The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e., sedentary: <1.5 metabolic equivalents (MET); light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e., >480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-week preparation period was assessed by the wrist-worn multisensory device Microsoft Band II (MSBII). A total of 11 rowers provided valid data (i.e., >480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63 ± 1.25 h per day during the week and 12.49 ± 1.10 h per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27 ± 1.15, 0.76 ± 0.37, 0.51 ± 0.44 h per day, and 0.67 ± 0.43, 0.59 ± 0.37, 0.53 ± 0.32 h per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69). Based on our pilot study of 11 national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 h/day.

Spin defects in solid-state materials are strong candidate systems for quantum information technology and sensing applications. Here we explore in details the recently discovered negatively charged boron vacancies (V\(_B\)\(^−\)) in hexagonal boron nitride (hBN) and demonstrate their use as atomic scale sensors for temperature, magnetic fields and externally applied pressure. These applications are possible due to the high-spin triplet ground state and bright spin-dependent photoluminescence of the V\(_B\)\(^−\). Specifically, we find that the frequency shift in optically detected magnetic resonance measurements is not only sensitive to static magnetic fields, but also to temperature and pressure changes which we relate to crystal lattice parameters. We show that spin-rich hBN films are potentially applicable as intrinsic sensors in heterostructures made of functionalized 2D materials.

Masers as telecommunication amplifiers have been known for decades, yet their application is strongly limited due to extreme operating conditions requiring vacuum techniques and cryogenic temperatures. Recently, a new generation of masers has been invented based on optically pumped spin states in pentacene and diamond. In this study, we pave the way for masers based on spin S = 3/2 silicon vacancy (V\(_{Si}\)) defects in silicon carbide (SiC) to overcome the microwave generation threshold and discuss the advantages of this highly developed spin hosting material. To achieve population inversion, we optically pump the V\(_{Si}\) into their m\(_S\) = ±1/2 spin sub-states and additionally tune the Zeeman energy splitting by applying an external magnetic field. In this way, the prerequisites for stimulated emission by means of resonant microwaves in the 10 GHz range are fulfilled. On the way to realising a maser, we were able to systematically solve a series of subtasks that improved the underlying relevant physical parameters of the SiC samples. Among others, we investigated the pump efficiency as a function of the optical excitation wavelength and the angle between the magnetic field and the defect symmetry axis in order to boost the population inversion factor, a key figure of merit for the targeted microwave oscillator. Furthermore, we developed a high-Q sapphire microwave resonator (Q ≈ 10\(^4\)–10\(^5\)) with which we find superradiant stimulated microwave emission. In summary, SiC with optimized spin defect density and thus spin relaxation rates is well on its way of becoming a suitable maser gain material with wide-ranging applications.

Quantum systems can provide outstanding performance in various sensing applications, ranging from bioscience to nanotechnology. Atomic-scale defects in silicon carbide are very attractive in this respect because of the technological advantages of this material and favorable optical and radio frequency spectral ranges to control these defects. We identified several, separately addressable spin-3/2 centers in the same silicon carbide crystal, which are immune to nonaxial strain fluctuations. Some of them are characterized by nearly temperature independent axial crystal fields, making these centers very attractive for vector magnetometry. Contrarily, the zero-field splitting of another center exhibits a giant thermal shift of −1.1 MHz/K at room temperature, which can be used for thermometry applications. We also discuss a synchronized composite clock exploiting spin centers with different thermal response.

The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 “Somewhat hard to hard” on Borg’s 6–20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 % for the whole dataset, 81.8 % for the trained runners, and 86.1 % for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.