Social media use, mental health and sleep: A systematic review with meta-analyses

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: 367, P. 701 - 712

Published: Sept. 4, 2024

Language: Английский

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Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree‐based machine learning models

Health Science Reports, Journal Year: 2024, Volume and Issue: 7(4)

Published: April 1, 2024

Abstract Background and Aims Mental health problem is a rising public concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective study was to use tree‐based machine learning (ML) models identify major risk factors predict anxiety, depression, insomnia in students. Methods A social media‐based cross‐sectional survey employed for data collection. We used Generalized Anxiety Disorder (GAD‐7), Patient Health Questionnaire (PHQ‐9) Insomnia Severity Index (ISI‐7) scale measuring students' depression problems. The supervised decision tree (DT), random forest (RF) robust eXtreme Gradient Boosting (XGBoost) ML algorithms were build prediction their predictive performance evaluated using confusion matrix receiver operating characteristic (ROC) curves. Results Of 1250 students surveyed, 64.7% male 35.3% female. ages ranged from 18 26 years old, with an average age 22.24 (SD = 1.30). Majority (72.6%) rural areas media addicted (56.6%). Almost 83.3% had moderate severe 84.7% 76.5% Students' addiction, age, academic performance, smoking status, monthly family income morningness‐eveningness main insomnia. highest observed XGBoost model Conclusion findings offer valuable insights stakeholders, families policymakers enabling profound comprehension pressing mental disorders. This understanding can guide formulation improved policy strategies, initiatives promotion, development effective counseling services within campus. Additionally, our proposed might play critical role diagnosing predicting problems among similar settings.

Language: Английский

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Neurobiological risk factors for problematic social media use as a specific form of Internet addiction: A narrative review

World Journal of Psychiatry, Journal Year: 2023, Volume and Issue: 13(5), P. 160 - 173

Published: May 19, 2023

Problematic social media use (PSMU) is a behavioral addiction, specific form of problematic Internet associated with the uncontrolled networks. It typical mostly for modern adolescents and young adults, which are first generations fully grown up in era total digitalization society. The biopsychosocial model formation addictions, postulating impact large number biological, psychological, factors on addictive behavior formation, may be quite applicable to PSMU. In this narrative review, we discussed neurobiological risk addiction focus current evidence association between PSMU structural/ functional characteristics brain autonomic nervous system, neurochemical correlations, genetic features. A review literature shows that vast majority mentioned studies were focused computer games generalized (without taking into account consumed content). Even though certain neuroimaging have been conducted PSMU, there practically no research neuropeptide associations date. This fact points extremely high relevance such studies.

Language: Английский

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Enhancing students’ learning outcomes through smartphones: A case study of using instagram in higher management education

The International Journal of Management Education, Journal Year: 2023, Volume and Issue: 21(3), P. 100885 - 100885

Published: Oct. 21, 2023

Social media have become an integral part of people's lives worldwide, particularly for students in higher education, most whom belong to Generation Z. Hence, there is a need universities develop technological content adapted the preferences today's students. One popular social platforms Instagram (IG); however, studies investigating how it can be used support learning are scant, especially context education institutions. Accordingly, using structural equation modelling (SEM), this study analyses results project IG as supporting tool that complements traditional lectures promote subject Bachelor Business Administration (BBA) degree. The show perceived usefulness main predictor students' satisfaction and outcomes. Additionally, they highlight value platform enhance user-friendliness courses increase student engagement management contexts.

