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Using Global Positioning Systems in Health Research
Using Global Positioning Systems in Health Research A Practical Approach to Data Collection and Processing Jacqueline Kerr, PhD, Scott Duncan, PhD, Jasper Schipperjin, PhD Abstract: The use of GPS devices in health research is increasingly popular. There are currently no best-practice guidelines for collecting, processing, and analyzing GPS data. The standardization of data collection and processing procedures will improve data quality, allow more-meaningful comparisons across studies and populations, and advance this fıeld more rapidly. This paper aims to take researchers, who are considering using GPS devices in their research, through device-selection criteria, device settings, participant data collection, data cleaning, data processing, and integration of data into GIS. Recommendations are outlined for each stage of data collection and analysis and indicates challenges that should be considered. This paper highlights the benefıts of collecting GPS data over traditional self-report or estimated exposure measures. Information presented here will allow researchers to make an informed decision about incorporating this readily available technology into their studies. This work reflects the state of the art in 2011. (Am J Prev Med 2011;41(5):532–540) © 2011 American Journal of Preventive Medicine
Background
T
he relationship between where you live and health has long been recognized.1,2 In the past decade, rapid progress has been made in understanding the role of neighborhoods in obesity prevention, with much of the research focusing on the relationships between the built environment around a residential address and physical activity.3 Some studies4,5 have also considered other locations of importance such as environments around schools and workplaces. Focusing on the environment around static address points has several drawbacks. Health behaviors occur in multiple locations and along routes to destinations. Focusing on one location, such as residential address, underestimates the exposure to multiple environments. Further, health behaviors are usually assessed in an aggregate manner, for example, total physical activity across the day. Relating one location to total behavior confounds the relationship and
From the Department of Family and Preventive Medicine (Kerr), University of California San Diego, San Diego, California; the Centre for Physical Activity and Nutrition Research (Duncan), Auckland University of Technology, Auckland, New Zealand; and the Institute of Sports Science and Clinical Biomechanics (Schipperjin), University of Southern Denmark, Odense, Denmark Address correspondence to: Jacqueline Kerr, PhD, Department of Family and Preventive Medicine, University of California San Diego, 9500 Gilman Drive, #0811, La Jolla CA 92093-0811. E-mail: [email protected]. 0749-3797/$17.00 doi: 10.1016/j.amepre.2011.07.017
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results in small effect sizes,6 especially if the behavior does not primarily occur at that location. The emergence of lightweight, low-cost, and accurate GPS devices has enabled researchers to objectively track the location of an individual. These devices have been shown to be more accurate than self-reported travel surveys or activity diaries.7–9 Several reviews10 –12 have now been published that expound the potential of combining GPS receivers with objective accelerometer measures of physical activity. Most recently, Krenn et al.12 identifıed 24 studies that implemented GPS to monitor physical activity location, most of which reported some form of data loss (missing data are relatively common when using GPS because of the requirement for a consistent line-of-sight to orbiting satellites). The authors concluded that although data loss did not appear to be related to sample size, longer measurement periods were associated with more lost data. They also noticed shortcomings in the reporting of data treatment and processing procedures, making it diffıcult to determine the reasons for data loss in previous studies. This is unsurprising, given that there are currently no best practice guidelines in place for collecting, processing, and analyzing GPS data. Thus, this paper aims to take researchers who are considering using GPS through device selection and preparation, participant data collection, data cleaning, data processing, and integration of data into GIS. The standardization of data collection and pro-
© 2011 American Journal of Preventive Medicine • Published by Elsevier Inc.
Kerr et al / Am J Prev Med 2011;41(5):532–540
cessing procedures will help this emerging fıeld to continue to advance and allow more-meaningful comparisons across studies and populations.
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they can make more-specifıc recommendations to policymakers on land use and transportation planning.
Accuracy of Portable GPS Receivers Use of GPS Data in Health Research
It is important to establish the measurement accuracy of Several studies now have collected GPS data across differGPS devices prior to implementation. Previous studies ent population groups demonstrating feasibility and genhave investigated the accuracy of portable GPS receivers erating new research questions.12 Some studies13–16 have from three perspectives: (1) accuracy relative to a fıxed location; (2) accuracy recording speed and movement; combined GPS with objective physical activity data using and (3) accuracy of third-party algorithms for deriving accelerometers, more accurately assessing the influence contextual information (e.g., activity type, transport of specifıc locations on behavior that occurs in these mode) from movement patterns. Establishing the acculocations, such as parks. Other studies have focused on racy under static conditions is the fırst priority; if the GPS journeys and assessed route choice7,8,17 or activity levels18 –20 during these specifıc travel times. GPS data have receiver cannot provide an accurate assessment of locaalso been used to supplement accelerometer data to better tion when stationary, it is unlikely to perform well under identify specifıc behaviors such as walking, driving, or dynamic conditions. cycling.21,22 Studies have assessed how far adolescents Previous studies33,34 of single devices at one geodetic 23,24 and where in a school or neighborhood chiltravel point found accuracies of 1.5–10 m. Duncan et al.35 re25–29 GPS data can indicate dren expend the most energy. cently expanded on these studies by testing seven differtime spent indoors or outdoors,30 and this may be particent off-the-shelf GPS devices on six geodetic points under ularly important for populations at risk for vitamin D a variety of conditions (e.g., open space, urban canyon 31 defıciency, such as older adults. GPS [with interference from high buildings], unstudies can also provide a more accurate der canopy). Although there was variation assessment of exposure to pollutants.32 in performance among devices, most were See Studies of risky behaviors like smoking able to detect the geodetic location to within article by Krenn and drug or alcohol use could utilize GPS meters when unobstructed. Under obon the same to identify locations that prompt such structed conditions, however, GPS accuracy topic in this issue. behaviors. Interventions can then be dedecreased substantially (typical error of veloped to target avoidance of specifıc 40 –50 m). These results highlight the shortlocations. Studies that make environcomings of using GPS to monitor location mental improvements or promote utilization of specifıc in covered environments, when the communication with facilities can use GPS data to assess whether healthy acthe satellite system is compromised. Table 1 outlines typtivities increase in these specifıc environments. If reical circumstances when GPS errors occur. searchers can better understand how resources such as The advantage of determining the validity of GPS deparks are used, or where to locate healthy food stores, vices under static conditions is that the geodetic point is Table 1. Causes of GPS data errors Interference
Description
Slow connectivity
When the device is first turned on or is trying to get a fix after a prolonged time without a connection (as on exiting a building), there is often a delay while the device attempts to gain a fix. This process is known as a cold start. The device may take a while to connect, and if the participant is moving at high speeds (⬎50 km/hour) in a vehicle, for example, the device may constantly attempt to update the fix point, resulting in an inability to get a fix. If the participant then enters a building with limited satellite view, it can look like he or she has not traveled at all or the data are registered as missing. Devices with faster fix functions are less likely to encounter this problem.
Physical structures
Satellite communication can be interfered with by surrounding high buildings (know as urban canyons), by tree cover, and by building materials such as tunnels or indoor locations. Older GPS models simply lost signal under such conditions. More-advanced models are able to get satellite fixes even indoors, depending on the building material and design. The result of this random interference is either signal loss or signal scatter.
