Demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies

The dataset is provided in the form of an excel files with 5 tabs. The first three excel tabs constitute demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies while the two last excel tabs include the full dataset with actual data collected using the consumer wearable devices in Cyprus and Greece respectively during the Spring of 2020. The data from the last two tabs were used to assess the compliance of asthmatic schoolchildren (n=108) from both countries to public health intervention levels in response to COVID-19 pandemic (lockdown and social distancing measures), using wearable sensors to continuously track personal location and physical activity. Asthmatic children were recruited from primary schools in Cyprus and Greece (Heraklion district, Crete) and were enrolled in the LIFE-MEDEA public health intervention project (Clinical.Trials.gov Identifier: NCT03503812). The LIFE-MEDEA project aimed to evaluate the efficacy of behavioral recommendations to reduce exposure to particulate matter during desert dust storm (DDS) events and thus mitigate disease-specific adverse health effects in vulnerable groups of patients. However, during the COVID-19 pandemic, the collected data were analysed using a mixed effect model adjusted for confounders to estimate the changes in 'fraction time spent at home' and 'total steps/day' during the enforcement of gradually more stringent lockdown measures. Results of this analysis were first presented in the manuscript titled “Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic” published by Scientific Reports (https://doi.org/10.1038/s41598-021-85358-4). The dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, include variables: Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable). All data were collected following approvals from relevant authorities at both Cyprus and Greece, according to national legislation. In Cyprus, approvals have been obtained from the Cyprus National Bioethics Committee (EEBK EΠ 2017.01.141), by the Data Protection Commissioner (No. 3.28.223) and Ministry of Education (No 7.15.01.23.5). In Greece, approvals have been obtained from the Scientific Committee (25/04/2018, No: 1748) and the Governing Board of the University General Hospital of Heraklion (25/22/08/2018). Overall, wearable sensors, often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessment in clinical studies. Nevertheless, the real-life application of these technologies in studies involving many participants for a significant observation period may be hindered by several practical challenges. Using a small subset of the LIFE-MEDEA dataset, in the first excel tab of dataset, we provide demonstration data from a small subset of asthmatic children (n=17) that participated in the LIFE MEDEA study that were equipped with a smartwatch for the assessment of physical activity (heart rate, pedometer, accelerometer) and location (exposure to indoor or outdoor microenvironment using GPS signal). Participants were required to wear the smartwatch, equipped with a data collection application, daily, and data were transmitted via a wireless network to a centrally administered data collection platform. The main technical challenges identified ranged from restricting access to standard smartwatch features such as gaming, internet browser, camera, and audio recording applications, to technical challenges such as loss of GPS signal, especially in indoor environments, and internal smartwatch settings interfering with the data collection application. The dataset includes information on the percentage of time with collected data before and after the implementation of a protocol that relied on setting up the smartwatch device using publicly available Application Lockers and Device Automation applications to address most of these challenges. In addition, the dataset includes example single-day observations that demonstrate how the inclusion of a Wi-Fi received signal strength indicator, significantly improved indoor localization and largely minimised GPS signal misclassification (excel tab 2). Finally excel tab 3, shows the tasks Overall, the implementation of these protocols during the roll-out of the LIFE MEDEA study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality. The protocol and the representative results have been submitted for publication to the Journal of Visualised experiments (submission: JoVE63275). The Variables included in the first three excel tabs were the following: Participant ID (Unique serial number for patient participating in the study), % Time Before (Percentage of time with data before protocol implementation), % Time After (Percentage of time with data after protocol implementation), Timestamp (Date and time of event occurrence), Indoor/Outdoor (Categorical- Classification of GPS signals to Indoor and Outdoor and null(missing value) based on distance from participant home), Filling algorithm (Imputation algorithm), SSID (Wireless network name connected to the smartwatch), Wi-Fi Signal Strength (Connection strength via Wi-Fi between smartwatch and home’s wireless network. (0 maximum strength), IMEI (International mobile equipment identity. Device serial number), GPS_LAT (Latitude), GPS_LONG (Longitude), Accuracy of GPS coordinates (Accuracy in meters of GPS coordinates), Timestamp of GPS coordinates (Obtained GPS coordinates Date and time), Battery Percentage (Battery life), Charger (Connected to the charger status). Important notes on data collection methodology: Global positioning system (GPS) and physical activity data were recorded using LEMFO-LM25 smartwatch device which was equipped with the embrace™ data collection application. The smartwatch worked as a stand-alone device that was able to transmit data across 5-minute intervals to a cloud-based database via Wi-Fi data transfer. The software was able to synchronize the data collected from the different sensors, so the data are transferred to the cloud with the same timestamp. Data synchronization with the cloud-based database is performed automatically when the smartwatch contacts the Wi-Fi network inside the participants’ homes. According to the study aims, GPS coordinates were used to estimate the fraction of time spent in or out of the participants' residences. The time spent outside was defined as the duration of time with a GPS signal outside a 100-meter radius around the participant’s residence, to account for the signal accuracy in commercially available GPS receivers. Additionally, to address the limitation that signal accuracy in urban and especially indoor environments is diminished, 5-minute intervals with missing GPS signals were classified as either “indoor classification” or “outdoor classification” based on the most recent available GPS recording. The implementation of this GPS data filling algorithm allowed replacing the missing 5-minute intervals with estimated values. Via the described protocol, and through the use of a Device Automation application, information on WiFi connectivity, WiFi signal strength, battery capacity, and whether the device was charging or not was also made available. Data on these additional variables were not automatically synchronised with the cloud-based database but had to be manually downloaded from each smartwatch via Bluetooth after the end of the study period.