Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument

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.

Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model

Despite the increasing availability and spatial granularity of individuals' time-activity (TA) data, the missing data problem, particularly long-term gaps, remains as a major limitation of TA data as a primary source of human mobility studies. In the present study, we propose a two-step imputation method to address the missing TA data with long-term gaps, based on both efficient representation of TA patterns and high regularity in TA data. The method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily TA sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual Convolutional Neural Network (CNN)-stacked Long Short-Term Memory (LSTM) model to capture multi-scale temporal dependencies across historical observations and to predict the missing TAs. We evaluated the performance of the proposed imputation method using the mobile phone-based TA data collected from 180 individuals in western New York, USA, from October 2016 to May 2017, with a 10-fold out-of-sample cross-validation method. We found that the proposed imputation method achieved excellent performance with 84% prediction accuracy, which led us to conclude that the proposed imputation method was successful at reconstructing the sequence, duration, and spatial extent of activities from incomplete TA data. We believe that the proposed imputation method can be applied to impute incomplete TA data with relatively long-term gaps with high accuracy.

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.

"Psyosphere": A GPS Data-Analysing Tool for the Behavioural Sciences

Positioning technologies, such as GPS are widespread in society but are used only sparingly in behavioural science research, e.g., because processing positioning technology data can be cumbersome. The current work attempts to unlock positioning technology potential for behavioural science studies by developing and testing a research tool to analyse GPS tracks. This tool—psyosphere—is published as open-source software, and aims to extract behaviours from GPSs data that are more germane to behavioural research. Two field experiments were conducted to test application of the research tool. During these experiments, participants played a smuggling game, thereby either smuggling tokens representing illicit material past border guards or not. Results suggested that participants varied widely in variables, such as course and speed variability and distance from team members in response to the presence of border guards. Subsequent analyses showed that some of these GPS-derived behavioural variables could be linked to self-reported mental states, such as fear. Although more work needs to be done, the current study demonstrates that psyosphere may enable researchers to conduct behavioural experiments with positioning technology, outside of a laboratory setting.

The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults

BACKGROUND

There is increasing interest in examining the life space mobility and activity participation of older adults in the community using sensor technology. Objective data from these technologies may overcome the limitations of self-reported surveys especially in older adults with age-associated cognitive impairment. This paper describes the development and validation of a prototype hybrid mobility tracker for assessing life space mobility and out-of-home activities amongst 33 community-ambulant older adults in Singapore.

METHODS

A hybrid mobility tracker was developed by combining a passive Global Positioning System logger, tri-axial accelerometer and radio-frequency identification. Objective measures of life space, derived from 1 week of tracking data using Geographic Information Systems, were the maximum Euclidean distance from home (Max Euclid) and the area of the minimum convex polygon surrounding all GPS waypoints (MCP area). Out-of-home activities were quantified by visually identifying the total number of activity nodes, or places where participants spent ≥5 min, from mobility tracks. Self-reported measure of life space in 4 weeks was obtained using the University of Alabama at Birmingham Study of Life Space Assessment (UAB-LSA) questionnaire. Self-reported out-of-home activities were recorded daily in a travel diary for 1 week. Bivariate correlations were used to examine convergent validity between objective and subjective measures of life space and out-of-home activities.

RESULTS

The mean age of participants was 69.2 ± 7.1 years. The mean UAB-LSA total score was 79.1 ± 17.4. The median (range) Max Euclid was 2.44 km (0.26-7.50) per day, and the median (range) MCP area was 3.31 km² (0.03-34.23) per day. The UAB-LSA total score had good correlation with Max Euclid (r = 0.51, p = 0.002), and moderate correlation with MCP area (r = 0.46, p = 0.007). The median (range) total number of activity nodes measured by tracker of 20 (8-47) per week had a good correlation with the total activity count recorded in the travel diaries of 15 (6-40) per week (r = 0.52, p = 0.002).

CONCLUSIONS

The tracking system developed to understand out-of-home travel was feasible and reliable. Comparisons with the UAB-LSA and travel diaries showed that it provided reliable and valid spatiotemporal data to assess the life space mobility and activity participation of older adults.

Measuring objective and subjective well-being: dimensions and data sources

Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.

