Objective: To determine the frequency of smartphone addiction and poor sleep in high school adolescents to evaluate relationship between phone sleep, together with demographic characteristics.
 Methods: A face-to-face survey was applied 730 students Kahta district Adıyaman, Turkey, November 2021 January 2022. The Smartphone Addiction Inventory-Short Form (SPAI-SF) used assess addiction, Pi...

BACKGROUND Constant social change brings about new forms of behavior, such as smartphone use, social networking,indoor tanning, cosmetic surgery, etc., that could become excessive or even lead to new forms of addictive disorders. METHODS We report the case of a woman who starts consulting for “clairvoyance addiction”. We then discuss the addictive nature of her disorder, based on several clas...

This study examines the effect of smartphone addiction on academic performance medical students with mediating role self-regulation and bedtime procrastination. The framework this is based social cognitive theory. Data was collected through an adopted questionnaire from at different institutes in Pakistan. A survey distributed among 300 however 260 participated returned it. SmartPLS3 used to an...

BACKGROUND AND AIMS Smartphones allow users to access social networking sites (SNSs) whenever and wherever they want. Such easy availability and accessibility may increase their vulnerability to addiction. Based on the social cognitive theory (SCT), we examined the impacts of outcome expectancies, self-efficacy, and impulsivity on young Chinese smartphone users' addictive tendencies toward SNSs...

E-health, and in particular smartphone-based technology, is increasingly becoming commonplace in healthcare. While psychiatry has tapped onto these innovations for conditions, such as affective disorders, and schizophrenia and psychosis, the usage of these technologies in addiction is limited. Addiction psychiatry could harness the potential of smartphone technologies. Given the increasing inci...

The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20-49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Ac...