Portable Global Positioning Units to Complement Accelerometry-Based Physical Activity Monitors : Medicine & Science in Sports & Exercise

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Objective Monitoring of Physical Activity: Closing the Gaps in the Science of Accelerometry

Portable Global Positioning Units to Complement Accelerometry-Based Physical Activity Monitors


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Medicine & Science in Sports & Exercise 37(11):p S572-S581, November 2005. | DOI: 10.1249/01.mss.0000185297.72328.ce
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Emerging theoretical models aimed at understanding the role of neighborhood contextual factors as barriers or supporters for physical activity have shown promise in explaining individual behavior (14,25). An expanded set of factors hypothesized to influence physical activity behavior, such as social, social-environmental, and physical environmental factors, are now being studied with the aim of identifying relationships and testing potential interventions.

Through its relevance as a social-environmental factor or community-level factor, the built environment has emerged as an area of interest to promote physical activity. Prevailing development patterns, with separated residential and commercial land uses, increased reliance on automobile travel, and a lack of adequate infrastructure to support bicycling and walking, may act as barriers or inhibitors to physical activity (20). Indeed, built environment interventions may be one of the most effective strategies for improving physical activity and weight status (5,21). Despite finding moderately positive relationships between environmental factors and physical activity behavior among adults, recent reviews also have identified a considerable number of studies showing no statistical association (8,11,15).

Several explanations for discrepancies among the findings of recent research are likely. First, physical activity is measured and analyzed in different ways. Similarly, objective environmental measures are often disparate, limiting comparability and the ability to replicate results across studies (11,19). Second, the environments examined tend to be limited to the vicinity of participants' place of residence even though individuals may be physically active at other locations. A third explanation is that, faced with an intervention that removes physical activity barriers, some individuals may shift the locations where their physical activity occurs, from elsewhere to the environment with lowered barriers. The latter may explain why recent studies evaluating trail interventions have shown increased trail use but no changes in total physical activity (1,12).

Critical to validating some of these explanations is an understanding of where physical activity takes place. The current study examined the usefulness of complementing accelerometry-based physical activity measures with spatial data from a portable global positioning system (GPS) in order to determine where physical activity occurs. GPS is now used in a variety of commercial and research applications, such as farming (22), geology (6), environmental epidemiology (16), transportation planning (27,31), and exercise science (9,24). Recent technological improvements have resulted in portable GPS units with adequate memory to store positional data over time, thus offering opportunities for obtaining locational information at low cost. However, GPS technologies have limitations: they regularly fail to record positions indoors and they can fail to record under heavy tree canopy and in dense urban areas. In the context of urbanized areas, the application of GPS technology to understanding activity location requires evaluative pilot-testing for reliability and validity to ensure feasibility and lack of systematic bias. Our study contributes to the evaluation of GPS to complement objectively measured physical activity first by examining the GPS units' battery life and their reliability and validity in different environmental contexts. Then we conducted a pilot study in which accelerometers and GPS units were concurrently worn by adults to identify and examine physical activity location.



Units and data recorded.

We tested six off-the-shelf Foretrex 201 portable (83.8 × 43.2 × 15.2 mm) GPS units (Garmin Ltd., Olathe, KS), each weighing 78 g. The units are designed to be worn on the wrist, upper arm, or attached to a belt around the waist. An internal nonvolatile memory card provides the unit with the capacity to store 10,000 points before the data require downloading. The units were set to record the positional coordinates of their location at 30-s intervals with the Wide Area Augmentation System (WAAS) enabled. The WAAS consists of satellites and ground stations that correct GPS signal errors caused by atmospheric disturbances, timing, and satellite orbit errors, resulting in increased positional accuracy.

The map datum used was World Geodetic Survey 1984 and the position format was latitude and longitude in degrees and minutes (HD° MM′). Recorded data, including latitude, longitude, date, and time of day, were downloaded to a personal computer using MapSource Trip and Waypoint Manager (Garmin Ltd.).

