How Artificial Intelligence Influences Psychological Well-Being and Physical Performance in Sports

Summary

A study of 385 Chinese athletes found that the use of artificial intelligence (AI) and self-efficacy positively influence psychological well-being, which, in turn, enhances perceived sport performance. Psychological well-being also mediates the relationships between AI/self-efficacy and performance. Findings suggest integrating AI tools with strategies to boost self-efficacy and well-being.

Abstract

This study examined how AI influences psychological well-being and perceived sport performance among athletes in China. Specifically, it aimed to investigate the direct effect of artificial intelligence use on psychological well-being, the effect of athlete self-efficacy on psychological well-being, the effect of psychological well-being on perceived sport performance, and the mediating role of psychological well-being in these relationships. A quantitative research approach was adopted using a cross-sectional survey design. Data were collected from 385 competitive athletes in China using a structured questionnaire, and analyzed using the Statistical Package for the Social Sciences (SPSS) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicated that artificial intelligence use and athlete self-efficacy both positively influenced psychological well-being, while psychological well-being positively affected perceived sport performance. In addition, psychological well-being significantly mediated the relationships between artificial intelligence use and perceived sport performance, and between athlete self-efficacy and perceived sport performance. These findings implied that both technological and psychological resources were important for enhancing athlete outcomes. The study suggested that sport organizations, coaches, and policymakers should integrate AI-supported systems with strategies that strengthen athletes’ self-efficacy and psychological well-being. Overall, the study concluded that artificial intelligence contributes to sport performance not only through technical support but also through its positive influence on athletes’ psychological functioning.

Introduction

Artificial intelligence is becoming a major force in contemporary sport, enabling coaches, analysts, and athletes to process large volumes of data for performance monitoring, injury prevention, tactical planning, and personalized training adaptation¹. Recent reviews confirm that AI and machine learning are increasingly applied to biomechanical monitoring, workload analysis, rehabilitation, motion tracking, and training optimization, signaling a decisive shift toward data-driven, technology-supported systems of preparation and competition². Yet, as these technologies become embedded in athletes’ daily routines, scholars have cautioned that their influence extends well beyond technical and biomedical domains: AI tools also shape how athletes interpret feedback, evaluate their own progress, and regulate confidence and motivation in training and competition³. In China, this issue carries urgency. National sport authorities have reported that AI-assisted training systems providing real-time technical feedback are already deployed among elite teams, making it essential to understand not only the performance gains but also the psychological consequences of such integration⁴.

While the applied benefits of AI in sport are increasingly documented, a growing body of research has begun to probe its psychological implications, revealing a more nuanced picture than the early literature suggested⁵. On the one hand, studies have identified negative repercussions, including feedback anxiety arising from constant performance monitoring, algorithmic dependency that can undermine autonomous decision-making, and heightened pressure when data-driven social comparisons become pervasive⁶. On the other hand, researchers have pointed to potential psychological benefits: when AI feedback is perceived as supportive, it can reduce performance uncertainty, enhance athletes' sense of control over their development, and strengthen self-regulatory capacity⁷. This emerging debate demonstrates that the psychological impact of AI is not monolithic; rather, it depends on how athletes appraise and integrate technological inputs into their self-concept and daily practice⁸. Critically, existing work remains fragmented. Studies typically isolate single psychological outcomes such as anxiety, motivation, or burnout without examining how these outcomes interconnect, and very few have positioned psychological well-being as a central explanatory mechanism linking AI use to sport performance.

The present study addresses this gap by focusing on psychological well-being (PWB) as a pivotal mediating mechanism. Drawing on the eudaimonic tradition, PWB encompasses six dimensions: environmental mastery, personal growth, purpose in life, self-acceptance, autonomy, and positive relations with others that are particularly relevant in sport⁹, where athletes must manage intense expectations, cope with uncertainty, and maintain balanced functioning while pursuing competitive excellence¹⁰. Recent evidence confirms that athlete well-being is not merely a desirable by-product but a critical foundation for sustainable performance, adaptive coping, and long-term athletic development¹¹. Importantly, PWB may serve as the psychological conduit through which both technological resources and personal competencies translate into better performance evaluations: AI can foster well-being by satisfying basic needs for competence, autonomy, and relatedness, while self-efficacy can sustain well-being by promoting confidence and resilience^12,13.

