A Study of the Correlation Between the Severity of Substance Use Disorder and the Intensity of Dependence on Generative Artificial Intelligence (ADDICT-IA)
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).
Обзор исследования
Статус
Условия
Вмешательство/лечение
Тип исследования
Регистрация (Оцененный)
Фаза
- Непригодный
Контакты и местонахождение
Контакты исследования
- Имя: Bruno GIORDANA, Dr
- Номер телефона: +33 4 92 03 87 75
- Электронная почта: giordana.b@chu-nice.fr
Места учебы
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Nice, Франция
- Chu De Nice
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Контакт:
- Bruno GIORDANA, Dr
- Номер телефона: +33 4 92 03 87 75
- Электронная почта: giordana.b@chu-nice.fr
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Критерии участия
Критерии приемлемости
Возраст, подходящий для обучения
- Взрослый
- Пожилой взрослый
Принимает здоровых добровольцев
Описание
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.
Учебный план
Как устроено исследование?
Детали дизайна
- Основная цель: Другой
- Распределение: Н/Д
- Интервенционная модель: Одногрупповое задание
- Маскировка: Нет (открытая этикетка)
Оружие и интервенции
Группа участников / Армия |
Вмешательство/лечение |
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Экспериментальный: CSAPA outpatients using generative AI
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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).
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Что измеряет исследование?
Первичные показатели результатов
Мера результата |
Мера Описание |
Временное ограничение |
|---|---|---|
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Correlation coefficient between substance use disorder severity and generative AI dependency
Временное ограничение: Baseline (single cross-sectional assessment, Day 0)
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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).
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Baseline (single cross-sectional assessment, Day 0)
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Вторичные показатели результатов
Мера результата |
Мера Описание |
Временное ограничение |
|---|---|---|
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Generative artificial intelligence dependency score broken down by primary substance
Временное ограничение: Baseline (Day 0)
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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).
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Baseline (Day 0)
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Generative artificial intelligence dependency score broken down by psychiatric comorbidities
Временное ограничение: Baseline (Day 0)
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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).
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Baseline (Day 0)
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Generative artificial intelligence dependency score broken down by sociodemographic characteristics
Временное ограничение: Baseline (Day 0)
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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).
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Baseline (Day 0)
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Соавторы и исследователи
Даты записи исследования
Изучение основных дат
Начало исследования (Оцененный)
Первичное завершение (Оцененный)
Завершение исследования (Оцененный)
Даты регистрации исследования
Первый отправленный
Впервые представлено, что соответствует критериям контроля качества
Первый опубликованный (Действительный)
Обновления учебных записей
Последнее опубликованное обновление (Действительный)
Последнее отправленное обновление, отвечающее критериям контроля качества
Последняя проверка
Дополнительная информация
Термины, связанные с этим исследованием
Ключевые слова
Дополнительные соответствующие термины MeSH
Другие идентификационные номера исследования
- 26-PP-15
Планирование данных отдельных участников (IPD)
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Информация о лекарствах и устройствах, исследовательские документы
Изучает лекарственный продукт, регулируемый FDA США.
Изучает продукт устройства, регулируемый Управлением по санитарному надзору за качеством пищевых продуктов и медикаментов США.
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