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Importance: The period from childhood to early adulthood involves increased susceptibility to the onset of mental disorders, with implications for policy making that may be better appreciated by disaggregated analyses of narrow age groups., Objective: To estimate the global prevalence and years lived with disability (YLDs) associated with mental disorders and substance use disorders (SUDs) across 4 age groups using data from the 2019 Global Burden of Disease (GBD) study., Design, Setting, and Participants: Data from the 2019 GBD study were used for analysis of mental disorders and SUDs. Results were stratified by age group (age 5 to 9, 10 to 14, 15 to 19, and 20 to 24 years) and sex. Data for the 2019 GBD study were collected up to 2018, and data were analyzed for this article from April 2022 to September 2023., Exposure: Age 5 to 9 years, 10 to 14 years, 15 to 19 years, and 20 to 24 years., Main Outcomes and Measures: Prevalence rates with 95% uncertainty intervals (95% UIs) and number of YLDs., Results: Globally in 2019, 293 million of 2516 million individuals aged 5 to 24 years had at least 1 mental disorder, and 31 million had an SUD. The mean prevalence was 11.63% for mental disorders and 1.22% for SUDs. For the narrower age groups, the prevalence of mental disorders was 6.80% (95% UI, 5.58-8.03) for those aged 5 to 9 years, 12.40% (95% UI, 10.62-14.59) for those aged 10 to 14 years, 13.96% (95% UI, 12.36-15.78) for those aged 15 to 19 years, and 13.63% (95% UI, 11.90-15.53) for those aged 20 to 24 years. The prevalence of each individual disorder also varied by age groups; sex-specific patterns varied to some extent by age. Mental disorders accounted for 31.14 million of 153.59 million YLDs (20.27% of YLDs from all causes). SUDs accounted for 4.30 million YLDs (2.80% of YLDs from all causes). Over the entire life course, 24.85% of all YLDs attributable to mental disorders were recorded before age 25 years., Conclusions and Relevance: An analytical framework that relies on stratified age groups should be adopted for examination of mental disorders and SUDs from childhood to early adulthood. Given the implications of the early onset and lifetime burden of mental disorders and SUDs, age-disaggregated data are essential for the understanding of vulnerability and effective prevention and intervention initiatives.

To determine the effect of mobile health (mHealth) focused on diet and lifestyle on blood pressure (BP). We performed a systematic review with meta-analysis using the mean difference (MD) of change from baseline as an effect measure. MEDLINE via PubMed, Cochrane Central, and EMBASE were reviewed until 6 May 2020. We included randomized controlled trials of adults who participated in mHealth focused on diet and lifestyle. Interventions were grouped according to the presence of health professional intervention (PI) (PI + mHealth or mHealth only). Eligible controls did not participate in mHealth and were classified as active comparator (PI) or no intervention (NI). Subgroup analyses were performed according to the presence of prior cardiovascular disease and hypertension status. We included 44 trials involving 24 692 participants. Mobile health interventions were superior to NI in reducing SBP in both situations: alone [MD = -1.8 mmHg; 95% confidence interval (CI): -3.6; 0.0] or with PI (MD = -5.3 mmHg; 95% CI: -7.5; -3.1), with a greater effect size in the latter group (P = 0.016). This benefit was not observed when the control was PI. DBP and SBP had consistent results. There was a marked effect of PI + mHealth vs. NI on the BP reduction among hypertensive participants. Current evidence shows that mHealth focused on diet and lifestyle can reduce BP, especially when implemented in hypertensive participants, and PI may provide additional benefit. PROSPERO ID CRD42019141475., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2022. For permissions, please email: journals.permissions@oup.com.)

Objective: Binge eating (BE) is associated with gestational weight gain, which is a risk factor for gestational diabetes (GDM). Little is known about this association in women with GDM. To evaluate the relationship of BE in pregnancy with gestational weight gain, BE at postpartum and postpartum weight retention in women with GDM., Method: Lifestyle INtervention for Diabetes prevention After pregnancy (LINDA-Brasil) is a multicenter cohort study with 1,958 women with GDM. BE was assessed by interview during recruitment in pregnancy and at a phone interview at ~4 months postpartum. Gestational weight gain was classified according to the 2009 Institute of Medicine (IOM) recommendations. Poisson regression with robust variance was used to estimate adjusted relative risks (RR)., Results: Prevalence of BE was 31.6% (95% confidence interval [CI] 29.5-33.6%) during pregnancy and 30.0% (95% CI 28.0-32.1%) at postpartum. The risk of exceeding the IOM's recommendation for gestational weight gain was 45% higher (RR 1.45, 95% CI 1.29-1.63) in women who had BE during pregnancy compared to those who did not. The risk of having postpartum weight retention above the 75th percentile was 33% higher (RR 1.33, 95% CI 1.10-1.59) among those with BE compared to those without., Discussion: Among these women with GDM, BE was frequent and was associated with excessive gestational weight gain and weight retention at postpartum. Thus, given the vulnerability of these periods of the life cycle, tracking this eating behavior is important for the management of gestational weight gain and for the prevention of excessive postpartum retention., (© 2020 Wiley Periodicals LLC.)

Background: The aim of this study was to investigate physical activity and associated factors in women with gestational diabetes mellitus (GDM)., Methods: This cross-sectional study was performed on 2706 women as part of the Lifestyle INtervention for Diabetes prevention After pregnancy (LINDA-Brasil) study, recruited between 2012 and 2016 in Brazil. A structured questionnaire was used to assess physical activity. Descriptive and Poisson regression analyses were used to investigate physical activity and associated factors., Results: Counseling regarding physical activity practices was reported by 47.4% and 34.3% of women before and after a diagnosis of GDM, respectively. During pregnancy, 26% of women were classified as inactive, 39.7% were classified as insufficiently active, and 34.3% were classified as active. Compared with prepregnancy, 63.1% of women reported a decrease in physical activity levels during pregnancy. The prevalence of being inactive during pregnancy was higher among women who did not live with a partner (P = 0.003), had a lower household income (P = 0.01), were employed (P < 0.001), and who had four or more children (P < 0.001)., Conclusion: Among Brazilian women with GDM, physical activity levels and practices were low, with most women reporting decreased physical activity during pregnancy. A low socioeconomic status was associated with lower physical activity. These findings indicate the need to reinforce the importance of physical activity in the management of GDM in Brazil., (© 2018 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

A cross-sectional study was conducted to evaluate and compare dietary intake, type of assistance (Basic Health Units - UBS and Family Health Strategies - ESF) and participation in the "Bolsa Família" Program (PBF) among users of the Brazilian Unified Health System, (SUS). The sample was composed of individuals of both sexes between 18 and 78 years of age in Porto Alegre, state of Rio Grande do Sul. Socioeconomic, clinical and food consumption data were collected via a questionnaire adapted from the SISVAN and VIGITEL national surveys. The analyses were conducted using R3.1 software. Of the 187 patients, 91 were affiliated to the ESF, 96 to UBS and 40 were registered with the PBF. A healthy eating pattern was identified in only 41% of SUS users. It was observed that 55% did not consume raw salad (37% p = 0.04) and vegetable consumption was lower among the PBF users (67.5% versus 75.9%; p = 0.02). There was no significant difference in food consumption considering the kind of assistance (ESF or UBS). A healthy consumption pattern was not associated with demographic and socioeconomic variables. The majority of beneficiaries of the PBF did not admit to healthy eating patterns. Therefore, effective health promotion and prevention is needed for this population, mainly among the beneficiaries of the PBF.