BACKGROUND AND PURPOSE
Stroke is a leading cause of disability. Despite advances in therapeutic interventions, many stroke survivors leave inpatient rehabilitation unable to independently navigate the community.1 However, 75% of these individuals report that getting out and about is either essential or very important to their daily lives.2 Therefore, measurement of community mobility is a meaningful part of the postrehabilitation process for stroke survivors as well as health care providers and third-party payers.
Typically, community mobility is equated with community ambulation and gait speed.3 Although gains in gait speed are correlated with improved function and quality of life for stroke survivors, correlation between clinically measured gait speed and community ambulation is dependent on a threshold speed.4 Clinically obtained values likely overestimate a person's ability to ambulate in the community. For example, impairments such as imbalance, motor dysfunction, fatigability, and cognition may impact community walking independent of gait speed.4 Moreover, nonambulatory stroke survivors who navigate the community through assisted transportation would necessarily be excluded when community mobility is evaluated through gait speed.
Social, cognitive, and environmental factors play a role in community mobility,5,6 limiting the use of gait speed as a predictor of return to community activities and life roles within the person's environment. If community mobility is more than the capacity to ambulate outside the home, then the extent to which and means by which people navigate within their local and broader environments should be included in the assessment of community mobility and will require a new measurement approach to capture these attributes.
Although the effect of environment (eg, shopping malls, city streets, workplaces) is being examined with respect to gait speed and the Six-Minute Walk Test (SMWT) in stroke survivors,7 a new method for the examination of community navigation has begun to be evaluated. Global positioning system technology (GPSt) is now being used to augment accelerometry-based physical activity monitoring, to evaluate walking disability in persons with neurologic disorders, and to track wandering and out-of-home mobility in persons with Alzheimer disease. In addition, it has been proposed as a way to define a community mobility envelope for older adults.8–14
The inherent value in GPSt is that it affords the ability to discern the extent of one's community mobility, the means by which a person navigates to places and events, and whether they return to meaningful destinations for their social, leisure, and occupational goals. GPSt has also begun to be used to identify the purpose of a particular navigation/mobility event (often defined as a trip), thus informing its relevance to participation goals. The importance of this attribute is evident when one considers that mobility, quality of life, and participation have been observed to decline in stroke survivors.5,15–17
The accuracy of GPS has been reported to be 99.4% for static location (within 15 m of a known reference point).12 It is well known that being indoors, proximal to buildings, or under a canopy of trees decreases the reliability of GPS; nevertheless, GPS calculations of walking have been shown to be highly correlated with direct measures of walking speed,18–20 with less than 2% error for community-dwelling older subjects.14 For example, compared with a standardized SMWT, GPS-measured gait distance in 6 minutes in a community environment has been shown to be valid and reliable.21 These studies support the use of GPS as a tool to monitor community mobility. Information on the reliability of GPS for measuring community mobility in our hands is included in Supplemental Digital Content 1 (data on the reliability of GPS, available at https://links.lww.com/JNPT/A21).
Examination methods for community mobility that elucidate the ecological relevance of that mobility with respect to participation in life situations (eg, traveling to work, navigating the shopping mall, attending religious services) may begin to bridge quality of life and participation for persons with disabilities, as delineated in the International Classification of Functioning, Disability, and Health.22 Linking life situations with the meaningful exploration of surroundings may in fact capture the essence of the construct “environment” from an ecological perspective.23 Given the increasing interest and priority of measuring participation and its relationship to contextual (particularly environmental) factors in stroke survivors,24–26 it would appear that the investigation of the role GPSt might play in this endeavor is prudent and timely. The purpose of this case study was to describe the community mobility and navigational characteristics of a stroke survivor using GPSt following inpatient rehabilitation.
HISTORY AND REVIEW OF SYSTEMS
This study was approved by the institutional review boards at Midwestern University and the Marianjoy Rehabilitation Hospital, where the subject was a patient. The subject provided written informed consent to participate. This 56-year-old man sustained a right pontine cerebrovascular accident. He was admitted through the emergency department and then was hospitalized for several days in an acute care hospital before being admitted to the rehabilitation center. He received inpatient rehabilitation on a dedicated stroke unit for 3 weeks. Medical history was significant for hypertension and gastroesophageal reflux. The subject lived in a two-story townhouse with his wife, who was in good health and worked full-time. His hobbies and leisure activities included being active in his church, playing the piano, performing in amateur theater productions, and serving as the music director for local musical productions at high schools and a community college. Prior to this stroke, he worked at a local library as an information technologist, and he reported an active social life. His goals in therapy were to return home as independently as possible, return to driving, and resume his job at the library (see Supplemental Digital Content 1, available at https://links.lww.com/JNPT/A21, for additional details).
