The Impact of Social Media Influencers on Children’s Dietary Behaviors

Over the past years vlogs rapidly have become an attractive platform for food industries, sponsoring social media influencers to promote their products. As with more traditional media, social media influencers predominantly promote unhealthy drinks and foods that are high in sugar, fat, and salt – consumption of which may increase the risk of overweight, obesity, and non-communicable diseases. The aim of the current Brief Research Report is to examine the impact of vlogs on children’s unhealthy dietary behaviors. Drawing on longitudinal survey data from 453 8- to 12-year-old children, we analyzed the longitudinal relations between children’s frequency of watching vlogs and their consumption of unhealthy beverages and snacks. Structural path modeling analyses of three waves of data with 1-year intervals showed that children’s self-reported frequency of watching vlogs influenced consumption of unhealthy beverages 2 years later. The analyses did not yield significant relat

Testing a Social Network Intervention Using Vlogs to Promote Physical Activity Among Adolescents: A Randomized Controlled Trial

There is a need to stimulate physical activity among adolescents, but unfortunately, they are hard to reach with traditional mass media interventions. A promising alternative is to carry out social network interventions. In social network interventions, a small group of individuals (influence agents) is selected to promote health-related behaviors within their social network. This study investigates whether a social network intervention is more effective to promote physical activity, compared to a mass media intervention and no intervention. Ad

A subtype of childhood acute lymphoblastic leukaemia with poor treatment outcome: a genome-wide classification study

Background: Genetic subtypes of acute lymphoblastic leukaemia (ALL) are used to determine risk and treatment in children. 25% of precursor B-ALL cases are genetically unclassified and have intermediate prognosis. We aimed to use a genome-wide study to improve prognostic classification of ALL in children. Methods: We constructed a classifier based on gene expression in 190 children with newly diagnosed ALL (German Cooperative ALL [COALL] discovery cohort) by use of double-loop cross-validation and validated this in an independent cohort of 107 newly diagnosed patients (Dutch Childhood Oncology Group [DCOG] independent validation cohort). Hierarchical cluster analysis with classifying gene-probe sets revealed a new ALL subtype, the underlying genetic abnormalities of which were characterised by comparative genomic hybridisation-arrays and molecular cytogenetics. Findings: Our classifier predicted ALL subtype with a median accuracy of 90·0% (IQR 88·3-91·7) in the discovery cohort and correctly identified 94 of 107 patients (accuracy 87·9%) in the independent validation cohort. Without our classifier, 44 children in the COALL cohort and 33 children in the DCOG cohort would have been classified as B-other. However, hierarchical clustering showed that many of these genetically unclassified cases clustered with BCR-ABL1-positive cases: 30 (19%) of 154 children with precursor B-ALL in the COALL cohort and 14 (15%) of 92 children with precursor B-ALL in the DCOG cohort had this BCR-ABL1-like disease. In the COALL cohort, these patients had unfavourable outcome (5-year disease-free survival 59·5%, 95% CI