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Background Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. Methods We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. Discussion By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation. Trial status IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024. Trial registration ClinicalTrials.govNCT04780542. First submission date: February 28, 2021.

Our objective was to evaluate levels of trait and state anxiety in a group of peri- and postmenopausal women and to explore the relation of hormonal therapy to levels of anxiety. Peri- (n = 63) and postmenopausal (n = 236) women were evaluated between March and September 2013. The assessed variables were menopausal status, anxiety (using the state and trait anxiety inventory), and sociodemographic and clinical variables. Use of psychotropic medications and hormone therapy was also ascertained. The mean age of the participants was 51.9 years, ranging from 31 to 69 years. The mean state anxiety scores, as well as the mean trait anxiety scores, were higher in perimenopausal than postmenopausal women. High state anxiety (above the 75th percentile), but not high trait anxiety, was related to perimenopausal status. Anxiety levels appeared to be higher among perimenopausal than postmenopausal women, as also occurs with depressive symptoms. Anxiety state provides data about recent anxiety symptoms in women; however, anxiety trait could be present in some women before perimenopause. Our findings suggest that perimenopause is a period with increased anxiety levels in some women.

High prevalence of depression and anxiety symptoms has been observed in medical students.To identify predictors of depressive symptoms according to CES-D in students during their first year of medical education.We evaluated 517 students in their first year of medical education with the following instruments: Centers for Epidemiology Studies for Depression Scale, State-Trait anxiety inventory, and stressors list. A linear regression analysis was carried out to evaluate variables predicting high CES-D scores.We observed that anxiety and depressive symptoms were more frequent in women, as well as stressors. Predictors of depression were state of anxiety, stressors, and low socioeconomic level.It is important to implement support measures for students who live with stress factors, suffering anxiety, or with a low socioeconomic level.