Environments
GPS data can be spurious for other reasons, but a substantial number of tracks can include speeds and distances that are not plausible. Even the location of the satellites in the sky can impede signal transfer, the higher the satellite in its orbit the better the signal. Normal atmospheric conditions can also interfere with signal accuracy even in ideal conditions on earth (e.g., in a clear open space).
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Table 2. Features to consider in GPS purchasing decisions Feature
Recommendation
Accuracy
The chip set within the GPS device determines the sensitivity and accuracy of the location estimates. Sensitivity refers to the minimum signal strength required for a GPS receiver to provide accurate results. The most recent chip sets (MTK II, SirfStar III) provide a sensitivity close to ⫺160 dBm and an accuracy of 3–5 m.
Battery life
Ideally a battery should last more than 12 hours to collect a representative daily sample. Some devices have more than 40 hours of battery life. Researchers should expect participants to charge the device every evening.
Memory
This depends on the setting. If data are collected more frequently or additional features such as signal strength or elevation are collected, the memory is saturated more quickly. It is desirable to have enough memory to collect a sufficient number of days of data without having to exchange a device. Devices can record multiple features for up to 14 days at a 15-second epoch.
Fix time
This is the time to gain a satellite fix from a cold, warm, or hot start. Faster fix times will lead to more-accurate trip detection. Devices that implement assisted GPS (AGPS) are likely to produce faster fix times. A reasonable fix time is up to 35 seconds.
GPS software
Manufacturers provide their own software and this can vary in its speed, flexibility, consistency, and usability. Using manufacturer-independent open source software, usable for multiple devices, such as the free BT747 (bt747.org), may improve the initialization and download process.
Satellite information
Some devices provide information about the number of satellites in view and in communication; this allows researchers to better assess the likely validity of the data and indicate systematic errors or attribute data as being from the indoors or outdoors.
Practical considerations
Size, cost, interface, and availability from manufacturers. Most devices are now small (e.g., 65 g), come with comfortable pouches, cost under $100, and do not have a participant interface that can interfere with inconspicuous monitoring. However, manufacturers may be ill prepared to distribute large numbers of devices, and tend to offer little customer support.
an acceptable criterion standard. In contrast, assessments of positional accuracy under dynamic conditions require the use of less-than-ideal criterion measures, such as the average location recorded from multiple units of the same device. The latter technique was used by Rodriguez et al.33 to assess accuracy under a variety of free-living scenarios. The average distance between each unit and the average of fıve other identical units was 10.7⫾11.9 m in open-space scenarios and 20.1⫾21.8 m in clustered development scenarios. Another area that requires more attention is the interunit reliability of off-the-shelf GPS receivers. It is essential that there is limited variation in accuracy among individual units. Rodriguez et al.33 reported a mean bias of 0.90⫾0.74 m among six units under static and unobstructed conditions.
Device Selection and Testing One of the fırst decisions is which device to use. Most GPS manufacturers retail different units marketed for specifıc purposes (e.g., running, hiking, cycling, orienteering), resulting in a confusing array of choice for researchers. Technical diffıculties have been reported from several devices, but it is anticipated that such problems will be resolved as devices improve and manufacturers recognize the growing fıeld of GPS research. Table 2 outlines the functions that are important in the decision to purchase a particular device. In our experi-
ence, the three most important factors are chipset sensitivity/accuracy, battery life, and fıx time. The relative importance of these and other functions will vary from study to study. In any case, researchers should be prepared to conduct their own performance tests, as manufacturer specifıcations do not always reflect true performance in the fıeld.35 Table 3 outlines the steps that should be followed when testing selected GPS devices. Table 3. Device testing conditions 1. Multiple devices need to be carried in the same way, often mounted on a board 2. All devices should be set to a low epoch (e.g., 15 seconds) so that the maximum amount of data can be collected, devices may perform similarly at a 60-second epoch, a lower epoch will distinguish device accuracy more clearly 3. Devices should be tested under identical conditions and in multiple conditions including urban canyons and indoors 4. Devices should be compared at different times of day as satellite availability and position change during the day and satellite connectivity can affect performance 5. Devices should compare accuracy in fixed locations (such as geodetic points) for static validity and while moving for dynamic validity 6. Time to acquire signal should also be tested
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Kerr et al / Am J Prev Med 2011;41(5):532–540
Preparing Devices for Deployment Clearly device initialization and set up will vary according to study aims and device capacity. The main decisions researchers have to make include how many days they need to represent the behavior under study and how much data can be stored on the device. If elevation is not important, recording of these data could be disabled to free up memory; however, we recommend including elevation where possible as it is often a useful indicator of spurious data. The epoch length is important for several reasons. First, the smaller the epoch, the fewer days on which data can be collected because of memory-capacity limitations. Second, smaller epochs allow greater precision in location detection. Researchers investigating children, for example, often wish to have small epochs (5–15 seconds) to detect the sporadic nature of children’s behavior.36 Third, the smaller the epoch, the greater the amount of data that must be downloaded and processed. This can have implications if data storage capacity is limited or costly and can increase the processing time and turnaround of devices during fıeld deployment. If data reduction is required to enable the researcher to make sense of the vast numbers of data points, then the researcher may reconsider the epoch length and opt for a longer period between data points, especially if this allows him or her to collect data over a more representative number of days. Another important step in preparing multiple devices for fıeld deployment is the time matching. Accelerometers, used by physical activity researchers, are usually initialized on a computer. Computer clocks are notoriously inaccurate and can drift in a very short period, or if connected to a network. In contrast, the GPS data are time-stamped from the Universal Time Clock (UTC). The UTC does not account for different time zones or changes resulting from daylight savings. Researchers must remember to reset their computer clock to match UTC before initializing their accelerometer so that the time-stamps match. If data are collected in multiple time zones or across daylight-saving seasons, researchers need to adjust for this in postprocessing.
Participant Compliance Issues and Best Practice Wearing Location There has been some research on older GPS models indicating that accuracy is affected by the position on the body.33 With newer devices, however, there appears to be little impact of wearing position. For physical activity research, the authors recommend participants wear it on the waist on the opposite hip to their accelerometer deNovember 2011
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vice, on a single elastic belt (provided by the researchers) so that the devices do not become separated and to improve wear compliance.
Wear Time To date, wear time for GPS devices used in physical activity research has been driven by the accelerometer wear-time demands to represent weekly habitual behavior. Intense activity bouts are often infrequent, so several days of accelerometer data may be needed to detect typical activity levels. In contrast, every minute of GPS data can identify a location and speed, meaning multiple locations and trips are recorded every day. Studies of specifıc physical activity locations may require longer periods, however, as behavior in specifıc locations of interest may occur less frequently. Clearly the research question will determine the wear-time criteria, but our experience from accelerometer research is to ensure that wear- time expectations are communicated to participants upfront and that frequent prompts (e.g., text messages, phone calls, or e-mail) are used to increase compliance.
Logging of Wear Time Physical activity researchers commonly ask participants to complete an accelerometer wear-time log. This enables them to track when the device was worn and screen for days that do not meet wear-time criteria. The authors recommend that GPS researchers also ask participants to note when the device was worn, when it was not worn, forgotten at home or left in a car, and whether the device was charged. In the log, it is helpful to include a check box for whether the participant stayed in one location all day, as this may look like they did not wear the device.
