Validity of Estimating the Maximal Oxygen Consumption by Consumer Wearables: A Systematic Review with Meta-analysis and Expert Statement of the INTERLIVE Network

Abstract

Background Technological advances have recently made possible the estimation of maximal oxygen consumption (VO2max) by consumer wearables. However, the validity of such estimations has not been systematically summarized using meta-analytic methods and there are no standards guiding the validation protocols. Objective The aim was to (1) quantitatively summarize previous studies investigating the validity of the VO2max estimated by consumer wearables and (2) provide best-practice recommendations for future validation studies. Methods First, we conducted a systematic review and meta-analysis of studies validating the estimation of VO2max by wearables. Second, based on the state of knowledge (derived from the systematic review) combined with the expert discussion between the members of the Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) consortium, we provided a set of best-practice recommendations for validation protocols. Results Fourteen validation studies were included in the systematic review and meta-analysis. Meta-analysis results revealed that wearables using resting condition information in their algorithms significantly overestimated VO2max (bias 2.17 ml·kg−1·min−1; limits of agreement − 13.07 to 17.41 ml·kg−1·min−1), while devices using exercise-based information in their algorithms showed a lower systematic and random error (bias − 0.09 ml·kg−1·min−1; limits of agreement − 9.92 to 9.74 ml·kg−1·min−1). The INTERLIVE consortium proposed six key domains to be considered for validating wearable devices estimating VO2max, concerning the following: the target population, reference standard, index measure, testing conditions, data processing, and statistical analysis. Conclusions Our meta-analysis suggests that the estimations of VO2max by wearables that use exercise-based algorithms provide higher accuracy than those based on resting conditions. The exercise-based estimation seems to be optimal for measuring VO2max at the population level, yet the estimation error at the individual level is large, and, therefore, for sport/clinical purposes these methods still need improvement. The INTERLIVE network hereby provides best-practice recommendations to be used in future protocols to move towards a more accurate, transparent and comparable validation of VO2max derived from wearables. PROSPERO ID CRD42021246192.

Wearable activity trackers–advanced technology or advanced marketing?

“…This consortium of six European universities and one industrial partner develops best-practice guidelines for evaluating consumer wearables. A recent review from INTERLIVE (Molina-Garcia et al 2022 ) regarding the validity of wearables in estimating and concluded that population-level estimates of demonstrate some degree of accuracy, but the margin of error is large for estimates at the individual level. Within the realm of oxygen utilization, some devices also report arterial oxygen saturation (SaO 2 or SpO 2 ).…”

Nutzen und Grenzen mobiler und tragbarer Technologien zur Individualisierung von hochintensivem Intervalltraining in der Bewegungstherapie

“…Ergänzende Ergebnisse liefert eine kürzlich durchgeführte Metaanalyse zur Validität der VO 2max -Messung mithilfe von Smartwatches 32 . Diese Metaanalyse stellte fest, dass die „Limits of Agreement“ über verschiedene Smartwatches hinweg eine Spannweite von -16.79 bis zu+16.61 ml/min/kg aufweisen 32 . Der Einsatz der VO 2max -Messung mithilfe von Smartwatches kann daher auf individueller Ebene begrenzt sein und falsche Ergebnisse liefern, obwohl auf Gruppenebene (in Abhängigkeit vom Wertebereich) ggf.…”

Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors: Validation Study

“…Wearable technology provides an approach to estimate CRF for routine individualized risk prediction in everyday clinical practice. The validity of several wearable technologies that provide CRF estimations has been studied [12][13][14][15][16]. Similar to the CRF estimation method examined in this study, most of the technologies have used HR and body acceleration data [12,[14][15][16], whereas some technologies combine HR and body acceleration data with data from respiratory bands [13].…”

“…Easily available technology provides a strategy to estimate CRF as recent technological advances in wearable devices, such as patches, clothing, and wristband monitors, enable the measurement of HR and multiple other health-related physiological signals in free-living conditions [11]. The validity of several wearable technologies in estimating CRF has been evaluated in healthy young individuals [12][13][14][15]. However, although one such study has also included a small number of individuals who are middle-aged and obese [16], the validity of wearable technologies to estimate CRF in clinically relevant populations with CVD risk factors, chronic diseases and medications, and heterogeneous fitness levels is poorly known [15].…”

“…The validity of several wearable technologies in estimating CRF has been evaluated in healthy young individuals [12][13][14][15]. However, although one such study has also included a small number of individuals who are middle-aged and obese [16], the validity of wearable technologies to estimate CRF in clinically relevant populations with CVD risk factors, chronic diseases and medications, and heterogeneous fitness levels is poorly known [15].…”