Problematische Nutzung sozialer Medien von Kindern und Jugendlichen im Schulalter in Luxembourg – Ergebnisse der HBSC Umfrage 2018

Report (2022)

Insbesondere unter Jugendlichen hat die Nutzung sozialer Medien in den letzten Jahren zugenommen. Wenn die Nutzung Merkmale einer Sucht aufweist (z.B. Gewöhnung und sozialer Rückzug), dann wird von einer ... [more ▼]

Insbesondere unter Jugendlichen hat die Nutzung sozialer Medien in den letzten Jahren zugenommen. Wenn die Nutzung Merkmale einer Sucht aufweist (z.B. Gewöhnung und sozialer Rückzug), dann wird von einer problematischen Nutzung sozialer Medien gesprochen – im Folgenden PSMU genannt. Dieser Kurzbericht gibt einen Überblick darüber, wie häufig PSMU unter luxemburgischen Schülern vorkommt und welche Merkmale mit einem höheren Risiko für PSMU einhergehen. Dazu wurden Daten der luxemburgischen Health Behavior in School-aged Children (HBSC)-Studie 2018 ausgewertet, an der 8 687 Jugendliche im Alter von 11 bis 18 Jahren teilnahmen. Laut dieser Umfrage liegt die Häufigkeit von PSMU in dieser Altersgruppe bei 5,9 %. Eine Reihe von Merkmalen aus den Bereichen Soziodemografie, soziale Unterstützung, Wohlbefinden und Mediennutzung wurden als potenzielle Risikofaktoren untersucht. PSMU tritt häufiger bei Mädchen und jüngeren Schülern sowie bei Schülern mit Migrationshintergrund auf. Bei Schülern, die bei beiden Elternteilen aufwachsen, ist PSMU seltener im Vergleich zu Schülern, die bei Alleinerziehenden oder in anderen Familienkonstellationen aufwachsen. Ein Vergleich der Risikofaktoren hat ergeben, dass das Alter, Cybermobbing, Stress, psychosomatische Beschwerden, eine Vorliebe für Online-Interaktion und die Intensität der Kommunikation über elektronische Medien die wichtigsten Risikofaktoren sind. Das Risiko für einen problematischen Umgang mit sozialen Medien ist somit höher bei jüngeren Schülern; Schülern, die andere online mobben; gestressten Schülern; Schülern mit häufigen psychosomatischen Beschwerden; Schülern, die Online-Kommunikation gegenüber einer Kommunikation in der realen Welt vorziehen sowie Schülern, die elektronische Medien generell häufig nutzen. Speziell zur Prävention von PSMU haben sich noch keine Maßnahmen etabliert, aber es gibt gut erforschte Maßnahmen zur Prävention von Internetsucht, die sich in abgewandelter Form möglicherweise auch bei PSMU eignen. [less ▲]

Die Bedeutung der Schule für Gesundheit und Wohlbe- finden von Schülerinnen und Schülern: Ergebnisse der HBSC Surveys 2018 in Luxemburg

Scientific Conference (2022, March 10)

Neben der Familie ist die Schule für Kinder und Jugendliche ein wichtiges Umfeld. Hier verbringen sie einen großen Teil des Tages, hier treffen sie ihre Freunde, und ihre schulischen Leistungen bestimmen ... [more ▼]