Battery Charging Many devices have only a limited battery life, which is a critical issue in studies that span several days. Table 4 outlines recommendations for increasing compliance when researchers expect study participants to charge the device.
Data Processing and Cleaning As highlighted already, GPS data are likely to include some data points that are affected by interference (Table 1). Researchers must therefore decide how to handle missing or erroneous data. Filtering methods may be employed for systematic errors and smoothing techniques may remove random errors. In addition, the raw GPS data require some processing and aggregating to create meaningful variables for analyses such as distance traveled or mode of
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Table 4. Compliance to GPS device charging 1. Send reminder messages to charge the device, preferably daily 2. Instruct participants to log device charging as this reminds participants to wear and charge, but also allows researchers to track if the battery dies or the device is not worn 3. Provide participants with tips to improve compliance including: Clearly explaining that the study requires a certain amount of wear time and that this is being tracked Reminding participants that battery charging will ensure that valuable days of wear time are not lost because of battery failure Suggesting participants charge the device in a location where they will see the device the next day, for example, next to a cell phone, toothbrush, or bed
transportation. In the future, journal reviewers may become more aware of GPS data issues and begin to require detailed processing information as is now common practice for accelerometry research. Disclosure of data processing and testing of the assumptions in validation studies will be key to rapid progress in this new and emerging fıeld.
Computing Derived Variables and Data Smoothing The fırst step in processing GPS data is to compute derived variables (e.g., duration, distance traveled, direction [bearing], speed, acceleration, elevation delta, grade) from the inputs supplied by the GPS (time-stamp, latitude, longitude, elevation). Filtering for unrealistic speed and acceleration values should take place after datasmoothing techniques are employed. Data smoothing reduces random noise in complex data sets by focusing on the primary pattern in the data and replacing points outside that pattern with plausible points that match the pattern. Some researchers suggest points with speeds above 50 m/s or accelerations above 10 m/s2 should be deleted.37 Excessive speed, acceleration, distance traveled, and/or elevation deltas are often caused by multipath reflections. Likewise, small (⬍30 m) or repeated back-and-forth movements are typical of indoor jitter where the building interferes with the GPS signal and creates multiple points within a short distance that do not represent normal human movement. The researcher has to decide what constitutes implausible movement for their environment and population. Smoothing methods employed by transportation researchers include a moving average approach,38,39 a modifıed Kalman fılter,40 a Gauss kernel smoothing approach,37 correc-
tions for the warm start/cold start problem,41 and interpolation of missing data.38
Detecting Locations, Trips, and Modes of Transportation Once the errors have been identifıed and either reduced by smoothing or replaced with plausible estimates of likely location (imputations), additional analyses such as location clustering, trips, and modes of transportation can be performed.
Location Detection A cluster process can identify locations of interest by determining areas containing a high concentration of points and then computing the centroid of each location. Although there are many different cluster-detection algorithms, Kernel methods and k-means algorithms are the most common. Once clusters are detected, the processing may collapse points within a given radius of the centroid to the coordinates of the centroid and thereby creating a location. This can be particularly helpful for indoor locations where jitter can be distracting.
Identifying Indoor GPS Points Some GPS devices can be confıgured to report the number of satellites detected overhead, the number of satellites used to compute the location, and the signal strength of each satellite. When outdoors, the GPS detects more satellites and receives a stronger signal from each. By considering the ratio of satellites used or satellites detected and the total signal strength, the indoor/outdoor condition can be estimated.
Trip Detection Transportation researchers commonly use “dwell time” to detect a journey end point. Dwell time is the length of time when there is no movement (i.e., very small distances between two points). The defınition of dwell time in the literature ranges among 45,42 300,43,44 and 900 seconds,37 with most studies applying a 120-second threshold. Transportation researchers have also established potential trip end points by point density calculations: counting how many of the 30 preceding and succeeding GPS points are positioned within a 15-m radius. If the sequence of points with a density higher than 15 lasts for at least 10 points or 300 seconds, then it is assumed that a moving journey (i.e., a trip) has ended. Other options include a criterion of zero speed37,45,46 or a change in bearing of 180 degrees47,48 to defıne trip end. Tsui and Shalaby46 reasoned that most mode changes indicating the start or end of a vehicle trip require a walk trip, and that walking speeds can reveal a change in mode. www.ajpmonline.org
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Clearly, accelerometer data could help refıne such algorithms with large changes in accelerometer counts from sitting to walking behavior.21
Identifying Mode of Transportation Once trips are identifıed, the mode of transportation can be assigned. A simple algorithm would average the speed over the course of the trip and classify transport mode based on predefıned cutoff values. A problem arises on multi-modal trips; for example, the average speed of a walking–vehicle–walking trip may result in the entire trip being classifıed as a bicycle trip. If pause points and changes in speed can be identifıed, then analyses can be performed on each of the trip segments, resulting in a better classifıcation. Because average speeds can be similar across transportation modes in certain circumstances (e.g., bus, car, and bicycle in a downtown area with traffıc and multiple stop lights), studies have also employed GIS layers to identify transit stops and distinguish some modes more clearly.49 Stopher et al.41 worked with a stepwise elimination of modes based on average and maximum speeds; proximity to certain network elements, such as bus stops or train stations; and the deviation from the street network. Other studies employ median speed, 95th percentiles, and acceleration distributions to identify modes. These statistical parameters were explicitly chosen over the average speed or the maximum speed and acceleration to make the algorithm more robust against outliers. Bohte and Maat49 provide clear logic rules for removing track points, detecting trips, and attributing modes in the appendices of their publication. Others50,51 have started to use machine-learning algorithms to detect transportation modes. Although some sample techniques and references have been provided for researchers to be aware of the possibilities for data processing, these procedures are complex and require support from computer scientists or mathematicians. This is another resource that GPS researchers should be prepared to employ. Studies21,52 have demonstrated up to and beyond 90% accuracy to detect transportation modes and trips. Although these results indicate some level of confıdence in trip prediction, it is not clear under which circumstances the algorithms work most effectively. Algorithms, however, have not been validated against a criterion standard such as observation data in free-living conditions. They are often correlated with participant recall data, which are inherently biased.39 Another issue with current processing algorithms is they have been developed in isolation for single research problems with a single device and thus algorithms may not transfer well to other studies or populations. Researchers at the University of California, San Diego, have November 2011
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developed a flexible web-based program that processes GPS data but allows the researcher to set the parameters best suited to the device and study context (ucsd-palmsproject.wikispaces.com).
