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

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.

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.

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.

Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to Estimate Exposure to Ambient Air Pollution among Individuals Experiencing Unsheltered Homelessness

Individuals experiencing homelessness represent a growing population in the United States. Air pollution exposure among individuals experiencing homelessness has not been quantified. Utilizing local knowledge mapping, we generated activity spaces for 62 individuals experiencing homelessness residing in a semi-rural county within the United States. Satellite derived measurements of fine particulate matter (PM2.5) were utilized to estimate annual exposure to air pollution experienced by our participants, as well as differences in the variation in estimated PM2.5 at the local scale compared with stationary monitor data and point location estimates for the same period. Spatial variation in exposure to PM2.5 was detected between participants at both the point and activity space level. Among all participants, annual median PM2.5 exposure was 16.22 μg/m³, exceeding the National Air Quality Standard. Local knowledge mapping represents a novel mechanism to capture mobility patterns and investigate exposure to air pollution within vulnerable populations. Reliance on stationary monitor data to estimate air pollution exposure may lead to exposure misclassification, particularly in rural and semirural regions where monitoring is limited.

Effects of Large-Scale Municipal Safe Routes to School Infrastructure on Student Active Travel and Physical Activity: Design, Methods, and Baseline Data of the Safe Travel Environment Evaluation in Texas Schools (STREETS) Natural Experiment

Past evaluations of Safe Routes to School (SRTS) programs have been relatively small in scope and have lacked objective measurements of physical activity. A 2016 Mobility Bond in Austin, Texas, USA, allocated USD 27.5 million for infrastructure changes to facilitate active commuting to schools (ACS). The Safe TRavel Environment Evaluation in Texas Schools (STREETS) study aims to determine the health effects of these infrastructure changes. The purpose of this paper is to describe the STREETS study design, methods, and selected baseline results. The STREETS study is comprised of two designs: (1) a serial cross-sectional design to assess changes in ACS prevalence, and (2) a quasi-experimental, prospective cohort to examine changes in physical activity. Differences between study arms (Austin SRTS and comparison) were assessed for school demographics, ACS, and school programs. At baseline, 14.3% of school trips were made by ACS, with non-significant differences between study arms. Only 26% of schools implemented ACS-related programs. Some significant differences across SRTS and comparison schools were identified for several school- and neighborhood-level characteristics. Substantial changes are needed across area schools and neighborhoods to promote optimum ACS. STREETS study longitudinal findings will be critical for informing optimal future implementations of SRTS programs.

Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic

Between March and April 2020, Cyprus and Greece health authorities enforced three escalated levels of public health interventions to control the COVID-19 pandemic. We quantified compliance of 108 asthmatic schoolchildren (53 from Cyprus, 55 from Greece, mean age 9.7 years) from both countries to intervention levels, using wearable sensors to continuously track personal location and physical activity. Changes in 'fraction time spent at home' and 'total steps/day' were assessed with a mixed-effects model adjusting for confounders. We observed significant mean increases in 'fraction time spent at home' in Cyprus and Greece, during each intervention level by 41.4% and 14.3% (level 1), 48.7% and 23.1% (level 2) and 45.2% and 32.0% (level 3), respectively. Physical activity in Cyprus and Greece demonstrated significant mean decreases by - 2,531 and - 1,191 (level 1), - 3,638 and - 2,337 (level 2) and - 3,644 and - 1,961 (level 3) total steps/day, respectively. Significant independent effects of weekends and age were found on 'fraction time spent at home'. Similarly, weekends, age, humidity and gender had an independent effect on physical activity. We suggest that wearable technology provides objective, continuous, real-time location and activity data making possible to inform in a timely manner public health officials on compliance to various tiers of public health interventions during a pandemic.

Comparing GPS-Based Community Mobility Measures with Self-report Assessments in Older Adults with Parkinson's Disease

BACKGROUND

Real-life community mobility (CM) measures for older adults, especially those with Parkinson's disease (PD), are important tools when helping individuals maintain optimal function and quality of life. This is one of the first studies to compare an objective global positioning system (GPS) sensor and subjective self-report CM measures in an older clinical population.

