ECSS Paris 2023: OP-AP44
INTRODUCTION: Respiratory frequency (fR) is an objective marker of physical effort and is very sensitive to changes in exercise tolerance, especially during high-intensity exercise (1). An abundance of technological solutions is currently available to meet the increasing demand for breathing monitoring during training and competitions, with strain sensors representing a promising solution to estimate fR via chest-wall movements (2). However, robust methodological approaches are required to validate commercial breathing sensors before encouraging their widespread use. Thus, the current study aims to validate a commercial breathing device with a thorough cycling protocol designed to describe its accuracy and precision during both progressive and abrupt changes in fR. METHODS: Ten competitive cyclists (M/F: 8/2; stature: 173 ± 10 cm; body mass: 64 ± 8 kg) performed a single-visit validation protocol wearing the Tyme Wear (TW) strap (Tyme Wear Inc, Boston, MA, USA). The protocol was composed of: i) a 10-min warm-up (80 W M, 60 W F); ii) a ramp incremental test until exhaustion (25 W/min M, 20 W/min F); iii) a self-paced 8-min intermittent protocol (20:40 s); and iv) a 5-min time trial. The performance of the TW device was compared against that of a reference metabolic cart (Quark PFT, Cosmed, Rome, Italy). Both reference and TW breathing waveforms were recorded at 25 Hz and processed with custom-based algorithms to extract fR. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean of difference (MOD), and limits of agreement (LoAs) were computed to verify the accuracy and precision of the TW strap during the cycling protocol. The validation analysis was conducted breath-by-breath and on 30-s windows. RESULTS: The fR time course measured with the TW strap fairly resembled that measured with the reference system throughout the cycling protocol. The breath-by-breath comparison showed an average MAE value of 1.56 breaths·min-1 and an average MAPE value of 3.76 %. The 30-s comparison showed average MAE e MAPE values of 0.60 breaths·min-1 and 2.00 % respectively. In addition, a good agreement was found between the TW strap and the reference system for both breath-by-breath (MOD ± LoAs: -0.03 ± 5.71 breaths·min-1) and 30-s (MOD ± LoAs: -0.13 ± 3.34 breaths·min-1) values. CONCLUSION: The present findings suggest that the TW strap is a suitable device to measure fR in cycling. The good performance and wearability of this commercial device may facilitate the daily measurement of fR in cycling and potentially in other endurance sports. (1) A. Nicolò, C. Massaroni, L. Passfield, Front. Physiol. 8 (2017) 922. (2) C. Massaroni, A. Nicolò, D. Lo Presti, M. Sacchetti, S. Silvestri, E. Schena, Sensors 19 (2019) 908.
Read CV Giuseppe GrecoECSS Paris 2023: OP-AP44
INTRODUCTION: The integration of wearable technology in sports has advanced rapidly, enabling athletes and recreational users alike to gain personalized insights into their performance. In skiing, where skill development and technique are essential for safety and enjoyment, tools that provide real-time feedback have the potential to transform the user experience. The Connected Boot sensor system, which consists of two inertial measurement units (IMUs) mounted at the upper posterior cuff of the ski boot, offers an innovative approach to assessing skiing style by capturing objective sensor data through a calculated ski quality score [1]. This study aims to evaluate the usability, learning curve when using the app, and perceived effectiveness of the Connected Boot among recreational skiers. By analyzing user feedback and engagement, this research seeks to assess the devices potential as a tool for enhancing skiing style. METHODS: Data were collected during the 2022/23 season from a sample of 89 recreational skiers (23 female 66 male; age: avg 46.3, sd = 14.8). Participants were equipped with IMUs to collect sensor data that was used to calculate turn size, turn speed, edge angle velocity, edging of the ski boot and other metrics. The participants installed a specially developed mobile application to process and visualize this objective sensor data and to calculate a ski quality score. In addition, the app was used to collect subjective questionnaire data at the beginning and end of the study to determine the acceptance of the Connected Boot sensor system. RESULTS: Survey results indicate a generally positive reception