Integrating GPS Data in a GIS Much of the true potential of GPS data for health research can be maximized when GPS data are compiled in a GIS to be combined with other spatial data.7,8,13–16,18,20,22,26 –29 Integration of GPS data into a GIS allows researchers to categorize their data in multiple ways based on location (e.g., using a GIS layer containing all public parks to identify GPS data points within these parks). GIS also allows researchers to construct exposure measures to various environmental characteristics. Environmental exposure measures in this context include a wide range of topics. More traditional environmental exposure measures such as air pollution on a daily cycle trip can be determined by plotting a GPS route against air pollution (model) data. Exposure to environmental characteristics that are thought to affect the physical activity (e.g., presence of intersections, mixed land use, green space along travel routes, and living and work environments) can also be determined. The addition of temporal weightings for exposures is also possible because of the time-stamps in GPS data. Unfortunately, the added detail and precision that come with GPS data increasingly reveal a lack of detail in commonly available GIS layers. The use of GIS also has some disadvantages that should be mentioned. As with other comprehensive software packages, GIS software requires a relatively high skill level and substantial experience to master. For researchers without GIS skills, cooperation with other researchers with the necessary GIS expertise is advisable. In addition, researchers need to know what GIS data are available from which agencies, another task that requires specialist knowledge and experience.
Future Directions Although the challenges of incorporating GPS devices and data into research are currently still multiple, there is great potential for progress in this fıeld of research. Researchers may detect temporal and spatial patterns of multiple behaviors that relate more closely to health outcomes, moving us away from focusing on single specifıc behaviors or locations that can explain only part of the picture. Future challenges will include the use of advanced spatiotemporal analysis techniques to assess behavior patterns across time and space. Other issues include appropriate participant selection and sample sizes, given increased variability in environ-
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mental exposure but perhaps a greater focus on lessfrequent specifıc behaviors. The increased environmental variability from the study of multiple locations and the improved matching of specifıc behaviors to specifıc locations will likely lead to greater effect sizes. The scope of GIS layers available will also have to reflect the large distances covered by individuals in their daily travel, possibly crossing county or city lines. Cummins et al.53 have already suggested how advances in GPS will inform theories of health and place. The new data collection methods also will challenge how environmental influences are determined. It will be challenging, however, to pull apart specifıc environmental cues given the potential to accumulate exposures across days and environments. In addition, the strength and draw of multiple environmental cues is unclear. It may be important to weight locations that are large and those situated at corners (e.g., fast-food outlets). Speed and mode of travel may also influence reactivity to exposures. The integration of GPS chipsets and accelerometers into present-day smartphones has introduced another potential method for monitoring health-behavior locations.23,24 Many smartphones make use of assisted GPS technology, such as wifı and cellular positioning. This results in faster fıxes and less drop-out: When the GPS signal is lost, the position of the phone within a circular buffer is estimated using backup technologies. The device is a fully functional mobile phone, which means the burden to users associated with carrying one or more research instruments is decreased. In addition to datalogging capabilities, data can be streamed in real time to researchers via the mobile network. User prompts can be delivered in real time to increase compliance, charge the battery, or even distribute intervention material. Ecological Momentary Assessments can be programmed to collect a wide range of behavioral information at a certain time or given a specifıc trigger (e.g., in a specifıc location).54 More research is required to establish the effıcacy and usability of these devices in the fıeld. Finally, the ethical, and to some extent legal, issues associated with collecting GPS data should also be considered. GPS data have the potential to be very personal and private; researchers can literally follow people’s every move. In many other research fıelds, data are made anonymous before analysis, and results are aggregated before they are presented, published, or shared with other researchers. No information that results in identifıcation of respondent locations should be provided. Useful techniques to protect the confıdentiality of participant data are described in the book Putting People on the Map.55 However, GPS data that are spatially aggregated may not contain the needed detail to reveal possible relationships between behaviors and environmental ex-
posure. Analyses need to be run with the maximum amount of spatial detail, and although this of course does not reveal the name of a respondent, it does reveal where he or she lives and works, which routes he or she takes, and which places he or she visits, for how long, and at what time. Depending on national legislation and data protection laws, this might present problems for data analysis and, in particular, data sharing or pooling among researchers.
Conclusion The use of GPS in health research is clearly becoming more prevalent. This paper reflects the state of the art in 2011. As collecting GPS data in large quantities becomes easier, data processing and analysis become increasingly challenging. If GPS procedures can be standardized, it will advance the fıeld and allow more-meaningful comparisons across studies and populations. This paper provides an overview of device-selection criteria, GPS settings, participant data collection, data cleaning, data processing, and integration of data into GIS. Before commencing on a GPS study, it is suggested here that researchers select and fıeld-test relevant devices, and carefully consider battery life, memory, satellite communication, fıx time, device features, and software. The GPS device should be set at an epoch short enough for suffıcient detail, but long enough for the device memory. Data processing and cleaning should not be taken lightly and benefıts from expertise in advanced data-handling techniques. The creation of relevant environmentalexposure parameters in GIS also requires considerable expertise. Collaboration with other researchers is therefore highly recommended. To this end, the GPS in Health Research Network (GPS-HRN) was founded in 2009 and currently includes more than 150 members from 22 countries (www.gps-hrn.org). Ongoing academic discussion and standardized guidelines like those provided herein will help to advance understanding of the influence of time and space on health behaviors. No fınancial disclosures were reported by the authors of this paper.
