Development and evaluation of smartphone usage management system for preventing problematic smartphone use.

Objective: The increased use of smartphones has led to several problems, including excessive smartphone use and the decreased self-ability to control smartphone use. To prevent these problems, the MindsCare app was developed as a method of self-management and intervention based on an evaluation of smartphone usage. We designed the MindsCare app to manage smartphone usage and prevent problematic smartphone use by providing personalized interventions. Methods: We recruited 342 Korean participants over the age of 20 and asked them to use MindsCare for 13 weeks. Subsequently, we evaluated the changes in average smartphone usage time and the usability of the app. We designed a usability evaluation questionnaire based on the Technology Acceptance Model and conducted factor and reliability analyses on the participants' responses. In the eighth week of the study, participants responded to a survey on the usability of the app. We ultimately collected data from 190 participants. Results: The average score for the usability of the system was 3.61 on a five-point Likert scale, and approximately 58% of the participants responded positively to the evaluation items. In addition, our analysis of MindsCare data revealed a significant reduction in average smartphone use time in the eighth week compared to the baseline (t = 3.47, p = 0.001). Structural equation model analysis revealed that effort expectancy and performance expectancy had a positive relation with behavior intention for the app. Conclusions: Through this study, we confirmed the MindsCare app's smartphone usage time reduction effect and proved its good usability. As a result, MindsCare may contribute to achieving users' goals of reducing problematic smartphone use.

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.

Factors Affecting Smartphone Usage Self-Report Levels.

For the treatment of smartphone addiction, it is important to understand users' smartphone usage patterns. Most of the studies are based on self-report surveys. However, there are differences between self-reported usage and real usage. For a better understanding of usage patterns, this study identified demographic and social factors that affect smartphone usage self-report levels. Also, it was confirmed that the influencing factors differ depending on the smartphone usage content by application category.

Corrigendum: Factors Affecting User Acceptance in Overuse of Smartphones in Mobile Health Services: An Empirical Study Testing a Modified Integrated Model in South Korea.

[This corrects the article DOI: 10.3389/fpsyt.2018.00658.].

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer.

Object: Pathologic prediction of prostate cancer can be made by predicting the patient's prostate metastasis prior to surgery based on biopsy information. Because biopsy variables associated with pathology have uncertainty regarding individual patient differences, a method for classification according to these variables is needed. Method: We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer. The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs. Uncertainty regarding the predicted output was removed from the DBN and combined with information from DS to make a correct decision. Result: The new method was validated on pathology data from 6342 patients with prostate cancer. The pathology stages consisted of organ-confined disease (OCD; 3892 patients) and non-organ-confined disease (NOCD; 2453 patients). The results showed that the accuracy of the proposed DBN-DS was 81.27%, which is higher than the 64.14% of the Partin table. Conclusion: The proposed DBN-DS is more effective than other methods in predicting pathology stage. The performance is high because of the linear combination using the results of pathology-related features. The proposed method may be effective in decision support for prostate cancer treatment.

Factors Affecting User Acceptance in Overuse of Smartphones in Mobile Health Services: An Empirical Study Testing a Modified Integrated Model in South Korea.

Smartphones have become crucial in people's everyday lives, including in the medical field. However, as people become close to their smartphones, this leads easily to overuse. Overuse leads to fatigue due to lack of sleep, depressive symptoms, and social relationship failure, and in the case of adolescents, it hinders academic achievement. Self-control solutions are needed, and effective tools can be developed through behavioral analysis. Therefore, the aim of this study was to investigate the determinants of users' intentions to use m-Health for smartphone overuse interventions. A research model was based on TAM and UTAUT, which were modified to be applied to the case of smartphone overuse. The studied population consisted of 400 randomly selected smartphone users aged from 19 to 60 years in South Korea. Structural equation modeling was conducted between variables to test the hypotheses using a 95% confidence interval. Perceived ease of use had a very strong direct positive association with perceived usefulness, and perceived usefulness had a very strong direct positive association with behavioral intention to use. Resistance to change had a direct positive association with behavioral intention to use and, lastly, social norm had a very strong direct positive association with behavioral intention to use. The findings that perceived ease of use influenced perceived usefulness, that perceived usefulness influenced behavioral intention to use, and social norm influenced behavioral intention to use were in accordance with prior related research. Other results that were not consistent with previous research imply that these are unique behavioral findings regarding smartphone overuse. This research identifies the critical factors that need to be considered when implementing systems or solutions in the future for tackling the issue of smartphone overuse.

A Performance Comparison on the Machine Learning Classifiers in Predictive Pathology Staging of Prostate Cancer.

This study objectives to investigate a range of Partin table and several machine learning methods for pathological stage prediction and assess them with respect to their predictive model performance based on Koreans data. The data was used SPCDB and gathered records from 944 patients treated with tertiary hospital. Partin table has low accuracy (65.68%) when applied on SPCDB dataset for comparison on patients with OCD NOCD conditions. SVM (75%) represents a promising alternative to Partin table from which pathology staging can benefit.