Effects of bilateral tDCS over DLPFC on response inhibition, craving, and brain functional connectivity in Internet gaming disorder: A randomized, double-blind, sham-controlled trial with fMRI.

Background and aims: Impaired inhibitory control accompanied by enhanced craving is hallmark of addiction. This study investigated the effects of transcranial direct current stimulation (tDCS) on response inhibition and craving in Internet gaming disorder (IGD). We examined the brain changes after tDCS and their correlation with clinical variables. Methods: Twenty-four males with IGD were allocated randomly to an active or sham tDCS group, and data from 22 participants were included for analysis. Participants self-administered bilateral tDCS over the dorsolateral prefrontal cortex (DLPFC) for 10 sessions. Stop-signal tasks were conducted to measure response inhibition and participants were asked about their cravings for Internet gaming at baseline and post-tDCS. Functional magnetic resonance imaging data were collected at pre- and post-tDCS, and group differences in resting-state functional connectivity (rsFC) changes from the bilateral DLPFC and nucleus accumbens were examined. We explored the relationship between changes in the rsFC and behavioral variables in the active tDCS group. Results: A significant group-by-time interaction was observed in response inhibition. After tDCS, only the active group showed a decrease in the stop-signal reaction time (SSRT). Although craving decreased, there were no significant group-by-time interactions or group main effects. The anterior cingulate cortex (ACC) showed group differences in post- versus pre-tDCS rsFC from the right DLPFC. The rsFC between the ACC and left middle frontal gyrus was negatively correlated with the SSRT. Discussion and conclusion: Our study provides preliminary evidence that bilateral tDCS over the DLPFC improves inhibitory control and could serve as a therapeutic approach for IGD.

Developing a Cost-Effectiveness Model of Digital Therapeutics for Smoking Cessation.

The purpose of this research was to construct a Markov model of digital therapeutics to predict the lifetime costs and consequences that would be incurred by a hypothetical group of adult smokers in Korea who only made a single attempt to stop smoking. To determine the efficacy of DTx, we created an annual cycle Markov model. The result shows that the NRT strategy is determined as the dominant strategy. Digital therapeutics acts as a complement to pharmacotherapy and is a low-cost option.

Investigating psychological and motivational predictors of problematic smartphone use among Smartphone-based Social Networking Service (SNS) users.

Given that the active use of certain smartphone applications is associate with problematic smartphone use, it has been proposed that certain smartphone applications are more addictive than others, such as Social Networking Services (SNS). Still, studies that consider smartphone users' main usage application which are known to influence the users' problematic smartphone use, such as SNS, remain to be explored. Thus, the current study aims to investigate the psychological and motivational predictors of problematic smartphone use in a sample of smartphone-based SNS users whose main device usage is SNS. A series of mean comparison tests and binary logistic regression were performed in this study. Of the 433 smartphone-based SNS users, 218 were male (50.3%) and 215 were female (49.7%). Age of 433 participants ranged from 20 to 40, and mean age was 30.75 (SD = 7.84). 73 participants (16.9%) were sorted into the high-risk problematic smartphone use group and 360 participants (83.1%) were categorized as the normal user group. The finding from binary regression analysis showed that reward responsiveness from the Behavioral Activation System (BAS), a lack of self-control, and anxiety significantly increased the odds of problematic smartphone use of the smartphone-based SNS users. Reward responsiveness was found to be the most powerful predictor. Our findings broaden the existing literature and provide implications to reduce addictive smartphone use relating to smartphone-based SNS usage.

Characteristics and Potential Challenges of Digital-Based Interventions for Children and Young People: Scoping Review.