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26. Fjortoft T, Kristoffersen B, Sageie J. Children in schoolyards: tracking movement patterns and physical activity in schoolyards using global positioning system and heart rate monitoring. Landsc Urban Plann 2009;93:210 –7. 27. Cooper AR, Page AS, Wheeler BW, et al. Mapping the walk to school using accelerometry combined with a global positioning system. Am J Prev Med 2010;38(2):178 – 83. 28. Fjortoft I, Lofman O, Halvorsen Thoren K. Schoolyard physical activity in 14-year-old adolescents assessed by mobile GPS and heart rate monitoring analysed by GIS. Scand J Public Health 2010;38(5S):28 –37. 29. Maddison R, Jiang Y, Vander Hoorn S, Exeter D, Mhurchu CN, Dorey E. Describing patterns of physical activity in adolescents using global positioning systems and accelerometry. Pediatr Exerc Sci 2010; 22(3):392– 407. 30. Cooper AR, Page AS, Wheeler BW, Hillsdon M, Griew P, Jago R. Patterns of GPS measured time outdoors after school and objective physical activity in English children: the PEACH project. Int J Behav Nutr Phys Act 2010;7:31. 31. Lauretani F, Maggio M, Valenti G, Dall’Aglio E, Ceda GP. Vitamin D in older population: new roles for this “classic actor”? Aging Male 2010;13(4):215–32. 32. Morabia A, Mirer FE, Amstislavski TM, et al. Potential health impact of switching from car to public transportation when commuting to work. Am J Public Health 2010;100(12):2388 –91. 33. Rodriguez DA, Brown AL, Troped PJ. Portable global positioning units to complement accelerometry-based physical activity monitors. Med Sci Sports Exerc 2005;37(11S):S572– 81. 34. Townshend AD, Worringham CJ, Stewart IB. Assessment of speed and position during human locomotion using nondifferential GPS. Med Sci Sports Exerc 2008;40(1):124 –32. 35. Duncan JS, Oliver M, MacRae D. Positional accuracy of seven portable GPS receivers under a variety of environmental conditions. In: Active Living Research Annual Conference; 2011 Feb 22–24; San Diego CA; www.activelivingresearch.org/node/12191. 36. Bailey RC, Olson J, Pepper SL, Porszasz J, Barstow TJ, Cooper DM. The level and tempo of children’s physical activities: an observational study. Med Sci Sports Exerc 1995;27(7):1033– 41. 37. Schuessler N, Axhausen KW. Processing raw data from global positioning systems without additional information. Trans Res Record 2009;2105(1):28 –36. 38. Ogle J, Guensler R, Bachman W, Koutsak M, Wolf J. Accuracy of global positioning system for determining driver performance parameters. Trans Res Record 2002;1818:12–24. 39. Chung EH, Shalaby A. A trip based reconstruction tool for GPS-based personal travel surveys. Transport Plan Techn 2005;28(5):381– 401. 40. Jun J, Guensler R, Ogle J. Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profıle estimates. Trans Res Record 2007;1972:141–50. 41. Stopher P, Jiang Q, FitzGerald C. Processing GPS data from travel surveys. In: 2nd International Colloqium on the Behavioural Foundations of Integrated Land-Use and Transportation Models: Frameworks, Models and Applications; 2005 Jun 12–15; Toronto, Canada. 42. Pearson D. Global positioning system (GPS) and travel surveys: Results from the 1997 Austin household survey. In: 8th Conference on the Application of Transportation Planning Methods; 2001 Apr 22–26; Corpus Christi TX. 43. Wolf J, Schonfelder S, Samaga U, Oliveira M, Axhausen KW. Eighty weeks of global positioning system traces. Trans Res Record 2004; 1870:46 –54. 44. Doherty ST, Noel C, Lee-Gosselin MEH, Sirois C, Ueno M, Theberge F. Moving beyond observed outcomes: integrating global positioning systems and interactive computer-based travel behaviour surveys. Trans Res E 2001;C026:449 – 66. 45. Schonfelder S, Li H, Guensler R, Ogle J, Axhausen KW. Analysis of commute Atlanta instrumented vehicle GPS data: destination choice
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behavior and activity spaces. In: 85th Annual Meeting of the Transportation Research Board; 2006 Jan 22–26; Washington DC. Tsui SYA, Shalaby A. An enhanced system for link and mode identifıcation for GPS-based personal travel surveys. Trans Res Record 2006;1972:38 – 45. Du J, Aultman-Hall L. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: automatic trip end identifıcation issues. Trans Res A 2007;41:220 –32. de Jong R, Mensonides W. Wearable GPS device as a data collection method for travel research: working paper. Sydney: Institute of Transport Studies, 2003. Bohte W, Maat K. Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Trans Res C 2009;17(3):285–97. Liao L, Patterson D, Fox D, Kautz H. Learning and inferring transportation routines. Artif Intell 2007;171(5– 6):311–31.
51. Gogate V, Dechter R, Rindt C, Marca J. Modeling transportation routines using hybrid dynamic mixed networks. In: Conference on Uncertainty in Artifıcial Intelligence 2005; Citeseer. 52. Ermes M, Parkka J, Mantyjarvi J, Korhonen I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed 2008;12(1):20 – 6. 53. Cummins S, Curtis S, Diez-Roux AV, Macintyre S. Understanding and representing “place” in health research: a relational approach. Soc Sci Med 2007;65(9):1825–38. 54. Dunton GF, Whalen CK, Jamner LD, Henker B, Floro JN. Using ecologic momentary assessment to measure physical activity during adolescence. Am J Prev Med 2005;29(4):281–7. 55. Panel on Confıdentiality Issues Arising from the Integration of Remotely Sensed and Self-Identifying Data, National Research Council. Putting people on the map: protecting confıdentiality with linked social-spatial data. Washington DC: National Academies Press, 2007.
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Background
T
he relationship between where you live and health has long been recognized.1,2 In the past decade, rapid progress has been made in understanding the role of neighborhoods in obesity prevention, with much of the research focusing on the relationships between the built environment around a residential address and physical activity.3 Some studies4,5 have also considered other locations of importance such as environments around schools and workplaces. Focusing on the environment around static address points has several drawbacks. Health behaviors occur in multiple locations and along routes to destinations. Focusing on one location, such as residential address, underestimates the exposure to multiple environments. Further, health behaviors are usually assessed in an aggregate manner, for example, total physical activity across the day. Relating one location to total behavior confounds the relationship and
From the Department of Family and Preventive Medicine (Kerr), University of California San Diego, San Diego, California; the Centre for Physical Activity and Nutrition Research (Duncan), Auckland University of Technology, Auckland, New Zealand; and the Institute of Sports Science and Clinical Biomechanics (Schipperjin), University of Southern Denmark, Odense, Denmark Address correspondence to: Jacqueline Kerr, PhD, Department of Family and Preventive Medicine, University of California San Diego, 9500 Gilman Drive, #0811, La Jolla CA 92093-0811. E-mail: [email protected]. 0749-3797/$17.00 doi: 10.1016/j.amepre.2011.07.017
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results in small effect sizes,6 especially if the behavior does not primarily occur at that location. The emergence of lightweight, low-cost, and accurate GPS devices has enabled researchers to objectively track the location of an individual. These devices have been shown to be more accurate than self-reported travel surveys or activity diaries.7–9 Several reviews10 –12 have now been published that expound the potential of combining GPS receivers with objective accelerometer measures of physical activity. Most recently, Krenn et al.12 identifıed 24 studies that implemented GPS to monitor physical activity location, most of which reported some form of data loss (missing data are relatively common when using GPS because of the requirement for a consistent line-of-sight to orbiting satellites). The authors concluded that although data loss did not appear to be related to sample size, longer measurement periods were associated with more lost data. They also noticed shortcomings in the reporting of data treatment and processing procedures, making it diffıcult to determine the reasons for data loss in previous studies. This is unsurprising, given that there are currently no best practice guidelines in place for collecting, processing, and analyzing GPS data. Thus, this paper aims to take researchers who are considering using GPS through device selection and preparation, participant data collection, data cleaning, data processing, and integration of data into GIS. The standardization of data collection and pro-
© 2011 American Journal of Preventive Medicine • Published by Elsevier Inc.
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cessing procedures will help this emerging fıeld to continue to advance and allow more-meaningful comparisons across studies and populations.
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they can make more-specifıc recommendations to policymakers on land use and transportation planning.
