Mental issues, internet addiction and quality of life predict burnout among Hungarian teachers: a machine learning analysis.

BACKGROUND: Burnout is usually defined as a state of emotional, physical, and mental exhaustion that affects people in various professions (e.g. physicians, nurses, teachers). The consequences of burnout involve decreased motivation, productivity, and overall diminished well-being. The machine learning-based prediction of burnout has therefore become the focus of recent research. In this study, the aim was to detect burnout using machine learning and to identify its most important predictors in a sample of Hungarian high-school teachers. METHODS: The final sample consisted of 1,576 high-school teachers (522 male), who completed a survey including various sociodemographic and health-related questions and psychological questionnaires. Specifically, depression, insomnia, internet habits (e.g. when and why one uses the internet) and problematic internet usage were among the most important predictors tested in this study. Supervised classification algorithms were trained to detect burnout assessed by two well-known burnout questionnaires. Feature selection was conducted using recursive feature elimination. Hyperparameters were tuned via grid search with 10-fold cross-validation. Due to class imbalance, class weights (i.e. cost-sensitive learning), downsampling and a hybrid method (SMOTE-ENN) were applied in separate analyses. The final model evaluation was carried out on a previously unseen holdout test sample. RESULTS: Burnout was detected in 19.7% of the teachers included in the final dataset. The best predictive performance on the holdout test sample was achieved by random forest with class weigths (AUC = 0.811; balanced accuracy = 0.745, sensitivity = 0.765; specificity = 0.726). The best predictors of burnout were Beck's Depression Inventory scores, Athen's Insomnia Scale scores, subscales of the Problematic Internet Use Questionnaire and self-reported current health status. CONCLUSIONS: The performances of the algorithms were comparable with previous studies; however, it is important to note that we tested our models on previously unseen holdout samples suggesting higher levels of generalizability. Another remarkable finding is that besides depression and insomnia, other variables such as problematic internet use and time spent online also turned out to be important predictors of burnout.

Problematic usage of the internet among Hungarian elementary school children: a cross-sectional study.

INTRODUCTION: Problematic usage of the internet (PUI) is perhaps one of the most frequently studied phenomena of the 21st century receiving increasing attention in both scientific literature and the media. Despite intensive research there have been relatively few meaningful studies among elementary school students in Hungary and worldwide, who may be considered as a high-risk population with regard to problematic internet use. The aim of our study was to carry out a complex research focusing on the prevalence and risk factors of PUI among elementary school children aged 10-15 years (Grade 5-8). METHODS: Demographics included were gender, age, place of stay, type of residence, family type, parental education, start of internet use, used devices, daily internet use, purpose of internet use, internet accounts, ways of keeping in touch with friends and sporting activities. PUI was evaluated using the paper-based version of the Potentially Problematic Use of the Internet Questionnaire. RESULTS: Overall, 2000 paper-based questionnaires were successfully delivered and the final analysis included 1168 responses (overall response rate 58.4%). Mean age was 12.55 ± 1.24 years. Female gender (OR = 2.760, p = 0,006, CI 95% 0.065 to 0.384), younger age (11-12 years) (OR = 3.812, p < 0.001, 95% CI: 1.747-4.731), early exposure to the internet (OR = 3.466, p = 0.001, 95% CI 1.535-5.446), living in a small village (OR = 1.081, p = 0.002, 95% CI 1.041-1.186) urgency to answer online (OR = 4.677, p < 0.001, 95% CI: 2.714-6.639), decreased frequency of personal contact with friends (OR = 2.897, p = 0.004, 95% CI: 1.037-1.681), spending more than 6 h online (OR = 12.913, p < 0.001, 95% CI: 10.798-14.892), morning and nighttime internet use (OR = 3.846, p < 0.001, CI 95% 1.886-5.810) and never doing any sports (OR = 2.016, p = 0.044, 95% CI: 1.050-3.354) were independently associated with problematic internet use. CONCLUSIONS: Based on the results of our questionnaire survey more than 10% seemed to be problematic users in our study population, which is a relatively high rate. Early exposure to the internet as well as younger age were strongly related to this phenomenon. Duration of being online as well as daily time interval of internet use are important predisposing factors. Scarcely studied social factors such as being online at the expense of personal relationships and the lack of physical activity should be payed more attention to prevent the development of PUI.

Internet Addiction and COVID-19 Misbeliefs Among Hungarian Online Gamers: A Cross-Sectional Study.

