG and self-reported sleep logs (3).  With the WHOOP\u00ae device, recovery metrics of HRV and RHR are collected during the last cycle of sleep, the preferred time to measure CAF.  The data collected during this cycle was used for analysis.  In addition to the HRV, RHR and SD measures, the device also uses accelerometry to measures exercise which is given in a score termed strain.  While this measure was collected during the data collection period, the data were not analyzed for this study.  The WHOOP\u00ae device is worn on the wrist about 2 cm from the distal end of the radius and ulna and is connected via Bluetooth technology to an application on a smartphone.  Data were collected continuously throughout the day and downloaded to the application on a smartphone via Bluetooth\u00ae technology by the wearer.   The smartphone application displayed physiological data in real time as well as provided a historical overview.  The researchers were able to view and analyze the outputs of the raw data provided by the manufacturer.  The participants had no access to the metrics provided by the device to limit bias.<\/p>\n\n\n\n

For the purpose of this study, each time period was defined as a 42-day training cycle (preseason and in-season).  The mean of the three variables across both six-week training periods (42-days per athlete) for those who contributed a minimum of five days of data per week for both cycles were calculated for statistical comparisons.  Thus, data from the athletes during both the preseason and in-season periods were analyzed for the impact on HRV (RMSSD reported in ms), RHR (bpm) and SD (reported in hrs\/day).  The Statistical Package for the Social Sciences SPSS (v24, IBM Corp., Chicago, IL, USA) was used for the statistical analyses.  Prior to data comparisons, a Kolmogorov-Smirnov test of normality was performed for each of the three variables and data were determined to be from a normal distribution.  A series of paired sample t-tests were performed to compare mean preseason and in-season data for each of the three variables (HRV, RHR, and SD) with the significance level set at p = <\/em>0.05.  Effect sizes were calculated utilizing Cohen\u2019s d<\/em> (1).<\/p>\n\n\n\n

RESULTS<\/strong><\/p>\n\n\n\n

Preseason HRV was significantly greater than in-season HRV (see table 1) indicating greater parasympathetic activity during the preseason with a medium effect size (d <\/em>= 0.516).\u00a0 Sleep duration was significantly longer (medium effect size: d <\/em>= .0513) in-season when athletes had mandatory breakfast at 7:00am and practice at 3:30pm (see table 1) compared to the preseason when training sessions started at 6:00am.\u00a0 The correlation between HRV and SD, while positive, was not significant during either the preseason (r = 0.67, n = 80, p <\/em>= 0.278) or in-season (r = 0.028, n = 81, p <\/em>= 0.402).\u00a0 Resting HR was not different between the two time periods (see Table 1) and reported a small effect size (d <\/em>= 0.214).\u00a0<\/p>\n\n\n\n

Table 1.<\/strong> Training Cycle Comparisons: Preseason & In-Season Paired Samples t-test<\/p>\n\n\n\n\n \n \n \n \n \n \n \n
 <\/td>\n  <\/td>\n Mean<\/strong><\/td>\n Std. Deviation<\/strong><\/td>\n p<\/em><\/strong><\/td>\n d<\/em><\/strong><\/td>\n Effect Size<\/strong><\/td>\n <\/tr>\n
HRV (ms)<\/td>\n HRV Preseason<\/td>\n 100<\/td>\n 35<\/td>\n 0.002*<\/td>\n 0.516<\/td>\n medium<\/td>\n <\/tr>\n
HRV In-season<\/td>\n 82<\/td>\n 34<\/td>\n <\/tr>\n
RHR (bpm)<\/td>\n RHR Preseason<\/td>\n 56<\/td>\n 8<\/td>\n 0.201<\/td>\n 0.214<\/td>\n small<\/td>\n <\/tr>\n
RHR In-season<\/td>\n 58<\/td>\n 6<\/td>\n <\/tr>\n
SD (hrs)<\/td>\n SD Preseason<\/td>\n 4<\/td>\n 1.49<\/td>\n 0.002*<\/td>\n 0.513<\/td>\n medium<\/td>\n <\/tr>\n <\/tbody>\n<\/table>\n\n\n\n

