The Influence of Built Environment on Walkability Using Geographic Information System The main goal of this study was to analyze the residential environment (built environment), by exploring the relationship between neighborhood walkability characteristics using Geographic Information System (GIS) and level of physical activity (PA). A randomly selected sample of 135 adults from Olomouc, Czech Republic was divided into low and high walkability neighborhoods based on the walkability index (calculated using GIS). The average daily number of steps (using Yamax SW-700 pedometer) between the groups also was measured. Result showed that participants living in the high walkable areas took more steps a day than participant in the low walkable areas. Furthermore, the mean body mass index (BMI) level was inversely correlated with the number of steps on weekdays and during the whole week. On weekend days it was still inversed throughout with the number of steps; taken but the but the relationship was not statistically significant.

The authors describe and compare how physical education classes and healthy lifestyle concepts are taught in selected Czech and U.S. schools for the deaf. Professionals who participated in the study included principals and teachers employed by 4 schools for the deaf. Data from schools were collected during the summer and fall semesters, and subsequent interviews were conducted with the principals and physical education teachers. Unique characteristics were exhibited by each of the 4 schools. The settings for extracurricular physical and sports activities varied by school type (residential or nonresidential). Findings indicated that the general trend in physical education has changed from a focus on sports performance to health-promoting activities. There were opportunities for teachers to revise curriculum programs to further promote the health and academic success of students who are deaf or hard of hearing.

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The pedometer is a widely used research tool for measuring the level and extent of physical activity (PA) within population subgroups. The sample used in this study was drawn from a population of university students to examine the influence of the monitoring interval and alternate starting days on step-count activity patterns. The study was part of a national project during 2008–2010. Eligible subjects (641) were selected from a sample of 906 university students. The students wore pedometers continuously for 7 days excluding time for sleep and personal hygiene. Steps per day were logged on record sheets by each student. Data gathering spanned an entire week, and the results were sorted by alternate starting days, by activity for an entire week, by activity for only the weekdays of the one-week monitoring interval and for the two-day weekend. The statistical analysis included ANOVA, intra-class correlation (ICC) analysis, and regression analysis. The ICC analysis suggested that monitoring starting on Monday (ICC = 0.71; 95%CI (0.61–0.79)), Tuesday (ICC = 0.67; 95%CI (0.59–0.75)) or Thursday (ICC = 0.68; 95%CI (0.55–0.79)) improved reliability. The results of regression analysis also indicated that any starting day except Sunday is satisfactory as long as a minimum of four days of monitoring are used.