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: Downhill walking induces eccentric muscle damage (EIMD), which impairs lower limb function and alters movement strategies, particularly during high-impact tasks like stair descent [1]. While laboratory-based force plates (measuring vertical ground reaction forces, vGRF) are the gold standard for assessing these kinetic changes [2], they are restricted to controlled environments. Smart insoles equipped with inertial measurement units (IMUs) offer a portable alternative [3]. This study aimed to investigate the effect of lower limb fatigue on impact mechanics during stair descent and to validate the utility of smart insole G-force metrics by examining their correlation with laboratory vGRF data. METHODS: Fifteen healthy males (Age: 21.5 ± 1.8 yrs; BMI: 23.89 ± 3.20 kg/m²) performed a 30-minute downhill walk (-25% gradient, 6 km/h) to induce EIMD. Stair descent kinetics were assessed at baseline (pre), 0h, 24h, and 48h post-exercise using a force plate (HPS400600, AMTI, USA) and a novel smart insole prototype (LSM6DS3tr IMU, STMicroelectronics, Switzerland). Data were analyzed using repeated measures ANOVA and Pearson correlation. RESULTS: The vGRF significantly decreased across all recovery time points compared to baseline (pre: 22.16 ± 2.46 N/kg; 0h: 20.48 ± 3.45 N/kg; 24h: 20.26 ± 3.44 N/kg; 48h: 21.35 ± 4.92 N/kg, all p < 0.05). This trend was mirrored by smart insole data, which detected a significant decline in impact acceleration (pre: 2.59 ± 0.33 g vs. 0h: 2.09 ± 0.44 g; 24h: 2.16 ± 0.30 g; 48h: 2.20 ± 0.46 g, all p < 0.05). Crucially, a strong positive correlation between vGRF and G-force emerged during the recovery phase, becoming pronounced at 24 hours (r = 0.763) and peaking at 48 hours (r = 0.866). CONCLUSION: The findings reveal that EIMD elicits a distinct "soft-landing" strategy during stair descent, characterized by significantly reduced impact forces. This adaptation serves as a protective mechanism to attenuate loading on fatigued muscles and compensate for compromised neuromuscular control [4]. Crucially, smart insole G-force metrics mirrored laboratory vGRF trends, detecting significant deviations at all post-exercise time points. The strong positive correlation observed during the recovery phase (24-48h) aligns with recent validation studies of IMU-based force estimation [5, 6]. By capturing these subtle shifts in "movement quality", novel smart insole prototypes are validated as a sensitive, portable tool for monitoring neuromuscular fatigue and managing injury risk in field settings where traditional force plates are unavailable.
Read CV SzuKai FuECSS 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