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The aim of this study was to investigate the risk factors of smartphone addiction in high school students.A total of 880 adolescents were recruited from a vocational high school in Taiwan in January 2014 to complete a set of questionnaires, including the 10-item Smartphone Addiction Inventory, Chen Internet Addiction Scale, and a survey of content and patterns of personal smartphone use. Of those recruited, 689 students (646 male) aged 14 to 21 and who owned a smartphone completed the questionnaire. Multiple linear regression models were used to determine the variables associated with smartphone addiction.Smartphone gaming and frequent smartphone use were associated with smartphone addiction. Furthermore, both the smartphone gaming-predominant and gaming with multiple-applications groups showed a similar association with smartphone addiction. Gender, duration of owning a smartphone, and substance use were not associated with smartphone addiction.Our findings suggest that smartphone use patterns should be part of specific measures to prevent and intervene in cases of excessive smartphone use.

Background and aimsInternet gaming disorder (IGD) is an increasingly important topic and has been included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) research criteria. This study aims to validate the Chinese version of the Ten-Item Internet Gaming Disorder Test (IGDT-10), a self-reported questionnaire based on DSM-5 IGD criteria, and to estimate the prevalence of IGD in adolescents.MethodsThe IGDT-10 was translated to Chinese as a 10-item questionnaire rated on a 3-point Likert scale to evaluate the symptoms of IGD. Overall, 8,110 students from grade four to senior high who played Internet games were administered the questionnaire. In addition, 76 senior high-school students were interviewed using DSM-5 criteria to determine the optimal cut-off point that ensures adequate sensitivity, specificity, and diagnostic accuracy. The cut-off point was determined using the Youden’s index and optimal diagnostic accuracy.ResultsThe Chinese version of the IGDT-10 showed good internal consistency (Cronbach’s α = .85) and adequate diagnostic efficiency (area under the curve = 0.810). Through interviews, the optimal cut-off point was determined to be five out of the nine criteria (Youden’s index: 42.1%, diagnostic accuracy: 86.8%, sensitivity: 43.8%, and specificity: 98.3%). In this study, the prevalence of IGD among adolescent gamers was 3.1%.ConclusionFindings evidence the validity and diagnostic accuracy of the IGDT-10 in the assessment of IGD.

Smartphone addiction is considered a form of technological addiction that has attracted increasing attention. The present study developed and validated the short-form Smartphone Addiction Inventory (SPAI-SF) and established cutoff point for screening smartphone addiction based on diagnostic criteria established by psychiatric interview. A total of 268 participants completed an online survey that collected demographic data, smartphone use behaviours, and responses to the 26-item SPAI. Each participant also completed a psychiatric interview. Confirmatory factor analysis (CFA) revealed that the 10-item SPAI-SF replicated the structure of original 26-item SPAI accurately, yielding a four-factor model consisting of compulsive behaviour, functional impairment, withdrawal, and tolerance. For maximal diagnostic accuracy, a cutoff point of 24/25 best discriminated cases of smartphone addiction from diagnostic negatives. The present findings suggest that both the 26-item SPAI and SPAI-SF manifest the four constructs of behavioural addiction and the characteristics of smartphone addiction. The cutoff point determined by psychiatrists' diagnostic interview will be useful for clinical screening and epidemiologic research.

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The coronavirus disease 2019 (COVID-19) pandemic results in a profound physical and mental burden on healthcare professionals. This study aims to evaluate burnout status and mood disorder of healthcare workers during this period. An online questionnaire was voluntarily answered by eligible adult employees in a COVID-19 specialized medical center. The major analysis included the burnout status and mood disorder. Factors related to more severe mood disorder were also identified. A total of 2029 participants completed the questionnaire. There were 901 (44.4%) and 923 (45.5%) participants with moderate to severe personal and work-related burnout status, respectively. Nurses working in the emergency room (ER), intensive care unit (ICU)/isolation wards, and general wards, as well as those with patient contact, had significantly higher scores for personal burnout, work-related burnout, and mood disorder. This investigation identified 271 participants (13.35%) with moderate to severe mood disorder linked to higher personal/work-related burnout scores and a more advanced burnout status. Univariate analysis revealed that nurses working in the ER and ICU/isolation wards were associated with moderate to severe mood disorder risk factors. Multivariate analysis demonstrated that working in the ER (OR, 2.81; 95% CI, 1.14–6.90) was the only independent risk factor. More rest, perquisites, and an adequate supply of personal protection equipment were the most desired assistance from the hospital. Compared with the non-pandemic period (2019), employees working during the COVID-19 pandemic (2020) have higher burnout scores and percentages of severe burnout. In conclusion, this study suggests that the COVID-19 pandemic has had an adverse impact on healthcare professionals. Adequate measures should be adopted as early as possible to support the healthcare system.

Background Modern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. Objective This study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. Methods The mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. Results No significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. Conclusions The circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm.

Background Assessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. Objective The study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. Methods Using the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. Results Our findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. Conclusions This study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.