The growing integration of AI into sport settings through intelligent wearables, motion tracking, and personalized performance analytics has the potential to reshape not only training efficiency but also athletes' psychological experiences¹⁴. While much of the existing discourse frames AI as a technical tool for injury prevention or tactical decision-making, a more theoretically grounded view suggests that AI-driven sport environments can act as a meaningful psychological resource¹⁵. Drawing on self-determination theory, AI systems can support the fulfillment of basic psychological needs, autonomy, competence, and relatedness that are widely established as precursors to eudaimonic well-being¹⁶. When athletes receive individualized, data-informed feedback that clarifies their progress and reduces performance ambiguity, their sense of competence is strengthened; when AI-powered platforms offer tailored training options and adaptable schedules, autonomy is enhanced; and when shared dashboards or collaborative analytics facilitate constructive coach-athlete communication, the need for relatedness is nurtured¹⁷. Through these pathways, AI use can positively influence the core components of psychological well-being (PWB)¹⁷, environmental mastery (by increasing predictability and control over one's training environment), personal growth (by visualising incremental improvements over time), purpose in life (by aligning daily training with long-term athletic goals), self-acceptance (by normalising performance fluctuations through objective data), and positive relations (by fostering supportive interpersonal exchanges)¹⁸. Therefore, the present study hypothesizes: H1: Artificial intelligence use positively affects psychological well-being.

Athlete self-efficacy refers to an individual’s belief in their ability to successfully perform tasks, overcome challenges, and achieve desired results in sports contexts¹⁹. According to self-efficacy theory, individuals who have greater confidence in their abilities are more likely to persist in difficult situations, regulate their effort, and respond constructively to setbacks²⁰. In sport environments, self-efficacy is especially important because athletes regularly face competitive pressure, physical demands, and performance uncertainty^21,22. In this study, athlete self-efficacy is represented through sport discipline efficacy, psychological efficacy, professional thought efficacy, and personality efficacy, reflecting the multidimensional nature of confidence in athletic functioning²³. Athletes with high self-efficacy are generally better able to cope with stress, maintain motivation, and preserve emotional balance under pressure²⁴. Such athletes are more likely to view challenges as manageable rather than threatening, which can improve their psychological functioning and overall sense of well-being^25,26, Since psychological well-being includes elements such as self-acceptance²⁷, autonomy, and purpose in life, self-efficacy can be understood as an important internal resource that supports these positive psychological states²⁸. In demanding sport environments, stronger self-efficacy may therefore help athletes maintain a healthier, more adaptive psychological state. H2: Athlete self-efficacy positively affects psychological well-being.

Psychological well-being (PWB) is a multidimensional construct reflecting positive functioning, personal growth, meaningful engagement, and healthy adjustment in life²⁹. Following Ryff’s eudaimonic framework, it encompasses six dimensions: environmental mastery (the capacity to manage one’s surroundings effectively), personal growth (a sense of ongoing development), purpose in life (holding meaningful goals), self-acceptance (a positive attitude toward oneself), autonomy (self-determination and independence), and positive relations with others (warm, trusting interpersonal connections)³⁰. In the sport domain, each dimension carries distinct relevance for how athletes evaluate their own performance³¹. Environmental mastery equips athletes to control training demands and competitive stressors; personal growth fuels sustained effort and skill refinement; purpose in life anchors commitment to long-term athletic aspirations; self-acceptance buffers against self-criticism after setbacks; autonomy supports intrinsic motivation and decision-making under pressure³². Thus, PWB is not merely the absence of distress but a positive psychological foundation that enables athletes to remain focused, resilient, and motivated during training and competition³³. When athletes feel purposeful, self-confident, and emotionally balanced, they are better able to handle adversity, sustain concentration, and appraise their own performance favorably, making PWB a powerful predictor of perceived sport performance³⁴. In line with these considerations, the present study hypothesizes: H3: Psychological well-being positively affects perceived sport performance.