CareConekta: study protocol for a randomized controlled trial of a mobile health intervention to improve engagement in postpartum HIV care in South Africa

BACKGROUND

South Africa is home to the world's largest antiretroviral therapy program but sustaining engagement along the HIV care continuum has proven challenging in the country and throughout the wider region. Population mobility is common in South Africa, but there are important research gaps in describing this mobility and its impact on engagement in HIV care. Postpartum women and their infants in South Africa are known to be at high risk of dropping out of HIV care after delivery and are frequently mobile.

METHODS

In 2017, we developed a beta version of a smartphone application (app) - CareConekta - that detects a user's smartphone location to allow for prospective characterization of mobility. Now we will adapt and test CareConekta to conduct essential formative work on mobility and evaluate an intervention - the CareConekta app plus text notifications and phone calls and/or WhatsApp messages - to facilitate engagement in HIV care during times of mobility. During the 3-year project period, our first objective is to evaluate the feasibility, acceptability, and initial efficacy of using CareConekta as an intervention to improve engagement in HIV care. Our second objective is to characterize mobility among South African women during the peripartum period and its impact on engagement in HIV care. We will enroll 200 eligible pregnant women living with HIV and receiving care at the Gugulethu Midwife Obstetric Unit in Cape Town, South Africa.

DISCUSSION

This work will provide critical information about mobility during the peripartum period and the impact on engagement in HIV care. Simultaneously, we will pilot test an intervention to improve engagement with rigorously assessed outcomes. If successful, CareConekta offers tremendous potential as a research and service tool that can be adapted and evaluated in multiple geographic regions, study contexts, and patient populations.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT03836625. Registered on 8 February 2019.

Attributing Air Pollutant Exposure to Emission Sources with Proximity Sensing

Biomass burning for home energy use contributes to negative health outcomes and environmental degradation. As part of the REACCTING study (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana), personal exposure to carbon monoxide (CO) was measured to gauge the effects of introducing two different cookstove types over four intervention groups. A novel Bluetooth Low-Energy (BLE) Beacon system was deployed on a subset of those CO measurement periods to estimate participants’ distances to their most-used cooking areas during the sampling periods. In addition to presenting methods and validation for the BLE Beacon system, here we present pollution exposure assessment modeling results using two different approaches, in which time-activity (proximity) data is used to: (1) better understand exposure and behaviors within and away from homes; and (2) predict personal exposure via microenvironment air quality measurements. Model fits were improved in both cases, demonstrating the benefits of the proximity measurements.

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.

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.

Mobility assessment of a rural population in the Netherlands using GPS measurements

Background

The home address is a common spatial proxy for exposure assessment in epidemiological studies but mobility may introduce exposure misclassification. Mobility can be assessed using self-reports or objectively measured using GPS logging but self-reports may not assess the same information as measured mobility. We aimed to assess mobility patterns of a rural population in the Netherlands using GPS measurements and self-reports and to compare GPS measured to self-reported data, and to evaluate correlates of differences in mobility patterns.

Method

In total 870 participants filled in a questionnaire regarding their transport modes and carried a GPS-logger for 7 consecutive days. Transport modes were assigned to GPS-tracks based on speed patterns. Correlates of measured mobility data were evaluated using multiple linear regression. We calculated walking, biking and motorised transport durations based on GPS and self-reported data and compared outcomes. We used Cohen’s kappa analyses to compare categorised self-reported and GPS measured data for time spent outdoors.

Results

Self-reported time spent walking and biking was strongly overestimated when compared to GPS measurements. Participants estimated their time spent in motorised transport accurately. Several variables were associated with differences in mobility patterns, we found for instance that obese people (BMI > 30 kg/m²) spent less time in non-motorised transport (GMR 0.69–0.74) and people with COPD tended to travel longer distances from home in motorised transport (GMR 1.42–1.51).

Conclusions

If time spent walking outdoors and biking is relevant for the exposure to environmental factors, then relying on the home address as a proxy for exposure location may introduce misclassification. In addition, this misclassification is potentially differential, and specific groups of people will show stronger misclassification of exposure than others. Performing GPS measurements and identifying explanatory factors of mobility patterns may assist in regression calibration of self-reports in other studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-017-0103-y) contains supplementary material, which is available to authorized users.

Classifying Human Activity Patterns from Smartphone Collected GPS data: a Fuzzy Classification and Aggregation Approach

Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone-measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone-collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g., up to 96% in activity identification) with use of this method. Two examples were provided in the appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics.

