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

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

Human movement patterns of farmers and forest workers from the Thailand-Myanmar border

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality among study participants. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology

Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (p_value = 0.098) in the control group and -62.4 min (p_value < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction p_value < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (p_value = 0.350) in the control group and -1039.5 (p_value = 0.003) in the intervention group (interaction p_value = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations.

Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border

BACKGROUND

Human mobility is a driver for the reemergence or resurgence of malaria and has been identified as a source of cross-border transmission. However, movement patterns are difficult to measure in rural areas where malaria risk is high. In countries with malaria elimination goals, it is essential to determine the role of mobility on malaria transmission to implement appropriate interventions.

METHODS

A study was conducted in Mutasa District, Zimbabwe, to investigate human movement patterns in an area of persistent transmission along the Mozambique border. Over 1 year, a convenience sample of 20 participants/month was recruited from active malaria surveillance cohorts to carry an IgotU^® GT-600 global positioning system (GPS) data logger during all daily activities. Consenting participants were tested for malaria at data logger distribution using rapid antigen diagnostic tests and completed a survey questionnaire. GPS data were analyzed using a trajectory analysis tool, and participant movement patterns were characterized throughout the study area and across the border into Mozambique using movement intensity maps, activity space plots, and statistical analyses.

RESULTS

From June 2016-May 2017, 184 participants provided movement tracks encompassing > 350,000 data points and nearly 8000 person-days. Malaria prevalence at logger distribution was 3.7%. Participants traveled a median of 2.8 km/day and spent a median of 4.6 h/day away from home. Movement was widespread within and outside the study area, with participants traveling up to 500 km from their homes. Indices of mobility were higher in the dry season than the rainy season (median km traveled/day = 3.5 vs. 2.2, P = 0.03), among male compared to female participants (median km traveled/day = 3.8 vs. 2.0, P = 0.0008), and among adults compared to adolescents (median total km traveled = 104.6 vs. 59.5, P = 0.05). Half of participants traveled outside the study area, and 30% traveled into Mozambique, including 15 who stayed in Mozambique overnight.

CONCLUSIONS

Study participants in Mutasa District, Zimbabwe, were highly mobile throughout the year. Many participants traveled long distances from home, including overnight trips into Mozambique, with clear implications for malaria control. Interventions targeted at mobile populations and cross-border transmission may be effective in preventing malaria introductions in this region.

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution

BACKGROUND

Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity.

METHODS

We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants.

RESULTS

Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs.

CONCLUSIONS

Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.

Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification

In this study fine particulate matter (PM_2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R² values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM_2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.

Redefining Exposure: Using Mobile Technology and Geospatial Analysis to Explore When and Where Chicago Adolescents are Exposed to Neighborhood Characteristics

Differences in how individuals navigate and interact with physical space have clear implications for when and where they are exposed to environmental characteristics. To address this reality, we propose and test a novel method with a sample of Chicago adolescents that links individual GPS coordinates with locations of environmental characteristics as a strategy to increase precision in the measurement of environmental exposures. We use exposure to violent crime as an example and link the GPS coordinates of 51 youth collected over a one-week period during the summer of 2016 to locations and times of violent crime. We explore different spatial and temporal parameters to determine whether an exposure occurred. Using the 660-foot (201 m), 24-hour operationalization, we found that youth were exposed to a total of 126 violent crimes, with an average of 3.82 (SD = 3.24) per respondent. This was higher than the 12 that were identified when exposure was calculated as the number of violent crimes occurring within 660 feet (201 m) of youths' residential addresses during the week-long assessment period. Examining correlations between the different exposure variables and measures of youths' psychological functioning, we found the largest relationships when using the GPS-based indices. We present a strategy for measuring exposure to environmental characteristics using GPS data. Higher rates of crime exposure are found based on GPS coordinates than with residential address. GPS-based exposure measures are related to youths' psychological functioning.

Human movement patterns of farmers and forest workers from the Thailand-Myanmar border

Background: Human travel patterns play an important role in infectious disease epidemiology and ecology. Movement into geographic spaces with high transmission can lead to increased risk of acquiring infections. Pathogens can also be distributed across the landscape via human travel. Most fine scale studies of human travel patterns have been done in urban settings in wealthy nations. Research into human travel patterns in rural areas of low- and middle-income nations are useful for understanding the human components of epidemiological systems for malaria or other diseases of the rural poor. The goal of this research was to assess the feasibility of using GPS loggers to empirically measure human travel patterns in this setting, as well as to quantify differing travel patterns by age, gender, and seasonality. Methods: In this pilot study we recruited 50 rural villagers from along the Myanmar-Thailand border to carry GPS loggers for the duration of a year. The GPS loggers were programmed to take a time-stamped reading every 30 minutes. We calculated daily movement ranges and multi-day trips by age and gender. We incorporated remote sensing data to assess patterns of days and nights spent in forested or farm areas, also by age and gender. Results: Our study showed that it is feasible to use GPS devices to measure travel patterns, though we had difficulty recruiting women and management of the project was relatively intensive. We found that older adults traveled farther distances than younger adults and adult males spent more nights in farms or forests. Conclusion: The results of this study suggest that further work along these lines would be feasible in this region. Furthermore, the results from this study are useful for individual-based models of disease transmission and land use.

Characterization of geographic mobility among participants in facility- and community-based tuberculosis case finding in urban Uganda

Background

International and internal migration are recognized risk factors for tuberculosis (TB). Geographic mobility, including travel for work, education, or personal reasons, may also play a role in TB transmission, but this relationship is poorly defined. We aimed to define geographic mobility among participants in facility- and community-based TB case finding in Kampala, Uganda, and to assess associations between mobility, access to care, and TB disease.

Methods

We included consecutive individuals age ≥15 years diagnosed with TB disease through either routine health facility practices or community-based case finding (consisting of door-to-door testing, venue-based screening, and contact investigation). Each case was matched with one (for community-based enrollment) or two (health facility enrollment) TB-negative controls. We conducted a latent class analysis (LCA) of eight self-reported characteristics to identify and define mobility; we selected the best-fit model using Bayesian Information Criterion. We assessed associations between mobility and TB case status using multivariable conditional logistic regression.

