The on-road experiences and awareness of sleepiness in a sample of Australian highway drivers: A roadside driver sleepiness study

Objective Driver sleepiness contributes substantially to road crash incidents. Simulator and on-road studies clearly reveal an impairing effect from sleepiness on driving ability. However, the degree to which drivers appreciate the dangerousness of driving while sleepy is somewhat unclear. This study sought to determine drivers’ on-road experiences of sleepiness, their prior sleep habits, and personal awareness of the signs of sleepiness. Methods Participants were a random selection of 92 drivers travelling on a major highway in the state of Queensland, Australia, who were stopped by police as part of routine drink driving operations. Participants completed a brief questionnaire that included demographic information, sleepy driving experiences (signs of sleepiness and on-road experiences of sleepiness), and prior sleep habits. A modified version of the Karolinska Sleepiness Scale (KSS) was used to assess subjective sleepiness in the 15 minutes prior to being stopped by police. Results Participants rating of subjective sleepiness were quite low, with 90% reporting being alert to extremely alert on the KSS. Participants were reasonably aware of the signs of sleepiness, with many signs of sleepiness associated with on-road experiences of sleepiness. Additionally, the number of hours spent driving was positively correlated with the drivers’ level of sleep debt. Conclusions The results suggest the participants had moderate experience of driving while sleepy and many were aware of the signs of sleepiness. The relationship between driving long distances and increased sleep debt is a concern for road safety – increased education regarding the dangers of sleepy driving seems warranted.

Estimating link-dependent origin-destination matrices from sample trajectories and traffic counts

In transport networks, Origin-Destination matrices (ODM) are classically estimated from road traffic counts whereas recent technologies grant also access to sample car trajectories. One example is the deployment in cities of Bluetooth scanners that measure the trajectories of Bluetooth equipped cars. Exploiting such sample trajectory information, the classical ODM estimation problem is here extended into a link-dependent ODM (LODM) one. This much larger size estimation problem is formulated here in a variational form as an inverse problem. We develop a convex optimization resolution algorithm that incorporates network constraints. We study the result of the proposed algorithm on simulated network traffic.

An autonomous mobile manipulator for collecting sample containers

In this work we present an autonomous mobile ma- nipulator that is used to collect sample containers in an unknown environment. The manipulator is part of a team of heterogeneous mobile robots that are to search and identify sample containers in an unknown environment. A map of the environment along with possible positions of sample containers are shared between the robots in the team by using a cloud-based communication interface. To grasp a container with its manipulator arm the robot has to place itself in a position suitable for the manipulation task. This optimal base placement pose is selected by querying a precomputed inverse reachability database.

Characteristics of a sample of first time drink driving offenders in Queensland - a qualitative analysis

Drink driving incidents in the Australian community continue to be a major road safety problem resulting in a third of all fatalities. Drink driving prevalence remains high; with the rate of Australians who self report drink driving remaining at 11%-12.1% [1,2]. The focus of research in the area to date has been with recidivist offenders who have a higher probability of reoffending, while there is comparatively limited research regarding first time offenders. An important and understudied area relates to the characteristics of first offenders and predictors of recidivism. This study examined the findings of in-depth focussed interviews with a sample of 20 individual first time drink driving offenders in Queensland recruited at the time of court mention.

The modified card sorting test: Test-retest stability and relationships with demographic variables in a healthy older adult sample

