Trust, verify and replicate

Adolescent crash rates and school start times in two central Virginia counties, 2009-2011: a follow-up study to a southeastern Virginia study, 2007-2008

BACKGROUND AND OBJECTIVE

Early high school start times (EHSST) may lead to sleep loss in adolescents ("teens"), thus resulting in higher crash rates. (Vorona et al., 2011). In this study, we examined two other adjacent Virginia counties for the two years subsequent to the above-mentioned study. We again hypothesized that teens from jurisdictions with EHSST (versus later) experience higher crash rates.

METHODS

Virginia Department of Motor Vehicles supplied de-identified aggregate data on weekday crashes and time-of-day for 16-18 year old (teen) and adult drivers for school years 2009-2010 and 2010-2011 in Henrico and Chesterfield Counties. Teen crash rates for counties with early versus later school start-times were compared using two-sample Z-tests and these compared to adult crash rates using pair-wise tests.

RESULTS

Henrico teens manifested a statistically higher crash rate of 48.8/1000 licensed drivers versus Chesterfield's 37.9/1000 (p = 0.04) for 2009-2010. For 2010-2011, HC 16-17 year old teens demonstrated a statistically significant higher crash rate (53.2/1000 versus 42.0/1000), while for 16-18 teens a similar trend was found, albeit nonsignificant (p = 0.09). Crash peaks occurred 1 hour earlier in the morning and 2 hours earlier in the afternoon in Chesterfield, consistent with commute times. Post hoc analyses found significantly more run-off road crashes to the right (potentially sleep-related) in Chesterfield teens. Adult crash rates and traffic congestion did not differ between counties.

CONCLUSIONS

Higher teen crash rates occurred in jurisdictions with EHSST, as in our prior study. This study contributes to and extends existing data on preventable teen crashes and high school start times.

Technologically sensed social exposure related to slow-wave sleep in healthy adults

OBJECTIVE

The aim of this study is to understand the relationship between automatically captured social exposure and detailed sleep parameters of healthy young adults.

METHODS

This study was conducted in a real-world setting in a graduate-student housing community at a US university. Social exposure was measured using Bluetooth proximity sensing technology in mobile devices. Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of 2 weeks. The analysis included a total of 11 subjects (6 males and 5 females) aged 24-35 (149 subject nights).

RESULTS

Slow-wave sleep showed a significant positive correlation (Spearman's rho = 0.51, p < 0.0001) with social exposure, whereas light non-REM (N1 + N2) sleep and wake time were found to be negatively correlated (rho = -0.25, p < 0.01; rho = -0.21, p < 0.01, respectively). The correlation of median slow-wave sleep with median social exposure per subject showed a strong positive significance (rho = 0.88, p < 0.001). On average, within subjects, following day's social exposure was higher when (slow-wave NREM + REM) percentage was high (Wilcoxon sign-ranked test, p < 0.05).

CONCLUSIONS

Subjects with higher social exposure spent more time in slow-wave sleep. Following day's social exposure was found to be positively affected by previous night's (slow-wave NREM + REM) percentage. This suggests that sleep affects following day's social exposure and not vice versa. Capturing an individual's dynamic social behavior and sleep from their natural environment can provide novel insights into these relationships.