BACKGROUND: Digital health technologies are becoming increasingly available to children and young people and their families. However, there are no scoping reviews that provide both an overview of the characteristics of digital interventions for children and young people and potential challenges to be considered when developing and implementing them. OBJECTIVE: This study aimed to systematically review scientific publications to identify the current characteristics and potential complications of digital interventions for children and young people. METHODS: This scoping review was conducted using the framework of Arksey and O'Malley and adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews. A search of 5 databases (PubMed, Scopus, Embase, MEDLINE, and CINAHL) and Google Scholar was performed for eligible clinical trials published between January 1, 2018, and August 19, 2022. RESULTS: The initial search of the 5 databases yielded 3775 citations; duplicates and those not meeting the inclusion criteria were eliminated. In total, 34 articles were included in the final review and relevant information, such as the descriptive characteristics and potential challenges, were classified. Mental health (26/34, 76%) was the most common target for the digital intervention for children and young people, exceeding physical health (8/34, 24%) by more than 3 times. In addition, a substantial number of digital interventions were dedicated solely to children and young people. Digital interventions for children and young people were more likely to be delivered via computers (17/34, 50%) rather than smartphones (13/34, 38%). More than one-third of the studies (13/34, 38%) applied cognitive behavioral theory as the theory of digital interventions. The duration of the digital intervention for children and young people was more likely to vary depending on the target user rather than the target disease. Intervention components were classified into 5 categories: guidance, task and activity, reminder and monitoring, supportive feedback, and reward system. Potential challenges were subcategorized into ethical, interpersonal, and societal challenges. For ethical challenges, the consent of children and young people or caregivers, potential risk of adverse events, and data privacy issues were considered. For interpersonal challenges, the engagement of children and young people was affected by the preference or barrier of caregivers to participate in studies. For societal challenges, restricted ethnicity in recruitment, limited availability of digital technology, differences in internet use patterns between girls and boys, unified clinical settings, and language barriers were described. CONCLUSIONS: We identified potential challenges and provided suggestions about ethical, interpersonal, and societal aspects to consider when developing and deploying digital-based interventions for children and young people. Our findings provide a thorough overview of the published literature and may serve as a comprehensive, informative foundation for the development and implementation of digital-based interventions for children and young people.

Development of depression detection algorithm using text scripts of routine psychiatric interview.

Background: A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients. Methods: Seventy-seven clinical patients [with depression (n = 60); without depression (n = 17)] with a prior psychiatric diagnosis history participated in this study. The study was conducted with 24 male and 53 female subjects with the mean age of 33.8 (± 3.0). Psychiatrists conducted a conversational interview with each patient that lasted at least 30 min. All interviews with the subjects between August 2021 and November 2022 were recorded and transcribed into text scripts, and a text emotion recognition module was used to indicate the subject's representative emotions of each sentence. A machine learning algorithm discriminates patients with depression and those without depression based on text scripts. Results: A machine learning model classified text scripts from depressive patients with non-depressive ones with an acceptable accuracy rate (AUC of 0.85). The distribution of emotions (surprise, fear, anger, love, sadness, disgust, neutral, and happiness) was significantly different between patients with depression and those without depression (p < 0.001), and the most contributing emotion in classifying the two groups was disgust (p < 0.001). Conclusion: This is a qualitative and retrospective study to develop a tool to detect depression against patients without depression based on the text scripts of psychiatric interview, suggesting a novel and practical approach to understand the emotional characteristics of depression patients and to use them to detect the diagnosis of depression based on machine learning methods. This model could assist psychiatrists in clinical settings who conduct routine conversations with patients using text transcripts of the interviews.

Echinochrome A Inhibits Melanogenesis in B16F10 Cells by Downregulating CREB Signaling.

Excessive increase in melanin pigment in the skin can be caused by a variety of environmental factors, including UV radiation, and can result in spots, freckles, and skin cancer. Therefore, it is important to develop functional whitening cosmetic reagents that regulate melanogenesis. In this study, we investigated the effects of echinochrome A (Ech A) on melanogenesis in the B16F10 murine melanoma cell line. We triggered B16F10 cells using α-MSH under Ech A treatment to observe melanin synthesis and analyze expression changes in melanogenesis-related enzymes (tyrosinase, tyrosinase-related protein 1 (TYRP1), and tyrosinase-related protein 2 (TYRP2)) at the mRNA and protein levels. Furthermore, we measured expression changes in the microphthalmia-associated transcription factor (MITF), CREB, and pCREB proteins. Melanin synthesis in the cells stimulated by α-MSH was significantly reduced by Ech A. The expression of the tyrosinase, TYRP1, and TYRP2 mRNA and proteins was significantly decreased by Ech A, as was that of the MITF, CREB, and pCREB proteins. These results show that Ech A suppresses melanin synthesis by regulating melanogenesis-related enzymes through the CREB signaling pathway and suggest the potential of Ech A as a functional agent to prevent pigmentation and promote skin whitening.

Differential Gene Expression in the Hippocampi of Nonhuman Primates Chronically Exposed to Methamphetamine, Cocaine, or Heroin.

