[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122869-en":3,"doc-seo-122869-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122869,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Using Ecological Momentary Assessment and Machine Learning techniques to predict depressive symptoms in emerging adults - Research study","This study predicts depressive symptom levels in emerging adults by combining sociodemographic variables, affect, and emotion regulation strategies. Thirty-three participants completed ecological momentary assessment via a smartphone app six times daily over seven days, responding to 1,233 of 2,058 prompts. Machine learning on these observations showed Random Forest as the most accurate model, using 13 of 36 variables. Age, worried emotion, and a social-exchange-related emotion regulation strategy were key predictors of severe depressive symptoms, offering supportive information beyond conventional diagnostic methods.","Document downloaded from:  \n[http://hdl.handle.net/10251/201750](http://hdl.handle.net/10251/201750)  \n[This paper must be cited as:](This paper must be cited as:)  \nDe La Barrera, U. ; Arrigoni, F. ; Monserrat, C. ; Montoya-Castilla, I. ; Gil-Gómez, J. (2024) . Using Ecological Momentary Assessment and Machine Learning techniques to predict depressive symptoms in emerging adults. Psychiatry Research. 332. [https://doi.org/10.1016/j.psychres.2023.115710](https://doi.org/10.1016/j.psychres.2023.115710)  \nThe final publication is available at  \n[https://doi.org/10.1016/j.psychres.2023.115710](https://doi.org/10.1016/j.psychres.2023.115710)  \nCopyright Elsevier  \nAdditional Information  \nUsing ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults  \nDe la Barrera, Usue 1 ; Arrigoni, Flavia2 ; Monserrat, Carlos3 ; Montoya-Castilla, Inmaculada4 ; Gil-Gómez, José -Antonio5,*  \n1 Departamento de Psicología Evolutiva y de la Educación. Facultad de Psicología y Logopedia. Universitat de València. ORCID: 0000-0003-1510-9669. Email: [usue.barrera@uv.es](usue.barrera@uv.es)  \n2 Departamento de Psicología, Facultad de Ciencias de la Educación. Universidad de Cádiz. ORCID: 0000-0001-5664-6167. Email: [flavia.arrigoni@uca.es](flavia.arrigoni@uca.es)  \n3 Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València. ORCID: 0000-0003-1790-8085. Email: [cmonserr@upv.es](cmonserr@upv.es)  \n4 Departamento de Personalidad, Evaluación y Tratamientos Psicológicos. Facultad de Psicología y Logopedia. Universitat de València. ORCID: 0000-0003-2536-2019. Email: [inmaculada.montoya@uv.es](inmaculada.montoya@uv.es)  \n5 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València. Valencia, Spain. ORCID: 0000-0001-9954-2480. Email: [jgil@upv.es](jgil@upv.es)  \n*Correspondence author  \nCorrespondence concerning this paper should be addressed to José -Antonio Gil-Gómez. Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València. Camino de Vera s/n, 46022, Valencia, Spain. Phone: +34 963 879 550; Fax:+34 963879 816; E-mail: [jgil@upv.es](jgil@upv.es)  \nAbstract  \nThe objective of this study was to predict the level of depressive symptoms in emerging adults by analyzing sociodemographic variables, affect, and emotion regulation strategies. Participants were 33 emerging adults (M=24.43; SD=2.80; 56.3% women) . They were asked to assess their current emotional state (positive or negative affect), recent events that may relate to that state, and emotion regulation strategies through ecological momentary assessment. Participants were prompted randomly by an app 6 times per day between 10 am and 10 pm for a seven-day period. They answered 1233 of the 2058 surveys (beeps), collectively. The analysis of observations, using Machine Learning (ML) techniques, showed that the Random Forest algorithm yields significantly better predictions than other models. The algorithm used 13 out of the 36 variables adopted in the study. Furthermore, the study revealed that age, emotion of worried and a specific emotion regulation strategy related to social exchange were the most accurate predictors of severe depressive symptoms. By carefully selecting predictors and utilizing appropriate sorting techniques, these findings may provide valuable supplementary information to traditional diagnostic methods and psychological assessments  \nKeywords: emerging adults, emotional regulation strategies, positive and negative affect, depressive symptoms, ecological momentary assessment (EMA), machine learning techniques  \n1. Introduction  \n1.1. Depressive symptoms in emerging adults  \nEmerging adults are individuals between the ages of 18 and 29 (Arnett, 2014) . This stage is considered to be the period between the end of adolescence and the assumption of adult responsibilities, such as securing stable job, getting marriage orb","cbCaishbRiIOy57z","https://ap.wps.com/l/cbCaishbRiIOy57z","pdf",631550,1,45,"English","en",105,"# Introduction\n## Depressive symptoms in emerging adults\n# Methods and Data Collection\n## Ecological momentary assessment procedure\n# Machine Learning Analysis\n## Model comparison and feature selection\n# Results\n## Key predictors of severe depressive symptoms\n# Discussion and Implications","[{\"question\":\"How were depressive symptoms predicted in emerging adults?\",\"answer\":\"The study used ecological momentary assessment data covering affect, recent events, and emotion regulation strategies, then applied machine learning models to predict depressive symptom severity.\"},{\"question\":\"What data collection approach did the participants complete?\",\"answer\":\"Participants completed in-the-moment assessments prompted randomly by an app six times per day between 10 am and 10 pm across a seven-day period.\"},{\"question\":\"Which machine learning model performed best?\",\"answer\":\"Random Forest produced significantly better predictions than other models in the analysis.\"},{\"question\":\"What variables were most accurate for predicting severe depressive symptoms?\",\"answer\":\"Age, the emotion of worry, and a social exchange-related emotion regulation strategy were identified as the most accurate predictors of severe depressive symptoms.\"}]","Using Ecological Momentary Assessment and Machine Learning techniques to predict depressive symptoms in emerging adults - Research study | PDF",1785813437,113,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"using-ecological-momentary-assessment-and-machine-learning-techniques-to-predict-depressive-symptoms-in-emerging-adults-research-study","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-ecological-momentary-assessment-and-machine-learning-techniques-to-predict-depressive-symptoms-in-emerging-adults-research-study/122869/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"How were depressive symptoms predicted in emerging adults?","Question",{"text":75,"@type":76},"The study used ecological momentary assessment data covering affect, recent events, and emotion regulation strategies, then applied machine learning models to predict depressive symptom severity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data collection approach did the participants complete?",{"text":80,"@type":76},"Participants completed in-the-moment assessments prompted randomly by an app six times per day between 10 am and 10 pm across a seven-day period.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best?",{"text":84,"@type":76},"Random Forest produced significantly better predictions than other models in the analysis.",{"name":86,"@type":73,"acceptedAnswer":87},"What variables were most accurate for predicting severe depressive symptoms?",{"text":88,"@type":76},"Age, the emotion of worry, and a social exchange-related emotion regulation strategy were identified as the most accurate predictors of severe depressive symptoms.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]