Challenges in using wearable GPS devices in low-income older adults: Can map-based interviews help with assessments of mobility?

Challenges in using wearable GPS devices in low-income older adults: Can map-based interviews... Abstract Daily mobility, defined as the ability to move oneself within one’s neighborhood and regions beyond, is an important construct, which affects people as they age. Having a feasible and valid measure of daily mobility is essential to understand how it affects older adults’ everyday life. Given the limitations of existing measures, new tools may be needed. The purpose of the study is to assess the feasibility and practicality of using the map-based questionnaire system VERITAS and GPS devices to measure daily mobility in older adults living in a deprived neighborhood in Denmark. Older adults were recruited from two senior housing areas, completed an interview using VERITAS and wore a GPS for 7 days. Feasibility of both methods was assessed by looking at practicalities, recruitment and compliance, and ability to measure daily mobility. Thirty-four older adults completed the VERITAS questionnaire, of which 23 wore the GPS device. Remembering to wear and charge the GPS was difficult for 48% participants, whereas remembering street names and drawing routes in VERITAS was difficult for two. Both the GPS and VERITAS were able to measure 10 out of the 13 identified components of mobility; however, VERITAS seemed more qualified at measuring daily mobility for this target population. The feasibility of assessing mobility may vary by specific context and study population being investigated. Wearable technology like a GPS may not be acceptable to low socioeconomic older adults, whereas interview led self-reported measurements like VERITAS might be more suitable for a low socioeconomic elderly population. Implications Practice: Using VERITAS, researchers, community stakeholders, and city planners can assess low-income senior citizens’ daily mobility to identify their ability to age in place. Policy: Policymakers who want to promote aging-in-place to decrease costs of health services should explore the concept of daily mobility and how it affects older adults’ ability to age in place. Research: The feasibility of measuring mobility using wearable GPS and self-report mapping tools like VERITAS seems to vary by specific context and study population being investigated, which is why future research should further examine the two methods in different population groups. BACKGROUND The worldwide population of older adults (60+) is expected to double from 11% in 2006 to 22% by 2050 [1]. By this time, in Western countries, there will be more older adults than children and local governments may struggle to deliver appropriate services to this large population. With the increase in older adults worldwide, there has also been an increased interest in enabling people to age-in-place. The World Health Organization has supported a program of age-friendly communities to increase capacity for aging in place [2]. Aging-in-place is a term typically defined as “remaining living in the community, with some level of independence, rather than in residential care” [3]. The majority of older Americans state that they want to age-in-place as long as possible [4], and several factors seem to affect one’s ability to do so. The ability to conduct everyday activities is an important predictor of aging in place, and limitations in daily mobility may represent an important barrier. Although there are several accepted definitions of mobility [5, 6] in this study, mobility is a concept that describes “the ability to move oneself (e.g., by walking, using assistive devices, or by using transportation) within community environments that expand from one’s home, to the neighborhood, and to regions beyond” [7]. It describes people’s ability to move around in their homes as well as inside and outside of their neighborhood, to do daily activities such as going to the grocery store, accessing health care services, meeting friends, and performing recreational activities. In the present study, we used the following components to describe daily mobility: destination type, number of and distance to destinations; routes used from home to destinations; number of times visiting destinations (frequency) and amount of time spent at destinations; mode of transportation (vehicle, public transportation, walk, bike) as well as distance traveled, frequency of use and time spent in these modes; use of assistive devices during trips (cane, walker, wheelchair); total time spent outdoors; and social interactions during trips and at destinations [8–10]. Mobility is fundamentally important for being able to maintain physical and psychological health and is especially important for elderly people because their mobility usually declines with age. In particular, socioeconomically disadvantaged older adults are at greater risk for declines in mobility and, consequently, institutionalization [11]. Studies indicate that elderly people travel shorter distances and less often than younger adults and have identified changes in activity patterns, health constraints, and traffic safety as potential contributors to their decreased mobility [12–14]. Features of the built environment have also been linked to mobility among older adults, underlining the fact that some types of environments offer greater opportunities to be mobile than others. Additionally, mobility is strongly related to social interaction (i.e., going somewhere to meet with a peer) and, consequently, loss in mobility might translate into reduced social participation [15, 16]. Even passive mobility, for example in vehicle versus walking, seems to have some mental and social health benefits [17]. Furthermore, mobility represents the translation of people’s need to access various resources like health services and grocery stores. That is, a certain level of mobility is important to be able to access these various resources. Thus, investigating how well older adults can conduct their everyday activities, how it affects their health, and how the built environment and the social environment affect their mobility is important to support aging-in-place. The complex concept of daily mobility has led to a range of different methods to measure mobility in older adults, like physical functioning tests [18, 19] or the self-reported Life-Space Questionnaire (LSQ) [20]. Even though these methods capture some of the elements of daily mobility, they do neglect some other important components, such as different modes of transportation and the social components of daily mobility. Consequently, there is a need to explore other measurement tools that might better capture daily mobility in older adults. One method that is increasingly being used to assess daily mobility and exposure to multiple environments is Global Positioning System (GPS) devices. GPS devices are an objective tool used to capture location and, in combination with data from a Geographic Information System [21], they can reveal movement through different environments in all age groups. Typically, GPS studies have collected data for 1 week although this is changing with increased use of smartphones and embedded GPS trackers. In the last few years, GPS has also been used in studies investigating daily mobility in older adults [22–25] by assessing a persons’ time spend in different spaces, different destinations visited, or mode of transportation. GPS may be a better tool to assess daily mobility than LSQ as it can capture many of the different components of daily mobility described earlier objectively. A relatively new method to assess daily mobility is spatial interviews or questionnaires like the Visualization and Evaluation of Route Itineraries, Travel Destinations, and Activity Spaces (VERITAS). VERITAS is a web-based application that combines interactive mapping (using Google Maps) with activity and travel questions [26, 27]. VERITAS facilitates the collection of self-reported data about participants’ destinations, routes, modes of transportation, and on related social dimensions (i.e., whom is generally met at these destinations), and provides a more general picture of daily mobility [26]. Please visit the SPHERELAB website (http://www.spherelab.org/tools) to see a demonstration of VERITAS. To our knowledge, VERITAS has only been used in a few studies, for respondents with different socioeconomic statuses [28] and ages. One study focusing on older adults (65+ years old) was conducted in Canada [27] and another which to some degree included an older study population (range: 33–84, mean age 51 years) was conducted in France [29]. However, none of these studies specifically looked at the feasibility of using VERITAS to assess daily mobility. Identifying a suitable method for measuring daily mobility, especially among older adults and deprived populations, is important to fully understand what affects daily mobility. Measurement tools need to be adaptable to different population groups (age and socioeconomic status (SES)) and measurements need to be sensitive toward change because they might have to measure relatively small behavior changes in older adults. Both VERITAS and GPS devices seem promising and their strengths and weaknesses need to be assessed to improve the next generation of built environment studies focusing on aging-in-place. Aim This paper is looking at two methods to collect daily mobility data in older adults: one that is self-reported and focuses on regular destinations and the other one that is objective and uses passive tracking capabilities providing acute measurement of daily mobility. More specifically, the aim of this study was to assess the feasibility and practicality of using the map-based questionnaire system VERITAS and wearable GPS devices to measure daily mobility in a sample of older adults living in senior housing in a socially deprived neighborhood of Copenhagen, Denmark. This includes assessing tool acceptability, adherence to protocol, barriers to their use (cost, time, etc.), and ability to provide measures of daily mobility in low-income older adults. METHODS Case study sample and recruitment procedures This study is part of a multicomponent intervention study (Move the Neighborhood!) focusing on using co-creation to, in collaboration with local seniors, design and build installations that have the potential to increase physical activity and decrease sedentary behavior among older adults living in a deprived neighborhood (Sydhavnen) of Copenhagen [30]. With only 73.0 years, Sydhavnen has one of the lowest life expectancies in Denmark (the average in Denmark is 80.6 years) [31], 32% of the population only attended primary school and 40.2% have a low income [32]. The study population was recruited within the neighborhood from two housing areas (350 tenants, age 50+) for the elderly that accommodate seniors who have a low socioeconomic status. Many tenants cannot pay the usual rent for an apartment in Copenhagen and might need extra help with cleaning, grocery shopping, and/or personal care. Older adults were recruited from the two housing areas through letters, local stakeholders, and existing social activities. Information about the intervention study, this substudy, and its purpose of providing valuable knowledge to this study and future studies targeting open spaces for older adults was provided during each recruitment procedure. Participants were introduced to both methods GPS and VERITAS and were encouraged to “use” both of them. However, they were given the option to refuse to participate in one or both of the measurements. Those who agreed on participating in both measurements completed the VERITAS interview and were afterwards given a GPS to wear for the following 7 days. We collected information on participants’ reasons for refusal. Participants were offered to be interviewed at their home or at our offices; all of them chose at home interviews. The study was registered in the ISRCTN registry (ISRCTN50036837). Furthermore, the study and its data-management procedures were approved by the Danish Data Protection Agency (2015-57-0008). GPS Those participants who agreed to wear a GPS wore a Qstarz BT-Q1000xt GPS device and were given several choices on how to wear the device (belt, ankle holder, key hanger, in their pocket or bag). Participants were asked to wear the device for seven consecutive days and to take the GPS off only when there was a risk of contact with water and at night while charging. Participants received short reminder text messages on their mobile phones—one in the morning and one in the evening—to increase compliance and remind them to charge their GPS device. Additionally, reminder flyers were posted next to their apartment door and next to their nightstand with information on how to charge and wear the GPS device because some participants did not use their phone a lot or were not used to text messages. GPS devices were collected after 7 days and GPS data were processed using a web-based application called the Personal Activity and Location Measurement System (PALMS) [33]. PALMS uses a range of user-defined settings to identify different variables like trips, mode of transportation and time spent outdoors, to assess mobility. In this study, trips were categorized as continuous movement of at least 3 min, with stationary periods of maximum 5 min. Mode of transportation during each trip was calculated using speed and included three modes: walking (≥1 km/h, <10 km/h), biking (≥10 km/h, <25 km/h), and in a vehicle (≥25 km/h) [34]. Data were afterwards visualized in QGIS version 2.18.3. QGIS is a free and open source GIS used to create, edit, visualize, and analyze geospatial information [21]. VERITAS VERITAS is a map-based retrospective questionnaire that allows for identification of locations (points), routes between locations (lines) or areas (polygons) such as perceived neighborhoods, on a map. Google Map search functionalities can be used to facilitate the identification of specific locations. Participants were first asked to confirm the location of their home address (point), which served as a basis for all other identified locations to determine distance. The specific VERITAS questions used in this study were based on the CURHA study conducted in Canada by Yan Kestens et al. [27] focusing on older adults with a mix of socioeconomic statuses. The