Fatigue analysis in recreational active runners & development of a real-time fatigue prediction model

• Main Author: Muhammad Jafar Ali
• Other Authors: Hoon Kay Hiang
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Mechanical engineering
• Online Access: http://hdl.handle.net/10356/73344
• Institution: Nanyang Technological University

Millions of recreational active runners participate in endurance events to have better fitness, cardiovascular health, and longevity. These endurance events lead to fatigue in the individual. Fatigue is a sensation of tiredness and deterioration of the physical performance. It is a critical factor that has the potential to disrupt the running performance of the individuals by stressing the physiological as well as the biomechanical system. Fatigue due to endurance running can lead to wide-ranging effects, such as cardiovascular stress, metabolic derangements, and musculoskeletal injuries. To achieve positive outcomes of endurance training and reduce the risks of injury, it is important to monitor the running intensity and examine the implications of fatigue on physiological, neuromuscular and kinematic systems. This can be achieved by determining the physiological and biomechanical predictors of fatigue. Considering the classes of athletes, different demographics of runners have different fatigue thresholds. This study focused on male recreational endurance runners. This group was selected for several reasons. Firstly, there is a dearth of literature related to this group. Secondly, this group is much larger as compared to amateur and professional competitive runners. Lastly, there is no real-time metabolic estimation and fatigue prediction model for this group to help them monitor their running intensity and fatigue in real time. This thesis analyzed endurance performance and evaluated fatigue during two scenarios. In the first scenario, the data was gathered during non-continuous running in which participants ran for 4 minutes followed by rest for 1 minute until exhaustion. In the second scenario, participants ran continuously and data was gathered until exhaustion. A total of 17 healthy male recreational active runners (age 22.94±1.48 years, BMI 22.16±1.92, V̇O2max 57.63±5.46 ml.kg-1.min-1, HRmax 190.47±7.89 beats.min-1, % body fat 13.01±3.31) were recruited. The physiological data included oxygen consumption (V̇O2), Heart Rate (HR), Respiratory Rate (RR), Respiratory Exchange Ratio (RER), Maximum HR (HRmax) and maximal aerobic capacity (V̇O2max). Kinematic data included hip angle, knee angle, ankle angle, pronation angle, landing impact on the tibia (IT), landing impact on the hip (IH) and hip push-off acceleration (ACCH). The neuromuscular data included Integrated EMG (iEMG) and Time-Dependent Median Power Frequency (TDMdPF). Perceived exertion data for the chest (RPEC), leg muscles (RPEM) and overall body feel (RPEO) were recorded to determine fatigue. Non-continuous running test was administered to determine blood lactate (BLa) response. Both scenarios were evaluated at the Critical Speed (CS), determined at Blood Lactate (BLa) of 4.0 mmol.l-1. Cardio-respiratory data was obtained using the ‘Parvo Medics TrueOne 2400’ with a POLAR chest strap heart rate sensor. To evaluate kinematics and neuromuscular activation in the lower extremity, the dominant leg was selected through the three manipulative tasks (back push-off test, stepping on the chair and kicking a soccer ball). Kinematic data were obtained using 2D video analysis system to track dominant leg joint angles. Two Trigno wireless IMU sensors were placed on the tibia of both legs to determine IT in the lower extremity. One Trigno wireless accelerometer was placed on the sacrum to determine IH and ACCH. Neuromuscular data from the dominant leg was obtained using seven ‘Trigno Wireless EMG sensors’, attached to the belly of Rectus Femoris (RF), Vastus Lateralis (VL), Bicep Femoris (BF), Semitendinosus (ST), Gastro-medial (GM), Gastro-lateral (GL) and Tibialis Anterior (TA) muscles in the dominant leg. A combinatorial data analysis approach for both tests was devised to study the kinematic changes in the lower extremity, neuromuscular fatigue (power ‘Pi’ and fatigue index ‘Fi’), cardiorespiratory stress, metabolic stress, time to exhaustion and perceived exertion. As the 1-min