METHODS

Over 14 days, 54 people in Ontario, Canada with early to mid-stage PD (mean age = 67.5 ± 6.3 years; 47 men; 46 retired) wore a wireless inertial measurement unit with GPS (WIMU-GPS), and completed the Life Space Assessment and mobility diaries. We assessed the convergent validity, reliability and agreement on mobility outcomes using Spearman's correlation, intraclass correlation coefficient, and Bland-Altman analyses, respectively.

RESULTS

Convergent validity was attained by the WIMU-GPS for trip frequency (rs = .69, 95% confidence interval [CI] = 0.52-0.81) and duration outside (rs = .43, 95% CI = 0.18-0.62), but not for life space size (rs = .39, 95% CI = 0.14-0.60). The Life Space Assessment exhibited floor and ceiling effects. Moderate agreements were observed between WIMU-GPS and diary for trip frequency and duration (intraclass correlation coefficients = 0.71, 95% CI = 0.51-0.82; 0.67, 95% CI = 0.42-0.82, respectively). Disagreement was more common among nonretired individuals.

CONCLUSIONS

WIMU-GPS could replace diaries for trip frequency and duration assessments in older adults with PD. Both assessments are best used for retired persons. However, the Life Space Assessment may not reflect actual mobility.

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.

Differences in adolescent activity and dietary behaviors across home, school, and other locations warrant location-specific intervention approaches

BACKGROUND

Investigation of physical activity and dietary behaviors across locations can inform "setting-specific" health behavior interventions and improve understanding of contextual vulnerabilities to poor health. This study examined how physical activity, sedentary time, and dietary behaviors differed across home, school, and other locations in young adolescents.

METHODS

Participants were adolescents aged 12-16 years from the Baltimore-Washington, DC and the Seattle areas from a larger cross-sectional study. Participants (n = 472) wore an accelerometer and Global Positioning Systems (GPS) tracker (Mean days = 5.12, SD = 1.62) to collect location-based physical activity and sedentary data. Participants (n = 789) completed 24-h dietary recalls to assess dietary behaviors and eating locations. Spatial analyses were performed to classify daily physical activity, sedentary time patterns, and dietary behaviors by location, categorized as home, school, and "other" locations.

RESULTS

Adolescents were least physically active at home (2.5 min/hour of wear time) and school (2.9 min/hour of wear time) compared to "other" locations (5.9 min/hour of wear time). Participants spent a slightly greater proportion of wear time in sedentary time when at school (41 min/hour of wear time) than at home (39 min/hour of wear time), and time in bouts lasting ≥30 min (10 min/hour of wear time) and mean sedentary bout duration (5 min) were highest at school. About 61% of daily energy intake occurred at home, 25% at school, and 14% at "other" locations. Proportionately to energy intake, daily added sugar intake (5 g/100 kcal), fruits and vegetables (0.16 servings/100 kcal), high calorie beverages (0.09 beverages/100 kcal), whole grains (0.04 servings/100 kcal), grams of fiber (0.65 g/100 kcal), and calories of fat (33 kcal/100 kcal) and saturated fat (12 kcal/100 kcal) consumed were nutritionally least favorable at "other" locations. Daily sweet and savory snacks consumed was highest at school (0.14 snacks/100 kcal).

CONCLUSIONS

Adolescents' health behaviors differed based on the location/environment they were in. Although dietary behaviors were generally more favorable in the home and school locations, physical activity was generally low and sedentary time was higher in these locations. Health behavior interventions that address the multiple locations in which adolescents spend time and use location-specific behavior change strategies should be explored to optimize health behaviors in each location.

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey

Introduction

Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India.

Methods

Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits.

Results

We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status.

Conclusions

The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.

Beyond the bus stop: where transit users walk

OBJECTIVES

Extending the health benefits of public transit requires understanding how transit use affects pedestrian activity, including pedestrian activity not directly temporally or spatially related to transit use. In this study, we identified where transit users walked on transit days compared with non-transit days within and beyond 400m and 800m buffers surrounding their home and work addresses.