questionnaire was originally in French and English. We translated it into high-school level Danish and made some adjustments to account for the Danish context. The questionnaire consisted of 32 categories each including one to five questions. Depending on whether the participant answered “yes” or “no,” the participant was asked to specify each of the 32 categories by drawing destinations or routes on the digital map, and identify how often, how (mode and use of assistive devices) and with whom they go there (social interaction). For example, “Do you shop for groceries at a supermarket at least once per month?”, if they answered “yes,” we asked them to locate the supermarket on the map. Questions considering specific places (e.g., supermarket, doctor, bank) all use the phrase, “…at least once per month,” that is VERITAS does not ask questions about the last 7 days or other specific period, but provides a more general picture of activities and destinations. We decided to deliver the questionnaire as an interview instead of a self-administered questionnaire because interviewing has shown to be a more reliable method for older adults [35]. Questions in the VERITAS interview encouraged participants to identify the following regular destinations: recreational destinations, different types of destinations for grocery shopping (supermarket, butcher, etc.) and services (bank, doctor, post office). Participants were asked to identify their perceived neighborhood by “drawing” a polygon picturing the perceived boundaries of their neighborhood. Participants reported destinations visited at least once a month to account for several different destinations, for example the post office, which may not be frequently visited. After completion of data collection, VERITAS data were downloaded as csv files and processed in QGIS version 2.18.3. Feasibility To fully evaluate the feasibility of the GPS device and VERITAS in measuring daily mobility in low socioeconomic older adults, three criteria were used: practicalities, recruitment and compliance, and mobility variables. Each of these three criteria had several subcriteria that were assessed and compared. The three criteria are described in the following section. Practicalities First, we evaluated several practical barriers to using the two methods, which included costs and time spent on equipment preparations, data collection, and data processing. Second, we assessed the practicalities of method acceptability qualitatively by interviewing participants before and after data collection. Seniors who did not want to be part of the study were asked, “What are your reasons for not wanting to participate in this study?” If participants only wanted to participate in one part of the data collection (e.g., only VERITAS interview) they were asked, “What are your reasons for not wanting to participate in the other part of the data collection?” Seniors who wore the GPS device were asked two questions after the 7 days of wear time: (a) “Have you had any difficulties or other experiences with the device while wearing it?” (b) “Did you at any time during the last 7 days forget to wear your device during all waking hours?” Three researchers were responsible for the home visits and interviewing the participants and non-participants. Responses of all three researchers were combined and assessed after completion of the data collection. After all practicalities were assessed, the scalability of the two methods was evaluated. Recruitment and compliance Second, we assessed recruitment difficulties and recruitment rates for both methods, that is how easy or difficult is recruitment and how many agree to participate in the VERITAS interview versus the GPS data collection. We further assessed compliance with the data collection procedure and data quality, that is difficulties answering the VERITAS questionnaire or wearing the GPS device, and the quality of the data when downloaded. Mobility variables Lastly, to fully evaluate the feasibility of the two methods, we assessed their ability to measure daily mobility. In this study, the concept of daily mobility is defined by several components listed in the Background section. These components are conceptualized through 13 variables, divided into three themes: Destination, Transportation, and Other. Destinations: number of destinations visited; type of destination; routes from home to destinations; time spent at a location; frequency of destinations visited; and distance to destinations. Transportation: mode of transportation; frequency of transportation mode; time spent in different transportation modes; use of assistive devices during trips; and distance traveled by mode. Other: time spent outdoors and social interaction. The 13 variables were evaluated by assessing GPS’s and VERITAS’s technical capability to measure the variables using PALMS (GPS) and QGIS (VERITAS). Finally, we present an example map of a participant’s GPS mobility data (for walking) and VERITAS mobility data (recreational walking), which was used for a visual assessment of the spatial data. The map was generated using QGIS. We recognize that caution should be taken when mapping two different types of data for the same person because they might not be fully comparable as VERITAS data and GPS data are not necessarily representing the same time points during data collection. GPS data were captured for seven specific days, whereas VERITAS data show a general picture of destinations visited during a usual month. But even though the two methods represent different ways of measuring daily mobility, they have a similar goal, providing a description of daily mobility which can be used in health research. Analyses On completion of the fieldwork, qualitative data from three researchers including field notes and notes from the unstructured interviews with participants were combined and coded to assess the feasibility of using VERITAS or GPS. During the first step of the thematic analysis, the combined data were analyzed and three themes emerged based on the participants’ feedback on the measurement procedure: GPS difficulties, VERITAS experiences, and no participation. VERITAS difficulties were not reported. To assess the two methods’ ability to measure the different aspects of the concept of mobility, we used a descriptive approach looking at a range of different variables identified in the methods section. These variables were assessed by visualizing VERITAS data in QGIS using the available geographical coordinates that allowed us to locate destinations and areas, as well as look at questions about the use of assistive devices, transportation mode, and social activities. GPS data were processed in PALMS and afterwards visualized in QGIS, allowing us to visually assess GPS processed data about transportation mode, routes, and time spent outside. To compare the spatial information collected using the two methods, we created a map for each participant using QGIS and visually assessed the percentage of agreement between GPS and VERITAS measurements, by first, counting the number of times were VERITAS identified destinations and recreational walking routes were visually on or next to GPS calculated trips using the QGIS illustrated map. Second, the percentage of agreement was calculated by dividing the total number of matches by the total number of VERITAS identified destinations and recreational walking routes and multiply by 100. RESULTS Recruitment and compliance Recruitment was challenging and time-consuming. No one responded to flyers in their mail (180 out of 350 households received flyers)—normally, the method used by local stakeholders to communicate with residents—which meant that we had to recruit them on the spot by participating in their social activities. Out of 340 older adults asked (including the 180 households), 34 (10%) agreed to complete the VERITAS questionnaire. Ages ranged from 51 to 90 years, of which 26.5% were men (see Table 1). During the VERITAS interview, two participants had difficulty remembering street names and drawing specific routes in the VERITAS map. Of the 34 older adults completing the VERITAS interview, 23 (6.8% out of 340 asked) agreed to wear the GPS device. Ages ranged from 53 to 86 years, and 24% were men (see Table 1). Of the 23 agreeing to wear the GPS device, only 12 participants (52%) had valid GPS data for at least 1 day (>8 hr of wear time), and only five participants had 7 days of GPS data. The 12 participants with GPS data had on average worn the GPS for 9 h/day for 5.6 days. Eleven participants had difficulties remembering to charge the device and to carry it on them at all time. Participants who did not want to wear the GPS said they did not want to be responsible for remembering to charge the device and to take it with them wherever they went (10 participants). Those who did not want to participate in the study at all stated that they did not have anything interesting to say (64 persons) or that they almost never left their apartment (6 persons) and thus, wearing the device did not make sense for them. Fifty-six older adults did not give any reason for not wanting to participate. Table 1 | Overview of descriptive, recruitment, and compliance Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 View Large Table 1 | Overview of descriptive, recruitment, and compliance Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 View Large Practicalities Table 2 shows an overview of the practicalities related to using GPS and VERITAS in the study. Setting up a VERITAS questionnaire costs a one-time payment of at least $1500 regardless of the size of your study population, whereas a GPS device + key hanger costs roughly $100 per person and consequently will get more expensive the more devices are needed for a study. When there is no need for simultaneous GPS tracking, one GPS device can typically be used for two consecutive participants per month. Time spent on equipment preparation, data collection, and data processing varied a lot for the two methods. VERITAS took approximately 1 min to prepare per participant; however, it took around 45 min to do the interview. In contrast, each GPS took about 10 min to prepare beforehand, around 20 min to explain and attach to the participant, and additionally 5 min to pick up after the 7 days. VERITAS only required transport time for one home visit, whereas the GPS required two home visits. Moreover, the participant needed to wear the device for 7 days and charge it every night, which might be considered as an extra burden for the participant. Table 2 | Overview of practicalities relevant to the use of VERITAS and GPS Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) View Large Table 2 | Overview of practicalities relevant to the use of VERITAS and GPS Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) View Large Because this study was a pilot study with a relatively low number of participants, we considered whether the methods were applicable in larger studies investigating mobility in older adults, based on results displayed in Table 2. Both methods seem to be applicable to larger studies. However, the costs for each GPS device and the specific target population must be considered because this study population had low participation rates and difficulty complying with the protocol. The interview version of VERITAS was applicable in the small sample used in this study, but might be difficult to implement on a larger scale, because interviewing takes a long time. An online version of VERITAS might be more applicable if the study population were more acquainted with using computers and the internet. Mobility variables Table 3 provides an overview of the two methods’ capability to measure the variables we identified to be important for daily mobility in older adults. Both methods seem to be able to measure most of the daily mobility variables. However, the quality of the measurement varies between the two tools. VERITAS was able to record the specific mode of transportation, for example whether the person used a bus or a car and whether the person was driving himself or someone else was driving him. On the other hand, VERITAS was not able to capture the actual distance traveled in different modes of transportation or the specific time spent in different modes of transportation. Whereas GPS data processed in PALMS were able to measure the specific distance traveled in different modes. However, PALMS was not able to capture the difference between car driving or taking the bus, nor was it able to assess the use of assistive devices (e.g., cane or walker), which VERITAS was able to do. Table 3 | Overview of variables explaining daily mobility through VERITAS and GPS data Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No View Large Table 3 | Overview of variables explaining daily mobility through VERITAS and GPS data Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No View Large Other components that explain daily mobility in older adults are the type and frequency of destinations visited, distance, and specific routes taken from home to destinations. VERITAS provides measures of type, frequency and number of destination visited, as well as the distance to destinations. Each destination drawn on Google Maps is followed by a question asking to specify the type of destination, at what frequency they visit that place, and how they generally go there. Participants were able to choose several options for the same trip, which means that VERITAS captured the use of different assistive devices and modes of transportation (e.g., walking with a cane to a bus stop and taking the bus). However, this version of VERITAS was not able to measure the specific routes taken from home to destinations and only poorly measures time spent at destinations (by asking participants to give an estimate of time), whereas GPS was able to measure (all types of) time with high temporal precision, as well as routes from a to b. Social interaction is another variable of daily mobility which is captured by VERITAS, as participants were asked whether they traveled by themselves or with someone else to each specific destination, and whether they regularly visited friends or family in their homes, or friends and family visited the participant in their home. If they answered “yes,” they were asked to elaborate on how well they knew this person, how often they met and how they communicated (mail, phone, in