Lauri Stenroth

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.

Low-fidelity, artificial intelligence (AI)-generated approaches are increasingly used to estimate kinematics and kinetics of human movement, which are traditionally measured by high-fidelity motion capture (Mocap) approaches. However, there are no methods to study knee joint tissue mechanics using such low-fidelity approaches. To do so, we investigated knee cartilage stresses and strains using finite element (FE) models driven by both high- and low-fidelity motion capture methods. We performed subject-specific FE modeling on nine healthy participants to evaluate tissue mechanical responses by two different approaches. High-fidelity kinematic and kinetic data were obtained through motion capture and musculoskeletal modeling, respectively, whereas artificial neural networks (ANNs) were used to estimate kinetic data from low-fidelity data (e.g., subject mass, height, age, gender, static knee abduction-adduction angle, and walking speed). These data were then used as loading inputs in the knee joint FE models that were generated from magnetic resonance images. The results indicated that the high- and low-fidelity approaches provided comparable estimates of maximum principal stress, maximum shear strain, and collagen fibril strain of tibial cartilage at the first peak of knee contact force ( p > 0.05). Significant differences between the methods in the peak values of the analyzed parameters were observed at the second peak of knee contact force ( p < 0.05), larger differences observed in the lateral joint compartment. However, most of these differences disappeared when comparing average values over the cartilage–cartilage contact areas. Our findings support the potential of the low-fidelity, AI-generated approach to assess tibial cartilage mechanics. This out-of-laboratory tool may enable cartilage failure prediction and improved management of osteoarthritis.

The objective of this paper is to explore methodologies used to estimate the mechanical cost of locomotion and evaluate their validity as indicators of metabolic cost within rehabilitation contexts. Aging societies face rising healthcare costs and fewer workers. Remote monitoring of rehabilitation progress may help address these challenges. Because locomotion mechanics are easier to measure than metabolic cost, understanding whether mechanical cost of transport can serve as a surrogate is of interest. We conducted a systematic search for studies on mechanical work or power and metabolic energy consumption in adults under physical rehabilitation contexts. Abstract, full text screening, and data extraction were performed. Ten articles were included. We found no direct relationship between mechanical cost and metabolic cost in the included studies. However, consistent trends were observed: interventions that provided net positive work generally reduced both mechanical and metabolic costs. Similar trends were found in wheelchair propulsion, where mechanical advantage and power assistance reduced both mechanical cost and metabolic demand for wheelchair users. Rehabilitation progress can be monitored using metabolic cost, and given the observed parallel trends, mechanical cost may also serve as a practical indicator, although further research is needed to establish its validity as a surrogate.

Individuals with anterior cruciate ligament reconstruction and knee pain (symptomatic) have demonstrated reduced knee extensor strength and similar knee joint loading during a forward lunge (FL) as compared with those without pain (asymptomatic) 3–10 years after anterior cruciate ligament reconstruction. Still, the dynamic knee joint stability during FL may differ between the groups. This study examined dynamic knee stability, measured by knee wobble through 3D kinematic modeling, to explore if symptomatic individuals exhibit more mediolateral knee wobble than asymptomatic individuals. Additionally, kinematic and kinetic movement strategy differences between the groups were investigated. Musculoskeletal modeling of the FL was applied to quantify knee wobble and other biomechanical parameters. In total, 33 symptomatic and 89 asymptomatic participants were included. No significant difference in knee wobble was observed between groups (mean difference: −0.16 [95% CI, −0.61 to 0.29], P = .480). Overall, only negligible between-group movement pattern differences were observed, most notably, the symptomatic group exhibited less concentric knee work (mean difference: 0.16 J/kg [95% CI, 0.04 to 0.28], P = .012) compared to the asymptomatic group. These findings suggest that symptomatic and asymptomatic individuals exhibit similar dynamic knee stability and movement strategies during the FL, 3–10 years after anterior cruciate ligament reconstruction.

Musculoskeletal finite element modeling is used to estimate mechanical responses of knee joint tissues but involves uncertainties in muscle activations, marker locations, cartilage stiffness, maximum isometric forces, and gait parameter personalization. This study investigates how these uncertainties affect cartilage mechanical responses in knee joint finite element models during walking. We selected three subjects and constructed five musculoskeletal models for each, representing different variations of modeling assumptions, along with a reference model using conventional assumptions. We then ran finite element simulations of knee joints using both personalized gait inputs (motion and loading boundary conditions) and non-personalized gait inputs from literature. Our results demonstrated that varying modeling assumptions, such as optimization function for muscle activation patterns, knee marker position, knee cartilage stiffness, and maximum isometric force, produced highly subject-specific effects. Differences between the reference and altered models ranged from 3% to 30% in musculoskeletal modeling and from 1% to 61% in finite element modeling results. The largest effects occurred with non-personalized gait data, resulting in up to 6- and 2-fold changes in musculoskeletal and finite element modeling results, respectively. This study highlights the sensitivity of knee mechanics to different modeling assumptions and underscores the importance of applying personalized gait parameters for accurate finite element simulations.

Purpose: Anterior cruciate ligament injury increases the risk of knee osteoarthritis, possibly via early onset of knee pain and changes in musculoskeletal function. This study compared knee muscle strength and movement biomechanics during walking and forward lunge between individuals with and without knee pain after anterior cruciate ligament reconstruction. Methods: Cross‐sectional study including participants at least 3 years post anterior cruciate ligament reconstruction, aged 18–40 at the time of surgery, and body mass index ≤30. Symptomatic participants were defined by a knee pain score (reconstructed knee) of ≥3 on a 0–10 scale during activities of daily living in the past week. Asymptomatic participants were defined by a pain score of 0. Maximal isometric quadriceps and hamstring muscle strength (Nm/kg) and 3D walking, and forward lunge movement biomechanics were measured. Results: A total of 122 participants (30% females) were included: 33 symptomatic and 89 asymptomatic (average age: 33.7, range 23.7–51.3 years). The average post‐surgery time was 6 (range 3–10) years. The symptomatic group exhibited lower isometric quadriceps and hamstring strength with mean group differences (95% confidence interval [CI]) of 0.33 (0.10–0.56) Nm/kg and 0.19 (0.07–0.31) Nm/kg, respectively. There were no important group differences in the walking and forward lunge movement biomechanics. Conclusions: Symptomatic individuals with anterior cruciate ligament reconstruction demonstrated weaker knee muscles compared to their asymptomatic counterparts. The comparable walking and forward lunge biomechanics suggest that knee pain has no substantial impact on movement biomechanics up to 10 years post‐surgery. Level of Evidence: Level III.