Accuracy of Portable GPS Receivers Use of GPS Data in Health Research
It is important to establish the measurement accuracy of Several studies now have collected GPS data across differGPS devices prior to implementation. Previous studies ent population groups demonstrating feasibility and genhave investigated the accuracy of portable GPS receivers erating new research questions.12 Some studies13–16 have from three perspectives: (1) accuracy relative to a fıxed location; (2) accuracy recording speed and movement; combined GPS with objective physical activity data using and (3) accuracy of third-party algorithms for deriving accelerometers, more accurately assessing the influence contextual information (e.g., activity type, transport of specifıc locations on behavior that occurs in these mode) from movement patterns. Establishing the acculocations, such as parks. Other studies have focused on racy under static conditions is the fırst priority; if the GPS journeys and assessed route choice7,8,17 or activity levels18 –20 during these specifıc travel times. GPS data have receiver cannot provide an accurate assessment of locaalso been used to supplement accelerometer data to better tion when stationary, it is unlikely to perform well under identify specifıc behaviors such as walking, driving, or dynamic conditions. cycling.21,22 Studies have assessed how far adolescents Previous studies33,34 of single devices at one geodetic 23,24 and where in a school or neighborhood chiltravel point found accuracies of 1.5–10 m. Duncan et al.35 re25–29 GPS data can indicate dren expend the most energy. cently expanded on these studies by testing seven differtime spent indoors or outdoors,30 and this may be particent off-the-shelf GPS devices on six geodetic points under ularly important for populations at risk for vitamin D a variety of conditions (e.g., open space, urban canyon 31 defıciency, such as older adults. GPS [with interference from high buildings], unstudies can also provide a more accurate der canopy). Although there was variation assessment of exposure to pollutants.32 in performance among devices, most were See Studies of risky behaviors like smoking able to detect the geodetic location to within article by Krenn and drug or alcohol use could utilize GPS meters when unobstructed. Under obon the same to identify locations that prompt such structed conditions, however, GPS accuracy topic in this issue. behaviors. Interventions can then be dedecreased substantially (typical error of veloped to target avoidance of specifıc 40 –50 m). These results highlight the shortlocations. Studies that make environcomings of using GPS to monitor location mental improvements or promote utilization of specifıc in covered environments, when the communication with facilities can use GPS data to assess whether healthy acthe satellite system is compromised. Table 1 outlines typtivities increase in these specifıc environments. If reical circumstances when GPS errors occur. searchers can better understand how resources such as The advantage of determining the validity of GPS deparks are used, or where to locate healthy food stores, vices under static conditions is that the geodetic point is Table 1. Causes of GPS data errors Interference
Description
Slow connectivity
When the device is first turned on or is trying to get a fix after a prolonged time without a connection (as on exiting a building), there is often a delay while the device attempts to gain a fix. This process is known as a cold start. The device may take a while to connect, and if the participant is moving at high speeds (⬎50 km/hour) in a vehicle, for example, the device may constantly attempt to update the fix point, resulting in an inability to get a fix. If the participant then enters a building with limited satellite view, it can look like he or she has not traveled at all or the data are registered as missing. Devices with faster fix functions are less likely to encounter this problem.
Physical structures
Satellite communication can be interfered with by surrounding high buildings (know as urban canyons), by tree cover, and by building materials such as tunnels or indoor locations. Older GPS models simply lost signal under such conditions. More-advanced models are able to get satellite fixes even indoors, depending on the building material and design. The result of this random interference is either signal loss or signal scatter.
Environments
GPS data can be spurious for other reasons, but a substantial number of tracks can include speeds and distances that are not plausible. Even the location of the satellites in the sky can impede signal transfer, the higher the satellite in its orbit the better the signal. Normal atmospheric conditions can also interfere with signal accuracy even in ideal conditions on earth (e.g., in a clear open space).
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Table 2. Features to consider in GPS purchasing decisions Feature
Recommendation
Accuracy
The chip set within the GPS device determines the sensitivity and accuracy of the location estimates. Sensitivity refers to the minimum signal strength required for a GPS receiver to provide accurate results. The most recent chip sets (MTK II, SirfStar III) provide a sensitivity close to ⫺160 dBm and an accuracy of 3–5 m.
Battery life
Ideally a battery should last more than 12 hours to collect a representative daily sample. Some devices have more than 40 hours of battery life. Researchers should expect participants to charge the device every evening.
Memory
This depends on the setting. If data are collected more frequently or additional features such as signal strength or elevation are collected, the memory is saturated more quickly. It is desirable to have enough memory to collect a sufficient number of days of data without having to exchange a device. Devices can record multiple features for up to 14 days at a 15-second epoch.
Fix time
This is the time to gain a satellite fix from a cold, warm, or hot start. Faster fix times will lead to more-accurate trip detection. Devices that implement assisted GPS (AGPS) are likely to produce faster fix times. A reasonable fix time is up to 35 seconds.
GPS software
Manufacturers provide their own software and this can vary in its speed, flexibility, consistency, and usability. Using manufacturer-independent open source software, usable for multiple devices, such as the free BT747 (bt747.org), may improve the initialization and download process.
Satellite information
Some devices provide information about the number of satellites in view and in communication; this allows researchers to better assess the likely validity of the data and indicate systematic errors or attribute data as being from the indoors or outdoors.
Practical considerations
Size, cost, interface, and availability from manufacturers. Most devices are now small (e.g., 65 g), come with comfortable pouches, cost under $100, and do not have a participant interface that can interfere with inconspicuous monitoring. However, manufacturers may be ill prepared to distribute large numbers of devices, and tend to offer little customer support.
an acceptable criterion standard. In contrast, assessments of positional accuracy under dynamic conditions require the use of less-than-ideal criterion measures, such as the average location recorded from multiple units of the same device. The latter technique was used by Rodriguez et al.33 to assess accuracy under a variety of free-living scenarios. The average distance between each unit and the average of fıve other identical units was 10.7⫾11.9 m in open-space scenarios and 20.1⫾21.8 m in clustered development scenarios. Another area that requires more attention is the interunit reliability of off-the-shelf GPS receivers. It is essential that there is limited variation in accuracy among individual units. Rodriguez et al.33 reported a mean bias of 0.90⫾0.74 m among six units under static and unobstructed conditions.
Device Selection and Testing One of the fırst decisions is which device to use. Most GPS manufacturers retail different units marketed for specifıc purposes (e.g., running, hiking, cycling, orienteering), resulting in a confusing array of choice for researchers. Technical diffıculties have been reported from several devices, but it is anticipated that such problems will be resolved as devices improve and manufacturers recognize the growing fıeld of GPS research. Table 2 outlines the functions that are important in the decision to purchase a particular device. In our experi-
ence, the three most important factors are chipset sensitivity/accuracy, battery life, and fıx time. The relative importance of these and other functions will vary from study to study. In any case, researchers should be prepared to conduct their own performance tests, as manufacturer specifıcations do not always reflect true performance in the fıeld.35 Table 3 outlines the steps that should be followed when testing selected GPS devices. Table 3. Device testing conditions 1. Multiple devices need to be carried in the same way, often mounted on a board 2. All devices should be set to a low epoch (e.g., 15 seconds) so that the maximum amount of data can be collected, devices may perform similarly at a 60-second epoch, a lower epoch will distinguish device accuracy more clearly 3. Devices should be tested under identical conditions and in multiple conditions including urban canyons and indoors 4. Devices should be compared at different times of day as satellite availability and position change during the day and satellite connectivity can affect performance 5. Devices should compare accuracy in fixed locations (such as geodetic points) for static validity and while moving for dynamic validity 6. Time to acquire signal should also be tested
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Preparing Devices for Deployment Clearly device initialization and set up will vary according to study aims and device capacity. The main decisions researchers have to make include how many days they need to represent the behavior under study and how much data can be stored on the device. If elevation is not important, recording of these data could be disabled to free up memory; however, we recommend including elevation where possible as it is often a useful indicator of spurious data. The epoch length is important for several reasons. First, the smaller the epoch, the fewer days on which data can be collected because of memory-capacity limitations. Second, smaller epochs allow greater precision in location detection. Researchers investigating children, for example, often wish to have small epochs (5–15 seconds) to detect the sporadic nature of children’s behavior.36 Third, the smaller the epoch, the greater the amount of data that must be downloaded and processed. This can have implications if data storage capacity is limited or costly and can increase the processing time and turnaround of devices during fıeld deployment. If data reduction is required to enable the researcher to make sense of the vast numbers of data points, then the researcher may reconsider the epoch length and opt for a longer period between data points, especially if this allows him or her to collect data over a more representative number of days. Another important step in preparing multiple devices for fıeld deployment is the time matching. Accelerometers, used by physical activity researchers, are usually initialized on a computer. Computer clocks are notoriously inaccurate and can drift in a very short period, or if connected to a network. In contrast, the GPS data are time-stamped from the Universal Time Clock (UTC). The UTC does not account for different time zones or changes resulting from daylight savings. Researchers must remember to reset their computer clock to match UTC before initializing their accelerometer so that the time-stamps match. If data are collected in multiple time zones or across daylight-saving seasons, researchers need to adjust for this in postprocessing.
