Nowadays, pulse oximetry has become the standard in primary and intensive care units, especially as a triage tool during the current COVID-19 pandemic. Hence, a deeper understanding of the measurement errors that can affect precise readings is a key element in clinical decision-making. Several factors may influence the accuracy of pulse oximetry, such as skin color, body temperature, altitude, or patient movement. The skin pigmentation effect on pulse oximetry accuracy has long been studied reporting some contradictory conclusions. Recent studies have shown a positive bias in oxygen saturation measurements in patients with darkly pigmented skin, particularly under low saturation conditions. This review aims to study the literature that assesses the influence of skin pigmentation on the accuracy of these devices. We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to conduct a systematic review retrospectively since February 2022 using WOS, PubMed, and Scopus databases. We found 99 unique references, of which only 41 satisfied the established inclusion criteria. A bibliometric and scientometrics approach was performed to examine the outcomes of an exhaustive survey of the thematic content and trending topics.

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

In this work, we study numerically the periodicity of regular regions embedded in chaotic states for the case of an anisotropic magnetic particle. The particle is in the monodomain regime and subject to an applied magnetic field that depends on time. The dissipative Landau–Lifshitz–Gilbert equation models the particle. To perform the characterization, we compute several two-dimensional phase diagrams in the parameter space for the Lyapunov exponents and the isospikes. We observe multiple transitions among periodic states, revealing complex topological structures in the parameter space typical of dynamic systems. To show the finer details of the regular structures, iterative zooms are performed. In particular, we find islands of synchronization for the magnetization and the driven field and several shrimp structures with different periods.

The myths surrounding women’s participation in sport have been reflected in respiratory physiology. This study aims to demonstrate that continuous monitoring of blood oxygen saturation during a maximal exercise test in female athletes is highly correlated with the determination of the second ventilatory threshold ($V{T}_2$) or anaerobic threshold (AnT). The measurements were performed using a pulse oximeter during a maximum effort test on a treadmill on a population of 27 healthy female athletes. A common behavior of the oxygen saturation evolution during the incremental exercise test characterized by a decrease in saturation before the aerobic threshold (AeT) followed by a second significant drop was observed. Decreases in peripheral oxygen saturation during physical exertion have been related to the athlete’s physical fitness condition. However, this drop should not be a limiting factor in women’s physical performance. We found statistically significant correlations between the maximum oxygen uptake and the appearance of the ventilatory thresholds ($V{T}_1$ and $V{T}_2$), the desaturation time, the total test time, and between the desaturation time and the $V{T}_2$. We observed a relationship between the desaturation time and the $V{T}_2$ appearance. Indeed, a linear regression model between the desaturation time and the $V{T}_2$ appearance can predict $80\%$ of the values in our sample. Besides, we suggest that pulse oximetry is a simple, fairly accurate, and non-invasive technique for studying the physical condition of athletes who perform physical exertion.

Blood oxygen saturation (SpO₂) is vital for patient monitoring, particularly in clinical settings. Traditional SpO₂ estimation methods have limitations, which can be addressed by analyzing photoplethysmography (PPG) signals with artificial intelligence (AI) techniques. This systematic review, following PRISMA guidelines, analyzed 183 unique references from WOS, PubMed, and Scopus, with 26 studies meeting the inclusion criteria. The review examined AI models, key features, oximeters used, datasets, tested saturation intervals, and performance metrics while also assessing bias through the QUADAS-2 criteria. Linear regression models and deep neural networks (DNNs) emerged as the leading AI methodologies, utilizing features such as statistical metrics, signal-to-noise ratios, and intricate waveform morphology to enhance accuracy. Gaussian Process models, in particular, exhibited superior performance, achieving Mean Absolute Error (MAE) values as low as 0.57% and Root Mean Square Error (RMSE) as low as 0.69%. The bias analysis highlighted the need for better patient selection, reliable reference standards, and comprehensive SpO₂ intervals to improve model generalizability. A persistent challenge is the reliance on non-invasive methods over the more accurate arterial blood gas analysis and the limited datasets representing diverse physiological conditions. Future research must focus on improving reference standards, test protocols, and addressing ethical considerations in clinical trials. Integrating AI with traditional physiological models can further enhance SpO₂ estimation accuracy and robustness, offering significant advancements in patient care.