OBJECTIVE: Methamphetamine (MA), cocaine, and heroin cause severe public health problems as well as impairments in neural plasticity and cognitive function in the hippocampus. This study aimed to identify the genes differentially expressed in the hippocampi of cynomolgus monkeys in response to these drugs. METHODS: After the monkeys were chronically exposed to MA, cocaine, and heroin, we performed large-scale gene expression profiling of the hippocampus using RNA-Seq technology and functional annotation of genes differentially expressed. Some genes selected from RNA-Seq analysis data were validated with reverse transcription-quantitative polymerase chain reaction (RT-qPCR). And the expression changes of ADAM10 protein were assessed using immunohistochemistry. RESULTS: The changes in genes related to axonal guidance (PTPRP and KAL1), the cell cycle (TLK2), and the regulation of potassium ions (DPP10) in the drug-treated groups compared to the control group were confirmed using RT-qPCR. Comparative analysis of all groups showed that among genes related to synaptic long-term potentiation, CREBBP and GRIN3A were downregulated in both the MA- and heroin-treated groups compared to the control group. In particular, the mRNA and protein expression levels of ADAM10 were decreased in the MA-treated group but increased in the cocaine-treated group compared to the control group. CONCLUSION: These results provide insights into the genes that are upregulated and downregulated in the hippocampus by the chronic administration of MA, cocaine, or heroin and basic information for developing novel drugs for the treatment of hippocampal impairments caused by drug abuse.

Effects of Ethanol on Expression of Coding and Noncoding RNAs in Murine Neuroblastoma Neuro2a Cells.

Excessive use of alcohol can induce neurobiological and neuropathological alterations in the brain, including the hippocampus and forebrain, through changes in neurotransmitter systems, hormonal systems, and neuroimmune processes. We aimed to investigate the effects of ethanol on the expression of coding and noncoding RNAs in a brain-derived cell line exposed to ethanol. After exposing Neuro2a cells, a neuroblastoma cell line, to ethanol for 24 and 72 h, we observed cell proliferation and analyzed up- and downregulated mRNAs and long noncoding RNAs (lncRNAs) using total RNA-Seq technology. We validated the differential expression of some mRNAs and lncRNAs by RT-qPCR and analyzed the expression of Cebpd and Rnu3a through knock-down of Cebpd. Cell proliferation was significantly reduced in cells exposed to 100 mM ethanol for 72 h, with 1773 transcripts up- or downregulated by greater than three-fold in ethanol-treated cells compared to controls. Of these, 514 were identified as lncRNAs. Differentially expressed mRNAs and lncRNAs were mainly observed in cells exposed to ethanol for 72 h, in which Atm and Cnr1 decreased, but Trib3, Cebpd, and Spdef increased. On the other hand, lncRNAs Kcnq1ot1, Tug1, and Xist were changed by ethanol, and Rnu3a in particular was greatly increased by chronic ethanol treatment through inhibition of Cebpd. Our results increase the understanding of cellular and molecular mechanisms related to coding and noncoding RNAs in an in vitro model of acute and chronic exposure to ethanol.

Study of the factors influencing the use of MyData platform based on personal health record data sharing system.

BACKGROUND: The application of telemedicine and electronic health (eHealth) technology has grown in importance during the COVID-19 pandemic, and a new approach in personal data management and processing MyData, has emerged. Data portability and informational self-determination are fundamental concepts of MyData. This study analysed the factors that influence acceptance of the MyData platform, which, reflects the right to self-determine personal data. METHODS: The study involved participants having experience using the MyData platform, and the key factors of the unified theory of acceptance and use of technology were used in the research model (performance expectancy, effort expectancy, social influence, facilitation condition and behavioural intention to use). The questionnaire comprided 27 items, and system usage log data were used to confirm that behavioural intention to use affected actual use behaviour through structural equation modeling. RESULTS: In total, 1153 participants completed the survey. The goodness of fit in the structural equation model indices indicates that the data fit the research model well. Performance expectancy, social influence, and facilitating conditions had direct effects on behavioural intention to use. We used system usage log data to confirm that behavioural intention to use positively affected actual use behaviour. The impact of the main factors in the unified theory of acceptance and use of technology was not moderated by age or gender, except for performance expectancy. CONCLUSIONS: This study is the first to examine the factors influencing the use of the MyData platform based on the personal health record data sharing system in Korea. In addition, the study confirmed the use behaviour of the MyData platform utilising the system's actual usage log for each function and analysing the effect of the intention of use on actual use. Our study serves as a significant foundation for the acceptance of data portability and sharing concepts. It also lays the foundation for expanding the data economy and ecosystem in the pandemic era.