After the data for each test were collected, we cleaned each data file using a computer program developed for this study (available on request). This procedure removed data headers, removed data when the unit was unable to record a position, converted coordinate information into decimal degrees, and transformed the data into wide character ASCII format to enable further processing with geographic information system (GIS) software.

Static tests.

We conducted tests of battery life and recharge time, validity, interunit reliability, and body placement. Battery life was tested by the time it took to fully recharge the battery of a single unit on seven trials (N = 7) and, for the same unit, the time to exhaustion when fully charged (N = 14). Data for the validity and interunit reliability were collected by simultaneously placing each unit for 1 h on a geodetic point (latitude 35°54′40.12898" and longitude 79°3′2.54092" NAD 83) maintained by the North Carolina Geodetic Survey. The simultaneous placement of the units controls experimentally for any weather and time-of-day effects. Each unit recorded 121 time-synchronized data points (N = 726). Because the units did not move from the known geodetic point, these tests could be considered assessments of “static validity” and “static interunit reliability.” Static validity was determined by visually inspecting a GIS map of recorded coordinates vis-à-vis the known coordinates of the geodetic point. Static interunit reliability was assessed using Bland–Altman plots by comparing the distance between each unit and the geodetic point relative to the same distance from all other units. This resulted in six plots, one for each unit.

Free-living tests.

We collected positional data from the six GPS units in each of three different built environment scenarios: open space with some tree canopy, clustered development (high-density development with open space surrounding it), and urban. Each scenario contained 25 locations, spaced an average of 15, 42, and 24 m from each other for the open, clustered, and urban scenario, respectively. Every unit was worn by study staff three times as the staff moved from location to location in each scenario (yielding 18 points per location). Upon arrival at each location, study staff manually pressed a button available in the GPS units, which automatically records the coordinates of the location. Interunit reliability was assessed by displaying the cumulative distribution of distances between each unit's recorded position at each location and the average position indicated by the other five units at that location.

Body placement tests examined how placement of the unit (waist or ankle relative to wrist) influenced the amount of positional data collected by each unit. Study staff simultaneously wore units on the right wrist, waist, and on the right ankle while moving from location to location in the open space and clustered development scenarios described above (25 per scenario). Unfortunately, manually pressing a button for three units at once was cumbersome and introduced measurement error. Thus, the protocol was changed so that when each location was reached, staff recorded the time and remained at the location for 40 s before proceeding to the next location. This ensured that each unit recorded the coordinates of the location at least once while controlling experimentally for atmospheric and time-of-day conditions that may influence the units' accuracy. By comparing the staff-recorded timestamps to the timestamps collected by the GPS units, it was possible to determine which points were recorded at each location. We used two-sample t-tests with unequal variances to compare wearing the GPS unit on the waist and ankle relative to the wrist.


Battery tests showed that units operated continuously an average of 15.97 h (SD 1.01) and recharge time averaged 2.66 h (SD 0.1). For evaluating static validity and static reliability, Figure 1 shows the spatial distribution of 726 observed GPS points relative to the known geodetic point. Many points overlap because the position recorded was the same. The average distance recorded from the units to the geodetic point was 3.02 m (SD 2.51) with 81.1% of observations within 5 m and 99.4% of observations within 15 m of the known point.

FIGURE 1—Spatial distribution of observations relative to geodetic point. The cumulative frequency of points by distance to the geodetic point is 589 (81.1%) within a 5-m buffer, 710 (97.8%) within a 10-m buffer, 722 (99.4%) within a 15-m buffer, and 725 (99.9%) within a 20-m buffer.