METHODS

We used data collected from 2008-2013 in King County, Washington, from 221 non-physically-disabled adult transit users, who were equipped with an accelerometer, global positioning system (GPS), and travel diary. We assigned walking activity to the following buffer locations: less than and at least 400m or 800m from home, work, or home/work (the home and work buffers comprised the latter buffer). We used Poisson generalized estimating equations to estimate differences in minutes per day of total walking and minutes per day of non-transit-related walking on transit days compared with non-transit days in each location.

RESULTS

We found that durations of total walking and non-transit-related walking were greater on transit days than on non-transit days in all locations studied. When considering the home neighborhood in isolation, most of the greater duration of walking occurred beyond the home neighborhood at both 400m and 800m; results were similar when considering the work neighborhood in isolation. When considering the neighborhoods jointly (i.e., by using the home/work buffer), at 400m, most of the greater duration of walking occurred beyond the home/work neighborhood. However, at 800m, most of the greater duration of walking occurred within the home/work neighborhood.

CONCLUSIONS

Transit days were associated with greater durations of total walking and non-transit related walking within and beyond the home and work neighborhoods. Accordingly, research, design, and policy strategies focused on transit use and pedestrian activity should consider locations outside the home and work neighborhoods, in addition to locations within them.

Validation of a Modified Life-Space Assessment in Multimorbid Older Persons With Cognitive Impairment

BACKGROUND AND OBJECTIVES

To investigate the validity, reliability, sensitivity to change, and feasibility of a modified University of Alabama at Birmingham Study of Aging Life-Space Assessment (UAB-LSA) in older persons with cognitive impairment (CI).

RESEARCH DESIGN AND METHODS

The UAB-LSA was modified for use in persons with CI Life-Space Assessment for Persons with Cognitive Impairment (LSA-CI). Measurement properties of the LSA-CI were investigated using data of 118 multimorbid older participants with CI [mean age (SD): 82.3 (6.0) years, mean Mini-Mental State Examination score: 23.3 (2.4) points] from a randomized controlled trial (RCT) to improve motor performance and physical activity. Construct validity was asessed by Spearman's rank (rs) and point-biseral correlations (rpb) with age, gender, motor, and cognitive status, psychosocial factors, and sensor-derived (outdoor) physical activity variables. Test-retest reliability was analyzed using intra-class correlation coefficients (ICCs). Sensitivity to change was determined by standardized response means (SRMs) calculated for the RCT intervention group.

RESULTS

The LSA-CI demonstrated moderate to high construct validity, with significant correlations of the LSA-CI scores with (outdoor) physical activity (rs = .23-.63), motor status (rs = .27-.56), fear of falling-related psychosocial variables (rs = |.24-.44|), and demographic characteristics (rpb = |.27-.32|). Test-retest reliability was good to excellent (ICC = .65-.91). Sensitivity to change was excellent for the LSA-CI composite score (SRM = .80) and small to moderate for the LSA-CI subscores (SRM = .35-.60). A completion rate of 100% and a mean completion time of 4.1 min) documented good feasibility.

DISCUSSION AND IMPLICATIONS

The LSA-CI represents a valid, reliable, sensitive, and feasible interview-based life-space assessment tool in multimorbid older persons with CI.

Applications of Space Technologies to Global Health: Scoping Review

BACKGROUND

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations' Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

OBJECTIVE

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

METHODS

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs' Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

RESULTS

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

CONCLUSIONS

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Analyzing individual exposure in urban areas offers several challenges where both the individual's activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals' activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual's time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

