DOI: 10.5061/dryad.bvq83bkg2
title: Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study

description: Background There is interest in identifying the most reliable method for
 detecting early mobility limitations. Accelerometry and Global Positioning
 System (GPS) could provide insight into declines in mobility, but few
 studies have used this multi-sensor approach to monitor mobility in older
 adults.  Methods Thirty-two volunteers (66.2±6.3 years) agreed to
 participate in our validation study. We conducted two experiments to
 determine the validity of the TicWatch S2 and Pro 3 Ultra GPS models
 against the Qstarz receiver in measuring life-space mobility, trip
 frequency, duration, and mode. We also assessed the accuracy of the
 TicWatch in measuring step count and agreement with the ActiGraph wGT3X-BT
 for activity counts and sedentary behavior. Participants wore devices
 simultaneously for three consecutive days and recorded activity and trip
 information. Results The TicWatch Pro 3 Ultra GPS performed better than
 the S2 model and was similar to the Qstarz in all tested trip-related
 measures, and it was able to estimate both passive and active trip modes.
 Both models showed similar results to the Qstarz in life-space-related
 measures. The TicWatch S2 demonstrated good to excellent overall agreement
 with the ActiGraph algorithms for the time spent in sedentary and
 non-sedentary activities, with 84% and 87% agreement rates, respectively.
 Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step
 count consistently with the gold standard observer, with a bias of 0.4
 steps. The thigh-worn ActiGraph algorithm accurately classified sitting
 and lying postures (97%) and standing postures (90%). Conclusion Our
 multi-sensor approach to monitoring mobility has the potential to capture
 both accelerometer-derived movement data and trip/life-space data only
 available through GPS. In this study, we found that the TicWatch models
 are valid devices for capturing GPS and raw accelerometer data, making
 them useful tools for assessing real-world mobility in older adults and
 advancing our knowledge of early mobility decline.
description: We conducted two experiments to validate the TicWatch for
 collecting movement and navigation data: 1) we compared the TicWatch S2
 against the Qstarz BT-Q1000X GPS Data Logger to measure life-space
 mobility, trip frequency, and duration. We also assessed the agreement of
 the TicWatch S2 in measuring step count against an observer, activity
 counts per minute (CPM), and sedentary and non-sedentary activity using
 ActiGraph's proprietary algorithms; 2) we compared the TicWatch Pro 3
 Ultra GPS against a stand-alone Qstarz BT-Q1000X GPS Data Logger to
 measure life-space mobility, trip frequency and duration, and mode of
 transportation. We evaluated the level of agreement between the TicWatch
 Pro 3 Ultra GPS in measuring steps compared to direct measures of step
 count reported by the participants. Additionally, we tested two different
 GPS configurations of the TicWatch Pro 3 Ultra GPS in a free-living study
 setting to observe battery life performance: a) periodic fix collection
 every 10 seconds (i.e., the GPS receiver turned off for 10 seconds before
 searching for a new location point), and b) stay connected fix collection
 every 5 seconds (i.e., the GPS receiver never turns off, allowing for more
 continuous data collection). Finally, for assessing body posture, we
 evaluated the agreement of the thigh-worn algorithms of the ActiGraph
 wGT3X-BT with an observer in identifying lying and sitting from standing.
 The study results not only inform the use of the TicWatch for assessing
 and monitoring early changes in mobility in the MacM3 cohort study but
 also offer valuable information on the validity of wearable devices for
 researchers considering collecting movement and navigation data in their
 research protocols.
 <strong>Protocol</strong> In
 Experiment 1, participants were
 provided with three devices, the TicWatch S2, Qstarz GPS Data Logger, and
 the ActiGraph. They were instructed to wear the TicWatch S2 and the
 ActiGraph on the non-dominant wrist simultaneously and to carry the GPS
 data logger with them whenever they travelled outside their homes. They
 were instructed to charge the TicWatch S2 and Qstarz every night using the
 chargers provided. In Experiment 2, participants were provided with
 three devices, the TicWatch Pro 3 Ultra GPS, Qstarz GPS Data Logger, and
 the ActiGraph. They were instructed to wear the TicWatch Pro 3 Ultra GPS
 on the non-dominant wrist, to carry the GPS whenever they travelled
 outside the home, and to record the time they put the watch on and off.