Impacts of geocoding uncertainty on reconstructed PFOA exposures and their epidemiological association with preeclampsia

Many epidemiology studies have investigated associations of perfluorooctanoate (PFOA) exposures with a variety of adverse health outcomes for participants in the C8 Health Project. The exposure concentrations (i.e., air and groundwater) used in these studies were determined primarily based on participant's residential locations. However, for residential addresses that could not be geocoded to the street level, the exposure concentrations were assigned based on population-weighted ZIP code centroid, which may result in exposure mischaracterization. The aim of this current study is to evaluate the potential impact of mischaracterized exposure concentrations due to geocoding uncertainty on the predicted serum PFOA concentrations and the epidemiological association between PFOA exposure and preeclampsia. For both workplace addresses and incompletely geocoded residential addresses, we used Monte Carlo (MC) simulation to assign alternate geographic locations within the reported ZIP code (instead of population-weighted ZIP code centroids) and the corresponding exposure concentrations. We found that mischaracterization of residential exposure due to population-weighted ZIP code centroid assignment had no significant impact on the serum PFOA concentration predictions and the epidemiological association of PFOA exposure with preeclampsia. In contrast, the uncertainty in workplace exposure moderately impacted the rank exposure among the participants. We observed a 41% increase in the average adjusted odds ratio of preeclampsia occurrence that may be due to differing proportions of cases (64.3%) and controls (54.5%) with workplace address geocodes during pregnancy. This finding suggests that differential exposure mischaracterization can be reduced by obtaining accurate exposure information such as street addresses and tap water consumption, for both workplaces and residences. The analysis we present is one approach for estimating the potential impacts of positional errors in a geocoding-based exposure assessment on exposure estimates and epidemiological study results.

A Human Open Field Test Reveals Thigmotaxis Related to Agoraphobic Fear

BACKGROUND

Thigmotaxis refers to a specific behavior of animals (i.e., to stay close to walls when exploring an open space). Such behavior can be assessed with the open field test (OFT), which is a well-established indicator of animal fear. The detection of similar open field behavior in humans may verify the translational validity of this paradigm. Enhanced thigmotaxis related to anxiety may suggest the relevance of such behavior for anxiety disorders, especially agoraphobia.

METHODS

A global positioning system was used to analyze the behavior of 16 patients with agoraphobia and 18 healthy individuals with a risk for agoraphobia (i.e., high anxiety sensitivity) during a human OFT and compare it with appropriate control groups (n = 16 and n = 19). We also tracked 17 patients with agoraphobia and 17 control participants during a city walk that involved walking through an open market square.

RESULTS

Our human OFT triggered thigmotaxis in participants; patients with agoraphobia and participants with high anxiety sensitivity exhibited enhanced thigmotaxis. This behavior was evident in increased movement lengths along the wall of the natural open field and fewer entries into the center of the field despite normal movement speed and length. Furthermore, participants avoided passing through the market square during the city walk, indicating again that thigmotaxis is related to agoraphobia.

CONCLUSIONS

This study is the first to our knowledge to verify the translational validity of the OFT and to reveal that thigmotaxis, an evolutionarily adaptive behavior shown by most species, is related to agoraphobia, a pathologic fear of open spaces, and anxiety sensitivity, a risk factor for agoraphobia.

Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA)

PURPOSE

Overview of geographically explicit momentary assessment research, applied to the study of mental health and well-being, which allows for cross-validation, extension, and enrichment of research on place and health.

METHODS

Building on the historical foundations of both ecological momentary assessment and geographic momentary assessment research, this review explores their emerging synergy into a more generalized and powerful research framework.

RESULTS

Geographically explicit momentary assessment methods are rapidly advancing across a number of complimentary literatures that intersect but have not yet converged. Key contributions from these areas reveal tremendous potential for transdisciplinary and translational science.

CONCLUSIONS

Mobile communication devices are revolutionizing research on mental health and well-being by physically linking momentary experience sampling to objective measures of socio-ecological context in time and place. Methodological standards are not well-established and will be required for transdisciplinary collaboration and scientific inference moving forward.

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.

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.

