ECSS Paris 2023: CP-AP32
INTRODUCTION: Soft and deep tissue massage are routine approaches utilised both in the rehabilitation and recovery processes of performance and recreational athletes, with therapeutic benefits purported to be increased blood flow and oxygenation rates. However, the evidence remains contradictory. Therefore, the purpose of this preliminary study was to examine the impact of both soft and deep tissue massage on consequent muscle oxygenation levels. METHODS: Following local institutional ethics approval, 10 recreationally active participants (7 males, 3 females) volunteered (mean ± SD: age 21 ± 2, body mass 67.8 ± 11.2 kg, height 175.3 ± 9.2 cm). All participants were assessed using the ankle-to-brachial index for suitability to undergo blood flow occlusion. For all conditions, the participant lay supine on a massage bench, where muscle oxygenation was recorded throughout using NIRS sampling at 10 Hz, across the mid-point of rectus-femoris. An occlusion cuff (20 cm width) was placed midway between ASIS and mid-patella. Following 7 min in the rested state (REST), muscle blood flow was regulated using occlusive and reperfusion phases of 7 min using 80% of the individualised limb occlusion pressure. Immediately followed by 7 min of SOFT then DEEP tissue massage, which were proceeded by a second occlusive-reperfusion phase. RESULTS: There was a significant difference (P< 0.01, effect size (ES) = 2.03; 95% CI 3.01 - 0.88 between minimum (66.43 ± 12.13%) and maximum (88.24 ± 9.12%) tissue saturation index (TSI) values when comparing REST to SOFT following reperfusion. For DEEP, when comparing to REST for minimum to maximum responses (82.34 ± 6.84%), a large ES: 1.51; 95% CI 2.54 - 0.55; (P< 0.01) was also observed. Oxyhaemoglobin (O2Hb) increased from a minimum [baseline value] at REST of 26.63 ± 18.38% to a maximum of 91.53 ± 37.65% following SOFT; ES = 2.19; 95% CI 3.19 – 1.01; (P< 0.01), while for DEEP there was an increase of 201.6% in O2Hb: ES = 3.19; 95% CI 1.77 – 4.35; (P< 0.01). CONCLUSION: These data tentatively suggest that both soft and deep tissue massage increased O2 availability at the muscle, as reflected in the increases for both TSI and O2Hb. It is probable that vasodilation, possibly linked to the nitric oxide cascade influenced by the pressure applied and thermoregulatory responses are the primary candidates attributable. Caution should be applied to these findings as both procedures occurred subsequent to each other, potentially explaining the mixed results across conditions.
Read CV Dan GordonECSS Paris 2023: CP-AP32
INTRODUCTION: Energy Expenditure (EE), a key indicator reflecting exercise intensity and volume, has been widely researched in public health and sports training. Accurate monitoring of EE is crucial in physiology, health management, and exercise prescription design. Consequently, accurately predicting or estimating energy expenditure using simple, easy-to-measure indicators combined with artificial intelligence (AI) algorithms has become a critical direction in energy consumption research. Building on previous research, this study integrates heart rate and acceleration data from incremental load exercises, constructing multiple dynamic time series prediction models. The model with the best fit was selected based on performance evaluation metrics, providing a theoretical reference for future energy expenditure prediction studies. METHODS: Twenty-five college students participated as subjects, undergoing an incremental load exercise test on a treadmill. Data collection comprised exercise load metrics and heart rate readings captured by accelerometers, with energy expenditure predictions made using dynamic time-series models (e.g., LSTM, BiLSTM, TCN, GRU, CNN+LSTM). Model performance was evaluated using metrics such as RMSE, R², and Bias, with prediction consistency further assessed through Bland-Altman plots. RESULTS: The LSTM model achieved the lowest RMSE (0.2423) and Bias (0.1060), indicating its high accuracy and consistency in predicting energy expenditure. It also displayed the