Comparison of global positioning system (GPS) tracking and parent-report diaries to characterize children's time-location patterns

A multi-sensor monitoring system of human physiology and daily activities

OBJECTIVE

To present the design and pilot test results of a continuous multi-sensor monitoring system of real-world physiological conditions and daily life (activities, travel, exercise, and food consumption), culminating in a Web-based graphical decision-support interface.

MATERIALS AND METHODS

The system includes a set of wearable sensors wirelessly connected to a "smartphone" with a continuously running software application that compresses and transmits the data to a central server. Sensors include a Global Positioning System (GPS) receiver, electrocardiogram (ECG), three-axis accelerometer, and continuous blood glucose monitor. A food/medicine diary and prompted recall activity diary were also used. The pilot test involved 40 type 2 diabetic patients monitored over a 72-h period.

RESULTS

All but three subjects were successfully monitored for the full study period. Smartphones proved to be an effective hub for managing multiple streams of data but required attention to data compression and battery consumption issues. ECG, accelerometer, and blood glucose devices performed adequately as long as subjects wore them. GPS tracking for a full day was feasible, although significant efforts are needed to impute missing data. Activity detection algorithms were successful in identifying activities and trip modes but could benefit by incorporating accelerometer data. The prompted recall diary was an effective tool for augmenting algorithm results, although subjects reported some difficulties with it. The food and medicine diary was completed fully, although end times and medicine dosages were occasionally missing.

CONCLUSIONS

The unique combination of sensors holds promise for increasing accuracy and reducing burden associated with collecting individual-level activity and physiological data under real-world conditions, but significant data processing issues remain. Such data will provide new opportunities to explore the impacts of human geography and daily lifestyle on health at a fine spatial/temporal scale.

Modeling personal particle-bound polycyclic aromatic hydrocarbon (pb-pah) exposure in human subjects in Southern California

BACKGROUND

Exposure to polycyclic aromatic hydrocarbon (PAH) has been linked to various adverse health outcomes. Personal PAH exposures are usually measured by personal monitoring or biomarkers, which are costly and impractical for a large population. Modeling is a cost-effective alternative to characterize personal PAH exposure although challenges exist because the PAH exposure can be highly variable between locations and individuals in non-occupational settings. In this study we developed models to estimate personal inhalation exposures to particle-bound PAH (PB-PAH) using data from global positioning system (GPS) time-activity tracking data, traffic activity, and questionnaire information.

METHODS

We conducted real-time (1-min interval) personal PB-PAH exposure sampling coupled with GPS tracking in 28 non-smoking women for one to three sessions and one to nine days each session from August 2009 to November 2010 in Los Angeles and Orange Counties, California. Each subject filled out a baseline questionnaire and environmental and behavior questionnaires on their typical activities in the previous three months. A validated model was used to classify major time-activity patterns (indoor, in-vehicle, and other) based on the raw GPS data. Multiple-linear regression and mixed effect models were developed to estimate averaged daily and subject-level PB-PAH exposures. The covariates we examined included day of week and time of day, GPS-based time-activity and GPS speed, traffic- and roadway-related parameters, meteorological variables (i.e. temperature, wind speed, relative humidity), and socio-demographic variables and occupational exposures from the questionnaire.

RESULTS

We measured personal PB-PAH exposures for 180 days with more than 6 h of valid data on each day. The adjusted R2 of the model was 0.58 for personal daily exposures, 0.61 for subject-level personal exposures, and 0.75 for subject-level micro-environmental exposures. The amount of time in vehicle (averaging 4.5% of total sampling time) explained 48% of the variance in daily personal PB-PAH exposure and 39% of the variance in subject-level exposure. The other major predictors of PB-PAH exposures included length-weighted traffic count, work-related exposures, and percent of weekday time.

CONCLUSION

We successfully developed regression models to estimate PB-PAH exposures based on GPS-tracking data, traffic data, and simple questionnaire information. Time in vehicle was the most important determinant of personal PB-PAH exposure in this population. We demonstrated the importance of coupling real-time exposure measures with GPS time-activity tracking in personal air pollution exposure assessment.

