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Aging and Possible Benefits or Negatives of Lifelong Endurance Running: How Master Male Athletes Differ from Young Athletes and Elderly Sedentary?

ABSTRACT: Regular physical activity, recommended by the WHO, is crucial in maintaining a good physical fitness level and health status and slows down the effects of aging. However, there is a lack of knowledge of whether lifelong endurance running, with a volume and frequency above the WHO limits, still brings the same benefits, or several negative effects too. The present study aims to examine the protentional benefits and risks of lifelong endurance running training in Master male athletes, as this level of physical activity is above the WHO recommendations. Within the study, four main groups of participants will be included: (1) endurance-trained master athletes, (2) endurance-trained young athletes, (3) young sedentary adults, and (4) elderly sedentary. Both groups of athletes are strictly marathon runners, who are still actively running. The broad spectrum of the diagnostic tests, from the questionnaires, physical fitness testing, and blood sampling to muscle biopsy, will be performed to obtain the possibility of complexly analyzing the effects of lifelong endurance physical activity on the human body and aging. Moreover, the study will try to discover and explain new relationships between endurance running and diagnostic parameters, not only within aging.

SUBMITTER: Vajda M

PROVIDER: S-EPMC9602696 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Aging and Possible Benefits or Negatives of Lifelong Endurance Running: How Master Male Athletes Differ from Young Athletes and Elderly Sedentary?

Vajda Matej M Oreská Ľudmila Ľ Černáčková Alena A Čupka Martin M Tirpáková Veronika V Cvečka Ján J Hamar Dušan D Protasi Feliciano F Šarabon Nejc N Zampieri Sandra S Löfler Stefan S Kern Helmut H Sedliak Milan M

International journal of environmental research and public health 20221013 20

Regular physical activity, recommended by the WHO, is crucial in maintaining a good physical fitness level and health status and slows down the effects of aging. However, there is a lack of knowledge of whether lifelong endurance running, with a volume and frequency above the WHO limits, still brings the same benefits, or several negative effects too. The present study aims to examine the protentional benefits and risks of lifelong endurance running training in Master male athletes, as this leve ...[more]

PMID: 36293774

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Project description:Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m-2; VO2max = 53.20 ± 7.17 mL·min-1·kg-1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Dataset Information

Aging and Possible Benefits or Negatives of Lifelong Endurance Running: How Master Male Athletes Differ from Young Athletes and Elderly Sedentary?

Publications

Aging and Possible Benefits or Negatives of Lifelong Endurance Running: How Master Male Athletes Differ from Young Athletes and Elderly Sedentary?

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