Tremor, finger and hand dexterity and force steadiness, do not change after mental fatigue in healthy humans

Does the Effect of Mental Fatigue Created by Motor Imagery on Upper Extremity Functions Change with Diaphragmatic Breathing Exercises? A Randomized, Controlled, Single-Blinded Trial

Background and Objectives: This study focused on the impact of mental fatigue induced by motor imagery on upper limb function, an area with limited research compared to lower limb performance. It aimed to explore how diaphragmatic breathing exercises influence these effects. Materials and Methods: This study included 30 participants, and Group 1 participated in 12 sessions of diaphragmatic breathing exercises under the supervision of a physiotherapist; Group 2 did not receive any intervention. For all the participants, mental fatigue was induced with motor imagery before and after the intervention, and evaluations were performed before and after mental fatigue. Upper extremity functions were evaluated using isometric elbow flexion strength, hand grip strength, upper extremity reaction time and endurance, finger reaction time, the nine-hole peg test, shoulder position sense, light touch-pressure threshold, and two-point discrimination. Results: The study results showed that after mental fatigue, there was a decrease in isometric elbow flexion strength, nondominant hand grip strength, and nondominant upper extremity endurance, and an increase in nondominant tactile sensation (p < 0.05). No changes were found in two-point discrimination, nine-hole peg test time, and position sense on either side (p > 0.05). The effect of mental fatigue on isometric elbow flexion strength and nondominant grip strength showed significant improvement following diaphragmatic breathing exercises (p < 0.05). Conclusions: This study found that mental fatigue from motor imagery can impact elbow flexion, hand grip strength, upper extremity endurance, and tactile sensitivity. Breathing exercises may help improve strength parameters affected by mental fatigue. It is crucial to consider these effects on upper extremity functions in rehabilitation programs.

Effect of mental fatigue on hand force production capacities

Mental fatigue is common in society, but its effects on force production capacities remain unclear. This study aimed to investigate the impact of mental fatigue on maximal force production, rate of force development-scaling factor (RFD-SF), and force steadiness during handgrip contractions. Fourteen participants performed two randomized sessions, during which they either carried out a cognitively demanding task (i.e., a visual attention task) or a cognitively nondemanding task (i.e., documentary watching for 62 min). The mental fatigue was evaluated subjectively and objectively (performances and electroencephalography). Maximal voluntary contraction (MVC) force, RFD-SF, and force steadiness (i.e., force coefficient of variation at submaximal intensities; 25, 50, and 75% of MVC) were recorded before and after both tasks. The feeling of mental fatigue was much higher after completing the cognitively demanding task than after documentary watching (p < .001). During the cognitively demanding task, mental fatigue was evidenced by increased errors, missed trials, and decreased N100 amplitude over time. While no effect was reported on force steadiness, both tasks induced a decrease in MVC (p = .040), a force RFD-SF lower slope (p = .011), and a reduction in the coefficient of determination (p = .011). Nevertheless, these effects were not explicitly linked to mental fatigue since they appeared both after the mentally fatiguing task and after watching the documentary. The study highlights the importance of considering cognitive engagement and mental load when optimizing motor performance to mitigate adverse effects and improve force production capacities.

Neuromuscular behaviour in the first dorsal interosseus following mental fatigue

We examined sex-specific changes to neuromuscular function in response to mental fatigue. Twenty-five young, healthy adults (13 F, 12 M) performed a mentally fatiguing task and control condition for 30 min on two separate days. Neuromuscular function was assessed in the first dorsal interosseous before and after each condition. Reaction time decreased after the mentally fatiguing task (P < 0.001, η2  = 0.47). Males and females reported higher levels of subjective fatigue after the mentally fatiguing task (P < 0.02, η2  = 0.07). Motor unit firing rate increased over time at 10% maximal voluntary contraction (MVC; P < 0.04, η2  = 0.16), and decreased over time at 50% MVC (P < 0.01, η2  = 0.14); however, this was not unique to either sex. During a variable force contraction, error decreased in females over time and increased in males (P < 0.05, η2  = 0.13), although changes were not unique to mental fatigue. Physiological function of the neuromuscular system was not specifically affected by mental fatigue in males or females.

Transforming fatigue assessment: Smartphone-based system with digitized motor skill tests

BACKGROUND

The condition of fatigue is a complex and multifaceted disorder that encompasses physical, mental, and psychological dimensions, all of which contribute to a decreased quality of life. Smartphone-based systems are gaining significant research interest due to their potential to provide noninvasive monitoring and diagnosis of diseases.

OBJECTIVE

This paper studies the feasibility of using smartphones to collect motor skill related data for machine learning based fatigue detection. The authors' main goal is to provide valuable insights into the nature of fatigue and support the development of more effective interventions to manage it.

