Four teenagers are demanding action on violence against women and girls. Adolescent-led advocacy is increasingly recognised by WHO an… #Adolescenthealth #Genderbasedviolence #Healthpolicy #Publichealthadvocacy #Violenceprevention

In this 1-group pretest-posttest pilot of WISEBOx (Wise Intervention for Adolescents Who Purchase Loot Boxes) with adolescents who had previously purchased loot boxes (N=38), fewer participants reported purchasing loot boxes at the 1-month posttest than in the year before baseline. These findings support the #feasibility of WISEBOx and the need for controlled efficacy testing.

Background: Little is known about the composition and function of the social networks of adolescent and young adult survivors of #Cancer, despite the importance of social health for adolescent and young adult development. For adolescent and young adult survivors of #Cancer, social networks may be particularly important sources of support and coping. Understanding this group’s social health through understanding their social networks can inform intervention development to improve various facets of health and life productivity. Objective: The objective of this descriptive study was to explore the #feasibility and acceptability of collecting and analyzing social network data using the Network Canvas platform among a subset of adolescent and young adult survivors of childhood #Cancer. Methods: We used cognitive interviewing, a qualitative method to assess survey comprehension and #usability, to assess the #feasibility and acceptability of using the Network Canvas social network data collection platform among 10 adolescent and young adult survivors of #Cancer. Participants (ie, egos) were asked about people (ie, alters) in their networks before #Cancer diagnosis (via recall) and currently (after #Cancer diagnosis). #feasibility was explored in three ways: (1) the proportion of participants who completed the full Network Canvas survey; (2) qualitative analysis of cognitive interviews addressing #usability and comprehension; and (3) demonstration of whether self-administered, role-anonymized responses yielded data complete enough to support the creation of standard egocentric network measures through preliminary descriptive statistics. Acceptability was explored through qualitative analysis of the desire of participants to use Network Canvas again and features they liked when using Network Canvas. Results: In terms of #feasibility, all 10 participants completed the Network Canvas survey. In qualitative analysis, participants described the ease of using Network Canvas (including engagement) while also describing ways to improve its #usability. In terms of acceptability, when asked if they would use Network Canvas again, participants overall answered positively. Conclusions: Network Canvas use among our sample of adolescent and young adult survivors of #Cancer was feasible and acceptable based on this exploratory analysis. The collection and analysis of social network data using Network Canvas was feasible in this pilot study. Additionally, the potentially therapeutic experience reported by some participants after completing the Network Canvas survey deserves further exploration. This work provides a starting point for future social health studies, psychosocial programming, and identification of those at risk of social disconnect.

Background: #SocialMedia #SoMe #hcsm use is ubiquitous among young people, yet its relationship with #MentalHealth may depend on why it is used. Understanding the motives underlying #SocialMedia #SoMe #hcsm engagement may provide greater insights into its potential risks and benefits than considering overall use alone. Objective: This study aimed to identify latent dimensions of motives for #SocialMedia #SoMe #hcsm use and examine their associations with #anxiety, #depression, well-being, and resilience in a sample of older adolescents. Methods: Cross-sectional data were drawn from the BrainWaves study (N=6820; mean age 16.72, SD 0.74 years). #SocialMedia #SoMe #hcsm use motives were assessed using 10 items and exploratory factor analysis identified 2 latent dimensions: avoidant-escapist use (boredom, addiction, distraction, and procrastination) and social-recreational use (socializing, communication, entertainment, and observation). Confirmatory factor analysis was used to estimate factor scores, which were examined using linear regression. Structural equation modeling estimated associations with #anxiety, #depression, well-being, and resilience, with secondary analyses accounting for gender and deprivation. Results: Linear regression analyses showed that avoidant-escapist motives were associated with higher #anxiety and #depressive symptoms, whereas social-recreational motives were associated with lower symptoms. No interactions with gender or deprivation were observed. The structural equation model showed acceptable model fit (comparative fit index=0.945; root mean squared error of #Approximation=0.057; standardized root mean squared residual=0.054). Avoidant-escapist motives were associated with higher #anxiety (β=0.543) and #depressive symptoms (β=0.629) and lower well-being (β=−0.610) and resilience (β=−0.469). Conversely, social-recreational motives were associated with lower #anxiety (β=−0.228) and #depressive symptoms (β=−0.342) and higher well-being (β=0.362) and resilience (β=0.242;

During puberty, the body clock shifts by approximately two hours, delaying melatonin release and making early mornings biologically difficult, not a matter of effort or attitude. The research on what chronic sleep deprivation does to teenagers is clear, and it should change how we talk to them in the morning.

mumblog.org · Lila Sjöberg During puberty, the body clock shifts by approximately two hours, delaying melatonin release and making early mornings biologically difficult, not a matter of effort or attitude. The research on what chronic sleep deprivation does to teenagers is clear, and it should change how we talk to them in the morning.