For the six Bland–Altman plots to assess static interunit reliability, mean bias ranged between −0.22 and 1.86 m, with an average of 0.90 m (SD 0.74). The slopes of the scatter of points in the plots (Pearson correlation coefficients) ranged between 0.28 and 0.76, with an average of 0.53 (SD 0.18). A sample Bland–Altman plot of unit three and the average distance recorded by all other GPS units are provided in Figure 2. All other plots (not shown) display similar cases of positive correlations, suggesting that error is increasing with the magnitude of the mean.

FIGURE 2—Bland–Altman plot of GPS unit 3 and average distance from remaining units. The horizontal solid line represents the mean bias. The:
dashed lines represent the 95% band around the mean bias. The sloping solid line represents the Pearson correlation with slope r and significance P.

For the 25 locations in the open space scenario, the mean estimated distance between each unit's recorded position and the average position of the other five units was 10.7 m (SD 11.9). For the clustered development scenario and the urban scenario the mean values were 20.1 m (SD 21.8) and 18.5 m (SD 18.4), respectively. Figure 3 shows the cumulative distribution of distances by location in the clustered development scenario. The same figures for the open space and urban scenarios (not shown) indicate higher accuracy in those scenarios.

FIGURE 3—Distribution of distances between recorded positions and the estimated average position of remaining GPS units by clustered development scenario location (18 points per location).

Finally, the body placement tests show that wearing the GPS unit on the wrist yielded data points 100% of the time in the open space scenario and 86.8% of the time in the clustered development scenario. Compared with the wrist, wearing the GPS unit on the ankle resulted in fewer records. In the open space scenario, wearing the unit on the ankle resulted in 1.6% fewer records (P < 0.05), but wearing the unit on the belt was no different than wearing it on the wrist (P = 0.16). In the clustered development scenario, wearing the unit on the ankle resulted in 21% fewer records (P < 0.01), and wearing the unit on the belt resulted in 5.5% more records than wearing it on the wrist (P < 0.05).


In this study, we tested the battery life and recharge time, static validity, static reliability, interunit reliability in simulated free-living conditions, and body placement of portable GPS units. Our results suggest that limited battery life is a significant limitation of the portable GPS units we evaluated. Fully charged batteries lasted an average of 15.97 h, suggesting that GPS data are likely to be missing for the later part of a day for adult participants. However, this figure is slightly higher than the typical amount of time in a day participants wear accelerometers (10,29). Although other GPS units on the market appear to have higher battery life, we believed that the ease of portability, memory capacity, functionality, and compactness of the GPS units used in our study were more important than prolonged battery life. The degree to which the limited battery life biases analyses of the locations in which physical activity takes place is a matter for further empirical investigation. Limited battery life also implies that, in multiday studies, participants would be required to recharge the unit, adding burden to their participation, and increasing the probability of noncompliance. Another limitation is that we did not compare positional accuracy to other portable GPS units available on the market.

The multiple tests of validity and interunit reliability suggest that the positional information recorded by the units is sufficiently precise to track participant's movements, provided that a position is recorded. Figure 1 suggests that most observations are scattered in close proximity of the known geodetic point. The fact that 99.4% of recorded observations are within 15 m of the geodetic point is noteworthy. The Bland–Altman plots suggest that most observations are well within the limits of agreement. For Figure 2, only 2.5% of the observations lie beyond a 95% confidence band (−0.40, 1.04) around the mean bias. Because differences within the 95% confidence interval are acceptable for our current purposes, static interunit reliability is deemed high. The increase in the difference as the mean increases suggests that the limits of agreement are wider apart than necessary for small means and narrower than they should be for large means. However, our assessment regarding interunit reliability does not change. Kinetic interunit reliability results show the effect of the built environment on positional accuracy. Locations next to buildings or under dense tree canopy showed higher interunit error than locations that were more open. The error in the open space scenario was also lower than in the other two scenarios.

Finally, results from testing the body placement of the GPS units provide two findings of importance. First, the usable data yield from the units (86.8 and 100% in each scenario tested) appears satisfactory. It means that in the worst case scenario 13.2% of the time the units either recorded data that were not usable or failed to record any data. Second, wearing the unit on the ankle is not recommended.

