This study examined the validity, precision and accuracy of the predictions of distance running performances in female runners from three nomograms. Official rankings of French women for the 3000-m, 5000-m, and 10,000-m track-running events from 2005 to 2019 were examined. Only female runners who performed in the three distance events within the same year were included (n=158). Each performance over any distance was predicted using the three nomograms from the two other performances. The 3000-m, 5000-m and 10,000-m performances were 11min17 s± 1min20 s, 19min29 s ± 2min20 s, 41min18 s ± 5min7 s, respectively. No difference was found between the actual and predicted running performances regardless of the nomogram (p>0.05). All predicted running performances were significantly correlated with the actual ones, with a very high correlation coefficient (p<0.001; r>0.90). Bias and 95% limits of agreement were acceptable because, whatever the nomogram, they were less than or equal to -0.0±6.2% on the 3000-m, 0.0±3.7% on the 5000-m, and 0.1±9.3% on the 10,000-m. The study confirms the validity of the three nomograms to predict track-running performance with a high level of accuracy. The predictions from these nomograms are similar and may be used in training programs and competitions.

This systematic review evaluated the literature pertaining to the effect of shoes on lower limb venous status in asymptomatic populations during gait or exercise. The review was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed-NCBI, EBSCO Host, Cochrane Library and Science Direct databases were searched (March 2019) for words around two concepts: shoes and venous parameters. The inclusion criteria were as follows: (1) the manuscript had to be published in an English-language peer-reviewed journal and the study had to be observational or experimental and (2) the study had to suggest the analysis of many types of shoes or orthotics on venous parameters before, during and/or after exercise. Out of 366 articles, 60 duplications were identified, 306 articles were analyzed, and 13 articles met the eligibility criteria after screening and were included. This review including approximately 211 participants. The methodological rigor of these studies was evaluated with the modified Downs and Black quality index. Nine studies investigated the effect of shoes on blood flow parameters, two on venous pressure and two on lower limb circumferences with exercise. Evidence was found that unstable shoes or shoes with similar technology, sandals, athletic or soft shoes, and customized foot orthotics elicited more improvement in venous variables than high-heeled shoes, firm shoes, ankle joint immobilization and barefoot condition. These venous changes are probably related to the efficiency of muscle pumps in the lower limbs, which in turn seem to be dependent on shoe features associated with changes in the kinetics, kinematics and muscle activity variables in lower limbs during gait and exercise.

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Although studies used machine learning algorithms to predict performances in sports activities, none, to the best of our knowledge, have used and validated two artificial intelligence techniques: artificial neural network (ANN) and k-nearest neighbor (KNN) in the running discipline of marathon and compared the accuracy or precision of the predicted performances. Official French rankings for the 10-km road and marathon events in 2019 were scrutinized over a dataset of 820 athletes (aged 21, having run 10km and a marathon in the same year that was run slower...). For the KNN and ANN the same inputs (10-km race time, body mass index, age and sex) were used to solve a linear regression problem to estimate the marathon race time. No difference was found between the actual and predicted marathon performances for either method (p > 0.05). All predicted performances were significantly correlated with the actual ones, with very high correlation coefficients (r> 0.90; p < 0.001). KNN outperformed ANN with a mean absolute error of 2.4 vs 5.6%. The study confirms the validity of both algorithms, with better accuracy for KNN in predicting marathon performance. Consequently, the predictions from these artificial intelligence methods may be used in training programs and competitions.