Participant Compliance Issues and Best Practice Wearing Location There has been some research on older GPS models indicating that accuracy is affected by the position on the body.33 With newer devices, however, there appears to be little impact of wearing position. For physical activity research, the authors recommend participants wear it on the waist on the opposite hip to their accelerometer deNovember 2011
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vice, on a single elastic belt (provided by the researchers) so that the devices do not become separated and to improve wear compliance.
Wear Time To date, wear time for GPS devices used in physical activity research has been driven by the accelerometer wear-time demands to represent weekly habitual behavior. Intense activity bouts are often infrequent, so several days of accelerometer data may be needed to detect typical activity levels. In contrast, every minute of GPS data can identify a location and speed, meaning multiple locations and trips are recorded every day. Studies of specifıc physical activity locations may require longer periods, however, as behavior in specifıc locations of interest may occur less frequently. Clearly the research question will determine the wear-time criteria, but our experience from accelerometer research is to ensure that wear- time expectations are communicated to participants upfront and that frequent prompts (e.g., text messages, phone calls, or e-mail) are used to increase compliance.
Logging of Wear Time Physical activity researchers commonly ask participants to complete an accelerometer wear-time log. This enables them to track when the device was worn and screen for days that do not meet wear-time criteria. The authors recommend that GPS researchers also ask participants to note when the device was worn, when it was not worn, forgotten at home or left in a car, and whether the device was charged. In the log, it is helpful to include a check box for whether the participant stayed in one location all day, as this may look like they did not wear the device.
Battery Charging Many devices have only a limited battery life, which is a critical issue in studies that span several days. Table 4 outlines recommendations for increasing compliance when researchers expect study participants to charge the device.
Data Processing and Cleaning As highlighted already, GPS data are likely to include some data points that are affected by interference (Table 1). Researchers must therefore decide how to handle missing or erroneous data. Filtering methods may be employed for systematic errors and smoothing techniques may remove random errors. In addition, the raw GPS data require some processing and aggregating to create meaningful variables for analyses such as distance traveled or mode of
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Table 4. Compliance to GPS device charging 1. Send reminder messages to charge the device, preferably daily 2. Instruct participants to log device charging as this reminds participants to wear and charge, but also allows researchers to track if the battery dies or the device is not worn 3. Provide participants with tips to improve compliance including: Clearly explaining that the study requires a certain amount of wear time and that this is being tracked Reminding participants that battery charging will ensure that valuable days of wear time are not lost because of battery failure Suggesting participants charge the device in a location where they will see the device the next day, for example, next to a cell phone, toothbrush, or bed
transportation. In the future, journal reviewers may become more aware of GPS data issues and begin to require detailed processing information as is now common practice for accelerometry research. Disclosure of data processing and testing of the assumptions in validation studies will be key to rapid progress in this new and emerging fıeld.
Computing Derived Variables and Data Smoothing The fırst step in processing GPS data is to compute derived variables (e.g., duration, distance traveled, direction [bearing], speed, acceleration, elevation delta, grade) from the inputs supplied by the GPS (time-stamp, latitude, longitude, elevation). Filtering for unrealistic speed and acceleration values should take place after datasmoothing techniques are employed. Data smoothing reduces random noise in complex data sets by focusing on the primary pattern in the data and replacing points outside that pattern with plausible points that match the pattern. Some researchers suggest points with speeds above 50 m/s or accelerations above 10 m/s2 should be deleted.37 Excessive speed, acceleration, distance traveled, and/or elevation deltas are often caused by multipath reflections. Likewise, small (⬍30 m) or repeated back-and-forth movements are typical of indoor jitter where the building interferes with the GPS signal and creates multiple points within a short distance that do not represent normal human movement. The researcher has to decide what constitutes implausible movement for their environment and population. Smoothing methods employed by transportation researchers include a moving average approach,38,39 a modifıed Kalman fılter,40 a Gauss kernel smoothing approach,37 correc-
tions for the warm start/cold start problem,41 and interpolation of missing data.38
Detecting Locations, Trips, and Modes of Transportation Once the errors have been identifıed and either reduced by smoothing or replaced with plausible estimates of likely location (imputations), additional analyses such as location clustering, trips, and modes of transportation can be performed.
Location Detection A cluster process can identify locations of interest by determining areas containing a high concentration of points and then computing the centroid of each location. Although there are many different cluster-detection algorithms, Kernel methods and k-means algorithms are the most common. Once clusters are detected, the processing may collapse points within a given radius of the centroid to the coordinates of the centroid and thereby creating a location. This can be particularly helpful for indoor locations where jitter can be distracting.
Identifying Indoor GPS Points Some GPS devices can be confıgured to report the number of satellites detected overhead, the number of satellites used to compute the location, and the signal strength of each satellite. When outdoors, the GPS detects more satellites and receives a stronger signal from each. By considering the ratio of satellites used or satellites detected and the total signal strength, the indoor/outdoor condition can be estimated.
