Smartphone dependence classification using tensor factorization

The unique role of smartphone addiction and related factors among university students: a model based on cross-sectional and cross-lagged network analyses

Smartphone addiction is a global problem affecting university students. Previous studies have explored smartphone addiction and related factors using latent variables. In contrast, this study examines the role of smartphone addiction and related factors among university students using a cross-sectional and cross-lagged panel network analysis model at the level of manifest variables. A questionnaire method was used to investigate smartphone addiction and related factors twice with nearly six-month intervals among 1564 first-year university students (M = 19.14, SD = 0.66). The study found that procrastination behavior, academic burnout, self-control, fear of missing out, social anxiety, and self-esteem directly influenced smartphone addiction. Additionally, smartphone addiction predicted the level of self-control, academic burnout, social anxiety, and perceived social support among university students. Self-control exhibited the strongest predictive relationship with smartphone addiction. Overall, self-control, self-esteem, perceived social support, and academic burnout were identified as key factors influencing smartphone addiction among university students. Developing prevention and intervention programs that target these core influencing factors would be more cost-effective.

Problematic internet usage: the impact of objectively Recorded and categorized usage time, emotional intelligence components and subjective happiness about usage

Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI (sociability, emotionality, well-being, self-control), and happiness with phone use can predict PIU and its components (obsession, neglect, and control disorder). A total of 268 participants (Female: 61.6%) reported their demographic and completed a questionnaire that included Problematic Internet Usage Questionnaire short form (PIUQ-SF-6), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), level of happiness with the amount and frequency of smartphone use, and living conditions (whether alone or with others). Their smartphone usage was objectively recorded through a dedicated app. A series of one-way ANOVA revealed no significant difference in PIU for different living conditions and a significant difference in the subjective level of happiness with phone usage (F (3, 264) = 7.55, p < .001), as well as of the frequency of usage where the unhappy group had higher PIU (F (3, 264) = 6.85, p < .001). Multiple linear regression analysis showed that happiness with phone usage (β = -.17), the actual usage of communication (β = .17), social media (β = .19) and gaming apps (β = .13), and trait EI component of self-control (β = -.28) were all significant predictors of PIU. Moreover, gender, age, and happiness with the frequency of phone usage were not significant predictors of PIU. The whole model accounted for the total variance of PIU by 32.5% (Adjusted R2 = .287). Our study contributes to the literature by being among the few to rely on objectively recorded smartphone usage data and utilizing components of trait EI as predictors.

Exploring the Associations Between Self-reported Tendencies Toward Smartphone Use Disorder and Objective Recordings of Smartphone, Instant Messaging, and Social Networking App Usage: Correlational Study

Background

Social communication via instant messaging (IM) and social networking (SN) apps makes up a large part of the time that smartphone users spend on their devices. Previous research has indicated that the excessive use of these apps is positively associated with problematic smartphone use behaviors. In particular, image-based SN apps, such as Instagram (Facebook Inc) and Snapchat (Snap Inc), have been shown to exert stronger detrimental effects than those exerted by traditional apps, such as Facebook (Facebook Inc) and Twitter (Twitter Inc).

Objective

In this study, we investigated the correlation between individuals’ tendencies toward smartphone use disorder (SmUD) and objective measures of the frequency of smartphone usage. Additionally, we put to test the hypothesis that the pathway linking the frequency of actual smartphone usage to self-reported tendencies toward SmUD was mediated by the increased frequency of IM and SN app usage.

Methods

We recruited a sample of 124 adult smartphone users (females: 78/124, 62.9%; age: mean 23.84 years, SD 8.29 years) and collected objective information about the frequency of smartphone and SN app usage over 1 week. Participants also filled in a self-report measure for assessing the multiple components of tendencies toward SmUD. Bivariate associations were investigated by using Spearman correlation analyses. A parallel mediation analysis was conducted via multiple regression analysis.

Results

The frequency of smartphone usage, as well as the use of IM apps (Messenger, Telegram, and WhatsApp [Facebook Inc]), Facebook, and image-based apps (Instagram and Snapchat), had significant positive associations with at least 1 component of SmUD, and the cyberspace-oriented relationships factor exhibited the strongest associations overall. We found support for an indirect effect that linked actual smartphone usage to SmUD tendencies via the frequency of the use of image-based SN apps.

Conclusions

Our novel results shed light on the factors that promote SmUD tendencies and essentially indicate that image-based SN apps seem to be more strongly associated with problematic smartphone behaviors compared to IM apps and traditional SN apps, such as Facebook.

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.

Smartphone use patterns and problematic smartphone use among preschool children

Background

The purpose of the present study was to identify smartphone use patterns associated with problematic smartphone use (PSU) among preschool children. Little is known about PSU patterns in younger children, although the age for first smartphone use is decreasing.