The Demands of a Women's College Soccer Season

The purpose of this study was to use GPS, accelerometers, and session rating of perceived exertion (sRPE) to examine the demands of a Division II women’s soccer team. Data was collected on 25 collegiate Division II women’s soccer players over an entire regular season (17 matches and 24 practices). Zephyr^TM BioHarnesses (BHs) were used to collect tri-axial acceleration information and GPS derived variables for all matches and practices. Acceleration data was used to calculate Impulse Load, a measure of mechanical load that includes only locomotor related accelerations. GPS was used to quantify total distance and distance in six speed zones. Internal Training Loads were assessed via sRPE. Mean Impulse Load, total distance, and sRPE during match play was 20,120 ± 8609 N·s, 5.48 ± 2.35 km, and 892.50 ± 358.50, respectively. Mean Impulse Load, total distance, and sRPE during practice was 12,410 ± 4067 N·s, 2.95 ± 0.95 km, and 143.30 ± 123.50, respectively. Several very large to nearly perfect correlations were found between Impulse Load and total distance (r = 0.95; p < 0.001), Impulse Load and sRPE (r = 0.84; p < 0.001), and total distance and sRPE (r = 0.82; p < 0.001). This study details the mechanical demands of Division II women’s soccer match play. This study also demonstrates that Impulse Load is a good indicator of total distance.

Differences in Behavior, Time, Location, and Built Environment between Objectively Measured Utilitarian and Recreational Walking

OBJECTIVES

Utilitarian and recreational walking both contribute to physical activity. Yet walking for these two purposes may be different behaviors. We sought to provide operational definitions of utilitarian and recreational walking and to objectively measure their behavioral, spatial, and temporal differences in order to inform transportation and public health policies and interventions.

METHODS

Data were collected 2008-2009 from 651 Seattle-King County residents, wearing an accelerometer and a GPS unit, and filling-in a travel diary for 7 days. Walking activity bouts were classified as utilitarian or recreational based on whether walking had a destination or not. Differences between the two walking purposes were analyzed, adjusting for the nested structure of walking activity within participants.

RESULTS

Of the 4,905 observed walking bouts, 87.4% were utilitarian and 12.6% recreational walking. Utilitarian walking bouts were 45% shorter in duration (-12.1 min) and 9% faster in speed (+0.3km/h) than recreational walking bouts. Recreational walking occurred more frequently in the home neighborhood and was not associated with recreational land uses. Utilitarian walking occurred in areas having higher residential, employment, and street density, lower residential property value, higher area percentage of mixed-use neighborhood destinations, lower percentage of parks/trails, and lower average topographic slope than recreational walking.

CONCLUSION

Utilitarian and recreational walking are substantially different in terms of frequency, speed, duration, location, and related built environment. Policies that promote walking should adopt type-specific strategies. The high occurrence of recreational walking near home highlights the importance of the home neighborhood for this activity.

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.

Ultrafine particles and black carbon personal exposures in asthmatic and non-asthmatic children at school age

Traffic-related air pollution (TRAP) exposure during childhood is associated with asthma; however, the contribution of the different TRAP pollutants in each microenvironment (home, school, transportation, others) in asthmatic and non-asthmatic children is unknown. Daily (24-h) personal black carbon (BC), ultrafine particle (UFP), and alveolar lung-deposited surface area (LDSA) individual exposure measurements were obtained from 100 children (29 past and 21 current asthmatics, 50 non-asthmatics) aged 9±0.7 years from the INMA-Sabadell cohort (Catalonia, Spain). Time spent in each microenvironment was derived by the geolocation provided by the smartphone and a new spatiotemporal map-matching algorithm. Asthmatics and non-asthmatics spent the same amount of time at home (60% and 61%, respectively), at school (20% and 23%), on transportation (8% and 7%), and in other microenvironments (7% and 5%). The highest concentrations of all TRAPs were attributed to transportation. No differences in TRAP concentrations were found overall or by type of microenvironment between asthmatics and non-asthmatics, nor when considering past and current asthmatics, separately. In conclusion, asthmatic and non-asthmatic children had a similar time-activity pattern and similar average exposures to BC, UFP, and LDSA concentrations. This suggests that interventions should be tailored to general population, rather than to subgroups defined by disease.

Within-person associations of young adolescents' physical activity across five primary locations: is there evidence of cross-location compensation?