* Significantly different at p \u2264 0.05.<\/p>\n\n\n\n

Table 1. compares the variables Heart Rate Variability (HRV) in milliseconds (ms) and Resting Heart Rate (RHR) in beats per minute (bpm) taken during the lasts sleep cycle and Sleep Duration (SD) in hours (hrs) as averaged over 6-week time frames during preseason and in-season for 8 NCAA Division I Collegiate Football players.<\/p>\n\n\n\n

DISCUSSION<\/strong><\/p>\n\n\n\n

During preseason period the athletes averaged less than five hours of sleep per night, consistent with previous research on athletes with practices that began between 5:00am and 6:00am (16).  The increase of an average of about one hour in sleep during the in-season period coincided with one hour later report time (7:00am mandatory breakfast) indicating that the athletes were going to sleep at about the same time during the preseason and in-season, gaining the hour with the later wake-up time.  Based on circadian rhythms, it is probable that the athletes were not just unable to go to sleep earlier during the summer conditioning period based on their schedule, but also based on their own biorhythms (9, 16).  In both time periods the athletes were sleeping less than the recommended seven to nine hours per night.  While the easy solution to gain sleep would be to recommend later practices, this may not be a viable solution in all cases.  For best practices, the coaching staff may choose to schedule conditioning during the mornings to avoid the warmer ambient temperatures of the summer afternoons that place increased heat stress on the athletes.  Therefore, from a clinical standpoint, the better solution might be to educate the athletes about the need for more sleep or for the utilization of naps when early morning practice or training is scheduled.  Taking a 20-minute nap has proven to be beneficial in endurance runners and provides a more practical solution than either an earlier bedtime or a later wake-up (4). <\/p>\n\n\n\n

Even with the later practice schedule and the increase in sleep, the athletes showed higher sympathetic activity in the in-season data collection period as indicated by the lower HRV values.  The pre-season schedule consisted of conditioning and weight training with limited non-contact football activities while the in-season schedule added practices with contact, games and travel.  It is possible that the increase in contact activity as well as the sympathetic activity associated with performance in games led to the decrease in HRV (16, 19).  Based on NCAA regulations, athletes are limited to 8 hours of conditioning during the summer which increases to 20 hours per week of activity in-season, therefore the number of hours per week of activity increased from pre-season to in-season in addition to the type of activity changing.  The demands of an in-season schedule also include travel and games that fall outside of the activity limit, potentially adding additional stressors.  Therefore, it is considerable that the type and length of activity, contact practices and games, led to the increase in sympathetic activity which was not offset by the significant increase in SD or the later wake-up time (9, 19). <\/p>\n\n\n\n

Although during these time periods none of the monitored athletes had an injury, sleep deprivation is also associated with neurocognitive deficits therefore making sleep monitoring important in athletes who have a suspected or diagnosed concussion (13).  Diminished sleep can also lead to increased bodily discomfort and an amplification of pain (11).  Sleep is suggested to play a vital role in synaptic plasticity as well as having multiple functions for physical recovery, thereby making sleep an important part of injury prevention and treatment (11).<\/p>\n\n\n\n

Even though the results associate a later wake-up time for collegiate football players with an increase in SD, the study had its limitations.  First, athlete compliance was reported at an average of 5 days\/week which is not totally inclusive of all time during the 24\/7 monitoring recommended by WHOOP\u00ae.  There was inconsistency in the days during which the athletes wore the device making it difficult to examine specific days of the week, for example how the response on a Monday differed from a Thursday, or to track changes in one individual over a consistent five-day period consistently throughout either the preseason or in-season.  In addition, the preseason and in-season periods contained different activities due to the nature of collegiate sports and the training cycle.  Therefore, many factors that were not measured during these time periods may have contributed to changes in HRV, RHR, and SD.  Few studies have investigated sleep duration and cardiac autonomic function in collegiate football players during different training periods.  The need to establish individualized trends in football athlete adaptation and recovery is still needed in the sport studies literature.  Future research in this area may include the investigation of the impact of workload on these aspects of recovery.   <\/p>\n\n\n\n

