Objectives: The study provided a comprehensive analysis exploring the association between athletes’ injury characteristics and the choice of physiotherapy treatments applied at the Polyclinic Tokyo 2020. Methods: Data from all physiotherapy treatments offered in the Polyclinic Olympic Village in Tokyo between 13 July and 10 August were recorded electronically. The physiotherapy team consisted of approximately 150 Japanese physiotherapists. Physiotherapy modalities analyzed were cryotherapy, electrotherapy, exercise therapy, laser therapy, manual therapy, shockwave therapy, taping, and ultrasound therapy. Logistic regression assessed the association between athletes’ injury characteristics and the physiotherapy treatment the Polyclinic Physiotherapy Services offered at the Tokyo 2020 Olympic Games using odds ratio. Results: This study analyzed 808 athletes who received physiotherapy, of which 66.6% (n = 1023) were for lower limb chronic injuries affecting muscles and tendons (72.8%, n = 1209) and were the most prevalent ones treated. Chronic injuries accounted for 56.7% (n = 942). Manual therapy was associated with trunk injuries (odds ratio 1.8, 95% confidence interval 1.4–2.3) and chronic injuries (1.5, 1.2–1.9). Ultrasound therapy was associated with injuries of the upper limbs (3.6, 1.9–6.7) and lower limbs (3.0, 1.7–5.2). Taping was associated with bone/joint/ligament injuries (2.1, 1.4–3.0). Shockwave was associated with muscle/tendon injuries (2.0, 1.2–3.4). Cryotherapy was related to acute injuries (1.8, 1.1–3.0) and lower limbs (3.6, 1.4–9.4). Laser was associated with bone/joint/ligament injuries (14.4, 4.5–45.8). Conclusions: Chronic injuries affecting lower limb muscles and tendons were the most prevalent musculoskeletal injuries treated at the Tokyo 2020 Polyclinic. Overall, Japanese physiotherapists used manual therapy, taping, exercise, and ultrasound therapy to treat athlete injuries.

Objective: Assessment of the vertical ground reaction force (VGRF) during landing tasks is crucial for physical therapy in sports. The purpose of this study was to determine whether the VGRF during a single-leg landing can be estimated from a two-dimensional (2D) video image and pose estimation artificial intelligence (AI). Methods: Eighteen healthy male participants (age: 23.0 ± 1.6 years) performed a single-leg landing task from a 30-cm height. The VGRF was measured using a force plate and estimated using center of mass (COM) position data from a 2D video image with pose estimation AI (2D-AI) and three-dimensional optical motion capture (3D-Mocap). The measured and estimated peak VGRFs were compared using a paired t-test and Pearson’s correlation coefficient. The absolute errors of the peak VGRF were also compared between the two estimations. Results: No significant difference in the peak VGRF was found between the force plate measured VGRF and the 2D-AI or 3D-Mocap estimated VGRF (force plate: 3.37 ± 0.42 body weight [BW], 2D-AI: 3.32 ± 0.42 BW, 3D-Mocap: 3.50 ± 0.42 BW). There was no significant difference in the absolute error of the peak VGRF between the 2D-AI and 3D-Mocap estimations (2D-AI: 0.20 ± 0.16 BW, 3D-Mocap: 0.13 ± 0.09 BW, P = 0.163). The measured peak VGRF was significantly correlated with the estimated peak by 2D-AI (R = 0.835, P <0.001). Conclusion: The results of this study indicate that peak VGRF estimation using 2D video images and pose estimation AI is useful for the clinical assessment of single-leg landing.