person). Combined, this information can give an in-depth knowledge about participants’ social relationships. GPS data on the other hand, is not able to detect social interactions; however, identification tagging on different GPS devices may allow assessment of social interaction in the future. Finally, we created a map for each participant using QGIS for illustrative purposes and visual assessment of the spatial information collected using the two different methods. The map in Fig. 1 displays one participant’s identified destinations (yellow dots) and recreational walking route (yellow line) in VERITAS and GPS measured walking using PALMS (orange lines). As can be seen, the two types of visualizations are very different, providing different information. For the example person, the GPS identified walking represents 25 different trips, which may be recreational walking or walking for transportation to specific destinations. The VERITAS data for the same person revealed 21 different destinations and 3 routes for recreational walking. Due to many GPS tracks starting and ending inside buildings, determining the exact start and end points of trips is difficult. GPS accuracy is lower inside buildings which causes trip start and end data to contain noise, that is incorrect GPS points. The advantage of the GPS data, however, is that it is certain that the person actually walked the specific routes seen in Fig. 1, as he or she was wearing the GPS device. From these data, detailed information can be retrieved about the specific trips taken, for example the actual distance traveled, and specific duration and time of day when the trip was made. Fig 1 View largeDownload slide QGIS map displaying a participant’s VERITAS identified destinations and GPS measured walking. = VERITAS destinations; ▬ = VERITAS recreational walking; ▬ = GPS walking trips; ⌂ = home Fig 1 View largeDownload slide QGIS map displaying a participant’s VERITAS identified destinations and GPS measured walking. = VERITAS destinations; ▬ = VERITAS recreational walking; ▬ = GPS walking trips; ⌂ = home Looking more closely at the VERITAS data, we see that most of the destinations (yellow dots) are on or close to GPS routes, which indicates that these destinations were most likely also visited during the days of GPS monitoring. In fact, comparing all participants’ data by visually matching each participant’s GPS trips with VERITAS destinations, we see that 58.4% of VERITAS identified destinations are on or next to GPS measured trips. However, the mismatch between VERITAS identified recreational walking trip (yellow line in Fig. 1) and GPS trips may indicate that the recreational trip was not taken during the 7 days of GPS monitoring. For each destination in VERITAS, detailed information about the context and purpose of the trip can be retrieved. Furthermore, we can retrieve information about mode of transportation and use of assistive devices (i.e., bus, train, walking without any assistance), and how often the participant visits each destination, ranging from several times per week to a couple of times per month or year. This information depicts a more general picture of the participant’s life space, but does not provide detailed information about specific time points or routes taken to these destinations. DISCUSSION This pilot study aimed to assess the challenges and benefits of two different spatial measurement methods by descriptively investigating the feasibility of using an interview version of the map-based VERITAS questionnaire compared to GPS devices to assess daily mobility in low-income older adults. We investigated feasibility by assessing three main criteria: practicalities; recruitment and compliance; and mobility variables, using both qualitative fieldwork data during data collection and objective quantitative data from the two methods. Practicalities Using a GPS device on low-income older adults imposed numerous practical issues. First, one GPS device costs $100, which might be considered prohibitive for a larger study. However, often researchers are able to get bulk discounts, borrow devices, or rotate a smaller pool of devices among a larger group since each person only wears the device for 7 days. Second, even though 10 min to prepare one GPS device does not seem burdensome, the amount of time and staff needed to do this at scale mount up. Third, the actual data collection, in this case, was done through home visits, explaining the device, how to wear and charge it, and setting up for reminder texts and reminder posters. This procedure was very time-consuming. However, because this study population was disadvantaged in several ways (low-income, physical disabilities, disadvantages neighborhood), it was not considered feasible to mail the device to their homes. Fourth, participants had to wear the device for 7 days and remember to charge it daily, which was found to be a challenge, and might explain the low participation rate (6.8%). However, other studies did succeed in using the GPS device on older adults [36–38], although study participants in these studies were mainly Caucasians with high levels of physical functioning and education. Lastly, one might consider the specific context (low income and socially disadvantaged community) to be the reason for the several identified limitations and not the GPS device itself. One study by Paz-Soldan et al. [39] found several limitations in the use of GPS on adults in a low SES city in the Amazon Basin of Peru. Consequently, it might be the specific age group in combination with its vulnerability by being a disadvantaged population group, which might make the GPS device less feasible to use in studies focusing on disadvantaged older population groups. The map-based interview version of VERITAS used in this study seemed to have both benefits and some practical limitations when used on a disadvantaged population of older adults. The benefits of using VERITAS were as follows: first, the lower burden of participation (one interview of around 45 min vs. 7 days of wear) consequently, higher participation rates. Second, it took a relatively short time to prepare the VERITAS procedure before visiting the participant in their home, which saved time. Third, VERITAS was completed during one guided home visit instead of seven consecutive days of wear time (GPS), which avoids the challenges of incomplete data over multiple days. On the other hand, we might have incomplete VERITAS data which we might not be aware of because there is no actual “golden standard” to compare VERITAS data to. Assessing the practical limitations of VERITAS, a 1-hr home-based interview with a stranger (researcher) may be more burdensome for the participant than a 20-min visit followed by 7 days of wear time proposed for the GPS. However, results from this study did not confirm one or the other method to be better. Additionally, costs for the VERITAS software are relatively high and data processing takes time. Lastly, this was only a small pilot study with a sample of 34 participants. If VERITAS had to be used on a larger scale, it might not be feasible using the same approach as in our study because home visits are time-consuming and require several trained researchers and travel costs. Even though VERITAS can be self-administered, it implies that participants have sufficient internet and map literacy, which might not be the case among older adults in general and older adults of lower socioeconomic status. First, low-income older adults may not have the money to own a computer and thus be able to learn to use one. Second, lower educational level older adults may not have had a job where they worked on a computer and, thus, have never been able to learn to use one. Third, older adults in general grew up in a time without computers and, thus, have difficulties to learn to use it in such late age. However, because more and more seniors are getting used to different technological devices as well as the internet, an online version of VERITAS might be possible in a few years. A report from Pew Research Center showed an increase in internet use by American older adults (65+) from 14% in 2000 to 58% in 2015 [40]. Because this increase is exponential, we might expect that this number has increased further since 2015 and will keep increasing in the coming years. However, an online process might not elicit the quality of data leveraged in an interview and does not include the human contact with the researcher which may be very valuable to older adults. Recruitment and compliance More participants wanted to participate in the VERITAS interview and experienced the interview to be pleasant, compared to wearing the GPS device, although recruitment was difficult and time-consuming for both methods. Only a few participants had difficulties with the VERITAS interview, whereas most of the participants wearing the GPS reported difficulties remembering to wear the device and charging it, which might be due to the old age and less experience with technology. Additionally, only 52% of the 23 participants had GPS data for at least 1 day and only 5 participants had 7 days with at least 8 hr of wear time, which poses another limitation to the GPS approach. As mentioned in the Results section, participants reported that they did not want to take the responsibility of remembering to charge the device and to take it with them when leaving their apartment. However, our communication strategy when introducing the project and the GPS device to this specific target population might not have been the most suitable or sufficient in this case. That is, a more targeted communication strategy considering the specific context and population in the development of the communication strategy might have increased participation. Measurement of daily mobility Daily mobility can be measured in several different ways, depending on the specific context, the population being investigated, and the concept of mobility of interest. Our interpretation of daily mobility goes beyond the commonly considered “ability to move oneself within community environments that expand from one’s home, to the neighborhood, and to regions beyond” [7]. We are identifying several additional components as important variables in explaining mobility, which will allow researchers and health promoters to better target individual or environmental interventions to support increased mobility and consequently, aging in place. For example, the use of different modes of transportation—both active and passive—the use of assistive devices, and social interaction as being important to maintain mobility, by having family or friends who can help you with transportation or who you can visit and socially interact with. Using this definition, we found VERITAS and the Qstarz BT-Q1000xt GPS device to be useful in measuring mobility to some extent. GPS is able to objectively measure the traditional main components of mobility, as it can measure movement from home, in the neighborhood and further afield. The benefit of using an objective measurement tool like the GPS is its ability to capture a persons’ actual movement with great temporal precision throughout an entire week, without being affected by recall bias or other biases, which self-reported measurements typically involve [41, 42]. However, when digging deeper into mobility, it becomes clear that GPS has some limitations. First, the GPS device itself is potentially capable of providing some of the daily mobility components, but limitations in data processing and data transformation (algorithms), diminish our possibilities (i.e., transportation mode detection). However, complementary sensors like the accelerometer might enhance the algorithm performance. Second, the GPS device itself has some limitations, which cannot be improved by creating better algorithms, and, consequently, is not able to measure some daily mobility variables like use of assistive devices and social interaction. Although recent studies have used complementary diaries or wearable data streams (e.g., smartphones), that can serve to complement the GPS derived data with the missing information. VERITAS, on the other hand, is a self-reported map-based measurement tool that allows researchers to comprehensively assess spatial behavior along daily mobility. In contrast to GPS that provides objective information on seven specific days, VERITAS provides a general picture of the participants’ behavior, by asking questions about predefined activities which the respondent has to map and specify in terms of how often they visit this destination, how they generally reach this destination, and whether they go there by themselves or meet with someone. Although we were able to measure almost all components of mobility using VERITAS, we need to weigh this against the bias of using a self-reported measurement tool—especially when the target population is older adults, who might have difficulties in recalling their behavior, or be more susceptible to social desirability bias or social approval bias [41, 42]. Future research could address this issue by including a social desirability measure within the questionnaire to control for potential bias. As depicted in Fig. 1, VERITAS collected daily mobility and GPS measured daily mobility are very different and have each their strengths and limitations. Almost 60% of participants’ GPS measured trips and VERITAS identified destinations within 7 days seemed to match, which may be argued as either a high or a low number. Assessing each participant’s data in QGIS, it became clear that a lot more destinations were identified through VERITAS than measured by GPS trips. This might be due to the fact that most of the participants did not wear the GPS device for 7 days, so trips may have been missed. Alternatively, participants may have overestimated the number of destinations when self-reporting. Additionally, none of VERITAS recreational routes matched with the GPS measured walking trips. There may be several reasons for this; because we do not compare the same time periods, the participants might not have walked those identified VERITAS-routes while wearing the GPS device for only 7 days; or as participants are older adults, they may have reported on walks that they would like to have taken or been seen to have taken suggesting social desirability bias or social approval bias, as well as recall bias might be an issue. This issue is particularly