Results

We enrolled 267 cases and 432 controls. Cases were more likely than controls to have been born in Kampala (p<0.001); there was no difference between cases and controls for remaining mobility characteristics. We selected a two-class LCA model; the “mobile” class was perfectly correlated with a single variable: travel (>3 km) from residence ≥2 times per month. Mobility was associated with a 28% reduction in odds of being a TB case (adjusted matched odds ratio 0.72 [95% confidence interval 0.49, 1.06]).

Conclusion

Frequency of out-of-neighborhood travel is an easily measured variable that correlates closely with predicted mobility class membership. Mobility was associated with decreased risk of TB disease; this may be in part due to the higher socioeconomic status of mobile individuals in this population. However, more research is needed to improve assessment of mobility and understand how mobility affects disease risk and transmission.

The MEDEA childhood asthma study design for mitigation of desert dust health effects: implementation of novel methods for assessment of air pollution exposure and lessons learned

BACKGROUND

Desert dust events in Mediterranean countries, originating mostly from the Sahara and Arabian deserts, have been linked to climate change and are associated with significant increase in mortality and hospital admissions from respiratory causes. The MEDEA clinical intervention study in children with asthma is funded by EU LIFE+ program to evaluate the efficacy of recommendations aiming to reduce exposure to desert dust and related health effects.

METHODS

This paper describes the design, methods, and challenges of the MEDEA childhood asthma study, which is performed in two highly exposed regions of the Eastern Mediterranean: Cyprus and Greece-Crete. Eligible children are recruited using screening surveys performed at primary schools and are randomized to three parallel intervention groups: a) no intervention for desert dust events, b) interventions for outdoor exposure reduction, and c) interventions for both outdoor and indoor exposure reduction. At baseline visits, participants are enrolled on MEDena® Health-Hub, which communicates, alerts and provides exposure reduction recommendations in anticipation of desert dust events. MEDEA employs novel environmental epidemiology and telemedicine methods including wearable GPS, actigraphy, health parameters sensors as well as indoor and outdoor air pollution samplers to assess study participants' compliance to recommendations, air pollutant exposures in homes and schools, and disease related clinical outcomes.

DISCUSSION

The MEDEA study evaluates, for the first time, interventions aiming to reduce desert dust exposure and implement novel telemedicine methods in assessing clinical outcomes and personal compliance to recommendations. In Cyprus and Crete, during the first study period (February-May 2019), a total of 91 children participated in the trial while for the second study period (February-May 2020), another 120 children completed data collection. Recruitment for the third study period (February-May 2021) is underway. In this paper, we also present the unique challenges faced during the implementation of novel methodologies to reduce air pollution exposure in children. Engagement of families of asthmatic children, schools and local communities, is critical. Successful study completion will provide the knowledge for informed decision-making both at national and international level for mitigating the health effects of desert dust events in South-Eastern Europe.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03503812 , April 20, 2018.

Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors

Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.

A statistical framework for measuring the temporal stability of human mobility patterns

Despite the growing popularity of human mobility studies that collect GPS location data, the problem of determining the minimum required length of GPS monitoring has not been addressed in the current statistical literature. In this paper, we tackle this problem by laying out a theoretical framework for assessing the temporal stability of human mobility based on GPS location data. We define several measures of the temporal dynamics of human spatiotemporal trajectories based on the average velocity process, and on activity distributions in a spatial observation window. We demonstrate the use of our methods with data that comprise the GPS locations of 185 individuals over the course of 18 months. Our empirical results suggest that GPS monitoring should be performed over periods of time that are significantly longer than what has been previously suggested. Furthermore, we argue that GPS study designs should take into account demographic groups.

Measuring Social Communication in the Community: Novel Tools for Advancing Family Participation

Natural contexts and family involvement are key features of effective intervention approaches. However, the measurement of community participation and parent engagement with children remains complex. Therefore, we examined the feasibility of combining use of the Language ENvironment Analysis (LENA^®) system and a global positioning system (GPS; i.e., Qstarz^® BT-Q1000XT). The LENA is a small speech recognition device that captures and quantifies full-day recordings of the natural language environment. The Qstarz BT-Q1000XT is a wearable GPS data logger that allows identification of the locations a person visits. The marrying of these measures allows for an accurate representation of community settings that afford children greater social communication opportunities. Our results show that the combination of measures provides meaningful social communication location data. Also, the participating caregiver reported that the collection of measures was feasible across community settings.

Concordance between GPS-based smartphone app for continuous location tracking and mother's recall of care-seeking for child illness in India

Background

Traditionally, health care-seeking behaviour for child illness is assessed through population-based national demographic and health surveys. GPS-based technologies are increasingly used in human behavioural research including tracking human mobility and spatial behaviour. This paper assesses how well a care-seeking event to a health care facility for child illness, as recalled by the mother in a survey setting using questions sourced from Demographic and Health Surveys, concurs with one that is identified by TrackCare, a GPS-based location-aware smartphone application.

Methods

Mothers residing in the Vadu HDSS area in Pune district, India having at least one young child were randomly assigned to receive a GPS-enabled smartphone with a pre-installed TrackCare app configured to record the device location data at one-minute intervals over a 6-month period. Spatio-temporal parameters were derived from the location data and used to detect a care-seeking event to any of the health care facilities in the area. Mothers were asked to recall a child illness and if, where and when care was sought, using a questionnaire during monthly visits over a 6-month period. Concordance between the mother's recall and the TrackCare app to identify a care-seeking event was estimated according to percent positive agreement.

Results

Mean concordance for a care-seeking event between the two methods (mother's recall and TrackCare location data) ranged up to 45%, was significantly higher (P-value <0.001) for care-seeking at a hospital as compared to a clinic and for a health care facility in the private sector compared to that in the public sector. Overall, the proportion of disagreement for a care-seeking event not detected by TrackCare but reported by mother ranged up to 77% and was significantly higher (P-value <0.001) compared to those not reported by mother but detected by TrackCare.

