Longitudinal active living research to address physical inactivity and sedentary behaviour in children in transition from preadolescence to adolescence.

BACKGROUND: Children can be highly active and highly sedentary on the same day! For instance, a child can spend a couple of hours playing sports, and then spend the rest of the day in front of a screen. A focus on examining both physical activity and sedentary behaviour throughout the day and in all seasons in a year is necessary to generate comprehensive evidence to curb childhood obesity. To achieve this, we need to understand where within a city are children active or sedentary in all seasons. This active living study based in Saskatoon, Canada, aims to understand the role played by modifiable urban built environments in mitigating, or exacerbating, seasonal effects on children's physical activity and sedentary behaviour in a population of children in transition from preadolescence to adolescence. METHODS/DESIGN: Designed as an observational, longitudinal investigation this study will recruit 800 Canadian children 10-14 years of age. Data will be obtained from children representing all socioeconomic categories within all types of neighbourhoods built in a range of urban designs. Built environment characteristics will be measured using previously validated neighbourhood audit and observational tools. Neighbourhood level socioeconomic variables customized to Saskatoon neighbourhoods from 2011 Statistics Canada's National Household Survey will be used to control for neighbourhood social environment. The validated Smart Cities Healthy Kids questionnaire will be administered to capture children's behaviour and perception of a range of factors that influence their activity, household (including family socioeconomic factors), parental, peer and neighbourhood influence on independent mobility. The outcome measures, different intensities of physical activity and sedentary behaviour, will be collected using global positioning system equipped accelerometers in all four seasons. Each accelerometry cycle will be matched with weather data obtained from Environment Canada. Extensive weather data will be accessed and classified into one of six distinct air mass categories for each day of accelerometry. Computational and spatial analytical techniques will be utilized to understand the multi-level influence of environmental exposures on physical activity and sedentary behaviour in all seasons. DISCUSSION: This approach will help us understand the influence of urban environment on children's activity, thus paving the way to modify urban spaces to increase physical activity and decrease sedentary behaviour in children in all four seasons. Lack of physical activity and rising sedentariness is associated with rising childhood obesity, and childhood obesity in turn is linked to many chronic conditions over the life course. Understanding the interaction of children with urban spaces will reveal new knowledge, and when translated to actions will provide a strong basis for informing future urban planning policy.

Temporal aggregation impacts on epidemiological simulations employing microcontact data.

BACKGROUND: Microcontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population's contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes. METHODS: We employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks. RESULTS: No aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs - which extrapolate from data collected over a brief period - exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation. CONCLUSIONS: The interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

The Effect of Season and Neighbourhood-Built Environment on Home Area Sedentary Behaviour in 9-14 Year Old Children.

There is little understanding of how the built environment shapes activity behaviours in children over different seasons. This study sought to establish how seasonal weather patterns, in a given year in a mid-western Canadian city, affect sedentary time (SED) in youth and how the relationship between season and SED are moderated by the built environment in their home neighbourhood. Families with children aged 9-14 years were recruited from the prairie city of Saskatoon, Canada. Location-specific, device-based SED was captured in children during three timeframes over a one-year period using GPS-paired accelerometers. Multilevel models are presented. Children accumulated significantly greater levels of SED in spring but significantly less SED in the fall months in comparison to the winter months. Children living in neighbourhoods with the highest density of destinations accumulated significantly less SED while in their home area in comparison to their counterparts, and this effect was more pronounced in the spring and summer months. On weekends, the rise in sedentariness within the home area was completely diminished in children living in neighbourhoods with the greatest number of destinations and highest activity friendliness. These results suggested that increasing neighbourhood amenities can lead to a reduced sedentariness of youth, though more so in the warmers months of the year.

The future of activity space and health research.

We discuss the future of activity space and health research in the context of a recently published systematic review. Our discussion outlines a number of elements for reflection among the research community. We need to think beyond activity space and reconceptualize exposure in era of high volume, high precision location data. We need to develop standardized methods for understanding global positioning system data. We must adopt replicable scientific computing processes and machine learning models. Finally, we must embrace modern notions of causality in order to contend with the conceptual challenges faced by our research field.

Intrinsic dimensionality of human behavioral activity data.

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.

Multiscale entropy rate analysis of complex mobile agents.

Accurate prediction of the motion of objects is a central scientific goal. For deterministic or stochastic processes, models exist which characterize motion with a high degree of reliability. For complex systems, or those where objects have a degree of agency, characterizing motion is far more challenging. The information entropy rate of motion through a discrete space can place a limit on the predictability of even the most complex or history-dependent actor, but the variability in measured encountered locations is inexorably tied to the spatial and temporal resolutions of those measurements. This relation depends on the path of the actor in ways that can be used to derive a general law in closed form relating the mobility entropy rate to different spatial and temporal resolutions, and the path properties within each cell along the path. Correcting for spatial and temporal effects through regression yields the path properties and a measure of mobility entropy rate robust to changes in dimension, allowing comparison of mobility entropy rates between datasets. Employing this measure on empirical datasets yields novel findings, from the similarity of taxicabs to drifters, to the predictable motions of undergraduates, to the browsing habits of Canadian moose.

A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns.

Characterizing how people move through space has been an important component of many disciplines. With the advent of automated data collection through GPS and other location sensing systems, researchers have the opportunity to examine human mobility at spatio-temporal resolution heretofore impossible. However, the copious and complex data collected through these logging systems can be difficult for humans to fully exploit, leading many researchers to propose novel metrics for encapsulating movement patterns in succinct and useful ways. A particularly salient proposed metric is the mobility entropy rate of the string representing the sequence of locations visited by an individual. However, mobility entropy rate is not scale invariant: entropy rate calculations based on measurements of the same trajectory at varying spatial or temporal granularity do not yield the same value, limiting the utility of mobility entropy rate as a metric by confounding inter-experimental comparisons. In this paper, we derive a scaling relationship for mobility entropy rate of non-repeating straight line paths from the definition of Lempel-Ziv compression. We show that the resulting formulation predicts the scaling behavior of simulated mobility traces, and provides an upper bound on mobility entropy rate under certain assumptions. We further show that this formulation has a maximum value for a particular sampling rate, implying that optimal sampling rates for particular movement patterns exist.