highest R² value (0.9255), demonstrating its strong ability to interpret the data.The TCN model also performed well, with an RMSE of 0.2710, an R² of 0.9068, and the lowest Bias (0.0216) among the models. The low Bias value indicates that TCN offers a reduced systematic error in predictions compared to other models. TCNs ability to capture long-term contextual information through its convolutional structure helped it mitigate biases effectively. BiLSTM, GRU, and CNN+LSTM showed moderate performance. Overall, LSTM and TCN were the top-performing models. CONCLUSION: In this study, we developed an EE model for incremental loading exercise by utilizing acceleration and heart rate data, comparing the predictive performance of multiple time series models, including LSTM, BiLSTM, GRU, TCN, and CNN+LSTM. Through evaluation metrics such as RMSE, R², and Bias, along with Bland-Altman plots to assess prediction consistency, we identified LSTM and TCN as the most accurate and stable models for EE prediction. Our findings underscore the effectiveness of time series-based algorithms in capturing dynamic patterns in incremental loading exercise. Moreover, incorporating joint internal (heart rate) and external (acceleration) metrics as input features proves effective in reflecting exercise intensity. This approach suggests that the integration of multiple metrics enhances the accuracy of EE estimation, providing a reliable means for quantifying exercise load and calculating exercise intensity.
Read CV yingzhe songECSS Paris 2023: CP-AP32
INTRODUCTION: Biofeedback technologies are increasingly applied in sports science to optimize neuromuscular control and psychophysiological adaptation [1-3]. Current research demonstrates methodological inconsistencies in efficacy assessments across athlete populations, particularly regarding long-term outcomes and modality-specific mechanisms. METHODS: This systematic review aim at evaluating biomechanical pathways and proposes evidence-based training through multilevel data synthesis. Following PRISMA guidelines, Web of Science, PubMed, and SPORTDiscus were systematically searched. 32 studies (2019–2024) were analyzed from using search terms: ("biofeedback" OR "neurofeedback") AND ("athletic performance" OR "sport*") . Dual-blind AMSTAR2 assessments excluded studies scoring <22% on methodological rigor (κ=0.81). Data extraction categorized outcomes by European College of Sport Science biofeedback taxonomy: physiological (HRV/EMG), biomechanical (kinematic), and neurocognitive (EEG) . RESULTS: HRV biofeedback demonstrated moderate effects on endurance performance (d=0.62–1.15) and stress resilience, while EMG interventions improved strength output variability by 18–27% in power sports . Neurocognitive protocols enhanced decision-making accuracy (p<0.05, ES=0.89) but showed conflicting results in motor learning retention between RCTs and non-randomized trials . Biomechanical feedback reduced injury-risk movement patterns (Cohen’s f²=0.31) yet exhibited limited transfer to competition contexts. CONCLUSION: Theoretical integration of psychophysiological adaptation models clarifies biofeedback’s task-specific efficacy. Practitioners should prioritize multimodal protocols combining HRV and kinematic feedback for compound adaptations. Limitations include heterogeneity in outcome measures and underrepresentation of female athletes (18% of samples). 1.Morales-Sánchez, V., Falcó, C., Hernández-Mendo, A., & Reigal, R. E. (2022). Efficacy of Electromyographic Biofeedback in Muscle Recovery after Meniscectomy in Soccer Players. 2.Mackay, E. J., Robey, N. J., Suprak, D. N., Buddhadev, H. H., & San Juan, J. G. (2023). The effect of EMG biofeedback training on muscle activation in an impingement population. Journal of Electromyography and Kinesiology, 70, 102772. 3.Prończuk, M., Trybek, G., Terbalyan, A., Markowski, J., Pilch, J., Krzysztofik, M., ... & Maszczyk, A. (2023). The Effects of EEG Biofeedback Training on Visual Reaction Time in Judo Athletes. Journal of Human Kinetics, 89, 247.
Read CV Hao TIANECSS Paris 2023: CP-AP32