The structural anisotropy of various poly(alkylthiophene) films have been studied by X-ray diffraction, using both conventional methods and synchrotron radiation at grazing incidence. Solutioncast films orient with the side chains preferably normal to the film surface, whereas spin-cast films of nonstereoregular material orient with both the main and the side chains in the film plane. For thick (10-50 µm) solution-cast films, the degree of orientation depends strongly on the solvent used for casting, and on the stereoregularity of the polymer, films of stereoregular materials being more oriented than those of nonregular materials. The most oriented nonregular films are those cast from mixtures of chloroform and tetrahydrofuran. Thin (50-500 nm) solution-cast films are more oriented than the thicker ones, and the effects of different stereoregularity or different casting solvents are small. For spin-cast films, the degree of orientation is independent of substrate and solvent. Spin-cast films of stereoregular material have two different phases: One with the side chains normal to the substrate, and another where they are parallel to the substrate. The diffraction peaks of spin-cast poly(octylthiophene) narrow considerably upon heating.

ObjectivesWe 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.MethodsSix 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.ResultsMen 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).ConclusionsOur findings provide novel insights into sex-based differences in speed, sub-technique selection, and kinematic patterns during LIT and HIT for classical skiing.

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.

Commercial systems utilizing data from inertial measurement units (IMUs) to analyse movement patterns have not yet been adapted to monitor daily training in cross-country (XC) skiing. The main purposes of this study are to investigate: 1) the feasibility and potential of a multi-sensor system consisting of a heart rate sensor, Global Navigation Satellite Systems (GNSS) data and seven IMUs placed at multiple locations on the body for outdoor XC skiing, and 2) the validity of employing hard decision rules based on the correlation between arms and legs for detecting subtechniques in classical XC skiing. All sensor data were synchronously sampled and synchronized with GNSS data from a commercially available sports watch while XC skiing on varying tracks, from amateur skiers and world-class athletes. An algorithm based on the correlation of the angular velocity of arms and legs was developed to detect the three main classic sub-techniques, diagonal, double poling with a kick and double poling. Other sub-techniques were classified as miscellaneous (0−20%). The system is shown to work well outdoors on snow during different conditions, and the implemented algorithm was validated by video analyses to detect the three sub-techniques with a sensitivity of 99-100%. This study is the first to detect and link sub-techniques in XC skiing to GNSS data, thereby associating the detection and distribution of sub-techniques to different terrains. Such information gives insight on technical and tactical aspects of skiers' daily training and competitions, thereby providing a tool for coaches and athletes.

Objectives High physical work demands are believed to be partly responsible for the high sickness absence among home care workers, but no studies have assessed their physical work demands using precise device-based measurements. Hence, the objective of this observational study was to assess physical work demands in home care, using wearable sensors. Methods From six home care units in a large municipality in Norway, 114 of 195 eligible home care workers filled in a questionnaire, a diary about work hours, and wore five accelerometers, and a heart rate sensor for up to six consecutive workdays. Results On average, the homecare workers spent 50% of the working hours sitting, 25.2% standing, 11.4% moving, 8.3% walking fast, 1.9% walking slow, 1.2% stair-climbing, 0.3% cycling, and 0.05% running. We found the following exposures to demanding postures: arm-elevation in an upright body position ≥30° was 36.7%, ≥60° was 4.1%, and ≥90°was 0.5%; forward trunk inclination in an upright body position ≥30° was 9.9%, ≥60° was 4%, and ≥90° was 1%; and for kneeling it was 0.8%. We found the average cardiovascular load (%heart rate reserve) during work to be 28%. There was considerable individual variation in these physical exposures at work. Conclusions This study presents precise information on various physical work demands of home care workers in Norway. Home care workers spent on average half the workday sitting and the remaining time in various occupational physical activities. Presently, few device-based exposure limits have been proposed for acceptable amounts of occupational physical exposures, but the level of arm-elevation, forward trunk inclination, and the considerable variation of physical workloads among home care workers, indicate that preventive measures should be taken.