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

Portillo-Van Diest, Ana ; Ballester Coma, Laura ; Mortier, Philippe ; Vilagut, Gemma ; Amigo, Franco ; Puértolas Gracia, Beatriz ; García-Mieres, Helena ; Alayo, Itxaso ; Blasco, Maria Jesus ; Carrasco Espi, Paula ; Falcó, Raquel ; Forteza-Rey, Ines ; Garcia-Pazo, Patricia ; Gili, Margalida ; Giménez-García, Cristina ; Machancoses, Francisco H ; Marzo Campos, Juan Carlos ; Navarra-Ventura, Guillem ; Piqueras, Jose A ; Rebagliato, Marisa ; Roca, Miquel ; Rodriguez Jiménez, Tiscar (Universidad de Zaragoza) ; Roldan, Lorenzo ; Ruiz-Palomino, Estefanía ; Soto-Sanz, Victoria ; Alonso, Jordi
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
Resumen: 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.
Idioma: Inglés
DOI: 10.1136/bmjopen-2023-072641
Año: 2023
Publicado en: BMJ Open 13, 7 (2023), e072641 [9 pp]
ISSN: 2044-6055

Factor impacto JCR: 2.4 (2023)
Categ. JCR: MEDICINE, GENERAL & INTERNAL rank: 81 / 329 = 0.246 (2023) - Q1 - T1
Factor impacto CITESCORE: 4.4 - Medicine (all) (Q1)

Factor impacto SCIMAGO: 0.971 - Medicine (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/ISCIII/CD18-00049
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/CP21-00078
Financiación: info:eu-repo/grantAgreement/ES/ISCIII/PI20-00006
Tipo y forma: Article (Published version)
Área (Departamento): Área Person.Eval.Trat.Psicoló. (Dpto. Psicología y Sociología)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes.


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 Record created 2023-11-08, last modified 2024-11-25


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