Neben der Familie ist die Schule für Kinder und Jugendliche ein wichtiges Umfeld. Hier verbringen sie einen großen Teil des Tages, hier treffen sie ihre Freunde, und ihre schulischen Leistungen bestimmen wesentlich mit, welchen beruflichen Weg sie später einschlagen können. Dementsprechend hat die Schule als sozialer Kontext einen großen Einfluss auch auf das Wohlbefinden und gesundheitliche Befindlichkeiten der Schüler. Positive schulische Erfahrungen können eine Ressource für Wohlbefinden sein, negative Erfahrungen können die psychische und physische Gesundheit beeinträchtigen. Da es sich bei HBSC um eine Befragung handelt, die in der Schule durchgeführt wird und da die Schule ein wichtiges soziales Umfeld ist, werden auch mehrere Fragen zur Schule gestellt. Im Vortrag werden zunächst aktuelle Ergebnisse aus Luxemburg zum schulischen Kontext präsentiert und international eingeordnet. So wird schon seit mehreren Befragungen erhoben, ob die Schüler die Schule mögen, ob sie sich durch die Schularbeit gestresst fühlen und wie sie die Beziehungen zu ihren Klassenkameraden und den Lehrern bewerten und ob sie viel auf der Ebene der Klasse und der Schule mitbestimmen können. In einem zweiten Schritt wird mit Hilfe einer Clusteranalyse untersucht, ob es typische Konstellationen gibt, d. h. Gruppen von Schülern, die sich in ihren Schulerfahrungen möglichst stark ähneln. Aus der HBSC-Studie ist bekannt, dass es zahlreiche Zusammenhänge zwischen diesen Erfahrungen gibt. Wenn solche kohärenten Gruppen identifiziert werden können, dann kann eine darauf basierende Typologie helfen, die komplexen Zusammenhänge zwischen zahlreichen Variablen in komprimierter Form darzustellen. Im konkreten Fall wurden 4 schulbezogene Einstellungen und Bewertungen dazu genutzt, um solche Gruppen zu identifizieren. Diese Gruppen/Cluster werden zunächst anhand der Variablen beschrieben, die zur Gruppenbildung genutzt wurden. Die Analyse zeigt, dass es bezüglich der Schulerfahrungen fünf in sich homogene Cluster gibt. Cluster 1, in dem sich der größte Anteil der Schüler (28.8%) befindet, ist durch allgemein positive Schulerfahrungen gekennzeichnet. Die Schüler in dieser Gruppe mögen die Schule, sie berichten von einem guten Klassenklima und sie geben an, kaum durch die Schularbeit gestresst zu sein. Darüber hinaus berichten diese Schüler über ein gutes Verhältnis zu ihren Lehrern. Cluster 2 ist durch Schüler gekennzeichnet, deren Schulerfahrungen überwiegend positiv sind (sie mögen die Schule und sie haben ein gutes Verhältnis zu den Mitschülern und Lehrern), die sich aber durch die Schularbeit gestresst fühlen, was sich negativ auf ihr allgemeines Wohlbefinden auswirkt. Cluster 3 zeichnet sich durch Schüler aus, die über ein geringes Maß an Stress berichten und insgesamt durchschnittliche Werte für die schulbezogenen Variablen wie Beziehungen zu ihrem Lehrer und das Klassenklima aufweisen. In Cluster 4 befinden sich die Schüler, die viel Schulstress erfahren, relativ schlechte Beziehungen zu ihren Lehrern haben und die Schule im Allgemeinen nicht mögen. Allerdings berichten diese Schüler von einem guten Klassenklima. Dieses Cluster umfasst 14.7% der Schüler und ist damit das kleinste Cluster. Cluster 5 besteht aus Schülern mit durchweg negativen Schulerfahrungen und ist damit das Gegenteil von Cluster 1. Die Schüler in Cluster 5 haben viel Schulstress, sind nicht gerne in der Schule und berichten von schlechteren Beziehungen zu ihren Lehrern und Mitschülern im Vergleich zu den Schülern in den anderen Clustern. Die weitere Beschreibung der Cluster mit Hilfe von soziodemografischen Variablen, anderen schulbezogenen Variablen und gesundheitsbezogenen Ergebnisvariablen wird zeigen, dass die gefundenen Gruppen in sich kohärent sind bezüglich der schulbezogenen Variablen, und dass sie zudem mit gesundheitsbezogenen und mit soziodemografischen Variablen korrelieren. [less ▲]

Types of health-related behaviours: a cluster analysis of the Luxembourgish HBSC data