Our examination thus far is limited in several ways. First, we did not test for any potential interference that may exist among GPS units even though many of our tests were conducted wearing several GPS units at the same time to guarantee time synchronicity. Second, the tests based on data collected at different times may suffer from random error related to the location of satellites. Third, we did not examine whether disabling the WAAS could be an effective strategy for saving battery power. Further tests may determine the tradeoff between the additional power saved and the potential decrease in positional accuracy resulting from disabling the units' WAAS. In sum, this first part of our study shows that the positional information collected with the portable GPS units is sufficiently precise to track the movements of individuals carrying the units.



Study purpose and units used.

Having established the acceptable performance of the GPS units, the principal aim of this second part of the study was to determine the degree to which the units can complement accelerometer-based physical activity measurements in order to identify where physical activity takes place. A secondary aim was to show how the combination of these two data sources provides useful information for testing hypotheses regarding the relationship between physical activity and the built environment. The same six GPS units described previously were used for this study, but they were set to record every minute. For the activity monitor, we used the dual mode ActiGraph model 7164, formerly known as the Computer Science and Applications (CSA) and Manufacturing Technology Inc. (MTI) (ActiGraph, LLC, Fort Walton Beach, FL). Previous studies have demonstrated the ActiGraph 7164 to be reliable and valid (13,30). Each activity monitor was calibrated and tested to ensure adequate operation and was set to record activity in 1-min epochs. The clock in the activity monitors and the GPS units were synchronized to ensure the data could be matched correctly.


Volunteer participants were recruited using University of North Carolina–supported e-mail lists and fliers posted in the town of Chapel Hill. A cash incentive was provided to all participants upon their completion of the study. All potential participants underwent a screening procedure to ensure they were at least 18 yr old, resided at a local nonuniversity address, and had the ability to walk continuously and unassisted for 20 min. Participants were recruited as evenly as possible to achieve a balanced gender distribution. The study sample (N = 35) comprised 21 (60%) females aged 23–61 (mean 32.1, SD 10.7) and 14 (40%) males aged 20–58 (mean 31.5, SD 10.6). Participants were recruited on a rolling basis during a 4-wk period in September and October 2004. The study procedures were approved by the university's institutional review board and written consent was provided by all participants.

Data collection.

Each participant was asked to wear a GPS unit and activity monitor for three consecutive days. Participants were instructed to wear the GPS and activity monitor during all waking hours except when showering, bathing, swimming, or during activities that would result in submerging the units in water. Consistent with the literature, participants were instructed to wear the ActiGraph unit on the right hip using either a belt clip or nylon carrying pouch (7,23,26). They were also instructed to carry the GPS units on either their wrist or waist and always to charge the GPS unit overnight. After the 3 d, study staff interviewed each participant to collect information on their experience carrying the devices. GPS data from one participant were not recorded because the participant did not adhere to the GPS protocol. GPS data from two other participants were not recorded because one unit malfunctioned with a participant and the unit was mistakenly given to a second participant. The unit was exchanged by a retailer and no further problems arose. The three participants with no GPS data were excluded from the analysis. Eight other participants had no GPS readings for one of their three days. Because the units for these eight participants were determined to be working properly, their accelerometer data (and lack of GPS data) were included in our analysis.

Processing of ActiGraph, GPS, and neighborhood characteristics data.

GPS data were downloaded using the same process described earlier. ActiGraph data were downloaded using a reader interface unit and Actisoft Analysis Software 3.2 (Fort Walton Beach, FL). A computer program developed for this study (available on request) was used to identify and calculate the number and duration of moderate physical activity (MPA), vigorous physical activity (VPA), and moderate to vigorous physical activity (MVPA) bouts per participant. The program also merged participant's activity monitor data with the corresponding GPS data according to the date and time information in each unit.