important for use of these devices in an intervention trial. Ability to measure change over time depends on a reliable baseline. If participants miss report at baseline, the chances of detecting change over time are diminished. Furthermore, from self-reported physical activity data in trials we know that participants also over-report change due to even greater social desirability than likely here in this observational study. Based on the assessment of the three main criteria and their subcriteria, VERITAS seems to be more qualified at measuring our definition of daily mobility for this specific target population. Recruitment was easier, compliance was higher, data quality was better, and the daily mobility variables were assessed not only for the last 7 days (GPS approach), but through a more general picture of participants’ daily life (habitual approach). Whereas the GPS device might be sufficient to use for other mobility measures like lifespace. CONCLUSION This is the first study to assess the feasibility and practicality of VERITAS in measuring mobility in older adults. Our findings suggest that using a GPS device on older adults living in a socially deprived community on a larger scale may not be feasible. Recruitment rates were low and its ability to measure daily mobility was somewhat limited. Better communication strategies might have increased participation. Greater compliance with VERITAS completion and the depth and quality of the responses may support its use in some studies, especially in low socioeconomic populations who respond well to a guided interview. Researchers need to invest personal time in this particular population, as relationship development is key to conducting research with vulnerable populations. In-person spatial interviews like VERITAS might be a better fit when studying older adults living in disadvantages communities. We believe that this study will contribute to the next generation of built environment studies focusing on older adults’ mobility, physical activity, and aging-in-place, as it provides recommendations on specific methods to assess mobility in specific populations. Acknowledgments We thank Copenhagen Municipality in particular the Areal Renewal Office in Sydhavn for their support and collaboration. We also thank all involved persons at the two social housing areas “Tranehavegård” and “Engholmen Nord” for their enthusiastic participation in the study. Y.K. holds a Canada Institutes of Health Research applied public health Chair in Urban Interventions and Population Health. All data and the findings reported have not been previously published, and the manuscript is not being simultaneously submitted elsewhere. Funding: This study was funded by The Danish Foundation for Culture and Sports Facilities and The Velux Foundation. The funders have no role or authority in conducting the research project. Authors’ Contributions: T.S. conceived and coordinated the study, was responsible for its design, acquisition of data, data cleaning, data analyses, and drafted the manuscript. J.K. contributed with significant input to the outline of the manuscript, the introduction, and discussion section. Y.K. contributed with input to the methods section and discussion section. J.S. was part of developing the idea for the manuscript, handled and processed the data, and contributed to input and proofreading in all sections. All authors revised the manuscript critically, and read and approved the final manuscript. Ethical Approval and Informed Consent: All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and national research committee in Denmark and with the 1964 Helsinki declaration and its later amendments. The study and its data management procedures have been approved by the Danish Data Protection Agency (2015–57-0008). According to the Danish National Committee on Health Research Ethics, formal ethical approval was not required as the project was not a biomedical research project. Each face-to-face appointment with the senior participants started with a researcher explaining the purpose and procedures of the study once more and explicitly asking the respondent if he or she had understood everything and wanted to participate. It was important that the respondents felt at ease answering our questions and we aimed at creating a conversational atmosphere. We felt that requiring a written consent would disturb this atmosphere, and for that reason, we asked all seniors who participated for their informed consent orally before starting on their researcher facilitated survey. All participants could withdraw from the study at any time. This article does not contain any studies with animals performed by any of the authors. Primary Data: The authors have full control of all primary data and agree to allow the journal to review our data if requested. Compliance with Ethical Standards Conflict of Interest: We hereby declare that coauthor Yan Kestens holds shares in the company that sells VERITAS (Treksoft Solutions). The remaining authors declare that they have no potential conflict of interest. References 1. Harper S . Economic and social implications of aging societies . Science . 2014 ; 346 ( 6209 ): 587 – 591 . Google Scholar CrossRef Search ADS PubMed 2. World Health Organization . Global Age-Friendly Cities: A Guide . Geneva, Switzerland: World Health Organization ; 2007 . 3. Davey JA , de Joux V, Nana G, Arcus M. Accommodation Options for Older People in Aotearoa/New Zealand . Wellington, New Zealand: Centre for Housing Research Christchurch ; 2004 . 4. Bayer A-H , Harper L. Fixing to Stay: A National Survey of Housing and Home Modification Issues . Washington, D.C.: AARP ; 2000 . 5. Vincent HK , Vincent KR , Lamb KM . Obesity and mobility disability in the older adult . Obes Rev . 2010 ; 11 ( 8 ): 568 – 579 . Google Scholar CrossRef Search ADS PubMed 6. Baker PS , Bodner EV , Allman RM . Measuring life-space mobility in community-dwelling older adults . J Am Geriatr Soc . 2003 ; 51 ( 11 ): 1610 – 1614 . Google Scholar CrossRef Search ADS PubMed 7. Webber SC , Porter MM , Menec VH . Mobility in older adults: a comprehensive framework . Gerontologist . 2010 ; 50 ( 4 ): 443 – 450 . Google Scholar CrossRef Search ADS PubMed 8. Cagney KA , Browning CR, Jackson AL, et al. Networks, Neighborhoods, and Institutions: An Integrated “Activity Space” Approach for Research on Aging. In: Panel on New Directions in Social Demography, Social Epidemiology, and the Sociology of Aging; Committee on Population; Division on Behavioral and Social Sciences and Education; National Research Council; Waite LJ, Plewes TJ, editors. New Directions in the Sociology of Aging. Washington (DC): National Academies Press (US); 2013:8. Available from: https://www.ncbi.nlm.nih.gov/books/NBK184369/ 9. Haustein S , Siren A . Older people’s mobility: segments, factors, trends . Transp. Rev . 2015 ; 35 ( 4 ): 466 – 487 . Google Scholar CrossRef Search ADS 10. Kestens Y , Wasfi R , Naud A , Chaix B . “Contextualizing context”: reconciling environmental exposures, social networks, and location preferences in health research . Curr Environ Health Rep . 2017 ; 4 ( 1 ): 51 – 60 . Google Scholar CrossRef Search ADS PubMed 11. Yeom HA , Fleury J , Keller C . Risk factors for mobility limitation in community-dwelling older adults: a social ecological perspective . Geriatr Nurs . 2008 ; 29 ( 2 ): 133 – 140 . Google Scholar CrossRef Search ADS PubMed 12. Schmöcker J-D , Quddus M, Noland R, Bell M. Estimating trip generation of elderly and disabled people: analysis of London data . Transp Res Rec J Transp Res Board . 2005 ; 1924 : 9 – 18 . Google Scholar CrossRef Search ADS 13. Collia DV , Sharp J , Giesbrecht L . The 2001 National Household Travel Survey: a look into the travel patterns of older Americans . J Safety Res . 2003 ; 34 ( 4 ): 461 – 470 . Google Scholar CrossRef Search ADS PubMed 14. Rosso AL , Auchincloss AH , Michael YL . The urban built environment and mobility in older adults: a comprehensive review . J Aging Res . 2011 ; 2011 : 816106 . Google Scholar CrossRef Search ADS PubMed 15. Steptoe A , Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women . Proc Natl Acad Sci USA . 2013 ; 110 ( 15 ): 5797 – 5801 . Google Scholar CrossRef Search ADS PubMed 16. Bertera EM . Physical activity and social network contacts in community dwelling older adults . Act Adapt Aging . 2003 ; 27 ( 3–4 ): 113 – 127 . 17. Takemoto M , Carlson JA , Moran K , Godbole S , Crist K , Kerr J . Relationship between objectively measured transportation behaviors and health characteristics in older adults . Int J Environ Res Public Health . 2015 ; 12 ( 11 ): 13923 – 13937 . Google Scholar CrossRef Search ADS PubMed 18. Steffen TM , Hacker TA , Mollinger L . Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds . Phys Ther . 2002 ; 82 ( 2 ): 128 – 137 . Google Scholar CrossRef Search ADS PubMed 19. VanSwearingen JM , Paschal KA , Bonino P , Chen TW . Assessing recurrent fall risk of community-dwelling, frail older veterans using specific tests of mobility and the physical performance test of function . J Gerontol A Biol Sci Med Sci . 1998 ; 53 ( 6 ): M457 – M464 . Google Scholar CrossRef Search ADS PubMed 20. Stalvey BT , Owsley C, Sloane ME, Ball K. The Life Space Questionnaire: a measure of the extent of mobility of older adults . J Appl Gerontol . 1999 ; 18 ( 4 ): 460 – 478 . Google Scholar CrossRef Search ADS 21. QGIS . Open Source Geographic Information System . 2017 (cited June 15, 2017) . Available at http://www.qgis.org/en/site/. 22. Shoval N , Wahl H-W , Auslander G et al. Use of the global positioning system to measure the out-of-home mobility of older adults with differing cognitive functioning . Ageing Soc . 2011 ; 31 ( 5 ): 849 – 869 . Google Scholar CrossRef Search ADS 23. Shoval N , Auslander G, Cohen-Shalom K, Landau R, Heinik J. What can we learn about the mobility of the elderly in the GPS era ? J Transp Geogr . 2010 ; 18 ( 5 ): 603 – 612 . Google Scholar CrossRef Search ADS 24. Yen IH , Leung CW , Lan M , Sarrafzadeh M , Kayekjian KC , Duru OK . A pilot study using global positioning systems (GPS) devices and surveys to ascertain older adults’ travel patterns . J Appl Gerontol . 2015 ; 34 ( 3 ): NP190 – NP201 . Google Scholar CrossRef Search ADS PubMed 25. 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 . Google Scholar CrossRef Search ADS PubMed 26. Chaix B , Kestens Y , Perchoux C , Karusisi N , Merlo J , Labadi K . An interactive mapping tool to assess individual mobility patterns in neighborhood studies . Am J Prev Med . 2012 ; 43 ( 4 ): 440 – 450 . Google Scholar CrossRef Search ADS PubMed 27. Kestens Y , Chaix B , Gerber P et al. Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol) . BMC Geriatr . 2016 ; 16 : 96 . Google Scholar CrossRef Search ADS PubMed 28. Sessford JD , Jung M , Brawley LR , Forbes JL . Do older adults’ beliefs about their community mobility predict walking performance ? J Aging Phys Act . 2015 ; 23 ( 2 ): 272 – 278 . Google Scholar CrossRef Search ADS PubMed 29. Perchoux C , Kestens Y , Thomas F , Van Hulst A , Thierry B , Chaix B . Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study) . Soc Sci Med . 2014 ; 119 : 64 – 73 . Google Scholar CrossRef Search ADS PubMed 30. Pawlowski CS , Winge L , Carroll S et al. Move the neighbourhood: study design of a community-based participatory public open space intervention in a Danish deprived neighbourhood to promote active living . BMC Public Health . 2017 ; 17 ( 1 ): 481 . Google Scholar CrossRef Search ADS PubMed 31. Forskningscenter for Forebyggelse og Sundhed . Sundhedsprofil for Vesterbro/Kongens Enghave . Copenhagen, Denmark : Københavns Kommune ; 2008 . 32. Sydhavnen O. Kvarterplan områdefornyelse Sydhavnen . Copenhagen, Denmark : Københavns Kommune ; 2014 . 33. Anonymous . The Physical Activity and Location Measurement (PALMS) . 2014 (cited June 15, 2017) . Available at http://ucsd-palms-project.wikispaces.com/. 34. Carlson JA , Jankowska MM, Meseck K et al. Validity of PALMS GPS scoring of active and passive travel compared to SenseCam . Med Sci Sports Exerc . 2015;47(3):662–667. doi: 10.1249/MSS.0000000000000446 35. Hayes V , Morris J , Wolfe C , Morgan M . The SF-36 health survey questionnaire: is it suitable for use with older adults ? Age Ageing . 1995 ; 24 ( 2 ): 120 – 125 . Google Scholar CrossRef Search ADS PubMed 36. Prins RG , Pierik F , Etman A , Sterkenburg RP , Kamphuis CB , van Lenthe FJ . How many walking and cycling trips made by elderly are beyond commonly used buffer sizes: results from a GPS study . Health Place . 2014 ; 27 : 127 – 133 . Google Scholar CrossRef Search ADS PubMed 37. Boruff BJ , Nathan A , Nijënstein S . Using GPS technology to (re)-examine operational definitions of ‘neighbourhood’ in place-based health research . Int J Health Geogr . 2012 ; 11 : 22 . Google Scholar CrossRef Search ADS PubMed 38. Kerr J , Marshall S , Godbole S et al. The relationship between outdoor activity and health in older adults using GPS . Int J Environ Res Public Health . 2012 ; 9 ( 12 ): 4615 – 4625 . Google Scholar CrossRef Search ADS PubMed 39. Paz-Soldan VA , Stoddard ST , Vazquez-Prokopec G et al. Assessing and maximizing the acceptability of global positioning system device use for studying the role of human movement in dengue virus transmission in Iquitos, Peru . Am J Trop Med Hyg . 2010 ; 82 ( 4 ): 723 – 730 . Google Scholar CrossRef Search ADS PubMed 40. Pew Research Center. Americans’ Internet Access: 2000–2015 . 2015 (cited June 15, 2017). Available at http://www.pewinternet.org/2015/06/26/americans-internet-access-2000–2015/. 41. Adams SA , Matthews CE , Ebbeling CB et al. The effect of social desirability and social approval on self-reports of physical activity . Am J Epidemiol . 2005 ; 161 ( 4 ): 389 – 398 . Google Scholar CrossRef Search ADS PubMed 42. Sallis JF , Saelens BE . Assessment of physical activity by self-report: status, limitations, and future directions . Res Q Exerc Sport . 2000 ; 71 ( suppl 2 ): 1 – 14 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Translational Behavioral Medicine Oxford University Press

Challenges in using wearable GPS devices in low-income older adults: Can map-based interviews help with assessments of mobility?

Loading next page...
 
/lp/ou_press/challenges-in-using-wearable-gps-devices-in-low-income-older-adults-ckK0j7MFMS
Copyright
© Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
1869-6716
eISSN
1613-9860
D.O.I.
