Objectives: The International Association for the Study of Pain defines 3 pain types presumed to involve different mechanisms—nociceptive, neuropathic, and nociplastic. Based on the hypothesis that pain types should guide the matching of patients with treatments, work has been undertaken to identify features to discriminate between them for clinical use. This study aimed to evaluate the validity of features to discriminate between pain types. Materials and Methods: Subjective and physical features were evaluated in a cohort of 350 individuals with chronic musculoskeletal pain attending a chronic pain management program. The analysis tested the hypothesis that, if features nominated for each pain type represent 3 different groups, then (1) cluster analysis should identify 3 main clusters of patients, (2) these clusters should align with the pain type allocated by an experienced clinician, (3) patients within a cluster should have high expression of the candidate features proposed to assist identification of that pain type. Supervised machine learning interrogated features with the greatest and least importance for discrimination, and probabilistic analysis probed the potential for the coexistence of multiple pain types. Results: Results confirmed that data could be best explained by 3 clusters. Clusters were characterized by a priori specified features and agreed with the designation of the experienced clinician with 82% accuracy. Supervised analysis highlighted features that contributed most and least to the classification of pain type, and probabilistic analysis reinforced the presence of mixed pain types. Discussion: These findings support the foundation for further refinement of a clinical tool to discriminate between pain types.

In the field of sport psychology, trait theories view personality characteristics as the main determinants of behavior. This study explored personality traits in athletes of a growing sport, trail or mountain ultrarunning, a group for which previous studies have yielded inconclusive results regarding the dominant traits and which traits are associated with sporting success. The NEO-FFI questionnaire by Costa and McCrae (1992) was applied online to a sample of 86 trail runners (60 men and 26 women), who participated in a competition in Chile. It sought to determine what the personality profile of these athletes was like; to identify if there were significant differences between this population and the normal population; and if there were differences among them according to gender, the distance in which they competed, the results in the competition, and the motivations they stated for running. For each comparison, a Wilcoxon Rank Sum Test was performed to measure its differences and respective statistical significance. Results showed that the personality profile of the mountain runners matched that of successful athletes in terms of high Conscientiousness and low Neuroticism, however, Openness to Experience scores showed no significant differences with the normal population. Neither did significant differences appear in any of the traits between ultradistance and shorter distance runners, nor according to their motivations for running, nor between male and female runners (except in the Agreeableness dimension). Finally, a clustering of the runners was outlined according to their personality profiles and performance, to see if they fit existing classifications that distinguish between recreational and results-oriented athletes, observing that it was possible to establish distinct profiles among runners. These results are of relevance to sports practitioners, who can design tailored interventions according to athletes´ personality profiles and prevent negative consequences when these traits become associated with unhealthy behaviors. The modest gender differences found, allow questioning gender stereotypes within the sport and fostering a more equitable approach to athlete training. This knowledge can contribute to further growing the sport of trail and ultrarunning in Chile and Latin America. Keywords: trail runners; ultrarunners; endurance sports; personality; Big Five

This study examined associations among training load (external and internal), heart rate variability (HRV), perceptual fatigue, sleep, and injury in endurance athletes during a 12-week training mesocycle. The aims were to (i) compare these parameters between injured and non-injured weeks and (ii) assess differences between high- and low-severity injuries. Fifteen endurance athletes (80% males; median age: 36.5 years; running experience: 5.5 years) participated. Injury severity was measured using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems (OSTRC-H). Training load (training stress score [TSS], Session-Rate of Perceived Exertion [sRPE]), HRV, perceptual fatigue (readiness to train and wellbeing), and sleep were collected through daily and weekly assessments over 12 weeks. The weekly injury prevalence over the 12-week period ranged from 7%–40%, with 47% classified as high severity. Injured weeks were characterised by significantly higher sleep-related impairment ( p = .004, Hedge's g = 0.704), while all other variables did not significantly differ. Athletes who sustained more severe injuries exhibited lower TSS ( p < .001, Hedge's g = 0.84), lower sRPE ( p = .029, Hedge's g = 0.705), poorer subjective wellbeing ( p = .003, Hedge's g = 0.753), and lower HRV ( p = .059, Hedge's g = 0.487) in the week preceding the injury. This exploratory study provides preliminary insights into the potential role of sleep-related impairment, training load fluctuations, and HRV in injury severity among endurance athletes. While findings suggest benefits of integrating these measures into monitoring strategies, the small sample size limits generalisability and should be interpreted with caution.

The aim of this article is to examine the validity of elevation gain measures in mountain activities, such as hiking and mountain running, using different wearable devices and post-processing procedures. In particular, a total of 202 efforts were recorded and evaluated using three standard devices: GPS watch, GPS watch with barometric altimeter, and smartphone. A benchmark was based on orthorectified aerial photogrammetric survey conducted by the Chilean Air Force. All devices presented considerable elevation gain measuring errors, where the barometric device consistently overestimated elevation gain, while the GPS devices consistently underestimated elevation gain. The incorporation of secondary information in the post-processing can substantially improve the elevation gain measuring accuracy independently of the device and altitude measuring technology, reducing the error from −5% to −1%. These results could help coaches and athletes correct elevation gain estimations using the proposed technique, which would serve as better estimates of physical workload in mountain physical activities.

This study investigates how fractal complexity affects Trail and mountain running (TMR) race course measurements at varying GPS resolutions and emphasizes the need for standardized course measurement protocols. GPX files from 34 UTMB World Series race courses, including final events in Chamonix, were analyzed. Horizontal distance, elevation gain, km-effort, and fractal complexity were computed at varying GPS spatial resolutions (0.2–100 m). Elevation data were refined using a 20-cm resolution Digital Elevation Model (DEM) to ensure consistency across the dataset. The courses were systematically resampled and compared to assess the effects of spatial resolution on race measurements and classifications. The findings reveal that a decrease in GPS spatial resolution significantly reduces measured distances and elevation gains. Discrepancies in kilometer-effort reached up to 14% (mean = 7.0%, SD = 3.8%), horizontal distance up to 6.3% (mean = 2.9%, SD = 1.5%), and elevation gain up to 32% (mean = 14.0%, SD = 9.5%). Adopting a 1-m resolution, chosen for its practical balance between capturing terrain complexity at a human scale and computational efficiency, would enhance the reliability of distance, elevation gain, and km-effort calculations, ensuring fairer race classifications and improved comparability across events.

La delimitación de áreas quemadas es un paso importante para el estudio de incendios forestales, y el uso de teledetección satelital permite una metodología escalable. Estudios previos utilizan un umbral de dNBR para determinar la presencia de áreas incendiadas, pero este umbral se ve afectado por la variabilidad vegetacional determinada por la geografía del área de estudio y la cobertura de uso de suelos. Por ello, se utilizó la diferencia del índice normalizado de áreas quemadas (dNBR) para estudiar los mega incendios que afectaron la zona central de Chile en el verano de 2017. Se desarrolló una metodología automatizada que a partir de imágenes satelitales y de polígonos de las áreas incendiadas provistos por la Corporación Nacional Forestal de Chile (CONAF) genera un set de umbrales de dNBR diferenciados por región administrativa y uso de suelo. La aplicación de umbrales de dNBR diferenciados permite mejorar significativamente la precisión del modelo de delimitación de áreas quemadas, aunque no logra resultados satisfactorios para todos los usos de suelo. Este avance metodológico permitirá mejorar el diseño y fiscalización de políticas de prevención, conservación y restauración de ecosistemas afectados por incendios forestales.