Stress responses in surgical trainees during simulation-based training courses in laparoscopy.

BACKGROUND: Simulation-based training courses in laparoscopy have become a fundamental part of surgical training programs. Surgical skills in laparoscopy are challenging to master, and training in these skills induces stress responses in trainees. There is limited data on trainees' stress levels, the stress responses related to training on different laparoscopic simulators, and how previous experiences influence trainees' stress response during a course. This study investigates physiologic, endocrine and self-reported stress responses during simulation-based surgical skills training in a course setting. METHODS: We conducted a prospective observational study of trainees attending basic laparoscopic skills training courses at a national training centre. During the three-day course, participants trained on different laparoscopic simulators: Two box-trainers (the D-box and P.O.P. trainer) and a virtual reality simulator (LAPMentor™). Participants' stress responses were examined through heart rate variability (HRV), saliva cortisol, and the State Trait Anxiety Inventory-6 (STAI-6). The correlation between previous laparoscopic experiences and stress response measurements was explored. RESULTS: Twenty-four surgical trainees were included in the study. Compared to resting conditions, stress measures were significantly higher during simulation-training activity (the D-box (SDNN = 58.5 ± 23.4; LF/HF-ratio = 4.58 ± 2.71; STAI-6 = 12.3 ± 3.9, P < 0.05), the P.O.P trainer (SDNN = 55.7 ± 7.4; RMSSD = 32.4 ± 17.1; STAI-6 = 12.1 ± 3.9, P < 0.05), and the LAPMentor™ (SDNN = 59.1 ± 18.5; RMSSD = 34.3 ± 19.7; LF/HF-ratio = 4.71 ± 2.64; STAI-6 = 9.9 ± 3.0, P < 0.05)). A significant difference in endocrine stress response was seen for the simulation-training activity on the D-box (saliva cortisol: 3.48 ± 1.92, P < 0.05), however, no significant differences were observed between the three simulators. A moderate correlation between surgical experience, and physiologic and endocrine stress response was observed (RMSSD: r=-0.31; SDNN: r=-0.42; SD2/SD1 ratio: r = 0.29; Saliva cortisol: r = 0.46; P < 0.05), and a negative moderate correlation to self-reported stress (r=-0.42, P < 0.05). CONCLUSION: Trainees have a significant higher stress response during simulation-training compared to resting conditions, with no difference in stress response between the simulators. Significantly higher cortisol levels were observed on the D-box, indicating that simulation tasks with time pressure stress participants the most. Trainees with more surgical experience are associated with higher physiologic stress measures, but lower self-reported stress scores, demonstrating that surgical experience influences trainees' stress response during simulation-based skills training courses.

Performance Effects of Video- and Sensor-Based Feedback for Implementing a Terrain-Specific Micropacing Strategy in Cross-Country Skiing.

PURPOSE: To investigate the performance effects of video- and sensor-based feedback for implementing a terrain-specific micropacing strategy in cross-country (XC) skiing. METHODS: Following a simulated 10-km skating time trial (Race1) on snow, 26 national-level male XC skiers were randomly allocated into an intervention (n = 14) or control group (n = 12), before repeating the race (Race2) 2 days later. Between races, intervention received video- and sensor-based feedback through a theoretical lecture and a practical training session aiming to implement a terrain-specific micropacing strategy focusing on active power production over designated hilltops to save time in the subsequent downhill. The control group only received their overall results and performed a training session with matched training load. RESULTS: From Race1 to Race2, the intervention group increased the total variation of chest acceleration on all hilltops (P < .001) and reduced time compared with the control group in a specifically targeted downhill segment (mean group difference: -0.55 s; 95% confidence interval [CI], -0.9 to -0.19 s; P = .003), as well as in overall time spent in downhill (-14.4 s; 95% CI, -21.4 to -7.4 s; P < .001) and flat terrain (-6.5 s; 95% CI, -11.0 to -1.9 s; P = .006). No between-groups differences were found for either overall uphill terrain (-9.3 s; 95% CI, -31.2 to 13.2 s; P = .426) or total race time (-32.2 s; 95% CI, -100.2 to 35.9 s; P = .339). CONCLUSION: Targeted training combined with video- and sensor-based feedback led to a successful implementation of a terrain-specific micropacing strategy in XC skiing, which reduced the time spent in downhill and flat terrain for intervention compared with a control group. However, no change in overall performance was observed between the 2 groups of XC skiers.

Physiological and Biomechanical Determinants of Sprint Ability Following Variable Intensity Exercise When Roller Ski Skating.

