Wearable activity trackers-advanced technology or advanced marketing?

Abstract

Wearable devices represent one of the most popular trends in health and fitness. Rapid advances in wearable technology present a dizzying display of possible functions: from thermometers and barometers, magnetometers and accelerometers, to oximeters and calorimeters. Consumers and practitioners utilize wearable devices to track outcomes, such as energy expenditure, training load, step count, and heart rate. While some rely on these devices in tandem with more established tools, others lean on wearable technology for health-related outcomes, such as heart rhythm analysis, peripheral oxygen saturation, sleep quality, and caloric expenditure. Given the increasing popularity of wearable devices for both recreation and health initiatives, understanding the strengths and limitations of these technologies is increasingly relevant. Need exists for continued evaluation of the efficacy of wearable devices to accurately and reliably measure purported outcomes. The purposes of this review are (1) to assess the current state of wearable devices using recent research on validity and reliability, (2) to describe existing gaps between physiology and technology, and (3) to offer expert interpretation for the lay and professional audience on how best to approach wearable technology and employ it in the pursuit of health and fitness. Current literature demonstrates inconsistent validity and reliability for various metrics, with algorithms not publicly available or lacking high-quality validation studies. Advancements in wearable technology should consider standardizing validation metrics, providing transparency in used algorithms, and improving how technology can be tailored to individuals. Until then, it is prudent to exercise caution when interpreting metrics reported from consumer-wearable devices.

Keywords: Energy expenditure; Fitness; Health monitoring; Physical activity; Step count; VO2max; Wearable technology.

Fig. 1

Web of variables assessed by wearable devices and factors that must be considered in accurately reporting these variables. Variables directly connected to the athlete are those recorded by technologies. Outer variables are factors that influence the inner measure. For example, “training load” must consider both “external load” and “internal load,” which themselves must consider “work,” “distance,” “duration,” etc

Fig. 2

Normalized representation of average metric acceptance. Consensus is taken from expert opinion and other literature. ✔ = not generally accepted. ✔✔✔✔ = widely accepted

Fig. 3

Mean absolute percentage error (MAPE) for various physiological variables recorded by wearable devices from recent investigations. MAPE indicates the predictive accuracy of devices. Although no standardized thresholds exist for high or low error, MAPE > 3% has been considered high for laboratory-based studies and > 10% has been considered high for studies in free-living conditions. Note: studies normally found a range for MAPE. Therefore, “X” indicates the approximate average of various devices and scenarios tested. 1, Passler (2019); 2, Henriksen (2020); 3, Nelson (2016); 4, Montoye (2017); 5, Wallen (2016); 6, Navalta (2020); 7, LeBoeuf (2014); 8, Carrier (2020); 9, Henriksen (2021)

Fig. 4

Comparison between how users are relying upon wearable devices with expert recommendation and 5-star rating on how best to rely upon wearable devices. 5-star, excellent reliability; 4-star, good reliability; 3-star, moderate reliability; 2-star, poor reliability; 1-star, very poor reliability. 1, Evenson (2015); 2, Global Web Index (2020); 3, Canhoto (2017); 4, Rose (2019); 5, Li (2017); 6, McDonough (2021); 7, Kerner (2017); 8, Montgomery-Downs (2012)

Fig. 4

Comparison between how users are relying upon wearable devices with expert recommendation and 5-star rating on how best to rely upon wearable devices. 5-star, excellent reliability; 4-star, good reliability; 3-star, moderate reliability; 2-star, poor reliability; 1-star, very poor reliability. 1, Evenson (2015); 2, Global Web Index (2020); 3, Canhoto (2017); 4, Rose (2019); 5, Li (2017); 6, McDonough (2021); 7, Kerner (2017); 8, Montgomery-Downs (2012)