Conclusions

Given the uncertainty and limitations in use of continuous location tracking data in a field setting and the complexity of classifying human activity patterns, additional research is needed before continuous location tracking can serve as a gold standard substitute for other methods to determine health care-seeking behaviour. Future performance may be improved by incorporating other smartphone-based sensors, such as Wi-Fi and Bluetooth, to obtain more precise location estimates in areas where GPS signal is weakest.

The measurement of time spent outdoors in child myopia research: a systematic review

The purpose of this article is to summarize the methods most commonly used to measure time spent outdoors and provide a comprehensive review of time and activity recording methods with the aim of encouraging the development of new methods. PubMed, Embase and the Cochrane Library were searched from Jan. 1^st, 1990 to Aug. 31^th, 2017. Studies including the following specific terms: "outdoor", "outside", "outdoor activity", "outside activity", "outdoor time", "outside time", and "outdoor AND measurement of time spent outdoors" were considered for this review. In total, three kinds of outdoor time measurements were discussed. Questionnaires have the longest history and are the most thoroughly revised instruments for assessing time spent outdoors, but recall bias is their most substantial drawback. Global positioning system (GPS) tracking can distinguish between indoor and outdoor locations, but its utility is limited due to several factors such as subject compatibility. Light exposure measurement devices are newly emerging, but all of these devices require good subject cooperation. Further efforts and exploration are needed to develop better methods and new tools to record exposure to the outdoors in real time. Moreover, inventing a new device by combining two or more types of devices mentioned above and using the latest technology of en ergy supplementation and autoswitching may make the best use of the advantages and bypass the disadvantages of each tool.

Physical activity and activity space in patients with pulmonary fibrosis not prescribed supplemental oxygen

Background

Patients with pulmonary fibrosis (PF) have impaired quality of life, and research suggests that dyspnea and physical activity are primary drivers. As PF progresses, some patients notice the disease “shrinks their worlds”. The objective of this study is to describe movement (both physical activity and activity space) in a cohort of patients with PF of various etiologies who have not been prescribed supplemental oxygen (O₂).

Methods

Subjects with PF not on supplemental O₂ during the day were enrolled from across the U.S. from August 2013 to October 2015. At enrollment, each subject completed questionnaires and, for seven consecutive days, wore an accelerometer and GPS tracker.

Results

One hundred ninety-four subjects had a confirmed diagnosis of PF and complete, analyzable GPS data. The cohort was predominantly male (56%), Caucasian (95%) and had idiopathic pulmonary fibrosis (30%) or connective tissue disease related-PF (31%). Subjects walked a median 7497 (interquartile range [IQR] 5766-9261) steps per day. Steps per day were correlated with symptoms and several quality of life domains. In a model controlling for age, body mass index, wrist- (vs. waist) worn accelerometer and percent predicted diffusing capacity (DLCO%), fatigue (beta coefficient = −51.5 ± 11.7, p < 0.0001) was an independent predictor of steps per day (model R²=0.34).

Conclusions

Patients with PF, who have not been prescribed O₂ for use during the day, have wide variability in their mobility. Day-to-day physical activity is related to several domains that impact quality of life, but GPS-derived activity space is not. Wearable data collection devices may be used to determine whether and how therapeutic interventions impact movement in PF patients.

Trial registration

NCT01961362. Registered 9 October, 2013.

Electronic supplementary material

The online version of this article (10.1186/s12890-017-0495-2) contains supplementary material, which is available to authorized users.

Addressing location uncertainties in GPS-based activity monitoring: A methodological framework

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.

Community-level characteristics and environmental factors of child respiratory illnesses in Southern Arizona

BACKGROUND

Lower respiratory illnesses (LRIs) and asthma are common diseases in children <5 years of age. Few studies have investigated the relationships between multiple, home-based social and environmental risk factors and asthma and LRIs in children. Of those that have, none have focused exclusively on children <5 years of age, who are more physiologically vulnerable and spend more time at home compared to older children. Further, no studies have done so at the community level.

METHODS

We modeled relationships between emergency department visits and hospitalization rates for asthma and LRIs for children <5 years and geographic risk factors, including socio-economic and housing characteristics, ambient air pollution levels, and population density in Maricopa and Pima Counties, Arizona, from 2005 to 2009. We used a generalized linear model with a negative binomial observation distribution and an offset for the population of very young children in each tract. To reduce multicollinearity among predictors, socio-economic characteristics, and ambient air pollutant levels were combined into unit-less indices using the principal components analysis (PCA). Housing characteristics variables did not exhibit moderate-to-high correlations and thus were not included in PCA. Spatial autocorrelation among regression model residuals was assessed with the Global Moran's I test.

RESULTS

Following the regression analyses, almost all predictors were significantly related to at least one disease outcome. Lower socio-economic status (SES) and reduced population density were associated with asthma hospitalization rates and both LRI outcomes (p values <0.001). After adjusting for differences between counties, Pima County residence was associated with lower asthma and LRI hospitalization rates. No spatial autocorrelation was found among multiple regression model residuals (p values >0.05).

CONCLUSIONS

Our study revealed complex, multi-factorial associations between predictors and outcomes. Findings indicate that many rural areas with lower SES have distinct factors for childhood respiratory diseases that require further investigation. County-wide differences in maternal characteristics or agricultural land uses (not tested here) may also play a role in Pima County residence protecting against hospitalizations, when compared to Maricopa County. By better understanding this and other relationships, more focused public health interventions at the community level could be developed to reduce and better control these diseases in children <5 years, who are more physiologically vulnerable.

How Sensors Might Help Define the External Exposome

The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.

