Sports technology has rapidly reshaped the measurement and governance of athletic performance. Wearables, smart fabrics, and AI-linked sensors now generate constant biometric and tactical streams used for analytics, injury prevention, and fan-facing applications. As these tools become embedded in everyday training, the line between assistance and surveillance becomes harder to distinguish, raising new concerns about autonomy and control(1). Although the data originate from athletes' bodies, legal ownership remains uncertain (2). Existing privacy law and international sports governance frameworks offer no clear allocation of rights over the digital traces produced through play or training (3). The concept of data sovereignty-first developed in Indigenous data governance and later expanded into digital rights debates-suggests that individuals should retain authority over information tied to their bodily identity (4). Yet, despite its relevance, this principle has rarely been applied to the sporting context (5). This absence of legal clarity creates a paradox. Athletes generate the data that underpin modern sports technology, yet remain largely excluded from decision-making about its use. Without a recognised ownership framework, performance data sit in an ambiguous space: deeply personal yet institutionally controlled, private yet commercially valuable (6).To avoid uncertainty in interpretation, several key terms are defined at the outset. First, performance data refers to biometric or sport-generated information produced during training or competition. This category includes measurable signals such as heart-rate output, GPS tracking traces, neuromuscular load, movement efficiency markers, reaction-time scores, and other analytics derived directly from an athlete's body or actions. Unlike ordinary personal data collected outside a workplace, these data exist only because the athlete performs. Second, the term data sovereignty is used here to describe an individual's authority to decide how information linked to their physical identity is collected, accessed, shared, stored, and potentially erased. The idea originally emerged in Indigenous data governance debates, but it now informs wider discussions about digital autonomy and informational rights. Finally, co-ownership is not presented as a settled legal rule but as an emerging governance model relevant to sport. Under this model, data rights and responsibilities are shared among athletes, clubs, leagues, and technology providers. Rather than permitting unilateral control by employers or vendors, co-ownership assumes that access, secondary use, retention, and commercial exploitation must be negotiated and justified.Athlete performance data differ from ordinary personal information because they are generated through professional activity and combine physiological, biomechanical, and tactical elements(1). Their production is inseparable from physical labour and competitive action, meaning they exist only because the athlete performs. This hybrid character-both biological and professional-complicates the question of who holds legitimate control. Despite this, prevailing regulatory and contractual structures treat such information as employer-managed output rather than an extension of personhood (3). Many leagues include provisions permitting extensive use of biometric or performance data for analytics, marketing, or third-party licensing(3). In these contexts, consent is typically formal rather than substantive due to structural bargaining inequality between athletes and organisations (5). Over time, this asymmetry has normalised treatment of athlete-generated data as proprietary institutional assets rather than material tied to identity.Although the GDPR recognises individual rights including access (Art. 15), correction (Art. 16), erasure (Art. 17), restriction (Art. 18), portability (Art. 20), and objection (Art. 21), supported by transparency duties under Articles 12-14, exercising these rights in elite sport remains challenging.Once biometric records enter layered analytic ecosystems, athletes encounter fragmented storage, opaque processing, and procedural barriers, rendering legal rights difficult to operationalise (7). Institutional platforms often integrate medical, performance, and commercial modules, making data lineage unclear and deletion or correction technically difficult. Although bodies such as the IOC and FIFA provide guidance on secure handling, they refrain from addressing ownership or benefitsharing (6). As a result, athletes often do not know how long their information is retained, whether it is monetised, or who profits from derivative datasets (2). What begins as performance optimisation can therefore evolve into an opaque economic system that privileges institutional control.This unresolved framework creates a conceptual gap: athlete data are simultaneously personal, professional, and commercial, yet legal regimes force a binary classification(1). The result is an athlete positioned primarily as a passive data subject rather than a rights-bearing contributor(4). Real-world disputes demonstrate that this ambiguity is not theoretical. NBA players have questioned whether teams may claim rights over analytic outputs derived from wearables even where collection was negotiated through union channels (8). Comparative labour analysis similarly shows that although leagues such as the NFL and MLB reference wearable technology, most agreements fail to define ownership, access conditions, or benefit-sharing obligations (9).Korea's Personal Information Protection Act (PIPA) focuses on controller duties but does not recognise any proprietary claim athletes may hold over data produced from their bodies (5). Although it regulates consent, purpose limitation, and retention, it frames personal information as institutionmanaged rather than individually controlled. For athletes, this offers procedural compliance but no substantive...