Introduction: The internet has become a part of everyday life, and during the COVID-19 pandemic the rate of internet use has raised even higher, which increases the possibility of compulsive and problematic use leading to the acceptance of online misbeliefs and conspiration theories. This cross-sectional study aimed to explore the relationship between COVID-19-related misconceptions and internet addiction among adult recreational online gamers. Methods: A sample of 1671 recreational video game users completed the online survey (male: n = 1522 (91.08%), mean age = 21.83, SD = 4.18; female: n = 149 (8.91%), mean age = 24.33, SD = 8.38). Demographic questions, risk factors and health-related questions, internet use and addiction were measured alongside a short questionnaire about common COVID-19-related topics, such as its origin and risk of infection. Results: Out of all participants 248 (14.8%) answered all the COVID-19-related questions properly, thus having no misconceptions, while 545 (32.6%) had one wrong answer, 532 (31.8%) had 2 wrong answers, 251 (15.0%) had 3 wrong answers, 78 (4.7%) had 4 wrong answers and 17 (1.0%) had 5 wrong answers. Significant factors to a higher number of COVID-misconceptions were time spent studying (χ2 (35,1671) = 63.86, p = 0.002), marital status (χ2 (15,1671) = 30.65 p = 0.01) and secondary employment (χ2 (51,671) = 14.88, p = 0.01). Although 17.1% of the participants reached the threshold score for internet addiction, the predictors of COVID-19 misconceptions were marital status (ß = -0.06, p = 0.01) and time spent studying (ß = 0.05, p = 0.03), while neither daily internet use, internet addiction scores or risk factors predicted these misconceptions in a linear regression model. Discussion: Our study concludes that Internet addiction did not directly influence misconceptions about the COVID-19 pandemic in this population despite the surprisingly high rate of problematic users.

Prevalence and Risk Factors of Problematic Internet Use among Hungarian Adult Recreational Esports Players.

Background: Esports are highly prevalent in modern culture, particularly among young people, and are a healthy hobby for the majority of users. However, there is a possible link between video gaming (including esports) and problematic internet use (so-called internet addiction, IA), mostly involving adolescents. Methods: Here we present an online survey focusing on the prevalence and risk factors of internet addiction among adult esports players. Demographics included age, gender, family type, type of work, working years and daily internet use. Medical conditions associated with IA such as smoking, alcohol and drug intake, hypertension, diabetes, ischemic heart disease, musculoskeletal pain and history of depression were also recorded. Results: Overall, 2313 players including 176 females (7.6%) and 2137 males (92.4%) participated in our online survey. Age distribution was the following: 18−25 years 90.3% (2088/2313), 26−35 years 7.95% (184/2313), 36−45 years 0.86% (20/2313), 46−55 years 0.82% (19/2313), 56−62 years 0.04% (1/2313) and 62 years or older 0.04% (1/2313). Internet addiction was detected in 19.9% of players (461/2313) based on the Problematic Internet Use Questionnaire. In a multivariate analysis internet addiction was significantly associated with age between 18 and 25 (OR: 1.675, p = 0.002), being single (OR = 1.505, p = 0.014), internet use > 6 h daily (OR = 4.338, p < 0.001), having < 3 children (OR: 2.037, p = 0.023) and having secondary employment (OR = 1.789, p = 0.037). Regular alcohol intake (OR = 18.357, p < 0.001) and history of depression (OR= 5.361, p = 0.032) were also strongly correlated with IA. Conclusion: This is the first study from Hungary investigating the prevalence and risk factors of internet addiction among adult esports players. One out of five adult gamers suffered from IA. Our study also draws attention to increased risk within this group and risk factors such as younger age, family status and type of employment.

Internet addiction: the medical challenge of the 21st century? br / Internetfüggoség: a 21. század orvosi kihívása?

As a result of digitalisation and the increasing use of the internet, its problematic use is on the rise in the 21st cen-tury, with a predominant impact on minors and a potentially increasing challenge for health care systems in the fu-ture. The main risk factors for this phenomenon are age, inadequate social and family relationships, and can be as-sociated with mental problems such as depression and anxiety, somatic illnesses, often with additional dependencies. Imaging studies can detect abnormally functioning brain areas in the affected individuals, however, there is a signifi-cant heterogeneity among them. Similar to other addictions, extensive internet use negatively affects the individual in all areas of life. We do not have a high level of evidence for treatment yet, but it appears that treatments used in other (classic) addictive diseases may be effective.