2.5 Factors that Influence Squat-Jump Performance
2.5.5 Body Mass Index
Body composition is crucial to physical sports performance (Acar and Eler, 2019). Body mass index (BMI), one of the parameters for body composition, is the global standard used for the classification between underweight, normal weight, overweight, and obese (World Health Organization, 1995). It is more expensive but easier to administer when compared with the percentage of body fat assessment method (Nikolaidis, 2012). BMI is correlated with reduced physical fitness. Comparison has been made between groups that differ in BMI and the results have shown that better performance in physical fitness tests was observed in the groups with lower or normal BMI than in the groups with higher BMI or overweight/obese (Mak et al., 2010; Artero et al., 2010). Nikolaidis et al. (2015; 2019) also reported that higher BMI was associated with reduced scores in the key parameters of sport-related physical fitness like jumping. The authors believed that BMI optimization has to be considered for sports performance improvement. Many other studies investigated the impact of overweight and obesity on health-related physical fitness. Studies discovered that the increase in BMI resulted in a reduction in fitness (Huang and Malina, 2007; Fogelholm et al., 2008). Compared to this type of study, the studies on the relationship between underweight and health-related fitness were lesser. But it has been proven by Artero et al. (2010) that not only overweight and obese but also underweight may be associated with health-related fitness.
sports injuries, which is 67 %.
Overtraining is the accumulation of training (Posabella, 2020), resulting in the loss of balance between training and recovery (Kibler, Chandler and Stracener, 1992). In view of the musculoskeletal system, it is the combination of stresses from physiologically, biomechanically, and anatomically, that ultimately cause an overload of forces on the musculotendinous unit (Kibler, Chandler and Stracener, 1992). One of the possible negative effects resulting from overtraining is the long-term decline in physical performance (Halson and Jeukendrup, 2004). Besides, the researchers also claimed that the time needed to recover from the overtraining syndrome will be much longer, which might need several months up to several years whereas the performance capacity might need weeks or months to recover.
Figure 2.3 has highlighted the cycle of overload injury vicious. It can be seen that the unhealed damage may decrease the performance. Therefore, the musculotendinous unit must be allowed to heal before a player can return to sports activities. Nonetheless, the study on the relationship between injury and sports performance is limited. Hence, no real statistic has reported how much muscle damage resulting from overtraining reduces sports performance.
Figure 2.3: The Overload Injury Vicious Cycle (Kibler, Chandler and Stracener, 1992).
2.6 Wearable System for the Functional Analysis of Movement According to Yazici et al. (2020), the typical laboratory-based assessment methods (e.g., 3-dimensional motion capture systems) used in the jump analysis are accurate but accompanied by some limitations. They are costly and require a longer time for setup and analysis as well as more technical expertise and equipment. In contrast, wireless inertial sensors have become a good replacement for the assessment of jump performance since they are lightweight, easy to use, cost-effective, and allow unrestricted movement. Nevertheless, WIS is accompanied by several drawbacks. According to Vítečková et al.
(2020), the signals are likely to be influenced by noise and other uncontrollable external factors. Besides, it has a great power consumption which may cause a limited duration of usage. Not only that, it may cause subjects to feel uncomfortable when devices are placed on their bodies (Muro-de-la-Herran, García-Zapirain and Méndez-Zorrilla, 2014).
BTS Bioengineering has introduced an innovative motion analysis system known as the BTS G-Walk® sensor as shown in Figure 2.4. It is a wearable and portable device held in a specialized belt that should be worn in the lumbosacral region as shown in Figure 2.5. It is composed of a 3-axis 16 bits accelerometer, a 3-axis 13 bits magnetometer, and a 3-axis 16 bits gyro (D’Addio et al., 2019). The new approach introduced by the BTS G-Walk® sensor allows the clinical test (e.g., running and jumping) to be carried out
various analysis protocols) running on a computer through a Bluetooth connection (D’Addio et al., 2019).
Figure 2.4: BTS G-Walk® sensor (BTS Bioengineering, 2019).
Figure 2.5: BTS G-Walk® sensor Positioning on Spinal L5 (Falso et al., 2017).
According to BTS Bioengineering (2019), SJ is one of the jump tests included in the BTS G-Walk® sensor. It allows sports performance monitoring as it can examine the kinetic and kinematic asymmetrical movement as well as features useful global indexes (e.g., the Bosco index, coordination, elasticity, and endurance). In 2020, Yazici et al. have concluded that the BTS G-Walk® sensor was reliable in the assessment of jump performance in healthy adults since the measurements of the jump parameters had shown excellent test-retest reliability.
study was commenced with subject recruitment and selection. Based on the literature review in Chapter 2, the methodology was designed to answer the problem as well as achieve the aim and objectives defined in Chapter 1. To examine the acceptability and feasibility of the experimental design, pre-tests and a pilot study as reported in Chapter 4 were conducted. With the manipulation of the initial knee flexion angle, quantitative data (i.e., flight time, peak speed, propulsive peak force, maximum concentric power, and flight height) were collected from 15 healthy subjects by using the BTS G-Walk® sensor. The gender, age, and BMI of the subjects were controlled, and subjects were limited to wearing sports shoes and sports clothing during the study.
Statistical analysis was run using IBM Statistic® SPSS (version 25.0. Armonk, NY: IBM Corp) and the results are reported in Chapter 5.
Figure 3.1: Methodological Flow Chart.
3.2 Experimental Design