The Role of Motivation Systems, Anxiety, and Low Self-Control in Smartphone Addiction among Smartphone-Based Social Networking Service (SNS) Users.

Given that Social Networking Service (SNS) has emerged as the most influential platform, which can lead users to addictive smartphone use, it is necessary to investigate which psychological variables lead smartphone-based SNS users to addictive smartphone use. Still, studies on the relationship between psychological variables and addictive smartphone use among smartphone-based SNS users remain to be explored. Therefore, this study aims to investigate the role of psychological factors on smartphone addiction proneness (SAP). A total of 433 smartphone-based SNS users were collected from 5003 adults in Korea. Data were analyzed with descriptive statistics, Pearson's correlation coefficients, and path analysis using SPSS 21.0 and AMOS 23.0. The results of a parallel-mediation path analysis demonstrated that Behavioral Inhibition (BIS), Behavioral activation (BAS) drive, anxiety, and low self-control directly influenced SAP, separately. BIS and BAS _drive also had significant indirect effects on SAP through the effect of anxiety. BIS and BAS_fun had significant indirect effects on SAP through the effect of low self-control. The study variables accounted for 38.4 of the total variances of SAP. Thus, when establishing interventions to reduce the users' addictive smartphone use, these interactive relationships of the variables should be considered.

The Kynurenine Pathway and Mediating Role of Stress in Addictive Disorders: A Focus on Alcohol Use Disorder and Internet Gaming Disorder.

Stress plays an important role in the pathophysiology of addictive disorders. The kynurenine (KYN) pathway involved in neuroimmune and cognitive functions is activated under stress. However, the neuroimmunological-neurocognitive mechanisms in the role of stress in addictive disorders are unclear still now. Ninety-nine young adults aged 18-35 years [alcohol use disorder (AUD), N = 30; Internet gaming disorder (IGD), N = 34; healthy controls (HCs), N = 35] participated in this study. Stress levels, resilience, addiction severity, and neurocognitive functions were evaluated, and serum levels of tryptophan (TRP), 5-hydroxytryptamine (5-HT), KYN, and kynurenine acid (KYNA) were determined using liquid chromatography coupled with tandem mass spectrometry through blood samples. Both addictive disorder groups showed higher levels of stress, lower resilience, and impaired executive functions compared to the HC group. Importantly, the AUD group revealed significantly increased KYN levels and KYN/TRP ratios, as well as decreased KYNA levels and KYNA/KYN ratios compared to HCs (p < 0.001, p < 0.001, p = 0.033, and p < 0.001, respectively). The IGD group showed KYN levels and KYNA/KYN ratios intermediate between those of the AUD group and HCs. Furthermore, in the AUD group, the mediating effect of AUD on KYN through stress level was moderated by resilience [index of moderated mediation = -0.557, boot S.E = 0.331, BCa CI (-1.349, -0.081)]. Stress may induce an imbalance in downstream of KYN pathway metabolites, and the KYN/TRP ratio may play as a neuromediator between stress and behavioral changes in both addictive disorders. This study suggests that regulation of the KYN pathway is critical in the pathophysiology of addictive disorders and it may serve as an important target for future treatment modalities.

Altered connectivity in the right inferior frontal gyrus associated with self-control in adolescents exhibiting problematic smartphone use: A fMRI study.

BACKGROUND: With the continued spread of smartphones and development of the internet, the potential negative effects arising from problematic smartphone use (PSU) in adolescents are being reported on an increasing basis. This study aimed to investigate whether altered resting-state functional connectivity (rsFC) is related to the psychological factors underlying PSU in adolescents. METHODS: Resting-state functional magnetic resonance images were acquired from 47 adolescents with PSU and 46 healthy control adolescents (the CON group). Seed-based functional connectivity analyses were then performed to compare the two groups with respect to rsFC in the right inferior frontal gyrus, associated with various forms of self-control, and rsFC in the left inferior frontal gyrus. RESULTS: Compared to the CON group, the PSU group exhibited a reduction in rsFC between the right inferior frontal gyrus and limbic areas, including the bilateral parahippocampal gyrus, the left amygdala, and the right hippocampus. In addition, a reduction in fronto-limbic rsFC was associated with the severity of PSU, the degree of self-control, and the amount of time the subjects used their smartphones. CONCLUSION: Adolescents with PSU exhibited reduced levels of fronto-limbic functional connectivity; this mechanism is involved in salience attribution and self-control, attributes that are critical to the clinical manifestation of substance and behavioral addictions. Our data provide clear evidence for alterations in brain connectivity with respect to self-control in PSU.

