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NCT Number: NCT07819851

A Study of the Correlation Between the Severity of Substance Use Disorder and the Intensity of Dependence on Generative Artificial Intelligence

This bicentric, cross-sectional observational study conducted in France evaluates the relationship between substance use disorder (SUD) severity and generative artificial intelligence dependency among outpatients treated in specialized addiction care centers (CSAPA).

While conversational generative artificial intelligence tools have seen rapid widespread adoption, potential problematic usage and cognitive dependency remain poorly documented in clinical addictology. Outpatients followed for substance use disorders present shared cognitive, reward-processing, and behavioral vulnerabilities that may heighten their susceptibility to emerging digital dependencies.

Eligible adult patients complete a single 15-minute evaluation comprising the Generative Artificial Intelligence Dependency Scale (GAIDS; 11 items rated on a 5-point Likert scale from 1 to 5, total score range: 11 to 55) and the DSM-5 diagnostic criteria checklist for their primary substance of abuse, alongside sociodemographic characteristics. Clinical data, including documented psychiatric comorbidities, are extracted in parallel from electronic health records. Following questionnaire completion, participants receive a dedicated debriefing and clinical restitution interview with an investigator.

The primary objective is to evaluate the linear correlation between SUD severity (number of validated DSM-5 criteria, from 0 to 11) and generative artificial intelligence dependency intensity (total raw GAIDS score). Secondary objectives aim to describe generative artificial intelligence dependency levels across specific primary substance classes (alcohol, tobacco, cannabis, cocaine, opioids, etc.), documented comorbid psychiatric disorders (e.g., mood disorders, ADHD, anxiety, personality disorders), and sociodemographic subgroups (age brackets, sex, education, and occupational status).

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Key information

Conditions

Substance-related Disorders Artificial Intelligence (AI) Behavior Behavior, Addictive Chemically-Induced Disorders Compulsive Behavior Impulsive Behavior Mental Disorders

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Adult patient (aged 18 years or older), with or without legal protection measures
  • Actively followed for a substance use disorder (SUD) characterized according to DSM-5 criteria at a participating specialized addiction care center (Nice University Hospital or Sainte-Marie Hospital in Nice, France).
  • Self-reported use of a conversational generative artificial intelligence tool at least once in the past 12 months.
  • Ability to understand, read, and complete a self-administered questionnaire in French.
  • Oral non-opposition obtained from the patient (and from their legal representative if applicable).
  • Affiliated with or beneficiary of a French social security healthcare system.

Exclusion criteria

  • Minor patient (< 18 years old).
  • Major neurocognitive disorders, intellectual disability, or acute psychiatric decompensation precluding comprehension or questionnaire completion.
  • Explicit opposition to participate expressed by the patient or their legal representative.
  • Withdrawal of non-opposition during the study.
  • Incomplete questionnaire or clinical record preventing computation of primary scores.

Treatment and study plan

Questionnaire Assessment

Other

Administration of a single cross-sectional self-questionnaire assessing generative AI dependency (11-item GAIDS scale), DSM-5 substance use disorder criteria (0 to 11 criteria), and sociodemographic data, followed by a personalized debriefing and clinical restitution interview with an investigator (total duration: approximately 15 minutes).

Primary outcomes

  1. Correlation coefficient between substance use disorder severity and generative AI dependency

    Time frame: Baseline (single cross-sectional assessment, Day 0)

    Linear correlation coefficient (Pearson or Spearman, depending on distribution normality) between the number of validated DSM-5 criteria for the primary substance (score ranging from 0 to 11, higher scores indicate greater severity) and the total raw score on the Generative Artificial Intelligence Dependency Scale (GAIDS; 11 items rated on a 5-point Likert scale from 1 to 5; total score range: 11 to 55; higher scores suggest greater dependency).

Secondary outcomes

  1. Generative artificial intelligence dependency score broken down by primary substance

    Time frame: Baseline (Day 0)

    Descriptive statistics (mean +/- standard deviation or median) of the total raw score on the Generative Artificial Intelligence Dependency Scale (GAIDS; 11 items rated on a 5-point Likert scale from 1 to 5; total score range: 11 to 55; higher scores suggest greater dependency) broken down by primary substance classes (alcohol, tobacco, cannabis, cocaine hydrochloride, crack cocaine, opioids, benzodiazepines, amphetamines, other substances).

  2. Generative artificial intelligence dependency score broken down by psychiatric comorbidities

    Time frame: Baseline (Day 0)

    Descriptive statistics (mean +/- standard deviation or median) of the total raw score on the Generative Artificial Intelligence Dependency Scale (GAIDS; 11 items rated on a 5-point Likert scale from 1 to 5; total score range: 11 to 55; higher scores suggest greater dependency) broken down by documented DSM-5 psychiatric comorbidities (unipolar depressive disorders, bipolar disorders, schizophrenia spectrum and other psychotic disorders, ADHD, ASD, anxiety disorders, OCD, PTSD, borderline personality disorder, antisocial personality disorder, eating disorders, other, or absence of disorder).

  3. Generative artificial intelligence dependency score broken down by sociodemographic characteristics

    Time frame: Baseline (Day 0)

    Descriptive statistics (mean +/- standard deviation or median) of the total raw score on the Generative Artificial Intelligence Dependency Scale (GAIDS; 11 items rated on a 5-point Likert scale from 1 to 5; total score range: 11 to 55; higher scores suggest greater dependency) broken down by sociodemographic characteristics: age brackets (18-24, 25-39, 40-59, 60+), sex, occupational status (employed, student/in training, unemployed), and highest educational level (less than high school, high school diploma, short higher education, long higher education).

Study contacts

Contact information is provided by the study sponsor or research team.

Bruno GIORDANA, Dr

CONTACT

[email protected]

4 92 03 87 75 ext. +33

Sponsors and collaborators

Lead sponsor

Centre Hospitalier Universitaire de Nice

Other

Registry information

Acronym: ADDICT-IA

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Sep 15, 2026
Registry last updated
Sep 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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