CONCLUSIONS<\/strong><\/p>\n\n\n\n

Both SD and HRV differed between pre-season and in-season in college football players.  Athletes had higher HRV, indicative of greater parasympathetic activity, during the preseason period, while SD increased during the in-season period.  The RHR did not change between the two time periods.<\/p>\n\n\n\n

APPLICATIONS IN SPORT<\/strong><\/p>\n\n\n\n

Optimal training requires the balance of physiological demands with appropriate recovery.  This study indicates that the time of year or type of training may result in differing SD and HRV levels in college football players as recorded by the WHOOP\u00ae device.  The ability to translate this into usable clinical data in regards to recovery requires further investigation. <\/p>\n\n\n\n

ACKNOWLEDGEMENTS<\/strong><\/p>\n\n\n\n

The authors would like to acknowledge The University of Tulsa Athletic Performance and Sports Medicine staff for their cooperation on the project.  The project was supported by the American Athletic Conference Academic Consortium Research Grant Program.<\/p>\n\n\n\n

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    Authors: Portia Resnick1, Davis Hale2, Roger Kollock2, Tori Stafford2, Erich […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[994,898],"tags":[379,1763,1697],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-22k","jetpack-related-posts":[{"id":5391,"url":"https:\/\/thesportjournal.org\/article\/academic-fraud-in-revenue-and-nonrevenue-sports\/","url_meta":{"origin":7832,"position":0},"title":"Academic Fraud in Revenue and Nonrevenue Sports","date":"November 23, 2017","format":false,"excerpt":"Authors: John Adamek Corresponding Author: John Adamek, CSCS 4 Truman Place Moonachie NJ, 07074 Jfadamek21@gmail.com 201-543-9142 John Adamek is a strength and conditioning coach owner of Sports Science Integration. 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Gladstone Aurora, IL 60506 eheller@aurora.edu C: 630-217-2358 \u201cSo, Who\u2019s Our New Coach?\u201d: NCAA Student Athletes\u2019 Perceptions After a Head Coaching Change \u2003 ABSTRACT Coaches play an important role in athlete\u2019s collegiate experience,\u2026","rel":"","context":"In "Sports Management"","img":{"alt_text":"Table 1","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2016\/11\/Table1-2.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":6322,"url":"https:\/\/thesportjournal.org\/article\/student-athletes-vs-athlete-students-the-academic-success-campus-involvement-and-future-goals-of-division-i-student-athletes-who-were-university-bound-compared-to-those-who-would-not-have-attended\/","url_meta":{"origin":7832,"position":5},"title":"Student-Athletes vs. Athlete-Students: The academic success, campus involvement, and future goals of Division I student athletes who were university bound compared to those who would not have attended a university had they not been an athlete.","date":"February 21, 2019","format":false,"excerpt":"Authors: Brenda L. Vogel, Jeff Kress, and Daniel R. Jeske Corresponding Author:Jeff Kress, Ph.D. Department of Kinesiology1250 Bellflower Blvd. \u2013 MS 4901, HHS2-103Long Beach, CA 90840jeff.kress@csulb.edu949-375-3958 Brenda L.Vogel is a Professor of Criminology and Criminal Justice and the Director of the School of Criminology, Criminal Justice, and Emergency Management at\u2026","rel":"","context":"In "Sports Studies and Sports Psychology"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2019\/02\/Figure-5.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/7832"}],"collection":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/comments?post=7832"}],"version-history":[{"count":2,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/7832\/revisions"}],"predecessor-version":[{"id":7835,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/posts\/7832\/revisions\/7835"}],"wp:attachment":[{"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/media?parent=7832"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/categories?post=7832"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/thesportjournal.org\/wp-json\/wp\/v2\/tags?post=7832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}