10.1093/tbm/iby009
Publisher site
See Article on Publisher Site

Abstract

Abstract Daily mobility, defined as the ability to move oneself within one’s neighborhood and regions beyond, is an important construct, which affects people as they age. Having a feasible and valid measure of daily mobility is essential to understand how it affects older adults’ everyday life. Given the limitations of existing measures, new tools may be needed. The purpose of the study is to assess the feasibility and practicality of using the map-based questionnaire system VERITAS and GPS devices to measure daily mobility in older adults living in a deprived neighborhood in Denmark. Older adults were recruited from two senior housing areas, completed an interview using VERITAS and wore a GPS for 7 days. Feasibility of both methods was assessed by looking at practicalities, recruitment and compliance, and ability to measure daily mobility. Thirty-four older adults completed the VERITAS questionnaire, of which 23 wore the GPS device. Remembering to wear and charge the GPS was difficult for 48% participants, whereas remembering street names and drawing routes in VERITAS was difficult for two. Both the GPS and VERITAS were able to measure 10 out of the 13 identified components of mobility; however, VERITAS seemed more qualified at measuring daily mobility for this target population. The feasibility of assessing mobility may vary by specific context and study population being investigated. Wearable technology like a GPS may not be acceptable to low socioeconomic older adults, whereas interview led self-reported measurements like VERITAS might be more suitable for a low socioeconomic elderly population. Implications Practice: Using VERITAS, researchers, community stakeholders, and city planners can assess low-income senior citizens’ daily mobility to identify their ability to age in place. Policy: Policymakers who want to promote aging-in-place to decrease costs of health services should explore the concept of daily mobility and how it affects older adults’ ability to age in place. Research: The feasibility of measuring mobility using wearable GPS and self-report mapping tools like VERITAS seems to vary by specific context and study population being investigated, which is why future research should further examine the two methods in different population groups. BACKGROUND The worldwide population of older adults (60+) is expected to double from 11% in 2006 to 22% by 2050 [1]. By this time, in Western countries, there will be more older adults than children and local governments may struggle to deliver appropriate services to this large population. With the increase in older adults worldwide, there has also been an increased interest in enabling people to age-in-place. The World Health Organization has supported a program of age-friendly communities to increase capacity for aging in place [2]. Aging-in-place is a term typically defined as “remaining living in the community, with some level of independence, rather than in residential care” [3]. The majority of older Americans state that they want to age-in-place as long as possible [4], and several factors seem to affect one’s ability to do so. The ability to conduct everyday activities is an important predictor of aging in place, and limitations in daily mobility may represent an important barrier. Although there are several accepted definitions of mobility [5, 6] in this study, mobility is a concept that describes “the ability to move oneself (e.g., by walking, using assistive devices, or by using transportation) within community environments that expand from one’s home, to the neighborhood, and to regions beyond” [7]. It describes people’s ability to move around in their homes as well as inside and outside of their neighborhood, to do daily activities such as going to the grocery store, accessing health care services, meeting friends, and performing recreational activities. In the present study, we used the following components to describe daily mobility: destination type, number of and distance to destinations; routes used from home to destinations; number of times visiting destinations (frequency) and amount of time spent at destinations; mode of transportation (vehicle, public transportation, walk, bike) as well as distance traveled, frequency of use and time spent in these modes; use of assistive devices during trips (cane, walker, wheelchair); total time spent outdoors; and social interactions during trips and at destinations [8–10]. Mobility is fundamentally important for being able to maintain physical and psychological health and is especially important for elderly people because their mobility usually declines with age. In particular, socioeconomically disadvantaged older adults are at greater risk for declines in mobility and, consequently, institutionalization [11]. Studies indicate that elderly people travel shorter distances and less often than younger adults and have identified changes in activity patterns, health constraints, and traffic safety as potential contributors to their decreased mobility [12–14]. Features of the built environment have also been linked to mobility among older adults, underlining the fact that some types of environments offer greater opportunities to be mobile than others. Additionally, mobility is strongly related to social interaction (i.e., going somewhere to meet with a peer) and, consequently, loss in mobility might translate into reduced social participation [15, 16]. Even passive mobility, for example in vehicle versus walking, seems to have some mental and social health benefits [17]. Furthermore, mobility represents the translation of people’s need to access various resources like health services and grocery stores. That is, a certain level of mobility is important to be able to access these various resources. Thus, investigating how well older adults can conduct their everyday activities, how it affects their health, and how the built environment and the social environment affect their mobility is important to support aging-in-place. The complex concept of daily mobility has led to a range of different methods to measure mobility in older adults, like physical functioning tests [18, 19] or the self-reported Life-Space Questionnaire (LSQ) [20]. Even though these methods capture some of the elements of daily mobility, they do neglect some other important components, such as different modes of transportation and the social components of daily mobility. Consequently, there is a need to explore other measurement tools that might better capture daily mobility in older adults. One method that is increasingly being used to assess daily mobility and exposure to multiple environments is Global Positioning System (GPS) devices. GPS devices are an objective tool used to capture location and, in combination with data from a Geographic Information System [21], they can reveal movement through different environments in all age groups. Typically, GPS studies have collected data for 1 week although this is changing with increased use of smartphones and embedded GPS trackers. In the last few years, GPS has also been used in studies investigating daily mobility in older adults [22–25] by assessing a persons’ time spend in different spaces, different destinations visited, or mode of transportation. GPS may be a better tool to assess daily mobility than LSQ as it can capture many of the different components of daily mobility described earlier objectively. A relatively new method to assess daily mobility is spatial interviews or questionnaires like the Visualization and Evaluation of Route Itineraries, Travel Destinations, and Activity Spaces (VERITAS). VERITAS is a web-based application that combines interactive mapping (using Google Maps) with activity and travel questions [26, 27]. VERITAS facilitates the collection of self-reported data about participants’ destinations, routes, modes of transportation, and on related social dimensions (i.e., whom is generally met at these destinations), and provides a more general picture of daily mobility [26]. Please visit the SPHERELAB website (http://www.spherelab.org/tools) to see a demonstration of VERITAS. To our knowledge, VERITAS has only been used in a few studies, for respondents with different socioeconomic statuses [28] and ages. One study focusing on older adults (65+ years old) was conducted in Canada [27] and another which to some degree included an older study population (range: 33–84, mean age 51 years) was conducted in France [29]. However, none of these studies specifically looked at the feasibility of using VERITAS to assess daily mobility. Identifying a suitable method for measuring daily mobility, especially among older adults and deprived populations, is important to fully understand what affects daily mobility. Measurement tools need to be adaptable to different population groups (age and socioeconomic status (SES)) and measurements need to be sensitive toward change because they might have to measure relatively small behavior changes in older adults. Both VERITAS and GPS devices seem promising and their strengths and weaknesses need to be assessed to improve the next generation of built environment studies focusing on aging-in-place. Aim This paper is looking at two methods to collect daily mobility data in older adults: one that is self-reported and focuses on regular destinations and the other one that is objective and uses passive tracking capabilities providing acute measurement of daily mobility. More specifically, the aim of this study was to assess the feasibility and practicality of using the map-based questionnaire system VERITAS and wearable GPS devices to measure daily mobility in a sample of older adults living in senior housing in a socially deprived neighborhood of Copenhagen, Denmark. This includes assessing tool acceptability, adherence to protocol, barriers to their use (cost, time, etc.), and ability to provide measures of daily mobility in low-income older adults. METHODS Case study sample and recruitment procedures This study is part of a multicomponent intervention study (Move the Neighborhood!) focusing on using co-creation to, in collaboration with local seniors, design and build installations that have the potential to increase physical activity and decrease sedentary behavior among older adults living in a deprived neighborhood (Sydhavnen) of Copenhagen [30]. With only 73.0 years, Sydhavnen has one of the lowest life expectancies in Denmark (the average in Denmark is 80.6 years) [31], 32% of the population only attended primary school and 40.2% have a low income [32]. The study population was recruited within the neighborhood from two housing areas (350 tenants, age 50+) for the elderly that accommodate seniors who have a low socioeconomic status. Many tenants cannot pay the usual rent for an apartment in Copenhagen and might need extra help with cleaning, grocery shopping, and/or personal care. Older adults were recruited from the two housing areas through letters, local stakeholders, and existing social activities. Information about the intervention study, this substudy, and its purpose of providing valuable knowledge to this study and future studies targeting open spaces for older adults was provided during each recruitment procedure. Participants were introduced to both methods GPS and VERITAS and were encouraged to “use” both of them. However, they were given the option to refuse to participate in one or both of the measurements. Those who agreed on participating in both measurements completed the VERITAS interview and were afterwards given a GPS to wear for the following 7 days. We collected information on participants’ reasons for refusal. Participants were offered to be interviewed at their home or at our offices; all of them chose at home interviews. The study was registered in the ISRCTN registry (ISRCTN50036837). Furthermore, the study and its data-management procedures were approved by the Danish Data Protection Agency (2015-57-0008). GPS Those participants who agreed to wear a GPS wore a Qstarz BT-Q1000xt GPS device and were given several choices on how to wear the device (belt, ankle holder, key hanger, in their pocket or bag). Participants were asked to wear the device for seven consecutive days and to take the GPS off only when there was a risk of contact with water and at night while charging. Participants received short reminder text messages on their mobile phones—one in the morning and one in the evening—to increase compliance and remind them to charge their GPS device. Additionally, reminder flyers were posted next to their apartment door and next to their nightstand with information on how to charge and wear the GPS device because some participants did not use their phone a lot or were not used to text messages. GPS devices were collected after 7 days and GPS data were processed using a web-based application called the Personal Activity and Location Measurement System (PALMS) [33]. PALMS uses a range of user-defined settings to identify different variables like trips, mode of transportation and time spent outdoors, to assess mobility. In this study, trips were categorized as continuous movement of at least 3 min, with stationary periods of maximum 5 min. Mode of transportation during each trip was calculated using speed and included three modes: walking (≥1 km/h, <10 km/h), biking (≥10 km/h, <25 km/h), and in a vehicle (≥25 km/h) [34]. Data were afterwards visualized in QGIS version 2.18.3. QGIS is a free and open source GIS used to create, edit, visualize, and analyze geospatial information [21]. VERITAS VERITAS is a map-based retrospective questionnaire that allows for identification of locations (points), routes between locations (lines) or areas (polygons) such as perceived neighborhoods, on a map. Google Map search functionalities can be used to facilitate the identification of specific locations. Participants were first asked to confirm the location of their home address (point), which served as a basis for all other identified locations to determine distance. The specific VERITAS questions used in this study were based on the CURHA study conducted in Canada by Yan Kestens et al. [27] focusing on older adults with a mix of socioeconomic statuses. The questionnaire was originally in French and English. We translated it into high-school level Danish and made some adjustments to account for the Danish context. The questionnaire consisted of 32 categories each including one to five questions. Depending on whether the participant answered “yes” or “no,” the participant was asked to specify each of the 32 categories by drawing destinations or routes on the digital map, and identify how often, how (mode and use of assistive devices) and with whom they go