scholarly journals Association between Subjective Indicators of Recovery Status and Heart Rate Variability among Divison-1 Sprint-Swimmers

Sports ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 93 ◽  
Author(s):  
Andrew Flatt ◽  
Michael Esco ◽  
Fabio Nakamura
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Quality ◽  
Self Report ◽  
Z Score ◽  
Related Factors ◽  
Targeted Interventions ◽  
Natural Logarithm ◽  
Z Scores ◽  
Better Than

Heart rate variability (HRV) is a physiological marker of training adaptation among athletes. However, HRV interpretation is challenging when assessed in isolation due to its sensitivity to various training and non-training-related factors. The purpose of this study was to determine the association between athlete-self report measures of recovery (ASRM) and HRV throughout a preparatory training period. Ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD) and subjective ratings of sleep quality, fatigue, muscle soreness, stress and mood were acquired daily for 4 weeks among Division-1 sprint-swimmers (n = 17 males). ASRM were converted to z-scores and classified as average (z-score −0.5–0.5), better than average (z-score > 0.5) or worse than average (z-score < −0.5). Linear mixed models were used to evaluate differences in LnRMSSD based on ASRM classifications. LnRMSSD was higher (p < 0.05) when perceived sleep quality, fatigue, stress and mood were better than average versus worse than average. Within-subject correlations revealed that 15 of 17 subjects demonstrated at least one relationship (p < 0.05) between LnRMSSD and ASRM variables. Changes in HRV may be the result of non-training related factors and thus practitioners are encouraged to include subjective measures to facilitate targeted interventions to support training adaptations.

Season-Long Heart-Rate Variability Tracking Reveals Autonomic Imbalance in American College Football Players

2021 ◽  
pp. 1-10
Author(s):  
Andrew A. Flatt ◽  
Jeff R. Allen ◽  
Clay M. Keith ◽  
Matthew W. Martinez ◽  
Michael R. Esco
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
College Football ◽  
Time Of Day ◽  
Football Players ◽  
Resting Heart Rate ◽  
Autonomic Imbalance ◽  
Natural Logarithm ◽  
Competitive Season ◽  
Red Flag

Purpose: To track cardiac-autonomic functioning, indexed by heart-rate variability, in American college football players throughout a competitive period. Methods: Resting heart rate (RHR) and the natural logarithm root mean square of successive differences (LnRMSSD) were obtained throughout preseason and ∼3 times weekly leading up to the national championship among 8 linemen and 12 nonlinemen. Seated 1-minute recordings were performed via mobile device and standardized for time of day and proximity to training. Results: Relative to preseason, linemen exhibited suppressed LnRMSSD during camp-style preparation for the playoffs (P = .041, effect size [ES] = −1.01), the week of the national semifinal (P < .001, ES = −1.27), and the week of the national championship (P = .005, ES = −1.16). As a combined group, increases in RHR (P < .001) were observed at the same time points (nonlinemen ES = 0.48–0.59, linemen ES = 1.03–1.10). For all linemen, RHR trended upward (positive slopes, R2 = .02–.77) while LnRMSSD trended downward (negative slopes, R2 = .02–.62) throughout the season. Preseason to postseason changes in RHR (r = .50, P = .025) and LnRMSSD (r = −.68, P < .001) were associated with body mass. Conclusions: Heart-rate variability tracking revealed progressive autonomic imbalance in the lineman position group, with individual players showing suppressed values by midseason. Attenuated parasympathetic activation is a hallmark of impaired recovery and may contribute to cardiovascular maladaptations reported to occur in linemen following a competitive season. Thus, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.

Combining LORETA Z-Score Neurofeedback with Heart Rate Variability Training

2015 ◽  
pp. 159-188 ◽  
Author(s):  
Michael Thompson ◽  
Lynda Thompson ◽  
Andrea Reid-Chung
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Z Score

scholarly journals Poor Sleep Quality and Compromised Visual Working Memory Capacity

2019 ◽  
Vol 25 (6) ◽  
pp. 583-594 ◽  
Author(s):  
Weizhen Xie ◽  
Anne Berry ◽  
Cindy Lustig ◽  
Patricia Deldin ◽  
Weiwei Zhang
Keyword(s):  
Working Memory ◽  
Sleep Quality ◽  
Depressed Mood ◽  
Working Memory Capacity ◽  
Community Sample ◽  
Information Storage ◽  
Self Report ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Related Factors

AbstractObjectives:Reduction in the amount of information (storage capacity) retained in working memory (WM) has been associated with sleep loss. The present study examined whether reduced WM capacity is also related to poor everyday sleep quality and, more importantly, whether the effects of sleep quality could be dissociated from the effects of depressed mood and age on WM.Methods:In two studies, WM was assessed using a short-term recall task, producing behavioral measures for both the amount of retained WM information (capacity) and how precise the retained WM representations were (precision). Self-report measures of sleep quality and depressed mood were obtained using questionnaires.Results:In a sample of college students, Study 1 found that poor sleep quality and depressed mood could independently predict reduced WM capacity, but not WM precision. Study 2 generalized these sleep- and mood-related WM capacity effects to a community sample (aged 21–77 years) and further showed that age was associated with reduced WM precision.Conclusions:Together, these findings demonstrate dissociable effects of three health-related factors (sleep, mood, and age) on WM representations and highlighte the importance of assessing different aspects of WM representations (e.g., capacity and precision) in future neuropsychological research.

