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Abstract Background Active transport can contribute to physical activity accumulation and improved health in adults. The built environment is an established associate of active transport behaviours; however, assessment of environmental features encountered during journeys remains challenging. The purpose of this study was to examine the utility of wearable cameras to objectively audit and quantify environmental features along work-related walking and cycling routes. Methods A convenience sample of employed adults was recruited in New Zealand, in June 2011. Participants wore a SenseCam for all journeys over three weekdays and completed travel diaries and demographic questionnaires. SenseCam images for work-related active transport journeys were coded for presence of environmental features hypothesised to be related to active transport. Differences in presence of features by transport mode and in participant-reported and SenseCam-derived journey duration were determined using two-sample tests of proportion and an independent samples t-test, respectively. Results Fifteen adults participated in the study, yielding 1749 SenseCam images from 30 work-related active transport journeys for coding. Significant differences in presence of features were found between walking and cycling journeys. Almost a quarter of images were uncodeable due to being too dark to determine features. There was a non-significant tendency for respondents to under-report their journey duration. Conclusion This study provides proof of concept for the use of the SenseCam to capture built environment data in real time that may be related to active transportation. Further work is required to test and refine coding methodologies across a range of settings, travel behaviours, and demographic groups.

Abstract Background Accelerometers can identify certain physical activity behaviours, but not the context in which they take place. This study investigates the feasibility of wearable cameras to objectively categorise the behaviour type and context of participants’ accelerometer-identified episodes of activity. Methods Adults were given an Actical hip-mounted accelerometer and a SenseCam wearable camera (worn via lanyard). The onboard clocks on both devices were time-synchronised. Participants engaged in free-living activities for 3 days. Actical data were cleaned and episodes of sedentary, lifestyle-light, lifestyle-moderate, and moderate-to-vigorous physical activity (MVPA) were identified. Actical episodes were categorised according to their social and environmental context and Physical Activity (PA) compendium category as identified from time-matched SenseCam images. Results There were 212 days considered from 49 participants from whom SenseCam images and associated Actical data were captured. Using SenseCam images, behaviour type and context attributes were annotated for 386 (out of 3017) randomly selected episodes (such as walking/transportation, social/not-social, domestic/leisure). Across the episodes, 12 categories that aligned with the PA Compendium were identified, and 114 subcategory types were identified. Nineteen percent of episodes could not have their behaviour type and context categorized; 59% were outdoors versus 39% indoors; 33% of episodes were recorded as leisure time activities, with 33% transport, 18% domestic, and 15% occupational. 33% of the randomly selected episodes contained direct social interaction and 22% were in social situations where the participant wasn’t involved in direct engagement. Conclusion Wearable camera images offer an objective method to capture a spectrum of activity behaviour types and context across 81% of accelerometer-identified episodes of activity. Wearable cameras represent the best objective method currently available to categorise the social and environmental context of accelerometer-defined episodes of activity in free-living conditions.

The primary aim of the current study was to investigate the accuracy of various automated rules for determining accelerometer nonwear time in a sample of predominantly desk-based office workers (using their self-reported nonwear times as a criterion). Second, the authors examined the effect of applying these rules to accelerometer data retention and classification of sedentary behaviors. The findings show that a threshold of 180 consecutive zero minutes and a 1-min allowance preceding or following the bout yielded the most accurate determination of accelerometer nonwear time in this sample. In keeping with Evenson and Terry (2009), the authors found that increasing threshold duration resulted in increased classification of sedentary time. They also found considerable individual differences in nonwear time threshold accuracy and quantification of sedentary time. In particular, accuracy of the longer bout criteria was reduced to classify (a) nonwear time in individuals who self-reported removing the belts for short periods; and (b) sedentary time in individuals whose activity patterns were more often sedentary than those who were less sedentary. (Contains 1 table.)

