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• Cyclist-vehicle passing distance data collected from 23 cyclists over a 4-week period in a naturalistic study. • Over 16,000 passing events detected. • All events associated with speed limit, presence of bike lane, and road type classification. • Regression analyses explored factors that influence passing distance and compliance with minimum passing distance laws. • Speed limit, bike lane, and road classification all affect passing distance and probability of non-compliant pass. In Australia, cycling hospitalisations are increasing and the cycling participation rate is stagnating. In an effort to improve cyclist safety, many Australian jurisdictions have mandated a minimum passing distance that vehicles much provide when overtaking a cyclist on a public road, including the Australian Capital Territory (ACT). However, it is not currently clear how vehicle-cyclist passing distances are affected by various parameters such as the road environment, the vehicles involved, or the speed limit. This naturalistic bicycle riding study examined data from passing distance measurement devices that were installed on the bicycles of volunteer cyclists who ride in the ACT, to explore how passing distances and compliance with the minimum passing distance were affected by several parameters. Over a four-week period, 23 volunteer cyclist participants undertook 465 journeys and travelled 6531 km over a total period of 271 h. There were 10,959 passing events identified on roads zoned greater than 60 km/h (high speed roads) of which 1349 (12.3 %) were non-compliant. On roads zoned 60 km/h or less (low speed roads) there were 5517 passing events of which 153 (2.8 %) were non-compliant. Regression analyses showed that differences in passing distance and non-compliance with the minimum passing distance were associated with road classification, bike lane presence, and speed limit. The results were mixed but, in general, passing distances were greater on roads with a lower (hierarchy) classification and on motorways as well as on roads with higher speed limits. An exception to this was roads with a speed limit of 50 km/h where passing distances were closer in comparison to roads with a speed limit of 60 km/h. Bike lanes were generally associated with an increase in passing distance except on 'trunk' classified roads, where a bike lane resulted in closer passing events. This suggests that on trunk roads, which are assumed to carry large amounts of traffic, bike lanes may be insufficient to offer protection to cyclists and additional measures may be required. [ABSTRACT FROM AUTHOR]

Abstract: Background: There are no effective indicators of graft dysfunction in islet transplantation. This study evaluated the role of the Continuous Glucose Monitoring System (CGMS) as an early indicator of graft dysfunction in islet transplant recipients. Methods: In 5 islet allograft recipients, we retrospectively determined the date of graft dysfunction: 3 fasting blood glucose levels >7.8 mmol/L (140 mg/dL) and/or 3 postprandial blood glucose levels >10 mmol/L (180 mg/dL) in 1 week. We then determined 2 time points in respect to graft dysfunction, 5 to 9 months before (time point A) and 2 to 3 months before (time point B). For these 2 time points, we assessed the following: HbA1c, C-peptide (CP), C-peptide glucose ratio (CPGR), 90-minute glucose from mixed meal tolerance test, and percentage of capillary blood glucose levels >7.8 mmol/L (%CBG >7.8) in a 15-day interval (1 week before and after CGMS placement). From the CGMS recordings, we calculated the glucose variability and the percentage of time spent in hyperglycemia >7.8 mmol/L (%HGT >7.8) and >10 mmol/L (%HGT >10). Results: No difference was found between time points A and B for the following parameters: HbA1c, CP, CPGR, 90-minute glucose, %CBG >7.8, and %HGT >10. We observed a statistically significant increase from time point A to time point B in glucose variability (1.1 ± 0.5 mmol/L to 1.6 ± 0.6 mmol/L; P = .004), and in the %HGT >7.8 (11 ± 12% to 22 ± 18%; P = .036). Conclusion: Glucose variability and %HGT >7.8 determined using CGMS are useful as early indicators of graft dysfunction in islet transplant recipients. Further studies with larger sample sizes will help validate these observations. [Copyright &y& Elsevier]