of the Connected Boot across multiple dimensions. Most participants found it easy to use (71%) and beneficial for improving skiing style (81%). Statements regarding ease of learning received particularly strong support, e.g., over half of respondents strongly agreed that both familiarizing themselves with the Connected Boot (54%) and learning to use it (48%) were easy. The high level of agreement on usability (72%) highlights effective design and alignment with user needs. However, a small proportion of negative responses regarding usefulness of the provided information about the individual skiing style (18%), learning of how to use the Connected Boot and ease of use (15% each) could indicate that some users find the Connected Boot less intuitive. Moreover, it suggests that individual differences, such as skill level or technological familiarity, may influence user experience. CONCLUSION: The Connected Boot is promising when it comes to improving the skiing experience through feedback and ease of use, as shown by the high agreement in the areas of ease of use and perceived benefits. Nevertheless, to improve the product’s appeal and satisfaction, further refinements might focus on increasing user enjoyment and addressing specific pain points highlighted by the minority of users who reported lower satisfaction. [1] Snyder et al., Sensors, 2021
Read CV Stefan KranzingerECSS Paris 2023: OP-AP44
INTRODUCTION: Accurately measuring training intensity in swimming is challenging due to a lack of validated measurement devices. Smart swim goggles (FORM goggles, Vancouver, Canada) that harbour a temple-mounted optical heart-rate (HR) sensor enable real-time feedback of HR during training. These HR data facilitate the calculation of HR-based training loads (TL) such as Banister training impulse (bTRIMP) and cumulative TRIMP (cTRIMP) (1). However, the HR measurement from these goggles has yet to be validated. The purpose of this study was to assess the validity of the FORM goggles HR measurements for use in swim training monitoring. METHODS: We applied a criterion validation approach in which the FORM goggles HR measurements were compared to those from a gold-standard electrocardiogram chest-strap (Garmin HRM-Swim). Twenty-five experienced recreational swimmers (proficient in freestyle, backstroke, and breaststroke) completed one of two workouts, with two participants completing both. The workouts differed in intensity and distance. Comparisons were made at four levels: individual heartbeat, interval, main-set, and workout. At each level, the average (HRmean), maximum (HRmax), and minimum (HRmin) HR were assessed using Lin’s concordance correlation, Bland-Altman, and regression. The analysis of cTRIMP and bTRIMP was performed at the interval and workout levels, respectively. Statistics are presented as ranges across all levels. RESULTS: HRmean from the FORM goggles and Garmin HRM-Swim were similar in both workouts, with respective mean±SD values of 131.1±16.4 bpm and 131.1±17.3 bpm (Workout 1), and 142.3±17.0 bpm and 141.8±17.7 bpm (Workout 2). HRmean, HRmax, and HRmin correlation ranged from 0.90-1.0, 0.87-0.92, and 0.59-0.90, respectively, while the TRIMPs correlation was 1.0. The fixed bias (limits of agreement) ranged from -0.34 to 0.11 (3.6-9.5) bpm, -0.11 to 2.6 (8.9-12.0) bpm, and -0.6 to 1.2 (10.1-26.9) bpm for HRmean, HRmax, and HRmin, respectively, and from -0.38 to -0.20 (4.3-4.6) arbitrary units (a.u.) for the TRIMPs. Proportional bias was observed for HRmean (individual heartbeat, interval, and workout levels), and for HRmax (interval level). Regression intercepts from the differences between devices showed no statistically significant difference from zero: HRmean (-0.20 to -0.05 bpm), HRmax (-0.25 to 2.1 bpm), HRmin (-0.66 to 0.89 bpm), and TRIMPs (-0.43 to -0.29 a.u.). CONCLUSION: This study revealed strong agreement between the FORM goggles and Garmin HRM-Swim for HR measurements, particularly for HRmean and TRIMP values. Given that HRmean serves as the primary input for HR-based TL metrics, the goggles provide valid data for monitoring swim training. Larger discrepancies between devices were noted for HRmin and HRmax. Further research should explore the impact of these observed discrepancies on adhering to HR intensity zones during training when utilizing real-time HR feedback. 1. García-Ramos et al., Eur J Sport Sci., 2015.
Read CV Aidan KitsECSS Paris 2023: OP-AP44