## Materials and Methods
### Variables/Data Sources/Measurement
#### 2.7.2 Objective 2: Test the Adherence of Participants to Using GPS and Fitness Tracking Technology  
Downloaded activity and GPS data was used to measure adherence by evaluating missing data, gaps in recording and periods of non-activity.  
7.3 Objective 3: Compare the 30-Day and 90-Day Post-Discharge Activity Levels between Those Who Live in Metropolitan Areas with Those Who Live in Rural Areas  
To compare the 30 to 90-days post-discharge activity levels of metropolitan participants to rural, the Google Fit app was used to collect daily step counts. Activity data were emailed daily to the research team from the respective applications in comma separated values (CSV) format. The daily step data obtained from the Google Fit application were aggregated and mean and median steps per person per day were calculated for the periods of 1-30, 31-60 and 61-90 days (the latter period data were adjusted to reflect those who exited the study prior to 90 days). A total of 151,859 rows of raw data were generated in CSV format from this study.  
7.4 Objective 4: Compare Levels of Access to Cardiac Healthcare Services between Patients Who Lived in Areas with High (Metropolitan) and Low (Rural)  
Participant home locations were stratified using the accessibility and remoteness index of Australia (ARIA) to determine rurality. Participants were asked to give the address of their local services (CR, pharmacy, general practice, pathology and emergency department) at base-line and these were mapped using VERITASTM software (Veritas Technologies LLC, Santa Clara, CA, USA). The FollowMeeTM application collected geospatial data (primary measure). Descriptive statistics were generated for the accuracy of the data point and stratified by type (GPS or Network) and a rural or metropolitan classification of the participant. A summary of data points, categorised by time, were produced to give an indication of device usage over the project, as was a summary of data availability based upon accuracy of data point. A total of 153,000 lines of raw data cleaned to 45,577 were generated in CSV format during the study period.  
The GPS tracking app was required to have a two-minute ping option (track interval), while the activity tracking app needed to include a step counter and measure of distance travelled. The FollowMeeTM GPS tracking application featuring a two-minute ping combined with the ability to automatically suspend tracking when the participant was inactive and reactivate upon movement offered both battery efficiency and a high level of location accuracy. Similarly, the GoogleFitTM application offered high accuracy with the required activity tracking features while conserving battery power. The cost of subscriptions for both applications needed to access all the required features, were moderate. The use of these applications facilitated the use of a single device by the participants without the need for manual data download or return of the device streamlined the data collection process significantly. The GPS data mapped the distance and locations travelled by the participants and were matched to service locations. The street addresses were geocoded and based on the accuracy of the data points within each of seven arbitrary buffers zone (See Table 1) a 50 m buffer was applied. Patients whose FollowMeeTM track data that entered the 50 m buffer zone were recorded as attended for that health service.