Incorporation of Mobile Application (App) Measures Into the Diagnosis of Smartphone Addiction.
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 (app-incorporated 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.
Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan, and Department of Psychiatry, College of Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Yu-Hsuan
YH
Lin
Department of Psychiatry, Koo Foundation Sun Yat-Sen Cancer Center, New Taipei City, Taiwan.
Po-Hsien
PH
Lin
Department of Psychiatry, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan.|School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan.
Chih-Lin
CL
Chiang
Department and Graduate School of Electrical Engineering, Tamkang University, New Taipei City, Taiwan.
Yang-Han
YH
Lee
Sleep Research Center, National Yang-Ming University, Taipei, Taiwan.|Brain Research Center, National Yang-Ming University, Taipei, Taiwan.|Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.
Cheryl C H
CCH
Yang
Sleep Research Center, National Yang-Ming University, Taipei, Taiwan.|Brain Research Center, National Yang-Ming University, Taipei, Taiwan.|Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan.|Institute of Translational and Interdisciplinary Medicine, National Central University, Taoyuan, Taiwan.
Terry B J
TBJ
Kuo
Department of Biostatistics, Columbia University Mailman School of Public Health, 722 West 168th St, New York, NY 10032. shl517@mail.harvard.edu.|Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.
Sheng-Hsuan
SH
Lin
D016739:Behavior, Addictive / Q000175:diagnosis* / Q000523:psychology*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007406:Interview, Psychological; D008403:Mass Screening / Q000706:statistics & numerical data; D063731:Mobile Applications; D011594:Psychometrics / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D000068997:Smartphone; D012984:Software; D013334:Students / Q000523:psychology