there (social interaction). For example, “Do you shop for groceries at a supermarket at least once per month?”, if they answered “yes,” we asked them to locate the supermarket on the map. Questions considering specific places (e.g., supermarket, doctor, bank) all use the phrase, “…at least once per month,” that is VERITAS does not ask questions about the last 7 days or other specific period, but provides a more general picture of activities and destinations. We decided to deliver the questionnaire as an interview instead of a self-administered questionnaire because interviewing has shown to be a more reliable method for older adults [35]. Questions in the VERITAS interview encouraged participants to identify the following regular destinations: recreational destinations, different types of destinations for grocery shopping (supermarket, butcher, etc.) and services (bank, doctor, post office). Participants were asked to identify their perceived neighborhood by “drawing” a polygon picturing the perceived boundaries of their neighborhood. Participants reported destinations visited at least once a month to account for several different destinations, for example the post office, which may not be frequently visited. After completion of data collection, VERITAS data were downloaded as csv files and processed in QGIS version 2.18.3. Feasibility To fully evaluate the feasibility of the GPS device and VERITAS in measuring daily mobility in low socioeconomic older adults, three criteria were used: practicalities, recruitment and compliance, and mobility variables. Each of these three criteria had several subcriteria that were assessed and compared. The three criteria are described in the following section. Practicalities First, we evaluated several practical barriers to using the two methods, which included costs and time spent on equipment preparations, data collection, and data processing. Second, we assessed the practicalities of method acceptability qualitatively by interviewing participants before and after data collection. Seniors who did not want to be part of the study were asked, “What are your reasons for not wanting to participate in this study?” If participants only wanted to participate in one part of the data collection (e.g., only VERITAS interview) they were asked, “What are your reasons for not wanting to participate in the other part of the data collection?” Seniors who wore the GPS device were asked two questions after the 7 days of wear time: (a) “Have you had any difficulties or other experiences with the device while wearing it?” (b) “Did you at any time during the last 7 days forget to wear your device during all waking hours?” Three researchers were responsible for the home visits and interviewing the participants and non-participants. Responses of all three researchers were combined and assessed after completion of the data collection. After all practicalities were assessed, the scalability of the two methods was evaluated. Recruitment and compliance Second, we assessed recruitment difficulties and recruitment rates for both methods, that is how easy or difficult is recruitment and how many agree to participate in the VERITAS interview versus the GPS data collection. We further assessed compliance with the data collection procedure and data quality, that is difficulties answering the VERITAS questionnaire or wearing the GPS device, and the quality of the data when downloaded. Mobility variables Lastly, to fully evaluate the feasibility of the two methods, we assessed their ability to measure daily mobility. In this study, the concept of daily mobility is defined by several components listed in the Background section. These components are conceptualized through 13 variables, divided into three themes: Destination, Transportation, and Other. Destinations: number of destinations visited; type of destination; routes from home to destinations; time spent at a location; frequency of destinations visited; and distance to destinations. Transportation: mode of transportation; frequency of transportation mode; time spent in different transportation modes; use of assistive devices during trips; and distance traveled by mode. Other: time spent outdoors and social interaction. The 13 variables were evaluated by assessing GPS’s and VERITAS’s technical capability to measure the variables using PALMS (GPS) and QGIS (VERITAS). Finally, we present an example map of a participant’s GPS mobility data (for walking) and VERITAS mobility data (recreational walking), which was used for a visual assessment of the spatial data. The map was generated using QGIS. We recognize that caution should be taken when mapping two different types of data for the same person because they might not be fully comparable as VERITAS data and GPS data are not necessarily representing the same time points during data collection. GPS data were captured for seven specific days, whereas VERITAS data show a general picture of destinations visited during a usual month. But even though the two methods represent different ways of measuring daily mobility, they have a similar goal, providing a description of daily mobility which can be used in health research. Analyses On completion of the fieldwork, qualitative data from three researchers including field notes and notes from the unstructured interviews with participants were combined and coded to assess the feasibility of using VERITAS or GPS. During the first step of the thematic analysis, the combined data were analyzed and three themes emerged based on the participants’ feedback on the measurement procedure: GPS difficulties, VERITAS experiences, and no participation. VERITAS difficulties were not reported. To assess the two methods’ ability to measure the different aspects of the concept of mobility, we used a descriptive approach looking at a range of different variables identified in the methods section. These variables were assessed by visualizing VERITAS data in QGIS using the available geographical coordinates that allowed us to locate destinations and areas, as well as look at questions about the use of assistive devices, transportation mode, and social activities. GPS data were processed in PALMS and afterwards visualized in QGIS, allowing us to visually assess GPS processed data about transportation mode, routes, and time spent outside. To compare the spatial information collected using the two methods, we created a map for each participant using QGIS and visually assessed the percentage of agreement between GPS and VERITAS measurements, by first, counting the number of times were VERITAS identified destinations and recreational walking routes were visually on or next to GPS calculated trips using the QGIS illustrated map. Second, the percentage of agreement was calculated by dividing the total number of matches by the total number of VERITAS identified destinations and recreational walking routes and multiply by 100. RESULTS Recruitment and compliance Recruitment was challenging and time-consuming. No one responded to flyers in their mail (180 out of 350 households received flyers)—normally, the method used by local stakeholders to communicate with residents—which meant that we had to recruit them on the spot by participating in their social activities. Out of 340 older adults asked (including the 180 households), 34 (10%) agreed to complete the VERITAS questionnaire. Ages ranged from 51 to 90 years, of which 26.5% were men (see Table 1). During the VERITAS interview, two participants had difficulty remembering street names and drawing specific routes in the VERITAS map. Of the 34 older adults completing the VERITAS interview, 23 (6.8% out of 340 asked) agreed to wear the GPS device. Ages ranged from 53 to 86 years, and 24% were men (see Table 1). Of the 23 agreeing to wear the GPS device, only 12 participants (52%) had valid GPS data for at least 1 day (>8 hr of wear time), and only five participants had 7 days of GPS data. The 12 participants with GPS data had on average worn the GPS for 9 h/day for 5.6 days. Eleven participants had difficulties remembering to charge the device and to carry it on them at all time. Participants who did not want to wear the GPS said they did not want to be responsible for remembering to charge the device and to take it with them wherever they went (10 participants). Those who did not want to participate in the study at all stated that they did not have anything interesting to say (64 persons) or that they almost never left their apartment (6 persons) and thus, wearing the device did not make sense for them. Fifty-six older adults did not give any reason for not wanting to participate. Table 1 | Overview of descriptive, recruitment, and compliance Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 View Large Table 1 | Overview of descriptive, recruitment, and compliance Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 Descriptive VERITAS GPS Recruitment (n) 34 23 Age (mean) 74 (SD 9.6) 73 (SD 8.9) Sex (female) 73.50% 76% Compliance issues (n) 2 11 View Large Practicalities Table 2 shows an overview of the practicalities related to using GPS and VERITAS in the study. Setting up a VERITAS questionnaire costs a one-time payment of at least $1500 regardless of the size of your study population, whereas a GPS device + key hanger costs roughly $100 per person and consequently will get more expensive the more devices are needed for a study. When there is no need for simultaneous GPS tracking, one GPS device can typically be used for two consecutive participants per month. Time spent on equipment preparation, data collection, and data processing varied a lot for the two methods. VERITAS took approximately 1 min to prepare per participant; however, it took around 45 min to do the interview. In contrast, each GPS took about 10 min to prepare beforehand, around 20 min to explain and attach to the participant, and additionally 5 min to pick up after the 7 days. VERITAS only required transport time for one home visit, whereas the GPS required two home visits. Moreover, the participant needed to wear the device for 7 days and charge it every night, which might be considered as an extra burden for the participant. Table 2 | Overview of practicalities relevant to the use of VERITAS and GPS Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) View Large Table 2 | Overview of practicalities relevant to the use of VERITAS and GPS Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) Factors VERITAS GPS Equipment costs $ At least $1500 (one-time payment) Around $100 (per device + key hanger) Time: equipment preparation 1 min (registration of 1 participant by name and login information on a computer before visit) 10 min (preparing 1 GPS software on a computer, nametag on GPS device, tap on “On” button, attach to key hanger) Time: data collection 30 min–1 hr (per interview) 25 min/7 days (explaining the GPS, how to wear, how to charge, hanging reminder poster on wall, collecting phone number for reminder text, 7-day wear time) Time: home visits 1 home visit 2 home visits (delivering and explaining the GPS and collecting it 7 days later) Scalability (can this be done on a larger study population?) Yes (if done online instead of interview) Yes (but need many devices which are expensive) View Large Because this study was a pilot study with a relatively low number of participants, we considered whether the methods were applicable in larger studies investigating mobility in older adults, based on results displayed in Table 2. Both methods seem to be applicable to larger studies. However, the costs for each GPS device and the specific target population must be considered because this study population had low participation rates and difficulty complying with the protocol. The interview version of VERITAS was applicable in the small sample used in this study, but might be difficult to implement on a larger scale, because interviewing takes a long time. An online version of VERITAS might be more applicable if the study population were more acquainted with using computers and the internet. Mobility variables Table 3 provides an overview of the two methods’ capability to measure the variables we identified to be important for daily mobility in older adults. Both methods seem to be able to measure most of the daily mobility variables. However, the quality of the measurement varies between the two tools. VERITAS was able to record the specific mode of transportation, for example whether the person used a bus or a car and whether the person was driving himself or someone else was driving him. On the other hand, VERITAS was not able to capture the actual distance traveled in different modes of transportation or the specific time spent in different modes of transportation. Whereas GPS data processed in PALMS were able to measure the specific distance traveled in different modes. However, PALMS was not able to capture the difference between car driving or taking the bus, nor was it able to assess the use of assistive devices (e.g., cane or walker), which VERITAS was able to do. Table 3 | Overview of variables explaining daily mobility through VERITAS and GPS data Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No View Large Table 3 | Overview of variables explaining daily mobility through VERITAS and GPS data Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No Variables VERITAS GPS Destination  Number of destinations visited Yes Yes  Type of destination Yes No  Routes from home to destinations No Yes  Frequency of destinations visited Yes (total) Yes (for 7 days)  Distance to destinations Yes Yes  Time spent at a location Yes Yes (high temporal precision) Transportation  Mode of transportation Yes (specific mode) Yes (walk, bike, vehicle)  Frequency of transportation mode Yes Yes  Time spent in different transportation modes No Yes (high temporal precision)  Use of assistive devices during trips Yes No  Distance traveled by mode No Yes Other  Time spent outdoors Yes (not precisely) Yes (high temporal precision)  Social interaction Yes No View Large Other components that explain daily mobility in older adults are the type and frequency of destinations visited, distance, and specific routes taken from home to destinations. VERITAS provides measures of type, frequency and number of destination visited, as well as the distance to destinations. Each destination drawn on Google Maps is followed by a question asking to specify the type of destination, at what frequency they visit that place, and how they generally go there. Participants were able to choose several options for the same trip, which means that VERITAS captured the use of different assistive devices and modes of transportation (e.g., walking with a cane to a bus stop and taking the bus). However, this version of VERITAS was