The relationship between artificial intelligence use and sport performance may not be fully direct, because the benefits of AI are often realized through athletes’ internal psychological responses³⁵. AI can provide performance-related information, training recommendations, and real-time feedback, but the actual influence of these technological inputs depends on how athletes interpret and utilize them³⁶. If AI use increases athletes’ feelings of control, competence, and growth, it may first improve their psychological well-being before influencing performance outcomes³⁷. This suggests that psychological well-being may explain the process through which AI use contributes to better performance-related perceptions in sports. In other words, athletes who regularly use AI-supported tools may feel more prepared, more confident in their training, and more satisfied with their progress, which can strengthen their psychological well-being³⁸. Once athletes experience higher well-being, they are likely to become more focused, emotionally stable, and motivated, and these qualities can positively shape how they perceive their sport performance³⁹. Thus, psychological well-being may function as an explanatory mechanism linking technological engagement with sport-related outcomes. Based on this reasoning, the present study proposes that the impact of artificial intelligence use on perceived sport performance is transmitted through psychological well-being. H4: Psychological well-being mediates the relationship between AI use and perceived sport performance.

Athlete self-efficacy is widely recognized as an important predictor of positive outcomes in sports because it influences persistence, confidence, and adaptive coping. However, the effect of self-efficacy on performance may also occur indirectly through psychological well-being. Athletes who strongly believe in their capabilities are more likely to feel autonomous, purposeful, and accepting of themselves, which are central elements of psychological well-being⁴⁰. In this sense, self-efficacy not only affects action and effort directly; it may also contribute to a healthier psychological state that supports long-term functioning and performance. When athletes possess high self-efficacy, they are generally better equipped to handle setbacks, manage pressure, and remain committed to their goals. These experiences can strengthen their psychological well-being, which in turn can improve their perceptions of sport performance. Therefore, psychological well-being may serve as the mechanism through which confidence in one’s athletic ability is translated into stronger performance-related outcomes⁴¹. Rather than assuming that self-efficacy influences performance only directly, this study argues that athletes perform better when their self-belief enhances their broader psychological functioning. H5: Psychological well-being mediates the relationship between athlete self-efficacy and perceived sport performance.

Protocol

Ethical considerations
The study was exempt from formal ethical review under the institution's policy as it met the criteria for minimal-risk, anonymous survey research. All procedures were conducted in strict accordance with the ethical standards of the Declaration of Helsinki. Prior to data collection, informed consent was obtained from all adult participants. For participants under 18 years of age, written informed consent was obtained from their parents or legal guardians, alongside written assent obtained from the minors themselves. All respondents were explicitly informed of the research purpose, their voluntary participation, their right to withdraw at any time without consequence, and the strict confidentiality measures applied to their data. No personally identifiable information was collected or reported, and all data were used exclusively for aggregated academic analysis. Particular attention was given to non-intrusive, respectful wording to ensure that the survey did not interfere with the athletes' training commitments or psychological well-being. For more details, please see the Table of Materials.

