Association between adolescents' problematic online behaviors and self-harm risk.

INTRODUCTION: Problematic online behaviors, including sexting, cyberbullying, and Internet gaming disorder (IGD), were associated with a higher risk of self-harm among adolescents. However, the independent effect on self-harm brought by each problematic online behavior remains unknown. METHODS: This cross-sectional nationwide survey investigated a representative sample of 8446 Taiwanese adolescents (49.7 % female, mean age = 15.4 ± 1.8, ranging from 10 to 18) in the summer of 2021. We used self-reported questionnaires to assess adolescents' problematic online behaviors. 18-item Risk-Taking and Self-Harm Inventory was applied to evaluate their self-harm risks. Adolescents with score ≥ 7 were considered self-harm identified. Multivariate regression analyses were performed to investigate the association between problematic online behaviors and self-harm risks. RESULTS: The prevalence of sexting, cyberbullying victimization, cyberbullying perpetration, and IGD was 1.3 %, 1.1 %, 0.9 %, and 0.7 %, respectively. Adolescents with sexting were associated with higher self-harm scores (ß = 4.30, 95 % CI [3.35, 5.25]), so did adolescents with IGD (ß = 3.33, 95 % CI [1.96, 4.71]), cyberbullying perpetration (ß = 2.61, 95 % CI [1.03, 4.20]), and cyberbullying victimization (ß = 1.40, 95 % CI [0.04, 2.76]). Also, sexting (OR = 5.16, 95 % CI [3.26, 8.07]) and IGD (OR = 3.11, 95 % CI [1.50, 6.15]) were associated with increased odds of identified self-harm behaviors. CONCLUSIONS: Sexting, cyberbullying victimization, cyberbullying perpetration, and IGD were all independently associated with an increased self-harm risk among adolescents. Among them, sexting was shown to be the most robust risk factor of identified self-harm behaviors. To prevent self-harm among adolescents, it is important to identify youth involved in problematic online behaviors, especially sexting.

Systematic Review and Meta-Analysis of Prevalence of Depression Among Caregivers of Cancer Patients.

Background: Caregivers of cancer patients commonly experience depressive symptoms due to the heavy burden of caregiving responsibility. Objective: This meta-analysis examined the prevalence of depression among caregivers of cancer patients. Methods: We included 85 studies covering 23,317 participants published between 2001 and 2021 (25 countries) that reported the prevalence of depression among caregivers of cancer patients. We examined the pooled prevalence of depression and hypothesized moderators, including year, age, sex, geographic regions, percentage of spousal caregivers, depression measures, and cancer stage. Results: All 85 effect sizes included 6,077 caregivers of patients with depression. The weighted average prevalence of depression was 25.14% (95% CI, 21.42-29.27%) among caregivers. The prevalence rates were moderated by geographic region, patients' cancer stage, and measures for depression. The prevalence rates also varied among the different measures assessing depression. The prevalence rate decreased with the mean age of the caregivers and the percentage of spousal caregivers. Conclusions: This study revealed a high prevalence of depression among caregivers of cancer patients. The prevalence rates also varied with the study design, demographics of caregivers, and patients' medical information. These findings highlight that psychological support and intervention may be crucial for patients and their caregivers in clinical practice.

Assessing User Retention of a Mobile App: Survival Analysis.

BACKGROUND: A mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. OBJECTIVE: The primary aim of this study was to quantify user retention for the "Staff Hours" app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. METHODS: We developed an app called "Staff Hours" to automatically calculate users' work hours through GPS data (passive data). "Staff Hours" not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the "Staff Hours" database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. RESULTS: A total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). CONCLUSIONS: Our findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

Investigation of the Role of Anxiety and Depression on the Formation of Phantom Vibration and Ringing Syndrome Caused by Working Stress during Medical Internship.

Phantom vibration syndrome (PVS) and phantom ringing syndrome (PRS) are prevalent hallucinations during medical internship. Depression and anxiety are probably understudied risk factors of PVS and PRS. The aim was to evaluate the role of anxiety and depression on the relationship between working stress during medical internship and PVS and PRS. A prospective longitudinal study, consisted of 74 medical interns, was carried out. The severity of phantom vibrations and ringing, as well as anxiety and depression as measured before, at the third, sixth, and 12th month during internship, and two weeks after internship. We conducted a causal mediation analysis to quantify the role of depression and in the mechanism of working stress during medical internship inducing PVS and PRS. The results showed that depression explained 21.9% and 8.4% for stress-induced PRS and PVS, respectively. In addition, anxiety explained 15.0% and 7.8% for stress-induced PRS and PVS, respectively. Our findings showed both depression and anxiety can explain a portion of stress-induced PVS and PRS during medical internship and might be more important in clinical practice and benefit to prevention of work-related burnout.

Systematic review and meta-analysis of epidemiology of internet addiction.

