Experience sampling methods for the personalised prediction of mental health problems in Spanish university students: protocol for a survey-based observational study within the PROMES-U project

IntroductionThere is a high prevalence of mental health problems among university students. Better prediction and treatment access for this population is needed. In recent years, short-term dynamic factors, which can be assessed using experience sampling methods (ESM), have presented promising results for predicting mental health problems.Methods and analysisUndergraduate students from five public universities in Spain are recruited to participate in two web-based surveys (at baseline and at 12-month follow-up). A subgroup of baseline participants is recruited through quota sampling to participate in a 15-day ESM study. The baseline survey collects information regarding distal risk factors, while the ESM study collects short-term dynamic factors such as affect, company or environment. Risk factors will be identified at an individual and population level using logistic regressions and population attributable risk proportions, respectively. Machine learning techniques will be used to develop predictive models for mental health problems. Dynamic structural equation modelling and multilevel mixed-effects models will be considered to develop a series of explanatory models for the occurrence of mental health problems.Ethics and disseminationThe project complies with national and international regulations, including the Declaration of Helsinki and the Code of Ethics, and has been approved by the IRB Parc de Salut Mar (2020/9198/I) and corresponding IRBs of all participating universities. All respondents are given information regarding access mental health services within their university and region. Individuals with positive responses on suicide items receive a specific alert with indications for consulting with a health professional. Participants are asked to provide informed consent separately for the web-based surveys and for the ESM study. Dissemination of results will include peer-reviewed scientific articles and participation in scientific congresses, reports with recommendations for universities’ mental health policy makers, as well as a well-balanced communication strategy to the general public

Experience sampling methods for the personalised prediction of mental health problems in Spanish university students: protocol for a survey-based observational study within the PROMES-U project

INTRODUCTION: There is a high prevalence of mental health problems among university students. Better prediction and treatment access for this population is needed. In recent years, short-term dynamic factors, which can be assessed using experience sampling methods (ESM), have presented promising results for predicting mental health problems. METHODS AND ANALYSIS: Undergraduate students from five public universities in Spain are recruited to participate in two web-based surveys (at baseline and at 12-month follow-up). A subgroup of baseline participants is recruited through quota sampling to participate in a 15-day ESM study. The baseline survey collects information regarding distal risk factors, while the ESM study collects short-term dynamic factors such as affect, company or environment. Risk factors will be identified at an individual and population level using logistic regressions and population attributable risk proportions, respectively. Machine learning techniques will be used to develop predictive models for mental health problems. Dynamic structural equation modelling and multilevel mixed-effects models will be considered to develop a series of explanatory models for the occurrence of mental health problems. ETHICS AND DISSEMINATION: The project complies with national and international regulations, including the Declaration of Helsinki and the Code of Ethics, and has been approved by the IRB Parc de Salut Mar (2020/9198/I) and corresponding IRBs of all participating universities. All respondents are given information regarding access mental health services within their university and region. Individuals with positive responses on suicide items receive a specific alert with indications for consulting with a health professional. Participants are asked to provide informed consent separately for the web-based surveys and for the ESM study. Dissemination of results will include peer-reviewed scientific articles and participation in scientific congresses, reports with recommendations for universities' mental health policy makers, as well as a well-balanced communication strategy to the general public. STUDY REGISTRATION: osf.io/p7csq

Associations of maternal education, area deprivation, proximity to greenspace during pregnancy and gestational diabetes with Body Mass Index from early childhood to early adulthood: A proof-of-concept federated analysis in seventeen birth cohorts

Background: International sharing of cohort data for research is important and challenging. The LifeCycle project aimed to harmonise data across birth cohorts and develop methods for efficient federated analyses of early life stressors on offspring outcomes. Aim: To explore feasibility of federated analyses of associations between four different types of pregnancy exposures (maternal education, area deprivation, proximity to green space and gestational diabetes) with offspring BMI from infancy to 17 years. Methods: We used harmonised exposure and outcome data from 17 cohorts (n=200,650 mother-child pairs) from the EU Child Cohort Network. For each child, we derived BMI at five age periods: (i) 0-1 years, (ii) 2-3, (iii) 4-7, (iv) 8-13 and (v) 14-17 years. Associations were estimated using linear regression via one-stage individual participant data meta-analysis using the federated analysis platform DataSHIELD. Results: Associations between lower maternal education and higher child BMI emerged from age 4 years and increased with age (difference in BMI z-score comparing low with high education age 0-1 years = 0.02 [95% CI 0.00, 0.03], 2-3 years = 0.01 [CI -0.02, 0.04], 4-7 years = 0.14 [CI 0.13, 0.16], 8-13 years = 0.22 [CI 0.20, 0.24], 14-17 years = 0.20 [CI 0.16, 0.23]). A similar pattern was found for area deprivation. Gestational diabetes was positively associated with BMI from 8 years (8-13 years = 0.17 [CI 0.10, 0.24], 14-17 years = 0.012 [CI -0.13, 0.38]) but not at younger ages. The normalised difference vegetation index measure of maternal proximity to green space was weakly associated with higher BMI in the first year of life but not at older ages. Conclusions: Associations between maternal education, area-based socioeconomic position and GDM with BMI increased with age. Maternal proximity to green space was not associated with offspring BMI, other than a weak association in infancy. Opportunities and challenges of cross-cohort federated analyses are discussed