not able to measure the specific routes taken from home to destinations and only poorly measures time spent at destinations (by asking participants to give an estimate of time), whereas GPS was able to measure (all types of) time with high temporal precision, as well as routes from a to b. Social interaction is another variable of daily mobility which is captured by VERITAS, as participants were asked whether they traveled by themselves or with someone else to each specific destination, and whether they regularly visited friends or family in their homes, or friends and family visited the participant in their home. If they answered “yes,” they were asked to elaborate on how well they knew this person, how often they met and how they communicated (mail, phone, in person). Combined, this information can give an in-depth knowledge about participants’ social relationships. GPS data on the other hand, is not able to detect social interactions; however, identification tagging on different GPS devices may allow assessment of social interaction in the future. Finally, we created a map for each participant using QGIS for illustrative purposes and visual assessment of the spatial information collected using the two different methods. The map in Fig. 1 displays one participant’s identified destinations (yellow dots) and recreational walking route (yellow line) in VERITAS and GPS measured walking using PALMS (orange lines). As can be seen, the two types of visualizations are very different, providing different information. For the example person, the GPS identified walking represents 25 different trips, which may be recreational walking or walking for transportation to specific destinations. The VERITAS data for the same person revealed 21 different destinations and 3 routes for recreational walking. Due to many GPS tracks starting and ending inside buildings, determining the exact start and end points of trips is difficult. GPS accuracy is lower inside buildings which causes trip start and end data to contain noise, that is incorrect GPS points. The advantage of the GPS data, however, is that it is certain that the person actually walked the specific routes seen in Fig. 1, as he or she was wearing the GPS device. From these data, detailed information can be retrieved about the specific trips taken, for example the actual distance traveled, and specific duration and time of day when the trip was made. Fig 1 View largeDownload slide QGIS map displaying a participant’s VERITAS identified destinations and GPS measured walking. = VERITAS destinations; ▬ = VERITAS recreational walking; ▬ = GPS walking trips; ⌂ = home Fig 1 View largeDownload slide QGIS map displaying a participant’s VERITAS identified destinations and GPS measured walking. = VERITAS destinations; ▬ = VERITAS recreational walking; ▬ = GPS walking trips; ⌂ = home Looking more closely at the VERITAS data, we see that most of the destinations (yellow dots) are on or close to GPS routes, which indicates that these destinations were most likely also visited during the days of GPS monitoring. In fact, comparing all participants’ data by visually matching each participant’s GPS trips with VERITAS destinations, we see that 58.4% of VERITAS identified destinations are on or next to GPS measured trips. However, the mismatch between VERITAS identified recreational walking trip (yellow line in Fig. 1) and GPS trips may indicate that the recreational trip was not taken during the 7 days of GPS monitoring. For each destination in VERITAS, detailed information about the context and purpose of the trip can be retrieved. Furthermore, we can retrieve information about mode of transportation and use of assistive devices (i.e., bus, train, walking without any assistance), and how often the participant visits each destination, ranging from several times per week to a couple of times per month or year. This information depicts a more general picture of the participant’s life space, but does not provide detailed information about specific time points or routes taken to these destinations. DISCUSSION This pilot study aimed to assess the challenges and benefits of two different spatial measurement methods by descriptively investigating the feasibility of using an interview version of the map-based VERITAS questionnaire compared to GPS devices to assess daily mobility in low-income older adults. We investigated feasibility by assessing three main criteria: practicalities; recruitment and compliance; and mobility variables, using both qualitative fieldwork data during data collection and objective quantitative data from the two methods. Practicalities Using a GPS device on low-income older adults imposed numerous practical issues. First, one GPS device costs $100, which might be considered prohibitive for a larger study. However, often researchers are able to get bulk discounts, borrow devices, or rotate a smaller pool of devices among a larger group since each person only wears the device for 7 days. Second, even though 10 min to prepare one GPS device does not seem burdensome, the amount of time and staff needed to do this at scale mount up. Third, the actual data collection, in this case, was done through home visits, explaining the device, how to wear and charge it, and setting up for reminder texts and reminder posters. This procedure was very time-consuming. However, because this study population was disadvantaged in several ways (low-income, physical disabilities, disadvantages neighborhood), it was not considered feasible to mail the device to their homes. Fourth, participants had to wear the device for 7 days and remember to charge it daily, which was found to be a challenge, and might explain the low participation rate (6.8%). However, other studies did succeed in using the GPS device on older adults [36–38], although study participants in these studies were mainly Caucasians with high levels of physical functioning and education. Lastly, one might consider the specific context (low income and socially disadvantaged community) to be the reason for the several identified limitations and not the GPS device itself. One study by Paz-Soldan et al. [39] found several limitations in the use of GPS on adults in a low SES city in the Amazon Basin of Peru. Consequently, it might be the specific age group in combination with its vulnerability by being a disadvantaged population group, which might make the GPS device less feasible to use in studies focusing on disadvantaged older population groups. The map-based interview version of VERITAS used in this study seemed to have both benefits and some practical limitations when used on a disadvantaged population of older adults. The benefits of using VERITAS were as follows: first, the lower burden of participation (one interview of around 45 min vs. 7 days of wear) consequently, higher participation rates. Second, it took a relatively short time to prepare the VERITAS procedure before visiting the participant in their home, which saved time. Third, VERITAS was completed during one guided home visit instead of seven consecutive days of wear time (GPS), which avoids the challenges of incomplete data over multiple days. On the other hand, we might have incomplete VERITAS data which we might not be aware of because there is no actual “golden standard” to compare VERITAS data to. Assessing the practical limitations of VERITAS, a 1-hr home-based interview with a stranger (researcher) may be more burdensome for the participant than a 20-min visit followed by 7 days of wear time proposed for the GPS. However, results from this study did not confirm one or the other method to be better. Additionally, costs for the VERITAS software are relatively high and data processing takes time. Lastly, this was only a small pilot study with a sample of 34 participants. If VERITAS had to be used on a larger scale, it might not be feasible using the same approach as in our study because home visits are time-consuming and require several trained researchers and travel costs. Even though VERITAS can be self-administered, it implies that participants have sufficient internet and map literacy, which might not be the case among older adults in general and older adults of lower socioeconomic status. First, low-income older adults may not have the money to own a computer and thus be able to learn to use one. Second, lower educational level older adults may not have had a job where they worked on a computer and, thus, have never been able to learn to use one. Third, older adults in general grew up in a time without computers and, thus, have difficulties to learn to use it in such late age. However, because more and more seniors are getting used to different technological devices as well as the internet, an online version of VERITAS might be possible in a few years. A report from Pew Research Center showed an increase in internet use by American older adults (65+) from 14% in 2000 to 58% in 2015 [40]. Because this increase is exponential, we might expect that this number has increased further since 2015 and will keep increasing in the coming years. However, an online process might not elicit the quality of data leveraged in an interview and does not include the human contact with the researcher which may be very valuable to older adults. Recruitment and compliance More participants wanted to participate in the VERITAS interview and experienced the interview to be pleasant, compared to wearing the GPS device, although recruitment was difficult and time-consuming for both methods. Only a few participants had difficulties with the VERITAS interview, whereas most of the participants wearing the GPS reported difficulties remembering to wear the device and charging it, which might be due to the old age and less experience with technology. Additionally, only 52% of the 23 participants had GPS data for at least 1 day and only 5 participants had 7 days with at least 8 hr of wear time, which poses another limitation to the GPS approach. As mentioned in the Results section, participants reported that they did not want to take the responsibility of remembering to charge the device and to take it with them when leaving their apartment. However, our communication strategy when introducing the project and the GPS device to this specific target population might not have been the most suitable or sufficient in this case. That is, a more targeted communication strategy considering the specific context and population in the development of the communication strategy might have increased participation. Measurement of daily mobility Daily mobility can be measured in several different ways, depending on the specific context, the population being investigated, and the concept of mobility of interest. Our interpretation of daily mobility goes beyond the commonly considered “ability to move oneself within community environments that expand from one’s home, to the neighborhood, and to regions beyond” [7]. We are identifying several additional components as important variables in explaining mobility, which will allow researchers and health promoters to better target individual or environmental interventions to support increased mobility and consequently, aging in place. For example, the use of different modes of transportation—both active and passive—the use of assistive devices, and social interaction as being important to maintain mobility, by having family or friends who can help you with transportation or who you can visit and socially interact with. Using this definition, we found VERITAS and the Qstarz BT-Q1000xt GPS device to be useful in measuring mobility to some extent. GPS is able to objectively measure the traditional main components of mobility, as it can measure movement from home, in the neighborhood and further afield. The benefit of using an objective measurement tool like the GPS is its ability to capture a persons’ actual movement with great temporal precision throughout an entire week, without being affected by recall bias or other biases, which self-reported measurements typically involve [41, 42]. However, when digging deeper into mobility, it becomes clear that GPS has some limitations. First, the GPS device itself is potentially capable of providing some of the daily mobility components, but limitations in data processing and data transformation (algorithms), diminish our possibilities (i.e., transportation mode detection). However, complementary sensors like the accelerometer might enhance the algorithm performance. Second, the GPS device itself has some limitations, which cannot be improved by creating better algorithms, and, consequently, is not able to measure some daily mobility variables like use of assistive devices and social interaction. Although recent studies have used complementary diaries or wearable data streams (e.g., smartphones), that can serve to complement the GPS derived data with the missing information. VERITAS, on the other hand, is a self-reported map-based measurement tool that allows researchers to comprehensively assess spatial behavior along daily mobility. In contrast to GPS that provides objective information on seven specific days, VERITAS provides a general picture of the participants’ behavior, by asking questions about predefined activities which the respondent has to map and specify in terms of how often they visit this destination, how they generally reach this destination, and whether they go there by themselves or meet with someone. Although we were able to measure almost all components of mobility using VERITAS, we need to weigh this against the bias of using a self-reported measurement tool—especially when the target population is older adults, who might have difficulties in recalling their behavior, or be more susceptible to social desirability bias or social approval bias [41, 42]. Future research could address this issue by including a social desirability measure within the questionnaire to control for potential bias. As depicted in Fig. 1, VERITAS collected daily mobility and GPS measured daily mobility are very different and have each their strengths and limitations. Almost 60% of participants’ GPS measured trips and VERITAS identified destinations within 7 days seemed to match, which may be argued as either a high or a low number. Assessing each participant’s data in QGIS, it became clear that a lot more destinations were identified through VERITAS than measured by GPS trips. This might be due to the fact that most of the participants did not wear the GPS device for 7 days, so trips may have been missed. Alternatively, participants may have overestimated the number of destinations when self-reporting. Additionally, none of VERITAS recreational routes matched with the GPS measured walking trips. There may be several reasons for this; because