Psychological Follow-up of 6 Months Training for a Half Ironman Triathlon
Bibliographic record
Abstract
Preparation for an endurance event amongst amateur athletes requires a major investment in time as well as a proper physical and mental training. Even if there is an increase of participation for endurance events in the country, there are few studies that have reported the psychological effects of such a demanding training for age groups athletes. PURPOSE: The aim of this study was to characterise the psychological state of recreational athletes during a six months training prior to a half Ironman triathlon. METHODS: Thirty-one amateur athletes were recruited for this observational study. Participants were 40 ± 9.1 years old; and had a body weight of 74 ± 12.7 kg and a height of 172 ± 10.12 cm. All participants underwent a physical fitness assessment in January and two weeks prior to the half ironman event, held in June. They followed a training program supervised by a registered kinesiologist based on the Ironman UniversityTM annual planning. The training volume was 410.4 ± 201.48 min per week. For each month of training, participants received an email with a link to complete a monthly series of questionnaires that included: Facets Mindfulness Questionnaire Short Version (FFMQsv), Profile mood scale (POMS), Positive and Negative Affect Schedule (PANAS) and Sport Motivation Scale 2 Revised (SMS-2R). RESULTS: Vigour, anxiety and fatigue, POMS sub scale, significantly changed during training (p <0.05). Positive emotions increased over 3 months to stabilise until the competition. Participants felt different types of motivation regulation: intrinsic (15.9 ± 1.76), integrated (15.9 ± 1.98), identified (15.7 ± 1.87) and a little less (p<0.05) for introjected regulation (13.3 ± 1.62). They were significantly different (p<0.01) from external (4.9 ± 1.08) regulation motivation and non-regulation (3.6 ± 0.73). Mindfulness sub scale, Observation and Describe factors, were the only factors that significantly increased or decreased during the 6-months training (r2= 0.35 and r2=0.30, respectively). CONCLUSIONS: Athletes who engages in this type of event have a high intrinsic motivation. We also noted that motivation, mindfulness and mood state follow the macrocycle of training. Thus, specific interventions and mental training could be structured around these important elements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".