Digital health technology applications and wearable sensors enable non-invasive and cost-effective patient monitoring both in healthcare and home settings1. At the same time, the large amount of data collected and the availability of wearable-based analytic platforms enable the development of algorithms for automated health event prediction, prevention, and intervention for a wide range of acute and chronic diseases2. Commercially available wearable sensors, primarily used for fitness tracking, are also increasingly being used by medical professionals in public health research and represent a promising tool for multimodal and continuous data collection under real-life conditions3. More importantly, though, unbiased data collection from wearables sensors allows researchers to overcome the challenges of recall bias that characterize traditional data collection methods such as interviews and diaries4.
However, for the purposes of clinical trials or other population studies, data accuracy, reliability, and integrity are essential. In addition, the credibility of the collected data may also be affected by several other parameters, such as age-group applicability as well as the memory capacity and energy efficiency of the device5. Recent systematic reviews of laboratory and field-based studies with limited numbers of participants have generally confirmed the applicability of commercial smartwatches for activity, heart rate, seizure, and behavior monitoring, although the reviews have also demonstrated poor suitability for elderly users, as well as battery, memory, and data quality limitations6,7. These limitations may be further amplified in larger population studies under real-life conditions where additional parameters such as inconsistent internet connectivity, device comfort, and incorrect smartwatch use come into play8. Specifically, appearance and inconvenience are significant barriers to wearing sensors daily9, while concerns relating to privacy and confidentiality issues may affect recruitment in studies involving wearable sensors10. Concerning the applicability of commercial smartwatches and fitness trackers for measuring physical activity in research studies, a recent study by Henriksen et al. suggested that the selection of an appropriate device for a particular study should not only be based on the available embedded sensors but rather also take into account validation and previous use in research, appearance, battery life, robustness, water resistance, connectivity, and usability11.
For the purposes of this study, we present a protocol to improve on the challenges encountered during the LIFE MEDEA project, an intervention study for the mitigation of the health effects of desert dust storms12. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a commercial smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch daily, and data were transmitted via a wireless network to a centrally administered data collection platform via the data collection application for the near real-time assessment of compliance. Additional details on the smartwatch and system setup are provided in a previous study13. During the first year of the project implementation, several technical and real-life challenges relating to the device emerged, which affected recruitment, the compliance of participants in wearing the device daily, and the completeness of the collected data. Some challenges were population-specific, such as the requirement of school administrators and many parents that the children wearing the smartwatches should not have access to standard smartwatch features, such as gaming, internet browser, camera, and audio-recording applications. Other challenges were technical in nature, such as loss of GPS signal, especially in indoor environments, and internal smartwatch settings interfering with the data collection application. A detailed overview of the main challenges identified as well as a brief description of their implications and solutions are presented in Table 1.
In this study, we suggest simple, cost-effective, and off-the-shelf solutions to improve user compliance, data quality, and data completeness in population studies employing wearable sensors and provide the relevant protocols. In addition, we demonstrate the data completeness improvements from the implementation of such protocols using representative results from the study13.