ECSS Paris 2023: OP-AP14
INTRODUCTION: Accurate assessment of total energy expenditure (TEE) is a key parameter in sport science, health research, and clinical practice. Body-worn activity sensors offer a practical and consumer-friendly approach for estimating TEE under free-living conditions; however, their validity is often insufficiently examined against the gold-standard doubly labeled water (DLW) method. Therefore, this study aimed to evaluate the validity of four body-worn activity sensors, including consumer wearables and a research-grade accelerometer, for assessing habitual TEE in daily life. METHODS: Fifty-six healthy adults (66% female; 29.4 ± 7.2 years, 22.9 ± 2.6 kg·m-2, estimated VO2max 49.5 ± 9.8 ml·kg-1·min-1) wore four activity sensors simultaneously at predefined and standardized body locations (Polar Ignite 3 [IG], Polar Verity Sense [VS], Garmin vivoactive 4 [VA], GENEActiv [GA]) during waking hours for 10 consecutive days under free-living conditions. Participants with at least 8 out of 10 valid days (≥12 h sensor wear per day), determined using a wear-time log, were included, resulting in sensor-specific sample sizes (IG: 51, VS: 41, VA: 47, GA: 41). For the wrist-worn consumer devices (IG and VA), TEE was derived directly from the manufacturers’ proprietary algorithms. For VS and GA, manufacturer algorithms were used to estimate energy expenditure (EE) during wear time only; non-wear EE was estimated based on resting metabolic rate (Harris & Benedict, 1918) and added to wear-time EE to derive daily TEE. DLW-derived TEE (TEEDLW) was calculated as mean daily TEE across the measurement period and served as the criterion measure. Statistical analyses included descriptive analysis (Mean±SD, MAPE), Wilcoxon signed-rank tests, Bland-Altman analyses, and linear regression (TEEDLW as dependent, sensor-derived TEE as independent variable; R², SEE; α=.05). RESULTS: Mean wear-time did not differ between sensors (~15 h·day⁻¹; p>.05), indicating that differences in TEE estimates were more likely algorithmic rather than compliance-related. VA and GA significantly underestimated TEE compared with TEEDLW (VA: 2528±457 kcal·d-1, GA: 2709±464 kcal·d-1, TEEDLW: 2930±697 kcal·d-1; p<.05), whereas no significant differences were observed for IG (3080±592 kcal·d-1) and VS (3003±632 kcal·d-1; all p>.05). Systematic bias [limits of agreement] amounted to -418 [-1334/498] kcal·d-1 for VA, -264 [-1273/746] kcal·d-1 for GA, 111 [-803/1025] kcal·d-1 for IG, and -4 [-1172/1164] kcal·d-1 for VS. MAPE ranged from 13.6–16.1%, with 31.7–56.9% of participants exhibiting a MAPE ≤10% across sensors. Associations with TEEDLW were moderate to high (R2 = 0.40–0.66), with substantial prediction errors (SEE: 448–587 kcal·d-1). CONCLUSION: Wearable activity sensors demonstrated moderate to high agreement with DLW-derived TEE at the group level. Nevertheless, substantial inter-individual variability and large limits of agreement limit their applicability for individual-level TEE assessment.
