Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experimental Approach
2.2. Recruitment
2.3. Exclusion and Inclusion Criteria
2.4. Data Collection
2.4.1. “Well-Being Questionnaire”
2.4.2. “Profile of Mood State—Adolescents (POMS-A)”
2.4.3. Training Log
2.4.4. Performance Outcome
2.5. Modelling Training Adaptation Using ANN Geometric Optimisation
2.5.1. Concept
2.5.2. Mathematical Considerations Regarding the Development of the Model
2.5.3. Inputs to the Model
2.5.4. Overfitting
2.5.5. Goodness of Fit of the Model
2.5.6. Geometric Activity Performance Index
2.5.7. Prerequisites for the Modelling
3. Results
3.1. Swimmers’ Characteristics
3.2. Modelling Training Adaptation using ANN Geometric Optimisation
3.2.1. Goodness of Fit of the Model
3.2.2. Geometric Activity Performance Index
4. Discussion
4.1. Strengths and Limitations
4.2. Future Studies
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Data Sharing Statement
References
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Frequency | Data Type | Reminders |
---|---|---|
Daily After every training session | Training log: Rating of perceived exertion (RPE) using the modified Borg CR-10 RPE scale Sport type Distance (meters, if swimming) Duration (minutes) | If no training was entered on the previous day, the web platform automatically sent a reminder email on the following day. |
Twice a week Every Tuesday and Friday | The Well-being questionnaire | An email was sent on the day of completion and if needed up to two additional reminders were sent (on the day after and on the day after next). |
Fortnightly Every second Sunday | The POMS-A | An email was sent on the day of completion and if needed up to two additional reminders were sent (on the day after and on the day after next). |
Time Series ► | x | y | z |
---|---|---|---|
Combinations ▼ | |||
1 | Distance | Session-RPE | %PBT (binary) |
2 | Session-RPE | Recovery | %PBT (binary) |
3 | Training strain | Recovery | %PBT (binary) |
4 | Training monotony | Recovery | %PBT (binary) |
5 | Distance | Acute: Chronic Workload Ratio | %PBT (binary) |
Load Parameters | Coping Parameters | |
---|---|---|
External Load | Internal Load | |
Distance | Session-RPE Training strain Training monotony Acute:Chronic Workload Ratio | Recovery Sleep quality Sleep quantity Soreness Pleasure Stress Total Mood Disturbance |
Swimmer | Sex | Age (Year) | Quartile | Best Discipline (meter) | FINA Points 2013 | Quartile | Best %PBT | Quartile | Weekly Mean Internal Training Load (AU) | Quartile | Weekly Mean Distance (meter) | Quartile |
---|---|---|---|---|---|---|---|---|---|---|---|---|
A2 | ♂ | 18 | 3 | 400 freestyle ld | 765 | 4 | 100.1 | 1 | 4258.46 | 4 | 30,100 | 4 |
B5 | ♀ | 14 | 1 | 200 breaststroke ld | 633 | 3 | 110 | 4 | 3144.23 | 3 | 18,826.92 | 2 |
B6 | ♀ | 15 | 1 | 100 freestyle sd | 459 | 1 | 100.7 | 1 | 2775 | 2 | 17,148 | 2 |
