Predictors of well-being among college students

Ridner, S. Lee; Newton, Karen; Staten, Ruth R.; Crawford, Timothy N.; Hall, Lynne A.

doi:10.1080/07448481.2015.1085057

Cited by 115 publications

(74 citation statements)

References 48 publications

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“…Furthermore, sleep quality emerging as a significant outcome in our sample parallels a recent article by Ridner and colleagues that found that sleep quality was the strongest predictor of well-being among college students. 52 Ridner called for interventions to promote sleep quality as a means of improving well-being in this population. 52 Our results add to this body of literature by exploring the determinants that affect sleep quality and therefore influence well-being among college students.…”

Section: Commentmentioning

confidence: 99%

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Multilevel analysis exploring the links between stress, depression, and sleep problems among two-year college students

Wallace

Boynton

Lytle

2016

Journal of American College Health

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Objective This study explored the association of stress and depression with a multidimensional sleep problems construct in a sample of 2-year college students. Participants The sample consisted of 440 students enrolled in 2-year study from Fall 2011 to Fall 2013. Methods Participants in an obesity prevention study completed surveys assessing sleep, stress, and depression at baseline, 4, 12, and 24 months. Multilevel models predicting sleep problems were conducted to distinguish episodic from chronic reports of stress and depression. Results Participants were primarily women (68%), white (73%), young adults (M age = 22.8), with an average of 8.4 hours of sleep per night. Neither stress nor depression was predictive of sleep quantity; however, they were predictive of sleep quality. Conclusions Results show that sleep quality rather than sleep quantity may be the greater health concern for young adults, suggesting that intervention programs targeting depression, stress management, and healthy sleep patterns are warranted.

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Section: Commentmentioning

confidence: 99%

“…52 Ridner called for interventions to promote sleep quality as a means of improving well-being in this population. 52 Our results add to this body of literature by exploring the determinants that affect sleep quality and therefore influence well-being among college students.…”

Section: Commentmentioning

confidence: 99%

Multilevel analysis exploring the links between stress, depression, and sleep problems among two-year college students

Wallace

Boynton

Lytle

2016

Journal of American College Health

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“…Este per l está em linha com estudos prévios nesta população quer nacionais quer internacionais Eurostudent, 2012(Hauschildt, Gwosć, Netz, & Mishra, 2015. Constatou-se que taxas de prevalência de consumos de álcool e drogas são ligeiramente inferiores aos valores reportados pela maioria dos estudos (Bayati, Beigi, & Salehi, 2009;Ridner et al, 2015). A prática moderada de exercício físico/desporto pelos participantes é um dado positivo inesperado, contrariando a generalidade os estudos que indicam que os EES deixam de ter disponibilidade para praticar exercício físico (Ferrara, 2009).…”

Section: Introductionunclassified

“…Também se constatou que aqueles que dormem mais horas de sono por dia em tempo aulas têm níveis de saúde mental mais elevados. Os nossos resultados estão em linha com outros estudos recentes nesta população (Ridner et al, 2015;Kenari, 2014 …”

Section: Introductionunclassified

A Saúde Mental Em Estudantes Do Ensino Superior. Relação Com O Género, Nível Socioeconómico E Os Comportamentos De Saúde

