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IntroductionPrevalence of walking and cycling for transport is low and varies greatly across countries. Few studies have examined neighborhood perceptions related to walking and cycling for transport in different countries. Therefore, it is challenging to prioritize appropriate built-environment interventions.ObjectivesThe aim of this study was to examine the strength and shape of the relationship between adults' neighborhood perceptions and walking and cycling for transport across diverse environments.MethodsAs part of the International Physical activity and Environment Network (IPEN) adult project, self-reported data were taken from 13,745 adults (18-65 years) living in physically and socially diverse neighborhoods in 17 cities across 12 countries. Neighborhood perceptions were measured using the Neighborhood Environment Walkability Scale, and walking and cycling for transport were measured using the International Physical Activity Questionnaire-Long Form. Generalized additive mixed models were used to model walking or cycling for transport during the last seven days with neighborhood perceptions. Interactions by city were explored.ResultsWalking-for-transport outcomes were significantly associated with perceived residential density, land use mix-access, street connectivity, aesthetics, and safety. Any cycling for transport was significantly related to perceived land use mix-access, street connectivity, infrastructure, aesthetics, safety, and perceived distance to destinations. Between-city differences existed for some attributes in relation to walking or cycling for transport.ConclusionsMany perceived environmental attributes supported both cycling and walking; however, highly walkable environments may not support cycling for transport. People appear to walk for transport despite safety concerns. These findings can guide the implementation of global health strategies.

Children consistently exposed to positive, stimulating environments, including their spatial neighbourhood, experience foundations for ongoing optimal development. Recognition of the neighbourhood as a mechanism to enhance wellbeing is reflected in the United Nation's New Urban Agenda. Through a series of innovations and advancements, this paper aims to describe a spatial indicators' framework, the Neighbourhood Early Childhood Development Framework. Once tested, it can be used to assess and monitor urban neighbourhoods for their ability to equitably support early childhood development. Eight domains were included in the framework: Early childcare and education services, family-friendly destinations, food outlets, housing, public open space, public transport, traffic, and walkability. Overall, 44 indicators were conceptualised for calculation at the smallest appropriate geographical scale available (i.e. a child's home address). Different scales of analysis were chosen to represent a child's local neighbourhood in an urban setting. The Framework supports commitment to action in the New Urban Agenda through improved measurement, monitoring, and research capabilities, alongside provision of tools to support evidence-based and interdisciplinary policy and practitioner decision-making. Once tested with child outcomes, it can inform more precise, evidence-based place-based interventions, while offering the potential to reduce childhood developmental inequities at scale. [ABSTRACT FROM AUTHOR]

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.