GPS or travel diary: Comparing spatial and temporal characteristics of visits to fast food restaurants and supermarkets

To assess differences between GPS and self-reported measures of location, we examined visits to fast food restaurants and supermarkets using a spatiotemporal framework. Data came from 446 participants who responded to a survey, filled out travel diaries of places visited, and wore a GPS receiver for seven consecutive days. Provided by Public Health Seattle King County, addresses from food permit data were matched to King County tax assessor parcels in a GIS. A three-step process was used to verify travel-diary reported visits using GPS records: (1) GPS records were temporally matched if their timestamps were within the time window created by the arrival and departure times reported in the travel diary; (2) the temporally matched GPS records were then spatially matched if they were located in a food establishment parcel of the same type reported in the diary; (3) the travel diary visit was then GPS-sensed if the name of food establishment in the parcel matched the one reported in the travel diary. To account for errors in reporting arrival and departure times, GPS records were temporally matched to three time windows: the exact time, +/- 10 minutes, and +/- 30 minutes. One third of the participants reported 273 visits to fast food restaurants; 88% reported 1,102 visits to supermarkets. Of these, 77.3 percent of the fast food and 78.6 percent supermarket visits were GPS-sensed using the +/-10-minute time window. At this time window, the mean travel-diary reported fast food visit duration was 14.5 minutes (SD 20.2), 1.7 minutes longer than the GPS-sensed visit. For supermarkets, the reported visit duration was 23.7 minutes (SD 18.9), 3.4 minutes longer than the GPS-sensed visit. Travel diaries provide reasonably accurate information on the locations and brand names of fast food restaurants and supermarkets participants report visiting.

Classification of indoor-outdoor location using combined global positioning system (GPS) and temperature data for personal exposure assessment

Objectives

The objectives of this study was to determine the accuracy of indoor-outdoor classification based on GPS and temperature data in three different seasons.

Methods

In the present study, a global positioning system (GPS) was used alongside temperature data collected in the field by a technician who visited 53 different indoor locations during summer, autumn and winter. The indoor-outdoor location was determined by GPS data alone, and in combination with temperature data.

Results

Determination of location by the GPS signal alone, based on the loss of GPS signal and using the used number of satellites (NSAT) signal factor, simple percentage agreements of 73.6 ± 2.9%, 72.9 ± 3.4%, and 72.1 ± 3.1% were obtained for summer, autumn, and winter, respectively. However, when temperature and GPS data were combined, simple percentage agreements were significantly improved (87.9 ± 3.3%, 84.1 ± 2.8%, and 86.3 ± 3.1%, respectively). A temperature criterion for indoor-outdoor determination of ~ Δ 2°C for 2 min could be applied during all three seasons.

Conclusion

The results showed that combining GPS and temperature data improved the accuracy of indoor-outdoor determination.

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Background

Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.

Objective

The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.

Methods

Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).

Results

The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).

Conclusions

The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment

Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person's movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time-activity data. Using Skyhook Wireless's hybrid geolocation module, we developed "Apolux" (Air, Pollution, Exposure), an Android(TM) smartphone application designed to track participants' location in 5-min intervals for 3 months. From 42 participants, we compared Apolux data with contemporaneous data from two self-reported, 24-h time-activity diaries. About three-fourths of measurements were collected within 5 min of each other (mean=74.14%), and 79% of participants reporting constantly powered-on smartphones (n=38) had a daily average data collection frequency of <10 min. Apolux's degree of temporal resolution varied across manufacturers, mobile networks, and the time of day that data collection occurred. The discrepancy between diary points and corresponding Apolux data was 342.3 m (Euclidian distance) and varied across mobile networks. This study's high compliance and feasibility for data collection demonstrates the potential for integrating smartphone-based time-activity data into long-term and large-scale air pollution exposure studies.

Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol)

BACKGROUND

Given the challenges of aging populations, calls have been issued for more sustainable urban re-development and implementation of local solutions to address global environmental and healthy aging issues. However, few studies have considered older adults' daily mobility to better understand how local built and social environments may contribute to healthy aging. Meanwhile, wearable sensors and interactive map-based applications offer novel means for gathering information on people's mobility, levels of physical activity, or social network structure. Combining such data with classical questionnaires on well-being, physical activity, perceived environments and qualitative assessment of experience of places opens new opportunities to assess the complex interplay between individuals and environments. In line with current gaps and novel analytical capabilities, this research proposes an international research agenda to collect and analyse detailed data on daily mobility, social networks and health outcomes among older adults using interactive web-based questionnaires and wearable sensors.

METHODS/DESIGN

Our study resorts to a battery of innovative data collection methods including use of a novel multisensor device for collection of location and physical activity, interactive map-based questionnaires on regular destinations and social networks, and qualitative assessment of experience of places. This rich data will allow advanced quantitative and qualitative analyses in the aim to disentangle the complex people-environment interactions linking urban local contexts to healthy aging, with a focus on active living, social networks and participation, and well-being.

DISCUSSION

This project will generate evidence about what characteristics of urban environments relate to active mobility, social participation, and well-being, three important dimensions of healthy aging. It also sets the basis for an international research agenda on built environment and healthy aging based on a shared and comprehensive data collection protocol.

Children's GPS-determined versus self-reported transport in leisure time and associations with parental perceptions of the neighborhood environment

Background

This study aimed to examine both GPS-determined and self-reported walking, cycling and passive transport in leisure time during week- and weekend-days among 10 to 12-year old children. Comparisons between GPS-determined and self-reported transport in leisure time were investigated. Second, associations between parental perceptions of the neighborhood environment and GPS-determined walking, cycling and passive transport in leisure time were studied.

Methods

Children (10 to 12-years old; n = 126) wore a GPS device and an accelerometer for 7 consecutive days to assess objectively measured transport in leisure time and filled out a diary to assess self-reported transport in leisure time. Parents completed a questionnaire to assess parental perceptions of the neighborhood environment. Pearson correlations and t-tests were used to test for concurrent validity and differences between GPS-determined and self-reported transport in leisure time. Generalized linear models were used to determine the associations between the parental perceptions of the neighborhood environment and GPS-determined transport in leisure time.

Results

Overall, children under-reported their walking and cycling in leisure time, compared to GPS-determined measures (all p values <0.001). However, children reported their passive transport in leisure time during weekend days quite accurate. GPS-determined measures revealed that children walked most during weekdays (M = 3.96 trips/day; 26.10 min/day) and used passive transport more frequently during weekend days (M = 2.12 trips/day; 31.39 min/day). Only a few parental perceived environmental attributes of the neighborhood (i.e. residential density, land use mix access, quality and availability of walking and cycling facilities, and aesthetics) were significantly associated with children’s GPS-determined walking, cycling or passive transport in leisure time.