Determination of minimal recording period to assess resting heart rate variability during pregnancy

2020 ◽  
Vol 45 (4) ◽  
pp. 431-436
Author(s):  
Danilo Fernandes da Silva ◽  
Shuhiba Mohammad ◽  
Kelly Ann Hutchinson ◽  
Kristi Bree Adamo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Exercise Training ◽  
Pregnant Women ◽  
Resting Heart Rate ◽  
Time Segment ◽  
Natural Logarithm ◽  
Resting Heart Rate Variability ◽  
Measurement Period

Traditionally, resting heart rate variability (rHRV) is measured for 10 min using the last 5 min for analyses (e.g., criterion period). It is unknown whether the measurement period can be shortened in pregnant women as there are currently no established standards. We aimed to compare shorter time segments (e.g., from the 1st to 10th minutes) of the parasympathetic index natural logarithm transformation of root mean square of successive R–R differences (Ln rMSSD) with the criterion period in pregnant and nonpregnant women. Twelve pregnant (age: 30.8 ± 3.4 years; gestational age: 20.1 ± 5.0 weeks) and 15 nonpregnant women (age: 29.8 ± 4.0 years) were included. rHRV was measured using a portable heart rate monitor for 10 min while sitting. Ln rMSSD difference/agreement between shorter time segments and criterion period was analyzed. The result observed between the 4th–5th minutes was the shortest time segment not different from/highly agreed with the criterion period in pregnant women (difference [95% confidence interval (CI)]: −0.10 [−0.22 to 0.02]/bias ± 1.96 × SD: −0.06 [−0.38 to 0.25]). In nonpregnant women, the 2nd–3rd-minute segment was the shortest with similar results (difference [95% CI]: −0.04 [−0.15 to 0.07]/bias ± 1.96 × SD: −0.03 [−0.39 to 0.32]). The Ln rMSSD was found to be stable from the 5th–10th minutes and the 3rd–10th minutes in pregnant and nonpregnant women, respectively. A shortened rHRV assessment can increase its applicability in clinical/exercise-training settings. Novelty Ln rMSSD can be measured for 5 min in pregnant women, with the last 1-min segment analyzed. The last 1-min segment from 3 min can be used for rHRV measurement in nonpregnant women. The shortened rHRV assessment can facilitate its applicability in clinical/exercise-training settings.

Heat Stress Training Camps for Endurance Sport: A Descriptive Case Study of Successful Monitoring in 2 Ironman Triathletes

2020 ◽  
Vol 15 (1) ◽  
pp. 146-150
Author(s):  
Ed Maunder ◽  
Andrew E. Kilding ◽  
Christopher J. Stevens ◽  
Daniel J. Plews
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heat Stress ◽  
Well Being ◽  
Self Report ◽  
Endurance Athletes ◽  
Resting Heart Rate ◽  
Resting Heart Rate Variability ◽  
Stress Training

A common practice among endurance athletes is to purposefully train in hot environments during a “heat stress camp.” However, combined exercise-heat stress poses threats to athlete well-being, and therefore, heat stress training has the potential to induce maladaptation. This case study describes the monitoring strategies used in a successful 3-week heat stress camp undertaken by 2 elite Ironman triathletes, namely resting heart rate variability, self-report well-being, and careful prescription of training based on previously collected physiological data. Despite the added heat stress, training volume very likely increased in both athletes, and training load very likely increased in one of the athletes, while resting heart rate variability and self-report well-being were maintained. There was also some evidence of favorable metabolic changes during routine laboratory testing following the camp. The authors therefore recommend that practitioners working with endurance athletes embarking on a heat stress training camp consider using the simple strategies employed in the present case study to reduce the risk of maladaptation and nonfunctional overreaching.

Quantifying Training Loads in Contemporary Dance

2017 ◽  
Vol 12 (6) ◽  
pp. 796-802 ◽  
Author(s):  
Annie C. Jeffries ◽  
Lee Wallace ◽  
Aaron J. Coutts
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Stepwise Multiple Regression ◽  
Contemporary Dance ◽  
Related Factors ◽  
Dance Training ◽  
Metabolic Equivalents

Purpose:To describe the training demands of contemporary dance and determine the validity of using the session rating of perceived exertion (sRPE) to monitor exercise intensity and training load in this activity. In addition, the authors examined the contribution of training (ie, accelerometry and heart rate) and non-training-related factors (ie, sleep and wellness) to perceived exertion during dance training.Methods:Training load and ActiGraphy for 16 elite amateur contemporary dancers were collected during a 49-d period, using heart-rate monitors, accelerometry, and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training.Results:Average weekly training load was 4283 ± 2442 arbitrary units (AU), monotony 2.13 ± 0.92 AU, strain 10677 ± 9438 AU, and average weekly vector magnitude load 1809,707 ± 1015,402 AU. There were large to very large within-individual correlations between training-load sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 49.7% of the adjusted variance in training-load sRPE was explained by peak heart rate, metabolic equivalents, soreness, motivation, and sleep quality (y = –4.637 + 13.817%HRpeak + 0.316 METS + 0.100 soreness + 0.116 motivation – 0.204 sleep quality).Conclusion:The current findings demonstrate the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads, and a combination of training and nontraining factors contribute to perceived exertion in dance training.

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