Research design and study context
This study employed a quantitative research approach using a cross-sectional survey design to examine the proposed relationships among artificial intelligence use, athlete self-efficacy, psychological well-being, and perceived sport performance among athletes in China. A quantitative design was considered appropriate because the study aimed to test theoretically derived hypotheses, examine the strength and direction of relationships among clearly specified constructs, and assess both direct and mediating effects through statistical procedures. The cross-sectional approach was selected because data on all study variables were collected from respondents at a single point in time, allowing the researcher to capture athletes’ current perceptions of AI use in sport, their efficacy beliefs, psychological well-being, and perceived performance outcomes. This design was widely used in social and behavioral sciences, particularly when the objective was to identify patterns of association among latent constructs and to test a theoretically grounded structural model in a natural setting. The study was conducted in China, which provided a highly relevant empirical setting for this research because the country had experienced rapid digitalization across multiple sectors, including sport, fitness, and athlete development. In recent years, Chinese sport institutions, universities, professional training centers, and athletic organizations have increasingly integrated AI-based systems into coaching, performance monitoring, technical evaluation, and injury-prevention practices. This expanding use of smart sport technologies created a suitable environment for examining whether exposure to AI-supported sport systems contributed to athletes’ psychological states and performance perceptions. Moreover, the highly competitive nature of the Chinese sport environment made it particularly important to understand how technological resources and psychological capabilities jointly influenced athlete functioning. Therefore, the Chinese context offered both practical relevance and theoretical value for investigating the proposed model.

Population and sample
The target population of the study consisted of competitive athletes in China, including those affiliated with universities, sports academies, professional clubs, provincial training centers, and other organized sports institutions, who were actively involved in regular training and competitive activities. The study focused on athletes because they were the most appropriate respondents for evaluating the role of artificial intelligence in sport-related contexts and for reporting on their self-efficacy, psychological well-being, and perceived performance. To ensure respondents had sufficient familiarity with the subject matter, only individuals currently engaged in organized sport and with some level of exposure to technology-supported sport environments were included in the survey. A sample size of 385 respondents was used for the study. This sample size was considered adequate for a quantitative study involving multiple latent variables, multidimensional constructs, and mediation analysis, particularly when using PLS-SEM, which required sufficient cases for stable parameter estimation and predictive assessment.

The respondents were selected through a purposive sampling technique, which enabled the researcher to target individuals who met specific inclusion criteria relevant to the study's objectives. Purposive sampling was particularly suitable because the research required data from athletes rather than from the general population. In practical terms, participants were approached through sports institutions, university athletic departments, clubs, training programs, and athlete networks. The sample was expected to represent a range of sports disciplines, competitive levels, and athlete backgrounds to broaden the findings and provide a more meaningful picture of how AI-related sports practices were experienced across different athletic contexts in China.

Measurement of variables
The study used a structured questionnaire to measure all variables, and all items were adapted from previously established scales reported in the literature. The questionnaire employed a Likert-type response format, which is widely accepted for measuring perceptions, attitudes, beliefs, and self-reported experiences in quantitative research. The independent variable, AI use, was measured using eight items adapted from a study⁴², and reworded to fit the sport context. Specifically, the items prompted athletes to indicate the extent to which they actively engaged with AI-supported sport technologies during training and competition. To ground the construct in concrete practice, the questionnaire explicitly listed examples of AI applications that athletes in the Chinese sport system commonly encounter, including wearable biosensors (e.g., smartwatches, heart-rate variability monitors, GPS-enabled performance trackers), AI-driven video analysis and motion-capture systems that provide automated technical feedback, personalised training recommendation platforms that adapt workloads based on real-time data, and predictive analytics dashboards used for injury risk alerts and recovery monitoring. The items thus captured athletes’ self-reported frequency of use and perceived integration of these intelligent tools into their daily sporting routines, rather than merely general attitudes toward technology. This operationalization allows the construct to reflect the breadth of AI exposure while acknowledging that athletes were not required to distinguish between different algorithmic subtypes; instead, the scale measured a holistic level of engagement with AI-enabled sport environments. It should be noted that, due to the heterogeneity of AI tools across sports and institutions, the construct treats AI use as a broad latent variable, a limitation discussed later.

