Relationships were present between lower-body muscle structure and strength and power qualities. Fifteen elite male surfing athletes performed a battery of lower-body strength and power tests, including countermovement jump (CMJ), squat jump (SJ), isometric midthigh pull (IMTP), and had their lower-body muscle structure assessed with ultrasonography. In addition, lower-body muscle-tendon complex (MTC) stiffness and dynamic strength deficit (DSD) ratio were calculated from the CMJ and IMTP. Significant relationships of large to very large strength were observed between the vastus lateralis (VL) thickness of the left (LVL) and right (RVL) leg and peak force (PF) (r = 0.54–0.77, p < 0.01–0.04), peak velocity (PV) (r = 0.66–0.83, p < 0.01), and peak jump height (r = 0.62–0.80, p < 0.01) in the CMJ and SJ, as well as IMTP PF (r = 0.53–0.60, p = 0.02–0.04). Furthermore, large relationships were found between left lateral gastrocnemius (LG) pennation angle and SJ and IMTP PF (r = 0.53, p = 0.04, and r = 0.70, p < 0.01, respectively) and between LG and IMTP relative PF (r = 0.63, p = 0.01). Additionally, large relationships were identified between lower-body MTC stiffness and DSD ratio (r = 0.68, p < 0.01), right (LG) pennation angle (r = 0.51, p = 0.05), CMJ PF (r = 0.60, p = 0.02), and jump height (r = 0.53, p = 0.04). These results indicate that greater VL thickness and increased LG pennation angle are related to improved performance in the CMJ, SJ, and IMTP. Furthermore, these results suggest that lower-body MTC stiffness explains a large amount of variance in determining an athlete's ability to rapidly apply force during a dynamic movement....

Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes. Objective: This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use. Methods: A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis. Results: Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems. Conclusions: The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.

Purpose: To determine whether a previously validated performance-testing protocol for competitive surfers is able to differentiate between Australian elite junior surfers selected (S) to the national team and those not selected (NS).Methods: Thirty-two elite male competitive junior surfers were divided into 2 groups (S = 16, NS = 16). Their age, height, body mass, sum of 7 skinfolds, and lean-body-mass ratio (mean ± SD) were 16.17 ± 1.26 y, 173.40 ± 5.30 cm, 62.35 ± 7.40 kg, 41.74 ± 10.82 mm, 1.54 ± 0.35 for the S athletes and 16.13 ± 1.02 y, 170.56 ± 6.6 cm, 61.46 ± 10.10 kg, 49.25 ± 13.04 mm, 1.31 ± 0.30 for the NS athletes. Power (countermovement jump [CMJ]), strength (isometric midthigh pull), 15-m sprint paddling, and 400-m endurance paddling were measured.Results: There were significant (P ≤ .05) differences between the S and NS athletes for relative vertical-jump peak force (P = .01, d = 0.9); CMJ height (P = .01, d = 0.9); time to 5-, 10-, and 15-m sprint paddle; sprint paddle peak velocity (P = .03, d = 0.8; PV); time to 400 m (P = .04, d = 0.7); and endurance paddling velocity (P = .05, d = 0.7).Conclusions: All performance variables, particularly CMJ height; time to 5-, 10-, and 15-m sprint paddle; sprint paddle PV; time to 400 m; and endurance paddling velocity, can effectively discriminate between S and NS competitive surfers, and this may be important for athlete profiling and training-program design.

The purpose of this study was to determine whether any significant associations were present between lower-body strength and power, and the performance of turning and aerial manoeuvres in elite surfing athletes. Eighteen competitive male surfers performed a battery of physical tests (countermovement jump (CMJ), squat jump (SJ), and isometric mid-thigh pull (IMTP)) during a single session, in addition to having their performance of turning and aerial manoeuvres ranked from highest to lowest. Significant associations were identified between turning manoeuvre ranking and; peak force in the CMJ, SJ and IMTP (ϱ=−0.737, p<0.01; ϱ=−0.856, p<0.01; ϱ=−0.683, p<0.01, respectively), as well as, peak velocity and jump height in the CMJ (ϱ=−0.560, p=0.02; ϱ=−0.529, p=0.02, respectively). No significant associations were identified between aerial manoeuvre ranking and any strength and power variables. These results suggest that surfing athletes that exhibit greater lower-body isometric and dynamic strength, and power also perform higher scoring turning manoeuvres during wave riding.

Purpose: To identify whether there are any significant differences in the lower-body muscle structure and countermovement-jump (CMJ) and squat-jump (SJ) performance between stronger and weaker surfing athletes.Methods: Twenty elite male surfers had their lower-body muscle structure assessed with ultrasonography and completed a series of lower-body strength and jump tests including isometric midthigh pull (IMTP), CMJ, and SJ. Athletes were separated into stronger (n = 10) and weaker (n = 10) groups based on IMTP performance.Results: Large significant differences were identified between the groups for vastus lateralis (VL) thickness (P = .02, ES = 1.22) and lateral gastrocnemius (LG) pennation angle (P = .01, ES = 1.20), and a large nonsignificant difference was identified in LG thickness (P = .08, ES = 0.89). Furthermore, significant differences were present between the groups for peak force, relative peak force, and jump height in the CMJ and SJ (P < .01−.05, ES = 0.90−1.47) and eccentric peak velocity, as well as vertical displacement of the center of mass during the CMJ (P < .01, ES = 1.40−1.41).Conclusion: Stronger surfing athletes in this study had greater VL and LG thickness and LG pennation angle. These muscle structures may explain their better performance in the CMJ and SJ. A unique finding in this study was that the stronger group appeared to better use their strength and muscle structure for braking as they had significantly higher eccentric peak velocity and vertical displacement during the CMJ. This enhanced eccentric phase may have resulted in a greater production and subsequent utilization of stored elastic strain energy that led to the significantly better CMJ performance in the stronger group.

Farley, ORL, Secomb, JL, Parsonage, JR, Lundgren, LE, Abbiss, CR, and Sheppard, JM. Five weeks of sprint and high-intensity interval training improves paddling performance in adolescent surfers. J Strength Cond Res 30(9): 2446–2452, 2016—The purpose of our study was to examine the effects of sprint interval training (SIT; 10 seconds) and high-intensity interval training (HIT; 30 seconds) on surfing athletes paddling performance (400-m time trial and repeat-sprint paddle performance). Twenty-four competitive adolescent surfers (19 male, 5 female; age = 14.4 ± 1.3 years, mass: 50.1 ± 10.7 kg, and stature: 159.9 ± 10.3 cm) were assigned to perform either 5 weeks of SIT and HIT. Participants completed a repeated-sprint paddle ability test (RSPT, 15-m surfboard sprint paddle initiated every 40 seconds × 10 bouts) and 400-m endurance surfboard paddle time trial before and after training. High-intensity interval training decreased the total time to complete the 400 m by 15.8 ± 16.1 seconds (p = 0.03), and SIT decreased the total time to complete the RSPT by 6.5 ± 4.3 seconds (p = 0.02). Fatigue index during the RSPT (first—slowest effort) was lower after HIT and SIT (p ≤ 0.001 and p = 0.02, respectively). There were no significant differences in performance changes in the 400 m (total time) and RSPT (total time, fastest 15 m time, and peak velocity) between HIT and SIT. Our study indicates that HIT and SIT may be implemented to the training program of surfers to improve aerobic and repeat-sprint paddle ability, both of which are identified as key aspects of the sport. In addition, these findings indicate that 400-m paddle and RSPT can discriminate between aerobic and anaerobic training adaptations, with aerobic gains likely from HIT and anaerobic gains from SIT.