The most common race format in cross-country (XC) skiing is the mass-start event, which is under-explored in the scientific literature. To explore factors important for XC skiing mass-starts, the main purpose of this study was to investigate physiological and biomechanical determinants of sprint ability following variable intensity exercise when roller ski skating. Thirteen elite male XC skiers performed a simulated mass-start competition while roller ski skating on a treadmill. The protocol consisted of an initial 21-min bout with a varying track profile, designed as a competition track with preset inclines and speeds, directly followed by an all-out sprint (AOS) with gradually increased speed to rank their performance. The initial part was projected to simulate the "stay-in-the-group" condition during a mass-start, while the AOS was designed to assess the residual physiological capacities required to perform well during the final part of a mass-start race. Cardiorespiratory variables, kinematics and pole forces were measured continuously, and the cycles were automatically detected and classified into skating sub-techniques through a machine learning model. Better performance ranking was associated with higher VO2Max (r = 0.68) and gross efficiency (r = 0.70) measured on separate days, as well as the ability to ski on a lower relative intensity [i.e., %HR Max (r = 0.87), %VO2Max (r = 0.89), and rating of perceived exertion (r = 0.73)] during the initial 21-min of the simulated mass-start (all p-values < 0.05). Accordingly, the ability to increase HR (r = 0.76) and VO2 (r = 0.72), beyond the corresponding values achieved during the initial 21-min, in the AOS correlated positively with performance (both p < 0.05). In addition, greater utilization of the G3 sub-technique in the steepest uphill (r = 0.69, p < 0.05), as well as a trend for longer cycle lengths (CLs) during the AOS (r = 0.52, p = 0.07), were associated with performance. In conclusion, VO2Max and gross efficiency were the most significant performance-determining variables of simulated mass-start performance, enabling lower relative intensity and less accumulation of fatigue before entering the final AOS. Subsequently, better performance ranking was associated with more utilization of the demanding G3 sub-technique in the steepest uphill, and physiological reserves allowing better-performing skiers to utilize a larger portion of their aerobic potential and achieve longer CLs and higher speed during the AOS.

Assessment of Basic Motions and Technique Identification in Classical Cross-Country Skiing.

Cross-country skiing is a popular Olympic winter sport, which is also used extensively as a recreational activity. While cross-country skiing primarily is regarded as a demanding endurance activity it is also technically challenging, as it contains two main styles (classical and skating) and many sub-techniques within these styles. To further understand the physiological demands and technical challenges of cross-country skiing it is imperative to identify sub-techniques and basic motion features during training and competitions. Therefore, this paper presents features for identification and assessment of the basic motion patterns used during classical-style cross-country skiing. The main motivation for this work is to contribute to the development of a more detailed platform for comparing and communicating results from technique analysis methods, to prevent unambiguous definitions and to allow more precise discussions and quality assessments of an athlete's technical ability. To achieve this, our paper proposes formal motion components and classical style technique definitions as well as sub-technique classifiers. This structure is general and can be used directly for other cyclic activities with clearly defined and distinguishable sub-techniques, such as the skating style in cross country skiing. The motion component features suggested in our approach are arm synchronization, leg kick, leg kick direction, leg kick rotation, foot/ski orientation and energy like measures of the arm, and leg motion. By direct measurement, estimation, and the combination of these components, the traditional sub-techniques of diagonal stride, double poling, double poling kick, herringbone, as well as turning techniques can be identified. By assuming that the proposed definitions of the classical XC skiing sub-techniques are accepted, the presented classifier is proven to map measures from the motion component definitions to a unique representation of the sub-techniques. This formalization and structure may be used on new motion components, measurement principles, and classifiers, and therefore provides a framework for comparing different methodologies. Pilot data from a group of high-level cross-country skiers employing inertial measurement sensors placed on the athlete's arms and skis are used to demonstrate the approach. The results show how detailed sub-technique information can be coupled with physical, track, and environmental data to analyze the effects of specific motion patterns, to develop useful debriefing tools for coaches and athletes in training and competition settings, and to explore new research hypotheses.

Sex-based differences in speed, sub-technique selection, and kinematic patterns during low- and high-intensity training for classical cross-country skiing.

OBJECTIVES: We investigated sex-based differences in speed, sub-technique selection, and kinematic patterns during low- (LIT) and high-intensity training (HIT) for classical cross-country (XC) skiing across varying terrain. METHODS: Six male and six female elite XC skiers with an approximately 15% differences in VO2max (men: 68.9±2.9 mL·min-1·kg-1, women: 60.1±3.3 mL·min-1·kg-1) were monitored using a multi-sensor system to collect time-synchronised data of heart rate, speed, and multiple tri-axial inertial measurements units while XC skiing on a 5-km competition track. RESULTS: Men skied 21% faster than women during HIT (5.9±0.3 m·s-1 vs. 4.9±0.2 m·s-1, P < .001), with the greatest difference (26%) while skiing on flat terrain, whereas skiing speed did not significantly differ between men and women during LIT. At similar instructed intensity and rating of perceived effort, women exhibited significantly higher relative heart rate (85±2% vs. 71±3% of maximum) and blood lactate levels (4.0±1.3 vs. 1.2±0.2 mmol/L) during LIT (all P < .001) than men, whereas physiological responses did generally not differ between the sexes during HIT. During both intensities and among both sexes, double poling (DP) was the sub-technique most used relative to distance, followed by miscellaneous sub-techniques (MISC), diagonal stride (DIA), kick double poling (DK) and herringbone (HRB). In relation to distance women used DIA more than men during LIT (22% vs. 17%, P = .009) and HIT (23% vs. 12%, P = .001), whereas men used MISC, including tucking and turning, more than women during LIT (39% vs. 25%, P = .017) and HIT (41% vs. 30%, P = .064). In particular, men used DP more than women while skiing the uphill sections during both LIT (24% vs. 11%, P = .015) and HIT (39% vs. 13%, P = .002). CONCLUSIONS: Our findings provide novel insights into sex-based differences in speed, sub-technique selection, and kinematic patterns during LIT and HIT for classical skiing.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data.

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier's arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier's arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.