The Effect of Stress on Compulsive Sexual Behavior Disorder: Active Coping Strategy and Self-Control as Mediators.

OBJECTIVE: The aim of this study was to verify the effects of stress on compulsive sexual behavior disorder and the mediating effects of active coping strategies and self-control. METHODS: A self-reporting scale was administered to 300 adults aged 20 to 39, and the collected data were analyzed. The measures used for the study included a perceived stress scale, the way of coping checklist, a brief self-control scale, and an online compulsive sexual behavior disorder scale. RESULTS: The correlations between stress and active coping strategy, stress and lack of self-control, and lack of self-control and compulsive sexual behavior disorder were -0.18 (p<0.01), 0.38 (p<0.01), and 0.26 (p<0.01), respectively. CONCLUSION: First, stress level was positively correlated with compulsive sexual behavior disorder. Second, self-control completely mediates the relationship between stress and compulsive sexual behavior disorder. Third, self-control and active coping strategies had sequential mediating effects in that order.

Reduced Volume of a Brainstem Substructure in Adolescents with Problematic Smartphone Use.

OBJECTIVES: Despite the growing concern regarding the adverse effects related to problematic smartphone use (PSU), little is known about underlying morphologic changes in the brain. The brainstem is a deep brain structure that consists of several important nuclei associated with emotions, sensations, and motor functions. In this study, we sought to examine the difference in the volume of brainstem substructures among adolescents with and without PSU. METHODS: A total of 87 Korean adolescents participated in this study. The PSU group (n=20, age=16.2±1.1, female:male=12:8) was designated if participants reported a total Smartphone Addiction Proneness Scale (SAPS) score of ≥42, whereas the remaining participants were assigned to the control group (n=67, age=15.3±1.7, female:male=19:48). High-resolution T1 magnetic resonance imaging was performed, and the volume of each of the four brainstem substructures [midbrain, pons, medulla, and superior cerebellar peduncle (SCP)] was measured. Analysis of covariance was conducted to reveal group differences after adjusting for effects of age, gender, whole brain-stem volume, depressive symptoms, and impulsivity. RESULTS: The PSU group showed a significantly smaller volume of the SCP than the control group (F=8.273, p=0.005). The volume of the SCP and the SAPS score were negatively correlated (Pearson's r=-0.218, p=0.047). CONCLUSION: The present study is the first to reveal an altered volume of the brainstem substructure among adolescents with PSU. This finding suggests that the altered white matter structure in the brainstem could be one of the neurobiological mechanisms underlying behavioral changes in PSU.

The Effect of Stress on Internet Game Addiction Trends in Adults: Mindfulness and Conscientiousness as Mediators.

OBJECTIVE: This study examined the mediating effect of mindfulness and consciousness among five factors on the effect of stress on Internet game addiction tendency. METHODS: An online survey was conducted on 400 men and women between their 20s and 40s across Korea. The scales used in the study were the Internet game addiction scale based on the DSM-5, Perceived Stress Scale, Mindfulness Scale, and Five-Factor Personality Scale. For data analysis, structural equation modeling was conducted, and Sobel verification was carried out to verify the significance of the mediating effects. RESULTS: The results of the study are as follows: Stress showed a positive correlation with Internet game addiction tendency, mindfulness mediated the effect of stress on Internet game addiction tendency. CONCLUSION: Of the five personality factors, conscientiousness also mediated the effect of stress on Internet game addiction tendency. This study highlights the need for further research for individuals with game addiction tendency.

Machine learning prediction of dropping out of outpatients with alcohol use disorders.

BACKGROUND: Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes. METHODS: A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models-logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost-and compared the prediction performances thereof. RESULTS: Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes. CONCLUSION: An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.

A Deep Learning Algorithm to Predict Hazardous Drinkers and the Severity of Alcohol-Related Problems Using K-NHANES.

