## 2 Materials and Methods
### Time-Activity Pattern Assessment  
The participants carried a compact waist belt with a smartphone (Samsung Galaxy Mini, developed by the Korean manufacturer Samsung Electronics) with integrated GPS sensor, which did not restrict movement and did not result in any bias. After finishing the study, none of the participants mentioned that the device had any impact on their daily activities, interfered with them or alter their behavior. The only activities during which the device was not wearied was sleeping, workout and bathing and these non-wear intervals and other relevant information were identified and reported on the weekly diary. GPS data were reviewed before collecting the devices from the study participants as well as the weekly activity and the wear time of the device of each participant were checked. There were 3 participants who needed to repeat the survey due to improper data collection. The smartphone with an integrated GPS sensor was used to track the time and location data of the study participants for 7 consecutive days (Figure 2). The Android operating system of smartphones is well-suited for localization due to its support of diverse sensors and hardware which enable localization and can collect data on the geographical location through GPS to obtain information on time spent in different environments (Figure 3). GPS satellite data were recorded using the triangulation method (data recorded once per second (1 Hz)). The file with summary information was displayed every 10 s and stored on the phone's memory card. Figure 1: The study design. Figure 2: The waist belt for the smartphone. Participants were asked to complete a daily diary for one week to record the time they put on or take the phone off, the reasons for not having the phone on and the type of activity that they were doing. In the analysis, these data were used to determine the wear time of the device, to avoid mistakes and inaccuracies when the device is left or not wearied. GPS coordinates, time, locations, and activities were analyzed and reviewed manually to ensure that the correct activity was assigned. Each individual data point was assigned to one of the six locations/activities and additional contextual information such as MEs was added. Based on the GPS data analysis, six locations/activities (home, work, other indoor, static outdoor, walking, in transit) were identified for each study participant to assess individual time-activity patterns [3; 9; 12; 21; 22]. The home and work addresses of the participants were obtained from a questionnaire survey and were geocoded using ArcGIS. Geocoding was performed to obtain latitude and longitude coordinates for each participant's home and work addresses. Initially, 95% of the records were matched and 5% were left unmatched. All unmatched records were reviewed and corrected, leading to another 5% matched addresses. Data of geographic coordinates and time from smartphones were transferred to ArcGIS software and used for the MEs analysis. A spatial join was performed to assign the attributes of one data layer (geographic coordinates point) to the attributes of the other layers (buildings, streets, pathways). A spatial analysis of the collected data was performed using ArcGIS 10.3 software, topographic and orthophoto maps ([[http://www.maps.lt/en/](http://www.maps.lt/en/)]([http://www.maps.lt/en/](http://www.maps.lt/en/))) and building (Open Street Map (2014)) layers. The indoor time was determined by analyzing GPS data with ArcGIS software using buildings, transport network, orthophotos, and other layers. In the analysis, when a person enters a building, the GPS signal disappears, thus setting the starting time indoors and when the person leaves the building, the GPS signal starts up again, thus, setting the end time indoors. A GPS signal is lost in all indoor MEs and while analyzing the building layer it was determined that a person was inside the building.