The patient was given the standard examination for admission to the rehabilitation stroke unit. He presented with significantly decreased left upper and lower extremity active movement, decreased strength, and a lack of fractionated movement. Manual muscle test scores are not reported here because he was unable to make isolated movements. Shortly after discharge from rehabilitation, his left upper extremity Fugl-Meyer Scale score was 11 (out of a possible 66) and the Modified Ashworth Scale scores for his left upper extremity were as follows: shoulder internal rotation = 3; elbow flexion = 2; forearm pronation = 1+; and wrist and finger flexors = 3 (out of a possible 4 points). He was rated on the Brunnstrom Scale at 2 for the left upper and 3 for the left lower extremity (on a 6-point scale). Results of the same tests on the right upper and lower extremity were normal. Moreover, his initial Functional Independence Measure score for the motor subscale was 70 out of a possible 91 and improved to 85 at discharge (see Supplemental Digital Content 1, available at https://links.lww.com/JNPT/A21, for additional details on his evaluation). At discharge, he required 59 seconds to complete the Timed Up and Go (TUG) test. The Timed Up and Go test has been shown to be strongly, negatively correlated with walking speed in people with a stroke.27 Although the Berg Balance Scale (BBS) and the SMWT could not be administered during evaluation, at discharge from rehabilitation his BBS score was 35 out of a possible 56 points and his SMWT distance was 73 m. The BBS has been shown to correlate with community ambulation for persons with chronic stroke and hemiparesis.28 The SMWT distance has been shown to correlate with reintegration to the community for persons with a stroke.29 He scored 29 out of a possible 30 on the Mini-Mental Status Examination, above the 27-point threshold reported for increased risk of dementia.30
Physical therapy treatment focused on strength and balance training and regaining motor control and functional ability. The patient made good progress over the 3 weeks of rehabilitation and was discharged home. On the basis of his improvements during rehabilitation and on the tests administered at discharge, this patient was deemed to be a good subject for the study because, although he was very limited in mobility at discharge, he had the potential to return to greater community mobility and independence. After discharge he received help from his wife and brother and was referred to outpatient physical and occupational therapy 3 times per week.
The subject exhibited impairments in muscle performance, tone, coordination, balance, and functional mobility, with the average gait speed after discharge well below the 0.66 m/s threshold for community ambulation.4 Yet, despite deficits in balance and gait, he still exhibited a high degree of motivation to return home and participate in life situations such as returning to work. Although his cognition and judgment were intact, like many other stroke survivors, he was at risk for declines in quality of life31 and participation16 following rehabilitation.
GPSt offered good potential to ascertain information on community mobility, given his low SMWT (Table 1) and high Timed Up and Go test scores. It seemed apparent that this subject was facing significant community ambulation challenges. Therefore, using technology that could capture community mobility beyond simple ambulation characteristics was deemed prudent.
Several days after discharge, he was visited in his home by the first two authors (C.C.E. and T.A.H.) and administered the Short Form 36-Item Questionnaire, version 2 (SF-36 V2, Quality Metric) and Reintegration to Normal Living Index (RNLI) surveys. These tools have been shown to be valid and reliable indices of quality of life32–34 and community reintegration,35,36 respectively, for persons with chronic disease and impaired mobility.
The participant was asked to identify 10 locations (targets) in the community he frequented prior to the stroke or that were relevant to his leisure, social, occupational, and cultural participation goals and community involvement. The address and location of the targets were verified on a map with the subject. He was fitted with a GPS unit (Data Logger, GlobalSat Inc., Taipei Hsien, Taiwan) and an accelerometer (GT3X, Actigraph, Actigraph, Pensacola, FL) attached to a single belt (Figure 1) and was instructed to wear the devices while awake except for bathing. The DG-100 Data Logger has been found to have acceptable accuracy when used to monitor human movement and speed in a community environment.37 We chose this unit because of its small size, accuracy, ease of use (allowing the subject to activate it by pushing only one button), and long battery life of up to 20 hours. He was instructed on how to activate the GPS unit and replace the batteries each day from a supply of new batteries issued to him. The participant was instructed to keep the belt buckled and to step into it and pull it up around his waist (see Video, Supplemental Digital Content 2, of subject donning the belt and the GPS device, available at https://links.lww.com/JNPT/A24), because his left hand lacked the dexterity to buckle the belt. He was also given written instructions on these procedures. He demonstrated the ability to perform these tasks. The accelerometer was activated prior to deployment and remained on for the entire week. No subject involvement was required to operate the accelerometer.
The subject was monitored with GPSt and the accelerometer for five separate 1-week periods, on the first, fifth, and ninth weeks as well as at 6 and 12 months after discharge from the inpatient rehabilitation unit. One-week monitoring periods were chosen to capture the subject's weekly, habitual routine. Accelerometry-based studies have shown that 7 days of consecutive monitoring are needed to achieve 90% reliability across a variety of activities.38 He completed the RNLI survey at the end of each monitoring period. At discharge, week 9, and 1 year, he completed the SF-36. The SMWT and BBS test (Table 1) were administered prior to the start of the study, at the end of the 9-week monitoring period, and at 6 and 12 months. The GPS unit recording frequency was set to 30 seconds. According to the manufacturer (GlobalSat, DG-100 Data Logger User Manual Version 1.21), the DG-100 Data Logger has an accuracy of 10 m when operating under standard conditions and 1 to 5 m when the Wide Area Augmentation System is enabled, as was used for this study (see Supplemental Digital Content 1, available at https://links.lww.com/JNPT/A21, for additional information on the reliability of GPS monitoring).