Conclusions

To accurately assess children’s active transport in leisure time, GPS measures are recommended over self-reports. More research using GPS with a focus on children’s transport in leisure time and investigating the associations with parental perceptions of the neighborhood environment is needed to confirm the results of the present study.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0045-9) contains supplementary material, which is available to authorized users.

Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

BACKGROUND

Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns.

METHODS

Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods.

RESULTS

Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions.

CONCLUSIONS

The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.

Urban-rural differences in daily time-activity patterns, occupational activity and housing characteristics

BACKGROUND

There is evidence that rural residents experience a health disadvantage compared to urban residents, associated with a greater prevalence of health risk factors and socioeconomic differences. We examined differences between urban and rural Canadians using data from the Canadian Human Activity Pattern Survey (CHAPS) 2.

METHODS

Data were collected from 1460 respondents in two rural areas (Haldimand-Norfolk, Ontario and Annapolis Valley-Kings County, Nova Scotia) and 3551 respondents in five urban areas (Vancouver, Edmonton, Toronto, Montreal, and Halifax) using a 24-h recall diary and supplementary questionnaires administered using computer-assisted telephone interviews. We evaluated differences in time-activity patterns, occupational activity, and housing characteristics between rural and urban populations using multivariable linear and logistic regression models adjusted for design as well as demographic and socioeconomic covariates. Taylor linearization method and design-adjusted Wald tests were used to test statistical significance.

RESULTS

After adjustment for demographic and socioeconomic covariates, rural children, adults and seniors spent on average 0.7 (p < 0.05), 1.2 (p < 0.001), and 0.9 (p < 0.001) more hours outdoors per day respectively than urban counterparts. 23.1% (95% CI: 19.0-27.2%) of urban and 37.8% (95% CI: 31.2-44.4%) of rural employed populations reported working outdoors and the distributions of job skill level and industry differed significantly (p < 0.001) between urban and rural residents. In particular, 11.4% of rural residents vs. 4.9% of urban residents were employed in unskilled jobs, and 11.5% of rural residents vs. <0.5% of urban residents were employ in primary industry. Rural residents were also more likely than urban residents to report spending time near gas or diesel powered equipment other than vehicles (16.9% vs. 5.2%, p < 0.001), more likely to report wood as a heating fuel (9.8% vs. <0.1%; p < 0.001 for difference in distribution of heating fuels), less likely to have an air conditioner (43.0% vs. 57.2%, p < 0.001), and more likely to smoke (29.1% vs. 19.0 %, p < 0.001). Private wells were the main water source in rural areas (68.6%) in contrast to public water systems (97.6%) in urban areas (p < 0.001). Despite these differences, no differences in self-reported health status were observed between urban and rural residents.

CONCLUSIONS

We identified a number of differences between urban and rural residents, which provide evidence pertinent to the urban-rural health disparity.

Comparing children's GPS tracks with geospatial proxies for exposure to junk food

Various geospatial techniques have been employed to estimate children's exposure to environmental cardiometabolic risk factors, including junk food. But many studies uncritically rely on exposure proxies which differ greatly from actual exposure. Misrepresentation of exposure by researchers could lead to poor decisions and ineffective policymaking. This study conducts a GIS-based analysis of GPS tracks--'activity spaces'--and 21 proxies for activity spaces (e.g. buffers, container approaches) for a sample of 526 children (ages 9-14) in London, Ontario, Canada. These measures are combined with a validated food environment database (including fast food and convenience stores) to create a series of junk food exposure estimates and quantify the errors resulting from use of different proxy methods. Results indicate that exposure proxies consistently underestimate exposure to junk foods by as much as 68%. This underestimation is important to policy development because children are exposed to more junk food than estimated using typical methods.

Active play opportunities at child care

BACKGROUND AND OBJECTIVES

Physical activity (PA) is important for children's health and development, yet preschoolers are not meeting PA recommendations. The objective of this study was to examine different PA opportunities at child care and how variation in indoor versus outdoor and free versus teacher-led opportunities relate to children's PA.

METHODS

An observational study of 98 children (mean age 4.5 years, 49% girls) from 10 child care centers. Classrooms were observed for at least 4 full days per center (total 50 days) to categorize time into (1) not an active play opportunity (APO); (2) naptime; (3) APO, outdoor free play; (4) APO, outdoor teacher-led; (5) APO, indoor free play; and (6) APO, indoor teacher-led. Children wore accelerometers during observations. Linear regression models examined the influence of APO categories on moderate-vigorous physical activity (MVPA) and sedentary time.

RESULTS

Children's activity was 73% sedentary, 13% light, and 14% MVPA. For 88% of time children did not have APOs, including 26% time as naptime. On average, 48 minutes per day were APOs (41% sedentary, 18% light, and 41% MVPA), 33 minutes per day were outdoors. The most frequent APO was outdoor free play (8% of time); outdoor teacher-led time was <1%. Children were more active and less sedentary outdoors versus indoors and during the child-initiated APOs (indoors and outdoors) versus teacher-led APOs.

CONCLUSIONS

Preschoolers were presented with significantly fewer than recommended opportunities for PA at child care. More APOs are needed for children to meet recommendations, particularly those that encourage more outdoor time, more teacher-led and child-initiated active play, and flexibility in naptime for preschoolers.

A Pilot Study Comparing Observational and Questionnaire Surrogate Measures of Pesticide Exposure Among Residents Impacted by the Ecuadorian Flower Industry

Self-reported measures of residential pesticide exposure are commonly used in epidemiological studies, especially when financial and logistical resources are limited. However, self-reporting is prone to misclassification bias. This pilot study assesses the agreement between self-report of residential pesticide exposure with direct observation measures, in an agricultural region of Ecuador, as a cross-validation method in 26 participants (16 rose workers and 10 controls), with percent agreement and kappa statistics calculated. Proximity of homes to nearby flower farms was found to have only fair agreement (kappa =.35). The use of discarded plastics (kappa =.06) and wood (kappa =.13) were found to have little agreement. Results indicate that direct observation or measurement may provide more accurate appraisals of residential exposures, such as proximity to industrial farmland and the use of discarded materials obtained from the flower farms.