METHODS

An application for smartphones running on Android OS is developed. Two aim-based reaction tests, an Archimedean spiral test, and a tremor test, were assembled. 41 subjects participated in the study. The resulting dataset consists of 131 trials of fatigue assessment alongside digital signals extracted from the motor skill tests. Six machine learning classifiers were trained on computed features extracted from the collected digital signals.

RESULTS

The collected dataset SmartPhoneFatigue is presented for further research. The real-world utility of this database was shown by creating a methodology to construct a fatigue predictive model. Our approach incorporated 60 distinct features, such as kinematic, angular, aim-based, and tremor-related measures. The machine learning models exhibited a high degree of prediction rate for fatigue state, with an accuracy exceeding 70%, sensitivity surpassing 90%, and an f1-score greater than 80%.

CONCLUSION

The results demonstrate that the proposed smartphone-based system is suitable for motion data acquisition in non-controlled environments and shows promise as a more objective and convenient method for measuring fatigue.

Strain Gauge Measuring System for Subsensory Micromotions Analysis as an Element of a Hybrid Human-Machine Interface

The human central nervous system is the integrative basis for the functioning of the organism. The basis of such integration is provided by the fact that the same neurons are involved in various sets of sensory, cognitive, and motor functions. Therefore, the analysis of one set of integrative system components makes it possible to draw conclusions about the state and efficiency of the other components. Thus, to evaluate a person's cognitive properties, we can assess their involuntary motor acts, i.e., a person's subsensory reactions. To measure the parameters of involuntary motor acts, we have developed a strain gauge measuring system. This system provides measurement and estimation of the parameters of involuntary movements against the background of voluntary isometric efforts. The article presents the architecture of the system and shows the organization of the primary signal processing in analog form, in particular the separation of the signal taken from the strain-gauge sensor into frequency and smoothly varying components by averaging and subtracting the analog signals. This transfer to analog form simplifies the implementation of the digital part of the measuring system and allowed for minimizing the response time of the system while displaying the isometric forces in the visual feedback channel. The article describes the realization of the system elements and shows the results of its experimental research.

A plausible link between the time-on-task effect and the sequential task effect

Mental fatigue can be studied by using either the time-on-task protocol or the sequential task protocol. In the time-on-task protocol, participants perform a long and effortful task and a decrease in performance in this task is generally observed over time. In the sequential task protocol, a first effortful or control task is followed by a second effortful task. The performance in the second task is generally worse after the effortful task than after the control task. The principal aim of the present experiment is to examine the relationship between these two decrements in performance while concomitantly using a sequential task protocol and assessing the performance of the first effortful task as a function of time-on-task. We expect a positive correlation between these two decrements in performance. A total of 83 participants performed a 30-min fatiguing mental task (i.e., a modified Stroop task) or a control task followed by a time-to-exhaustion handgrip task. As expected, this protocol combining the time-on-task and sequential task protocols allowed us to observe (1) a decrease in performance over time during the Stroop task, (2) a worst performance in the handgrip task after the Stroop task by comparison to the control task, (3) a positive correlation between these two effects. The decrease in performance during the Stroop task also correlated with the subjective measures of boredom and fatigue, whereas the detrimental effect observed in the handgrip task did not. Our findings suggest that the two fatigue-related phenomena share a common mechanism but are not completely equivalent.

Mental calculation increases physiological postural tremor, but does not influence physiological goal-directed kinetic tremor

Purpose

During a cognitive effort, an increase in cortical electrical activity, functional alterations in the anterior cingulate cortex, and modifications in cortical inputs to the active motor units have been reported. In light of this, an increase in tremor could be anticipated as result of a mental task. In the present work, we tested this hypothesis.

Methods

In 25 individuals, tremor was measured with a three-axial accelerometer during 300 s of postural and goal-directed tasks performed simultaneously to mental calculation, or during control (same tasks without mental calculation). Hand and finger dexterity were also evaluated. Electromyographic (EMG) recordings from the extensor digitorum communis were collected during the postural task.

Results

Hand and finger dexterity was negatively affected by the mental task (p = .003 and p = .00005 respectively). During mental calculation, muscle tremor increased in the hand postural (+ 29%, p = .00005) but not in the goal-directed task (− 1.5%, p > .05). The amplitude of the main frequency peak also increased exclusively in the hand postural task (p = .028), whilst no shift in the position of the main frequency peak was observed. EMG was not affected.

Conclusion

These results support the position of the contribution of a central component in the origin of physiological hand postural tremor. It is suggested that the different effect of mental calculation on hand postural and goal-directed tasks can be attributed to the different origins and characteristics of hand postural and goal-directed physiological tremor.