The GPS and accelerometer data were downloaded from the devices at the end of each monitoring period and analyzed with the software provided by the respective manufacturers (Data Logger PC Utility, GlobalSat, and ActiLife-5, Actigraph Pensacola, FL) as well as with Google Earth (Google Inc) and Excel (Microsoft Inc). The GPS analysis program allowed for calculation of speed, distance, and visualization of location of travel as well as limited categorization of amount of travel by speed zones. GPS trackpoint data were uploaded to Google Earth and Excel and analyzed by cross-referencing the location of trackpoints in Google Earth with speed and distances traveled on the basis of the Data Logger PC Utility data and calculations in Excel. An example of trackpoint data displayed in Google Earth for the subject, without identifiers, is shown in Figure 2. Data were calculated for each day of deployment to provide individual and daily trips and target visits and were categorized into activity mode (driving, walking, and traveling by public transportation) on the basis of the unique movement pattern and speed for each type of transportation.
Much of the GPS-related literature that reports “trip” data deals with young, healthy individuals and focuses either on nonvehicular travel (walking, running, or cycling) or vehicular travel.10,39,40 We were interested in capturing both vehicular travel and nonvehicular travel in a person with a physical disability. Therefore, we amended the definition of “trips” from work by Wolf et al41 and Cho et al,10 using the algorithm shown in Figure 3. A “trip” was defined as travel of more than 10 m (10 trackpoints all >10 m from a stationary location or from his home). Trips were further categorized as walking, nonwalking, or a combination of walking and nonwalking on the basis of gait speed and as either a complete or partial trip, depending on whether the subject returned to the original location for that trip. For example, if the subject drove to a coffee shop and stopped for 10 minutes and then returned home, this would be counted as both a partial trip (home to coffee shop) and a complete trip (from home and back to home).
On the basis of the subject's walking speed as determined from the SMWT, the cutoff speed for walking was set at 4 mph or less (1.78 m/s). Speeds greater than this were attributed to nonwalking movement (ie, driving or traveling by public transportation). Further categorization of travel mode was distinguished by examining the route, speed, and pattern of travel. For example, the one incident of this subject's travel by public transportation was verified by observing that his route coincided with a local train route and stops coincided with train stops.
Accelerometer data were analyzed with the manufacturer's software (ActiLife5, Actigraph). Accelerometer data shown in Figure 4 represent the percentage of time the subject spent in different activity categories based on “counts” of activity. According to the manufacturer, “counts” represent changes in acceleration with respect to time, where an acceleration of a threshold value or higher denotes 1 count. To avoid measurement error due to extraneous motion or noise, the digital signal is band-pass filtered to generate units of 0.01664 g/s per count, with a resolution of 0.001664 g per count. The data are displayed as the percentage of total time spent in different activity levels, as follows: sedentary, 0 to 100 counts; light, 101 to 760 counts; lifestyle, 761 to 1951 counts; moderate, 1952 to 5724 counts; vigorous, 5725 to 9498 counts; and very vigorous, 9499 counts and higher. Note that the y-axis in Figure 4 ranges from 50% to 100% to better distinguish activity in the moderate and vigorous range. Actigraph data were used to verify belt wear time and determine the overall activity level of the subject (ie, total time and intensity of activity). The percentage of time the subject spent in different categories of activity was compared with data from the National Health and Nutrition Examination Survey.41
The subject made fair progress toward greater independence in the 10 weeks after discharge as well as in the follow-up periods. Many of the physical limitations or challenges that he faced at discharge continued to be problematic over the course of the subsequent year. For example, loss of left upper extremity function was one of his main complaints, and this did not improve dramatically even at 1 year after stroke. The upper extremity Fugl-Meyer score at 1-year follow-up had improved only to 13, from an initial score of 11. However, he remained positive and motivated, continued to set goals, and had a supportive network of friends and family. He was able to access the community quickly and robustly even in the first week of monitoring. He was also able to start driving again by 6 months after stroke, and he returned to his work and many of his social activities, including taking up a new activity, participating in improvisational comedy at an urban workshop.
Compliance and monitoring time data for all of the monitoring periods by day are shown in Table 2. Although deployment time was fairly similar (ranging from 150 to 168 hours), compliance was highly variable (range, 11.9–77.6 h/wk). During some monitoring periods (week 5 and 6 months), there were entire days when the GPS unit did not record any data or recorded only a single trackpoint. Review of the accelerometer data indicated that the subject did wear the belt, but he may not have activated the GPS unit on those days since movement was recorded by the accelerometer. To save battery life, the GPS unit is set to turn off after a few minutes if no movement is detected (as might occur if kept indoors), but recording is reinitiated when motion is detected. Therefore, we chose not to correct trip and target data for “recording time” (ie, we did not present data as trips per hour or day of recording time) and presented these data only per week of recording.