A new analytical method for the classification of time-location data obtained from the global positioning system (GPS)

Although the global positioning system (GPS) has been suggested as an alternative way to determine time-location patterns, its use has been limited. The purpose of this study was to evaluate a new analytical method of classifying time-location data obtained by GPS. A field technician carried a GPS device while simulating various scripted activities and recorded all movements by the second in an activity diary. The GPS device recorded geological data once every 15 s. The daily monitoring was repeated 18 times. The time-location data obtained by the GPS were compared with the activity diary to determine selection criteria for the classification of the GPS data. The GPS data were classified into four microenvironments (residential indoors, other indoors, transit, and walking outdoors); the selection criteria used were used number of satellites (used-NSAT), speed, and distance from residence. The GPS data were classified as indoors when the used-NSAT was below 9. Data classified as indoors were further classified as residential indoors when the distance from the residence was less than 40 m; otherwise, they were classified as other indoors. Data classified as outdoors were further classified as being in transit when the speed exceeded 2.5 m s(-1); otherwise, they were classified as walking outdoors. The average simple percentage agreement between the time-location classifications and the activity diary was 84.3 ± 12.4%, and the kappa coefficient was 0.71. The average differences between the time diary and the GPS results were 1.6 ± 2.3 h for the time spent in residential indoors, 0.9 ± 1.7 h for the time spent in other indoors, 0.4 ± 0.4 h for the time spent in transit, and 0.8 ± 0.5 h for the time spent walking outdoors. This method can be used to determine time-activity patterns in exposure-science studies.

The German Environmental Survey for Children (GerES IV): reference values and distributions for time-location patterns of German children

Children's time-location patterns are important determinants of environmental exposure and other health-relevant factors. Building on data of the German Environmental Survey for Children (GerES IV), our study aimed at deriving reference values and distributions for time-location patterns of 3-14-year-old German children. We also investigated if GerES IV data are appropriate for evaluating associations with children's health determinants by linking them to data of the National Health Interview and Examination Survey for Children and Adolescents (KiGGS). Parents reported on the time their children usually spend at home, in other indoor environments, and outdoors. This information was characterized by statistical parameters, which were also calculated for different strata concerning socio-demography and the residential environment. Consequently, group differences were evaluated by t-tests and univariate ANOVA. Reference distributions were fitted to the time-location data by a Maximum Likelihood approach to make them also useable in probabilistic exposure modeling. Finally, associations between data on the children's physical activity as well as body weight and their outdoor time were investigated by bivariate correlation analysis and cross tabulation. On daily average, German children spend 15 h and 31 min at home, 4 h and 46 min in other indoor environments, and 3 h and 43 min outdoors. Time spent at home and outdoors decreases with age while time spent in other indoor environments increases. Differences in time-location patterns were also observed for the socio-economic status (SES) and immigration status. E.g., children with a high SES spend 24 min less outdoors than low SES children. Immigrants spend on daily average 20 min more at home and 15 min less outdoors than non-immigrant children. Outdoor time was associated with parameters of the residential environment like the building development. Children living in 1- or 2-family houses spend more time outdoors than children living in building blocks (3 h 48 min vs. 3 h 29 min). Physical activity correlates with outdoor time. For children with diminished age-specific outdoor time, a higher prevalence of obesity was observed (odds ratio: 3.2, 95% CI: 1.5-7.1). GerES IV provides a compilation of current time-location reference values and distributions on German children. This data hint to substantial differences in time-location patterns within the population to be considered in environmental health risk assessment.

Exploring blood glucose variation over geographical space

BACKGROUND

Type 2 diabetes mellitus is known to be associated with environmental, behavioral, and lifestyle factors such as a sedentary lifestyle, overly rich nutrition, and obesity. However, the day-to-day human-environment interactions and real-life activities that cause an individual's blood glucose to fluctuate remain relatively unexplored, owing in part to data collection challenges. This article presents a novel data collection system that overcomes these challenges and allows exploration of the spatial correlates of blood glucose fluctuation.

METHODS

An automated monitoring system was developed combining a Global Positioning System (GPS) receiver with a continuous blood glucose monitor. The GPS was used to elicit a second-by-second accounting of an individual's daily activities alongside blood glucose measurement every 5 min. A pilot study of 40 diabetes patients was conducted over a 72-h period. Geographic Information System software was used to generate blood glucose maps, incorporating methods to deal with scale issues, overlapping data, and to protect subject identity.

RESULTS

Individual blood glucose variation maps revealed a variety of distinct patterns. Most subjects had at least two major anchor points in their life combined with a variety of other activity locations at varying distances from home, many associated with quite distinct low or high blood glucose values. Further statistical analysis revealed location and distance from home were significantly correlated with blood glucose variation-although the strength and direction of the effect was quite mixed.