The second independent variable, athlete self-efficacy, was conceptualized as a multidimensional construct measured through four dimensions: sport discipline efficacy, psychological efficacy, professional thought efficacy, and personality efficacy, each with four items. This multidimensional treatment captures the theoretical breadth of self-efficacy in competitive sport, where confidence extends beyond physical execution to include belief in one's discipline and adherence to training regimens (sport discipline efficacy), mental readiness and emotional regulation under pressure (psychological efficacy), tactical understanding and decision-making capacity (professional thought efficacy), and resilient personal characteristics such as perseverance and adaptability (personality efficacy). These four dimensions are grounded in the Athlete Self-Efficacy Scale developed and validated by Koçak⁴³, where they were shown to collectively reflect an overarching sense of athletic capability. The mediating variable, psychological well-being, was measured using an adapted version of the Psychological Well-Being Scale for Children (PWB-c) by a group⁴⁴, which includes six dimensions: environmental mastery (7 items), personal growth (5 items), purpose in life (5 items), self-acceptance (5 items), autonomy (5 items), and positive relations (10 items). Although originally developed for younger populations, the PWB-c items are formulated with straightforward language grounded in Ryff’s eudaimonic framework, making the content conceptually appropriate for adults. For the present study, items were carefully reviewed and reworded where necessary, for example, replacing school-related references with training or sport contexts to ensure suitability for competitive adult athletes without altering the underlying constructs. The dependent variable, perceived sport performance, was adapted from another study⁴⁵, and comprised three dimensions: self-oriented performance (4 items), socially prescribed performance (4 items), and other-oriented performance (4 items). Because several constructs in the model were multidimensional, they were treated as higher-order latent constructs in the data analysis. To ensure linguistic and conceptual equivalence in the Chinese context, the full instrument underwent a rigorous translation and back-translation procedure by bilingual experts in sport psychology, followed by an expert panel review to assess content validity and cultural relevance. A pilot test was subsequently conducted with a small sample of athletes (n = 30) to evaluate item clarity, response process, and preliminary internal consistency; feedback led to minor wording refinements before the main data collection. The use of adapted, previously validated scales, combined with these procedural steps, enhanced the measurement model's content validity and theoretical consistency.

Data collection procedure
Data were collected through a self-administered online and in-person survey, depending on respondents' accessibility and practical availability. This mixed distribution approach was adopted to maximize participation from athletes across different institutions and sport settings in China. Prior to the full-scale data collection, the survey instrument was reviewed for wording, clarity, relevance, and contextual appropriateness. Because the original scales had been developed in different contexts, the items were adapted carefully to reflect the realities of athletic training and performance environments in China. Where necessary, minor wording changes were made to help respondents easily relate the items to their own sporting experiences without altering the constructs' underlying meanings.

The final questionnaire was divided into logical sections: a brief introduction explaining the purpose of the research, followed by demographic questions, and then the measurement items for artificial intelligence use, athlete self-efficacy, psychological well-being, and perceived sport performance. During data collection, participants were informed that their participation was entirely voluntary, that there were no right or wrong answers, and that the study was being conducted solely for academic purposes. They were assured that all responses would remain confidential and anonymous and that no personally identifying information would be disclosed in the reporting of results. These procedures were important for reducing response bias, encouraging honest answers, and increasing the credibility of the collected data. Completed questionnaires were screened before analysis to ensure that only valid and usable responses were retained for statistical testing.

Participants were recruited through a combination of purposive and snowball sampling across provincial training centers, university sports departments, and professional athletic clubs in eastern and southern China. Inclusion criteria required that participants be active athletes (age ≥ 16 years) with at least one year of systematic training experience and regular exposure to AI-based performance tools (e.g., wearable sensors, video analysis software, or smart coaching apps). Exclusion criteria included current injury preventing training, incomplete surveys, or non-consent to data use. All participants provided written informed consent prior to participation; for athletes under 18, parental or guardian consent was additionally secured. Regarding translation and cultural adaptation, all original English scales were independently translated into Chinese by two bilingual sport science researchers, reconciled into a single version, and then back-translated by a third translator blind to the original items. Discrepancies were resolved through expert panel discussion, followed by a pilot test with 30 athletes (not included in the final sample) to confirm semantic, conceptual, and contextual equivalence. This process ensured that the measurement instruments were both linguistically accurate and culturally appropriate for the Chinese athletic context.