The primary outcome measures for this case are the target visits and trip data (Tables 3 and 4). The subject made eight target visits and visited 60% of the target locations in the first week of monitoring (first week after discharge). All of the target visits involved both driving and some outdoor ambulation. In this same period, he made 30 trips. During the first 10 weeks after discharge, the subject visited targets regularly (70% visited), but he did so less regularly during the 6- and 12-month periods (40% visited). The trip data for the subject were more variable but demonstrated a pattern of regularly getting out and accessing the community. In the first three monitoring periods, he averaged 26.7 trips per week, and during the two follow-up periods, he averaged 24 trips per week.
The subject's accelerometry data is shown in Figure 4. No major upward trend, but a slight increase in physical activity, was observed over the course of the five data collection periods. The subject was sedentary approximately 91% to 96% of the time, based on count number (0–100). The subject's activity was categorized as light (101–760 counts) 3% to 4% of the time at weeks 1 and 9 and at 6 months, as well as 6% of the time at week 5 and 12 months. Higher physical activity categories were rarely obtained, with less than 3% for lifestyle (761–1951) and less than 1% for moderate or vigorous. Data from the National Health and Nutrition Examination Survey42 indicate that men 50 to 60 years of age spent the following percentages of time in these categories: sedentary, 77.7%; light, 15.1%; lifestyle, 5.6%; moderate, 1.6%; and for both vigorous and very vigorous, less than 1%. On the basis of these normative data, the subject was relatively sedentary and spent less time in light and lifestyle levels of activity than 50- to 60-year-old men without a history of stroke.
During the year of monitoring, the subject improved his gait speed and balance, according to the results of the SMWT and the BBS (Table 1). These improvements in gait and balance were consistent with subjective reports (that arose in conversation) that he was able to get around better. The SF-36 and RNLI survey data (Table 5) also showed a modest improvement in his satisfaction with recovery from the stroke and with quality of life. The component of the SF-36 that increased most was the physical functioning.
The purpose of this study was to describe the community mobility and navigational characteristics of a person who sustained a cerebrovascular accident, by means of GPSt following inpatient rehabilitation. There are several noteworthy results. First, from the trip and target visit data, it is clear that the subject routinely navigated the community and traveled to locations and events predetermined by him as meaningful to his social, leisure, occupational, and spiritual goals. Second, the accelerometry information identified that this subject was sedentary throughout the first year following his stroke, yet his functional mobility measures showed steady improvement over the course of the year after rehabilitation. Also, despite continued improvements in balance, gait function, and physical function, the number of target visits fluctuated within the 12-month period. The number of total trips into the community did not change appreciably from beginning to end. Finally, GPSt recording time and compliance were variable.
Trips and Target Visits
GPSt has been used to describe physical activity in a variety of settings and populations. However, our purpose was not to demonstrate the utility of the technology as a proxy for physical activity but rather to demonstrate its utility in identifying levels of community mobility with respect to continued participation in life situations after stroke. This is a unique and important endeavor because stroke survivors often exhibit restrictions in community reintegration, report a lower quality of life than persons unaffected by stroke, and are at risk for declines in mobility following rehabilitation and upon returning home.6,15,16
For this subject, the most frequently visited target destination was his place of employment. His place of spiritual worship, the local coffee shop, and his outpatient rehabilitation site were all accessed relatively frequently (four, six, and five total visits, respectively) over the course of the study. A favorite restaurant, the professional baseball stadium, and the gym each were accessed only one or two times. This suggests that frequency of visits is target dependent. Compare, for example, the place of worship and the baseball stadium as targets. The baseball stadium was 30 miles away, and travel depended on season, schedule, and potentially cost, particularly if the subject was employed or unemployed at that time. On the other hand, the subject's place of worship was local and accessed consistently during times of religious events (one time per week). Therefore, navigation to some targets may not necessarily increase over time.
The number of target visits per week's recording duration was also quite variable. The most targets visited (n = 10) occurred during week 9. Some targets, such as the coffee shop and the outpatient rehabilitation center, were visited three times during this week. The fewest targets visited during any 1-week recording period occurred at the 6-month point, where the subject visited only his place of employment three times.
The delineation of trips was another way we determined the extent to which the subject was getting out and about. Generally, trips were a combination of driving and walking, favoring driving, largely due to the suburban environment in which the subject lived. The ratio of walking to driving may have been different if the subject lived in an urban area rather than a suburban community.42 This person lived in the Midwest of the United States, where winter typically brings cold and snow. The low community mobility values at the 6-month recording period (December) might be at least partly explained by difficult driving conditions and inclement weather during winter months.
