Characteristics of school campuses and physical activity among youth.
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BACKGROUND: Previous research suggests that school characteristics may influence physical activity. However, few studies have examined associations between school building and campus characteristics and objective measures of physical activity among middle school students. METHODS: Students from ten middle schools (n=248, 42% female, mean age 13.7 years) wore TriTrac-R3D accelerometers in 1997 recording measures of minute-by-minute physical movements during the school day that were then averaged over 15-minute intervals (n=16,619) and log-transformed. School characteristics, including school campus area, play area, and building area (per student) were assessed retrospectively in 2004-2005 using land-use parcel data, site visits, ortho-photos, architectural plans, and site maps. In 2006, linear mixed models using SAS PROC MIXED were fit to examine associations between school environmental variables and physical activity, controlling for potentially confounding variables. RESULTS: Area per enrolled student ranged from 8.8 to 143.7 m2 for school campuses, from 12.1 to 24.7 m2 for buildings, and from 0.4 to 58.9 m2 for play areas. Play area comprised from 3% to 62% of total campus area across schools. In separate regression models, school campus area per student (beta=0.2244, p<0.0001); building area per student (beta=2.1302, p<0.02); and play area per student (beta=0.347, p<0.0001) were each directly associated with log-TriTrac-R3D vector magnitude. Given the range of area density measures in this sample of schools, this translates into an approximate 20% to 30% increase in average vector magnitude, or walking 2 extra miles over the course of a week. CONCLUSIONS: Larger school campuses, school buildings, and play areas (per enrolled student) are associated with higher levels of physical activity in middle school students. (+info)
A flexible routing scheme for patients with topographical disorientation.
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BACKGROUND: Individuals with topographical disorientation have difficulty navigating through indoor environments. Recent literature has suggested that ambient intelligence technologies may provide patients with navigational assistance through auditory or graphical instructions delivered via embedded devices. METHOD: We describe an automatic routing engine for such an ambient intelligence system. The method routes patients with topographical disorientation through indoor environments by repeatedly computing the route of minimal cost from the current location of the patient to a specified destination. The cost of a given path not only reflects the physical distance between end points, but also incorporates individual patient abilities, the presence of mobility-impeding physical barriers within a building and the dynamic nature of the indoor environment. We demonstrate the method by routing simulated patients with either topographical disorientation or physical disabilities. Additionally, we exemplify the ability to route a patient from source to destination while taking into account changes to the building interior. RESULTS: When compared to a random walk, the proposed routing scheme offers potential cost-savings even when the patient follows only a subset of instructions. CONCLUSION: The routing method presented reduces the navigational effort for patients with topographical disorientation in indoor environments, accounting for physical abilities of the patient, environmental barriers and dynamic building changes. The routing algorithm and database proposed could be integrated into wearable and mobile platforms within the context of an ambient intelligence solution. (+info)
Traffic safety and city structure: lessons for the future.
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OBJECTIVE: To understand the critical factors that are likely to influence road traffic fatality rates in large cities around the world in the next few decades. MATERIAL AND METHODS: Road traffic fatality data for 56 cities around the world and for cities with a population of greater than 100,000 in the USA were collected and analysed to understand factors affecting differences in fatality rates. RESULTS: There are wide variations in fatality rates across income levels and within similar incomes levels. The risk varies by a factor of about 20 between the best and the worst cities. CONCLUSIONS: These patterns appear to indicate that it is not enough to have the safest vehicle and road technology to ensure low road traffic fatality rates. City structure, modal share split, and exposure of motorists and pedestrians may have a significant role in determining fatality rates, in addition to enforcement, vehicle crashworthiness and road design. (+info)
The autonomous house: a bio-hydrogen based energy self-sufficient approach.
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