METHODS: Data of 328 eligible housewives who participated in the MyBFF@Home study was used. Intervention group of 169 subjects were provided with an intervention package which includes physical activity (brisk walking, dumbbell exercise, physical activity diary, group exercise) and 159 subjects in control group received various health seminars. Physical activity level was assessed using short-International Physical Activity Questionnaire. The physical activity level was then re-categorized into 4 categories (active intervention, inactive intervention, active control and inactive control). Physical activity, blood glucose and lipid profile were measured at baseline, 3rd month and 6th month of the study. General Linear Model was used to determine the effect of physical activity on glucose and lipid profile.
RESULTS: At the 6th month, there were 99 subjects in the intervention and 79 control group who had complete data for physical activity. There was no difference on the effect of physical activity on the glucose level and lipid profile except for the Triglycerides level. Both intervention and control groups showed reduction of physical activity level over time.
CONCLUSION: The effect of physical activity on blood glucose and lipid profile could not be demonstrated possibly due to physical activity in both intervention and control groups showed decreasing trend over time.
METHOD: This paper aims to develop a sustainable pedestrian gap crossing index model based on traffic flow density. It focusses on the gaps accepted by pedestrians and their decision for street crossing, where (Log-Gap) logarithm of accepted gaps was used to optimize the result of a model for gap crossing behavior. Through a review of extant literature, 15 influential variables were extracted for further empirical analysis. Subsequently, data from the observation at an uncontrolled mid-block in Jalan Ampang in Kuala Lumpur, Malaysia was gathered and Multiple Linear Regression (MLR) and Binary Logit Model (BLM) techniques were employed to analyze the results.
RESULTS AND CONCLUSIONS: From the results, different pedestrian behavioral characteristics were considered for a minimum gap size model, out of which only a few (four) variables could explain the pedestrian road crossing behavior while the remaining variables have an insignificant effect. Among the different variables, age, rolling gap, vehicle type, and crossing were the most influential variables. The study concludes that pedestrians' decision to cross the street depends on the pedestrian age, rolling gap, vehicle type, and size of traffic gap before crossing.
PRACTICAL APPLICATIONS: The inferences from these models will be useful to increase pedestrian safety and performance evaluation of uncontrolled midblock road crossings in developing countries.