CONCLUSIONS

Results suggests that blood glucose and space/location are highly correlated and should be considered further as a lifestyle-related risk factor for diabetes patients. In the future, patients and caregivers may benefit from individualized visualization tools that help identify problematic locations that require special attention.

Automated time activity classification based on global positioning system (GPS) tracking data

BACKGROUND

Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.

METHODS

We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.

RESULTS

Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.

CONCLUSIONS

Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.

Use of global positioning systems to study physical activity and the environment: a systematic review

CONTEXT

The GPS represents an innovative way to objectively assess the spatial locations of physical activity behavior. The aim of this systematic review was to determine the capability of GPS to collect high-quality data on the location of activities in research on the relationship between physical activity and the environment.

EVIDENCE ACQUISITION

Published and unpublished articles identified from seven electronic databases, reference lists, bibliographies, and websites up to March 2010 were systematically searched for, appraised, and analyzed in summer 2010. Included studies used GPS to measure the spatial locations of physical activity and some form of environmental analysis related to the GPS data. The capability of GPS was expressed in terms of data quality, which in turn was defined as the proportion of GPS data lost in each study.

EVIDENCE SYNTHESIS

24 studies met the inclusion criteria. Data loss was positively correlated with the measurement period for which participants were asked to wear the GPS device (r=0.81, p<0.001). Major reasons for data loss included signal drop-outs, loss of device battery power, and poor adherence of participants to measurement protocols. Data loss did not differ significantly between children and adults or by study sample size, year of publication, or GPS device manufacturer.

CONCLUSIONS

GPS is a promising tool for improving understanding of the spatial context of physical activity. The current findings suggest that the choice of an appropriate device and efforts to maximize participant adherence are key to improving data quality, especially over longer study periods.

Longitudinal variability of time-location/activity patterns of population at different ages: a longitudinal study in California

BACKGROUND

Longitudinal time-activity data are important for exposure modeling, since the extent to which short-term time-activity data represent long-term activity patterns is not well understood. This study was designed to evaluate longitudinal variations in human time-activity patterns.

METHOD

We report on 24-hour recall diaries and questionnaires collected via the internet from 151 parents of young children (mostly under age 55), and from 55 older adults of ages 55 and older, for both a weekday and a weekend day every three months over an 18-month period. Parents also provided data for their children. The self-administrated diary and questionnaire distinguished ~30 frequently visited microenvironments and ~20 activities which we selected to represent opportunities for exposure to toxic environmental compounds. Due to the non-normal distribution of time-location/activity data, we employed generalized linear mixed-distribution mixed-effect models to examine intra- and inter-individual variations. Here we describe variation in the likelihood of and time spent engaging in an activity or being in a microenvironment by age group, day-type (weekday/weekend), season (warm/cool), sex, employment status, and over the follow-up period.

RESULTS

As expected, day-type and season influence time spent in many location and activity categories. Longitudinal changes were also observed, e.g., young children slept less with increasing follow-up, transit time increased, and time spent on working and shopping decreased during the study, possibly related to human physiological changes with age and changes in macro-economic factors such as gas prices and the economic recession.

CONCLUSIONS

This study provides valuable new information about time-activity assessed longitudinally in three major age groups and greatly expands our knowledge about intra- and inter-individual variations in time-location/activity patterns. Longitudinal variations beyond weekly and seasonal patterns should be taken into account in simulating long-term time-activity patterns in exposure modeling.

Feasibility of using global positioning systems (GPS) with diverse urban adults: before and after data on perceived acceptability, barriers, and ease of use

BACKGROUND

Global positioning systems (GPS) have emerged as a research tool to better understand environmental influences on physical activity. This study examined the feasibility of using GPS in terms of perceived acceptability, barriers, and ease of use in a racially/ethnically diverse sample of lower socioeconomic position (SEP).

METHODS

Data were from 2 pilot studies involving a total of 170 African American, Hispanic, and White urban adults with a mean (standard deviation) age of 47.8 (±13.1) years. Participants wore a GPS for up to 7 days. They answered questions about GPS acceptability, barriers (wear-related concerns), and ease of use before and after wearing the GPS.

RESULTS

We found high ratings of GPS acceptability and ease of use and low levels of wear-related concerns, which were maintained after data collection. While most were comfortable with their movements being tracked, older participants (P < .05) and African Americans (P < .05) reported lower comfort levels. Participants who were younger, with higher education, and low incomes were more likely to indicate that the GPS made the study more interesting (P < .05). Participants described technical and wear-related problems, but few concerns related to safety, loss, or appearance.

