Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning

Objectives

To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier).

Methods

Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) unfavourable 20MSRT development (identification of individuals with 20MSRT development in the lowest tertile among adolescents with baseline 20MSRT below median level).

Results

Prediction performance for future 20MSRT status (Task 1) was (area under the receiver operating characteristic curve, AUC) 83% and 76%, sensitivity 80% and 60%, and specificity 78% and 79% in girls and boys, respectively. Twenty variables showed predictive power in boys, 14 in girls, including fitness characteristics, physical activity, academic scores, adiposity, life enjoyment, parental support, social status in school and perceived fitness.

Prediction performance for future development (Task 2) was lower and differed statistically from random level only in girls (AUC 68% and 40% in girls and boys).

Conclusion

RF classifier predicted future unfavourable status in 20MSRT and identified potential individuals for interventions based on a holistic profile (14‒20 baseline characteristics). The MATLAB script and functions employing the RF classifier of this study are available for future precision exercise medicine research.

Production oriented method for the determination of rare earth and other trace elements in rocks by ICP-MS

The rare earth elements along with Y, Sc, Co, Hf, Nb, Rb, Ta, Th, Ti, U, V and Zr have been determined in ten standard reference materials and ten granitoid samples by inductively coupled plasma mass spectrometry (ICP-MS). Two digestion methods have been used: (a) dissolution with hydrofluoric-perchloric acid followed by fusion of the residue with lithium metaborate, (b) dissolution with hydrofluoric acid-perchloric acid followed by fusion with lithium metaboratesodium perborate and addition of hydrogen peroxide. An evaluation of the two decomposition methods and a comparison of the ICP-MS and X-ray fluorescence results have been carried out. In comparing the two digestion methods, method (b) gave more accurate results for Nb and Ta, and it has been applied to a rock geochemistry project covering whole Finland (about 7000 samples). The statistical stability of the method has been monitored by frequent analyses of reference material SDC-1 (Mica Schist, USGS).