Background: Depression prevalence is rising in adolescents and young adults (ages 10-24 years). Traditional screening methods for depression, such as the Patient Health Questionnaire (PHQ-9), are particularly challenging for adolescents. AI has potential to help with this, but the scarcity of annotated datasets highlights the need for off-the-shelf approaches. In this study, we investigate the #feasibility of using state-of-the-art large language models (LLMs) to detect depressive symptoms in adolescent settings (ages 15-17 years). Objective: This study aimed to evaluate the #feasibility of using in-context learning with LLMs to identify depressive symptoms in free-text clinical notes for adolescent populations, to evaluate the #feasibility of off-the-shelf LLM configurations in detecting PHQ-9 symptom-related information under zero- and few-shot settings, and to demonstrate the potential clinical utility of LLM-driven evidence extraction through an interpretable AI-based depression screening tool. Methods: We examined free text of the electronic health records (EHRs) of adolescent patients with the diagnosis of depression or related mood disorders (age groups 15-17 years, 924 patients) from the Cincinnati Children’s Hospital Medical Center. We leveraged the combination of the Beck Depression Inventory (BDI) and the PHQ-9 to develop tailored categories suited for adolescent depression symptoms. We applied 3 state-of-the-art LLMs (Llama, smaller FLAN-T5, and Phi) to identify these symptom categories in zero- and few-shot configurations, allowing us to assess their performance without additional task-specific training. A word-search model was used as the baseline comparator. Results: We manually annotated clinical notes for 22 patients across 16 depression-related symptom categories. Our analysis shows that all LLMs substantially outperformed the word-matching baseline. Within the evaluated prompting configurations, FLAN-T5 achieved the highest precision (average F1-score=0.65, precision=0.78) and strong performance in detecting rare symptoms such as sleep problems (F1-score=0.92) and self-loathing (F1-score=0.80). Phi offered a balanced performance (precision=0.44, recall=0.60), while Llama 3 achieved the highest recall (0.90) but tended to overgeneralize, making it less suitable for precise screening. Within this pilot study, the best-performing LLM exceeded the baseline F1-score by 0.43. Finally, we demonstrate the clinical utility of FLAN-T5 annotations as features in a machine learning model, which distinguished depression cases from controls with high precision (0.78). Conclusions: We used a range of popular LLMs to identify indicators of adolescent (ages 15-17 years) depression symptoms in mental health notes. Within the evaluated configurations, FLAN-T5 demonstrated the most favorable balance between precision and recall. These results support the #feasibility of using off-the-shelf LLMs for symptom detection in adolescent clinical notes, while future work using standardized prompting strategies and task formulations will help establish more definitive comparisons between model architectures. The computational efficiency of FLAN-T5 further supports its potential for deployment in resource-limited clinical settings. Our findings require further validation for broader populations and other mental health conditions.

A Johns Hopkins Center for Indigenous Health research associate updated the board on Hojo Horizons work in local high schools and said a federal Teen Pregnancy Prevention grant was prematurely terminated in June; philanthropic funds are keeping programming going while legal challenges proceed.