Other factors may also need to be considered as potential barriers to mobility, affecting the subject's trips, such as his income (which declined after he stopped working), changes in his goals, and the influence of his home environment. For example, we did not adjust his meaningful target destinations as his new goals, events, and activities changed. Although his home environment might have influenced mobility, we did not specifically include barriers inside the home because the focus of this study was on community mobility and outdoor navigation.
Despite the potential for GPSt to provide information about community navigation, the impact of social and cognitive factors on community mobility5,6 requires different measurement approaches. We did administer the SF-36 and RNLI surveys, offering some insight into physical, social, emotional, and occupational factors. The RNLI survey reflects a summary score on issues about moving around within one's living quarters and community; work, recreational, and social activities; and family roles. The RNLI survey score increased from weeks 1 to 9 and then fluctuated, yet it remained relatively high thereafter. This was similar to the social functioning, physical functioning, and role limitations component scores of the SF-36. However, target visits and total trips fluctuated throughout the study. It may be fruitful for future investigations to categorize the nature of the meaningful targets with respect to their social (coffee shop), occupational (library), or spiritual (place of worship) purpose and compare navigation with these targets with specific measures related to social, occupational, and/or cognitive factors.
An interaction between family support and functional independence could also explain the fluctuations in trips and target visits. For example, the subject's brother stayed with him during the first week after discharge. They reported that it was important for both of them that the subject “get back out socially.” The subject had 30 complete trips and eight target visits in the first week, which was the second highest measure of community mobility across all recording sessions. As functional gains were made (see BBS, SMWT) and independent driving was achieved (around the 6-month recording period), the subject no longer had family assistance; his brother returned home after the first week, and his wife returned to work. Thus, the 12-month recording period data might be more reflective of the subject's independence, whereas the initial 1-week recording period might reflect the influence of family support.
Community Ambulation Versus Community Mobility
Most stroke survivors value navigating in and accessing the community yet remain dependent to some degree on others for mobility assistance and transportation.2 Using GPSt, we were able to identify that our subject accessed and navigated to all but one of his target destinations. The only destination he did not visit during the data collection periods was the local neighborhood intersection. Given its proximity to his home, this was the only target that could have been accessed solely by walking. It was also the one target destination we asked him to include for the sake of walking in his neighborhood. The SMWT data suggest that his functional mobility might have limited his community ambulation at the beginning of the data collection period and thus his desire to walk to the corner. However, by the 12-months monitoring period, such limitations in gait did not exist. It may be that walking to the corner for the sake of walking for exercise was simply not valued enough by this subject.
Given that persons with higher gait speeds tend to report higher levels of community ambulation,4 the steady increase in both SMWT distance and BBS scores suggests greater community ambulation potential at the 12-months follow-up period than at the initial assessment. However, trips, although fluctuating over time, were essentially unchanged from beginning to end. In addition, accelerometry data identified our subject as largely sedentary during all recording periods (ranging from 90% to 96% of the time), a finding consistent with the literature.43 Given that Rand et al43 found only moderate correlations between SMWT and physical activity at home, it is possible that the relationship between SMWT and the number of trips taken into the community might be even weaker. Although gait speed can discern household from community ambulators,44 gait speed alone may be insufficient to capture the breadth of community mobility and participation.45
Lord and Rochester45 have called for self-reporting instruments that depict different levels of task attainment in local (just outside the home) and more challenging locations (eg, a shopping center). Although surveys may provide insight into this and related community reintegration issues, they are prone to bias and recall error and may not be measuring the same macroscopic quantities afforded by GPSt. On the basis of our limited data, it seems that the relationship between physical functioning and target/trip navigation is complex and by no means unambiguous. For example, this subject's 6-months RNLI survey score decreased slightly, and this coincided with fewer target visits and trips during the winter season. On the contrary, the increase in physical functioning from week 1 to 12 months was not similarly demonstrated in target visits and number of trips. This suggests getting out to meaningful targets is conditioned by contextual factors (eg, societal environmental factors such as transportation access) and physical functioning. There may be a collective minimum threshold for community mobility, similar to gait-speed thresholds for home and community ambulation. Ongoing efforts to explore how contextual factors and domains of functioning interrelate46 may assist in understanding the presence and/or nature of such a collective threshold.
GPSt is versatile, yet it has not been fully exploited for research into community mobility of persons with movement disorders. It has been used to record gait and physical activity, and we contend that it is able to offer insight into ability to access the community. GPSt may offer a window into participation in life situations, through an understanding of the places and events an individual navigates to and from and the means by which these places and events are accessed.
This report raises several questions with respect to the measurement of community mobility and the use of GPSt. First, if GPSt can measure community navigation to meaningful places and events, then what are the best macroscopic quantities of participation offered by GPSt? For example, how many meaningful targets should be used to represent participation in life situations (and, more simply, what makes up a trip)? Second, what is a sufficient data-collection schedule? Third, what is the best data-collection platform (hardware and software) for GPSt for this population? Fourth, what is the relationship between GPSt data and outcome measures that describe impairments, physical activity, or quality of life?
