Behavioral sleep medicine · 2026
The Serial Mediating Roles of Repetitive Negative Thinking and Future Anxiety Between AI Competence Anxiety and Sleep Disturbance.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kara Berkan Can, Türk Fulya, Deniz Mehmet Engin
Аннотация
OBJECTIVES: Although artificial intelligence technologies are rapidly becoming widespread, the potential psychological risks associated with these technologies have not yet been sufficiently understood. Considering the expanding areas of use of artificial intelligence tools, examining the potential risk factors that may emerge from these technologies is considered important. Accordingly, the present study investigated the serial mediating roles of repetitive negative thinking (RNT) and future anxiety in the relationship between artificial intelligence competence anxiety and sleep disturbance.
METHOD: The study sample consisted of 364 individuals between the ages of 18 and 60 (71.98% female; Mage = 28.43, SD = 8.39). A two-step Structural Equation Modeling (SEM) approach was adopted in the analyses, and indirect effects were tested using the bootstrap method. In addition, gender, age, and frequency of artificial intelligence tool usage were included in the model as control variables.
RESULTS: The results indicated significant indirect associations between artificial intelligence competence anxiety and sleep disturbance through RNT and future anxiety.
CONCLUSIONS: Considering these variables together contributes to a more comprehensive understanding of the psychological mechanisms underlying the relationship between artificial intelligence competence anxiety and sleep disturbance. In this context, the findings contribute to the development of intervention and prevention programs aimed at reducing sleep disturbance and provide a guiding framework for practitioners.
METHOD: The study sample consisted of 364 individuals between the ages of 18 and 60 (71.98% female; Mage = 28.43, SD = 8.39). A two-step Structural Equation Modeling (SEM) approach was adopted in the analyses, and indirect effects were tested using the bootstrap method. In addition, gender, age, and frequency of artificial intelligence tool usage were included in the model as control variables.
RESULTS: The results indicated significant indirect associations between artificial intelligence competence anxiety and sleep disturbance through RNT and future anxiety.
CONCLUSIONS: Considering these variables together contributes to a more comprehensive understanding of the psychological mechanisms underlying the relationship between artificial intelligence competence anxiety and sleep disturbance. In this context, the findings contribute to the development of intervention and prevention programs aimed at reducing sleep disturbance and provide a guiding framework for practitioners.