Nocturnal Heart Rate Variability in Women Discordant for Hormonal Contraceptive Use

PURPOSE

The aim of this study was to investigate within-cycle differences in nocturnal heart rate (HR) and heart rate variability (HRV) in naturally menstruating women (NM) and women using combined hormonal contraceptives (CU) or progestin-only hormonal contraceptives (PU).

METHODS

Physically active participants were recruited into three groups: NM ( n = 19), CU ( n = 11), and PU ( n = 12). Participants' HR and HRV (with Bodyguard 2 HRV monitor) and blood hormones were monitored during one menstrual cycle (MC) (NM group) or for 4 wk (CU and PU groups). Estradiol, progesterone, and luteinizing hormone were analyzed from fasting blood samples collected four times in the NM (M1 = bleeding, M2 = follicular phase, M3 = ovulation, and M4 = luteal phase) and PU groups (M1 = lowest E 2 , M2 = M1 + 7 d, M3 = M1 + 14 d, and M4 = M1 + 21 d) and twice in the CU group (active and inactive pill phases). After every blood sample, nightly HR and HRV were recorded and examined as an average from two nights.

RESULTS

Hormonal concentrations differed ( P < 0.05) between MC phases in the NM and PU groups, but not ( P ≥ 0.116) between the active and the inactive phases in the CU group. In the NM and PU groups, some of the HRV values were higher, whereas in the NM group, HR was lower during M2 compared with M3 ( P < 0.049) and M4 ( P < 0.035). In the CU group, HRV values ( P = 0.014-0.038) were higher, and HR was lower ( P = 0.038) in the inactive phase compared with the first week of the active phase.

CONCLUSIONS

The MC and the hormonal cycle phases influence autonomic nervous system balance, which is reflected in measurements of nocturnal HR and HRV. This should be considered when monitoring recovery in physically active individuals.

Comparative characteristics of developing morphofunctional features of schoolchildren from different climatic and geographical regions

OBJECTIVES

Adaptation processes in body of schoolchildren, along with study load and social factors, are determined by influence of climatic and geographical factors of residence region. This research has been undertaken to study the morphofunctional characteristics of schoolchildren at the age of 7-17 years.

METHODS

The research involved 880 schoolchildren of both sexes in age from 7 to 17 years, studying in secondary school №22 in Pavlodar (Kazakhstan) and secondary school №7 in Kyzylorda (Kazakhstan). The Quetelet index of northern schoolchildren was within the norm but higher compared to southern peers.

RESULTS

The Quetelet index was within the normal range for all schoolchildren, but higher among northerners. The indicator of the life index in the boys of the south was higher up to 10 years, then the adaptive possibilities of breathing decreased; in the girls in the north this indicator was higher in all periods, except for 8-9 years. The physical performance index (PWC170/kg) was higher in children from the southern region.

CONCLUSIONS

This study revealed significant geographical differences in the morphofunctional development of children. The obtained data served allowed establishing the "Electronic map of schoolchildren's health" and indicated the need for systematic accounting of schoolchildren's health indicators for effective development of differentiated prevention programs.

Sleep and Daily Positive Emotions – Is Heart Rate Variability a Mediator?

Abstract. Sleep quality is considered a basic dimension of emotional health. The psychophysiological mechanisms underlying the associations between sleep quality and positive emotions are still largely unknown, yet autonomic regulation may play a role. This study employed a two-day ecological momentary assessment methodology in a sample of young adults to investigate whether subjective sleep quality reported in the morning was associated with daily positive emotional experience and whether this association was mediated by heart rate variability (HRV), a measure of cardiac vagal tone. Sleep quality was assessed using an electronic sleep diary upon awakening, while resting HRV and positive emotions were inspected at random times throughout the day using photoplethysmography and an electronic diary, respectively. Relevant confounding variables such as smoking, alcohol intake, and physical exercise between each measurement were also assessed. The sample included 121 participants (64.8% females, Mage = 25.97 ± 5.32 years). After controlling for relevant confounders including health behaviors and psychiatric comorbidities, mediation analysis revealed that greater sleep quality positively predicted daily HRV (β = .289, p < .001) which, in turn, had a direct influence on positive emotions (β = .244, p = .006). Also, sleep quality directly predicted positive emotional experience (β = .272, p = .001). Lastly, the model showed an indirect effect between sleep quality and positive emotions via HRV (β = .071, 95% BCI [.011, .146]). Results support the view of HRV as a process variable linking sleep to positive emotions. Experimental data is needed to consolidate the present findings.

