Measuring Physical Activity Using Triaxial Wrist Worn Polar Activity Trackers: A Systematic Review

International Journal of Exercise Science 13(4): 438-454, 2020. Collecting objective physical activity data from research participants are increasingly done using consumer-based activity trackers. Several validation studies of Polar devices are conducted to date, but no systematic review of the current level of accuracy for these devices exist. The aim of this study is therefore to investigate the accuracy of current wrist-worn Polar devices that equips a triaxial accelerometer to measure physical activity. We conducted a systematic review by searching six databases for validation studies on modern Polar activity trackers. Studies were grouped and examined by tested outcome, i.e. energy expenditure, physical activity intensity, and steps. We summarized and reported relevant metrics from each study. The initial search resulted in 157 studies, out of which fourteen studies were included in the final review. Energy expenditure was reviewed in seven studies, physical activity intensity was reviewed in four studies, and steps was reviewed in 11 studies. There is a large difference in study protocols with conflicting results between the identified studies. However, for energy expenditure there is some indication that Polar devices perform better in free-living, compared to lab-based studies. In addition, step counting seems to have less average error compared to energy expenditure and physical activity intensity. There is large heterogeneity between the identified studies, both in terms of study protocols and results, and the accuracy of Polar devices remains unclear. More studies are needed for more recently developed devices, and future studies should take care to follow guidelines for assessment of wearable sensors designed for physical activity monitoring

Collecting health-related research data using consumer-based wireless smart scales

Background: Serious public-health concerns such as overweight and obesity are in many cases caused by excess intake of food combined with decreases in physical activity. Smart scales with wireless data transfer can, together with smart watches and trackers, observe changes in the population’s health. They can present us with a picture of our metabolism, body health, and disease risks. Combining body composition data with physical activity measurements from devices such as smart watches could contribute to building a human digital twin. Objective: The objectives of this study were to (1) investigate the evolution of smart scales in the last decade, (2) map status and supported sensors of smart scales, (3) get an overview of how smart scales have been used in research, and (4) identify smart scales for current and future research. Method: We searched for devices through web shops and smart scale tests/reviews, extracting data from the manufacturer’s official website, user manuals when available, and data from web shops. We also searched scientific literature databases for smart scale usage in scientific papers. Result: We identified 165 smart scales with a wireless connection from 72 different manufacturers, released between 2009 and end of 2021. Of these devices, 49 (28%) had been discontinued by end of 2021. We found that the use of major variables such as fat and muscle mass have been as good as constant over the years, and that minor variables such as visceral fat and protein mass have increased since 2015. The main contribution is a representative overview of consumer grade smart scales between 2009 and 2021. Conclusion: The last six years have seen a distinct increase of these devices in the marketplace, measuring body composition with bone mass, muscle mass, fat mass, and water mass, in addition to weight. Still, the number of research projects featuring connected smart scales are few. One reason could be the lack of professionally accurate measurements, though trend analysis might be a more feasible usage scenario

Health research requires efficient platforms for data collection from personal devices

Data from consumer-based devices for collecting personal health-related data could be useful in diagnostics and treatment. This requires a flexible and scalable software and system architecture to handle the data. This study examines the existing mSpider platform, addresses shortcomings in security and development, and suggests a full risk analysis, a more loosely coupled component- based system for long term stability, better scalability, and maintainability. The goal is to create a human digital twin platform for an operational production environment