Scientific Conference (2021, June)

Background: Although it is known that health behaviours, socio-demographic variables and outcomes correlate, it is rarely investigated if there are typical patterns of these variables among the research ... [more ▼]

Background: Although it is known that health behaviours, socio-demographic variables and outcomes correlate, it is rarely investigated if there are typical patterns of these variables among the research subjects. Objectives: To find out whether the students can be divided into distinct groups based on their health behaviour and whether these groups differ in other ways (outcomes and socio-demographics). Method: In step 1, a hierarchical cluster analysis was carried out to determine the number of groups and to identify the cluster centres. In step 2, this information was entered as the initial values of a cluster centre analysis. In step 3, the clusters were characterised using additional variables. Results: The 8065 students surveyed could be divided into 5 distinct groups based on their data on smoking, drinking, soft drinks, exercising, fighting and bullying, with cluster 1 and cluster 5 representing the strongest contrast. Cluster 1 comprises students whose health behaviour is generally positive. It is the largest cluster with 49.5% of students. Cluster 5 comprises students whose behaviour is consistently negative. It is the smallest cluster with 7.1% of students. Students in cluster 2 are close to average on many variables, but their dental health is problematic because they frequently consume soft drinks and rarely brush their teeth. Students in cluster 3 are physically inactive, their mental health is poor, but they are also rarely injured. The students in cluster 4 stand out because of their aggressive behaviour. Conclusion: With the help of cluster analysis, it is possible to categorise the students into a small number of groups based on their health behaviour. These groups are coherent in terms of health behaviour, many outcome variables and socio-demographic variables. [less ▲]

Predictors of Problematic Social Media Use in a Nationally Representative Sample of Adolescents in Luxembourg

in International Journal of Environmental Research and Public Health (2021), 18(22),

Social media use has increased substantially over the past decades, especially among adolescents. A proportion of adolescents develop a pattern of problematic social media use (PSMU). Predictors of PSMU ... [more ▼]

Social media use has increased substantially over the past decades, especially among adolescents. A proportion of adolescents develop a pattern of problematic social media use (PSMU). Predictors of PSMU are insufficiently understood and researched. This study aims to investigate predictors of PSMU in a nationally representative sample of adolescents in Luxembourg. Data from the Health Behavior in School-aged Children (HBSC) study in Luxembourg were used, in which 8687 students aged 11–18 years old participated. The data were analyzed using hierarchical multiple regression. A range of sociodemographic, social support, well-being and media use predictors were added to the model in four blocks. The predictors in the final model explained 22.3% of the variance in PSMU. The block of sociodemographic predictors explained the lowest proportion of variance in PSMU compared with the other blocks. Age negatively predicted PSMU. Of the predictors related to social support, cyberbullying perpetration was the strongest predictor of PSMU. Perceived stress and psychosomatic complaints positively predicted PSMU. The intensity of electronic media communication and preference for online social interaction were stronger predictors of PSMU than the other predictors in the model. The results indicate that prevention efforts need to consider the diverse range of predictors related to PSMU. [less ▲]

Scanning of questionnaires as a tool to identify difficult questions - lessons learned

Scientific Conference (2020, November 10)

Background: In 2018, the Luxembourg HBSC team scanned the questionnaires to make the data available faster and to avoid entry errors. Scanning has been shown to be suitable for identifying difficult ... [more ▼]