Validity of geographically modeled environmental exposure estimates

Geographic modeling is increasingly being used to estimate long-term environmental exposures in epidemiologic studies of chronic disease outcomes. However, without validation against measured environmental concentrations, personal exposure levels, or biologic doses, these models cannot be assumed a priori to be accurate. This article discusses three examples of epidemiologic associations involving exposures estimated using geographic modeling, and identifies important issues that affect geographically modeled exposure assessment in these areas. In air pollution epidemiology, geographic models of fine particulate matter levels have frequently been validated against measured environmental levels, but comparisons between ambient and personal exposure levels have shown only moderate correlations. Estimating exposure to magnetic fields by using geographically modeled distances is problematic because the error is larger at short distances, where field levels can vary substantially. Geographic models of environmental exposure to pesticides, including paraquat, have seldom been validated against environmental or personal levels, and validation studies have yielded inconsistent and typically modest results. In general, the exposure misclassification resulting from geographic models of environmental exposures can be differential and can result in bias away from the null even if non-differential. Therefore, geographic exposure models must be rigorously constructed and validated if they are to be relied upon to produce credible scientific results to inform epidemiologic research. To our knowledge, such models have not yet successfully predicted an association between an environmental exposure and a chronic disease outcome that has eventually been established as causal, and may not be capable of doing so in the absence of thorough validation.

Physical activity patterns of ethnic children from low socio-economic environments within the UK

Many children fail to meet physical activity (PA) guidelines for health benefits. PA behaviours are complex and depend on numerous interrelated factors. The study aims to develop current understanding of how children from low Socio-economic environments within the UK use their surrounding built environments for PA by using advanced technology. The environment was assessed in 96 school children (7-9 years) using global positioning system (GPS) monitoring (Garmin Forerunner, 305). In a subsample of 46 children, the environment and PA were assessed using an integrated GPS and heart rate monitor. The percentage of time spent indoor, outdoor, in green and non-green environments along with time spent in moderate-to-vigorous PA (MVPA) in indoor and outdoor environments were assessed. A 2-by-2 repeated measures analysis of covariance, controlling for body mass index, BF%, assessed the environmental differences. The findings show that 42% of children from deprived wards of Coventry fail to meet PA guidelines, of which 43% was accumulated during school. Children engaged in more MVPA outdoor than indoor environments (P < 0.01) and a greater amount of time was spent in non-green environments (P < 0.01). Increased time outdoors was negatively associated with BF%. In conclusion, outdoor environments are important for health-enhancing PA and reducing fatness in deprived and ethnic children.

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.

A review of current information and communication technologies: can they be used to assess apathy?

BACKGROUND

Neuropsychiatric symptoms, such as apathy, have an important impact on the quality of life of both patients diagnosed with dementia and their caregivers and represent a strong predictor of progression of the illness. Current clinical assessment methods risk bias resulting from the assessor's subjectivity, pointing to a need for additional objective and systematic assessment tools. Therefore, the use of information and communication technologies (ICT) such as actigraphy and automatized video monitoring are of interest in addition to current assessment methods.

AIM

The goal of this study is to give an overview of current assessment tools for apathy in clinical practice and new approaches to assessment methods with the help ICT.

METHODS

This study was conducted with the use of narrative literature overview.

RESULTS

There is evidence that apart from the currently used assessment methods for apathy, new ICT approaches could provide clinicians with valuable additional information for an earlier detection and therefore more accurate diagnosis of apathy.

CONCLUSIONS

There are no ICT techniques specifically designed for the assessment of apathy, but nevertheless several techniques seem to be promising and deserve more study.

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.

Adolescent health-risk behavior and community disorder

BACKGROUND

Various forms of community disorder are associated with health outcomes but little is known about how dynamic context where an adolescent spends time relates to her health-related behaviors.

OBJECTIVE

Assess whether exposure to contexts associated with crime (as a marker of community disorder) correlates with self-reported health-related behaviors among adolescent girls.

METHODS

Girls (N = 52), aged 14-17, were recruited from a single geographic urban area and monitored for 1 week using a GPS-enabled cell phone. Adolescents completed an audio computer-assisted self-administered interview survey on substance use (cigarette, alcohol, or marijuana use) and sexual intercourse in the last 30 days. In addition to recorded home and school address, phones transmitted location data every 5 minutes (path points). Using ArcGIS, we defined community disorder as aggregated point-level Unified Crime Report data within a 200-meter Euclidian buffer from home, school and each path point. Using Stata, we analyzed how exposures to areas of higher crime prevalence differed among girls who reported each behavior or not.

