Development of a wearable global positioning system for place and health research

<p>Abstract</p> <p>Background</p> <p>An increasing number of studies suggest that characteristics of context, or the attributes of the places within which we live, work and socialize, are associated with variations in health-related behaviours and outcomes. The challenge for health research is to ensure that these places are accurately represented spatially, and to identify those aspects of context that are related to variations in health and amenable to modification. This study focuses on the design of a wearable global positioning system (GPS) data logger for the purpose of objectively measuring the temporal and spatial features of human activities. Person-specific GPS data provides a useful source of information to operationalize the concept of place.</p> <p>Results</p> <p>We designed and tested a lightweight, wearable GPS receiver, capable of logging location information for up to 70 hours continuously before recharging. The device is accurate to within 7 m in typical urban environments and performs well across a range of static and dynamic conditions.</p> <p>Discussion</p> <p>Rather than rely on static areal units as proxies for places, wearable GPS devices can be used to derive a more complete picture of the different places that influence an individual's wellbeing. The measures are objective and are less subject to biases associated with recall of location or misclassification of contextual attributes. This is important for two reasons. First, it brings a dynamic perspective to place and health research. The influence of place on health is dynamic in that certain places are more or less relevant to wellbeing as determined by the length of time in any location and by the frequency of activity in the location. Second, GPS data can be used to assess whether the characteristics of places at specific times are useful to explaining variations in health and wellbeing.</p

Gender equality predicts leisure-time physical activity: benefits for both sexes across 34 countries

Although countries’ gender equality is associated with important health outcomes, especially for females, it remains unclear whether gender equality is associated with leisure-time physical activity (LTPA). Data from 34 countries was acquired from the International Social Survey Program, the Pew Research Forum, the United Nations, and the World Bank. Separate analyses were conducted for 21,502 males and 26,652 females. Hierarchal nonlinear Bernoulli modeling was used to examine the association between gender equality and participation in LTPA. Both males and females residing in countries’ with higher gender equality were more likely (twice and three times more likely, respectively) to report weekly LTPA than those residing in countries characterized by low gender equality. These effects persisted even when controlling for individual (i.e. age, education) and country-level (i.e. population, gross domestic product) covariates. However, significant variation in LTPA persisted at the country level, suggesting the need for further research. These findings provide novel evidence that both males and females benefit from gender equality. To explain these findings, we hypothesize that increased gender equality decreases the average number of offspring and, in turn, allows mothers more time for leisure, and to invest more resources in both male and female offspring, which may increase LTPA

Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method

Background: Personal exposure studies of air pollution generally use self-reported diaries to capture individuals’ time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants’ locations. Improved time activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies. Methods: Data was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant’s position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary based methods. Results: There was good agreement between the automated method and the diary method; however, the automated method (means: outdoors = 5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors = 6.7%, indoors other = 14.4%). Agreement statistics (AC1 = 0.778) suggest ‘good’ agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time “Indoors Other” using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time “In Transit” was relatively consistent between the methods, the mean daily exposure to PM2.5 while “In Transit” was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 using the daily diary. Conclusions: Mean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location. Combined with continuous air measurements, the higher resolution GPS data could present a different and more accurate picture of personal exposures to air pollution

HealthcareLCA: an open-access living database of health-care environmental impact assessments

Anthropogenic environmental change negatively effects human health and is increasing health-care system demand. Paradoxically, the provision of health care, which itself is a substantial contributor to environmental degradation, is compounding this problem. There is increasing willingness to transition towards sustainable health-care systems globally and ensuring that strategy and action are informed by best available evidence is imperative. In this Personal View, we present an interactive, open-access database designed to support this effort. Functioning as a living repository of environmental impact assessments within health care, the HealthcareLCA database collates 152 studies, predominantly peer-reviewed journal articles, into one centralised and publicly accessible location, providing impact estimates (currently totalling 3671 numerical values) across 1288 health-care products and processes. The database brings together research generated over the past two decades and indicates exponential field growth

