ECSS Paris 2023: OP-AP23
INTRODUCTION: The quality of RR-intervals (RR) and heart rate (HR) measured by wearable heart rate monitors has been assessed extensively [1-3]; however, most of the validation protocols include only long steady-state acquisition periods, neglecting the dynamic responses and delaying effects of filters applied by manufacturers. While irrelevant in endurance sports, in applications such as high-intensity interval training, those characteristics become important; still, they are largely undocumented. Therefore, this study aims to quantify, evaluate, and compare the dynamic response of RR and HR measurements of commercially available chest-worn wearable monitors during induced sharp changes in HR. METHODS: A cheap, simple, and highly reproducible strategy was adopted, where a waveform generator was used to create ECG signals simulating the heart activity. RR and HR were recorded using the standard Bluetooth heart rate service for four ECG-based wearable monitors: Garmin HRM-Dual (G), Movesense Active (M), Polar H10 (P), and Wahoo TICKR (W). To simulate sharp changes in HR, four step functions were used (60-120 bpm, 120-60 bpm, 120-180 bpm, and 180-120 bpm), where each test was repeated ten times for each device. Dynamic response was quantified by resampling the signals to 1 Hz and time-aligning to the start of the test. Evaluation and comparison were based on latency, computed as RR latency (time elapsed from the step signal until the sensor RR response) and HR latency (time elapsed from the change in RR until the HR response was within ±3 bpm of the reference). RESULTS: The RR measurements of all devices responded nearly immediately to changes on the reference signal. RR latency was 1.7±0.2 s (G), 1.6±0.2 s (M), 3.2±0.7 s (P), 2.0±0.5 s (W) (mean±SD) across all step tests. Mean absolute error of RR measurements pre and post step (constant signal) was below 3 ms for all but one device (W: 21 ms). HR response was significantly delayed, and latency was different between devices and step tests. The longest HR latency was observed on the 120-60 bpm test: 38.9±1.2 s (G), 24.0±0.0 s (M), 23.0±0.3 s (P), 24.3±5.6 s (W). The shortest HR latency was observed on the 120-180 bpm test: 12.6±0.3 s (G), 8.3±0.1 s (M), 4.7±0.7 s (P), 5.2±0.8 s (W). CONCLUSION: HR measurements were significantly different between the four devices, where all presented some level of latency, indicating that manufacturers implemented different digital filters and thresholds to compute the HR values. Such filtering strategies had great impact on the dynamic response of the sensors, suggesting that those characteristics could be relevant in applications where sharp changes in HR are present. Open documentation of the processing steps is necessary, and future research involving sharp HR changes should be based on RR measurements rather than HR measurements. 1. Schaffarczyk et al. (2022) Sensors 2. Gilgen-Ammann et al. (2019) European Journal of Applied Physiology 3. Rogers et al. (2022) Sensors
Read CV Mariah SabioniECSS Paris 2023: OP-AP23
INTRODUCTION: Recent developments in wearable foot-pod technology have provided an equivalent measure of power for runners, similar to what cyclists have enjoyed for many years. A direct measure of power (Watts) during running, as with cycling, thus has the utility for developing a correlation from which to estimate a rate of oxygen uptake (VO2) for a given Watt. The aim of this study was to test the possibility of estimating VO2 from power output during free outdoor running at a self-selected pace, and then deriving a formula to account for changes in this relationship affected by the ground-surface gradient. METHODS: Fifteen healthy active participants (12 male: 26 ± 6 yrs; 3 female 28 ± 3 yrs) completed three mins of running on each of three different gradients: flat athletics track, 5° uphill and 5° downhill paved foot path. Power output was measured continuously via a shoe-lace attached foot-pod (Stryd Power Monitor, Boulder, Colorado) and similarly for VO2 via a portable wearable pulmonary gas exchange monitor (Cortex MetaMax 3B, Leipzig). RESULTS: The key results are summarised with four key measures as group means ± SD and analysis of variance statistic (ANOVA): 1. Running power (Watts/kg): Flat = 3.4 ± 0.5; Uphill = 3.9 ± 0.7 (+14.7%∆ vs flat); Downhill = 3.1 ± 0.5 (-8.8%∆ vs flat); ANOVA F=27.9, DF=2 p<0.001 2. Stryd Device equation Estimated VO2 (ml•kg-1•min-1) = 13.16 x Watts/kg: Flat = 46.0 ± 7.4; Uphill = 53.4 ± 9.6 (+16.1 %∆ vs flat); Downhill = 42.3 ± 6.9 (-8.0 %∆ vs flat); ANOVA F=27.7, DF=2 p<0.001 3. Actual VO2 (ml•kg-1•min-1): Flat = 38.7 ± 7.8; Uphill = 40.5 ± 5.6 (+4.7 %∆ vs flat); Downhill = 37.2 ± 6.7 (-3.9 %∆ vs flat); ANOVA F=5.58, DF=2 p=0.015 4. VO2 to Watts Ratio: Flat = 10.0; Uphill = 13.3 (+33 %∆ vs flat); Downhill = 4.3 (-57 %∆ vs flat); ANOVA F = 196.4, DF = 1 p<0.001 Power output and VO2 changed significantly (P<.001) across the three gradients. Compared to flat surface running, the proportional %changes of the respective foot-pod estimated VO2 for running uphill and downhill (+14.7%, -8.8%) remained similar to the respective %change in power output (+16.1, -8.8%). Whereas, the %change in actual VO2 for uphill and downhill running, respectively, were significantly smaller (+4.7%, -3.9%; p<.001). For all three conditions the foot-pod equation for estimating VO2 were significantly lower than actual VO2 (p <.001), and such differences were magnified when expressed as ratio of VO2 to Power. CONCLUSION: Whilst the Stryd Power foot-pod monitor is able to show reliable and valid changes in running power with changes in both running speed and surface gradient [1], there still remains the need to develop a much more accurate formula for estimating VO2 from power output that accounts for both running speed and surface gradient. 1. Cerezuela, V., et al. Eur J Sport Sci. 2021; 21(3):341-350
Read CV John BuckleyECSS Paris 2023: OP-AP23
INTRODUCTION: Transferring laboratory tests and data collection procedures to large-scale field tests presents several challenges due to the inherent differences in controlled laboratory environments versus dynamic field conditions. The extra manual labor required for following up a large-scale study and some logistical concerns such as equipment portability and data collection in remote locations further complicate the transition from lab to field. For a performance and health related study of military recruits, we are developing a continuous monitoring setup using consumer available smartwatches that requires minimal effort from the study operators to obtain the relevant data. METHODS: We developed an end-to-end pipeline which monitors study participants 24/7 using a Garmin smartwatch. Health parameters measured by multiple Garmin smartwatches are transmitted to a single phone using an operator sync (participants are not required to upload their own data). The proposed solution is tailored towards complex environments where the applicability must be non-invasive to not disrupt daily activities and has to be decoupled from external servers to ensure confidentiality. As such, study participants of the military recruits can be monitored using a smart watch with GPS capabilities, even during multi-day tactical exercises in the field or during prolonged periods without connectivity. Based on the weekly itinerary, the watch uses different monitoring modes and sampling rates to extend battery life while providing a wide range of (optional) parameters such as heart rate, GPS, movement dynamics, accelerometer, etc. This is linked together with other data sources such as itinerary metadata, test battery results, injuries and more. RESULTS: The proposed system has been used in an 8-week-long study. During this period, it was used to collect specific GPS based activities (map reading exercises, runs at aerobic and anaerobic thresholds, etc.) and various monitoring parameters (heartrate, steps, sleep quality, stress, etc.). Due to the operator synchronization mechanism, it is easy to deploy such a system in a field test (short or long deployments). The recorded parameters are immediately available when the operator chooses to synchronize the devices. The uploaded data is then automatically linked to the corresponding profile in our central platform and the annotations from other data sources are applied for improved analysis. CONCLUSION: First results show the feasibility of the proposed approach. However, some remaining issues still need to be resolved in regard to the Garmin Health SDK, which at the moment fails to transfer all the available data from the watch to the phone if a large number of watches are synchronized simultaneously. The valorization potential of the proposed set-up is high as it could also be used in other contexts. E.g. research laboratories that can quickly equip a group of people for their testing without having to manually process all the watches afterwards.
Read CV Maarten SlembrouckECSS Paris 2023: OP-AP23