Effects of a Chatbot-Based Intervention on Behavioral Responsiveness and Neural Anticipation of Social and Monetary Incentives in Individuals With Anhedonia
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 80
- 试验地点
- 1
研究概览
简要总结
The main aim of the present study is to investigate the effects of a Motivational Interviewing-based artificial intelligence chatbot on social incentive processing in college students with elevated levels of depression and anhedonia by combining a randomized active-control intervention design with pre- and post-intervention Social Incentive Delay Task assessments during fMRI.
详细描述
Anhedonia represents a core characteristic of depression and is characterized by reduced experience of pleasure. It is closely related to decreased motivation, altered reward processing, changes in affective responsiveness, and alterations in intrinsic brain network function. Anhedonia is not specifically targeted by currently available pharmacological interventions. Initial evidence indicates that an increased willingness to change and implementation of change in daily life can alleviate anhedonia.
The present study aims to examine whether a Motivational Interviewing-based AI chatbot can lead to changes in social incentive processing in college students with elevated anhedonia and depressive symptoms. Social incentive processing is included because social approval, social feedback, and interpersonal reward are important sources of motivation in daily life and may be altered in individuals with elevated anhedonia. The Social Incentive Delay Task allows the study to examine behavioral and neural responses during the anticipation and receipt of social incentives. To this end, eligible participants with a total score of 22 or higher on the Snaith-Hamilton Pleasure Scale and a score of 14 or higher on the Beck Depression Inventory will undergo a randomized, between-subjects, active-control intervention study. Participants will be assigned to either a Motivational Interviewing-based chatbot group or an active control chatbot group for 1 week. Pre- and post-intervention assessments will include self-report questionnaires and the Social Incentive Delay Task during functional magnetic resonance imaging to examine psychological, behavioral, and neural effects of the intervention.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Basic Science
- 盲法
- Double (Participant, Investigator)
入排标准
- 年龄范围
- 18 Years 至 40 Years(Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18-40 years
- •Right-handed
- •Normal or corrected normal visual acuity
- •Participants must show elevated anhedonia and depressive symptoms at screening, defined as a total score of 22 or higher on the Snaith-Hamilton Pleasure Scale and a score of 14 or higher on the Beck Depression Inventory
排除标准
- •History of major central nervous system disorders, such as epilepsy, traumatic brain injury, stroke, or brain tumors.
- •History of severe mental illness, including schizophrenia spectrum disorders, bipolar disorder, or other psychotic disorders.
- •History of substance or alcohol use disorder or substance or alcohol misuse within the past 12 months that may affect study participation or outcome assessment.
- •Individuals currently at high risk of suicide, severe self-harm, or experiencing an acute psychiatric crisis.
- •Individuals who are currently using psychiatric medications or have undergone psychotherapy within the past 4 weeks that may significantly affect mood, motivation, or reward processing.
- •Severe vision or hearing impairments that cannot be corrected and would interfere with task performance.
- •Contraindications to MRI scanning, including metallic implants, pacemakers, severe claustrophobia, or other conditions incompatible with MRI.
- •Pregnancy or breastfeeding.
研究组 & 干预措施
Motivational Interviewing-based AI chatbot group
Motivational Interviewing-based AI chatbot intervention
干预措施: MI Chatbot Interaction (Behavioral)
Active control chatbot group
Active control nature-story chatbot intervention
干预措施: Active Control Chatbot Interaction (Behavioral)
研究者
Benjamin Becker
Professor
University of Electronic Science and Technology of China