Data analysis technique
The collected data were analyzed using SPSS and PLS-SEM, as both tools were appropriate for handling quantitative survey data and for testing complex theoretical models involving mediation and multidimensional latent variables. In the first stage of analysis, SPSS was used for data preparation and preliminary statistical examination. This included coding the responses, checking for missing values, identifying outliers, screening for incomplete questionnaires, and generating descriptive statistics such as frequencies, means, and standard deviations for the demographic and study variables. SPSS was also used to assess the internal consistency of the scales at an initial level and to ensure that the data were suitable for further multivariate analysis. In the second stage, PLS-SEM was applied to evaluate both the measurement model and the structural model. PLS-SEM was selected because it was particularly suitable when the study involved higher-order constructs, prediction-oriented objectives, mediation relationships, and models that might not strictly require multivariate normality. The measurement model assessment focused on examining indicator reliability, outer loadings, composite reliability, Cronbach’s alpha, average variance extracted, and discriminant validity through accepted criteria such as the Fornell-Larcker criterion and the HTMT ratio.

After confirming the adequacy of the measurement model, the structural model was assessed to evaluate the hypothesized relationships. Path coefficients, t-values, p-values, and bias-corrected 95% confidence intervals were obtained through bootstrapping with 5,000 subsamples, a procedure recommended for stable and reliable significance testing in PLS-SEM. The model’s explanatory power was examined via the coefficient of determination (R²), effect sizes (f²), and predictive relevance (Q²). The hypothesized structural relationships among artificial intelligence use, athlete self-efficacy, psychological well-being, and perceived sport performance were evaluated first, followed by the assessment of psychological well-being as a mediator. Mediation analysis was conducted by evaluating the significance of the specific indirect effects from AI use to perceived sport performance through psychological well-being, and from athlete self-efficacy to perceived sport performance through psychological well-being, using the same bootstrapping procedure to generate robust confidence intervals for the indirect paths. Additionally, because all variables were collected from a single survey instrument, common method bias was assessed to strengthen the rigor of the analysis. Together, the use of SPSS for preliminary data screening and PLS-SEM for structural modeling provided a systematic and robust analytical framework for hypothesis testing.

Results

Descriptive statistics (N = 385)
Descriptive statistics were presented to summarize the basic characteristics of the study variables and to provide an initial understanding of the data before conducting advanced statistical analysis. In quantitative research, descriptive statistics are important because they show the average responses of participants and the extent to which responses varied across the sample. In this study, the mean values indicated the general level of agreement of the athletes rega...

Discussion

Before interpreting the substantive findings, it is necessary to clarify certain analytical and methodological boundaries of the present study. This research employs a cross-sectional survey design and analyzes the data via variance-based structural equation modeling (PLS-SEM). Consequently, the authors partitioned the data into model robustness and predictive relevance, which were assessed through bootstrapping (5,000 resamples) and the Stone-Geisser Q² statistic via the PLSpredict procedure, whi...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Artificial Intelligence Use Scale (adapted for sport)	Adapted from Abbas et al. [42]	[42]	N/A
Athlete Self-Efficacy Scale (multidimensional)	Adapted from Koçak [45]	[45]	N/A
IBM SPSS Statistics (Version 28)	IBM Corp., Armonk, NY, USA	https://www.ibm.com/products/spss-statistics	SCR_002865
Perceived Sport Performance Scale	Adapted from Hill et al. [44]	[44]	N/A
Pilot test questionnaire	In-house	In-house (instrument)	N/A
Psychological Well-Being Scale for Children (PWB-c) – adapted for adult athletes	Adapted from Opree et al. [43]	[43]	N/A
SmartPLS (Version 4.2)	SmartPLS GmbH, Oststeinbek, Germany	https://www.smartpls.com/	SCR_022040
Translation and back-translation procedure (bilingual experts)	In-house (sport psychology researchers)	In-house (procedure)	N/A