Language: Английский

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Understanding University Students' Perspectives towards Digital Tools for Mental Health Support: A Cross-country Study

Clinical Practice and Epidemiology in Mental Health, Journal Year: 2024, Volume and Issue: 20(1)

Published: Feb. 21, 2024

Background Organisational and individual barriers often prevent university students from seeking mental health support. Digital technologies are recognised as effective in managing psychological distress a source of health-related information, thus representing useful options to address needs terms accessibility cost-effectiveness. However, students' experiences perspectives towards such interventions little known. Objectives We aimed expand the existing base scientific knowledge, focusing on this special population. Methods Data were qualitative component “the CAMPUS study”, longitudinally assessing at University Milano-Bicocca (Italy) Surrey (UK). conducted in-depth interviews thematically analysed transcripts using framework approach. Results An explanatory model was derived five themes identified across 33 (15 for Italy, 18 UK). Students perceived that social media, apps, podcasts could deliver relevant content, ranging primary tertiary prevention. Wide availability anonymity advantages make tools suitable preventive interventions, reduce stigma, an extension standard treatment. These goals can be hindered by disadvantages, namely lower efficacy compared face-to-face contact, lack personalisation, problematic engagement. Individual cultural specificities might influence awareness use digital Conclusion Although considering some specific features, instrument support students. Since personal contact remains crucial, should integrated with through multi-modal

Language: Английский

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The adaptive community-response (ACR) method for collecting misinformation on social media

Journal Of Big Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Feb. 24, 2024

Abstract Social media can be a major accelerator of the spread misinformation, thereby potentially compromising both individual well-being and social cohesion. Despite significant recent advances, study online misinformation is relatively young field facing several (methodological) challenges. In this regard, detection has proven difficult, as large-scale data streams require (semi-)automated, highly specific therefore sophisticated methods to separate posts containing from irrelevant posts. present paper, we introduce adaptive community-response (ACR) method, an unsupervised technique for collection on Twitter (now known ’X’). The ACR method based previous findings showing that users occasionally reply with fact-checking by referring sites (crowdsourced fact-checking). first step, captured such misinforming but fact-checked tweets. These tweets were used in second step extract linguistic features (keywords), enabling us collect also those not at all third step. We initially mathematical framework our followed explicit algorithmic implementation. then evaluate basis comprehensive dataset consisting $$>25$$ > 25 million tweets, belonging $$>300$$ 300 stories. Our evaluation shows useful extension pool field, researchers more comprehensively. Text similarity measures clearly indicated correspondence between claims false stories even though performance was heterogeneously distributed across A baseline comparison showed detect story-related comparable degree, while being sensitive different types tweets: Fact-checked tend driven high outreach (as number retweets), whereas sensitivity extends exhibiting lower outreach. Taken together, ACR’s capacity valuable methodological contribution (i) adoption prior, pioneering research (ii) well-formalized (iii) empirical foundation via set indicators.

Language: Английский

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Mapping the Landscape of Internet Pornography, Loneliness, and Social Media Addiction: A CiteSpace Bibliometric Analysis

International Journal of Mental Health and Addiction, Journal Year: 2024, Volume and Issue: unknown

Published: April 9, 2024

Language: Английский

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The association between personality traits and Facebook addiction among adolescent students in a lower middle-income country, Nepal

International Journal of Adolescence and Youth, Journal Year: 2025, Volume and Issue: 30(1)

Published: Feb. 21, 2025

Language: Английский

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Internet Use, Internet Addiction, and Mental Health Among Adult Population: Bibliometric Analysis

Journal of Technology in Behavioral Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Language: Английский

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YouTube addiction scale (YAS): Adaptation to Turkish culture, validity and reliability study

Published: March 2, 2025

In the current research, YouTube Addiction Scale (YAS) developed by Pakpour et al. (2023) was adapted to Turkish culture, and scale's psychometric properties were examined. A cross-sectional survey conducted with 779 adults (Mage = 25.16 years, 56% female). Confirmatory factor analysis (CFA) performed validate whether original structure of YAS retained in version. addition, tests internal consistency, concurrent validity external criterion measures (Bergen Social Media Scale, Smartphone Application-Based Scale), gender differences analyzed. Jeffreys's Amazing Statistics Program (JASP) version 0.19.0 used for CFA consistency analyses, while IBM SPSS 25.0 employed remaining analyses. The consists six items, indicating that unidimensional aligns well culture. indicates good both validity. It shows acceptable levels can be as a reliable tool assess addiction future studies

Language: Английский

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