Digital mental health tools such as apps, virtual reality, and artificial intelligence (AI) hold great promise but continue to face barriers to widespread clinical adoption. The Society of Digital Psychiatry, in partnership with JMIR Mental Health , presents a 3-pronged road map to accelerate their safe, effective, and equitable implementation. First, education: integrate digital psychiatry into core training and professional development through a global webinar series, annual symposium, newsletter, and an updated open-access curriculum addressing AI and the evolving digital navigator role. Second, AI standards: develop transparent, actionable benchmarks and consensus guidance through initiatives like MindBench.ai to assess reasoning, safety, and representativeness across populations. Third, digital navigators: expand structured, train-the-trainer programs that enhance digital literacy, engagement, and workflow integration across diverse care settings, including low- and middle-income countries. Together, these pillars bridge research and practice, advancing digital psychiatry grounded in inclusivity, accountability, and measurable clinical impact.

: To determine whether there is an association between parent and sibling obesity status and obesity status in children with autism spectrum disorder (ASD). We examined predictors of obesity in children with ASD with body mass index data for the proband, 1 sibling, and 2 parents using data from the multisite Simons Simplex Collection. In a stepwise logistic regression model, proband obesity status was associated with obesity status of the sibling (odds ratio [OR] 2.66; 95% confidence interval [CI], 1.92–3.70), mother (OR 2.10; 95% CI, 1.59–2.77), and father (OR 1.51; 95% CI, 1.15–1.98). Proband obesity was also related to somatic complaints (OR 1.60; 95% CI, 1.006–2.53), mood stabilizers (OR 1.80; 95% CI, 1.19–2.72), internalizing problems (OR 1.60; 95% CI, 1.14–2.30), age (OR 1.01; 95% CI, 1.00–1.01), and some adaptive functioning domains (OR 0.987; 95% CI, 0.977–0.997). Race, ethnicity, income, sex, and maternal education were not significant predictors. Familial factors were generally the strongest predictors of obesity rather than medication use, demographics, or psychological characteristics. Results support a family-centered approach to treatment of obesity in children with ASD.