B29 | ♀ | 15 | 1 | 50 breaststroke ld | 504 | 1 | 105.9 | 3 | 2377.31 | 1 | 15,426.92 | 1 |
C10 | ♂ | 19 | 4 | 100 medley sd | 582 | 2 | 103.1 | 2 | 2504.81 | 1 | 13,905.77 | 1 |
C13 | ♂ | 16 | 2 | 100 freestyle ld | 471 | 1 | 103.7 | 3 | 3353.08 | 3 | 23,386.54 | 3 |
C14 | ♂ | 16 | 2 | 400 medley ld | 445 | 1 | 109.2 | 4 | 2365.38 | 1 | 16,350 | 1 |
D21 | ♀ | 15 | 1 | 400 freestyle ld | 640 | 3 | 106.1 | 4 | 4417.71 | 4 | 27,253.33 | 4 |
D22 | ♀ | 15 | 1 | 200 breaststroke ld | 617 | 2 | 102.4 | 1 | 2946.4 | 2 | 32,212.8 | 4 |
D35 | ♀ | 18 | 3 | 100 freestyle ld | 631 | 3 | 100.7 | 1 | 2850.38 | 2 | 25,732.69 | 3 |
E24 | ♂ | 19 | 4 | 100 freestyle ld | 646 | 4 | 104.9 | 3 | 2112.5 | 1 | 15,411.46 | 1 |
E27 | ♀ | 18 | 3 | 100 backstroke ld | 619 | 2 | 102.7 | 2 | 4703.27 | 4 | 23,744.23 | 3 |
E28 | ♂ | 20 | 4 | 50 butterfly ld | 673 | 4 | 103.0 | 2 | 3128.46 | 3 | 22,900 | 2 |
Combinations ► | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Swimmers▼ | |||||
A2 | 75 | 100 | 100 | 100 | 100 |
B5 | 88 | 75 | 88 | 88 | 88 |
B6 | 100 | 100 | 83 | 100 | 100 |
B29 | 100 | 80 | 80 | 100 | 100 |
C10 | 100 | 100 | 100 | 100 | 100 |
C13 | 100 | 100 | 100 | 100 | 100 |
C14 | 100 | 100 | 100 | 100 | 75 |
D21 | 100 | 100 | 100 | 100 | 86 |
D22 | 100 | 100 | 100 | 86 | 100 |
D35 | 100 | 100 | 100 | 100 | 100 |
E24 | 100 | 100 | 100 | 100 | 100 |
E27 | 100 | 100 | 100 | 100 | 100 |
E28 | 75 | 63 | 88 | 88 | 75 |
Average | 95 | 94 | 95 | 97 | 94 |
Global average | 95 |
Correlation Tests | Quartile | Best %PBT | |||||
---|---|---|---|---|---|---|---|
Statistic | Original p-Value | Corrected p-Value | Statistic | Original p-Value | Corrected p-Value | ||
GAPI_1 | Spearman rank | 0.85 | <0.01 | <0.01 | 0.85 | <0.01 | <0.01 |
Blomqvist β | 0.93 | <0.01 | <0.01 | 0.92 | <0.01 | <0.01 | |
GAPI_2 | Spearman rank | 0.35 | 0.25 | 0.51 | 0.33 | 0.28 | 0.28 |
Blomqvist β | 0.46 | 0.25 | 0.75 | 0.42 | 0.08 | 0.32 | |
GAPI_3 | Spearman rank | 0.56 | 0.05 | 0.13 | 0.59 | 0.03 | 0.13 |
Blomqvist β | 0.46 | 0.25 | 0.25 | 0.42 | 0.08 | 0.16 | |
GAPI_4 | Spearman rank | 0.62 | 0.02 | 0.02 | 0.65 | 0.01 | 0.04 |
Blomqvist β | 0.74 | 0.02 | 0.03 | 0.67 | <0.01 | <0.01 | |
GAPI_5 | Spearman rank | 0.39 | 0.20 | 0.40 | 0.47 | 0.10 | 0.31 |
Blomqvist β | 0.46 | 0.25 | 0.25 | 0.42 | 0.08 | 0.32 |
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Carrard, J.; Kloucek, P.; Gojanovic, B. Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation. Sports 2020, 8, 8. https://doi.org/10.3390/sports8010008
Carrard J, Kloucek P, Gojanovic B. Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation. Sports. 2020; 8(1):8. https://doi.org/10.3390/sports8010008
Chicago/Turabian StyleCarrard, Justin, Petr Kloucek, and Boris Gojanovic. 2020. "Modelling Training Adaptation in Swimming Using Artificial Neural Network Geometric Optimisation" Sports 8, no. 1: 8. https://doi.org/10.3390/sports8010008