Purpose: The number of patients with alcohol-related problems is steadily increasing. A large-scale survey of alcohol-related problems has been conducted. However, studies that predict hazardous drinkers and identify which factors contribute to the prediction are limited. Thus, the purpose of this study was to predict hazardous drinkers and the severity of alcohol-related problems of patients using a deep learning algorithm based on a large-scale survey data. Materials and Methods: Datasets of National Health and Nutrition Examination Survey of South Korea (K-NHANES), a nationally representative survey for the entire South Korean population, were used to train deep learning and conventional machine learning algorithms. Datasets from 69,187 and 45,672 participants were used to predict hazardous drinkers and the severity of alcohol-related problems, respectively. Based on the degree of contribution of each variable to deep learning, it was possible to determine which variable contributed significantly to the prediction of hazardous drinkers. Results: Deep learning showed the higher performance than conventional machine learning algorithms. It predicted hazardous drinkers with an AUC (Area under the receiver operating characteristic curve) of 0.870 (Logistic regression: 0.858, Linear SVM: 0.849, Random forest classifier: 0.810, K-nearest neighbors: 0.740). Among 325 variables for predicting hazardous drinkers, energy intake was a factor showing the greatest contribution to the prediction, followed by carbohydrate intake. Participants were classified into Zone I, Zone II, Zone III, and Zone IV based on the degree of alcohol-related problems, showing AUCs of 0.881, 0.774, 0.853, and 0.879, respectively. Conclusion: Hazardous drinking groups could be effectively predicted and individuals could be classified according to the degree of alcohol-related problems using a deep learning algorithm. This algorithm could be used to screen people who need treatment for alcohol-related problems among the general population or hospital visitors.

Behavioral Intention to Use a Smartphone Usage Management Application Between a Non-Problematic Smartphone Use Group and a Problematic Use Group.

Despite the many advantages of smartphone in daily life, there are significant concerns regarding their problematic use. Therefore, several smartphone usage management applications have been developed to prevent problematic smartphone use. The purpose of this study is to investigate the factors of users' behavioral intention to use smartphone usage management applications. Participants were divided into a smartphone use control group and a problematic use group to find significant intergroup path differences. The research model of this study is fundamentally based on the Technology Acceptance Model and Expectation-Confirmation Theory. Based on this theorem, models were modified to best suit the case of problematic smartphone use intervention by smartphone application. We conducted online surveys on 511 randomly selected smartphone users aged 20-60 in South Korea, in 2018. The Smartphone Addiction Proneness Scale was used to measure participants' smartphone dependency. Descriptive statistics were used for the demographic analysis and collected data were analyzed using IBM SPSS Statistics 24.0 and Amos 24.0. We found that in both non-problematic smartphone use group and problematic smartphone use group, facilitating factors and perceived security positively affect the intentions of users to use the application. One distinct difference between the groups was that the latter attributed a lower importance to perceived security than the former. Some of our highlighted unique points are envisioned to provide intensive insights for broadening knowledge about technology acceptance in the field of e-Addictology.

Caudate nucleus volume mediates the link between glutamatergic neurotransmission and problematic smartphone use in youth.

BACKGROUND AND AIMS: Problematic smartphone use (PSU) is growing rapidly among teens. It has similar presentations as other behavioral addictions in terms of excessive use, impulse control problems, and negative consequences. However, the underlying neurobiological mechanisms remain undiscovered. We hypothesized that structural changes in the striatum might serve as an important link between alteration in glutamate signaling and development of PSU. METHODS: Among 88 participants, twenty (F:M, 12:8; age 16.2 ± 1.1) reported high scores in the smartphone addiction proneness scale (SAPS) with a cut-off score of 42; the other 68 (F:M, 19:49; age 15.3 ± 1.7) comprised the control group. Sociodemographic data and depression, anxiety, and impulsivity traits were measured. Striatal volumes (caudate, putamen, and nucleus accumbens) were estimated from T1 imaging data. Serum glutamate levels were estimated from peripheral blood samples. Group comparisons of each data were performed after controlling for age and gender. Mediation analyses were conducted to test the indirect effects of glutamate level alteration on PSU through striatal volumetric alteration. RESULTS: The PSU group showed a decrease in both caudate volumes than the control group. Left caudate volume was positively correlated with serum glutamate level, and negatively with impulsivity traits and SAPS scores. The mediation model revealed a significant indirect effect of serum glutamate on SAS scores through the reduced left caudate volume. DISCUSSION AND CONCLUSIONS: This study suggests that altered glutamatergic neurotransmission may be associated with PSU among teens, possibly through reduced left caudate volume. Current findings might support neural mechanisms of smartphone addiction.