Objectives. To investigate the test-retest stability of a standardized version of Nelson's (1976) Modified Card Sorting Test (MCST) and its relationships with demographic variables in a sample of healthy older adults. Design. A standard card order and administration were devised for the MCST and administered to participants at an initial assessment, and again at a second session conducted a minimum of six months later in order to examine its test-retest stability. Participants were also administered the WAIS-R at initial assessment in order to provide a measure of psychometric intelligence. Methods. Thirty-six (24 female, 12 male) healthy older adults aged 52 to 77 years with mean education 12.42 years (SD = 3.53) completed the MCST on two occasions approximately 7.5 months (SD = 1.61) apart. Stability coefficients and test-retest differences were calculated for the range of scores. The effect of gender on MCST performance was examined. Correlations between MCST scores and age, education and WAIS-R IQs were also determined. Results. Stability coefficients ranged from .26 for the percent perseverative errors measure to .49 for the failure to maintain set measure. Several measures were significantly correlated with age, education and WAIS-R IQs, although no effect of gender on MCST performance was found. Conclusions. None of the stability coefficients reached the level required for clinical decision making. The results indicate that participants' age, education, and intelligence need to be considered when interpreting MCST performance. Normative studies of MCST performance as well as further studies with patients with executive dysfunction are needed.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

Estimating link-dependent origin-destination matrices from sample trajectories and traffic counts

Origin-Destination matrices (ODM) estimation can benefits of the availability of sample trajectories which can be measured thanks to recent technologies. This paper focus on the case of transport networks where traffic counts are measured by magnetic loops and sample trajectories available. An example of such network is the city of Brisbane, where Bluetooth detectors are now operating. This additional data source is used to extend the classical ODM estimation to a link-specific ODM (LODM) one using a convex optimisation resolution that incorporates networks constraints as well. The proposed algorithm is assessed on a simulated network.

Nintendo Wii Fit as an adjunct to physiotherapy following lower limb fractures: Preliminary feasibility, safety and sample size considerations

Objective The Nintendo Wii Fit integrates virtual gaming with body movement, and may be suitable as an adjunct to conventional physiotherapy following lower limb fractures. This study examined the feasibility and safety of using the Wii Fit as an adjunct to outpatient physiotherapy following lower limb fractures, and reports sample size considerations for an appropriately powered randomised trial. Methodology Ambulatory patients receiving physiotherapy following a lower limb fracture participated in this study (n = 18). All participants received usual care (individual physiotherapy). The first nine participants also used the Wii Fit under the supervision of their treating clinician as an adjunct to usual care. Adverse events, fracture malunion or exacerbation of symptoms were recorded. Pain, balance and patient-reported function were assessed at baseline and discharge from physiotherapy. Results No adverse events were attributed to either the usual care physiotherapy or Wii Fit intervention for any patient. Overall, 15 (83%) participants completed both assessments and interventions as scheduled. For 80% power in a clinical trial, the number of complete datasets required in each group to detect a small, medium or large effect of the Wii Fit at a post-intervention assessment was calculated at 175, 63 and 25, respectively. Conclusions The Nintendo Wii Fit was safe and feasible as an adjunct to ambulatory physiotherapy in this sample. When considering a likely small effect size and the 17% dropout rate observed in this study, 211 participants would be required in each clinical trial group. A larger effect size or multiple repeated measures design would require fewer participants.

Defining a developmental subtype of bipolar disorder in a sample of nonreferred adults by age at onset

Objective To test the hypothesis that the age at onset of bipolar disorder would identify a developmental subtype of bipolar disorder in adults characterized by increased levels of irritability, chronic course, rapid cycling, and comorbidity with attention deficit hyperactivity disorder. Methods Forty-four adult subjects diagnosed with bipolar disorder were selected from large family studies of youth with and without attention deficit hyperactivity disorder. These subjects were stratified by the age at onset in childhood (younger than 13 years; n = 8, 18%), adolescence (13–18 years; n = 12, 27%, or adulthood (older than 19 years; n = 24, 55%). All subjects were administered structure diagnostic interviews and a brief cognitive battery. Results In contrast with adult-onset bipolar disorder, child-onset bipolar disorder was associated with a longer duration of illness, more irritability than euphoria, a mixed presentation, a more chronic or rapid-cycling course, and increased comorbidity with childhood disruptive behavior disorders and anxiety disorders. Conclusion Stratification by age at onset of bipolar disorder identified subgroups of adult subjects with differing clinical correlates. This pattern of correlates is consistent with findings documented in children with pediatric bipolar disorder and supports the hypothesis that child-onset bipolar disorder may represent a developmental subtype of the disorder.

