Women with PTSD and controls completed actigraphy (N
= 124) as part of their participation in a larger study examining the relationships between hostility, health, and PTSD in women. Only women with complete actigraphy and BRS data (n
= 53) were included in the analyses conducted for this study. Excluded participants (n
= 71) were younger (M
age = 36 years) than included participants (M
age = 42 years), F
(1, 122) = 5.76, p
< .05, but did not differ in terms of race, marital status, BMI, cardiac medication use, PTSD severity, or self-reported sleep quality. Women with PTSD (n
= 32) were compared to women without PTSD or depression (n
= 21) on sleep disturbance and BRS. Recruitment flyers, consent procedures, and diagnostic evaluations based on the Clinician Administered PTSD Scale (Blake, et al., 1995
) are presented in detail elsewhere (Beckham, Flood, Dennis, & Calhoun, 2009
Participants with a diagnosis of PTSD were significantly older (PTSD M age = 45, SD = 13, control M age = 37, SD = 13), had higher BMI (PTSD M BMI = 32.07, SD = 7.41, control M BMI = 26.94, SD = 6.32), and were more likely to be unemployed (PTSD N = 13, 40.63%, control N = 2, 9.52%, χ2 (1, N = 52) =6.04 relative to control subjects. The two cohorts did not differ significantly on race, African American, Caucasian, and other, χ2 (1, N = 52) = 2.63, ns, marital status, married, and unmarried, χ2 < 1, or years of education, ≤ 12, 13–16, ≥ 17, χ2 (2, N = 53) = 2.40, ns. Veteran representation was greater in the PTSD group (31%) than the control group (0%), χ2 (1, N = 53) = 8.09, p < .05. All participants endorsed exposure to at least one lifetime traumatic experience. Participants with PTSD endorsed a median of seven events (range = 1–19) meeting criterion A for PTSD diagnosis as compared to a median of three events (range = 0–9) in the control group. BRS was negatively correlated with age in the sample, r (53) = −.51, p < .001, and subgroups. Participants with PTSD were more likely to be taking cardiac-affecting medications (37.5%) than the control group (4.8%), χ2 (1, N = 53) = 7.34, p < .01. The following medications were considered cardiac-affecting medications: beta blockers, other antihypertensives, anticholinergics, and alpha-adrenergics. Only one participant in the entire sample (in the PTSD group) endorsed current use of sedative/hypnotic medications.
Objective measures included estimation of sleep parameters based upon actigraphy, and estimation of BRS derived from blood pressure (BP) and inter-beat interval. Actigraphy data was collected by participants at home over a one-week period using a wrist-watch style actigraph (Mini-Mitter Inc., Sun River, OR) to derive objective estimates of time in bed, total sleep time, sleep onset latency, wake after sleep onset, wake after sleep onset wake after sleep onset percentage (percentage of time awake after falling asleep and before final awakening), and sleep efficiency (percentage of time sleeping while in bed), in addition to fragmentation index (a measure of sleep fragmentation). Actigraphy data was collected within one month of the laboratory collected BP and inter-beat interval readings.
A Finapres BP monitor (Ohmeda, Madison, WI) was used to assess BP, and inter-beat interval was estimated from blood pressure pulse wave. This data was collected in the laboratory on one occasion during a five-minute resting period. Average values were computed from the beat-to-beat BP record for BP and inter-beat interval. Mean heart rate was calculated as 60,000 divided by the mean of the inter-beat interval. An estimate of BRS was derived from systolic blood pressure and inter-beat interval values (Di Rienzo, Parati, Mancia, Pedotti, & Castiglioni, 1997
). Lower values of BRS reflect less parasympathetic nervous system activity. Additional information regarding calculation of BRS can be found in Hughes et al, 2007
Analyses were conducted on BRS and mean sleep parameter scores for participants providing at least three nights of actigraphy data. Group differences were assessed using standard Chi-square procedures for categorical measures, t-tests for normally distributed data, and nonparametric Wilcoxon rank tests for non-normal distributions. Within each cohort, Fisher’s z transformations were applied to compute partial correlation coefficients between BRS and sleep parameters, which were subsequently used to test for group differences. A generalized linear regression procedure was used to regress BRS on the following variables, controlling for covariates: sleep parameters (modeled singularly); a proxy for group status (PTSD = 1; Control= 0); and an interaction term (group status by sleep parameter). A main effects and an interaction model were estimated for each sleep parameter. The interaction term was used to assess for differential associations between sleep parameters and BRS, based upon group status. Because the BRS distribution was non-normal, models were estimated using an assumed gamma distribution with a log link (SAS 9.1; PROC GENMOD). Tests of model fit were based on Chi-square statistics associated with scaled deviance scores.