we do not compare the same time periods, the participants might not have walked those identified VERITAS-routes while wearing the GPS device for only 7 days; or as participants are older adults, they may have reported on walks that they would like to have taken or been seen to have taken suggesting social desirability bias or social approval bias, as well as recall bias might be an issue. This issue is particularly important for use of these devices in an intervention trial. Ability to measure change over time depends on a reliable baseline. If participants miss report at baseline, the chances of detecting change over time are diminished. Furthermore, from self-reported physical activity data in trials we know that participants also over-report change due to even greater social desirability than likely here in this observational study. Based on the assessment of the three main criteria and their subcriteria, VERITAS seems to be more qualified at measuring our definition of daily mobility for this specific target population. Recruitment was easier, compliance was higher, data quality was better, and the daily mobility variables were assessed not only for the last 7 days (GPS approach), but through a more general picture of participants’ daily life (habitual approach). Whereas the GPS device might be sufficient to use for other mobility measures like lifespace. CONCLUSION This is the first study to assess the feasibility and practicality of VERITAS in measuring mobility in older adults. Our findings suggest that using a GPS device on older adults living in a socially deprived community on a larger scale may not be feasible. Recruitment rates were low and its ability to measure daily mobility was somewhat limited. Better communication strategies might have increased participation. Greater compliance with VERITAS completion and the depth and quality of the responses may support its use in some studies, especially in low socioeconomic populations who respond well to a guided interview. Researchers need to invest personal time in this particular population, as relationship development is key to conducting research with vulnerable populations. In-person spatial interviews like VERITAS might be a better fit when studying older adults living in disadvantages communities. We believe that this study will contribute to the next generation of built environment studies focusing on older adults’ mobility, physical activity, and aging-in-place, as it provides recommendations on specific methods to assess mobility in specific populations. Acknowledgments We thank Copenhagen Municipality in particular the Areal Renewal Office in Sydhavn for their support and collaboration. We also thank all involved persons at the two social housing areas “Tranehavegård” and “Engholmen Nord” for their enthusiastic participation in the study. Y.K. holds a Canada Institutes of Health Research applied public health Chair in Urban Interventions and Population Health. All data and the findings reported have not been previously published, and the manuscript is not being simultaneously submitted elsewhere. Funding: This study was funded by The Danish Foundation for Culture and Sports Facilities and The Velux Foundation. The funders have no role or authority in conducting the research project. Authors’ Contributions: T.S. conceived and coordinated the study, was responsible for its design, acquisition of data, data cleaning, data analyses, and drafted the manuscript. J.K. contributed with significant input to the outline of the manuscript, the introduction, and discussion section. Y.K. contributed with input to the methods section and discussion section. J.S. was part of developing the idea for the manuscript, handled and processed the data, and contributed to input and proofreading in all sections. All authors revised the manuscript critically, and read and approved the final manuscript. Ethical Approval and Informed Consent: All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and national research committee in Denmark and with the 1964 Helsinki declaration and its later amendments. The study and its data management procedures have been approved by the Danish Data Protection Agency (2015–57-0008). According to the Danish National Committee on Health Research Ethics, formal ethical approval was not required as the project was not a biomedical research project. Each face-to-face appointment with the senior participants started with a researcher explaining the purpose and procedures of the study once more and explicitly asking the respondent if he or she had understood everything and wanted to participate. It was important that the respondents felt at ease answering our questions and we aimed at creating a conversational atmosphere. We felt that requiring a written consent would disturb this atmosphere, and for that reason, we asked all seniors who participated for their informed consent orally before starting on their researcher facilitated survey. All participants could withdraw from the study at any time. This article does not contain any studies with animals performed by any of the authors. Primary Data: The authors have full control of all primary data and agree to allow the journal to review our data if requested. Compliance with Ethical Standards Conflict of Interest: We hereby declare that coauthor Yan Kestens holds shares in the company that sells VERITAS (Treksoft Solutions). The remaining authors declare that they have no potential conflict of interest. References 1. Harper S . Economic and social implications of aging societies . Science . 2014 ; 346 ( 6209 ): 587 – 591 . Google Scholar CrossRef Search ADS PubMed 2. World Health Organization . Global Age-Friendly Cities: A Guide . Geneva, Switzerland: World Health Organization ; 2007 . 3. Davey JA , de Joux V, Nana G, Arcus M. Accommodation Options for Older People in Aotearoa/New Zealand . Wellington, New Zealand: Centre for Housing Research Christchurch ; 2004 . 4. Bayer A-H , Harper L. Fixing to Stay: A National Survey of Housing and Home Modification Issues . Washington, D.C.: AARP ; 2000 . 5. Vincent HK , Vincent KR , Lamb KM . Obesity and mobility disability in the older adult . Obes Rev . 2010 ; 11 ( 8 ): 568 – 579 . Google Scholar CrossRef Search ADS PubMed 6. Baker PS , Bodner EV , Allman RM . Measuring life-space mobility in community-dwelling older adults . J Am Geriatr Soc . 2003 ; 51 ( 11 ): 1610 – 1614 . Google Scholar CrossRef Search ADS PubMed 7. Webber SC , Porter MM , Menec VH . Mobility in older adults: a comprehensive framework . Gerontologist . 2010 ; 50 ( 4 ): 443 – 450 . Google Scholar CrossRef Search ADS PubMed 8. Cagney KA , Browning CR, Jackson AL, et al. Networks, Neighborhoods, and Institutions: An Integrated “Activity Space” Approach for Research on Aging. In: Panel on New Directions in Social Demography, Social Epidemiology, and the Sociology of Aging; Committee on Population; Division on Behavioral and Social Sciences and Education; National Research Council; Waite LJ, Plewes TJ, editors. New Directions in the Sociology of Aging. Washington (DC): National Academies Press (US); 2013:8. Available from: https://www.ncbi.nlm.nih.gov/books/NBK184369/ 9. Haustein S , Siren A . Older people’s mobility: segments, factors, trends . Transp. Rev . 2015 ; 35 ( 4 ): 466 – 487 . Google Scholar CrossRef Search ADS 10. Kestens Y , Wasfi R , Naud A , Chaix B . “Contextualizing context”: reconciling environmental exposures, social networks, and location preferences in health research . Curr Environ Health Rep . 2017 ; 4 ( 1 ): 51 – 60 . Google Scholar CrossRef Search ADS PubMed 11. Yeom HA , Fleury J , Keller C . Risk factors for mobility limitation in community-dwelling older adults: a social ecological perspective . Geriatr Nurs . 2008 ; 29 ( 2 ): 133 – 140 . Google Scholar CrossRef Search ADS PubMed 12. Schmöcker J-D , Quddus M, Noland R, Bell M. Estimating trip generation of elderly and disabled people: analysis of London data . Transp Res Rec J Transp Res Board . 2005 ; 1924 : 9 – 18 . Google Scholar CrossRef Search ADS 13. Collia DV , Sharp J , Giesbrecht L . The 2001 National Household Travel Survey: a look into the travel patterns of older Americans . J Safety Res . 2003 ; 34 ( 4 ): 461 – 470 . Google Scholar CrossRef Search ADS PubMed 14. Rosso AL , Auchincloss AH , Michael YL . The urban built environment and mobility in older adults: a comprehensive review . J Aging Res . 2011 ; 2011 : 816106 . Google Scholar CrossRef Search ADS PubMed 15. Steptoe A , Shankar A, Demakakos P, Wardle J. Social isolation, loneliness, and all-cause mortality in older men and women . Proc Natl Acad Sci USA . 2013 ; 110 ( 15 ): 5797 – 5801 . Google Scholar CrossRef Search ADS PubMed 16. Bertera EM . Physical activity and social network contacts in community dwelling older adults . Act Adapt Aging . 2003 ; 27 ( 3–4 ): 113 – 127 . 17. Takemoto M , Carlson JA , Moran K , Godbole S , Crist K , Kerr J . Relationship between objectively measured transportation behaviors and health characteristics in older adults . Int J Environ Res Public Health . 2015 ; 12 ( 11 ): 13923 – 13937 . Google Scholar CrossRef Search ADS PubMed 18. Steffen TM , Hacker TA , Mollinger L . Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds . Phys Ther . 2002 ; 82 ( 2 ): 128 – 137 . Google Scholar CrossRef Search ADS PubMed 19. VanSwearingen JM , Paschal KA , Bonino P , Chen TW . Assessing recurrent fall risk of community-dwelling, frail older veterans using specific tests of mobility and the physical performance test of function . J Gerontol A Biol Sci Med Sci . 1998 ; 53 ( 6 ): M457 – M464 . Google Scholar CrossRef Search ADS PubMed 20. Stalvey BT , Owsley C, Sloane ME, Ball K. The Life Space Questionnaire: a measure of the extent of mobility of older adults . J Appl Gerontol . 1999 ; 18 ( 4 ): 460 – 478 . Google Scholar CrossRef Search ADS 21. QGIS . Open Source Geographic Information System . 2017 (cited June 15, 2017) . Available at http://www.qgis.org/en/site/. 22. Shoval N , Wahl H-W , Auslander G et al. Use of the global positioning system to measure the out-of-home mobility of older adults with differing cognitive functioning . Ageing Soc . 2011 ; 31 ( 5 ): 849 – 869 . Google Scholar CrossRef Search ADS 23. Shoval N , Auslander G, Cohen-Shalom K, Landau R, Heinik J. What can we learn about the mobility of the elderly in the GPS era ? J Transp Geogr . 2010 ; 18 ( 5 ): 603 – 612 . Google Scholar CrossRef Search ADS 24. Yen IH , Leung CW , Lan M , Sarrafzadeh M , Kayekjian KC , Duru OK . A pilot study using global positioning systems (GPS) devices and surveys to ascertain older adults’ travel patterns . J Appl Gerontol . 2015 ; 34 ( 3 ): NP190 – NP201 . Google Scholar CrossRef Search ADS PubMed 25. 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 . Google Scholar CrossRef Search ADS PubMed 26. Chaix B , Kestens Y , Perchoux C , Karusisi N , Merlo J , Labadi K . An interactive mapping tool to assess individual mobility patterns in neighborhood studies . Am J Prev Med . 2012 ; 43 ( 4 ): 440 – 450 . Google Scholar CrossRef Search ADS PubMed 27. Kestens Y , Chaix B , Gerber P et al. Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol) . BMC Geriatr . 2016 ; 16 : 96 . Google Scholar CrossRef Search ADS PubMed 28. Sessford JD , Jung M , Brawley LR , Forbes JL . Do older adults’ beliefs about their community mobility predict walking performance ? J Aging Phys Act . 2015 ; 23 ( 2 ): 272 – 278 . Google Scholar CrossRef Search ADS PubMed 29. Perchoux C , Kestens Y , Thomas F , Van Hulst A , Thierry B , Chaix B . Assessing patterns of spatial behavior in health studies: their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study) . Soc Sci Med . 2014 ; 119 : 64 – 73 . Google Scholar CrossRef Search ADS PubMed 30. Pawlowski CS , Winge L , Carroll S et al. Move the neighbourhood: study design of a community-based participatory public open space intervention in a Danish deprived neighbourhood to promote active living . BMC Public Health . 2017 ; 17 ( 1 ): 481 . Google Scholar CrossRef Search ADS PubMed 31. Forskningscenter for Forebyggelse og Sundhed . Sundhedsprofil for Vesterbro/Kongens Enghave . Copenhagen, Denmark : Københavns Kommune ; 2008 . 32. Sydhavnen O. Kvarterplan områdefornyelse Sydhavnen . Copenhagen, Denmark : Københavns Kommune ; 2014 . 33. Anonymous . The Physical Activity and Location Measurement (PALMS) . 2014 (cited June 15, 2017) . Available at http://ucsd-palms-project.wikispaces.com/. 34. Carlson JA , Jankowska MM, Meseck K et al. Validity of PALMS GPS scoring of active and passive travel compared to SenseCam . Med Sci Sports Exerc . 2015;47(3):662–667. doi: 10.1249/MSS.0000000000000446 35. Hayes V , Morris J , Wolfe C , Morgan M . The SF-36 health survey questionnaire: is it suitable for use with older adults ? Age Ageing . 1995 ; 24 ( 2 ): 120 – 125 . Google Scholar CrossRef Search ADS PubMed 36. Prins RG , Pierik F , Etman A , Sterkenburg RP , Kamphuis CB , van Lenthe FJ . How many walking and cycling trips made by elderly are beyond commonly used buffer sizes: results from a GPS study . Health Place . 2014 ; 27 : 127 – 133 . Google Scholar CrossRef Search ADS PubMed 37. Boruff BJ , Nathan A , Nijënstein S . Using GPS technology to (re)-examine operational definitions of ‘neighbourhood’ in place-based health research . Int J Health Geogr . 2012 ; 11 : 22 . Google Scholar CrossRef Search ADS PubMed 38. Kerr J , Marshall S , Godbole S et al. The relationship between outdoor activity and health in older adults using GPS . Int J Environ Res Public Health . 2012 ; 9 ( 12 ): 4615 – 4625 . Google Scholar CrossRef Search ADS PubMed 39. Paz-Soldan VA , Stoddard ST , Vazquez-Prokopec G et al. Assessing and maximizing the acceptability of global positioning system device use for studying the role of human movement in dengue virus transmission in Iquitos, Peru . Am J Trop Med Hyg . 2010 ; 82 ( 4 ): 723 – 730 . Google Scholar CrossRef Search ADS PubMed 40. Pew Research Center. Americans’ Internet Access: 2000–2015 . 2015 (cited June 15, 2017). Available at http://www.pewinternet.org/2015/06/26/americans-internet-access-2000–2015/. 41. Adams SA , Matthews CE , Ebbeling CB et al. The effect of social desirability and social approval on self-reports of physical activity . Am J Epidemiol . 2005 ; 161 ( 4 ): 389 – 398 . Google Scholar CrossRef Search ADS PubMed 42. Sallis JF , Saelens BE . Assessment of physical activity by self-report: status, limitations, and future directions . Res Q Exerc Sport . 2000 ; 71 ( suppl 2 ): 1 – 14 . Google Scholar CrossRef Search ADS PubMed © Society of Behavioral Medicine 2018. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Journal

Translational Behavioral MedicineOxford University Press

Published: Mar 15, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off