This study examines the utility of current accelerometer threshold definitions and epoch durations for physical activity intensity classification in preschool-aged children. Using video footage of children engaged in active play, directly observed 1-sec epoch physical activity intensity scores were derived from a modified version of the Children's Activity Rating Scale, averaged into 15-sec epochs (corresponding to the accelerometer scale), and compared to accelerometer-measured classifications. Overall, 6,540 individual 1-sec epochs were captured and classified from six children, yielding 429 time-matched 15-sec epoch physical activity intensity classifications from both methods. Children participated in 25 distinct physical activity behaviors and spent 75% of free-play in one intensity level for less than or equal to 5 sec. The maximum amount of time spent participating in an activity prior to changing activity intensity was 98 sec. Agreement between methods was generally poor (overall: K = 0.22; child range: K = 0.00 to 0.66), and the difference between methods was significantly asymmetrical (McNemar's test overall: p = 0.01). Accelerometer thresholds and epoch durations used to classify preschoolers' physical activity intensity yielded biased estimates in the current study. Further investigation is necessary to establish optimal physical activity measurement methods with this population. (Contains 3 tables and 1 figure.)

A relatively new concept is that of "integrating" physical activity throughout the school curriculum, thereby teaching children about lifestyle physical activity in a variety of contexts. One method by which this may be achieved is by utilizing pedometers as a motivational and educational tool for measuring accumulated physical activity. No research is available that shows in-depth integration of physical activity into the curriculum or that investigates the efficacy of pedometer use for this purpose. The purposes of this study were to (1) design and implement a 4-week elementary school curriculum unit, based around pedometer walking and (2) quantify, using pedometry, the physical activity levels of children (N = 78) prior to, and during, the unit implementation. Results showed that more than one half of the participants were achieving more than 15,000 steps daily, and children were significantly more active on weekdays than weekends (p = 0.0001). Boys were more active than girls at baseline (p = 0.01) and during intervention weekdays (p = 0.03). Differences between baseline and intervention weekdays were nonsignificant for the complete sample; however, significant increases in step counts were observed when the children with low activity levels, especially females, were examined separately. Overall, the integration of physical activity using pedometer-based activities is feasible. However, any increases in activity may be restricted to children who are least active.

Background: The study aim was to determine the association between children's objectively assessed moderate-to-vigorous physical activity (MVPA) and active trips (AT) and independently mobile trips (IM) during out-of-school hours. Methods: Children aged 9 to 13 years (n = 254) were recruited from 9 schools in Auckland, New Zealand between 2011 and 2012. Children completed travel diaries and wore accelerometers for 7 days. Parents provided demographic information. Geographic information systems-derived distance to school was calculated. Accelerometer data were extracted for out of school hours only. Percentage of time spent in MVPA (%MVPA), AT, and IM were calculated. Generalized estimating equations were used to determine the relationship between daily %MVPA and AT and between daily %MVPA and IM, accounting for age, sex, ethnicity, distance to school, day of the week, and numeric day of data collection. Results: A significant positive relationship was observed between %MVPA and both AT and IM. For every unit increase in the daily percentage of trips made that were AT or IM, we found an average increase of 1.28% (95% Cl 0.87%. 1.70%) and 1.15% (95% Cl 0.71%, 1.59%) time in MVPA. respectively. Conclusion: Children's AT and IM are associated with increased MVPA during out-of-school hours. [ABSTRACT FROM AUTHOR]

Abstract 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.

Abstract Background Physical activity is essential for optimal physical and psychological health but substantial declines in children's activity levels have occurred in New Zealand and internationally. Children's independent mobility (i.e., outdoor play and traveling to destinations unsupervised), an integral component of physical activity in childhood, has also declined radically in recent decades. Safety-conscious parenting practices, car reliance and auto-centric urban design have converged to produce children living increasingly sedentary lives. This research investigates how urban neighborhood environments can support or enable or restrict children's independent mobility, thereby influencing physical activity accumulation and participation in daily life. Methods/Design The study is located in six Auckland, New Zealand neighborhoods, diverse in terms of urban design attributes, particularly residential density. Participants comprise 160 children aged 9-11 years and their parents/caregivers. Objective measures (global positioning systems, accelerometers, geographical information systems, observational audits) assessed children's independent mobility and physical activity, neighborhood infrastructure, and streetscape attributes. Parent and child neighborhood perceptions and experiences were assessed using qualitative research methods. Discussion This study is one of the first internationally to examine the association of specific urban design attributes with child independent mobility. Using robust, appropriate, and best practice objective measures, this study provides robust epidemiological information regarding the relationships between the built environment and health outcomes for this population.