CONCLUSIONS

Use of GPS was feasible in this racially/ethnically diverse, lower SEP sample.

Identifying Walking Trips Using GPS Data

PURPOSE

this study developed and tested algorithms to identify outdoor walking trips from portable global positioning system (GPS) units in free-living conditions.

METHODS

the study included a calibration and a validation phase. For the calibration phase, we determined the best algorithm from 35 person-days of data. Measures of agreement regarding the daily number and duration of diary-reported and GPS-identified trips were used. In the validation phase, the best algorithm was applied to an additional and separate 136 person-days of diary and GPS data.

RESULTS

the preferred algorithm in the calibration phase resulted in 90% of trips identified from the GPS data being found in the diary, whereas 81% of trips reported in the diary being found in the GPS data. The preferred algorithm used 1) a maximum 3-min gap between points to define a trip, 2) at least 5 min or more of continuous GPS points, 3) a speed range between 2 and 8.0 km·h, 4) at least 30 m of displacement between the start and end points of a trip, and 5) merged walking trips when the time gap between trips was less than 3 min. With the validation data, substantial agreement between the GPS and the diary was achieved, with 86% of trips identified from the GPS data found in the diary and 77% of trips reported in the diary found in the GPS data.

CONCLUSIONS

the algorithm identified free-living walking trips of more than 5 min in duration. The ability to identify outdoor walking trips from GPS data can be improved by reducing recording intervals used in the GPS units and monitoring participant compliance. Further research is desirable to determine whether concurrent wearing of an accelerometer may improve the ability to detect walking more accurately.

Identifying walking trips from GPS and accelerometer data in adolescent females

BACKGROUND

Studies that have combined accelerometers and global positioning systems (GPS) to identify walking have done so in carefully controlled conditions. This study tested algorithms for identifying walking trips from accelerometer and GPS data in free-living conditions. The study also assessed the accuracy of the locations where walking occurred compared with what participants reported in a diary.

METHODS

A convenience sample of high school females was recruited (N = 42) in 2007. Participants wore a GPS unit and an accelerometer, and recorded their out-of-school travel for 6 days. Split-sample validation was used to examine agreement in the daily and total number of walking trips with Kappa statistics and count regression models, while agreement in locations visited by walking was examined with geographic information systems.

RESULTS

Agreement varied based on the parameters of the algorithm, with algorithms exhibiting moderate to substantial agreement with self-reported daily (Kappa = 0.33-0.48) and weekly (Kappa = 0.41-0.64) walking trips. Comparison of reported locations reached by walking and GPS data suggest that reported locations are accurate.

CONCLUSIONS

The use of GPS and accelerometers is promising for assessing the number of walking trips and the walking locations of adolescent females.

Performances of different global positioning system devices for time-location tracking in air pollution epidemiological studies

BACKGROUND

People's time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people's time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies.

METHODS

We evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps.

RESULTS

The battery life of our tested instruments ranged from <9 hours to 48 hours. The acquisition of location time after startup ranged from a few seconds to >20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%-95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%-80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas.

CONCLUSIONS

All the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects' time location patterns in epidemiological studies.

Exposure science: a view of the past and milestones for the future

BACKGROUND

The study of human exposure to environmental toxicants has evolved as a scientific field over the past 30 years.

OBJECTIVES

This review provides a historical perspective on the growth of exposure science as a field, with some emphasis on the results from initial observational studies in obtaining information needed for generating hypotheses on significant human contact with environmental agents, testing the performance of models, and reducing exposures to protect public health.

DISCUSSION

Advances in activity pattern and behavioral research that established a suite of variables needed to accurately define contact and factors that influence contact are also discussed. The identification and characterization of these factors have played a pivotal role in the growth of the field and in developing exposure reduction strategies. Answers to two key questions on the relevance and fundamental value of exposure science to the fields of environmental health and risk management are presented as a path forward: a) What does one do with such exposure information? b) What roles does exposure science play in situations beyond observational analyses and interpretation?

CONCLUSIONS

The discussion identifies the need for more focused use of observational studies of exposure for epidemiologic analyses. Further, the introduction and use of new tools and approaches for hypothesis testing that can improve the use of exposure science in prevention research for risk management is needed to affect the source-to-effect continuum. A major restructuring of the field is not required to achieve innovation. However, additional resources for training and education are required to ensure that the potential for exposure science to play a central role in reducing and preventing excess risk within environmental/occupational health is achieved.