RESULTS

Participants lived and spent time in areas with variable crime prevalence within 200 meters of their home, school and path points. Significant differences in exposure occurred based on home location among girls who reported any substance use or not (p 0.04) and sexual intercourse or not (p 0.01). Differences in exposure by school and path points were only significant among girls reporting any substance use or not (p 0.03 and 0.02, respectively). Exposure also varied by school/non-school day as well as time of day.

CONCLUSIONS

Adolescent travel patterns are not random. Furthermore, the crime context where an adolescent spends time relates to her health-related behavior. These data may guide policy relating to crime control and inform time- and space-specific interventions to improve adolescent health.

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.

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.

Feasibility study to objectively assess activity and location of Hispanic preschoolers: a short communication

Both physical and social environmental factors influence young children's physical activity, yet little is known about where Hispanic children are more likely to be active. We assessed the feasibility of simultaneously measuring, then processing objective measures of location and physical activity among Hispanic preschool children. Preschool-aged Hispanic children (n = 15) simultaneously wore QStarz BT100X global positioning system (GPS) data loggers and Actigraph GT3X accelerometers for a 24- to 36-hour period, during which time their parents completed a location and travel diary. Data were aggregated to the minute and processed using the personal activity location measurement system (PALMS). Children successfully wore the GPS data loggers and accelerometers simultaneously, 12 of which yielded data that met quality standards. The average percent correspondence between GPS- and diary-based estimates of types of location was high and Kappa statistics were moderate to excellent, ranging from 0.49-0.99. The between-method (GPS monitor, parent-reported diary) correlations of estimated participant-aggregated minutes spent on vehicle-based trips were strong. The simultaneous use of GPS and accelerometers to assess Hispanic preschool children's location and physical activity is feasible. This methodology has the potential to provide more precise findings to inform environmental interventions and policy changes to promote physical activity among Hispanic preschool children.

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.

Tracking and visualization of space-time activities for a micro-scale flu transmission study

Background

Infectious diseases pose increasing threats to public health with increasing population density and more and more sophisticated social networks. While efforts continue in studying the large scale dissemination of contagious diseases, individual-based activity and behaviour study benefits not only disease transmission modelling but also the control, containment, and prevention decision making at the local scale. The potential for using tracking technologies to capture detailed space-time trajectories and model individual behaviour is increasing rapidly, as technological advances enable the manufacture of small, lightweight, highly sensitive, and affordable receivers and the routine use of location-aware devices has become widespread (e.g., smart cellular phones). The use of low-cost tracking devices in medical research has also been proved effective by more and more studies. This study describes the use of tracking devices to collect data of space-time trajectories and the spatiotemporal processing of such data to facilitate micro-scale flu transmission study. We also reports preliminary findings on activity patterns related to chances of influenza infection in a pilot study.

Methods

Specifically, this study employed A-GPS tracking devices to collect data on a university campus. Spatiotemporal processing was conducted for data cleaning and segmentation. Processed data was validated with traditional activity diaries. The A-GPS data set was then used for visual explorations including density surface visualization and connection analysis to examine space-time activity patterns in relation to chances of influenza infection.

Results

When compared to diary data, the segmented tracking data demonstrated to be an effective alternative and showed greater accuracies in time as well as the details of routes taken by participants. A comparison of space-time activity patterns between participants who caught seasonal influenza and those who did not revealed interesting patterns.

Conclusions

This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research. The findings revealed micro-scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.

Portable global positioning system receivers: static validity and environmental conditions

BACKGROUND

GPS receivers are becoming increasingly common as an objective measure of spatiotemporal movement in free-living populations; however, research into the effects of the surrounding physical environment on the accuracy of off-the-shelf GPS receivers is limited.

PURPOSE

The goal of the current study was to (1) determine the static validity of seven portable GPS receiver models under diverse environmental conditions and (2) compare the battery life and signal acquisition times among the models.

METHODS

Seven GPS models (three units of each) were placed on six geodetic sites subject to a variety of environmental conditions (e.g., open sky, high-rise buildings) on three separate occasions. The observed signal acquisition time and battery life of each unit were compared to advertised specifications. Data were collected and analyzed in June 2012.