BACKGROUND

Youth are active in multiple locations, but it is unknown whether more physical activity in one location is associated with less in other locations. This cross-sectional study examines whether on days with more physical activity in a given location, relative to their typical activity in that location, youth had less activity in other locations (i.e., within-person associations/compensation).

METHODS

Participants were 528 adolescents, ages 12 to 16 (M = 14.12, SD = 1.44, 50% boys, 70% White non-Hispanic). Accelerometer and Global Positioning System devices were used to measure the proportion of time spent in moderate-to-vigorous physical activity (MVPA) in five locations: home, home neighborhood, school, school neighborhood, and other locations. Mixed-effects regression was used to examine within-person associations of MVPA across locations and moderators of these associations.

RESULTS

Two of ten within-participant associations tested indicated small amounts of compensation, and one association indicated generalization across locations. Higher at-school MVPA (relative to the participant's average) was related to less at-home MVPA and other-location MVPA (Bs = -0.06 min/day). Higher home-neighborhood MVPA (relative to the participant's average) was related to more at-home MVPA (B = 0.07 min/day). Some models showed that compensation was more likely (or generalization less likely) in boys and non-whites or Hispanic youth.

CONCLUSIONS

Consistent evidence of compensation across locations was not observed. A small amount of compensation was observed for school physical activity, suggesting that adolescents partially compensated for high amounts of school activity by being less active in other locations. Conversely, home-neighborhood physical activity appeared to carry over into the home, indicating a generalization effect. Overall these findings suggest that increasing physical activity in one location is unlikely to result in meaningful decreases in other locations. Supporting physical activity across multiple locations is critical to increasing overall physical activity in youth.

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.

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.

Cross Sectional Association between Spatially Measured Walking Bouts and Neighborhood Walkability

Walking is the most popular choice of aerobic physical activity to improve health among U.S. adults. Physical characteristics of the home neighborhood can facilitate or hinder walking. The purpose of this study was to quantify neighborhood walking, using objective methods and to examine the association between counts of walking bouts in the home neighborhood and neighborhood walkability. This was a cross-sectional study of 106 adults who wore accelerometers and GPS devices for two weeks. Walking was quantified within 1, 2, and 3 km Euclidean (straight-line) and network buffers around the geocoded home location. Walkability was estimated using a commercially available index. Walking bout counts increased with buffer size and were associated with walkability, regardless of buffer type or size (p < 0.001). Quantification of walking bouts within (and outside) of pre-defined neighborhood buffers of different sizes and types allowed for the specification of walking locations to better describe and elucidate walking behaviors. These data support the concept that neighborhood characteristics can influence walking among adults.

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.

Quantifying Distance Overestimation From Global Positioning System in Urban Spaces

OBJECTIVES

To investigate accuracy of distance measures computed from Global Positioning System (GPS) points in New York City.

METHODS

We performed structured walks along urban streets carrying Globalsat DG-100 GPS Data Logger devices in highest and lowest quartiles of building height and tree canopy cover. We used ArcGIS version 10.1 to select walks and compute the straight-line distance (Geographic Information System-measured) and sum of distances between consecutive GPS waypoints (GPS-measured) for each walk.

RESULTS

GPS distance overestimates were associated with building height (median overestimate = 97% for high vs 14% for low building height) and to a lesser extent tree canopy (43% for high vs 28% for low tree canopy).

CONCLUSIONS

Algorithms using distances between successive GPS points to infer speed or travel mode may misclassify trips differentially by context. Researchers studying urban spaces may prefer alternative mode identification techniques.

Locations of Physical Activity as Assessed by GPS in Young Adolescents

OBJECTIVES

To compare adolescents' physical activity at home, near home, at school, near school, and at other locations.

METHODS

Adolescents (N = 549) were ages 12 to 16 years (49.9% girls, 31.3% nonwhite or Hispanic) from 447 census block groups in 2 US regions. Accelerometers and Global Positioning System devices assessed minutes of and proportion of time spent in moderate to vigorous physical activity (MVPA) in each of the 5 locations. Mixed-effects regression compared MVPA across locations and demographic factors.

