Notably the sleeping environment in

Notably, the sleeping environment in which home studies are conducted is often not standardized especially with respect to ambient temperatures due to seasonal changes. According to the data collected from Summer 2012 to Summer 2014 in Australian houses by the Faculty of Architecture, The University of Sydney, the recorded overnight average temperatures (from 2200 to 0600h) over the four seasons were: Summer, 24.3°C±1.6°C; Autumn, 21.6°C±2.2°C; Winter, 17.1°C±2.6°C; and Spring, 21.1°C±1.9°C (Australian Research Council’s Discovery Projects, DP 11010559). The observed large temperature variation, in spite of air-conditioning being used in the monitored rooms, would suggest an expected greater variation in homes that are not centrally heated. However, there have been no studies that examine the validity of actigraphy and SWA under different ambient temperature conditions. Given the differing principles of measurements between actigraphy and SWA, a validation study is warranted to evaluate the concordance rates for sleep variables simultaneously recorded from these devices and PSG. In this study, the Actiwatch 2 (AW2) (Phillips-Respironics) was used. Hence, agreement rates between AW2, SWA and PSG were examined under ambient temperatures of 17°C, 22°C and 29°C. The objective of the study was to test the validity of AW2 and SWA for sleep assessment against PSG. We hypothesized that the performance of SWA may be more affected by ambient temperatures since it also measures angiotensin ii receptor blockers temperature.
Method
Results Table 1 shows the mean and standard deviations for SOL, WASO, TST and SE for PSG, AW2 and SWA at ambient temperature conditions of 17°C, 22°C, 29°C. The sleep measures of SOL, TST and SE from AW2, at all temperature conditions, were not significantly different from those recorded during PSG. However, WASO was significantly overestimated when compared to PSG at 17°C and 22°C (Table 1). The sleep measures recorded from SWA show a significant underestimation of SOL (at 17°C and 22°C), and TST and SE (at 29°C), but an overestimation of WASO (at 29°C) compared to PSG. Fig. 1, B–A plots for single measurement, has been specifically chosen to display data sets from different ambient temperatures and the spread of data especially those that lie outside of the limits of agreement, i.e., outliers of WASO, TST and SE at 29°C (Fig. 1F–H). Table 2 presents the differences between AW2/SWA and PSG (mean bias), and LoA from B–A plots with multiple measurements per subject. Consistent with the data presented in Table 1 and Fig. 1, WASO, TST and SE for SWA show larger mean bias and LoA at 29°C than at 17°C and 22°C. Although the correlation coefficients between AW2, SWA and PSG were generally acceptable with values well above .49, SWA fared poorer compared to AW2 (Table 3). The sleep and wake epoch agreement rates with PSG were high for both AW2 (87.6%) and SWA (89.6%). Although the sensitivity (ability to detect sleep) was high for AW2 (95%) and SWA (93%), the specificity (ability to detect wake) was low for both AW2 (45%) and SWA (57%). The Cohen’s kappa coefficients showed moderate agreement for AW2 (.41) and SWA (.46), p<.001.
Discussion The present study evaluated the concordance between AW2, SWA and PSG under ambient temperatures of 17°C, 22°C and 29°C. AW2 shows good average agreements with PSG for SOL, TST and SE at all three temperatures, although it significantly overestimates WASO at 17°C and 22°C (Table 1). The mean bias for SOL were considered small (range from −6.7 to 3.6min) and clinically acceptable, consistent with the finding of a previous study except that their study showed a relatively wider range of LoA [16]. The larger variability may be the result of the proprietary software default setting. In contrast, in the present study, lower variability may be explained by the use of a standardized method for manual detection of the rest interval (equivalent to time-in-bed) in which sleep variables are calculated. Although a small bias was observed for WASO (range from 7.1 to 12.4min), AW2 significantly overestimated WASO at 17°C and 22°C but not at 29°C (Tables 1 and 2 and Fig. 1B). This finding contradicted previous studies, which found that AW2 underestimated WASO [2,16]. These differences may be explained by the different model of actigraphy and algorithms used [5], or different level of activity threshold set [3]. Interestingly, good agreements for SOL and WASO were observed when sleep onset was short and wake bouts were low during the sleep period. Such agreements faded as sleep onset became delayed and wake bouts increased (Fig. 1A and B) contributing to the widening of LoA. The present study found good sleep detection (95%) but poor wake detection (45%) consistent with that reported in a review study [4]. Good sleep detection may reflect the good agreement rates in TST and SE at all temperatures (Fig. 1C and D). Poor wake detection may reflect the large variability in WASO (Fig. 1B and Table 2). The high correlations observed for the various sleep variables were consistent with previous reports [1,2,17]. The correlation coefficient is the highest for TST (Table 3). However, correlations may reflect how well data points from two measurements lie along any straight line or the line of equality [13].