Paavo Vartiainen

This study investigated the transfer effects of lower-body strength gains on ice hockey skating acceleration performance. Elite Finnish U20 male ice hockey players ( n = 22) were randomized to either a knee- or hip-dominant lower-body strength training intervention lasting 8 weeks. Maximal lower-body strength (1RM) and 30-m skating sprint performance were assessed before and after the intervention. Horizontal force–velocity profiling was derived from sprint performance, and split times were analyzed for the initial and final 10 m segments of the sprint. As no significant between-group differences were observed in any outcome measure, the groups were pooled for further analyses. Following the intervention, maximal strength increased significantly, accompanied by improvements in initial 10 m skating performance, theoretical maximal horizontal force ( F ₀ ), maximal power output ( P _max ), and horizontal force orientation (RF _max ). In contrast, theoretical maximal velocity ( V ₀ ), observed maximal skating speed, and performance during the final 10 m of the sprint declined. Consequently, no significant change was observed in overall 30 m sprint time. These findings indicate that increases in lower-body strength primarily enhance the force- and power-related qualities underpinning early skating acceleration but do not necessarily improve maximal skating speed. The results highlight the task-specific transfer of strength adaptations to skating performance and provide practical guidance for selecting performance tests and designing training programs in ice hockey.

Purpose: Surgeons’ visual and ergonomical challenges are long-standing concerns since the use of microscopes in surgical procedures. Although devices have been improved in the last few decades, the problem of narrow visual fields in high magnification surgeries persists. This study aims to identify the visual ergonomics challenges among microsurgeons and assess the need for novel assistive surgical solutions. Methods: The study consisted of two parts: a survey and a hands-on experiment. Sixteen surgeons from neurosurgery and otolaryngology (ENT) specialties were invited to semi-structured interviews on challenges encountered when using surgical microscopes and evaluation of the two proposed technological solutions: CPM (context-preserving magnification) and IT (instrument transparency). Following a demonstration of these software solutions, a survey utilizing a Likert scale was administered. The second part of the study involved twelve practicing neurosurgeons who performed a task using a novel solution and compared it to a standard operative microscope setting. Results: The most common challenges reported were visual obstruction of field by instruments (93.75%), blurring of structures and light reflection (81.25%), and loss of context (68.75%). 50% of surgeons agreed that adjusting zoom and focus takes a considerable amount of time from surgery and 56% stated that they had at least one episode of difficulty seeing depth. Notably, 69% of respondents expressed interest in testing prototypes of both proposed solutions in real surgery, with a particular preference for instrument transparency. Furthermore, the context-preserving magnification solution hands-on trial demonstrated a 40% reduction in task completion time for 60% of participants. However, one participant found no advantage, and others took longer to complete tasks with the solution compared to standard settings. Conclusion: Our solution addresses the top visual challenges and instrument obstruction remains a top challenge in high magnification microsurgery. We showed that surgeons are highly likely to use novel assistive technologies that provide wider visual field and transparent instruments.

Purpose: Estimating loading of the knee joint may be helpful in managing degenerative joint diseases. Contemporary methods to estimate loading involve calculating knee joint contact forces using musculoskeletal modeling and simulation from motion capture (MOCAP) data, which must be collected in a specialized environment and analyzed by a trained expert. To make the estimation of knee joint loading more accessible, simple input predictors should be used for predicting knee joint loading using artificial neural networks. Methods: We trained feedforward artificial neural networks (ANNs) to predict knee joint loading peaks from the mass, height, age, sex, walking speed, and knee flexion angle (KFA) of subjects using their existing MOCAP data. We also collected an independent MOCAP dataset while recording walking with a video camera (VC) and inertial measurement units (IMUs). We quantified the prediction accuracy of the ANNs using walking speed and KFA estimates from (1) MOCAP data, (2) VC data, and (3) IMU data separately (i.e., we quantified three sets of prediction accuracy metrics). Results: Using portable modalities, we achieved prediction accuracies between 0.13 and 0.37 root mean square error normalized to the mean of the musculoskeletal analysis-based reference values. The correlation between the predicted and reference loading peaks varied between 0.65 and 0.91. This was comparable to the prediction accuracies obtained when obtaining predictors from motion capture data. Discussion: The prediction results show that both VCs and IMUs can be used to estimate predictors that can be used in estimating knee joint loading outside the motion laboratory. Future studies should investigate the usability of the methods in an out-of-laboratory setting.