What happens when you are not on social networks? A survey among middle-school teens in Switzerland.

AIM: The aim of the study was to describe the differences between users and non-users of social networks controlling for explanatory factors. METHODS: Data were drawn from a survey on media and Internet use carried out among 2893 10th graders in Switzerland. Participants were asked whether they were active in 10 different social networks and divided into two groups: Not active (answering no to the 10 networks; n = 176) and Active (answering positively to at least one; n = 2717). The groups were compared on sociodemographic, health, and screen-related variables. All variables significant in the bivariate analysis were included in a backward logistic regression. RESULTS: The backward logistic regression revealed that inactive participants were more likely to be males, younger, to live in an intact family, and to assess their screen time as below average, and less likely to practice extracurricular sport, to spend ≥4 h of screen time per day, to be around their smartphone at all times, to have parental rules about Internet content, or to discuss Internet use with parents. CONCLUSION: Most young adolescents use social networks. However, this activity does not seem to be associated with academic problems. Therefore, the use of social networks should not be demonized but considered part of their social life.

Is vulnerability associated with substance use? A study among youth in Switzerland.

Adolescence is a period of exploration and experimentation that includes risk behaviors such as substance use. Adolescents living in a situation of vulnerability could be more prone to using substances. In this cross-sectional study, we aimed to evaluate the association between the level of vulnerability and substance use considering explanatory factors. Data were obtained from the first wave of the GenerationFRee project (http://www.generationfree.ch), a longitudinal study based on data collected yearly between 2014 and 2019 on youth aged 15-24 years in high schools and professional schools. The sample included 5179 participants. We designated four risk behaviors: current tobacco smoking, alcohol misuse, cannabis use, and other illegal drug use. We defined vulnerability based on three criteria: the relationship with parents, school performance, and the family socioeconomic status (SES). According to this definition, participants were divided into three groups: no vulnerability, moderate vulnerability, and high vulnerability. Each substance was compared by vulnerability level and controlled with explanatory factors such as age, gender, perceived health, emotional well-being, academic track, nationality, living with parents, residence, family structure, money earned, and perceived personal financial situation. The results show that all substances except alcohol misuse are associated with vulnerability at the bivariate level. All the explanatory factors were also significant with the exception of academic track and amount of money earned per month. In the multinomial regression, for the moderate- and high-vulnerability groups, cannabis use was the only behavior that remained significant. In conclusion, this study shows the association between level of vulnerability and substance use, especially cannabis use. The results also demonstrate the complexity around vulnerability and how the interaction with social aspects influences vulnerability. Youths presenting familial, educational, or financial problems need to be especially screened for substance use by healthcare providers.

Transition from paediatric to adult care: what makes it easier for parents?

OBJECTIVE: To assess differences between parents of adolescents with chronic illness (CI) going through a self-reported easy or difficult transfer. METHODS: Seventy-two parents of CI youths who had already transferred to adult care were divided according to whether they considered that the transfer had been easy (n = 45) or difficult (n = 27). We performed a bivariate analysis comparing both groups and variables with a significance level < .1 were included in a logistic regression. Results are presented as adjusted odds ratio (aOR). RESULTS: Over one third of parents (27/72) reported a difficult transfer. At the multivariate level, higher socioeconomic status (aOR: 7.74), parents feeling ready for transfer (aOR: 6.54) and a good coordination between teams (aOR: 7.66) were associated with an easy transfer. CONCLUSIONS: An easy transfer for parents is associated with feeling ready and considering that the coordination between teams is good. Health providers should consider these requisites for a successful transfer.

The relation between internet use and overweight among adolescents: a longitudinal study in Switzerland.

OBJECTIVE: This longitudinal study aimed to investigate the characteristics and predictive risk factors of overweight among adolescents. The hypothesis was that baseline overweight predicted most overweight over time compared to other factors, especially excessive internet use. SUBJECTS: A sample of 621 youths were followed from age 14 (T0 Spring 2012) to age 16 (T1 Spring 2014) in Switzerland. Participants were divided into two groups according to their weight at the final assessment: overweight and non-overweight. At T0, participants reported demographic, health, substance use and internet use data. A logistic regression was performed to assess the explanatory variables of overweight at T1. Data are presented as adjusted odds ratios (aORs) with 95% confidence interval. RESULTS: The 2-year evolution showed a net BMI increase of 4.8%. Overweight adolescents were significantly more likely to be male, to live in an urban area, to be on a diet and to report using the internet more than 2 h per day on weekends at T0. However, with the addition of baseline overweight, only the excessive use of internet on weekends remained as an explanatory variable. An adolescent who was already overweight at T0 had a more than 20-fold risk (aOR 21.04) of being overweight 2 years later. Moreover, among adolescents becoming overweight between T0 and T1, internet use did not show any significant effect. CONCLUSION: The risk of being overweight is mostly influenced by weight status at baseline compared to excessive internet use. Thus, our results do not confirm the negative effect of internet on healthier activities. Internet use could at most reinforce an already existing risk of being overweight.