Assessing exposure of young children to common endocrine-disrupting chemicals in the home environment: a review and commentary of the questionnaire-based approach

BACKGROUND

Although infants and young children are particularly vulnerable to endocrine disrupting chemical (EDC) exposure, there is an absence of comprehensive exposure data for this age group. As young children spend the majority of their time indoors, improved methods of exposure assessment are needed to characterise the health risks from exposures in the home environment. Biologic assessment, which has been considered the gold standard for exposure assessment in recent years, is difficult to conduct in young children. Questionnaires are an alternative and indirect method of predicting exposure, which may overcome some of the limitations of direct exposure assessment.

RESEARCH PROBLEM

The feasibility of using a questionnaire-based approach to predict exposure of young children to EDCs in the home has yet to be comprehensively reviewed. Moreover, there is no one questionnaire that has been validated for predicting the exposure of infants to common EDCs in the home.

AIMS AND OBJECTIVES

The aim of this review is to discuss the use and validation of the questionnaire-based approach to predict exposure of children to chemicals from three common classes of EDCs in the home, namely, plasticisers, flame retardants, and insecticides. We discuss the strengths and weaknesses of the questionnaire-based approach as well as the important pathways of exposure in the home environment, by which to guide the design and validation of future exposure questionnaires.

RESULTS

The findings from our review indicate that the questionnaire-based approach is a valuable tool in the prediction of exposure to persistent organic pollutants, as well as to toxicants that have consistent patterns of exposure. With improvements to the design and validation process, the questionnaire-based approach may also prove to be a reliable instrument in predicting exposure to EDCs with short-half lives, including bisphenol A, phthalates, and pyrethroid and organophosphate insecticides.

Protocol for a mixed-methods study of supplemental oxygen in pulmonary fibrosis

BACKGROUND

Little is known about whether or how supplemental oxygen affects patients with pulmonary fibrosis.

METHODS/DESIGN

A mixed-methods study is described. Patients with pulmonary fibrosis, informal caregivers of pulmonary fibrosis patients and practitioners who prescribe supplemental oxygen will be interviewed to gather data on perceptions of how supplemental oxygen impacts patients. In addition, three hundred pulmonary fibrosis patients who do not use daytime supplemental oxygen will be recruited to participate in a longitudinal, pre-/post- study in which patient-reported outcome (PRO) and activity data will be collected at baseline, immediately before daytime supplemental oxygen is initiated, and then once and again 9-12 months later. Activity data will be collected using accelerometers and portable GPS data recorders. The primary outcome is change in dyspnea from before to one month after supplemental oxygen is initiated. Secondary outcomes include scores from PROs to assess cough, fatigue and quality of life as well as the activity data. In exploratory analyses, we will use longitudinal data analytic techniques to assess the trajectories of outcomes over time while controlling for potentially influential variables.

DISCUSSION

Throughout the study and at its completion, results will be posted on the website for our research program (the Participation Program for Pulmonary Fibrosis or P3F) at http://www.pulmonaryfibrosisresearch.org.

GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina

A critical aspect of air pollution exposure assessment is the estimation of the time spent by individuals in various microenvironments (ME). Accounting for the time spent in different ME with different pollutant concentrations can reduce exposure misclassifications, while failure to do so can add uncertainty and bias to risk estimates. In this study, a classification model, called MicroTrac, was developed to estimate time of day and duration spent in eight ME (indoors and outdoors at home, work, school; inside vehicles; other locations) from global positioning system (GPS) data and geocoded building boundaries. Based on a panel study, MicroTrac estimates were compared with 24-h diary data from nine participants, with corresponding GPS data and building boundaries of home, school, and work. MicroTrac correctly classified the ME for 99.5% of the daily time spent by the participants. The capability of MicroTrac could help to reduce the time-location uncertainty in air pollution exposure models and exposure metrics for individuals in health studies.

Strengths and weaknesses of Global Positioning System (GPS) data-loggers and semi-structured interviews for capturing fine-scale human mobility: findings from Iquitos, Peru

Quantifying human mobility has significant consequences for studying physical activity, exposure to pathogens, and generating more realistic infectious disease models. Location-aware technologies such as Global Positioning System (GPS)-enabled devices are used increasingly as a gold standard for mobility research. The main goal of this observational study was to compare and contrast the information obtained through GPS and semi-structured interviews (SSI) to assess issues affecting data quality and, ultimately, our ability to measure fine-scale human mobility. A total of 160 individuals, ages 7 to 74, from Iquitos, Peru, were tracked using GPS data-loggers for 14 days and later interviewed using the SSI about places they visited while tracked. A total of 2,047 and 886 places were reported in the SSI and identified by GPS, respectively. Differences in the concordance between methods occurred by location type, distance threshold (within a given radius to be considered a match) selected, GPS data collection frequency (i.e., 30, 90 or 150 seconds) and number of GPS points near the SSI place considered to define a match. Both methods had perfect concordance identifying each participant's house, followed by 80-100% concordance for identifying schools and lodgings, and 50-80% concordance for residences and commercial and religious locations. As the distance threshold selected increased, the concordance between SSI and raw GPS data increased (beyond 20 meters most locations reached their maximum concordance). Processing raw GPS data using a signal-clustering algorithm decreased overall concordance to 14.3%. The most common causes of discordance as described by a sub-sample (n=101) with whom we followed-up were GPS units being accidentally off (30%), forgetting or purposely not taking the units when leaving home (24.8%), possible barriers to the signal (4.7%) and leaving units home to recharge (4.6%). We provide a quantitative assessment of the strengths and weaknesses of both methods for capturing fine-scale human mobility.

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.