Methods

We applied a cross-sectional study design to analyze data obtained from a nationwide survey on smartphone overdependence conducted in 2017 by the South Korean Ministry of Science and ICT and the National Information Society Agency. Data from 1,378 preschool children were analyzed using binomial logistic regression analysis. This study was conducted in compliance with STROBE (Strengthening the Reporting of Observational Studies in Epidemiology).

Results

Seventeen percent of the sample met the criteria for PSU. The odds of PSU significantly increased with frequent smartphone use and in children who used a smartphone for more than two hours per day. Using smartphones to watch TV shows or videos for entertainment or fun significantly increased the odds of PSU, whereas using smartphones for education, games, and social networking did not.

Conclusions

The findings indicate that one of five preschool children using smartphones could experience PSU. Compared to other age groups, PSU in young children may be more associated with their caregivers. To prevent PSU in preschool children, caregivers need information about the total screen time recommended for children, smartphone use patterns associated with PSU, suggestions for other activities as possible alternatives to smartphone use, and strategies to strengthen children’s self-regulation with regards to smartphone use.

Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review

Despite the negative role of IT in digital addiction development, IT may have a positive role in dealing with digital addiction. The present study undertakes a systematic literature review to explore the state of play and the trend regarding the use of IT in digital addiction research. Using predefined keywords, the Scopus database was searched for relevant literature published from 2017 to 2020. The initial search found 1655 papers. Six stages of study selection were completed using a set of inclusion and exclusion criteria. The study selection and quality assessment process were applied, then 15 papers were selected for further review. The results show that addiction detection using IT is the most researched topic in digital addiction research. The most commonly used IT in the selected studies are AI methods and biosignal recording systems. Various approaches in detection, prevention, and intervention are suggested in the selected studies. The advantages and limitations of each approach are discussed. Based on these results, some future research directions are suggested.

Development of the Smartphone Addiction Risk Rating Score for a Smartphone Addiction Management Application

Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.

Discriminative Sleep Patterns of Alzheimer's Disease via Tensor Factorization

Sleep change is commonly reported in Alzheimer's disease (AD) patients and their brain wave studies show decrease in dreaming and non-dreaming stages. Although sleep disturbance is generally considered as a consequence of AD, it might also be a risk factor of AD as new biological evidence shows. Leveraging the National Sleep Research Resource (NSRR), we built a unique cohort of 83 cases and 331 controls with clinical variables and electroencephalography (EEG) signals. Supervised tensor factorization method was applied for this temporal dataset to extract discriminative sleep patterns. Among the 30 patterns extracted, we identified 5 significant patterns (4 patterns for AD likely and 1 pattern for normal ones) and their visual patterns provide interesting linkage to sleep with repeated wakefulness, abnormal REM sleep, and insomnia. This study is preliminary but findings are interesting, which is a first step to provide quantifiable evidences to measure sleep as a risk factor of AD.

Passive objective measures in the assessment of problematic smartphone use: A systematic review

Research focussing on problematic smartphone use has predominantly employed psychometric tests which cannot capture the automatic processes and behaviours associated with problematic use. The present review aimed to identify passive objective measures that have been used or developed to assess problematic smartphone use. A systematic search was conducted using Web of Science, Scopus, PsychInfo and PubMed databases to identify passive objective measures that have been employed to assess problematic smartphone use, resulting in 18 studies meeting the inclusion criteria. Objective data that were monitored predominantly focussed on general screen usage time and checking patterns. Findings demonstrate that passive monitoring can enable smartphone usage patterns to be inferred within a relatively short timeframe and provide ecologically valid data on smartphone behaviour. Challenges and recommendations of employing passive objective measures in smartphone-based research are discussed.

Smartphone Use Patterns of Smartphone-dependent Children

Purpose

The purpose of this study was to identify the smartphone use patterns of smartphone-dependent children.

Methods

The study design was a cross-sectional descriptive study. The data were derived from the 2017 survey on smartphone overdependence conducted by the Ministry of Science and ICT and the National Information Society Agency. The study sample was 595 elementary school students identified as being smartphone-dependent. The data were analyzed by frequency, percentage, mean, standard deviation, the independent t-test, the χ² test, the Pearson correlation coefficient, and multiple regression analysis.

Results

The frequency of smartphone use was the factor strongly associated with more severe smartphone dependence in participants. Games were the most commonly used application type among participants, regardless of their degree of dependence. More severe smartphone dependence was associated with greater use of applications such as learning and television/video.

Conclusion

As smartphone dependence becomes more severe, children tend to use their smartphones more frequently and to use applications that involve solitary play for the purposes of entertainment and pleasure. The findings suggest that the parents should attentively monitor their children’s smartphone use patterns and provide consistent discipline in a way that ensures appropriate smartphone use.

Does smartphone addiction affect social interaction: a study among dental students in Hyderabad

OBJECTIVE

Technology has revolutionized the way people communicate and changed communication in many ways. The evolution of the smartphone may shed some light on this. Though smartphones are user friendly, they can be detrimental to a user's health if they are used in an addictive manner. With easy accessibility to smartphones, dental students may fall prey to smartphone addiction (SA) which in turn may affect their social interaction. Therefore, this paper aimed to examine whether SA affects social interaction.