Recovery After Stress-Autonomic and Subjective Arousal in Individuals With Psychosis Compared to Healthy Controls

BACKGROUND AND HYPOTHESIS

Heightened stress levels in individuals with psychosis (PSY) are associated with psychotic symptom occurrence and may be partially attributed to well-established deficits in resting-state heart rate variability (HRV) and emotion regulation. In healthy participants, resting-state HRV and self-reported emotion regulation skills have been linked to recovery after a stressor; however, it is unclear whether stress recovery is altered in PSY. Thus, we compared the autonomic and subjective recovery of PSY to healthy controls (HC) and investigated the predictive value of resting-state HRV and emotion regulation skills.

STUDY DESIGN

We assessed resting-state HRV and self-reported emotion regulation one week prior to a combined physical and cognitive stress induction. After the stress exposure, we assessed the autonomic (decrease in heart rate [HR], increase in HRV) and subjective (decrease in subjective stress and negative affect) recovery in PSY (n = 50) and HC (n = 50) over 60 min.

STUDY RESULTS

Repeated-measures ANOVA revealed the expected interaction of time × group for subjective stress but not negative affect or autonomic stress. Resting-state HRV predicted recovery of HR, and emotion regulation skills predicted recovery of HRV but not of the other parameters.

CONCLUSIONS

Although subjective stress recovery was delayed in PSY, the absence of autonomic recovery deficits suggests that a prolonged stress response may not contribute to heightened stress levels to the expected extent. Improving resting-state HRV and emotion regulation may support autonomic recovery, but further investigation is required to test the impact of such improvements on psychotic symptoms.

Vagally Derived Heart Rate Variability and Training Perturbations With Menses in Female Collegiate Rowers

INTRODUCTION

The parasympathetically derived marker of heart rate variability, root mean square of successive R-R differences (RMSSD), and the daily fluctuations as measured by the coefficient of variation (RMSSDCV) may be useful for tracking training adaptations in athletic populations. These vagally derived markers of heart rate variability may be especially pertinent when simultaneously considering a female athlete's menstrual cycle.

PURPOSE

The purpose of this study was to observe the perturbations in RMSSDCV, while considering RMSSD, across a season in the presence and absence of menses with training load in female collegiate rowers.

METHODS

Thirty-six (20 [1] y, 25.6 [3.4] kg·m-2) National Collegiate Athletic Association Division I female rowers were monitored for 18 consecutive weeks across a full season. Seated, ultrashortened RMSSD measurements were obtained by the rowers on at least 3 mornings per week using a smartphone photoplethysmography device. Following the RMSSD measurement, athletes indicated the presence or absence of menstruation within the application. Individual meters rowed that week and sessions rate of perceived exertion were obtained to quantify training load.

RESULTS

Longitudinal mixed-effects modeling demonstrated a significant effect of menses and time, while also considering RMSSD, such that those who were on their period had a significantly greater RMSSDCV than those who were not (11.2% vs 7.5%, respectively; P < .001). These changes were independent of meters rowed, sessions rate of perceived exertion, body mass index, birth-control use, and years of rowing experience, which were all nonsignificant predictors of RMSSDCgV (P > .05).

CONCLUSION

The presence of menses appears to significantly impact RMSSDCV when also considering RMSSD, which may allow coaches to consider individualized training plans accordingly.

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.