Nogueira¹,

Sequeira²

2017

RPESM

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RESUMOCONTEXTO: O nível de saúde mental (SM) in uência o bem-estar e o sucesso académico dos estudantes do ensino superior (Chow, 2010), contudo esta problemática tem sido pouco estudada em Portugal. A literatura existente é pouco clara e, por vezes, contraditória no que respeita às variáveis psicossociais associadas positiva e/ou negativamente à SM dos estudantes do ensino superior (Eisenberg, et al., 2009 ABSTRACT " e Mental Health of Higher Education Students: Relationship with gender, socioeconomic status and health behaviours"BACKGROUND: e level of mental health (MH) a ects the well-being and academic success of students of higher education (Chow, 2010). However, in Portugal, this issue has been rarely studied. e literature is unclear and sometimes contradictory regarding to positive and/or negatively associated psychosocial variables of higher education students (Eisenberg, et al., 2009). AIM: (1) To characterize the MH of higher education students; (2) To explore the relationship between MH and sociodemographic and health behaviors variables. METHODS: is is a descriptive, cross-sectional and correlational study, and a non-probabilistic sample, composed of students of higher education in the District of Lisbon of the 1st and 2nd year enrolled in the 1st cycle of studies. Participants gave written informed consent and, voluntarily completed an online survey, which was sent to campus email during October and November 2014. To collect sociodemographic information, health behaviors and students' MH, were used a brief questionnaire and the Mental Health Inventory (ISM). RESULTS: Overall, the results indicate satisfactory levels of MH, both in the positive dimension -Psychological Well-Being and in the negative -Psychological Distress. However, 93 participants were found to have low MH level. Correlational analyzes show that women have lower levels of MH than men. Similarly, as the socioeconomic level increases, the levels of MH improve. e results, also, indicate that higher levels of MH are signi cantly associated with participants who exercise regularly and who sleep more than seven hours, during class period. In this sample, there were no signi cant associations between alcohol consumption and other drugs with students' MH. CONCLUSIONS: We conclude that the female gender and the low socioeconomic level are variables negatively associated with the MH of the participants. While regular practice of exercise and sleep seven or more hours per day were positively associated with MH.

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“…The rst and therefore strongest predictor selected into the submodel was sleep quality -how refreshed a participant felt after waking up, on their average day. Sleep quality has been consistently shown to be one of the strongest predictors of well-being, especially in young adults, with poor sleep quality being strongly linked to poor mental health outcomes including symptoms of depression (Pilcher et al, 1997;Ridner et al, 2016;Wallace et al, 2017;Wilson et al, 2014). Further, it is important to note that while sleep quality is often shown to be an important predictor of well-being, sleep quantity is not (Pilcher et al, 1997;Wallace et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Sleep quality, ability to concentrate, and fruit consumption suffice to predict young adult well-being: Findings from Bayesian predictive projection

Bartonicek¹,

Wickham²,

Pornpattananangkul³

et al. 2020

Preprint

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Background The search for reliable lifestyle predictors of psychological well-being is a key research pursuit in psychology and public health. Researchers often gather data on many candidate predictors and then are faced with the difficult task of variable selection – selecting only few “relevant” predictors from the large set of candidates. Many statistical methods that are currently being used for variable selection suffer from known shortfalls. Fortunately, there are newer methods available. The aim of the study is to illustrate the use of predictive projection – a novel Bayesian method for variable selection with many attractive properties – and identify the predictors of psychological well-being in a sample of young adults. Methods The sample consisted of 791 young adults (ages 18 to 25, 27.4% men) from New Zealand who had taken part in the Dunedin Daily Life Study in 2013–2014. For 13 days, participants completed an online daily diary assessment of their well-being and a range of modifiable lifestyle variables. Demographic characteristics were also measured. Participants’ diary data was averaged across days and analyzed cross-sectionally to identify predictors of average differences in flourishing. Predictive projection was used to select as few predictors as necessary to approximate the predictive accuracy of a large reference model with 28 predictors. Lastly, predictive projection was compared with other standard methods of variable selection. Results Three predictors were sufficient to make average daily flourishing to approximate the predictions of the reference model: higher sleep quality, less trouble concentrating, and more servings of fruit. Demographic factors were not selected into the model. The projected submodel performed adequately on the test data. Compared to other variable selection methods, projection had either slightly worse or matching predictive performance, but led to much more parsimonious inference. Conclusion Predictive projection was used to quickly arrive at a parsimonious submodel that approximated the predictive accuracy of the reference model. The variables selected into the submodel – felt refreshed after waking up, had less trouble concentrating, and ate more servings of fruit – were all theoretically meaningful. Our findings have broad implications for well-being research and public policy.

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Predictors of well-being among college students

Abstract: Interventions that promote sleep quality among college students may be most beneficial in improving well-being.

Cited by 115 publications

References 48 publications

Multilevel analysis exploring the links between stress, depression, and sleep problems among two-year college students

Multilevel analysis exploring the links between stress, depression, and sleep problems among two-year college students

A Saúde Mental Em Estudantes Do Ensino Superior. Relação Com O Género, Nível Socioeconómico E Os Comportamentos De Saúde

Sleep quality, ability to concentrate, and fruit consumption suffice to predict young adult well-being: Findings from Bayesian predictive projection

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