Monitoring mobility in older adults using a global positioning system (GPS) smartwatch and accelerometer: A validation study

Description

Notes

Methods

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.

Protocol

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.

Data Reduction

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.¹ We chose to use algorithm settings proposed by Fillekes et al² 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.³ and Vanwolleghem et al.⁴ 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.⁵  We first determined the activity counts per minute (CPM) using a Python script that generates the ActiGraph physical activity counts.⁶ 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 allowance⁵. 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.⁷ 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.⁷ 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.⁸

REFERENCES

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8. 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).