Abstract Background Built environment attributes are recognized as being important contributors to physical activity (PA) engagement and body size in adults and children. However, much of the existing research in this emergent public health field is hindered by methodological limitations, including: population and site homogeneity, reliance on self-report measures, aggregated measures of PA, and inadequate statistical modeling. As an integral component of multi-country collaborative research, the Understanding the Relationship between Activity and Neighbourhoods (URBAN) Study seeks to overcome these limitations by determining the strengths of association between detailed measures of the neighborhood built environment with PA levels across multiple domains and body size measures in adults and children. This article outlines the research protocol developed for the URBAN Study. Methods and design The URBAN Study is a multi-centered, stratified, cross-sectional research design, collecting data across four New Zealand cities. Within each city, 12 neighborhoods were identified and selected for investigation based on higher or lower walkability and Māori demographic attributes. Neighborhoods were selected to ensure equal representation of these characteristics. Within each selected neighborhood, 42 households are being randomly selected and an adult and child (where possible) recruited into the study. Data collection includes: objective and self-reported PA engagement, neighborhood perceptions, demographics, and body size measures. The study was designed to recruit approximately 2,000 adults and 250 children into the project. Other aspects of the study include photovoice, which is a qualitative assessment of built environment features associated with PA engagement, an audit of the neighborhood streetscape environment, and an individualized neighborhood walkability profile centered on each participant's residential address. Multilevel modeling will be used to examine the individual-level and neighborhood-level relationships with PA engagement and body size. Discussion The URBAN Study is applying a novel scientifically robust research design to provide urgently needed epidemiological information regarding the associations between the built environment and health outcomes. The findings will contribute to a larger, international initiative in which similar neighborhood selection and PA measurement procedures are utilized across eight countries. Accordingly, this study directly addresses the international priority issues of increasing PA engagement and decreasing obesity levels.

Background: This brief report provides grades for the 2014 New Zealand Report Card on Physical Activity for Children and Youth. The Report Card presents a review of current evidence across 9 key indicators, including physical activity (PA), organized sport and free play, sedentary behavior, and community and government initiatives across New Zealand. Methods: Nationally representative survey data were collated by researchers at the University of Auckland, New Zealand, between June and December 2013. The grade for each indicator is based on the percentage of children and youth meeting a defined benchmark: A is 81%-100%; B is 61%-80%; C is 41%-60%, D is 21%-40%; F is 0%-20%; INC is incomplete data. Results: Overall PA received a score of B, as did Organized Sport Participation and Active Play. PA participation in School Environment scored slightly less with a score of B-. Sedentary Behaviors, Family and Peers, and Community and Built Environment scored a grade of C. Active transportation received a score of C-. An inconclusive grade was given for the Government indicator due to a lack of established international criteria for assessment. Conclusions: PA participation in New Zealand is satisfactory, but could improve. However, sedentary behavior is high. Of particular concern is the age-related decline in PA participation, particularly among adolescent females, and the increase in sedentary behavior. [ABSTRACT FROM AUTHOR]

Background: Breaks in sedentary behavior are associated with reduced body size in general populations. This study is the first to consider the relationship between objectively assessed sedentary breaks and body size in Pacific children and their mothers. Methods: Pacific children aged 6 years (n = 393) and their mothers (n = 386) residing in New Zealand were invited to participate in 2006. Sedentary time was assessed via accelerometry. Average frequency, duration, and intensity of breaks in sedentary time per hour were calculated. Waist circumference was assessed and demographic factors collected via questionnaire. Relationships between waist circumference and potential associated factors for participants were assessed using linear regression analyses. Results: Accelerometer data were obtained from 126 children (52 boys) and 108 mothers. Mean (standard deviation) waist circumference values for mothers and children were 114 cm (20.1 cm) and 59.4 cm (7.8 cm), respectively. For mothers, time spent sedentary and being an ex/nonsmoker were positively related to waist circumference. For children, watching television every day and having a mother with a high waist circumference was associated with a greater waist circumference. Conclusion: Strategies that focus on reducing sedentary time in Pacific mothers and on encouraging television free days in young Pacific children are recommended. [ABSTRACT FROM AUTHOR]