Assessing and maximizing the acceptability of global positioning system device use for studying the role of human movement in dengue virus transmission in Iquitos, Peru

As use of global positioning system (GPS) technology to study disease transmission increases, it is important to assess possible barriers to its use from the perspective of potential study participants. Fifteen focus group discussions stratified by sex, age, and motherhood status were conducted in 2008 in Iquitos, Peru. All participants said they would accept using a GPS unit for study purposes for 2-4 weeks. Participants' main concerns included caring properly for the unit, whether the unit would audio/videotape them, health effects of prolonged use, responsibility for units, and confidentiality of information. A pilot study was then conducted in which 126 persons were asked to carry GPS units for 2-4 weeks; 98% provided consent. All persons used the units expressing minimal concerns, although 44% reported forgetting the device at least once. Our study is the first to highlight participant concerns related to use of GPS for long-term monitoring of individual behavior in a resource-limited setting.

Spatial modeling in environmental and public health research

THIS PAPER HAS TWO AIMS: (1) to summarize various geographic information science methods; and (2) to provide a review of studies that have employed such methods. Though not meant to be a comprehensive review, this paper explains when certain methods are useful in epidemiological studies and also serves as an overview of the growing field of spatial epidemiology.

Analyses of school commuting data for exposure modeling purposes

Human exposure models often make the simplifying assumption that school children attend school in the same census tract where they live. This paper analyzes that assumption and provides information on the temporal and spatial distributions associated with school commuting. The data were obtained using Oak Ridge National Laboratory's LandScan USA population distribution model applied to Philadelphia, PA. It is a high-resolution model used to allocate individual school-aged children to both a home and school location, and to devise a minimum-time home-to-school commuting path (called a trace) between the two locations. LandScan relies heavily on Geographic Information System (GIS) data. With respect to school children attending school in their home census tract, the vast majority does not in Philadelphia. Our analyses found that: (1) about 32% of the students walk across two or more census tracts going to school and 40% of them walk across four or more census blocks; and (2) 60% drive across four or more census tracts going to school and 50% drive across 10 or more census blocks. We also find that: (3) using a 5-min commuting time interval - as opposed to the modeled "trace" - results in misclassifying the "actual" path taken in 90% of the census blocks, 70% of the block groups, and 50% of the tracts; (4) a 1-min time interval is needed to reasonably resolve time spent in the various census unit designations; and (5) approximately 50% of both the homes and schools of Philadelphia school children are located within 160 m of highly traveled roads, and 64% of the schools are located within 200 m. These findings are very important when modeling school children's exposures, especially, when ascertaining the impacts of near-roadway concentrations on their total daily body burden. As many school children also travel along these streets and roadways to get to school, a majority of children in Philadelphia are in mobile source-dominated locations most of the day. We hypothesize that exposures of school children in Philadelphia to benzene and particulate matter will be much higher than if home and school locations and commuting paths at a 1-min time resolution are not explicitly modeled in an exposure assessment. Undertaking such an assessment will be the topic of a future paper.

Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus

Background

Our understanding of the effects of human movement on dengue virus spread remains limited in part due to the lack of precise tools to monitor the time-dependent location of individuals. We determined the utility of a new, commercially available, GPS data-logger for long-term tracking of human movements in Iquitos, Peru. We conducted a series of evaluations focused on GPS device attributes key to reliable use and accuracy. GPS observations from two participants were later compared with semi-structured interview data to assess the usefulness of GPS technology to track individual mobility patterns.

Results

Positional point and line accuracy were 4.4 and 10.3 m, respectively. GPS wearing mode increased spatial point error by 6.9 m. Units were worn on a neck-strap by a carpenter and a moto-taxi driver for 14-16 days. The application of a clustering algorithm (I-cluster) to the raw GPS positional data allowed the identification of locations visited by each participant together with the frequency and duration of each visit. The carpenter moved less and spent more time in more fixed locations than the moto-taxi driver, who visited more locations for a shorter period of time. GPS and participants' interviews concordantly identified 6 common locations, whereas GPS alone identified 4 locations and participants alone identified 10 locations. Most (80%) of the locations identified by participants alone were places reported as visited for less than 30 minutes.