RESULTS

Forty-two percent of adolescents' overall MVPA occurred at school, 18.7% at home, 18.3% in other (nonhome, nonschool) locations, and 20.6% near home or school. Youth had 10 more minutes (30% more) of overall MVPA on school days than on nonschool days. However, the percentage of location time spent in MVPA was lowest at school (4.8% on school days) and highest near home and near school (9.5%-10.4%). Girls had 2.6 to 5.5 fewer minutes per day of MVPA than boys in all locations except near school.

CONCLUSIONS

Although a majority of adolescents' physical activity occurred at school, the low proportion of active time relative to the large amount of time spent at school suggests potential for increasing school-based activity. Increasing time spent in the neighborhood appears promising for increasing overall physical activity, because a high proportion of neighborhood time was active. Increasing youth physical activity to support metabolic health requires strategies for increasing use of physical activity-supportive locations (eg, neighborhoods) and environmental and program improvements in unsupportive locations (eg, schools, homes).

Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference?

Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.

Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

Demographic, physical activity, and route characteristics related to school transportation: an exploratory study

PURPOSE

To investigate the demographic, physical activity, and route characteristics related to children's school trips.

DESIGN

Cross-sectional exploratory study.

SETTING

Eighteen elementary schools in the Austin Independent School District, Austin, Texas.

SUBJECTS

One hundred twelve children aged 7 to 12 years.

MEASURES

Accelerometer and Global Positioning System (GPS) devices provided objective measures of school travel and physical activity. Parental survey (response rate = 34.2%) provided children's demographic and household information.

ANALYSIS

Generalized linear regression analyses were used for unadjusted and adjusted models estimating correlates of total moderate to vigorous physical activity (MVPA) and school trip-related MVPA's contribution rate.

RESULTS

Walking trips were .44 miles (.71 km) on average. Those who walked to school had about 11 more minutes of daily MVPA than nonwalkers (35.03 vs. 24.06) and higher proportions of their daily MVPA obtained from school commute trips (21.78% vs. 2.41%). School trips accounted for 11.2% of total daily MVPA on average, 12.9% for those who met the physical activity recommendation, and 35.2% for the sedentary children who belonged to the lowest MVPA quartile.

CONCLUSION

Active school commuting appears to be a valuable means to promote physical activity, and its contributions toward total physical activity vary across different demographic groups and community settings. Objective and detailed data from GPS and accelerometer units can facilitate the assessment of route/trip characteristics and physical activity implications of school transportation.

Association between neighborhood walkability and GPS-measured walking, bicycling and vehicle time in adolescents

OBJECTIVES

To investigate relations of walking, bicycling and vehicle time to neighborhood walkability and total physical activity in youth.

METHODS

Participants (N=690) were from 380 census block groups of high/low walkability and income in two US regions. Home neighborhood residential density, intersection density, retail density, entertainment density and walkability were derived using GIS. Minutes/day of walking, bicycling and vehicle time were derived from processing algorithms applied to GPS. Accelerometers estimated total daily moderate-to-vigorous physical activity (MVPA). Models were adjusted for nesting of days (N=2987) within participants within block groups.

RESULTS

Walking occurred on 33%, active travel on 43%, and vehicle time on 91% of the days observed. Intersection density and neighborhood walkability were positively related to walking and bicycling and negatively related to vehicle time. Residential density was positively related to walking.

CONCLUSIONS

Increasing walking in youth could be effective in increasing total physical activity. Built environment findings suggest potential for increasing walking in youth through improving neighborhood walkability.

Occupational exposure to ultrafine particles among airport employees--combining personal monitoring and global positioning system

Background

Exposure to ultrafine particles (UFP) has been linked to cardiovascular and lung diseases. Combustion of jet fuel and diesel powered handling equipment emit UFP resulting in potentially high exposure levels among employees working at airports. High levels of UFP have been reported at several airports, especially on the apron, but knowledge on individual exposure profiles among different occupational groups working at an airport is lacking.

