An observational study in Psychosocial Risk Factors, sponsored by University Hospital, Clermont-Ferrand. Completed at 1 site in France. Per ClinicalTrials.gov, last updated 2026-10-07.
Sponsored by University Hospital, Clermont-Ferrand · Observational
The goal of this observational study was to assess themes generated by ChatGPT to help organize psychosocial risk factors in professional sport. These factors relate to aspects of athletes' working conditions and professional relationships that may affect their health. Academic experts and sport professionals evaluated the proposed themes and sub-themes. The main questions were :
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This study's enrollment of 42 is below the median of 200 across 146 observational studies indexed under Psychological Well-Being.
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Academic experts not working in professional sports clubs and sport professionals working within professional sports clubs. Academic experts included university professors and senior lecturers, medical doctors, nurses, osteopaths, and academic engineers. Sport professionals included head directors, coaching staff, sports physicians, sport psychologists, physiotherapists, strength and conditioning coaches, sport/data scientists, and scouts. A total of 83 experts (54 academic experts and 29 sport professionals) were contacted through individual email invitations.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
- Membership in either of the following two categories :
Academic experts not working in professional sports clubs: university professors, university senior lecturers, medical doctors, nurses, osteopaths, or academic engineers in the fields described in the study.
Sport professionals working within professional sports clubs: head directors, coaching staff, sports physicians, sport psychologists, physiotherapists, strength and conditioning coaches, sport/data scientists, or scouts.
Exclusion Criteria:
Academic experts and sport professionals who completed a one-time online questionnaire evaluating AI-generated themes and sub-themes on psychosocial risk factors in professional athletes.
Expert ratings of AI-generated psychosocial risk factor themes
Academic experts and sport professionals evaluated each of eight AI-generated psychosocial risk factor themes using a study-specific online questionnaire. Each theme was rated separately for relevance, quality of formulation, and exhaustiveness using numerical rating scales from 0 to 10. For relevance, 0 indicated "not relevant at all" and 10 "highly relevant". For quality of formulation, 0 indicated "incomprehensible" and 10 "fully comprehensible". For exhaustiveness, 0 indicated "not complete at all" and 10 "fully complete". Higher scores indicated more favorable evaluations of the corresponding dimension. Scores were summarized separately for each theme and dimension.
Time frame: At baseline (single assessment; no follow-up).
Association between expert characteristics and theme ratings.
Relevance of each of eight AI-generated psychosocial risk factor themes was assessed using a study-specific online questionnaire on a numerical rating scale from 0 ("not relevant at all") to 10 ("highly relevant"). Higher scores indicated greater perceived relevance. Associations between relevance scores and expert characteristics (age, sex, professional role, and highest competitive sport level achieved) were examined using separate multiple linear regression models for each theme. The outcome was the relevance score, expressed in scale points.
Time frame: At baseline (single assessment; no follow-up).
Association between expert characteristics and theme formulation scores.
Quality of formulation of each of eight AI-generated psychosocial risk factor themes was assessed using a study-specific online questionnaire on a numerical rating scale from 0 ("incomprehensible") to 10 ("fully comprehensible"). Higher scores indicated greater comprehensibility. Associations between formulation scores and expert characteristics (age, sex, professional role, and highest competitive sport level achieved) were examined using multiple linear regression models for each theme. The outcome was the formulation score, expressed in scale points.
Time frame: At baseline (single assessment; no follow-up).
Association between expert characteristics and theme exhaustiveness scores.
Exhaustiveness of each of eight AI-generated psychosocial risk factor themes was assessed using a study-specific online questionnaire on a numerical rating scale from 0 ("not complete at all") to 10 ("fully complete"). Higher scores indicated greater perceived completeness. Associations between exhaustiveness scores and expert characteristics (age, sex, professional role, and highest competitive sport level achieved) were examined using multiple linear regression models for each theme. The outcome was the exhaustiveness score, expressed in scale points.
Time frame: At baseline (single assessment; no follow-up).
Plan to share: No — There is no plan to share individual participant data.
No publications or documents are linked to this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is completed, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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