Effects of lifestyle behaviours and depressed mood on sleep quality in young adults. A machine learning approach.
Modern lifestyles may lead to high stress levels, frequently associated with mood disorders (e.g. depressed mood) and sleep disturbance. The objective of this study was to develop a machine learning model aimed at identifying risk factors for developing poor sleep quality in young adults. The sample...
| Publicado en: | Psychology & Health Vol. 39; no. 1; pp. 128 - 144 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174419930&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174419930 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08870446 7WK jtl: Psychology & Health issn: 08870446 maglogo: N pubinfo: dt: Jan2024 vid: 39 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 174419930 156524837 174419930 174419930 10.1080/08870446.2022.2067331 174419930 ppf: 128 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effects of lifestyle behaviours and depressed mood on sleep quality in young adults. A machine learning approach. aug: au: Sanchez-Trigo, Horacio Molina-Martínez, Emilio Grimaldi-Puyana, Moisés Sañudo, Borja affil: Physical Education and Sports Department, University of Seville, Sevilla, Spain sug: subj: Life Style Evaluation Sleep Quality Evaluation Stress, Psychological Complications Depression Risk Factors Sleep Disorders Risk Factors Machine Learning Risk Assessment Human Male Female Adolescence Young Adult Physical Activity Smartphone Utilization Algorithms Sitting Sedentary Behavior Sensitivity and Specificity Data Mining Funding Source Adolescent: 13-18 years Male Female ab: Modern lifestyles may lead to high stress levels, frequently associated with mood disorders (e.g. depressed mood) and sleep disturbance. The objective of this study was to develop a machine learning model aimed at identifying risk factors for developing poor sleep quality in young adults. The sample consisted of 383 college-aged students (mean age ± SD: 21 ± 1 years; 61% males). Sleep quality, mood state, physical activity, number of sitting hours, and smartphone use were measured. A decision tree algorithm distinguished participants' sleep quality with 74% accuracy using a combination of four features: depressed mood, physical activity, sitting time, and vigour. Together with depressed mood, both physical activity (>6432 metabolic equivalent tasks -METs- per week) and sedentary behaviour (sitting time greater than 7 h/day) were the primary features that could differentiate those with poor sleep quality from those with good sleep quality. We provided a decision tree model with a sensitivity of 90.7% and a specificity of 54.3%, with an AUC of 0.725. These findings could promote improvements in prevention strategies and contribute to the development of meaningful and evidence-based intervention programs. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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