 They were also instructed to attach the ActiGraph to the
 anterior aspect of the left or right thigh just above the kneecap using
 the adhesive patches provided to perform the body posture tasks.
 <strong>Data
 Reduction</strong> The GPS data collected by the Qstarz and
 TicWatch models were first cleaned by excluding any points with speeds
 above 160 km/h, as the fastest roadways in our study area have a maximum
 speed limit of 110 km/h. We then processed each participant’s
 data to ensure the time periods compared between devices were identical,
 such that discrepancies due to battery life or participant error were
 excluded from the analysis. Measures related to the life-space area, such
 as maximum distance from home, minimum convex hull (MCH), and standard
 deviational ellipse (SDE), were calculated using ArcGIS® Pro, a desktop
 Geographic Information System application developed by Esri®. Each
 participant’s trip frequency and trip
 duration were determined using GPS data collected by both Qstarz and
 TicWatch. To accomplish
 this, we adapted the stop and trip detection algorithm of Montoliu et
 al.<sup>1</sup> We chose to use
 algorithm settings proposed by Fillekes et
 al<sup>2</sup> for trip detection and used them
 to derive trip frequency and duration independently for each device. Using
 the Qstarz data, we manually verified the algorithm results and adjusted
 the values for accuracy. We then compared the algorithm-derived measures
 from the TicWatch against these results. Additionally, we used the method
 proposed for segmenting GPS segments into active (non-motorized) and
 passive (motorized) trips, which is adapted from the work of Carlson et
 al.<sup>3</sup> and Vanwolleghem et
 al.<sup>4</sup> Specifically, trips with 90th percentile speed
 ≥ 25 km/h were classified as passive, whereas trips below that threshold
 were classified as active.  Accelerometer data were
 collected at different frequencies for the ActiGraph and TicWatch devices.
 TicWatch data was adjusted to match ActiGraph's frequency for
 comparison. In
 Experiment 1, the accelerometer data from the ActiGraph and TicWatch S2
 were screened for the period of wear times using the method described by
 Choi et al.<sup>5</sup>  We first determined the activity counts per minute (CPM) using a Python
 script that generates the ActiGraph physical activity
 counts.<sup>6</sup> We applied the script on both the S2 and
 ActiGraph devices. Based on the activity counts and using an epoch length
 of 60 seconds, non-wear time was defined as 90 consecutive minutes of zero
 counts, with an allowance of 2 minutes of nonzero counts, provided there
 were 30-minute consecutive zero counts before and after that
 allowance<sup>5</sup>. Wear times in Experiment 2
 were obtained using the on-body sensor of the TicWatch Pro 3 Ultra
 GPS. To evaluate PA intensity, we computed the vector
 magnitude (VM) by taking the square root of the summed squared counts per
 minute for each axis on both devices. The VM counts were then calculated
 per 60-second epoch, and we applied the cut-off scores developed
 by Montoye et
 al.<sup>7</sup> specifically for
 wrist-worn devices. We classified activities as "sedentary" if
 the VM counts were below 2,860 and collapsed light and moderate/vigorous
 activity categories into "non-sedentary" which included VM
 counts of 2,860 or higher.<sup>7</sup> Following this, we determined the
 time, in minutes, spent on sedentary and non-sedentary
 behaviour. We also calculated
 the mean activity counts per epoch length of 60-second for
 various activities, including exercising, sitting, lying, and walking, as
 reported by the participants, for both
 ActiGraph and TicWatch S2. To ensure an accurate comparison
 of accelerometer data, we restricted our analysis to the periods when
 participants reported wearing both the TicWatch and ActiGraph devices
 simultaneously. Step count was obtained directly using
 the step count and step detector sensors from the TicWatch models. In
 Experiment 1, we selected the step counter that keeps track of the total
 number of steps taken over time. In Experiment 2, we used the step
 detector that detects when a step is taken and generates an event each
 time it detects a step, but it does not keep track of the total number of
 steps taken. Body posture
 classification in Experiment 2 was obtained using the thigh-worn algorithm
 from the ActiGraph that relies on movement and the thigh angle to
 accurately classify lying and sitting vs. standing
 positions.<sup>8</sup>
 <strong>REFERENCES</strong> 
 Montoliu, R., Blom, J. & Gatica-Perez, D. Discovering
 places of interest in everyday life from smartphone data. in Multimedia
 Tools and Applications vol. 62 179–207 (2013).
