Artificial Intelligence (AI), Behavior, Addictive, Substance-related Disorders
Conditions
Keywords
Artificial Intelligence, Addictive behavior, Substance-related disorders
Brief summary
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).
Interventions
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).
Sponsors
Centre Hospitalier Universitaire de Nice
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
OTHER
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation coefficient between substance use disorder severity and generative AI dependency | 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
| Measure | Time frame | Description |
|---|---|---|
| Generative artificial intelligence dependency score broken down by primary substance | 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). |
| Generative artificial intelligence dependency score broken down by psychiatric comorbidities | 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). |
| Generative artificial intelligence dependency score broken down by sociodemographic characteristics | 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). |
Countries
France
Contacts
CONTACTBruno GIORDANA, Dr
Outcome results
None listed