To identify intensities of physical activity and to classify bouts as MPA or VPA, we followed recommendations in Swartz et al. (26). Consistent with Catellier et al. (2), we defined a bout as 10 or more continuous and consecutive minutes of physical activity at or exceeding the defined threshold values. Each bout had a 30% tolerance for activity level not meeting the threshold values. For example, in a 10-min bout, 3 min could be under the threshold level. A participant was presumed to be wearing the activity monitor from the time when the first nonzero value was recorded and was followed by one or more nonzero values being recorded. A participant was considered as not wearing the monitor if 20 or more consecutive minutes had zero counts. Missing data were not imputed. No outliers (counts exceeding 15,000 for 5 min or more) in the data were identified.

All bout information with matching positional data was imported to ArcMap 8.3 (ESRI, Redlands, CA) for further processing. Participants' GPS data were overlaid with GIS layers containing land use (residential, commercial, office, park, or other), the road network, building footprint, and digital orthophotographic images (1-m resolution; 1:1200 scale) provided by municipal and/or county planning offices in the study area. A building footprint is the physical outline of the building on the property where it stands. The address of each participant's primary residence was geocoded using ESRI Streetmap in ArcCatalog (ESRI). Thirty-one addresses were successfully geocoded using default settings and one address was manually geocoded. Addresses of the three participants without GPS data were not geocoded.

The GIS software was used to classify bouts with GPS data in terms of the locations where the bouts occurred (indoors, outdoors in neighborhood, outdoors out of neighborhood). A bout was identified as occurring indoors if 33% or more of its GPS data were located within a building footprint. If not indoors, a bout was then classified as outdoors in neighborhood if 33% or more of the positional data for the bout fell within a 1.54 km (1 mi) circular buffer around each participant's geocoded address. Other studies have used similar neighborhood definitions (3). All other bouts with GPS data were classified as occurring outdoors out of neighborhood. This classification allowed for comparisons of physical activity (bout, average bout duration, and percent of total MVPA time) by where it occurred. When GPS data were available for part of the bout only, we relied on that data to identify the location where the bout occurred. Because this approach may introduce some misclassification for bouts that were partly outdoors but mostly indoors, we also compared bouts with any GPS data available for 1 min or more to a stricter classification criterion that relied on bouts having 30% or more of GPS data available for the duration of the bout.

To perform a preliminary exploration of the relationship between neighborhood characteristics and physical activity, we calculated measures of population density, housing density, street density, street connectivity (three- and four-way intersection density), and accessibility to commercial or office land uses and parks within each participant's neighborhood. Population and housing information were drawn from the 2000 population census' block-level data. Data for blocks not lying completely within the neighborhood boundary were adjusted proportionally based on the block area inside the boundary. Street connectivity was calculated using the ArcScript Point and Polyline Tools v.1.2 extension in ArcView 3.2 (ESRI). The presence of mixed land uses within the neighborhood was coded using a binary variable equaling 1 if the neighborhood had at least one parcel (entirely or partially) with a retail, institutional, or office land use code designation, and 0 otherwise. The presence of parks within the neighborhood was coded using a binary variable in a similar fashion to mixed land uses.

Data analysis.

Comparisons of average duration of indoors, outdoors in neighborhood, and outdoors out of neighborhood bouts were made using unpaired t-tests assuming unequal variances. Comparisons of percentages of bouts occurring at each location were made with χ2 tests. Statistical significance tests are two tailed.

Pearson's correlation coefficients were used to examine the association between each participant's neighborhood characteristics and MVPA time. We also described neighborhood characteristics stratified by where the majority of participants' MVPA time (with GPS data) occurred, indoors, outdoors in neighborhood or outdoors out of neighborhood. To determine whether neighborhood characteristics differ significantly between participants among the three strata, we used an unpaired t-test assuming unequal variances for continuous variables and Fisher's exact test for variables measuring percentages.


