The impact of precompetition state on athletic performance among track and field athletes using machine learning
ObjectiveThis study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athle...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1429510/full |
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| author | Yuting Zhang Pengyu Fu Qi Yu Qingmei Niu Dongfeng Nie Xiangya Dou Xiaoqin Zhang |
| author_facet | Yuting Zhang Pengyu Fu Qi Yu Qingmei Niu Dongfeng Nie Xiangya Dou Xiaoqin Zhang |
| author_sort | Yuting Zhang |
| collection | DOAJ |
| description | ObjectiveThis study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athletic performance.MethodsMultiple indicators, traditional methods, and machine learning methods are used to detect the exercise load, fatigue index, and precompetition state of athletes with different sports performances.Results(1) Two weeks before the competition, the fat mass in the left upper limb in the BP group was significantly higher than that in the BnP group (P < 0.05). The absolute values of blood basophils and triglycerides (TGs) in the BnP group were significantly higher than those in the BP group (P < 0.05). The positive detection rate of urinary leukocytes in the BnP group was higher than that in the BP group, and the positive detection rate of urinary occult blood and vitamin C in the BP group was higher than that in the BnP group. (2) One week before the competition, the blood lactate dehydrogenase (LDH) in the BP group was significantly higher than that in the BnP group (P < 0.05). The detection rate of positive urinary occult blood in the BnP group was higher than that in the BP group (P < 0.05). (3) No significant differences were found in the daily dietary intake, energy consumption values, physical activity, sleep efficiency, real-time heart rate, real-time respiratory rate, and real-time heart rate variability between the intensive and reduced periods. (4) The Rosenberg Self-Esteem Scale score of the BnP group was significantly higher than that of the BP group (P < 0.05).ConclusionPrecompetition absolute basophil, LDH, TG, white blood cells, creatine kinase, fat mass in the left upper limb, erythrocyte pressure (HCT), and individual failure anxiety can be used as training monitoring indicators that focus on tracking athlete status before the race. |
| format | Article |
| id | doaj-art-dd68bb2549f14b86a48bd1412191d0e9 |
| institution | Kabale University |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-dd68bb2549f14b86a48bd1412191d0e92025-02-07T06:49:21ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-02-011610.3389/fphys.2025.14295101429510The impact of precompetition state on athletic performance among track and field athletes using machine learningYuting Zhang0Pengyu Fu1Qi Yu2Qingmei Niu3Dongfeng Nie4Xiangya Dou5Xiaoqin Zhang6College of Public Policy and Management, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Physical Education, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Physical Education, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Physical Education, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Physical Education, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaCollege of Life Science, Northwestern Polytechnical University, Xi’an, Shaanxi, ChinaDepartment of Physical Education, The Affiliated School of Shanxi Agricultural University, Tai’yuan, Shaanxi, ChinaObjectiveThis study aims to compare the differences in the precompetition status (nutritional, physiological, biochemical, psychological, and sleep statuses) among college track and field athletes with different competition performances and to screen for key indicators of differences affecting athletic performance.MethodsMultiple indicators, traditional methods, and machine learning methods are used to detect the exercise load, fatigue index, and precompetition state of athletes with different sports performances.Results(1) Two weeks before the competition, the fat mass in the left upper limb in the BP group was significantly higher than that in the BnP group (P < 0.05). The absolute values of blood basophils and triglycerides (TGs) in the BnP group were significantly higher than those in the BP group (P < 0.05). The positive detection rate of urinary leukocytes in the BnP group was higher than that in the BP group, and the positive detection rate of urinary occult blood and vitamin C in the BP group was higher than that in the BnP group. (2) One week before the competition, the blood lactate dehydrogenase (LDH) in the BP group was significantly higher than that in the BnP group (P < 0.05). The detection rate of positive urinary occult blood in the BnP group was higher than that in the BP group (P < 0.05). (3) No significant differences were found in the daily dietary intake, energy consumption values, physical activity, sleep efficiency, real-time heart rate, real-time respiratory rate, and real-time heart rate variability between the intensive and reduced periods. (4) The Rosenberg Self-Esteem Scale score of the BnP group was significantly higher than that of the BP group (P < 0.05).ConclusionPrecompetition absolute basophil, LDH, TG, white blood cells, creatine kinase, fat mass in the left upper limb, erythrocyte pressure (HCT), and individual failure anxiety can be used as training monitoring indicators that focus on tracking athlete status before the race.https://www.frontiersin.org/articles/10.3389/fphys.2025.1429510/fulltrack and field athletespre-competition statuscompetition performancemachine learningtraining monitoring |
| spellingShingle | Yuting Zhang Pengyu Fu Qi Yu Qingmei Niu Dongfeng Nie Xiangya Dou Xiaoqin Zhang The impact of precompetition state on athletic performance among track and field athletes using machine learning Frontiers in Physiology track and field athletes pre-competition status competition performance machine learning training monitoring |
| title | The impact of precompetition state on athletic performance among track and field athletes using machine learning |
| title_full | The impact of precompetition state on athletic performance among track and field athletes using machine learning |
| title_fullStr | The impact of precompetition state on athletic performance among track and field athletes using machine learning |
| title_full_unstemmed | The impact of precompetition state on athletic performance among track and field athletes using machine learning |
| title_short | The impact of precompetition state on athletic performance among track and field athletes using machine learning |
| title_sort | impact of precompetition state on athletic performance among track and field athletes using machine learning |
| topic | track and field athletes pre-competition status competition performance machine learning training monitoring |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1429510/full |
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