The Effects of Verbal Encouragement during a Soccer Dribbling Circuit on Physical and Psychophysiological Responses: An Exploratory Study in a Physical Education Setting

none10Bilel Aydi; Okba Selmi; Mohamed A. Souissi; Hajer Sahli; Ghazi Rekik; Zachary J. Crowley-McHattan; Jeffrey Cayaban Pagaduan; Antonella Muscella; Makram Zghibi; Yung-Sheng ChenAydi, Bilel; Selmi, Okba; Souissi, Mohamed A.; Sahli, Hajer; Rekik, Ghazi; Crowley-McHattan, Zachary J.; Cayaban Pagaduan, Jeffrey; Muscella, Antonella; Zghibi, Makram; Chen, Yung-Shen

Moving Beyond the Stigma: Understanding and Overcoming the Resistance to the Acceptance and Adoption of Artificial Intelligence Chatbots

Artificial intelligence chatbots may fundamentally transform academic research, automate mundane tasks, and enhance productivity. However, the integration of artificial intelligence chatbots (AIc) is impeded by a complex stigma deeply rooted in individuals’ misconceptions and apprehension, including concerns about academic integrity, job displacement, data privacy, and algorithmic bias. The aim of this study was to scrutinize the origins and impacts of the stigma associated with artificial intelligence chatbots within the realm of academic research and to propose strategies to mitigate such stigmas. This study draws parallels between the reception of artificial intelligence chatbots and previous transformative technologies, presenting case studies illustrating the spectrum of responses to the integration of artificial intelligence chatbots into academic research. This study identifies the need for a shift in mindset from perceiving artificial intelligence chatbots as threats to recognizing them as facilitators of efficiency and innovation. It also underscores the importance of understanding these models as tools that aid researchers but do not replace the need for human expertise and judgment. We further highlighted the role of education, transparency, regulation, and ethical guidelines in overcoming the stigma associated with artificial intelligence chatbots. Given how adaptable people are, the surrounding stigma will likely fade with time. We support a cooperative strategy with continuing education and discussion to maximize the benefits of artificial intelligence chatbots while minimizing their drawbacks, hopefully paving the way for their ethical and successful application in scholarly research

Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI’s Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model’s ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model’s potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback

Effets directs de la verbalisation sur les stratégies d'action et les prises de décisions des élèves lors d'un cycle de football

Dans une approche socio-constructiviste où l'élève est mis au centre du système enseignement/apprentissage, le sujet est appelé à construire ses propres stratégies d'action. Recentrer l'attention sur le processus de verbalisation revient à intégrer de nouveaux paramètres faisant de l'investigation des productions langagières du point de vue de l'élève une condition sine qua non pour révéler les processus cognitifs sousjacents. Concernant les sports collectifs à l'école, les leçons d'EPS, ne consistent pas à enseigner aux élèves des gestes techniques à exécuter et des schémas tactiques à reproduire, mais à leur permettre aussi de s'engager dans un processus de pratique réflexive basée sur des relations, des principes et des règles en rapport avec une intentionnalité. Dans cet article, on s'intéresse aux interlocutions discursives des élèves à propos du jeu : il s'agit de décrire les processus impliqués pendant que l'attitude réflective est développée. L'objectif est d'accéder à la manière (ou aux manières) dont ils interprètent le jeu d'opposition. A partir de deux séries de paramètres, il s'agira de vérifier dans quelle mesure la verbalisation peut faire progresser les joueurs au cours d'un cycle de footbal

Testing the Psychometric Properties of an Arabic Version of the Brunel Mood Scale among Physical Education Students

In our study, we translated and tested the psychometric properties of an Arabic version of the Brunel Mood Scale (BRUMS), referred to as the Arabic Mood Scale (ARAMS), among physical education university students. A total of 681 participants completed the ARAMS in exploratory and confirmatory phases. Exploratory analyses were conducted on data from 253 students between the ages of 19 and 25 years (M = 21.14 ± 1.65 years) of whom 132 were women (52.2%) and 121 were men (47.8%). Confirmatory analyses were conducted on data from 428 students between the ages of 19 and 25 years (M = 20.93 ± 1.55 years) of whom 203 were women (52.6%) and 225 were men (47.4%). The measurement model of the ARAMS was initially evaluated using exploratory factor analysis (EFA) and was subsequently tested via confirmatory factor analysis (CFA). EFA identified a 24-item, 6-factor structure that aligned with the original BRUMS measurement model, and CFA demonstrated congruence between the two models. Internal consistency of the six subscales exceeded adequacy levels with good Cronbach’s alpha and McDonald’s Omega values respectively for anger (0.811; 0.812), confusion (0.830; 0.830), depression (0.858; 0.859), fatigue (0.823; 0.825), and tension (0.824; 0.825), and an acceptable value for vigor (0.749; 0.748). Findings support the factorial validity and internal consistency of the ARAMS, which appears to be a suitable measure for use in Arabic physical education contexts. Further validation studies are required before the ARAMS is used in other Arabic-language contexts

Small-sided-games with coaches’ verbal encouragement have a positive effect on aerobic performance, mood state, satisfaction and subjective effort in male semi-professional soccer players

The study aimed to examine the impact of high-intensity Small-Sided Games (SSGs) with coaches’ verbal encouragement (VE) on soccer players’ aerobic performance, mood state, satisfaction and subjective effort. Forty-three semi-professional male soccer players were randomly assigned to three distinct groups: a control group (CG, n = 14), an experimental group with verbal encouragement (EGVE, n = 14), and an experimental group without verbal encouragement (EGNE, n = 15). Participants performed the VAMEVAL aerobic test, Total-Mood-Disorder (TMD), and the Satisfaction Scale for Athletes (SSA) tests before and following the 6-week SGGs program that included ten training sessions. Rating of Perceived Exertion (RPE) was collected 5-minutes post-training session. The SGGs program with coaches’ VE showed a significant improvement in maximal aerobic velocity (MAV) and TMD scores (p < 0.05). Except for the SSA scores (p = 0.268), the percentage of change was higher for MAV (p = 0.001; d: 1.36–1.48 (large)) and TMD scores (p = 0.001; d: 1.45–1.48 (large)) in the EGVE group when compared with the other groups (i.e., EGNE and CG). Overall, RPE scores were significantly higher (p ˂ 0.05; d: 0.99–5.00 (large)) in the EGVE group than other groups. The present study highlights the positive effects of integrating SSGs with coaches VE to improve aerobic performance and mental well-being of semi-professional soccer players. Nevertheless, notably the SSA did not exhibit a statistically significant difference. Furthermore, the experimental EGVE group reported elevated RPE, potentially suggesting that SSGs may entail greater physical and mental challenges, yet may yield more sport-specific outcomes for soccer players

Using artificial intelligence for exercise prescription in personalised health promotion:A critical evaluation of OpenAI’s GPT-4 model

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI’s Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model’s ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model’s potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.</p