Exploring the connection between parental bonding and smartphone addiction in Chinese medical students

Xin, Chunyu; Ding, Ning; Jiang, Nan; Li, Honghe; Wen, Deliang

doi:10.1186/s12888-022-04355-7

Cited by 6 publications

(3 citation statements)

References 86 publications

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“…Conversely, our approach only requires capturing beef images using a smartphone and uploading them to the model deployed on the cloud platform. With smartphone subscriptions reaching 6.5 billion in 2022 and projected to reach 7.7 billion in 2023 (Xin et al., 2022), using smartphones can provide a convenient method for sample collection without the need for trained personnel. Therefore, the smartphone‐based approach using computer vision for hydrocolloid detection could provide a simple way for meat quality testing in developing countries and resource‐limited environments.…”

Section: Resultsmentioning

confidence: 99%

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

Gao,

Chen,

Huang

et al. 2024

Journal of Food Science

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Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real‐time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone‐based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre‐training and fine‐tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin‐Tiny (Swin‐T) achieved the highest trade‐off performance, with a Top‐1 accuracy of 0.997, a detection speed of 3.2 ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin‐T model was also capable of distinguishing between different types of hydrocolloids with a Top‐1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real‐time quantitative information about meat adulteration anytime, anywhere.Practical ApplicationThis research provides a practical solution for regulators and consumers to non‐destructively and quantitatively detect the content and type of hydrocolloids in beef in real‐time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.

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Section: Resultsmentioning

confidence: 99%

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

Gao,

Chen,

Huang

et al. 2024

Journal of Food Science

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“…Smartphone addiction is considered a behavioral addiction and refers to an excessive use of smartphones that interferes with users’ daily life and has harmful effects[ 11 ]. Evidence from China indicated that the prevalence of smartphone addiction among college students ranges from 20% to 50%[ 12 - 14 ]. Notably, smartphone addiction is associated with mental health problems[ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic

Gao¹,

Hu²,

Ji³

et al. 2023

World J Psychiatry

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BACKGROUND Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic. AIM To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates. METHODS Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version (SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep. RESULTS Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [ P < 0.01; 95% confidence interval (CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022 ( P < 0.01; 95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040 ( P < 0.01; 95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively. CONCLUSION The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

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“…The current global prevalence of IA among adolescents is 8.8% [7] and 11.3% among Chinese adolescents [8]. Studies have shown that IA may cause a range of serious problems in adolescents, including attention disorders [9], academic performance decline [10], interpersonal relationship problems [11,12], family dysfunction [13], anxiety [14], and depression [15]. IA has seriously harmed adolescents' physical and mental health.…”

Section: Introductionmentioning

confidence: 99%

Association between internet addiction and suicide and self-injury behavior in Chinese adolescents: A meta-analysis

Yang

et al. 2023

Preprint

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Background: Internet addiction (IA) has become a significant social issue, with studies indicating a correlation between adolescent suicide, self-injury, and IA. However, the nature of this correlation among Chinese teenagers remains unclear. This research aimed to clarify the relationships between IA and suicidal and self-injury behaviors among Chinese adolescents through a meta-analysis of observational studies. Methods: We searched seven electronic databases from inception to January 16, 2023. Study quality was assessed by the AHRQ and the NOS. We extracted the effect values (OR) of IA and suicide, and self-injury behaviors using STATA (version 16) software for data synthesis. Results: Out of 836 initial studies, 26 cross-sectional (n = 376742) and two prospective (n = 1831) studies were selected to examine the association between IA and suicide and self-injury behaviors in Chinese adolescents. IA was significantly linked to increased odds of suicidal ideation (OR = 1.81, 95% CI: 1.46-2.24), suicide planning (OR = 1.85, 95% CI: 1.36-2.52), suicide attempt (OR = 1.78, 95% CI: 1.34-2.36), and non-suicidal self-injury (OR = 1.91, 95% CI: 1.58-2.31). Subgroup analysis suggested that regional differences, age, and publication year may regulate the association between IA and non-suicidal self-injury behavior. The study found that western China had the highest risk of non-suicidal self-injury, and middle school students were more vulnerable than college students. Moreover, teenagers with IA were found to be at a higher risk of non-suicidal self-injury over time. Additionally, age was identified as a significant moderator of the association between IA and suicidal ideation, with a lower likelihood of suicidal ideation in middle school students compared to college students. Conclusion: This meta-analysis provides evidence of a positive association between IA and suicidal and self-injury behaviors among Chinese adolescents. It emphasizes the need for public health interventions that target IA and related mental health issues in this population.

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Exploring the connection between parental bonding and smartphone addiction in Chinese medical students

Cited by 6 publications

References 86 publications

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

Real‐time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone

Relationship between depression, smartphone addiction, and sleep among Chinese engineering students during the COVID-19 pandemic

Association between internet addiction and suicide and self-injury behavior in Chinese adolescents: A meta-analysis

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