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®.