机构:[1]Natl Taiwan Univ Hosp, Dept Psychiat, Taipei, Taiwan;[2]Natl Taiwan Univ Hosp, Dept Psychiat, Coll Med, Taipei, Taiwan;[3]Koo Fdn Sun Yat Sen Canc Ctr, Dept Psychiat, New Taipei, Taiwan;中山大学肿瘤防治中心[4]Shin Kong Wu Ho Mem Hosp, Dept Psychiat, Taipei, Taiwan;[5]Fu Jen Catholic Univ, Sch Med, New Taipei, Taiwan;[6]Tamkang Univ, Dept & Grad Sch Elect Engn, New Taipei, Taiwan;[7]Natl Yang Ming Univ, Sleep Res Ctr, Taipei, Taiwan;[8]Natl Yang Ming Univ, Brain Res Ctr, Taipei, Taiwan;[9]Natl Yang Ming Univ, Inst Brain Sci, Taipei, Taiwan;[10]Natl Cent Univ, Inst Translat & Interdisciplinary Med, Taoyuan, Taiwan;[11]Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, 722 West 168th St, New York, NY 10032 USA
Objective: Global smartphone expansion has brought about unprecedented addictive behaviors. The current diagnosis of smartphone addiction is based solely on information from clinical interview. This study aimed to incorporate application (app)-recorded data into psychiatric criteria for the diagnosis of smartphone addiction and to examine the predictive ability of the app-recorded data for the diagnosis of smartphone addiction. Methods: Smartphone use data of 79 college students were recorded by a newly developed app for 1 month between December 1, 2013, and May 31, 2014. For each participant, psychiatrists made a diagnosis for smartphone addiction based on 2 approaches: (1) only diagnostic interview (standard diagnosis) and (2) both diagnostic interview and app-recorded data (appincorporated diagnosis). The app-incorporated diagnosis was further used to build app-incorporated diagnostic criteria. In addition, the app-recorded data were pooled as a score to predict smartphone addiction diagnosis. Results: When app-incorporated diagnosis was used as a gold standard for 12 candidate criteria, 7 criteria showed significant accuracy (area under receiver operating characteristic curve [ AUC] > 0.7) and were constructed as app-incorporated diagnostic criteria, which demonstrated remarkable accuracy (92.4%) for app-incorporated diagnosis. In addition, both frequency and duration of daily smartphone use significantly predicted app-incorporated diagnosis (AUC = 0.70 for frequency; AUC = 0.72 for duration). The combination of duration, frequency, and frequency trend for 1 month can accurately predict smartphone addiction diagnosis (AUC = 0.79 for app-incorporated diagnosis; AUC = 0.71 for standard diagnosis). Conclusions: The app-incorporated diagnosis, combining both psychiatric interview and app-recorded data, demonstrated substantial accuracy for smartphone addiction diagnosis. In addition, the app-recorded data performed as an accurate screening tool for app-incorporated diagnosis. (C) Copyright 2017 Physicians Postgraduate Press, Inc.
通讯机构:[11]Columbia Univ, Mailman Sch Publ Hlth, Dept Biostat, 722 West 168th St, New York, NY 10032 USA
推荐引用方式(GB/T 7714):
Lin Yu-Hsuan,Lin Po-Hsien,Chiang Chih-Lin,et al.Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction[J].JOURNAL OF CLINICAL PSYCHIATRY.2017,78(7):866-872.doi:10.4088/JCP.15m10310.
APA:
Lin, Yu-Hsuan,Lin, Po-Hsien,Chiang, Chih-Lin,Lee, Yang-Han,Yang, Cheryl C. H....&Lin, Sheng-Hsuan.(2017).Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction.JOURNAL OF CLINICAL PSYCHIATRY,78,(7)
MLA:
Lin, Yu-Hsuan,et al."Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction".JOURNAL OF CLINICAL PSYCHIATRY 78..7(2017):866-872