Fatigue in patients with acquired brain damage

Does mental fatigue affect performance in racket sports? A systematic review

Mental fatigue impairs performance across several sports domains. However, a systematic review on its effects on racket sports performance has been lacking due to the previous scarcity of studies. This review aims to provide a comprehensive review the effects of mental fatigue on racket players' performance, with a discussion of the underlying mechanisms. A thorough search was conducted across five databases, including Web of Science, PubMed, SCOPUS, SPORTDiscus (via EBSCOhost), and the Psychological and Behavioral Science Collection (via EBSCOhost). The PICOS framework established the inclusion criteria: (1) healthy racket sports players; (2) induction of mental fatigue in both field and laboratory settings; (3) comparison of mental fatigue interventions with a control group (e.g., watching a movie or reading a magazine); (4) assessment of performance outcomes, including physical performance, skilled performance, and perceptual-cognitive performance; and (5) randomized controlled trials (RCTs), non-randomized controlled trials (non-RCTs), and non-randomized non-controlled trials. Mental fatigue manipulation, subjective evaluation, and (neuro)physiological markers were synthesized to support the successful induction of mental fatigue. Performance was categorized into tennis, table tennis, badminton, and padel based on the characteristics of specific racket sports domains. Secondary outcomes, such as the rate perception of effort (RPE) and motivation, were synthesized to explain the mechanisms based on the prominent theory of the Psychobiological model of endurance performance. Six studies revealed that mental fatigue impacts stroke performance in table tennis, affecting speed, accuracy, faults, and only second-serve accuracy in tennis. The response time of psychomotor performance increased in table tennis, padel, and badminton. Meanwhile, mental fatigue increased the RPE and remained unchanged in heart rate, blood glucose, and lactate, consistent with the Psychobiological model of endurance performance. Additionally, attention is suggested as a significant underlying psychobiological factor.

Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.