We had our subject choose 10 meaningful targets. This was intended to focus our analysis on places and events that were important to our subject. These were meaningful places for him for a period of time. Unfortunately, we did not gather subjective information on how frequently he went to the targets prior to his stroke. We recognize that “meaningful places and events” are represented by a dynamic list of locations that change as personal and environmental factors change. In this subject's case, outpatient rehabilitation had ended by the 6-months follow-up collection period. He later began attending classes at a comedy club in a nearby metropolitan city, 30 miles away. Removing outpatient rehabilitation and adding the comedy club would necessarily reconfigure his navigational landscape when viewed through the lens of a new set of meaningful places.
We believe one area where GPSt may prove valuable in disability research is in how it may contribute to understanding the ecological receptivity of defined spaces. That is, how receptive is a specific physical environment for a person with mobility impairments?26,35,47 Is getting to a coffee shop feasible, and is the coffee shop navigable? Formally identifying the receptivity of places and spaces is a labor-intensive process.26,47 It is simply not prudent to perform this sort of assessment for every potentially meaningful place or event a person encounters. However, by identifying which valued places a person navigates to with some frequency, one may then specifically evaluate these spaces' ecological receptivity. In this way, trip analysis using GPSt may also serve as a beacon for further analysis and understanding of the person's environment and associated environmental factors.
With respect to targets and trips, what is a trip and can one discern the purpose of a trip? This is a major analysis issue that has received some attention. The work of Cho et al10 is insightful in this regard. Using subjects without physical mobility problems, they evaluated community ambulation over a 7-day period to identify criteria that could be used to identify outdoor walking trips. They then followed up on a larger sample over the course of 4 days, using an algorithm based on the original data. They validated this information by comparing it with travel diaries. We developed an algorithm for trip definition by using similar criteria. Although the 10-minute threshold clearly identifies the subject is stationary at a location, we may be underreporting individual partial trips, given this threshold. For example, stopping at the post office for 2 minutes would not be an individual partial trip but would be included in an ongoing trip. Standardization of the construct “trip” is an important area for ongoing and future research if it is to be used to represent levels of community mobility.
We did not validate our GPSt data with a diary. Although cross-validation may prove to be an important process, we were not interested in the purpose of each trip, given that we preidentified visits to meaningful target destinations. Cho et al10 found a small number of discrepancies between GPSt and diary data, and much of this was related to being indoors and possibly poor compliance with diary completion and/or wearing the GPS unit. They suggested that walking-related discrepancies could be solved by the simultaneous use of an accelerometer or pedometer, and we agree. Because we used an accelerometer simultaneously with the GPS unit, we were able to identify, for example, that our subject was still active while the GPS unit was not recording, either due to the subject being indoors or due to the unit being shut off.
We measured community mobility to meaningful places and events in 1-week intervals over the course of a year following inpatient rehabilitation. Most studies to date have evaluated only single bouts of physical activity with GPSt8,11 or have not used GPSt to follow subjects over a longer period of time, as was our intention in the present case. The 7-day interval served to offer snapshots of functioning without being overly burdensome to the subject and to keep the amount of collected data to a reasonable level. Although the optimal recording time remains an open question, there is some evidence that a 7-consecutive-day monitoring period is reliable for measuring activity.38
A major disadvantage to the use of GPSt is the potential for lost data via equipment malfunction, dead batteries, or simply not turning on the unit. For example, the total monitoring time varied widely among recording periods. The lowest monitoring time occurred at week 5 and at 6 months, which coincided with the fewest target visits and the total number of trips. It is possible that the number of targets accessed is underreported simply because of how long the device was actually on during the recording period. In addition, our approach to data analysis was rather simplistic but with minimal automatization; it was therefore time consuming. Automated systems using expertise in the field of geospatial information systems will be needed to adequately collect, analyze, and distribute GPSt data in a uniform and understandable manner. Expense and lost data may be minimized by the evolving hardware and software platforms for GPS data collection. At present, we are exploring the use of a smartphone platform with GPSt versus traditional waist-worn GPS units. But this technology is not without its own limitations. For example, battery consumption on the smartphone platform is largely dependent on GPS unit recording frequency, an issue that can affect the identification of walking trips.10
Where We Go From Here: Conceptualizing GPSt and Disability Research
Disability is a dynamic process that emerges from the interaction between the environment and an individual's impairments, activity limitations, and personal factors.48 From an ecological viewpoint, mobility simultaneously serves a life situation purpose and is specified by the environment. Given that life situation performance is a major contributor to an individual's quality of life, community mobility and participation overlap as constructs within a physical-social environment. Therefore, measurement of the person-environment interaction can provide insight into disability.47
We believe that meaningful navigation is the foundation of community mobility and that community mobility exists for the purposes of exploration and life-situational goal attainment. Therefore, measurement of participation at the ecological scale is specified by the individual-environment relationship. On the basis of this assumption, GPSt may offer insight into participation germane to this scale of measurement. Ultimately, the use of GPSt for community mobility requires a conceptual model of functioning that goes beyond seeing one's physical environment as a separate contextual factor of health (ie, as described within the International Classification of Functioning, Disability, and Health), but rather merges individual and environment and thus participation in an ecological sense.
