Ten-Second Heart Rate Variability, Its Changes Over Time, and the Development of Hypertension

Vagal cardiac control in rats with LPS-induced lung injury

Heart rate variability (HRV) as an index of cardiac autonomic control in acute lung injury (ALI) has been evaluated in anaesthetized rats intratracheally instilled with bacterial lipopolysaccharide (LPS) and ventilated with breathing frequency of 60/min, 40% oxygen, inspiratory time 40%, tidal volume of 6 mL/kg. ECG was recorded before and 30, 60, 120, 180 and 240 min after LPS or saline (control) administration. HRV was quantified by time and frequency-domain analysis (mean RR interval, SDRR, RMSSD and spectral power in high frequency (HF) band. Lactate in plasma, and oxidative stress, IL-1β, IL-5, IL-12p70 and IL-13 and galectin-3 in heart tissue raised in LPS-injured rats. Overall HRV magnitude (SDRR) and marker of vagal heart rate control (RMSSD), as well as frequency domain parameter, spectral power HF was increased 120 and 180 min since ALI onset. In conclusion, LPS-induced ALI is accompanied by altered vagal cardiac control mediated by autonomic nervous system, likely based on the close relationship between immune response and vagally mediated autonomic nervous activity.

Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Self-recorded heart rate variability profiles are associated with health and lifestyle markers in young adults

PURPOSE

To quantify associations between self-recorded heart rate variability (HRV) profiles and various health and lifestyle markers in young adults.

METHODS

Otherwise healthy volunteers (n = 40, 50% male) recorded 60-s, post-waking HRV with a cost-free mobile application in supine and standing positions for 7 days. The 7-day average and coefficient of variation (CV, reflects daily fluctuation) for the mean RR interval and root mean square of successive differences (LnRMSSD) were assessed. 7-day sleep duration and physical activity profiles were characterized via wrist-worn accelerometer. Subsequent laboratory assessments included aerobic fitness ([Formula: see text]O₂peak) and markers of cardiovascular, metabolic, and psychoemotional health. Associations were evaluated before and after [Formula: see text]O₂peak adjustment.

RESULTS

Bivariate correlations (P < 0.05) demonstrated that higher 7-day averages and/or lower CV values were associated with higher activity levels and superior cardiovascular (lower systolic and diastolic blood pressure [BP] and aortic stiffness [cf-PWV]), metabolic (lower body fat percentage, fasting glucose, and low-density lipoprotein cholesterol [LDL-C]), and psychoemotional health (lower perceived stress) markers, with some variation between sexes and recording position. In males, associations between HRV parameters and cf-PWV remained significant following [Formula: see text]O₂peak adjustment (P < 0.05). In females, HRV parameters were associated (P < 0.05) with numerous cardiovascular (systolic and diastolic BP, cf-PWV) and metabolic (fasting glucose and LDL-C) parameters following [Formula: see text]O₂peak adjustment.

CONCLUSIONS

Higher or more stable supine and standing HRV were generally associated with superior health and lifestyle markers in males and females. These findings lay groundwork for future investigation into the usefulness of self-recorded ultra-short HRV as a health-promoting behavior-modification tool in young adults.

Does Exercise Training Improve Cardiac-Parasympathetic Nervous System Activity in Sedentary People? A Systematic Review with Meta-Analysis

The aim of this study was to investigate the training-induced effect on cardiac parasympathetic nervous system (PNS) activity, assessed by resting heart rate variability (HRV) and post-exercise heart rate recovery (HRR), in sedentary healthy people. Electronic searches were carried out in PubMed, Embase, and Web of Science. Random-effects models of between-group standardised mean difference (SMD) were estimated. Heterogeneity analyses were performed by means of the chi-square test and I² index. Subgroup analyses and meta-regressions were performed to investigate the influence of potential moderator variables on the training-induced effect. The results showed a small increase in RMSSD (SMD₊ = 0.57 [95% confidence interval (CI) = 0.23, 0.91]) and high frequency (HF) (SMD₊ = 0.21 [95% CI = 0.01, 0.42]) in favour of the intervention group. Heterogeneity tests reached statistical significance for RMSSD and HF (p ≤ 0.001), and the inconsistency was moderate (I² = 68% and 60%, respectively). We found higher training-induced effects on HF in studies that performed a shorter intervention or lower number of exercise sessions (p ≤ 0.001). Data were insufficient to investigate the effect of exercise training on HRR. Exercise training increases cardiac PNS modulation in sedentary people, while its effect on PNS tone requires future study.