Impact of microenvironments and personal activities on personal PM2.5 exposures among asthmatic children

Personal activity patterns have often been suggested as a source of unexplained variability when comparing personal particulate matter (PM2.5) exposure to modeled data using central site or microenvironmental data. To characterize the effect of personal activity patterns on asthmatic children's personal PM2.5 exposure, data from the Windsor, Ontario Exposure Assessment Study were analyzed. The children spent on an average 67.1±12.7% (winter) and 72.3±22.6% (summer) of their time indoors at home where they received 51.7±14.8% and 66.3±19.0% of their PM2.5 exposure, respectively. In winter, 17.7±5.9% of their time was spent at school where they received 38.6±11.7% of their PM2.5 exposure. In summer, they spent 10.3±11.8% 'indoors away from home', which represented 23.4±18.3% of their PM2.5 exposure. Personal activity codes adapted from those of the National Human Activity Pattern Survey and the Canadian Human Activity Pattern Survey were assigned to the children's activities. Of the over 100 available activity codes, 19 activities collectively encompassed nearly 95% of their time. Generalized estimating equation (GEE) models found that, while indoors at home, relative to daytime periods when sedentary activities were conducted, several personal activities were associated with significantly elevated personal PM2.5 exposures. Indoor playing represented a mean increase in PM2.5 of 10.1 μg/m(3) (95% CI 6.3-13.8) and 11.6 μg/m(3) (95% CI 8.1-15.1) in winter and summer, respectively, as estimated by a personal nephelometer.

Evaluating children's location using a personal GPS logging instrument: limitations and lessons learned

Global positioning system (GPS) technology is increasingly used to assess geographically varying exposure in population studies. However, there has been limited evaluation of accuracy and completeness of personal GPS data. The ability of a GPS data logger to assess location of children during usual activity was evaluated. Data collected for 4 days from 17 children wearing GPS loggers, recorded every 15 s, were evaluated for completeness by time of day during weekend and weekdays, and for accuracy during nighttime at home. Percentage of possible GPS-recorded points and of 5-min intervals with at least one recorded location were examined. Mean percentage of total possible 15-s interval locations recorded daily was less than 30%. Across participants, the GPS loggers recorded 1-47% of total possible location points on weekends and 1-55% on weekdays. More complete data were measured during travel to school (average 91%). The percentage of daily 5-min intervals with recorded data was as high as 53%. At least one location was recorded during 69% of 5-min intervals before school (0630-0800 h), 62% during school (0800-1400 h) and 56% after school (1400-1700 h). During night time (0000-0600 h), on average, location was recorded for less than 25% of 5-min intervals and accuracy was poor. The large proportion of missing data limits the usefulness of GPS logging instruments for population studies. They have potential utility for assessing on-road travel time and route. GPS technology has limitations, and lessons learned from this evaluation can be generalized to the use of GPS in other research settings.

Tracking human activity and well-being in natural environments using wearable sensors and experience sampling

A growing range of studies have begun to document the health and well-being benefits associated with contact with nature. Most studies rely on generalized self-reports following engagement in the natural environment. The actual in-situ experience during contact with nature, and the environmental features and factors that evoke health benefits have remained relatively unexplored. Smartphones offer a new opportunity to monitor and interact with human subjects during everyday life using techniques such as Experience Sampling Methods (ESM) that involve repeated self-reports of experiences as they occur in-situ. Additionally, embedded sensors in smartphones such as Global Positioning Systems (GPS) and accelerometers can accurately trace human activities. This paper explores how these techniques can be combined to comprehensively explore the perceived health and well-being impacts of contact with nature. Custom software was developed to passively track GPS and accelerometer data, and actively prompt subjects to complete an ESM survey at regular intervals throughout their visit to a provincial park in Ontario, Canada. The ESM survey includes nine scale questions concerning moods and emotions, followed by a series of open-ended experiential questions that subjects provide recorded audio responses to. Pilot test results are used to illustrate the nature, quantity and quality of data obtained. Participant activities were clearly evident from GPS maps, including especially walking, cycling and sedate activities. From the ESM surveys, participants reported an average of 25 words per question, taking an average of 15 s to record them. Further qualitative analysis revealed that participants were willing to provide considerable insights into their experiences and perceived health impacts. The combination of passive and interactive techniques is sure to make larger studies of this type more affordable and less burdensome in the future, further enhancing the ability to understand how contact with nature enhances health and well-being.

Innovation through Wearable Sensors to Collect Real-Life Data among Pediatric Patients with Cardiometabolic Risk Factors

Background. While increasing evidence links environments to health behavior, clinicians lack information about patients' physical activity levels and lifestyle environments. We present mobile health tools to collect and use spatio-behavioural lifestyle data for personalized physical activity plans in clinical settings. Methods. The Dyn@mo lifestyle intervention was developed at the Sainte-Justine University Hospital Center to promote physical activity and reduce sedentary time among children with cardiometabolic risk factors. Mobility, physical activity, and heart rate were measured in free-living environments during seven days. Algorithms processed data to generate spatio-behavioural indicators that fed a web-based interactive mapping application for personalised counseling. Proof of concept and tools are presented using data collected among the first 37 participants recruited in 2011. Results. Valid accelerometer data was available for 5.6 (SD = 1.62) days in average, heart rate data for 6.5 days, and GPS data was available for 6.1 (2.1) days. Spatio-behavioural indicators were shared between patients, parents, and practitioners to support counseling. Conclusion. Use of wearable sensors along with data treatment algorithms and visualisation tools allow to better measure and describe real-life environments, mobility, physical activity, and physiological responses. Increased specificity in lifestyle interventions opens new avenues for remote patient monitoring and intervention.

Travel patterns during pregnancy: comparison between Global Positioning System (GPS) tracking and questionnaire data

BACKGROUND

Maternal exposures to traffic-related air pollution have been associated with adverse pregnancy outcomes. Exposures to traffic-related air pollutants are strongly influenced by time spent near traffic. However, little is known about women's travel activities during pregnancy and whether questionnaire-based data can provide reliable information on travel patterns during pregnancy.

OBJECTIVES

Examine women's in-vehicle travel behavior during pregnancy and examine the difference in travel data collected by questionnaire and global positioning system (GPS) and their potential for exposure error.

METHODS

We measured work-related travel patterns in 56 pregnant women using a questionnaire and one-week GPS tracking three times during pregnancy (<20 weeks, 20-30 weeks, and >30 weeks of gestation). We compared self-reported activities with GPS-derived trip distance and duration, and examined potentially influential factors that may contribute to differences. We also described in-vehicle travel behavior by pregnancy periods and influences of demographic and personal factors on daily travel times. Finally, we estimated personal exposure to particle-bound polycyclic aromatic hydrocarbon (PB-PAH) and examined the magnitude of exposure misclassification using self-reported vs. GPS travel data.