Purpose

The aim of this study was to compare personal exposure to UFP among five different occupational groups working at Copenhagen Airport (CPH).

Method

30 employees from five different occupational groups (baggage handlers, catering drivers, cleaning staff and airside and landside security) at CPH were instructed to wear a personal monitor of particle number concentration in real time and a GPS device. The measurements were carried out on 8 days distributed over two weeks in October 2012. The overall differences between the groups were assessed using linear mixed model.

Results

Data showed significant differences in exposure levels among the groups when adjusted for variation within individuals and for effect of time and date (p<0.01). Baggage handlers were exposed to 7 times higher average concentrations (geometric mean, GM: 37×10³ UFP/cm³, 95% CI: 25–55×10³ UFP/cm³) than employees mainly working indoors (GM: 5×10³ UFP/cm³, 95% CI: 2–11×10³ UFP/cm³). Furthermore, catering drivers, cleaning staff and airside security were exposed to intermediate concentrations (GM: 12 to 20×10³ UFP/cm³).

Conclusion

The study demonstrates a strong gradient of exposure to UFP in ambient air across occupational groups of airport employees.

Applicability of global positioning system for the assessment of walking ability in patients with arterial claudication

OBJECTIVE

This study determined for the first time the clinical applicability of a global positioning system (GPS)-monitored community-based walking ability assessment in a large cohort of patients with peripheral artery disease (PAD).

METHODS

A multicenter study was conducted among PAD patients who complained of intermittent claudication. Patients equipped with a GPS device performed a community-based outdoor walk. We determined the number of technically satisfactory GPS recordings (attempt No. 1). Patients with unsatisfactory GPS recordings were asked to perform a second attempt (attempt No. 2). From the satisfactory recordings obtained after attempts No. 1 and No. 2, we analyzed several GPS parameters to provide clinical information on the patients' walking ability. Results are reported as median (interquartile range).

RESULTS

A total of 218 patients performed an outdoor walk. GPS recordings were technically satisfactory in 185 patients (85%) and in 203 (93%) after attempts No. 1 and No. 2, respectively. The highest measured distance between two stops during community walking was 678 m (IQR, 381-1333 m), whereas self-reported maximal walking distance was 250 m (IQR, 150-400 m; P < .001). Walking speed was 3.6 km/h (IQR, 3.1-3.9 km/h), with few variations during the walk. Among the patients who had to stop during the walk, the stop durations were <10 minutes in all but one individual.

CONCLUSIONS

GPS is applicable for the nonsupervised multicenter recording of walking ability in the community. In the future, it may facilitate objective community-based assessment of walking ability, allow for the adequate monitoring of home-based walking programs, and for the study of new dimensions of walking in PAD patients with intermittent claudication.

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.

Incorporating guidelines for use of mobile technologies in health research and practice

This commentary aims to create initial recommendations to guide researchers' decisions on the development and use of mobile technologies for public health research. We recommend that mobile technologies for public health research should be scalable and sustainable; draw on social, psychological and/or behavioral theoretical models; be able to be integrated with multiple communication devices; incorporate social network and/or geographic metrics and take a community-based participatory approach to development and implementation. All of these approaches are discussed.

Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings

The emergence of portable global positioning system (GPS) receivers over the last 10 years has provided researchers with a means to objectively assess spatial position in free-living conditions. However, the use of GPS in free-living conditions is not without challenges and the aim of this study was to test the dynamic accuracy of a portable GPS device under real-world environmental conditions, for four modes of transport, and using three data collection intervals. We selected four routes on different bearings, passing through a variation of environmental conditions in the City of Copenhagen, Denmark, to test the dynamic accuracy of the Qstarz BT-Q1000XT GPS device. Each route consisted of a walk, bicycle, and vehicle lane in each direction. The actual width of each walking, cycling, and vehicle lane was digitized as accurately as possible using ultra-high-resolution aerial photographs as background. For each trip, we calculated the percentage that actually fell within the lane polygon, and within the 2.5, 5, and 10 m buffers respectively, as well as the mean and median error in meters. Our results showed that 49.6% of all ≈68,000 GPS points fell within 2.5 m of the expected location, 78.7% fell within 10 m and the median error was 2.9 m. The median error during walking trips was 3.9, 2.0 m for bicycle trips, 1.5 m for bus, and 0.5 m for car. The different area types showed considerable variation in the median error: 0.7 m in open areas, 2.6 m in half-open areas, and 5.2 m in urban canyons. The dynamic spatial accuracy of the tested device is not perfect, but we feel that it is within acceptable limits for larger population studies. Longer recording periods, for a larger population are likely to reduce the potentially negative effects of measurement inaccuracy. Furthermore, special care should be taken when the environment in which the study takes place could compromise the GPS signal.

Modelling of human exposure to air pollution in the urban environment: a GPS-based approach

The main objective of this work was the development of a new modelling tool for quantification of human exposure to traffic-related air pollution within distinct microenvironments by using a novel approach for trajectory analysis of the individuals. For this purpose, mobile phones with Global Positioning System technology have been used to collect daily trajectories of the individuals with higher temporal resolution and a trajectory data mining, and geo-spatial analysis algorithm was developed and implemented within a Geographical Information System to obtain time-activity patterns. These data were combined with air pollutant concentrations estimated for several microenvironments. In addition to outdoor, pollutant concentrations in distinct indoor microenvironments are characterised using a probabilistic approach. An example of the application for PM2.5 is presented and discussed. The results obtained for daily average individual exposure correspond to a mean value of 10.6 and 6.0-16.4 μg m(-3) in terms of 5th-95th percentiles. Analysis of the results shows that the use of point air quality measurements for exposure assessment will not explain the intra- and inter-variability of individuals' exposure levels. The methodology developed and implemented in this work provides time-sequence of the exposure events thus making possible association of the exposure with the individual activities and delivers main statistics on individual's air pollution exposure with high spatio-temporal resolution.

Walking objectively measured: classifying accelerometer data with GPS and travel diaries

PURPOSE

This study developed and tested an algorithm to classify accelerometer data as walking or nonwalking using either GPS or travel diary data within a large sample of adults under free-living conditions.

METHODS

Participants wore an accelerometer and a GPS unit and concurrently completed a travel diary for seven consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or nonwalking based on a decision-tree algorithm consisting of seven classification scenarios. Algorithm reliability was examined relative to two independent analysts' classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts.

RESULTS

The 706 participants' (mean age = 51 yr, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified PA into 8170 walking bouts (58.5 %) and 5337 nonwalking bouts (38.2%); 464 bouts (3.3%) were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the nonwalking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean ± SD duration of PA bouts classified as walking was 15.2 ± 12.9 min. On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day.

CONCLUSIONS

GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or nonwalking behavior.

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.

Performance of GPS-devices for environmental exposure assessment

Integration of individual time-location patterns with spatially resolved exposure maps enables a more accurate estimation of personal exposures to environmental pollutants than using estimates at fixed locations. Current global positioning system (GPS) devices can be used to track an individual's location. However, information on GPS-performance in environmental exposure assessment is largely missing. We therefore performed two studies. First, a commute-study, where the commute of 12 individuals was tracked twice, testing GPS-performance for five transport modes and two wearing modes. Second, an urban-tracking study, where one individual was tracked repeatedly through different areas, focused on the effect of building obstruction on GPS-performance. The median error from the true path for walking was 3.7 m, biking 2.9 m, train 4.8 m, bus 4.9 m, and car 3.3 m. Errors were larger in a high-rise commercial area (median error=7.1 m) compared with a low-rise residential area (median error=2.2 m). Thus, GPS-performance largely depends on the transport mode and urban built-up. Although ~85% of all errors were <10 m, almost 1% of the errors were >50 m. Modern GPS-devices are useful tools for environmental exposure assessment, but large GPS-errors might affect estimates of exposures with high spatial variability.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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