Health Risks of Mobile Phone Addiction Among College Students in China

Mei, Songli; Hu, Yueyang; Xiao-gang, WU; Cao, Renyi; Kong, Yixi; Zhang, Liwei; Lin, Xinli; Liu, Qian; Hu, Yuanchao; Li, Li

doi:10.1007/s11469-021-00744-3

Cited by 46 publications

(25 citation statements)

References 48 publications

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“…The median prevalence of PSU among children and young people was 23.3% (3). Importantly, PSU is often associated with physical and psychological concerns, including depression, anxiety, sleep impairments, and spinal disorders (4)(5)(6). Given the high prevalence and negative consequences associated with PSU, there is a call to understand the mechanism underlying PSU (2,7).…”

Section: Introductionmentioning

confidence: 99%

Understanding the Association Between Intolerance of Uncertainty and Problematic Smartphone Use: A Network Analysis

Liu

Ren

et al. 2022

Front. Psychiatry

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BackgroundExisting research has demonstrated that intolerance of uncertainty (IU) is associated with problematic smartphone use (PSU). However, little is known about how different IU components such as uncertainty-related beliefs, emotions, and behaviors may impact on different PSU symptoms.MethodsExtending previous research, the current study examined the specific associations between IU components and PSU symptoms via a symptom-level network approach. A regularized partial correlation network consisting of different IU components and PSU symptoms was estimated among 1,849 Chinese university students. We examined pathways and influential nodes (i.e. central components/symptoms and bridge components/symptoms) within the IU-PSU network.ResultsThe strongest pathway linking IU and PSU was between emotional reactions to uncertainty and coping-motivated smartphone use. Importantly, emotional reactions toward not having enough information (a reflection of emotional reactions to uncertainty) may act as both a central and a bridge component in maintaining the whole IU-PSU network.ConclusionsThe results are in line with the I-PACE model and highlight that PSU may be a coping response for negative emotions derived from uncertainty. Finally, the current findings highlight the potential of interventions targeting intolerance of uncertainty for reducing PSU.

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Section: Introductionmentioning

confidence: 99%

Understanding the Association Between Intolerance of Uncertainty and Problematic Smartphone Use: A Network Analysis

Liu

Ren

et al. 2022

Front. Psychiatry

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“…In modern society, smartphone use is closely linked to daily life overall ( 29 ). Among adolescents, a phenomenon has emerged in which they are dependent on smartphones so as not to be isolated from peers ( 30 ) and to be emotionally secure ( 31 ). But, someone who is addicted to a smartphone will feel happy when using a smartphone, then feel stressed and anxious when they can't meet the needs of using their smartphone ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Association between smartphone overdependency and mental health in Korean adolescents during the COVID pandemic; Age-and gender-matched study

Kim

Jae-Moo

Yang

et al. 2022

Front. Public Health

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PurposeThis study aimed to examine the relationship between smartphone dependency (SD) and mental health (MH) in adolescents in order to develop and implement plans pertaining to SD control.MethodsRaw data from the 16th Online Adolescent Health Behavior Survey in 2020 were analyzed. A total of 482 respondents were selected as study subjects based on their experience of smartphone overdependence (SO), specifically, 241 participants whose score for SO was 37 or higher (Group 2) and age- and gender-matched 241 participants whose score was lower than 10 (Group 1).ResultsFrequency analysis, cross-tab analysis (χ2 test), and multinomial logistic regression were performed Analysis shows that the MH affecting the increase in SO is the subjective perception of happiness, subjective perception of stress, sadness and despair, and experience of Loneliness. But, the variable affecting the reduction is the subjective evaluation of sleep quality. The likelihood of SO increased as adolescents felt unhappier [Exp (β) = 2.408] and more stressed [Exp (β) = 4.453] and more often felt lonely [Exp (β) = 8.149], but the likelihood decreased as they had neither sufficient nor insufficient sleep duration [Exp (β) = 0.344]. The findings suggest that it is necessary to develop aggressive measures for the prevention and management of MH in adolescents showing SO because mental health is closely linked to SD. In developing the measures, realistic approaches to widely pervasive SO among adolescents should be explored by taking into account MH factors, that is, predictors of SO, and the characteristics of youths, such that they can self-control smartphone use and form desirable life habits.

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“…However, in real-world application scenarios, surveillance cameras usually have multiple perspectives, which will result in great differences for intelligent devices such as computers to understand and process scene content from different perspectives. At the same time, it is urgent to evaluate the mobile status of equipment with secure and real-time performance [ 23 ]. Therefore, the robustness, accuracy, and effectiveness of the method to evaluate human skeletal state are important challenges for future research.…”

Section: Related Workmentioning

confidence: 99%

Extraction and Analysis of Foot Bone Shape Features Based on Deep Learning

Zhi

2022

Computational and Mathematical Methods in Medicine

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With the rapid development of artificial intelligence, more and more researchers and research institutions begin to pay attention to the bone feature recognition field. Human bone movement is very complex, and human bone shape recognition technology can be widely used in medical treatment, sports, and other fields. At present, there are mainly two kinds of methods for extracting the shape features of human foot bone based on optical image acquisition technology and sensor information perception technology. However, due to the interference factors such as target posture change, camera shake, and individual behavior differences, it is still a very challenging task to design a robust algorithm for extraction and analysis of foot bone shape features. In recent years, convolutional neural network- (CNN-) based foot contour feature recognition methods emerge one after another and have made breakthrough progress. How to use and how to fully explore the potential relationship of various characteristics contained in the foot bone data and how to enhance the robustness of view changes and other aspects need to be further studied. In this context, this paper proposed an improved CNN model, which not only has the capability of deep feature extraction of the CNN model but also can obtain the optimal model parameters with the combination of particle swarm optimization algorithm. The effectiveness of the proposed method in the extraction and analysis of foot bone shape features is verified in the simulation experiment.

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Health Risks of Mobile Phone Addiction Among College Students in China

Cited by 46 publications

References 48 publications

Understanding the Association Between Intolerance of Uncertainty and Problematic Smartphone Use: A Network Analysis

Understanding the Association Between Intolerance of Uncertainty and Problematic Smartphone Use: A Network Analysis

Association between smartphone overdependency and mental health in Korean adolescents during the COVID pandemic; Age-and gender-matched study

Extraction and Analysis of Foot Bone Shape Features Based on Deep Learning

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