Practices and Applications of Heart Rate Variability Monitoring in Endurance Athletes.

Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.

Repeatability of Respiratory Exchange Ratio Time Series Analysis.

Currently, there are few studies on the repeatability of a time series analysis of respiratory exchange ratio (RER) under the same conditions. This repeated-measures study compared 2 trials completed under the same conditions. After an 8-hour fast, subjects (7 male and 5 female) (mean ± SD) of age 27.3 ± 3.7 years, body weight of 71.8 ± 8.4 kg, percent body fat of 16.4 ± 8.1%, and peak oxygen uptake (V[Combining Dot Above]O2peak) of 46.0 ± 5.3 ml·kg·min completed a V[Combining Dot Above]O2peak test followed 7 days later by a cycle ergometer test at 30% of ventilatory threshold (VT) and 60% of VT for 15 minutes each. These tests were repeated again 7 days later. Paired t-tests revealed no significant differences between the tests for mean RER or sample entropy (SampEn) score at both intensities. The coefficients of variance were generally similar for the mean and SampEn of the RER. The intraclass correlation coefficient (ICC) values for the mean RER at 30% of VT were 1.00 and at 60% of VT were 0.92. The ICC values for the SampEn RER at 30% of VT were 0.81 and at 60% of VT were the lowest at 0.25. Bland-Altman plots demonstrated a measure of agreement between both methods. We demonstrated that RER measurements at 30 and 60% of VT are repeatable during steady-state cycle ergometery. Future research should determine if this finding is consistent with a larger sample size and different exercise intensities.

Cardiovascular and ride time-to-exhaustion effects of an energy drink.

BACKGROUND: Currently, there are few studies on the cardiovascular and fatigue effects of commercially available energy drinks. This study investigated the effects of Monster energy drink (Monster Beverage Corporation, Corona, California), on resting heart rate (HR), heart rate variability (HRV), ride time-to-exhaustion, peak exercise HR, respiratory exchange ratio (RER), and peak rating of perceived exertion (RPE). METHODS: The study used a double-blind, randomized, placebo controlled, crossover design. After an 8-hr fast, 15 subjects consumed Monster Energy Drink (ED standardized to 2.0 mg * kg-1 caffeine) or a flavor-matched placebo preexercise. Resting HR and HRV were determined. After an initial submaximal workload for 30 minutes, subjects completed 10 min at 80% ventilatory threshold (VT) and rode until volitional fatigue at 100% VT. RESULTS: Resting HR was significantly different (ED: 65+/-10 bpm vs. placebo: 58+/-8 bpm, p = 0.02), but resting HRV was not different between the energy drink and placebo trials. Ride time-to-exhaustion was not significantly different between trials (ED: 45.5+/- 9.8 vs. placebo: 43.8+/-9.3 min, p = 0.62). No difference in peak RPE (ED: 9.1 +/- 0.5 vs. placebo: 9.0 +/- 0.8, p = 1.00) nor peak HR (ED: 177 +/- 11 vs. placebo: 175 +/- 12, p = 0.73) was seen. The RER at 30% of VT was significantly different (ED: 0.94 +/- 0.06 vs. placebo: 0.91 +/- 0.05, p = 0.046), but no difference between the two conditions were seen at the other intensities. CONCLUSION: Although preexercise ingestion of the energy drink does increase resting HR there was no alteration in HRV parameters. Ride time-to-exhaustion was not enhanced.

Simulated physician learning program improves glucose control in adults with diabetes.

OBJECTIVE: Inexpensive and standardized methods to deliver medical education to primary care physicians (PCPs) are desirable. Our objective was to assess the impact of an individualized simulated learning intervention on diabetes care provided by PCPs. RESEARCH DESIGN AND METHODS: Eleven clinics with 41 consenting PCPs in a Minnesota medical group were randomized to receive or not receive the learning intervention. Each intervention PCP was assigned 12 simulated type 2 diabetes cases that took about 15 min each to complete. Cases were designed to remedy specific physician deficits found in their electronic medical record observed practice patterns. General linear mixed models that accommodated the cluster randomized study design were used to assess patient-level change from preintervention to 12-month postintervention of A1C, blood pressure, and LDL cholesterol. The relationship between the study arm and the total of intervention and patient health care costs was also analyzed. RESULTS: Intervention clinic patients with baseline A1C >or=7% significantly improved glycemic control at the last postintervention A1C measurement, intervention effect of -0.19% mean A1C (P = 0.034) and +6.7% in A1C <7% goal achievement (P = 0.0099). Costs trended lower, with the cost per patient -$71 (SE = 142, P = 0.63) relative to nonintervention clinic patients. The intervention did not significantly improve blood pressure or LDL control. Models adjusting for age, sex, and comorbidity showed similar results. PCPs reported high satisfaction. CONCLUSIONS: A brief individualized case-based simulated learning intervention for PCPs led to modest but significant glucose control improvement in adults with type 2 diabetes without increasing costs.

Simulated physician learning intervention to improve safety and quality of diabetes care: a randomized trial.

OBJECTIVE: To assess two physician learning interventions designed to improve safety and quality of diabetes care delivered by primary care physicians (PCPs). RESEARCH DESIGN AND METHODS: This group randomized clinical trial included 57 consenting PCPs and their 2,020 eligible adult patients with diabetes. Physicians were randomized to no intervention (group A), a simulated case-based physician learning intervention (group B), or the same simulated case-based learning intervention with physician opinion leader feedback (group C). Dependent variables included A1C values, LDL cholesterol values, pharmacotherapy intensification rates in patients not at clinical goals, and risky prescribing events. RESULTS: Groups B and C had substantial reductions in risky prescribing of metformin in patients with renal impairment (P = 0.03). Compared with groups A and C, physicians in group B achieved slightly better glycemic control (P = 0.04), but physician intensification of oral glucose-lowering medications was not affected by interventions (P = 0.41). Lipid management improved over time (P < 0.001) but did not differ across study groups (P = 0.67). CONCLUSIONS: A simulated, case-based learning intervention for physicians significantly reduced risky prescribing events and marginally improved glycemic control in actual patients. The addition of opinion leader feedback did not improve the learning intervention. Refinement and further development of this approach is warranted.