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Background: The handstand is an essential skill in acrobatic sports. This skill requires the athlete to maintain an inverted upright stance with only the hands supported, which requires a great effort of muscular coordination and motor control. Several factors influence the ability to control the posture, including fatigue, which is a bit studied constraint of handstand performance. Research question: With the aim to find out whether variability in movement control can be an indicator of fatigue, the present study was carried out. Method: Fourteen male acrobatic gymnasts were required to perform handstands. The time series for analyzing variability were capturing using Force Platforms, which is a traditional laboratory instrument, and Inertial Measurement Units (IMU), which is a more recent and less widely used, but more accessible tool. For this purpose, an analysis of the amount of variability was carried out, using the standard deviation. And analysis of the structure of variability (or complexity), using Detrended Fluctuation Analysis (DFA) and Fuzzy Entropy (FuEn). Results: Our results reveal that fatigue causes significant increases in the amount of variability in the medio-lateral axis on the force platform, and in the IMU located in the area of the L5 vertebra. These changes are accompanied by increased auto-correlation in the medio-lateral axis of the force platform, and more unpredictable behavior in the L5 IMU. Key points: Amount of variability can discriminate between non-fatigued and fatigued states in force platform. Structure of variability can discriminate between non-fatigued and fatigued states in force platform. Movements are most affected in the medio-lateral axis. Forces produced to maintain balance exhibit smoother adjustments in fatigue state. Acceleration in the L5 tends to be less predictable in fatigue state. [ABSTRACT FROM AUTHOR]

Analysis of variability in physiological time series has been shown to be an indicator of the state of the organism. Although there is evidence of the usefulness of analysis of the amount and/or structure of variability (complexity) in cycling actions, there is limited knowledge about its application in resistance exercise. The aim of this study is to find out whether variability in acceleration signals can be an indicator of intensity level in a squat task. For this purpose, an experimental design was developed in which the following participated seventy-two participants (age = 25.7 ± 4.4 years; height = 169.2 ± 9.8 cm; body mass = 67.7 ± 11.2 kg; ratio 1RM/body mass = 1.4 ± 0.3). They performed four repetitions of back squat at loads of 10%, 30%, 50%, 70%, and 90% of 1RM. Acceleration during the exercise was recorded using an inertial measurement unit (IMU) and a force platform. The variability of the movement was then analyzed using Standard Deviation (SD), Detrended Fluctuation Analysis (DFA), Fuzzy Entropy (FuzzyEn), and Sample Entropy (SampEn). For the IMU and for the force platform, significant effects were observed in all variables (p < 0.001). In pairwise comparisons, IMU showed a significant increase in motor complexity with increasing intensity, among most intensities, in DFA, FuzzyEn and SampEn. Differences in force platform were more limited, and only DFA detected differences between most intensities. The results suggest that measures of signal and acceleration variability may be a useful indicator of the relative intensity at which a squat exercise is performed. [ABSTRACT FROM AUTHOR]

The aim of this experiment was to assess if the previously supported relationship between the structure of motor variability and performance changes when the task or organismic constraints encourage individuals to adjust their movement to achieve a goal. Forty-two healthy volunteers (aged 26.05 ± 5.02 years) performed three sets of cyclic pointing movements, 600 cycles each. Every set was performed under different conditions: 1) without a target; 2) with a target; 3) with a target and a financial reward. The amount of performance variability was analysed using the standard deviation of the medial-lateral (ML) and anterior-posterior (AP) axes and the bivariate variable error. The structure of the variability was assessed by Detrended Fluctuation Analysis (DFA) of the following time series: the coordinate values of the endpoint in ML, AP axes and resultant distance (RD), the hand orientation and the movement time. The performance of the task constrained with a target, or a target and reward, required higher implication to adjust an individual’s movements to achieve the task goal, showing a decrease in dispersions and lower autocorrelation. Under the condition without a target, variability dispersion was positively related to autocorrelation of the movement values from ML axis and RD time series, and negatively related to the values from the hand orientation time series. There was a loss of the relationship between variability structure and performance when the task was constrained by the target and the reward. That could indicate different strategies of the participants to achieve the objective. Considering the results and previous studies, the relationship between variability structure and performance could depend on task constraints such as feedback, difficulty or the skill level of participants and it is mediated by individual constraints such as implication or intentionality. [ABSTRACT FROM AUTHOR]