 Fillekes, M. P., Giannouli, E., Kim, E. K., Zijlstra, W.
 & Weibel, R. Towards a comprehensive set of GPS-based indicators
 reflecting the multidimensional nature of daily mobility for applications
 in health and aging research. Int J Health Geogr 18, 17 (2019).
 Carlson, J. A. et al. Association between neighbourhood
 walkability and GPS-measured walking, bicycling and vehicle time in
 adolescents. Health Place 32, 1 (2015). Vanwolleghem,
 G. et al. Children’s GPS-determined versus self-reported transport in
 leisure time and associations with parental perceptions of the
 neighborhood environment. Int J Health Geogr 15, (2016).
 Choi, L., Liu, Z., Matthews, C. E. & Buchowski, M. S.
 Validation of accelerometer wear and nonwear time classification
 algorithm. Med Sci Sports Exerc 43, 357–364 (2011).
 BrØnd, J. C., Andersen, L. B. & Arvidsson, D. Generating
 ActiGraph Counts from Raw Acceleration Recorded by an Alternative Monitor.
 Med Sci Sports Exerc 49, 2351–2360 (2017). Montoye,
 A. H. K. et al. Development of cut-points for determining activity
 intensity from a wrist-worn ActiGraph accelerometer in free-living adults.
 J Sports Sci 38, 1–10 (2020). ActiGraph. How is
 inclination determined (for thigh wear location)? 1–1
 https://actigraphcorp.my.site.com/support/s/article/How-is-Inclination-Determined-for-Thigh-Wear-Location (2019).
description: # MONITORING MOBILITY IN OLDER ADULTS USING A GLOBAL POSITIONING SYSTEM
 (GPS) SMARTWATCH AND ACCELEROMETER: A VALIDATION STUDY ### Brief
 description of dataset contents, experimental procedures and results.
 Twenty-five volunteers participated in our validation study. We conducted
 two experiments to validate the TicWatch S2 and Pro 3 Ultra GPS for
 collecting accelerometer data against the ActiGraph wBT3X and life space
 measure and trip frequency, duration and mode against the Qstarz BT-Q1000X
 GPS Data Logger. We also assessed the accuracy of the TicWatch in
 measuring step count and agreement with the ActiGraph wGT3X-BT for
 activity counts and sedentary behavior. Participants wore devices
 simultaneously for three consecutive days and recorded activity and trip
 information. In Experiment 1, participants were provided with three
 devices, the TicWatch S2, Qstarz GPS Data Logger and the ActiGraph. They
 were instructed to wear the TicWatch S2 and the ActiGraph on the
 non-dominant wrist simultaneously and to carry the GPS data logger with
 them whenever they travelled outside their homes. We asked participants to
 record meaningful bouts of activities in terms of their body posture
 (e.g., sitting, standing or lying) or whether they were exercising as they
 were able to throughout the day. Participants were also asked to write the
 address or the intersection of the places they visited. In Experiment 2,
 participants were provided with three devices, the TicWatch Pro 3 Ultra
 GPS, Qstarz GPS Data Logger and the ActiGraph. They were instructed to
 wear the TicWatch Pro 3 Ultra GPS on the non-dominant wrist, to carry the
 GPS whenever they travelled outside the home and to record the times they
 took the watch on and off. They were also instructed to attach the
 ActiGraph to the anterior aspect of the left or right thigh just above the
 kneecap using the adhesive patches provided to perform the body posture
 tasks described below. In both experiments, participants were asked to
 wear the devices for three consecutive days during waking hours and remove
 the devices for showering and water activity purposes. Our findings
 indicate that the TicWatch Pro 3 Ultra GPS outperformed the S2 model and
 was similar to the Qstarz in all tested trip-related measures, and it was
 able to estimate both passive and active trip modes. Both models showed
 similar results to the Qstarz in life-space-related measures. The TicWatch
 S2 demonstrated good to excellent overall agreement with the ActiGraph
 algorithms for the time spent in sedentary and non-sedentary activities.
 Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step
 count consistently with the gold standard observer and the thigh-worn
 ActiGraph algorithm accurately classified sitting and lying postures and
 standing postures. ### Description of the data and file structure 1.
 Demographics_Experiment 1 and Demographics_Experiment 2.xlsx: These files
 provide demographic data corresponding to Table 1 in the manuscript. They
 encompass demographic details for all 25 participants from Experiment 1
 and 10 participants from Experiment 2. The data includes information such
 as sex, age in years, history of falls in the previous year, number of
 falls in the previous year, total number of comorbidities, level of
 education, and mobility level for each participant. Each row in the files
 corresponds to an individual participant. 2. Data on time spent sedentary
 and non-sedentary and CPM_Experiment 1.xlsx: This file supports the
 findings presented in Table 3 and Figure 2 of the manuscript. In
 Experiment 1, participants were directed to record meaningful activity
 bouts, specifying body posture (e.g., sitting, standing, or lying) and
 indicating exercise engagement whenever feasible throughout the day.
 Subsequently, these activities were categorized into sedentary and
 non-sedentary groups using cutoff points from Montoye et al., 2020
 (reference 29 in the manuscript). The file incorporates data extracted
 from the ActiGraph and TicWatch S2, featuring the total duration of
 activities in minutes, the duration of sedentary and non-sedentary
 activities, as well as the total Counts per minute. (Mins = minutes and
 avg=average). The rows depict the activities recorded by each participant
 during the study period, with the participant numbers listed in the first
 column. 3. The files below contain selected activity counts per minute
 acquired from both the ActiGraph and TicWatch S2 during Experiment 1,
 serving as the data foundation for constructing Figures 3A to 3F in the
 manuscript. Figures 3A to 3F feature Bland-Altman plots, comparing the
 average counts per minute per epoch length of 60 seconds for select
 activities performed by six participants. These activities, chosen
 randomly, aim to demonstrate the agreement between the TicWatch S2 and the
 ActiGraph in providing counts per minute. Participants recorded these
 activities using the diary provided in Experiment 1. Each file is a
 recording of one participant's activity. Column A is the date the
 activity occurred, column B the time, and columns C and D the counts per
 minute determined by the Ticwatch S2 and Actigraph, respectively.
 Activity_Walking the dog_Experiment 1.xlsx Activity_Exercising dancing
 class_Experiment 1. xlsx Activity_Lying watching TV_Experiment 1. xlsx
 Activity_Sitting using a computer_Experiment 1. xlsx Activity_Playing
 Pickleball_Experiment 1. xlsx Activity_Standing Cleaning the
 house_Experiment 1. xlsx 4. Step count data\_ Experiment 1 and 2.xlsx:
 This file supports Figure 4 and step count data results. The file has two
 tabs. Tab 1 displays the data from Experiment 1, showcasing both the
 number of steps recorded by the participant and the number of steps
 extracted from the TicWatch S2. In Experiment 1, we utilized the step
 counter that monitors the total number of steps taken over time. Tab 2
 displays the data from Experiment 2, and shows the number of steps
 recorded by the participant and extracted from the TicWatch Pro 3 Ultra
 GPS. In Experiment 2, we employed the step detector, which identifies when
 a step is taken and generates an event each time it detects a step. Each
 row in the file corresponds to an individual participant. 5. Body Posture
 Analysis_Experiment 2. xlsx: This file supports the outcomes of body
 posture classification in Experiment 2, derived from the thigh-worn
 algorithm implemented on the ActiGraph. This algorithm relies on both
 movement patterns and thigh angle data to effectively distinguish between
 lying and sitting versus standing positions. Participants were instructed
 to undertake two tasks: sitting on the edge of a bed (or sofa) and
 standing still, with or without support, each for a duration of 5 minutes.