BACKGROUND: The field of internet addiction has experienced significant debates on conflicting epidemiology. This meta-analysis investigated the prevalence rates of generalized internet addiction (GIA) and internet gaming disorder (IGD). METHODS: We included 113 epidemiologic studies covering 693,306 subjects published from 1996 to 2018 (for 31 nations) that reported prevalence rates for GIA or IGD. We examined pooled prevalence of GIA and IGD and the hypothesized moderators including year, geographic regions, types of scales, and sample representativeness. RESULTS: All 133 effect sizes included 53,184 subjects with GIA or IGD. Weighted average prevalence for GIA and IGD were 7.02 % (95 % CI, 6.09 %-8.08 %) and 2.47 % (95 % CI, 1.46 %-4.16 %) respectively. For GIA, prevalence was increased over time and prevalence rates variated among different scales. IGD prevalence was neither moderated by year, regions, nor sample representativeness. CONCLUSIONS: The prevalence of GIA was higher than the prevalence of IGD. The GIA prevalence was increasing over time and variated with different assessments. Our results reveal that GIA may reflect a pattern of increasing human-machine interaction.

Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development.

BACKGROUND: There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties. OBJECTIVE: The specific aims of this study were to (1) identify the Staff Hours app users' GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants' self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations. METHODS: We developed an app, Staff Hours, to automatically calculate a user's work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non-health care professionals (non-HCPs; n=21). RESULTS: The total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004). CONCLUSIONS: Staff Hours is the first automatic GPS location-based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations.

Development of the Problematic Mobile Gaming Questionnaire and Prevalence of Mobile Gaming Addiction Among Adolescents in Taiwan.

Mobile gaming has gained popularity among adolescents, and an increase in problematic use has been reported. The aims of this study are as follows: (a) develop a self-report questionnaire, the Problematic Mobile Gaming Questionnaire (PMGQ); (b) establish a validated cutoff value using structured interviews; and (c) evaluate the prevalence of mobile gaming addiction in adolescents. The PMGQ was built as a 12-item questionnaire rated on a 4-point Likert scale to evaluate the symptoms of problematic mobile gaming (PMG). The construct validity of the PMGQ was examined using exploratory factor analysis. Overall, 10,775 students with smartphones from grade 4 to senior high school were recruited to complete the questionnaire. A total of 113 senior high school students were interviewed using previously developed criteria for PMG to develop an optimal cutoff point measuring sensitivity, specificity, and diagnostic accuracy. The cutoff point was determined using the Youden index and optimal diagnostic accuracy. The PMGQ showed good internal consistency (Cronbach's α = 0.92) and adequate diagnostic efficiency (area under the receiver operating characteristic curve = 0.802). The items revealed three factors of addiction: compulsion, tolerance, and withdrawal. For the PMGQ, a cutoff point of 29/30 demonstrated the most optimal Youden index and diagnostic accuracy. Demographic data showed that the proportion of PMG was 19.1 percent among elementary school students, 20.5 percent among junior high school students, and 19.0 percent among senior high school students. The PMGQ demonstrated appropriate validity and accuracy in the assessment for PMG.

Validation of the Mobile App-Recorded Circadian Rhythm by a Digital Footprint.

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.

Temporal Stability of Smartphone Use Data: Determining Fundamental Time Unit and Independent Cycle.

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.

Development of a mobile application (App) to delineate "digital chronotype" and the effects of delayed chronotype by bedtime smartphone use.

The widespread use and deep reach of smartphones motivate the use of mobile applications to continuously monitor the relationship between circadian system, individual sleep patterns, and environmental effects. We selected 61 adults with 14-day data from the "Know Addiction" database. We developed an algorithm to identify the "sleep time" based on the smartphone behaviors. The total daily smartphone use duration and smartphone use duration prior to sleep onset were identified respectively. We applied mediation analysis to investigate the effects of total daily smartphone use on sleep through pre-sleep use (PS). The results showed participants' averaged pre-sleep episodes within 1 h prior to sleep are 2.58. The duration of three pre-sleep uses (PS1∼3) maybe a more representative index for smartphone use before sleep. Both total daily duration and the duration of the last three uses prior to sleep of smartphone use significantly delayed sleep onset, midpoint of sleep and reduced total sleep time. One hour of increased smartphone use daily, delays the circadian rhythm by 3.5 min, and reduced 5.5 min of total sleep time (TST). One hour of increased pre-sleep smartphone use delayed circadian rhythm by 1.7 min, and reduced 39 s of TST. The mediation effects of PS1∼3 significantly impacted on these three sleep indicators. PS1∼3 accounted for 14.3% of total daily duration, but the proportion mediated of delayed circadian rhythm was 44.0%. We presented "digital chronotype" with an automatic system that can collect high temporal resolution data from naturalistic settings with high ecological validity. Smartphone screen time, mainly mediated by pre-sleep use, delayed the circadian rhythm and reduced the total sleep time.

Chinese adaptation of the Ten-Item Internet Gaming Disorder Test and prevalence estimate of Internet gaming disorder among adolescents in Taiwan.

BACKGROUND AND AIMS: Internet 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. METHODS: The 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. RESULTS: The 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%. CONCLUSION: Findings evidence the validity and diagnostic accuracy of the IGDT-10 in the assessment of IGD.

Development of short-form and screening cutoff point of the Smartphone Addiction Inventory (SPAI-SF).

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.

Smartphone gaming and frequent use pattern associated with smartphone addiction.

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.