RESULTS

Substantial variation in positional error was observed among the seven GPS models, ranging from 12.1 ± 19.6 m to 58.8 ± 393.2 m when averaged across the three test periods and six geodetic sites. Further, mean error varied considerably among sites: the lowest error occurred at the site under open sky (7.3 ± 27.7 m), with the highest error at the site situated between high-rise buildings (59.2 ± 99.2 m). While observed signal acquisition times were generally longer than advertised, the differences between observed and advertised battery life were less pronounced.

CONCLUSIONS

Results indicate that portable GPS receivers are able to accurately monitor static spatial location in unobstructed but not obstructed conditions. It also was observed that signal acquisition times were generally underestimated in advertised specifications.

GPS tracking in neighborhood and health studies: a step forward for environmental exposure assessment, a step backward for causal inference?

Recent studies have relied on GPS tracking to assess exposure to environmental characteristics over daily life schedules. Combining GPS and GIS allows for advances in environmental exposure assessment. However, biases related to selective daily mobility preclude assessment of environmental effects, to the extent that these studies may represent a step backward in terms of assessment of causal effects. A solution may be to integrate the Public health / Nutrition approach and the Transportation approach to GPS studies, so as to combine a GPS and accelerometer data collection with an electronic mobility survey. Correcting exposure measures and improving study designs with this approach may permit mitigating biases related to selective daily mobility.

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.

Recommendations for ICT use in Alzheimer's disease assessment: Monaco CTAD Expert Meeting

Alzheimer disease (AD) and other related dementia represent a major challenge for health care systems within the aging population. It is therefore important to develop better instruments for assessing disease severity and disease progression to optimize patient's care and support to care providers, and also provide better tools for clinical research. In this area, Information and Communication Technologies (ICT) are of particular interest. Such techniques enable accurate and standardized assessments of patients' performance and actions in real time and real life situations. The aim of this article is to provide basic recommendation concerning the development and the use of ICT for Alzheimer's disease and related disorders. During he ICT and Mental Health workshop (CTAD meeting held in Monaco on the 30th October 2012) an expert panel was set up to prepare the first recommendations for the use of ICT in dementia research. The expert panel included geriatrician, epidemiologist, neurologist, psychiatrist, psychologist, ICT engineers, representatives from the industry and patient association. The recommendations are divided into three sections corresponding to 1/ the clinical targets of interest for the use of ICT, 2/ the conditions, the type of sensors and the outputs (scores) that could be used and obtained, 3/ finally the last section concerns specifically the use of ICT within clinical trials.

An interactive mapping tool to assess individual mobility patterns in neighborhood studies

As their most critical limitation, neighborhood and health studies published to date have not taken into account nonresidential activity places where individuals travel in their daily lives. However, identifying low-mobility populations residing in low-resource environments, assessing cumulative environmental exposures over multiple activity places, and identifying specific activity locations for targeting interventions are important for health promotion. Daily mobility has not been given due consideration in part because of a lack of tools to collect locational information on activity spaces. Thus, the first aim of the current article is to describe VERITAS (Visualization and Evaluation of Route Itineraries, Travel Destinations, and Activity Spaces), an interactive web mapping application that can geolocate individuals' activity places, routes between locations, and relevant areas such as experienced or perceived neighborhoods. The second aim is to formalize the theoretic grounds of a contextual expology as a subdiscipline to better assess the spatiotemporal configuration of environmental exposures. Based on activity place data, various indicators of individual patterns of movement in space (spatial behavior) are described. Successive steps are outlined for elaborating variables of multiplace environmental exposure (collection of raw locational information, selection/exclusion of locational data, defining an exposure area for measurement, and calculation). Travel and activity place network areas are discussed as a relevant construct for environmental exposure assessment. Finally, a note of caution is provided that these measures require careful handling to avoid increasing the magnitude of confounding (selective daily mobility bias).

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.