Background: In 2018, the Luxembourg HBSC team scanned the questionnaires to make the data available faster and to avoid entry errors. Scanning has been shown to be suitable for identifying difficult questions. Objective: The presentation shows which questions were difficult to answer and what the difficulty was. Method: The questionnaires were scanned by student assistants and the data was validated by them if the scanning programme did not detect any errors. If errors occurred (e.g. missing answers or multiple answers), then these questionnaires were checked by HBSC team members. This gave us a systematic overview of which questions were difficult to answer. Results 1. The data from 10000 questionnaires were entered in 6 weeks (half the time needed compared to manual entry in 2014). 2. The MVPA question was frequently the subject of multiple answers. This may indicate that these students use the answer scale as a counting aid. 3. Students who state that they have never smoked in their lives often skip the question about tobacco use in the last month. This behaviour can be explained by Grice's conversational maxims. 4. Behaviours indicating that the answers are not serious (crossed-out questions, crosses outside the boxes, fun answers to open questions) are rare. Conclusions: Scanning is an efficient way to enter many questionnaires in a short time and high quality. Furthermore, it can help to discover difficult questions and to find out what the difficulty is. [less ▲]

Suicidal Behaviour in Youth in Luxembourg - Findings from the HBSC 2014 Luxembourg Study

Report (2020)

Suicide is one of the leading causes of death among young people worldwide. In order to prevent suicides, early identification of groups at risk is needed. In the Luxembourgish HBSC study, data on ... [more ▼]

Suicide is one of the leading causes of death among young people worldwide. In order to prevent suicides, early identification of groups at risk is needed. In the Luxembourgish HBSC study, data on suicidal behaviours among adolescents were collected in 2006, 2010 and 2014. These can be used to identify suicide risk factors and to develop comprehensive suicide prevention programs. In Luxembourg, the suicide rate has fluctuated around 15 deaths per 100 000 inhabitants per year, for more than ten years. In the period 2006 – 2016, 20 deaths were registered as suicide in the age group of 10 to 19-year-olds. These suicides represent approximately 19% of all deaths registered in this age group. In the Luxembourgish HBSC study conducted in 2014, 875 adolescents indicated to have contemplated suicide in the last 12 months, which amounts to 15.1% of the adolescents in the study. In the same year, 811 adolescents (14.0%) indicated to have made a suicide plan in the last 12 months, and 448 adolescents (7.7%) to have attempted suicide (at least once) in the last year. In first instance, bivariate logistic regressions analyses were conducted for 24 independent variables with three suicidal behaviours (contemplation of suicide, planning of suicide and suicide attempt) and sadness as dependent variables in order to identify potential risk factors. These risk factors were further tested in multivariate logistic regressions, in order to make a statement about the relevance of these factors for suicidal behaviour of adolescents in Luxembourg, while taking into account the dependence between the risk factors. Results from multivariate logistic regressions indicate that subjective health complaints are the most important risk factor for suicidal behaviour. Adolescents who have recurrent multiple health complaints are at higher risk for suicidal behaviour than adolescents who do not have health complaints. Life satisfaction is the second most important risk factor for suicidal behaviour. Adolescents with lower levels of life satisfaction are at higher risk for suicidal behaviour than adolescents who have higher levels of life satisfaction. Gender-specific analyses show that the risk factors differ between girls and boys for suicidal behaviour. [less ▲]

The influence of well-being, social support, media use and sociodemographic factors on problematic social media sue among Luxembourgish adolescents

in Cogent Medicine (2020), 7(1),

Background: Adolescents spend an increasing amount of time communicating online. Previous research has indicated that electronic media communication has been associated with positive outcomes on ... [more ▼]