rest during the non-continuous test may change response to fatigue development in comparison to the continuous test, its effects on TTE, BLa and physiological markers of fatigue were investigated. The continuous test data is used to develop the real-time fatigue model as continuous running is most common among recreational active runners. The results of the study showed that the time to exhaustion (TTE) during non-continuous run was almost 1.6 times of the continuous run. It also showed a decrease in RER and lower V̇O2, lower BLa and lower RPEO at termination (End) during non-continuous run in comparison to the continuous run. Critical speed was found to be associated with Maximum Lactate Steady State (MLSS) during non-continuous running. However, continuous running showed a significant increase in BLa above MLSS. The influence of several physiological and kinematic fatigue predictors was examined using multilinear regression analysis. The analysis showed significant influence of oxygen cost (V̇O2) (p=0.0002), respiratory rate (RR) (p=0.0002), HR Recovery (HRR) (p<0.00001), HR Intensity (%HRmax) (p<0.0001), ankle angle (p<0.0001), Non-dominant Tibia Impact (INDT) (p=0.004) and IH (p=0.001) and ACCH (p=0.0004) on RPEO during non-continuous run. During continuous run, analysis showed significant influence of %HRmax (p<0.0001), RER (p<0.0001), knee angle (p=0.036), pronation angle (p<0.0001), INDT (p=0.045), IH (p=0.019) and ACCH (p=0.003) on RPEO. To determine the physiological and endurance difference between both tests, paired sample t-test with 95% confidence interval was Paired test between ‘End’ stage of continuous run in comparison with the non-continuous run showed a significant mean increase in ‘V̇O2’ of 1.315 ml.kg-1.min-1 (p=0.012), ‘RER’ of 0.0316 (p=0.002) and ‘BLa’ of 1.285 mmol.l-1 (p=0.001), and mean decrease in ‘TTE’ of 13.94 mins (p=0.00005). The neuromuscular fatigue indicated that ‘Pi’ of the GM (tb=-0.124, p=0.006), GL (tb=-0.206, p<0.0001), and TA (tb=-0.144, p=0.001) muscle and ‘Fi’ of RF (tb=-0.237, p<0.0001), VL (tb=0.160, p<0.0001), BF (tb=0.136, p=0.004), GM (tb=0.191, p<0.0001), GL (tb=0.096, p=0.028) and TA (tb=0.105, p=0.017) muscles were significantly linked with RPEM during the continuous run. Whereas during the non-continuous run, ‘Pi’ of the RF (tb=-0.359, p<0.0001), VL (tb=-0.116, p=0.013), BF (tb=0.379, p<0.0001), ST (tb=0.133, p=0.002), GM (tb=-0.09, p=0.039), GL (tb=-0.103, p=0.011), and TA (tb=-0.183, p<0.0001) muscles and ‘Fi’ of BF (tb=-0.113, p=0.028), ST (tb= 0.169, p<0.0001), GM (tb=0.245, p<0.0001), GL (tb= 0.155, p=0.005), and TA (tb=-0.143, p=0.001) muscles were significantly correlated with RPEM. Metabolic stress (BLa) showed significant correlation with RPEO (tb=0.583, p=0.0001) and %HRmax (tb=0.581, p=0.0001). The significant fatigue predictors (%HRmax, knee angle, pronation angle, INDT, IH, ACCH) from the continuous test were used to build real-time fatigue prediction model using multilinear regression. The model showed 88.26% variance (p<0.0001) in the observed subjective fatigue (RPEO). Whereas the normalized fatigue model showed 87.7% variance (p<0.0001) by using normalized significant fatigue predictors (%HRmax, pronation angle, ACCH). This research concluded that continuous and non-continuous tests showed a difference in fatigue manifestations on the biomechanical and physiological system. Neuromuscular activation (iEMG) levels were different in response to fatigue development during both tests. The observed ‘Pi’ of BF & GL and ‘Fi’ of BF were similar during both tests whereas other muscles showed a different response in relation with fatigue development. Time to Exhaustion (TTE) was higher during the non-continuous test and participants experienced lower metabolic and cardiorespiratory stress. %HRmax has been observed to predict metabolic stress during continuous running and could be used as a proxy for RPEO and BLa. The proposed real-time statistical model for predicting fatigue has shown the potential to estimate subjective indicator of fatigue (RPEO) for the selected participants under the laboratory controlled conditions. Further studies are needed to develop a robust model with larger population size, varied fitness levels and both genders under recreational, environmental factors and realistic endurance conditions.