RESULTS

Subjects overestimated both trip duration and trip distance compared to the GPS data. We observed moderately high correlations between self-reported and GPS-recorded travel distance (home to work trips: r = 0.88; work to home trips: r = 0.80). Better agreement was observed between the GPS and the self-reported travel time for home to work trips (r = 0.77) than work to home trips (r = 0.64). The subjects on average spent 69 and 93 minutes traveling in vehicles daily based on the GPS and self-reported data, respectively. Longer daily travel time was observed among participants in early pregnancy, and during certain pregnancy periods in women with higher education attainment, higher income, and no children. When comparing self-reported vs. GPS data, we found that estimated personal exposure to PB-PAH did not differ remarkably at the population level, but the difference was large at an individual level.

CONCLUSION

Self-reported home-to-work data overestimated both trip duration and trip distance compared to GPS data. Significant differences in PAH exposure estimates were observed at individual level using self-reported vs. GPS data, which has important implications in air pollution epidemiological studies.

Development of Time-location Weighted Spatial Measures Using Global Positioning System Data

OBJECTIVES

Despite increasing availability of global positioning system (GPS), no research has been conducted to analyze GPS data for exposure opportunities associated with time at indoor and outdoor microenvironments. We developed location-based and time-weighted spatial measures that incorporate indoor and outdoor time-location data collected by GPS.

METHODS

Time-location data were drawn from 38 female subjects in California who wore a GPS device for seven days. Ambient standard deviational ellipse was determined based on outdoor locations and time duration, while indoor time weighted standard deviational ellipse (SDE) was developed to incorporate indoor and outdoor times and locations data into the ellipse measure.

RESULTS

Our findings indicated that there was considerable difference in the sizes of exposure potential measures when indoor time was taken into consideration, and that they were associated with day type (weekday/weekend) and employment status.

CONCLUSIONS

This study provides evidence that time-location weighted measure may provide better accuracy in assessing exposure opportunities at different microenvironments. The use of GPS likely improves the geographical details and accuracy of time-location data, and further development of such location-time weighted spatial measure is encouraged.

Improving estimates of air pollution exposure through ubiquitous sensing technologies

Traditional methods of exposure assessment in epidemiological studies often fail to integrate important information on activity patterns, which may lead to bias, loss of statistical power, or both in health effects estimates. Novel sensing technologies integrated with mobile phones offer potential to reduce exposure measurement error. We sought to demonstrate the usability and relevance of the CalFit smartphone technology to track person-level time, geographic location, and physical activity patterns for improved air pollution exposure assessment. We deployed CalFit-equipped smartphones in a free-living population of 36 subjects in Barcelona, Spain. Information obtained on physical activity and geographic location was linked to space-time air pollution mapping. We found that information from CalFit could substantially alter exposure estimates. For instance, on average travel activities accounted for 6% of people's time and 24% of their daily inhaled NO2. Due to the large number of mobile phone users, this technology potentially provides an unobtrusive means of enhancing epidemiologic exposure data at low cost.

Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment

Quantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activity-patterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.

Mobile phones and malaria: modeling human and parasite travel

Human mobility plays an important role in the dissemination of malaria parasites between regions of variable transmission intensity. Asymptomatic individuals can unknowingly carry parasites to regions where mosquito vectors are available, for example, undermining control programs and contributing to transmission when they travel. Understanding how parasites are imported between regions in this way is therefore an important goal for elimination planning and the control of transmission, and would enable control programs to target the principal sources of malaria. Measuring human mobility has traditionally been difficult to do on a population scale, but the widespread adoption of mobile phones in low-income settings presents a unique opportunity to directly measure human movements that are relevant to the spread of malaria. Here, we discuss the opportunities for measuring human mobility using data from mobile phones, as well as some of the issues associated with combining mobility estimates with malaria infection risk maps to meaningfully estimate routes of parasite importation.

Television viewing associated with adverse dietary outcomes in children ages 2-6

The aim of this paper was to systematically review the evidence for the association between television viewing and diet in children ages 2-6. Data sources included PubMed, PsycINFO, EMBASE, ERIC, SportDISCUS, Sociological Abstracts, Web of Science and hand searches of reference lists of relevant articles. Twelve studies were reviewed in which the relationship between television viewing and diet was assessed in children between the ages of 2 and 6. All but one study reported significant relationship between television viewing time and adverse dietary outcomes. Parent-reported television viewing time was used to assay child television viewing in all included studies. Food frequency survey was the most frequent method of dietary assessment, and parent served as proxies for children in all studies. Lower fruit and/or vegetable intake was the most frequently reported dietary outcome, followed by increased energy intake with increased television viewing. The majority of studies reported adverse dietary outcomes with as little as 1 h of daily television exposure. While these results are consistent with recommendations from child health advocates to limit television viewing in young children, they also suggest that further efforts to limit television viewing in young children may be needed to aid in obesity prevention.

Feasibility and Validity of a Wearable GPS Device for Measuring Outings after Stroke

Aim. Self-report diaries are a low-cost method of measuring community participation but may be inaccurate, while the “gold standard,” observation is time consuming and costly. This study aimed to investigate the feasibility and validity of a global positioning system (GPS) for measuring outings after stroke. Design. Cross-sectional cohort study. Methods. Twenty ambulant people with stroke wore a GPS device and kept a diary for 7 days, and 18 were observed for half a day. We recorded recruitment rate, user perceptions, and data extraction time. GPS data were analysed against Google maps. Percent exact agreement (PEA) with observation was calculated for GPS and diary. Results. Of 23 eligible participants, 20 consented (mean 3.6 years after stroke). GPS data recovery was high (87%). Some participants had difficulty operating the on/off switch and reading the small screen. Data extraction took an average of 5 hours per participant. PEA with observation was high for number of outings (GPS 94%; diary 89%) but lower for purpose of outings (GPS 71%; diary 82%). Conclusions. The GPS device and diary were both feasible and valid for measuring outings after stroke. Simultaneous use of GPS and diaries is recommended for comprehensive analysis of outings.

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.