Comparison of physiological responses to morning and evening submaximal running

The aim of this study was to assess the effect of time of day on physiological responses to running at the speed at the lactate threshold. After determination of the lactate threshold, using a standard incremental protocol, nine male runners (age 26.3 - 5.7 years, height 1.77 - 0.07 m, mass 73.1 - 6.5 kg, lactate threshold speed 13.6 - 1.6 km· h -1 ; mean - s ) completed a standardized 30 min run at lactate threshold speed, twice within 24 h (07:00- 09:00 h and 18:00-21:00 h). Core body temperature, heart rate, minute ventilation, oxygen uptake, carbon dioxide expired, respiratory exchange ratio and capillary blood lactate were measured at rest, after a warm-up and at 10, 20 and 30 min during the run. In addition, the rating of perceived exertion was reported every 10 min during the run. Significant diurnal variation was observed only for body temperature (36.9 - 0.9°C vs 37.3 - 0.3°C) and respiratory exchange ratio at rest (0.86 - 0.01 vs 0.89 - 0.07) ( P ? 0.05). Diurnal variation persisted for body temperature throughout the warm-up (37.1 - 0.2°C vs 37.5 - 0.3°C) and during exercise (36.2 - 0.6°C vs 38.6 - 0.4°C), but only during the warm-up for the respiratory exchange ratio (0.85 - 0.05 vs 0.87 - 0.02) ( P ? 0.05). The rating of perceived exertion was significantly elevated during the morning trial (12.7 - 0.9 vs 11.9 - 1.2) ( P ? 0.05). These findings suggest that, despite the diurnal variation in body temperature, other physiological responses to running at lactate threshold speed are largely unaffected. However, a longer warm-up may be required in morning trials because of a slower increase in body temperature, which could have an impact on ventilation responses and ratings of perceived exertion.     

Self-report vs. kinematic screening test: prevalence,demographics, and sports biography of yips-affected golfers

The yips is considered a task-specific movement disorder. Its estimated prevalence, however, is high compared to similar neurological movement disorders, possibly resulting from previous studies’ restriction of samples based on skill level, and self-report bias. Alternatively, this high prevalence might be an indication of additional aetiologies, for example the influence of previously played racket sports. We estimated the prevalence of the putting yips across the skill range, using self-reports in one study and a screening test in a second study. We explored if previously played sports matter for the development of the yips. In study 1, yips prevalence (N = 1,306) and golfers’ sports biographies (n = 264) were examined via two online surveys, in which golfers indicated if they were yips-affected. In study 2, golfers (N = 186) putted in a standardised putting test while kinematic and performance measures were recorded. Prevalence was estimated via a kinematic threshold. Sports biographies (n = 119) were obtained via an online survey. Prevalence of currently yips-affected golfers was 22.4% in study 1 and 16.7% in study 2. In both studies, more yips-affected than unaffected golfers had experience in playing racket sports. Yips prevalence remained higher than previously estimated prevalence of other movement disorders but decreased when the whole skill range including professionals and novices was considered. Future studies should use the kinematic screening test instead of self-reports to detect the yips and further investigate the influence of previously played racket sports.     

Impact of age on haematological markers pre- and post-marathon running

This study investigated whether haematological markers differ between young and masters marathon participants, running at similar performance levels. Nine young (31.89 ± 4.96 years) and eight masters (63.13 ± 4.61 years) runners participated. At five time points (pre-race through 54 h post-race), a complete blood cell count, basic metabolic panel and creatine kinase (CK) isoenzyme panel were assessed. Race performance was standardised using the World Masters Association Age Grading Performance Tables. Total CK levels were elevated for all participants at all time points post-race (P < 0.001). The CK-isoenzyme MB% was elevated across groups at 6, 30 and 54 h post-race (P < 0.01, P < 0.01 and P < 0.05), with masters runners having a higher CK-MB% at 30 and 54 h (P < 0.05, P < 0.05). Total white blood cell and neutrophil counts were elevated through 6 h post-race (P < 0.001), with higher levels found in younger runners (P < 0.001). When considering all blood work, masters runners had a higher number of abnormal values at 6, 30 and 54 h post-race (P < 0.05, P < 0.01 and P < 0.05). In conclusion, masters runners demonstrated sustained CK-MB elevation, which may suggest greater cardiac stress. However, future studies using additional cardiac markers should be completed to confirm these findings. In addition, masters runners showed an increased number of laboratory values outside normal range, indicating the body’s reduced capacity to respond to marathon running.     

Big and open linked data analytics ecosystem: Theoretical background and essential elements

Big and open linked data are often mentioned together because storing, processing, and publishing large amounts of these data play an increasingly important role in today's society. However, although this topic is described from the political, economic, and social points of view, a technical dimension, which is represented by big data analytics, is insufficient. The aim of this review article was to provide a theoretical background of big and open linked data analytics ecosystem and its essential elements. First, the key terms were introduced including related dimensions. Then, the key lifecycle phases were defined and involved stakeholders were identified. Finally, a conceptual framework was proposed. In contrast to previous research, the new ecosystem is formed by interactions of stakeholders in the following dimensions and their sub-dimensions: transparency, engagement, legal, technical, social, and economic. These relationships are characterized by the most important requisites and public policy choices affecting the data analytics ecosystem together with the key phases and activities of the data analytics lifecycle. The findings should contribute to relevant initiatives, strategies, and policies and their effective implementation.