Trip Detection Transportation researchers commonly use “dwell time” to detect a journey end point. Dwell time is the length of time when there is no movement (i.e., very small distances between two points). The defınition of dwell time in the literature ranges among 45,42 300,43,44 and 900 seconds,37 with most studies applying a 120-second threshold. Transportation researchers have also established potential trip end points by point density calculations: counting how many of the 30 preceding and succeeding GPS points are positioned within a 15-m radius. If the sequence of points with a density higher than 15 lasts for at least 10 points or 300 seconds, then it is assumed that a moving journey (i.e., a trip) has ended. Other options include a criterion of zero speed37,45,46 or a change in bearing of 180 degrees47,48 to defıne trip end. Tsui and Shalaby46 reasoned that most mode changes indicating the start or end of a vehicle trip require a walk trip, and that walking speeds can reveal a change in mode. www.ajpmonline.org
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Clearly, accelerometer data could help refıne such algorithms with large changes in accelerometer counts from sitting to walking behavior.21
Identifying Mode of Transportation Once trips are identifıed, the mode of transportation can be assigned. A simple algorithm would average the speed over the course of the trip and classify transport mode based on predefıned cutoff values. A problem arises on multi-modal trips; for example, the average speed of a walking–vehicle–walking trip may result in the entire trip being classifıed as a bicycle trip. If pause points and changes in speed can be identifıed, then analyses can be performed on each of the trip segments, resulting in a better classifıcation. Because average speeds can be similar across transportation modes in certain circumstances (e.g., bus, car, and bicycle in a downtown area with traffıc and multiple stop lights), studies have also employed GIS layers to identify transit stops and distinguish some modes more clearly.49 Stopher et al.41 worked with a stepwise elimination of modes based on average and maximum speeds; proximity to certain network elements, such as bus stops or train stations; and the deviation from the street network. Other studies employ median speed, 95th percentiles, and acceleration distributions to identify modes. These statistical parameters were explicitly chosen over the average speed or the maximum speed and acceleration to make the algorithm more robust against outliers. Bohte and Maat49 provide clear logic rules for removing track points, detecting trips, and attributing modes in the appendices of their publication. Others50,51 have started to use machine-learning algorithms to detect transportation modes. Although some sample techniques and references have been provided for researchers to be aware of the possibilities for data processing, these procedures are complex and require support from computer scientists or mathematicians. This is another resource that GPS researchers should be prepared to employ. Studies21,52 have demonstrated up to and beyond 90% accuracy to detect transportation modes and trips. Although these results indicate some level of confıdence in trip prediction, it is not clear under which circumstances the algorithms work most effectively. Algorithms, however, have not been validated against a criterion standard such as observation data in free-living conditions. They are often correlated with participant recall data, which are inherently biased.39 Another issue with current processing algorithms is they have been developed in isolation for single research problems with a single device and thus algorithms may not transfer well to other studies or populations. Researchers at the University of California, San Diego, have November 2011
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developed a flexible web-based program that processes GPS data but allows the researcher to set the parameters best suited to the device and study context (ucsd-palmsproject.wikispaces.com).
Integrating GPS Data in a GIS Much of the true potential of GPS data for health research can be maximized when GPS data are compiled in a GIS to be combined with other spatial data.7,8,13–16,18,20,22,26 –29 Integration of GPS data into a GIS allows researchers to categorize their data in multiple ways based on location (e.g., using a GIS layer containing all public parks to identify GPS data points within these parks). GIS also allows researchers to construct exposure measures to various environmental characteristics. Environmental exposure measures in this context include a wide range of topics. More traditional environmental exposure measures such as air pollution on a daily cycle trip can be determined by plotting a GPS route against air pollution (model) data. Exposure to environmental characteristics that are thought to affect the physical activity (e.g., presence of intersections, mixed land use, green space along travel routes, and living and work environments) can also be determined. The addition of temporal weightings for exposures is also possible because of the time-stamps in GPS data. Unfortunately, the added detail and precision that come with GPS data increasingly reveal a lack of detail in commonly available GIS layers. The use of GIS also has some disadvantages that should be mentioned. As with other comprehensive software packages, GIS software requires a relatively high skill level and substantial experience to master. For researchers without GIS skills, cooperation with other researchers with the necessary GIS expertise is advisable. In addition, researchers need to know what GIS data are available from which agencies, another task that requires specialist knowledge and experience.
Future Directions Although the challenges of incorporating GPS devices and data into research are currently still multiple, there is great potential for progress in this fıeld of research. Researchers may detect temporal and spatial patterns of multiple behaviors that relate more closely to health outcomes, moving us away from focusing on single specifıc behaviors or locations that can explain only part of the picture. Future challenges will include the use of advanced spatiotemporal analysis techniques to assess behavior patterns across time and space. Other issues include appropriate participant selection and sample sizes, given increased variability in environ-
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mental exposure but perhaps a greater focus on lessfrequent specifıc behaviors. The increased environmental variability from the study of multiple locations and the improved matching of specifıc behaviors to specifıc locations will likely lead to greater effect sizes. The scope of GIS layers available will also have to reflect the large distances covered by individuals in their daily travel, possibly crossing county or city lines. Cummins et al.53 have already suggested how advances in GPS will inform theories of health and place. The new data collection methods also will challenge how environmental influences are determined. It will be challenging, however, to pull apart specifıc environmental cues given the potential to accumulate exposures across days and environments. In addition, the strength and draw of multiple environmental cues is unclear. It may be important to weight locations that are large and those situated at corners (e.g., fast-food outlets). Speed and mode of travel may also influence reactivity to exposures. The integration of GPS chipsets and accelerometers into present-day smartphones has introduced another potential method for monitoring health-behavior locations.23,24 Many smartphones make use of assisted GPS technology, such as wifı and cellular positioning. This results in faster fıxes and less drop-out: When the GPS signal is lost, the position of the phone within a circular buffer is estimated using backup technologies. The device is a fully functional mobile phone, which means the burden to users associated with carrying one or more research instruments is decreased. In addition to datalogging capabilities, data can be streamed in real time to researchers via the mobile network. User prompts can be delivered in real time to increase compliance, charge the battery, or even distribute intervention material. Ecological Momentary Assessments can be programmed to collect a wide range of behavioral information at a certain time or given a specifıc trigger (e.g., in a specifıc location).54 More research is required to establish the effıcacy and usability of these devices in the fıeld. Finally, the ethical, and to some extent legal, issues associated with collecting GPS data should also be considered. GPS data have the potential to be very personal and private; researchers can literally follow people’s every move. In many other research fıelds, data are made anonymous before analysis, and results are aggregated before they are presented, published, or shared with other researchers. No information that results in identifıcation of respondent locations should be provided. Useful techniques to protect the confıdentiality of participant data are described in the book Putting People on the Map.55 However, GPS data that are spatially aggregated may not contain the needed detail to reveal possible relationships between behaviors and environmental ex-
posure. Analyses need to be run with the maximum amount of spatial detail, and although this of course does not reveal the name of a respondent, it does reveal where he or she lives and works, which routes he or she takes, and which places he or she visits, for how long, and at what time. Depending on national legislation and data protection laws, this might present problems for data analysis and, in particular, data sharing or pooling among researchers.
Conclusion The use of GPS in health research is clearly becoming more prevalent. This paper reflects the state of the art in 2011. As collecting GPS data in large quantities becomes easier, data processing and analysis become increasingly challenging. If GPS procedures can be standardized, it will advance the fıeld and allow more-meaningful comparisons across studies and populations. This paper provides an overview of device-selection criteria, GPS settings, participant data collection, data cleaning, data processing, and integration of data into GIS. Before commencing on a GPS study, it is suggested here that researchers select and fıeld-test relevant devices, and carefully consider battery life, memory, satellite communication, fıx time, device features, and software. The GPS device should be set at an epoch short enough for suffıcient detail, but long enough for the device memory. Data processing and cleaning should not be taken lightly and benefıts from expertise in advanced data-handling techniques. The creation of relevant environmentalexposure parameters in GIS also requires considerable expertise. Collaboration with other researchers is therefore highly recommended. To this end, the GPS in Health Research Network (GPS-HRN) was founded in 2009 and currently includes more than 150 members from 22 countries (www.gps-hrn.org). Ongoing academic discussion and standardized guidelines like those provided herein will help to advance understanding of the influence of time and space on health behaviors. No fınancial disclosures were reported by the authors of this paper.
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