 The first row in the table indicates the specific activity performed by
 the participant, namely sitting or standing. The shaded columns display
 the duration of time during which the ActiGraph accurately identified the
 corresponding body posture. Starting on row 4, each row corresponds to an
 individual participant. 6. ComparingCHulls_Experiment 1 and
 ComparingCHulls_Experiment 2. xlsx: These files support the data presented
 in Table 2 and contain data related to the convex hull, defined as the
 smallest polygon where no internal angle exceeds 180 degrees and
 encompasses all collected points. The area and perimeter, reported in
 square meters and meters respectively, were extracted from the gold
 standard Qstarz, as well as from the TicWatch S2 and Pro 3 Ultra GPS. Each
 row in the file corresponds to an individual participant. 7.
 ComparingMaximumDistanceFromHome_Experiment 1 and
 ComparingMaximumDistanceFromHome_Experiment 2. xlsx: These files support
 the data presented in Table 2 and contain the data related to the maximum
 distance from home, defined as the farthest planar/straight-line distance
 (in meters) from the participants’ home location that was travelled to,
 extracted from the gold standard Qstarz, as well as from the TicWatch S2
 and Pro 3 Ultra GPS. Each row in the file corresponds to an individual
 participant. 8. Trip Frequency and duration Experiment 1 and Trip
 Frequency and duration_Experiment 2. xlsx: These files support the data
 showcased in Table 2, encompassing life space mobility data, which
 includes trip frequency, duration in minutes, and mode of transportation
 (active or passive). In Experiment 1, the table presents data on trip
 frequency and duration in minutes directly sourced from participant
 diaries and the TicWatch S2. In Experiment 2, Tab 1 provides data on the
 trip frequency and duration in minutes directly sourced from the TicWatch
 Pro 3 Ultra GPS and the gold standard Qstarz. Tab 2 reports the mode of
 transportation (passive and active), obtained from both the gold standard
 Qstarz and the TicWatch Pro 3 Ultra GPS. Each row in the file corresponds
 to an individual participant. 9. OnBodyAnalysis_Experiment 2.xlsx: This
 file contains the wear time in hours reported by the participant and
 captured by the TicWatch Pro 3 Ultra GPS during Experiment 2. We present
 data from all 3 days of data collection as well as the average of all
 days. Each row in the file corresponds to an individual participant 10.
 PowerDischarge_Experiment 2.xlsx: This file contains data supporting the
 battery life modes, specifically "stay connected fix collection every
 5 seconds" and "periodic fix collection every 10 seconds"
 for the TicWatch Pro 3 Ultra GPS. The table presents the duration in hours
 during which the device actively collected data in various modes,
 including normal mode, power saver mode, and extreme power saver mode.
 Each row in the file corresponds to an individual participant. ####
 Sharing/Access information Data was derived from the following sources: 1.
 ActiGraph accelerometer data: raw data was extracted using the ActiLife
 software from ActiGraph. 2. Counts per minute: we determined the activity
 counts per minute (CPM) using a Python script (BrØnd et al., 2017) that
 generates the ActiGraph physical activity counts (BrØnd JC, Andersen LB,
 Arvidsson D. Generating ActiGraph Counts from Raw Acceleration Recorded by
 an Alternative Monitor. Med Sci Sports Exerc. 2017 Nov 1;49(11):2351–60).
 3. TicWatch accelerometer and GPS data: a custom data-collection
 application was developed by our software engineer to optimize data
 quality and battery life. Ivy is an app designed to run on any wearable
 device running Google Wear OS and to allow for continuous, non-intrusive,
 long-term measurements of key mobility-related parameters. It collects and
 stores data from the following onboard sensors: accelerometer, gyroscope,
 location (GPS), and heart rate. The app additionally collects information
 on step counts, current activity type predictions (using the Google
 Activity Recognition API), on-body detection, and device power/battery
 states. Ivy also provides a modular list of settings which control
 behavior of the TicWatch’s sensors, namely the frequency of accelerometry
 and GPS receiver to address battery constraints. Our second app, Clover,
 has been designed to support Ivy as a Windows companion app with a
 user-friendly graphical interface that automates the process of setting up
 Ivy on a smartwatch, downloading collected data, and preparing devices for
 distribution. Clover has the capability to conduct initial checks on data
 quality, including an assessment of the number of days the watch was worn.
 4. Measures related to the life-space area, such as maximum distance from
 home, minimum convex hull (MCH), and standard deviational ellipse (SDE),
 were calculated using ArcGIS® Pro, a desktop Geographic Information System
 application developed by Esri®.