Table 1 summarizes participants' physical activity. Participants wore the activity monitors an average of 13.7 ± 1.8 h·d−1 and had an average of 142.01 min of MVPA per day, of which 92.79 min were in a bout. The exit interviews confirmed that eight participants reported noticing that the GPS units ran out of battery life before the end of their day at least once. Figure 4 displays three bouts of MVPA for one participant. The map shows the route taken for each bout and select attributes of the environment in which the physical activity occurred. Table 2 summarizes the results of matching GPS positional data with physical activity accelerometry, stratified by the criterion used in determining GPS data availability for a bout (1 min or 30% of bout). GPS data were available for 59.3% of all bouts (67% of MVPA time) using the 1-min criterion and for 44.9% of all bouts (53% of MVPA time) using the 30% of bout criterion. Comparisons between bouts with and without GPS data suggest that those with GPS data tended to be longer than those without GPS data (P ≤ 0.00). Of the bouts with GPS data, there were fewer in-neighborhood bouts than out-of-neighborhood bouts. However, the in-neighborhood bouts were longer (P < 0.05) and contributed more to total MVPA time than did out-of-neighborhood bouts. These differences hold regardless of the GPS data inclusion criterion applied. No indoor bouts with GPS data were identified.

Daily average of participants' MVPA (N = 96).
FIGURE 4—Sample physical activity bouts for a participant and their location relative to streets and land use.
Descriptive statistics of the location of MVPA bouts stratified by the availability of GPS data in bouts.

The consequences of applying the stricter GPS data inclusion criterion are shown also in Table 2. The average duration of out-of-neighborhood bouts retained from applying the stricter criterion is not different than the average duration of out-of-neighborhood bouts rejected due to insufficient GPS data (P = 0.914). However, applying the stricter criterion leads to rejecting in-neighborhood bouts that tend to be shorter than the bouts that are retained (P < 0.1).

Table 3 summarizes the characteristics of the neighborhood environment where participants reside and shows correlations with total MVPA. These six attributes of the neighborhood built environment and MVPA had no statistically significant correlations. Using the positional information provided by the GPS units, we classified participants into two groups according to where the majority (>50%) of their physical activity occurred (in-neighborhood or out-of-neighborhood). Table 4 presents summary neighborhood characteristics stratified by each group. Participants who got most of their moderate and vigorous physical activity (MVPA) in their neighborhood tended to live in environments with higher population density (P ≤ 0.00), higher housing unit density (P ≤ 0.00), higher street connectivity (P ≤ 0.00), and higher street density (P < 0.05). When the stricter GPS data inclusion criterion was applied, the statistical significance of these associations increased and the variables measuring the presence of parks and mixed land uses became statistically significant (P < 0.05).

Characteristics of participants' neighborhoods and their unadjusted correlation with MVPA minutes in a bout (N = 32).
Characteristics (mean (SD) or percentage) of participants' neighborhood of residence stratified by where the majority of physical activity takes place.


In this part of the study, we piloted the concurrent deployment of portable GPS units and accelerometers to determine where physical activity takes place. Using time and date information, we determined that more than half of all MVPA bouts and more than two thirds of all MVPA time could be matched successfully with positional data from the portable GPS units. Our results also show that the bouts with GPS data are longer than the bouts without GPS data. This is likely due to the fact that indoor physical activity is not likely to yield GPS data and activity outdoors, where GPS data are more likely to be recorded, is generally longer than activity indoors. Further research should confirm these two potential explanations. We also identified limitations from the GPS units used. The data recorded did not allow for differentiating when the GPS unit was turned off (or fully discharged) from when the GPS unit was turned on but unsuccessfully determining and recording its position (e.g., indoors or under heavy tree canopy). In either case, the GPS units record no information to the memory card. Although this preserved memory space, it limits the ability to determine whether participants are adhering to the study protocol, and to identify the location of activity (indoors vs outdoors).