Integrating health geography and behavioral economic principles to strengthen context-specific behavior change interventions

The long-term economic viability of modern health care systems is uncertain, in part due to costs of health care at the end of life and increasing health care utilization associated with an increasing population prevalence of multiple chronic diseases. Control of health care spending and sustaining delivery of health care services will require strategic investments in prevention to reduce the risk of disease and its complications over an individual's life course. Behavior change interventions aimed at reducing a range of harmful and risky health-related behaviors including smoking, physical inactivity, excess alcohol consumption, and excess weight, are one approach that has proven effective at reducing risk and preventing chronic disease. However, large-scale efforts to reduce population-level chronic diseases are challenging and have not been very successful at reducing the burden of chronic diseases. A new approach is required to identify when, where, and how to intervene to disrupt patterns of behavior associated with high-risk factors using context-specific interventions that can be scaled. This paper introduces the need to integrate theoretical and methodological principles of health geography and behavioral economics as opportunities to strengthen behavior change interventions for the prevention of chronic diseases. We discuss how health geography and behavioral economics can be applied to expand existing behavior change frameworks and how behavior change interventions can be strengthened by characterizing contexts of time and activity space

Predicting intraurban airborne PM1.0-trace elements in a port city : Land use regression by ordinary least squares and a machine learning algorithm

Airborne particulate matter (PM) has been associated with cardiovascular and respiratory morbidity and mortality, and there is some evidence that spatially varying metals found in PM may contribute to adverse health effects. We developed spatially refined models for PM trace elements using ordinary least squares land use regression (OLS-LUR) and machine leaning random forest land-use regression (RF-LUR). Two-week integrated measurements of PM1.0 (median aerodiameter < 1.0 μm) were collected at 50 sampling sites during fall (2010), winter (2011), and summer (2011) in the Halifax Regional Municipality, Nova Scotia, Canada. PM1.0 filters were analyzed for metals and trace elements using inductively coupled plasma-mass spectrometry. OLS- and RF-LUR models were developed for approximately 30 PM1.0 trace elements in each season. Model predictors included industrial, commercial, and institutional/ government/ military land use, roadways, shipping, other transportation sources, and wind rose information. RF generated more accurate models than OLS for most trace elements based on 5-fold cross validation. On average, summer models had the highest cross validation R2 (OLS-LUR = 0.40, RF-LUR = 0.46), while fall had the lowest (OLS-LUR = 0.27, RF-LUR = 0.31). Many OLS-LUR models displayed overprediction in the final exposure surface. In contrast, RF-LUR models did not exhibit overpredictions. Taking overpredictions and cross validation performances into account, OLS-LUR performed better than RF-LUR in roughly 20% of the seasonal trace element models. RF-LUR models provided more interpretable predictors in most cases. Seasonal predictors varied, likely due to differences in seasonal distribution of trace elements related to source activity, and meteorology

Gender equality predicts leisure-time physical activity: Benefits for both sexes across 34 countries

Abstract: Although countries&apos; gender equality is associated with important health outcomes, especially for females, it remains unclear whether gender equality is associated with leisure-time physical activity (LTPA). Data from 34 countries was acquired from the International Social Survey Program, the Pew Research Forum, the United Nations, and the World Bank. Separate analyses were conducted for 21,502 males and 26,652 females. Hierarchal nonlinear Bernoulli modeling was used to examine the association between gender equality and participation in LTPA. Both males and females residing in countries&apos; with higher gender equality were more likely (twice and three times more likely, respectively) to report weekly LTPA than those residing in countries characterized by low gender equality. These effects persisted even when controlling for individual (i.e. age, education) and country-level (i.e. population, gross domestic product) covariates. However, significant variation in LTPA persisted at the country level, suggesting the need for further research. These findings provide novel evidence that both males and females benefit from gender equality. To explain these findings, we hypothesize that increased gender equality decreases the average number of offspring and, in turn, allows mothers more time for leisure, and to invest more resources in both male and female offspring, which may increase LTPA