Read CV Juliane HeydenreichECSS Paris 2023: OP-AP14
INTRODUCTION: Energy expenditure (EE) assessment is essential for exercise prescription and metabolic health management. While wearable devices have become popular for EE monitoring, their accuracy varies substantially across manufacturers due to differences in sensor technologies and proprietary algorithms. Most validation studies have used fixed-speed protocols that do not reflect individual aerobic capacity differences, and sex-stratified analyses remain limited despite known metabolic differences between males and females. METHODS: To systematically evaluate the accuracy of three commercial wearable devices (POLAR Vantage V3, GARMIN Forerunner 265, and VIVO Watch GT) for EE estimation across different running protocols, with particular emphasis on sex-specific differences.Thirty healthy adults (16 males, 14 females; age 18-33 years; body fat <32%) completed multiple running experiments: fixed-speed protocol (6.7-12.0 km/h), maximal speed test, and individualized-speed protocol (35-70% of maximum speed). EE from wearables was compared against indirect calorimetry (Metamax 3B-R2) as the criterion measure. Performance metrics included mean absolute percentage error (MAPE), accuracy (proportion within ±20% of criterion), bias, and root mean square error (RMSE). All devices demonstrated high heart rate measurement accuracy (r=0.997 with ECG). RESULTS: Device performance varied substantially by sex and intensity. Overall, all three devices systematically overestimated EE, with significantly lower accuracy in females than males. POLAR excelled at rest (accuracy >50%; MAPE=9.1%) and maximal speed conditions (male accuracy: 66.67%; MAPE: 16.50±16.63%). VIVO demonstrated superior performance during fixed-speed protocols (accuracy ≥71.43%; lowest MAPE and RMSE) and individualized protocols (male accuracy >93.3%, female >41.67%). GARMIN exhibited the largest errors across all conditions (male bias: 1.04-3.09 kcal/min; female accuracy: 21.43-25.00%; MAPE up to 38.38%). Longer exercise duration (10 vs 3 minutes) improved estimation accuracy for all devices. MAPE followed a U-shaped pattern with increasing intensity, suggesting better accuracy at low and high speeds compared to moderate intensities. CONCLUSION: Current wearables show substantial sex-specific and intensity-dependent errors in EE estimation during running. VIVO performed best overall in males, while POLAR showed advantages at rest and maximal intensity. All devices demonstrated lower accuracy in females, likely reflecting sex-related differences in metabolism, body composition, and aerobic capacity. Sex-specific algorithm calibration is urgently needed, particularly for female users and moderate-intensity exercise. Users should interpret wearable-derived EE estimates with caution, accounting for sex and exercise characteristics. Manufacturers should optimize algorithms specifically for women and incorporate greater sex weighting in their models.
Read CV HUI HEECSS Paris 2023: OP-AP14
INTRODUCTION: Understanding the mechanics of impacts and vibrations induced by running is a current challenge for injury prevention, quantification of training loads and equipment design. Transient soft tissue vibrations (STV), in particular, have been linked to muscle fatigue and muscle damage (1,2,3). Yet, inconsistency in the results and limited evidence remain due to the superimposition of vibration-related effects on those induced by the muscle contraction inherent to movement, preventing a mechanistic approach. This study aimed to evaluate the influence of STV on acute neuromuscular function, using an instrumented ergometer capable of reproducing the shocks encountered when running, while eliminating the interference of voluntary contractions necessary for running (4). METHODS: Sixteen participants, running at least once per week, underwent nine hundred simulated running impacts at 14 to 15 km/h on one randomly selected leg. Ground reaction forces (GRF) were acquired using a force plate embedded in the ergometer. Tri-axial accelerometers recorded heel impacts and STV of the Vastus Lateralis (VL). Muscle preactivation of the VL was measured using surface electromyographic sensors (EMG). The high frequency component (>10 Hz) of GRF has been computed via Discrete Fourier Transform to quantify impact peak and loading rates. STV parameters including total magnitude, amplitude, median frequency, and damping were extracted using Continuous Wavelet Transform. Knee extensors maximal voluntary contraction torque (MVC) and voluntary activation (VA), as well as torque evoked by 10 and 100 Hz doublet (Db10 and Db100, respectively) were assessed before and immediately after the exercise, together with perceived fatigue and muscle soreness. In addition, shear wave speed of the VL was measured to assess changes in muscle mechanical properties. RESULTS: Immediately after the exercise, MVC decreased (-9.48 %, p = 0.001) whereas VA remained unchanged (-3.76 %, p = 0.1). Both Db100 and Db10 increased (10.31 %, p < 0.001 and 23.38 %, p < 0.001, respectively). Shear wave speed of VL rose (p < 0.05), together with elevated perceived fatigue (p < 0.001) and mild muscle soreness (p < 0.05). CONCLUSION: Real time recordings of force and acceleration confirmed that running impacts were accurately simulated, while preactivation values remained lower (~17% of MVC activation) than running tasks as expected. Moreover, STV induced fatigue even though there was no muscle contraction to create movement. This fatigue appears to be of central origin, as suggested by the increase in Db100 and Db10, a finding of particular interest with regard to potential positive effects related to changes in mechanical properties of the VL. REFERENCES 1) Gassier et al., 2025 2) Ehrström et al. 2018 3) Play et al., 2024 4) Gassier et al., 2025
Read CV Robin GassierECSS Paris 2023: OP-AP14