2026-04-29T04:52:22Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/236632024-11-28T19:49:40Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967col_20.500.12105_16983
Repisalud author Portillo-Van Diest, Ana author Ballester Coma, Laura author Mortier, Philippe author Vilagut, Gemma author Amigo, Franco author Puértolas Gracia, Beatriz author García-Mieres, Helena author Alayo, Itxaso author Blasco, Maria Jesus author Carrasco Espi, Paula author Falcó, Raquel author Forteza-Rey, Ines author García-Pazo, Patricia author Gili, Margalida author Giménez-García, Cristina author Machancoses, Francisco H author Marzo Campos, Juan Carlos author Navarra-Ventura, Guillem author Piqueras, Jose A author Rebagliato, Marisa author Roca, Miquel author Rodriguez Jiménez, Tiscar author Roldán-Espínola, Lorenzo author Ruiz-Palomino, Estefanía author Soto-Sanz, Victoria author Alonso, Jordi 2024-10-09T06:34:19Z 2024-10-09T06:34:19Z 2023-07-14 Portillo-Van Diest A, Ballester Coma L, Mortier P, Vilagut G, Amigo F, Puértolas Gracia B, et al. 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. BMJ Open. 2023 Jul 14;13(7):e072641. https://hdl.handle.net/20.500.13003/19360 https://hdl.handle.net/20.500.12105/23663 37451741 10.1136/bmjopen-2023-072641 2044-6055 BMJ open 2-s2.0-85164759092 1042144500067 L2025910963 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. eng 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 research article