Conclusion

The present study demonstrates the feasibility of a novel, commercially available GPS data-logger for long-term tracking of humans and shows the potential of these units to quantify mobility patterns in relationship with dengue virus transmission risk in a tropical urban environment. Cost, battery life, size, programmability and ease of wear are unprecedented from previously tested units, proving the usefulness of GPS-dataloggers for linking movement of individuals and transmission risk of dengue virus and other infectious agents, particularly in resource-poor settings.

Global positioning system: a new opportunity in physical activity measurement

Accurate measurement of physical activity is a pre-requisite to monitor population physical activity levels and design effective interventions. Global Positioning System (GPS) technology offers potential to improve the measurement of physical activity. This paper 1) reviews the extant literature on the application of GPS to monitor human movement, with a particular emphasis on free-living physical activity, 2) discusses issues associated with GPS use, and 3) provides recommendations for future research. Overall findings show that GPS is a useful tool to augment our understanding of physical activity by providing the context (location) of the activity and used together with Geographical Information Systems can provide some insight into how people interact with the environment. However, no studies have shown that GPS alone is a reliable and valid measure of physical activity.

Monitoring Mobility in Older Adults Using Global Positioning System (GPS) Watches and Accelerometers: A Feasibility Study

This exploratory study examined the feasibility of using Garmin global positioning system (GPS) watches and ActiGraph accelerometers to monitor walking and other aspects of community mobility in older adults. After accuracy at slow walking speeds was initially determined, 20 older adults (74.4 ± 4.2 yr) wore the devices for 1 day. Steps, distances, and speeds (on foot and in vehicle) were determined. GPS data acquisition varied from 43 min to over 12 hr, with 55% of participants having more than 8 hr between initial and final data-collection points. When GPS data were acquired without interruptions, detailed mobility information was obtained regarding the timing, distances covered, and speeds reached during trips away from home. Although GPS and accelerometry technology offer promise for monitoring community mobility patterns, new GPS solutions are required that allow for data collection over an extended period of time between indoor and outdoor environments.

Development of a method for personal, spatiotemporal exposure assessment

This work describes the development and evaluation of a high resolution, space and time-referenced sampling method for personal exposure assessment to airborne particulate matter (PM). This method integrates continuous measures of personal PM levels with the corresponding location-activity (i.e. work/school, home, transit) of the subject. Monitoring equipment include a small, portable global positioning system (GPS) receiver, a miniature aerosol nephelometer, and an ambient temperature monitor to estimate the location, time, and magnitude of personal exposure to particulate matter air pollution. Precision and accuracy of each component, as well as the integrated method performance were tested in a combination of laboratory and field tests. Spatial data was apportioned into pre-determined location-activity categories (i.e. work/school, home, transit) with a simple, temporospatially-based algorithm. The apportioning algorithm was extremely effective with an overall accuracy of 99.6%. This method allows examination of an individual's estimated exposure through space and time, which may provide new insights into exposure-activity relationships not possible with traditional exposure assessment techniques (i.e., time-integrated, filter-based measurements). Furthermore, the method is applicable to any contaminant or stressor that can be measured on an individual with a direct-reading sensor.

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

Background

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.

Results

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.

Discussion

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.

Human biomonitoring and the INSPIRE directive: spatial data as link for environment and health research

Recently, there has been a rapid gain of interest in the availability, applicability, and integration of different types of spatial data for environment and health issues. The INSPIRE Directive (Directive 2007/2/EC) aims at providing better and easily accessible spatial information in Europe for the formulation and implementation of community policy on the environment by triggering the creation of a European spatial information infrastructure that delivers integrated spatial information services to potential users. Human biomonitoring (HBM) significantly contributes to the already existing data on environment and health because of its specific nature of providing information on the internal dose of chemicals rather than their mere presence in different environmental compartments. However, due to the intrinsic nature of HBM data, a number of issues need to be dealt with if HBM data are to be used to its full capacity in a geographic information systems (GIS) environment and within the INSPIRE directive. The current article highlights some of these issues, and discusses a number of options to improve the geographical relevance of HBM data for their optimal use within the INSPIRE Directive framework. The main aim of this publication is to illustrate that HBM has a significant contribution to make to the INSPIRE Directive, although some kind of data aggregation will be necessary to protect individual privacy. If HBM data wants to have a significant contribution to spatial information used to assist policymaking and on the surveillance or tracking of the direct or indirect impact of such policies, the HBM data need to be compatible with other data collected within the other themes of the INSPIRE Directive.