The GPSt data indicated that this subject was able to return to many of the destinations he self-identified as important to his social, occupational, and leisure goals in the community in weeks 1 through 9 after discharge from rehabilitation as well as in the 6- and 12-month follow-up periods. However, community mobility in this case did not seem to correlate with balance and walking speed or distance (ie, as balance, walking speed, and distance improved, there were only minor increases in community mobility as measured by trips and target visits). These results suggest that GPSt may offer a novel way to capture community mobility that is not reflected in traditional rehabilitation outcome measures for post-stroke survivors.
1. Hill K, Ellis P, Bernhardt J, Maggs P, Hull S. Balance and mobility outcomes for stroke patients: a comprehensive audit. Aust J Physiother. 1997;43(3):173–180.
2. Lord SE, McPherson K, McNaughton HK, Rochester L, Weatherall M. Community ambulation after stroke: how important and obtainable is it and what measures appear predictive? Arch Phys Med Rehabil. 2004;85(2):234–239.
3. Corrigan R, McBurney H. Community ambulation: perceptions of rehabilitation physiotherapists in rural and regional communities. Physiother Theory Pract. 2012;28(1):10–17.
4. van de Port IG, Kwakkel G, Lindeman E. Community ambulation in patients with chronic stroke: how is it related to gait speed? J Rehabil Med. 2008;40(1):23–27.
5. Bhogal SK, Teasell RW, Foley NC, Speechley MR. Community reintegration after stroke. Top Stroke Rehabil. 2003;10(2):107–129.
6. van de Port IG, Kwakkel G, van Wijk I, Lindeman E. Susceptibility to deterioration of mobility long-term after stroke: a prospective cohort study. Stroke. 2006;37(1):167–171.
7. Donovan K, Lord SE, McNaughton HK, Weatherall M. Mobility beyond the clinic: the effect of environment on gait and its measurement in community-ambulant stroke survivors. Clin Rehabil. 2008;22(6):556–563.
8. Duncan MJ, Badland HM, Mummery WK. Applying GPS to enhance understanding of transport-related physical activity. J Sci Med Sport. 2009;12(5):549–556.
9. Frank JS, Patla AE. Balance and mobility challenges in older adults: implications for preserving community mobility. Am J Prev Med. 2003;25(3)(suppl 2):157–163.
10. Cho GH, Rodríguez DA, Evenson KR. Identifying walking trips using GPS data. Med Sci Sports Exerc. 2011;43(2):365–372.
11. Créange A, Serre I, Levasseur M, et al. Walking capacities in multiple sclerosis measured by global positioning system odometer. Mult Scler. 2007;13(2):220–223.
12. Rodríguez DA, Brown AL, Troped PJ. Portable global positioning units to complement accelerometry-based physical activity monitors. Med Sci Sports Exerc. 2005;37(11)(suppl):S572-S581.
13. Shoval N, Auslander GK, Freytag T, et al. The use of advanced tracking technologies for the analysis of mobility in Alzheimer's disease and related cognitive diseases. BMC Geriatr. 2008;8:7.
14. Webber SC, Porter MM. Monitoring mobility in older adults using global positioning system (GPS) watches and accelerometers: a feasibility study. J Aging Phys Act. 2009;17(4):455–467.
15. Hopman WM, Verner J. Quality of life during and after inpatient stroke rehabilitation. Stroke. 2003;34(3):801–805.
16. Mayo NE, Wood-Dauphinee S, Côté R, Durcan L, Carlton J. Activity, participation
, and quality of life 6 months poststroke. Arch Phys Med Rehabil. 2002;83(8):1035–1042.
17. Paul SL, Sturm JW, Dewey HM, Donnan GA, Macdonell RA, Thrift AG. Long-term outcome in the North East Melbourne Stroke Incidence Study: predictors of quality of life at 5 years after stroke. Stroke. 2005;36(10):2082–2086.
18. Schutz Y, Chambaz A. Could a satellite-based navigation system (GPS) be used to assess the physical activity of individuals on earth? Eur J Clin Nutr. 1997;51(5):338–339.
19. Schutz Y, Herren R. Assessment of speed of human locomotion using a differential satellite global positioning system. Med Sci Sports Exerc. 2000;32(3):642–646.
20. Terrier P, Ladetto Q, Merminod B, Schutz Y. High-precision satellite positioning system as a new tool to study the biomechanics of human locomotion. J Biomech. 2000;33(12):1717–1722.