Following traditional nonspatial correlational analyses of physical activity and built environment characteristics, we found no association between our neighborhood measures and total MVPA (unadjusted analyses, Table 3). The limited sample size did not allow us to adjust for other covariates. By contrast, a second approach to examining the data highlights the usefulness of accounting for physical activity locational information by associating neighborhood envirronment characteristics with where physical activity occurs (Table 4). Despite the limited sample size, our preliminary evidence shows that in-neighborhood physical activity tended to occur in environments with characteristics often hypothesized to support physical activity (19,20). Similar results were obtained for participants having any physical activity in their neighborhood (as opposed to the majority) vis-à-vis those not being active in their neighborhood (results not shown). The findings are consistent with research suggesting that increased accessibility to opportunities for activity (8), denser areas (18), and areas with higher connectivity (18) are related to increased physical activity. They also echo recommendations to examine physical activity in specific settings in order to clarify its association with the built environment (11). Further analyses can examine the characteristics of locations away from participants' neighborhoods but in which participants were physically active.

Our pilot study had several methodological limitations, including the confined geographic scope and the limited sample size. Another limitation was the potential for misclassifying bouts as indoors, outdoors in neighborhood, and outdoors out of neighborhood. Because the building footprint data have a horizontal accuracy of 0.518 m, misclassification of the location of activity due to footprint error is unlikely. Thus, misclassification errors are likely to result mostly from measurement errors with the GPS units. Bouts were classified using the only and best locational information available to us collected by the GPS units. However, when bouts occurred mostly indoors but also outdoors, they would be incorrectly classified by our method. Our comparison of criteria to classify bouts (1 min and 30% of bout) was intended to address this limitation, although the criteria were selected arbitrarily. Despite the similarity between characteristics of in-neighborhood physical activity using both criteria to classify bouts and to minimize the potential for misclassification, we recommend that researchers use the more stringent criterion of 30% of bout (or higher). Further research can examine the sensitivity of the results to other criteria.

A final shortcoming is the lack of information about the type of physical activities (recreational or for travel) undertaken by participants. Because the built environment is expected to influence the various types of physical activity differently (17,19,28), supplementing positional data and accelerometer data with information on the type of physical activity could provide additional valuable information to better understand the role of environmental factors in explaining physical activity behavior. The application of GIS to measuring the built environment and understanding its role in physical activity behavior deserves additional research. Although our measures of the built environment parallel similar measures tested elsewhere (4), the high resolution of the positional data enable the calculation of finer grain built environment measures than before. Last, the use of GIS to classify the location of bouts as indoors, outdoors in neighborhood, and outdoors out of neighborhood needs further exploration. For example, land use information can be combined with the last GPS coordinate recorded before a bout and the next GPS coordinate available after a bout to make inferences about the location of the bout.

Additional research regarding how to complement accelerometry with GPS data is necessary. The study focused on a self-selected population with high levels of physical activity. The preliminary relationships identified here must be tested in more diverse populations and in various urban contexts. A larger and more diverse sample of participants may allow for stratifying the analysis by physical activity level to uncover potential behavioral differences by levels of activity.

This pilot study complements accelerometer data by contributing the richness of spatial behavior contained in GPS data. Inherent in the positional data for each participant is information about preferred and nonpreferred outdoor environments for physical activity. As shown by our analysis, the relative comparison between environments promises to clarify relationships and contribute to understanding of physical activity behavior. Coupled with GIS databases that show, for example, the location of green areas, buildings, population activity, and streets, spatial physical activity data provide distinct advantages compared with relying on nonspatial physical activity data. First, they expand the usefulness of objective environmental data captured through GIS databases. Second, our measures were aggregated to a level that made them consistent and comparable with other studies, but given the accuracy of the GPS data, using more disaggregate measures such as street width, presence of crosswalks, and sidewalk width shows increased promise. Finally, the availability of GPS data opens the possibility of validating physical activity diaries.


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