Assessment of personal exposure to manganese in children living near a ferromanganese refinery

Airborne exposure to manganese (Mn) can result in neurologic effects. Stationary air sampling is the traditional technique to assess Mn exposure for communities, yet may not accurately reflect children's personal exposure. The goal of the study was to characterize personal exposure to Mn and PM(2.5) in a cohort of children ages 7-9 years residing near a ferromanganese refinery. A subset of children living in non-smoking households ages 7-9 enrolled in the Marietta Community Actively Researching Exposure Study during March-June 2009 and 2010 were invited to participate. Blood and hair were collected and analyzed for Mn. Participants wore a PM(2.5) sampler (Personal Modular Impactor) for 48 h. TWD was based on time spent at home and school and the distance of each from the refinery. Stationary outdoor air sampling was conducted 8 km from the refinery using a Harvard-type PM(2.5) impactor. The relationship between personal Mn exposure and TWD was examined by multiple regression adjusting for stationary air Mn concentration, wind speed and direction, and precipitation. Complete personal air sampling data were collected on 38 children. TWD ranged from 4.7 km to 28.5 km with a mean distance of 11.1 (4.7 sd) km. Mn concentration in personal air samples ranged from 1.5 ng/m3 to 54.5 ng/m3 (geometric mean, 8.1 ng/m3). TWD was a significant predictor of natural log personal air Mn concentration (lnMn) with an associated decrease of 0.075 lnMn for each km TWD (p<0.05, 95% CI -0.13 to -0.01). Personal Mn exposures were positively associated with stationary air Mn levels and inversely associated with wind speed. A child's location (home and school) relative to the refinery is a significant predictor of personal Mn exposure. Wind speed is also an important contributor to personal Mn exposure.

Home versus nonhome neighborhood: quantifying differences in exposure to the built environment

BACKGROUND

Built environment and health research have focused on characteristics of home neighborhoods, whereas overall environmental exposures occur over larger spatial ranges.

PURPOSE

Differences in built environment characteristics were analyzed for home and nonhome locations using GPS data.

METHODS

GPS data collected in 2007-2008 were analyzed for 41 subjects in the Seattle area in 2010. Environmental characteristics for 3.8 million locations were measured using novel GIS data sets called SmartMaps, representing spatially continuous values of local built environment variables in the domains of neighborhood composition, utilitarian destinations, transportation infrastructure, and traffic conditions. Using bootstrap sampling, CIs were estimated for differences in built environment values for home (<833 m of home address) and nonhome (>1666 m) GPS locations.

RESULTS

Home and nonhome built environment values were significantly different for more than 90% of variables across subjects (p<0.001). Only 51% of subjects had higher counts of supermarkets near than away from home. Different measures of neighborhood parks yielded varying results.

CONCLUSIONS

SmartMaps helped measure local built environment characteristics for a large set of GPS locations. Most subjects had significantly different home and nonhome built environment exposures. Considering the full range of individuals' environmental exposures may improve understanding of effects of the built environment on behavior and health outcomes.

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

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

Feasibility of using web surveys to collect time-activity data

Time-activity data are traditionally collected by telephone interviews or through paper diaries, which are time consuming and costly. As a potential alternative that may greatly save staff time, a web survey to collect time-activity data was developed and tested in this study. We collected 24-h recall web diaries from 151 parents of young children mostly under 55 years of age (who also answered for their children) and 55 older adults (≥ 55 years of age) both on a weekday and a weekend day every 3 months during an 18-month period. The performance and reliability of the web surveys collected were evaluated, including the survey-completion rate, and the percentage of surveys with unreasonable time being reported as spent sleeping and with missing reports of being in transit between locations. We also compared the web-survey data with time-activity information we collected from the same subjects in telephone interviews and found that these data sources were fairly consistent with each other. However, we observed slightly more compliance issues for the web than the telephone survey, but most of these issues could be addressed and minimized by refining some questions or the survey interface. Our study suggests that it is critical to reduce participants' burden and improve survey interface design for optimal compliance and data quality. In conclusion, web surveys are a promising method to consider for time-activity data collection.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

CONTEXT

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

EVIDENCE ACQUISITION

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

EVIDENCE SYNTHESIS

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

CONCLUSIONS

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

Comparison of home and away-from-home physical activity using accelerometers and cellular network-based tracking devices

BACKGROUND

Measuring physical at home and away from home is essential for assessing health and well-being, and could help design interventions to increase physical activity. Here, we describe how physical activity at home and away from home can be quantified by combining information from cellular network-based tracking devices and accelerometers.

METHODS

Thirty-five working adults wore a cellular network-based tracking device and an accelerometer for 6 consecutive days and logged their travel away from home. Performance of the tracking device was determined using the travel log for reference. Tracking device and accelerometer data were merged to compare physical activity at home and away from home.

