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    Neighborhood Built Environment and Transport and Leisure Physical Activity: Findings Using Objective Exposure and Outcome Measures in New Zealand
    Witten, K ; Blakely, T ; Bagheri, N ; Badland, H ; Ivory, V ; Pearce, J ; Mavoa, S ; Hinckson, E ; Schofield, G (US DEPT HEALTH HUMAN SCIENCES PUBLIC HEALTH SCIENCE, 2012-07)
    BACKGROUND: Evidence of associations between neighborhood built environments and transport-related physical activity (PA) is accumulating, but few studies have investigated associations with leisure-time PA. OBJECTIVE: We investigated associations of five objectively measured characteristics of the neighborhood built environment-destination access, street connectivity, dwelling density, land-use mix and streetscape quality-with residents' self-reported PA (transport, leisure, and walking) and accelerometer-derived measures of PA. METHODS: Using a multicity stratified cluster sampling design, we conducted a cross-sectional survey of 2,033 adults who lived in 48 New Zealand neighborhoods. Multilevel regression modeling, which was adjusted for individual-level (sociodemographic and neighborhood preference) and neighborhood-level (deprivation) confounders, was used to estimate associations of built environment with PA. RESULTS: We found that 1-SD increases in destination access, street connectivity, and dwelling density were associated with any versus no self-reported transport, leisure, or walking PA, with increased odds ranging from 21% [street connectivity with leisure PA, 95% confidence interval (CI): 0%, 47%] to 44% (destination accessibility with walking, 95% CI: 17%, 79%). Among participants who self-reported some PA, a 1-SD increase in street connectivity was associated with a 13% increase in leisure PA (95% CI: 0, 28%). SD increases in destination access, street connectivity, and dwelling density were each associated with 7% increases in accelerometer counts. CONCLUSIONS: Associations of neighborhood destination access, street connectivity, and dwelling density with self-reported and objectively measured PA were moderately strong, indicating the potential to increase PA through changes in neighborhood characteristics.
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    Linking GPS and travel diary data using sequence alignment in a study of children's independent mobility
    Mavoa, S ; Oliver, M ; Witten, K ; Badland, HM (BIOMED CENTRAL LTD, 2011-12-05)
    BACKGROUND: Global positioning systems (GPS) are increasingly being used in health research to determine the location of study participants. Combining GPS data with data collected via travel/activity diaries allows researchers to assess where people travel in conjunction with data about trip purpose and accompaniment. However, linking GPS and diary data is problematic and to date the only method has been to match the two datasets manually, which is time consuming and unlikely to be practical for larger data sets. This paper assesses the feasibility of a new sequence alignment method of linking GPS and travel diary data in comparison with the manual matching method. METHODS: GPS and travel diary data obtained from a study of children's independent mobility were linked using sequence alignment algorithms to test the proof of concept. Travel diaries were assessed for quality by counting the number of errors and inconsistencies in each participant's set of diaries. The success of the sequence alignment method was compared for higher versus lower quality travel diaries, and for accompanied versus unaccompanied trips. Time taken and percentage of trips matched were compared for the sequence alignment method and the manual method. RESULTS: The sequence alignment method matched 61.9% of all trips. Higher quality travel diaries were associated with higher match rates in both the sequence alignment and manual matching methods. The sequence alignment method performed almost as well as the manual method and was an order of magnitude faster. However, the sequence alignment method was less successful at fully matching trips and at matching unaccompanied trips. CONCLUSIONS: Sequence alignment is a promising method of linking GPS and travel diary data in large population datasets, especially if limitations in the trip detection algorithm are addressed.