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An AI-Generated Integrated Exercise Addiction Screening Scale (EASS-10): A Methodological Demonstration.

Attila Szabo1

  • 1Department of Psychology and Health Management, Faculty of Health and Sport Sciences, Széchenyi István University, H-9026 Gyor, Hungary.

Behavioral Sciences (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
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Artificial intelligence (AI) assisted in creating a new 10-item screening tool for exercise addiction (EA). This AI-generated scale, the EASS-10, integrates key constructs for improved clinical specificity in EA assessment.

Area of Science:

  • Psychiatry
  • Computational Psychology
  • Digital Health

Background:

  • Exercise addiction (EA) research often uses self-report tools lacking functional impairment criteria.
  • Existing instruments may overestimate EA prevalence and lack clinical specificity due to unaddressed obsessive passion.
  • AI is emerging as a tool for scientific research, including psychiatric instrument development.

Purpose of the Study:

  • To evaluate Claude AI's capability in synthesizing a theoretically grounded screening tool for exercise addiction (EA).
  • To integrate validated constructs from existing scales (PS, EDS, EAI) into a novel EA screening instrument.
  • To enhance clinical specificity by incorporating a functional impairment criterion.

Main Methods:

  • Utilized structured prompting and instrument upload with Claude AI (Sonnet 4.6) for scale synthesis.
Keywords:
artificial intelligencehealthphysical activitypsychologyquestionnaire

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08:33

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Published on: September 4, 2019

  • Systematically mapped content from the Passion Scale (PS), Exercise Dependence Scale (EDS), and Exercise Addiction Inventory (EAI).
  • A Monte Carlo simulation (N=500) assessed computational plausibility of the unidimensional model for the AI-generated scale.

    • Main Results:

    • Claude AI generated a 10-item Exercise Addiction Screening Scale (EASS-10).
    • The EASS-10 includes nine Likert-type items and one binary item for functional impairment.
    • The Monte Carlo simulation confirmed internal coherence of the simulated response patterns under a unidimensional model.

    Conclusions:

    • AI-assisted theoretical synthesis presents a novel methodological approach for developing screening instruments.
    • The EASS-10 is a proof-of-concept tool requiring empirical validation in clinical and exercise populations.
    • Further research is needed to corroborate the utility of AI in psychiatric scale development.