RESULTS

The tracking device detected 98.6% of all away-from-home excursions, accurately measured time away from home and demonstrated few prolonged signal drop-out periods. Most physical activity took place away from home on weekdays, but not on weekends. Subjects were more physically active per unit of time while away from home, particularly on weekends.

CONCLUSIONS

Cellular network-based tracking devices represent an alternative to global positioning systems for tracking location, and provide information easily integrated with accelerometers to determine where physical activity takes place. Promoting greater time spent away from home may increase physical activity.

Organophosphate pesticide metabolite levels in pre-school children in an agricultural community: within- and between-child variability in a longitudinal study

This study characterized the within- and between-child variability in dimethylthiophosphate (DMTP) levels in the urine of 44 children living in an agricultural community in central Washington State in December 1997 and 1999. The goal of this analysis was to investigate these variability components during periods when organophosphate pesticides were and were not actively applied to orchards in that community. Each child provided between 10 and 26 biweekly urine samples over a 21-month period, and these samples were analyzed for six dialkylphosphate (DAP) metabolites common to organophosphate pesticides, including DMTP. Previous analysis of this dataset found that DAP concentrations were elevated during months when organophosphate pesticides were applied to orchards in this region. The current analysis demonstrates that the within-child component of day-to-day variability was much greater than the between-child component of variability by a factor of 3-7 across the DAP metabolites that were analyzed. Therefore, organophosphate pesticide exposure appeared to vary more than 3 times from day-to-day than from child-to-child. This finding has important implications for epidemiologic and exposure pathways research, since accounting for within-child variability may increase the power of a study and allow for the detection of differences that would not otherwise be possible without an analysis that separates out the within-child variability.

Identifying Walking Trips Using GPS Data

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Use of the global positioning system to measure the out-of-home mobility of older adults with differing cognitive functioning

Cognitive impairment manifests in changed out-of-home mobility. Until recently, the assessment of outdoor mobility relied on the reports of family care-givers and institutional staff and used observational approaches, activity monitoring or behavioural checklists. This article presents data for 41 mildly demented, mildly cognitively impaired and healthy men and women aged 64–90 years in Tel-Aviv (Israel) who took part in an interdisciplinary project, SenTra, that is studying the outdoor activities of elderly people using advanced tracking technologies and that involves researchers from geography, social work, gerontology, psychology and medicine. The participants were tracked for 28 consecutive days using a location kit that combined a global positioning system (GPS) with radio frequency identification. The high-resolution spatial and temporal data enabled detailed analysis of the differences in the timing and distance of the participants' daily outdoor mobility patterns. It was found that the spatial range of the mobility of elderly people with cognitive impairment is severely restricted, with most out-of-home time spent in close proximity to their residences. We conclude that GPS is an advanced research tool able to understand out-of-home behaviour better than was possible with previous methods.

Assessing the outcomes of spine surgery using global positioning systems

STUDY DESIGN

Pilot study.

OBJECTIVE

To examine whether surgical outcomes can be assessed objectively by advanced tracking technology, based on Global Positioning Systems (GPS).

SUMMARY OF BACKGROUND DATA

Outcome studies are the commonest way to assess the results of surgical procedures. The success of these efforts is impeded by a number of factors, including the lack of valid outcome measures, difficulty in assessing changes in patients' expectations (response shift) and confounding effects of secondary gains.

METHODS

The measurement of walking speed, distances, and number of walking events per day, claudication index (maximal walking distance), characteristics during motorized trips, and the amount of time spent outdoors were monitored in 2 patients undergoing spine surgery for several weeks using advanced tracking technologies.

RESULTS

In 1 patient, all parameters increased progressively from the time of surgery to the end of the recording period. These findings were consistent with her recovery from surgery. In a second patient, tracking showed the patient's difficulty in mobilizing, leading to the diagnosis of another orthopedic problem, and to total hip replacement surgery.

CONCLUSION

The technology presented in this pilot appears to be useful in understanding a patient's level and breathe of activity. These data will assist in better understanding the limitations imposed by specific musculoskeletal pathology and in monitoring perioperative function and complications and their related causes. Spatial data may indirectly reflect a patient's social and mental conditions. This interdisciplinary pilot may lead to the development of valid outcome measures for a range of medical conditions. Studies comparing questionnaires to this new outcome measure may shed light on issues like response shifts and secondary gain. Norms and clusters of spatial behaviors in different pathologies may enable better patient selection for medical, mental, and surgical interventions.