Background: Aims were to investigate sex, ethnicity, and age differences in achieving daily step count and television (TV) watching recommendations in schoolchildren. Methods: Participants were 615 children (n = 325) and adolescents (n = 290) aged 5 to 16 years. Activity was assessed over 5 days using pedometers; TV time was collected via parental proxy-report and self-report. Ethnic, sex, and age differences in step counts, TV time, and odds of meeting TV and step count recommendations were examined for weekdays, weekend days, and overall using generalized estimation equation modeling. Results: Overall, girls were more active than boys (P < .001). Adolescents were more active than children (P = .044), watched more TV (P = .005), and were less likely to meet TV watching recommendations (P = .004). Non-European children watched significantly more TV (P = .008), and were significantly less likely to meet TV recommendations than non-European children (P - .001). Participants watched more TV and accumulated less steps on weekend days than weekdays. Conclusions: Multifaceted interventions focusing on both increasing activity and decreasing TV time are needed, especially on weekends. Children and girls may benefit more from activity interventions, while ethnic-specific interventions focusing on TV habits may be most efficacious for adolescents. [ABSTRACT FROM AUTHOR]

Background: Accelerometers are widely used to quantify physical activity (PA) in preschoolers, yet no 'best practice' method for data treatment exists. The purpose of this study was to develop a robust method for data reduction using contemporary statistical methods and apply it to preschoolers' accelerometer data. Methods: Children age 2 to 5 years were recruited in Auckland, New Zealand, and asked to wear accelerometers over 7 days. Average daily PA rates per second were derived for participants, estimated using negative binomial generalized estimating equation (GEE) models. Overall participant rates were derived and compared using normal GEE models. Descriptive information for data analyzed were compared with that derived using traditional data inclusion approaches. Results: Data were gathered from 78 of the 93 enrolled children over a median of 7 days. Daily PA rates ranged from 1.27 to 17.64 counts per second (median 5.70). Compared with traditional approaches, this method had many advantages, including improved data retention, the computation of a continuous measure, and facilitating powerful multivariable regression analyses, while providing similar descriptive information to existing methods. Conclusion: PA rates were successfully calculated for preschoolers' activity description and advantages of the approach identified. This method holds promise for future use and merits further application and enhancement. [ABSTRACT FROM AUTHOR]

Background: Global positioning systems (GPS), geographic information systems (GIS), and accelerometers are powerful tools to explain activity within a built environment, yet little integration of these tools has taken place. This study aimed to assess the feasibility of combining GPS, GIS, and accelerometry to understand transport-related physical activity (TPA) in adults. Methods: Forty adults wore an accelerometer and portable GPS unit over 7 consecutive days and completed a demographics questionnaire and 7-day travel log. Accelerometer and GPS data were extracted for commutes to/from workplace and integrated into a GIS database. GIS maps were generated to visually explore physical activity intensity, GPS speeds and routes traveled. Results: GPS, accelerometer, and survey data were collected for 37 participants. Loss of GPS data was substantial due to a range of methodological issues, such as low battery life, signal drop out, and participant noncompliance. Nonetheless, greater travel distances and significantly higher speeds were observed for motorized trips when compared with TPA. Conclusions: Pragmatic issues of using GPS monitoring to understand TPA behaviors and methodological recommendations for future research were identified. Although methodologically challenging, the combination of GPS monitoring, accelerometry and GIS technologies holds promise for understanding TPA within the built environment. [ABSTRACT FROM AUTHOR]