Establishing Age-calibrated Normative PROMIS Scores for Hand and Upper Extremity Clinic

The purpose of our study is to investigate differences in normative PROMIS upper extremity function (PROMIS-UE), physical function (PROMIS-PF), and pain interference (PROMIS-PI) scores across age cohorts in individuals without upper extremity disability. Methods: Individuals without upper extremity disability were prospectively enrolled. Subjects were administered PROMIS-UE, PROMIS-PF, and PROMIS-PI forms. Retrospective PROMIS data for eligible subjects were also utilized. The enrolled cohort was divided into age groups: 20-39, 40-59, and 60-79 years old. ANOVA, ceiling and floor effect analysis, and kurtosis and skewness statistics were performed to assess PROMIS scores trends with age. Results: This study included 346 individuals. In the 20-39 age group, mean PROMIS scores were 56.2 ± 6.1, 59.8 ± 6.9, and 43.1 ± 6.7 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. In the 40-59 age group, mean PROMIS computer adaptive test scores were 53.3 ± 7.5, 55.3 ± 7.6, and 46.6 ± 7.8 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. In the 60-79 age group, mean PROMIS scores were 48.4 ± 7.6, 48.5 ± 5.6, and 48.7 ± 6.9 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. Differences in mean PROMIS scores were significant across all PROMIS domains and age cohorts (P \u3c 0.001). Conclusion: Younger individuals without hand or upper extremity disability show higher normative PROMIS-UE and PROMIS-PF scores and lower PROMIS-PI scores, indicating greater function and less pain than older counterparts. A universal reference PROMIS score of 50 appears suboptimal for clinical assessment and decision-making in the hand and upper extremity clinic. This study included 346 individuals. In the 20-39 age group, mean PROMIS scores were 56.2 ± 6.1, 59.8 ± 6.9, and 43.1 ± 6.7 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. In the 40-59 age group, mean PROMIS computer adaptive test scores were 53.3 ± 7.5, 55.3 ± 7.6, and 46.6 ± 7.8 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. In the 60-79 age group, mean PROMIS scores were 48.4 ± 7.6, 48.5 ± 5.6, and 48.7 ± 6.9 for PROMIS-UE, PROMIS-PF, and PROMIS-PI, respectively. Differences in mean PROMIS scores were significant across all PROMIS domains and age cohorts (P \u3c 0.001)

Analysis of over 1 million race records shows runners from East African countries as the fastest in 50-km ultra-marathons

The 50-km ultra-marathon is a popular race distance, slightly longer than the classic marathon distance. However, little is known about the country of affiliation and age of the fastest 50-km ultra-marathon runners and where the fastest races are typically held. Therefore, this study aimed to investigate a large dataset of race records for the 50-km distance race to identify the country of affiliation and the age of the fastest runners as well as the locations of the fastest races. A total of 1,398,845 50-km race records (men, n = 1,026,546; women, n = 372,299) were analyzed using both descriptive statistics and advanced regression techniques. This study revealed significant trends in the performance of 50-km ultra-marathoners. The fastest 50-km runners came from African countries, while the fastest races were found to occur in Europe and the Middle East. Runners from Ethiopia, Lesotho, Malawi, and Kenya were the fastest in this race distance. The fastest 50-km racecourses, providing ideal conditions for faster race times, are in Europe (Luxembourg, Belarus, and Lithuania) and the Middle East (Qatar and Jordan). Surprisingly, the fastest ultra-marathoners in the 50-km distance were found to fall into the age group of 20-24 years, challenging the conventional belief that peak ultra-marathon performance comes in older age groups. These findings contribute to a better understanding of the performance models in 50-km ultra-marathons and can serve as valuable insights for runners, coaches, and race organizers in optimizing training strategies and racecourse selection

What affects the e‐bicycle speed perception in the era of eco‐sustainable mobility: A driving simulator study

The increase in the number of electric bicycles worldwide has resulted in a rise in the number of traffic accidents involving e‐bicyclists. Previous studies have been based on analyzing the use, advantages and disadvantages of e‐bicycles, whereas only a small number of studies have been focused on analyzing the e‐bicycle traffic safety, particularly the factors leading to the occurrence of traffic accidents. One of the factors affecting the occurrence of traffic accidents is the incorrect perception of the e‐bicycle speed by other traffic participants. To examine the mentioned problem, the authors of this paper conducted an experimental study to determine what affects the e‐bicycle speed perception. The experiment included 175 participants, aged 18 to 50. The research was conducted under laboratory conditions using a driving simulator, at different e‐bicycle speeds (10 km/h, 20 km/h and 30 km/h), in the situations in which the e‐bicyclist was (not) using a reflective vest. The results show statistically significant differences in the e‐bicycle speed perception when the e‐bicyclist does not use/uses a reflective vest. Besides, the driving licence categories of traffic participants and their driving experience also have a significant impact on the perception of the e‐ bicycle speed