Exercise remains one of the most effective non-pharmacological strategies for improving non-alcoholic and metabolic-associated fatty liver disease (NAFLD/MASLD). Beyond its systemic effects, regular physical activity rewires hepatic energy metabolism and enhances mitochondrial efficiency. Within this adaptive process, members of the Sirtuin family-particularly SIRT1, SIRT3 and SIRT6-have received considerable attention. These proteins operate at distinct regulatory layers: SIRT1 modulates transcriptional programs, SIRT3 shapes mitochondrial metabolic fluxes, and SIRT6 influences chromatin architecture and epigenetic repression. Together, they are widely regarded as the principal molecular mediators linking exercise to improved hepatic metabolic function. However, the Sirtuin family consists of seven members, and accumulating evidence indicates that the remaining isoforms-SIRT2, SIRT4, SIRT5 and SIRT7-also participate in the hepatic response to exercise. Their potential roles include buffering metabolic stress, supporting protein quality control, and modulating inflammatory signaling, suggesting a broader regulatory network than currently emphasized. This Perspective revisits the exercise–Sirtuin axis from a mechanistic physiology standpoint. It examines why research focus has historically converged on SIRT1, SIRT3 and SIRT6, and considers emerging data implicating the full Sirtuin repertoire in exercise-induced metabolic remodeling. The argument put forward is that future work may benefit from a more integrated framework that views exercise not as a trigger for a few dominant pathways, but as a stimulus capable of reorganizing an interdependent Sirtuin network governing hepatic metabolic resilience. Collectively, these considerations prompt a shift from a single-axis understanding toward a distributed regulatory model of hepatic adaptation to exercise.

• Cognitive–motor dual-task (CMDT) research is concentrated in neurologic and geriatric rehabilitation. • Uneven CMDT growth reflects structural differences across rehabilitation fields. • Outcome sensitivity shapes whether CMDT is clinically central or peripheral. • Standardized CMDT metrics lower entry barriers and accelerate evidence synthesis. • Implementation barriers persist even in CMDT-mature settings (time, tools, training).

Understanding the tennis serve requires an integrated view of how forces generated at the ground are transmitted through the lower limbs, trunk, and upper extremities to the racket. Despite this, most wearable-sensor research has focused predominantly on upper-body kinematics, with leg drive and ground reaction forces remaining largely unexplored in field-based settings. This conceptual paper proposes a practical, on-court framework that combines wearable inertial measurement units (IMUs) with a portable force plate to capture key lower–upper body components of the kinetic chain during the tennis serve. Building on established proximal-to-distal sequencing principles, two novel indices—the Kinetic Chain Efficiency Index and the Lower–Upper Coupling Ratio—are introduced to characterize lower–upper body coordination. In addition, a Measurement Coverage Index is presented to explicitly define which biomechanical components of the serve are accessible with this measurement configuration. By synthesizing existing literature and highlighting key methodological gaps, the paper outlines a conceptually grounded pathway for integrating lower-body mechanics into tennis biomechanics without reliance on laboratory-based motion capture. Practical implications for coaching and strength and conditioning are discussed, including potential applications in performance profiling, serve consistency, and the identification of kinetic-chain patterns that may be relevant to mechanical inefficiency or injury-related loading. Overall, the framework situates a more integrated biomechanical assessment within a realistic field environment and provides a structured basis for future research and applied tennis practice.

Correction on: Kwon JW (2026) A conceptual field-based framework for lower-upper body kinetic chain assessment in the tennis serve using IMUs and portable force plates: measurement coverage, new performance indices, and applied implications. Front. Sports Act. Living 8:1784684. doi: 10.3389/fspor.2026.1784684.Adding/removing text [The author used generative artificial intelligence tools solely for the purpose of checking and improving grammar and spelling.]. A correction has been made to the section [Generative AI statement]:"[The author used generative artificial intelligence tools for checking and improving grammar and spelling, and for the preparation of one figure.]" The original version of this article has been updated.

Background: This study aimed to compare and analyze the effects of two training modalities—resistance training and power training—on maximal strength (one-repetition maximum, 1RM) and repetition-based muscular endurance in university students.Methods: Fifty-seven university students (34 males and 23 females) participated in a 12-week training intervention. The program was divided into two phases: Phase 1 (first 6 weeks) involved resistance training with a controlled 3-second concentric-eccentric tempo, while Phase 2 (last 6 weeks) incorporated power training with 20-second maximal repetition sets. A total of 12 exercises were included, comprising eight 1RM-based and four repetition-based exercises. Measurements were conducted at three time points—T1 (pre), T2 (mid), and T3 (post)—and included assessments of body composition and exercise performance. Data were analyzed using repeated measures ANOVA and paired t-tests.Results: Significant improvements (P<0.001) were observed across most 1RM-based exercises, particularly during the power training phase. Repetition-based exercises also showed positive trends, although performance declines were noted in the kettlebell exercise for some participants. In terms of body composition, skeletal muscle mass significantly increased across all participants, while male participants exhibited a slight decrease in body fat percentage.Conclusions: These findings suggest that power training is highly effective in improving maximal strength within a short period and that the effectiveness of exercise interventions may differ depending on contraction velocity and training modality. Based on a practical training design for general university students, this study offers foundational insights for developing efficient physical training programs.