MATERIALS AND METHODS

A cross-sectional survey was conducted among undergraduate dental students from five dental institutions in Hyderabad. Participants received a questionnaire assessing SA [15 item-Smartphone Addiction Proneness Scale (SAPS-15)], social interaction [20 item-Social Interaction Anxiety Scale (SIAS) ). The chi-square (χ²)-test was used to determine the association between SA and social interaction anxiety. p ≤ 0.05 was considered statistically significant.

RESULTS

The study comprised 470 (30.7%) males and 1060 (69.3%) females. Multiple logistic regression revealed females [1.65; confidence interval (CI) = 0.88-1.88] and third year students (2.12; CI: 1.45-2.23) had significantly higher odds for social interaction anxiety (p ≤ 0.05*).

CONCLUSION

Therefore, the present study supported the hypothesis that SA affects social interaction anxiety among undergraduate dental students.

Transitions in smartphone addiction proneness among children: The effect of gender and use patterns

OBJECTIVES

This study assessed the incidence of transitions in smartphone addiction proneness (SAP) among children and examined the effects of gender, use patterns (social networking sites (SNSs) use and smartphone gaming) and depression on smartphone addiction transitions.

METHODS

A representative sample of 2,155 children from Taipei completed longitudinal surveys in both 2015 (5th grade) and 2016 (6th grade). Latent transition analysis (LTA) was used to characterize transitions in SAP and to examine the effects of gender, use patterns and depression on SAP transitions.

RESULTS

LTA identified four latent statuses of SAP: about half of the children were in non-SAP status, one-fifth were in tolerance status, one-sixth were in withdrawal status, and one-seventh were in high-SAP status. Both boys and girls had a higher prevalence of high-SAP and tolerance in 6th grade than in 5th grade, whereas in both grades boys had a higher prevalence of high-SAP and withdrawal, and girls had a higher prevalence of non-SAP and tolerance. Controlling for parents' education, family structure, and household income, higher use of SNSs by children, increasing use of mobile gaming and higher levels of depression were individually associated with increased odds of being in one of the three SAP statuses other than non-SAP. When all three covariates were jointly entered into the model, usage of SNSs and depression remained significant predictors.

CONCLUSION

Both boys and girls tended to transition to tolerance or high-SAP statuses, while children's depression and their usage of SNSs increased the risk of smartphone addiction.

Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure

The purpose of this study was to carry out a review of observational studies that consider links between mobile phone use and mental health from a psychological or behavioral perspective. Systematic literature searches in PubMed and PsycINFO for articles published until 2017 were done. Exclusion criteria included: papers that considered radiofrequency fields, attention, safety, relational consequences, sexual behavior, cyberbullying, and reviews, qualitative, and case or experimental studies. A total of 4738 papers were screened by title and abstract, 404 were retrieved in full text, and 290 were included. Only 5% had any longitudinal design. Self-reporting was the dominating method of measurement. One third of the studies included children or youth. A majority of adult populations consisted of university students and/or self-selected participants. The main research results included associations between frequent mobile phone use and mental health outcomes, such as depressive symptoms and sleep problems. Mobile phone use at bedtime was associated with, e.g., shorter sleep duration and lower sleep quality. "Problematic use" (dependency) was associated with several negative outcomes. In conclusion, associations between mobile phone use and adverse mental health outcomes are found in studies that take a psychological or behavioral perspective on the exposure. However, more studies of high quality are needed in order to draw valid conclusions about the mechanisms and causal directions of associations.

Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization

Several studies have been conducted to evaluate the efficacy of statins in Korean and Asian patients. However, most previous studies only observed the percent reduction in low-density lipoprotein cholesterol (LDL-C) and did not consider the effects of various patient conditions simultaneously, such as abnormal test results, patient demographics, and prescribed drugs before taking a statin. Moreover, the characteristics of the patients whose percent reduction in LDL-C was higher than expected were not provided. Therefore, in this study, we aimed to derive meaningful phenotypes by using tensor factorization to observe the characteristics of the patients whose percent reduction in LDL-C was higher than expected among patients taking moderate-intensity statins. In addition, we used the derived phenotypes to predict how much the LDL-C levels of new patients decreased. We consequently identified eight phenotypes that represented the characteristics of the patients whose percent reduction in LDL-C was higher than expected. Moreover, the latent representations of the derived phenotypes achieved prediction performance similar to that obtained using the raw data. These results demonstrate that the derived phenotypes and latent representations are useful tools for observing the characteristics of patients and predicting LDL-C levels. Additionally, our findings provide direction on how to conduct clinical studies in the future.

Correction: Smartphone dependence classification using tensor factorization

[This corrects the article DOI: 10.1371/journal.pone.0177629.].