Background: Adolescents spend an increasing amount of time communicating online. Previous research has indicated that electronic media communication has been associated with positive outcomes on adolescent well-being and development, however, problematic social media use is on the rise. This study investigates factors that influence problematic social media use (PSMU), based on previous empirical research and the Differential Susceptibility to Media Effects Model by Valkenburg and Peter (2013). Methods: The data used in this study stems from the 2018 Health Behaviour for School-aged Children (HBSC) study in Luxembourg. Data from elementary and secondary school students aged 11 to 18 was used (N = 6164), which was collected through a written survey. A four-stage hierarchical multiple regression analysis was conducted using SPSS, with problematic social media use as the dependent variable. 14 independent variables were included in the model, added in four blocks: sociodemographic factors, social support factors, well-being factors and media use factors.Results: The results indicate that in stage one of the hierarchical regression, the sociodemographic predictors accounted for 3% of the variation in problematic social media use. The addition of the social support factors to the model in stage two explained an additional 7% of the variation in problematic social media use, and the addition of the well-being factors in stage three an additional 5.3%. In stage four of the hierarchical regression media use factors were added to the model, and the four blocks of predictors accounted for 22.2% of the variation in problematic social media use (Adjusted R2 = 0.222). The most important predictors for problematic social media use were preference for online social interaction (β = 0.205, p < .001), the intensity of electronic media communication (β = 0.155, p < .001), psychosomatic complaints (β = 0.136, p < .001), perceived stress (β = 0.122, p < .001) and cyberbullying perpetration (β = 0.117, p < .001). Conclusions: The block of sociodemographic factors contributed minimally to the explanation of the variance in problematic social media use in the model. The most important predictors for problematic social media use were preference for online social interaction, the intensity of electronic media communication, psychosomatic complaints, perceived stress and cyberbullying perpetration. This suggests that there are several starting points for the prevention of problematic social media use among adolescents. [less ▲]

Communication with father and mother differently impacts suicidal behaviour

Poster (2019, September)

Background: Positive relationships with parents can reduce the risk of suicidal behaviour in adolescents. Previous research has indicated that adolescents who report poor communication with their parents ... [more ▼]

Background: Positive relationships with parents can reduce the risk of suicidal behaviour in adolescents. Previous research has indicated that adolescents who report poor communication with their parents are more likely to display suicidal behaviour. The aim of this study is to find out whether communication with the father or mother is equally important for suicidal behaviour. Methods: A total of 5595 students aged from 12 to 18 years old in secondary school participated in the 2014 HBSC Luxembourg survey. They responded to a questionnaire including, among others: 4 questions regarding sadness, suicide ideation, planning and attempt, and 2 questions about ease of communication with their father and mother. Findings: Adolescents who indicate poorer communication with their mother or father have higher odds for all suicidal behaviours. Poor communication with fathers has a bigger influence on the odds for sadness, whereas poor communication with mothers has a bigger influence on the odds for attempted suicide. Lastly, adolescents who don`t have or don`t see their mother or father are at increased risk for the suicidal behaviours, although the odds are not as high as for those indicating very difficult communication with their parent(s). Discussion: The Luxembourgish findings confirm the results of previous research and go further showing that, as a determinant, communication with mother differs from the communication with father. More studies should confirm these findings and include other variables, such as social support and stress, in order to see their relation with the communication with both parental figures and suicidal behaviours. [less ▲]

"Are you a boy or girl?" Who are the non-responders

Scientific Conference (2019, June 20)

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some ... [more ▼]

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some participants may have understandable reasons for not reporting their gender, e.g. questioning their gender identity. Objective: How many students and which students do not answer the question on gender? Methods: HBSC 2018 raw data from Ireland, Luxembourg, Belgium and France are compared. To explore the reasons for non-response, we divided the participants into 3 groups: 1. Responders answered both sociodemographic questions (age and gender) 2. age non-responders did not answer the question on age. 3. Gender non-responders answered the question on age, but not the one on gender. Results: Between 0.8% (Ireland) and 1.2% (Luxembourg) of participants did not report their gender. About half of them did not answer the age question either. However, the other half belong to the group of gender non-responders and this group is disadvantaged compared to responders: they report lower life satisfaction, lower self-rated health, more health complaints, less peer support and their WHO-5 Well-being score is lower. Not answering the question on gender is rare. If the participants answered the question on age, but not the question on gender, then the variable gender is missing not at random. Implication: The question arises whether the group of gender non-responders should be included in the analysis and whether the question on gender should be asked differently in the future. [less ▲]

Using data from the HBSC study for evidence-based suicide prevention in Luxembourg

in European Journal of Public Health (2019), 29