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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 res...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351263/ https://www.ncbi.nlm.nih.gov/pubmed/37451741 http://dx.doi.org/10.1136/bmjopen-2023-072641 |
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author | 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 Roldan, Lorenzo Ruiz-Palomino, Estefanía Soto-Sanz, Victoria Alonso, Jordi |
author_facet | 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 Roldan, Lorenzo Ruiz-Palomino, Estefanía Soto-Sanz, Victoria Alonso, Jordi |
author_sort | Portillo-Van Diest, Ana |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10351263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-103512632023-07-18 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 Roldan, Lorenzo Ruiz-Palomino, Estefanía Soto-Sanz, Victoria Alonso, Jordi BMJ Open Epidemiology 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. BMJ Publishing Group 2023-07-14 /pmc/articles/PMC10351263/ /pubmed/37451741 http://dx.doi.org/10.1136/bmjopen-2023-072641 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Epidemiology 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 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351263/ https://www.ncbi.nlm.nih.gov/pubmed/37451741 http://dx.doi.org/10.1136/bmjopen-2023-072641 |
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