Background: TikTok is an important source of mental health information for children, adolescents, and young adults, but the accuracy of psychoeducational content remains uncertain. Misleading information may be particularly consequential for younger users, who may have limited digital and health literacy. Objective: This study aimed to characterize misinformation and disinformation in TikTok content related to child and adolescent mental health, identify mental health topics disproportionately represented in misleading content, examine whether platform-observable video characteristics could discriminate videos containing misinformation, and explore how viewers responded to this content. Methods: We conducted a cross-sectional mixed methods secondary analysis of 122 child and adolescent mental health videos identified from a previously collected dataset of 1000 publicly available TikTok videos. Videos were independently coded for content, intent, authenticity, clinical topic, presenter characteristics, and engagement. An L2-penalized logistic regression using only platform-observable characteristics was evaluated using stratified 5-fold cross-validation; 95% CIs for predictive metrics were estimated using 5000 stratified bootstrap resamples of out-of-fold predictions. Disinformation was analyzed descriptively because only 2 cases were identified. An exploratory thematic analysis was conducted on 116 selected viewer comments. Results: Misinformation was identified in 21 of 122 (17.2%; 95% CI 11.5%-24.9%) videos, whereas disinformation occurred in 2 of 122 (1.6%; 95% CI 0.5%-5.8%). Misinformation was most frequent among videos addressing autism spectrum disorders (11/27, 40.7%), impulse control disorders (1/3, 33.3%), and mood disorders (4/13, 30.8%). Professional credentials were not disclosed in 63 of 122 (51.6%) videos, and only 9 of 122 (7.4%) referenced an external scientific source. The observable-feature model demonstrated modest discrimination, with an area under the receiver operating characteristic curve of 0.70 (95% CI 0.56-0.82), balanced accuracy of 0.69 (95% CI 0.58-0.79), recall of 0.67 (95% CI 0.48-0.86), and F1-score of 0.44 (95% CI 0.32-0.56). Qualitative analysis identified 5 recurring patterns in viewer responses: sharing and validation of lived experiences, discussion of diagnosis and misdiagnosis, frustration with mental health care, peer support and encouragement, and exchanges about treatment and medication experiences. Conclusions: A substantial minority of the analyzed TikTok videos contained misinformation, with particularly high proportions among some neurodevelopmental and mood-related topics. Platform-observable characteristics provided only modest discrimination of misinformation, underscoring the difficulty of identifying misleading mental health content from surface-level features alone. Viewer comments further showed that TikTok mental health content is embedded within broader processes of self-interpretation, diagnostic discussion, peer validation, and treatment-related exchange. These findings support the need for developmentally appropriate digital health literacy initiatives and accurate, transparently sourced psychoeducational content for young social media users. Trial Registration:

Children living in areas with unhealthy levels of fine particulate matter or ozone showed smaller two-year gains in cognitive performance and brain maturation than demographically matched peers.

Wellness staff presented MetroWest adolescent health survey results showing alcohol as the most‑used substance locally (55.7% in a referenced metric), declines in cigarette and marijuana use, but rising concerns about stress, bullying in some subgroups, gambling, and continued work on supports.

Welcome! You are invited to join a meeting: STRIPED Eating Disorder Public Health Surveillance Webinar. After registering, you will receive a confirmation email about joining the meeting.

Background: A sedentary lifestyle is a global health crisis that contributes to chronic disease and premature mortality. Adolescents with mild intellectual disability attending Dutch practical education (PrO) schools face elevated health risks due to low levels of physical activity (PA) and limited conceptual, social, and practical skills. Digital health interventions offer scalable solutions, but require contextual adaptation informed by formative research. However, understanding of PA behavior in this population and context remains limited because existing research relies on partial lenses and lacks integration of theory, participatory methods, and multistakeholder insights. Objective: We aimed to identify and prioritize PA determinants among adolescents with mild intellectual disability in Dutch PrO schools, using a theory-driven, participatory, and multistakeholder approach to inform the contextual adaptation of digital health interventions. Methods: We used an exploratory sequential mixed methods study design integrating context mapping and concept mapping. Inclusive participatory methods were co-designed with PrO schools for adolescents. In the first qualitative phase, PA determinants were identified through data collected from experts (n=13), teachers (n=8), and adolescents (n=83‐90). Qualitative data from all stakeholder groups were integrated and analyzed using a framework approach grounded in the Theoretical Domains Framework and the Capability, Opportunity, Motivation-Behavior model. In the subsequent quantitative phase, the PA determinants identified across all data sources were translated into inclusive pictorial rating instruments and prioritized from the adolescents’ perspectives using a 7-point Likert scale ranging from barriers to facilitators. Quantitative analyses included descriptive statistics and hierarchical clustering to examine perceived importance and consensus across determinants. Results: Qualitatively, 29 PA determinants were identified and mapped to Capability, Opportunity, Motivation-Behavior components: opportunity (n=15), motivation (n=8), and capability (n=6). Experts and teachers focused on capability and opportunity barriers (eg, impaired skills and cognitive fatigue), while adolescents emphasized motivation and social opportunity facilitators (eg, fun activities, enjoyment, and peer or family support). Quantitatively, adolescents’ ratings showed a positive bias toward facilitators (mean 5.09/7, SD 1.72), and facilitators showed greater consensus than barriers (=0.74). Adolescents rated several determinants as facilitators that experts and teachers emphasized as barriers (eg, those related to impaired skills and self-efficacy). Conclusions: Three meta-inferences informing future intervention adaptation were derived: (1) substantial individual variation exists in adolescents’ perceptions of PA determinants, (2) school-related barriers are highly individualized, and (3) social and emotional facilitators tend to be relatively more shared across adolescents. Integrating findings suggests a dual approach to promoting PA in this population and context: personalized strategies for individual barriers and group-level strategies for shared facilitators. Digital health interventions seem promising to provide accessible and inclusive means of delivering this dual approach at scale.