21. Wevers LE, Kwakkel G, van de Port IG. Is outdoor use of the Six-Minute Walk Test with a global positioning system in stroke patients' own neighbourhoods reproducible and valid? J Rehabil Med. 2011;43(11):1027–1031.
22. World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF). WHO ICF Web page. ICF application areas. http://www.who.int/classifications/icf/appareas/en/index.html
. Published 2011. Accessed August 30, 2011.
23. Gibson JJ. The Ecological Approach to Visual Preception. Hillsdale, NJ: Lawrence Erlbaum Associates Inc Publishers; 1986.
24. Corrigan R, McBurney H. Community ambulation: environmental impacts and assessment inadequacies. Disabil Rehabil. 2008;30(19):1411–1419.
25. Corrigan R, McBurney H. Community ambulation: influences on therapists and clients reasoning and decision making. Disabil Rehabil. 2008;30(15):1079–1087.
26. Gray DB, Hollingsworth HH, Stark S, Morgan KA. A subjective measure of environmental facilitators and barriers to participation
for people with mobility limitations. Disabil Rehabil. 2008;30(6):434–457.
27. Ng SS, Hui-Chan CW. The Timed Up & Go Test: its reliability and association with lower-limb impairments and locomotor capacities in people with chronic stroke. Arch Phys Med Rehabil. 2005;86(8):1641–1647.
28. Michael KM, Allen JK, Macko RF. Reduced ambulatory activity after stroke: the role of balance, gait, and cardiovascular fitness. Arch Phys Med Rehabil. 2005;86(8):1552–1556.
29. Murtezani A, Hundozi H, Gashi S, Osmani T, Krasniqi V, Rama B. Factors associated with reintegration to normal living after stroke. Med Arch. 2009;63(4):216–219.
30. Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198.
31. Sturm JW, Donnan GA, Dewey HM, et al. Quality of life after stroke: the North East Melbourne Stroke Incidence Study (NEMESIS). Stroke. 2004;35(10):2340–2345.
32. Almborg AH, Berg S. Quality of life among Swedish patients after stroke: psychometric evaluation of SF-36. J Rehabil Med. 2009;41(1):48–53.
33. Anderson C, Laubscher S, Burns R. Validation of the Short Form 36 (SF-36) Health Survey Questionnaire among stroke patients. Stroke. 1996;27(10):1812–1816.
34. Ware JE, Gandek B. Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA) Project. J Clin Epidemiol. 1998;51(11):903–912.
35. Gray DB, Hollingsworth HH, Stark SL, Morgan KA. Participation
survey/mobility: psychometric properties of a measure of participation
for people with mobility impairments and limitations. Arch Phys Med Rehabil. 2006;87(2):189–197.
36. Stark SL, Edwards DF, Hollingsworth H, Gray DB. Validation of the Reintegration to Normal Living Index in a population of community-dwelling people with mobility limitations. Arch Phys Med Rehabil. 2005;86(2):344–345.
37. Noury-Desvaux B, Abraham P, Mahé G, Sauvaget T, Leftheriotis G, Le Faucheur A. The accuracy of a simple, low-cost GPS data logger/receiver to study outdoor human walking in view of health and clinical studies. PLoS One. 2011;6(9):e23027.
38. Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(11)(suppl):S531-S543.
39. Auld J, Williams CA, Mohammadian A, Nelson PC. An automated GPS-based prompted recall survey with learning algorithms. Transport Lett Int J Transport Res. 2009;1(1):59–79.
40. Wolf J, Guensler R, Bachman W. Elimination of the travel diary: an experiment to derive trip purpose from travel data. Transport Res Record J Transport Res Board. 2001;1768:125–134.
41. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–188.
42. Ewing R. Can the physical environment determine physical activity levels? Exerc Sport Sci Rev. 2005;33(2):69–75.
43. Rand D, Eng JJ, Tang PF, Jeng JS, Hung C. How active are people with stroke? Use of accelerometers to assess physical activity. Stroke. 2009;40(1):163–168.
44. Perry J, Garrett M, Gronley JK, Mulroy SJ. Classification of walking handicap in the stroke population. Stroke. 1995;26(6):982–989.
45. Lord SE, Rochester L. Measurement of community ambulation after stroke: current status and future developments. Stroke. 2005;36(7):1457–1461.
46. Tomey KM, Sowers MR. Assessment of physical functioning: a conceptual model encompassing environmental factors and individual compensation strategies. Phys Ther. 2009;89(7):705–714.
47. Stark S, Hollingsworth HH, Morgan KA, Gray DB. Development of a measure of receptivity of the physical environment. Disabil Rehabil. 2007;29(2):123–137.
48. Brandt ENJ, Pope AM, eds; Committee on Assessing Rehabilitation Science and Engineering DoHSP, Institute of Medicine, National Academy of Sciences. Enabling American, Assessing the Role of Rehabilitation Science and Engineering. Washington, DC: National Academies Press; 1997. http://www.nap.edu/catalog.php?record_id=5799
. Accessed April 26th, 2012.