Genetic analyses in a sample of individuals with high or low BMD shows association with multiple Wnt pathway genes

INTRODUCTION Although the high heritability of BMD variation has long been established, few genes have been conclusively shown to affect the variation of BMD in the general population. Extreme truncate selection has been proposed as a more powerful alternative to unselected cohort designs in quantitative trait association studies. We sought to test these theoretical predictions in studies of the bone densitometry measures BMD, BMC, and femoral neck area, by investigating their association with members of the Wnt pathway, some of which have previously been shown to be associated with BMD in much larger cohorts, in a moderate-sized extreme truncate selected cohort (absolute value BMD Z-scores = 1.5-4.0; n = 344). MATERIALS AND METHODS Ninety-six tag-single nucleotide polymorphism (SNPs) lying in 13 Wnt signaling pathway genes were selected to tag common genetic variation (minor allele frequency [MAF] > 5% with an r(2) > 0.8) within 5 kb of all exons of 13 Wnt signaling pathway genes. The genes studied included LRP1, LRP5, LRP6, Wnt3a, Wnt7b, Wnt10b, SFRP1, SFRP2, DKK1, DKK2, FZD7, WISP3, and SOST. Three hundred forty-four cases with either high or low BMD were genotyped by Illumina Goldengate microarray SNP genotyping methods. Association was tested either by Cochrane-Armitage test for dichotomous variables or by linear regression for quantitative traits. RESULTS Strong association was shown with LRP5, polymorphisms of which have previously been shown to influence total hip BMD (minimum p = 0.0006). In addition, polymorphisms of the Wnt antagonist, SFRP1, were significantly associated with BMD and BMC (minimum p = 0.00042). Previously reported associations of LRP1, LRP6, and SOST with BMD were confirmed. Two other Wnt pathway genes, Wnt3a and DKK2, also showed nominal association with BMD. CONCLUSIONS This study shows that polymorphisms of multiple members of the Wnt pathway are associated with BMD variation. Furthermore, this study shows in a practical trial that study designs involving extreme truncate selection and moderate sample sizes can robustly identify genes of relevant effect sizes involved in BMD variation in the general population. This has implications for the design of future genome-wide studies of quantitative bone phenotypes relevant to osteoporosis.

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

Estimation for the general sample selection models

Consider a general regression model with an arbitrary and unknown link function and a stochastic selection variable that determines whether the outcome variable is observable or missing. The paper proposes U-statistics that are based on kernel functions as estimators for the directions of the parameter vectors in the link function and the selection equation, and shows that these estimators are consistent and asymptotically normal.

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion: of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Parental divorce and adjustment in adulthood: Findings from a community sample

The current study examines the link between the experience of divorce in childhood and several indices of adjustment in adulthood in a large community sample of women. Results replicated previous research on the long-term correlation between parental divorce and depression and divorce in adulthood. Results further suggested that parental divorce was associated with a wide range of early risk factors, life course patterns, and several indices of adult adjustment. Regression analyses indicated that the long-term correlation between parental divorce and depression in adulthood is explained by quality of parent-child and parental marital relations (in childhood), concurrent levels of stressful life events and social support, and cohabitation. The long-term association between parental divorce and experiencing a divorce in adulthood was partly mediated through quality of parent-child relations, teenage pregnancy, leaving home before 18 years, and educational attainment.

Family type and depression in pregnancy: Factors mediating risk in a community sample

The rate of severe depression among women in single-parent and biological families and in a variety of stepfamilies was examined in a large community sample of 13,088 pregnant women in the United Kingdom. Compared with women in biological families and published population rates, women in single-parent families and step-families reported significantly elevated rates of depression. Family-type differences in several risk factors were examined, including cohabiting (vs. married) status, relationship history, and socioeconomic and psychosocial risks, such as crowding, social support, and stressful life events. Family-type differences in depression were mediated partly by differences in social support, stressful life events, and crowding, but a main effect of family type in predicting depression remained after statistically controlling for these risks.