for 48 hours. We controlled respiration rate using a metronome at a fixed rate of 12 breaths/min (0.2 Hz). HRV measurements were conducted at the same time (61 hour) of day for each assessment period. We used a two-channel ECG signal detected by a Polar Heart Rate Monitor and transmitted online to a PC through Polar Advantage Interface receiver. QRS timing was fixed at 1 ms. The computer program labeled each QRS complex, and the resulting signal was filtered to eliminate ectopic beats and artifact edited and replaced with an average value. Segments containing .15% artifact ectopy were interpreted as premature beats and were excluded from our data analysis [18]. We quantified HRV from the last 5 minutes of R–R interval recording. For our primary outcome measurement, we examined the parasympathetic nervous system by calculating the square root of the mean of the sum of the squares of differences between adjacent R–R intervals (rMSSD). rMSSD is considered to be a stable measure of parasympathetic modulations in heart rate [1]. As a secondary measurement of mixed signaling, we also calculated the standard deviation of all R–R intervals (SDNN), which reflects the cyclic components responsible for variability in the period of recording and reflective of both sympathetic and parasympathetic tone. As a tertiary aim, we analyzed frequency domain in three log normalized frequency bands define as: high frequency power (HF; 0.15–0.40 Hz), low frequency power (LF; 0.04–0.15 Hz), very low frequency power (VLF; 0.0033–0.04 Hz), and total frequency power (PT; 0.00–0.40 Hz). The filtering techniques are described in previous reports [18,19]. Statistical analysis We used a generalized linear model (GLM) to analyze the influences of the differing doses of exercise training on HRV characteristics. We adjusted all our outcomes among the randomization groups for select specified covariates including baseline HRV, race, and the use of antidepressant medication. When our GLM demonstrated a significant overall statistical effect and a significant treatment group effect, we further explored Exercise & HRV After Menopause pairwise comparisons between the exercise training groups vs. the control group using a Dunnett-Hsu post-hoc assessment. The Dunnett-Hsu test allows for specific multiple pair-wise compari- sons while still protecting against type I statistical errors. Between group differences at baseline and follow-up in prevalence were examined using Chi-square tests. We also used a subgroup analysis to compare dose-response effects across predefined baseline groups with significance of interactions relationships between variables were tested using a Spearman correlation analysis and denoted as rs. All reported P values are two-sided (P,0.05). All analyses were performed using SAS version 9.1 (Cary, NC). Results Baseline Heart Rate, Medication, and Physiologic vs. HRV Correlation Characteristics We have presented a schematic CONSORT flowchart our HRV analysis in Figure 1. Baseline health characteristics are presented in Table 1 and an examination of parasympathetic levels (rMSSD) by ethnicity, smoking status and medication use in Table 2. Overall, we were able to successfully obtain HRV measurements on 373 individuals at follow up. In our analysis of baseline characteristics (Table 2), we observed significant differ- ences for ethnicity (P,0.0001) and antidepressant medication use (P,0.002). Post-hoc assessments revealed that African American women had statistically higher levels of rMSSD vs. Caucasian women (P,0.0001). Spearman correlations characteristics for various physiologic variables and HRV indices are presented in Table 3. With the exception of VLF power, we observed small yet significant correlations between age and all indices of HRV. When analyzing HRV change from baseline to follow-up we did not observe any significant correlations between most HRV param- eters and changes in fitness (VO2 L/min), weight, and BMI. There were three exceptions as we did observe a negative correlation between BMI and SDNN (rs = 20.1; P,0.05), VLF (rs = 20.12; P,0.0004), and body weight (rs = 20.11; P,0.02). Figure 1. DREW Heart Rate Variability Assessment: Schematic diagram represents CONSORT flow chart of the DREW heart rate variability study analysis. doi:10.1371/journal.pone.0002288.g001 PLoS ONE | www.plosone.org 3 June 2008 | Volume 3 | Issue 6 | e2288