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Sleep in the Social World of College Students: Bridging Interpersonal Stress and Fear of Missing Out with Mental Health.

ABSTRACT: INTRODUCTION:The college years are characterized by psychosocial and biological phenomena that may impact mental health, such as heightened sensitivity to social stressors and compromises in sleep quantity and quality. The current study uses a biopsychosocial approach to examine the associations among interpersonal stress, Fear of Missing Out (FoMO), insomnia, and mental health. METHODS:Survey data were collected from 283 undergraduate students (90% female) with a mean age of 21.4 years. A path analysis was utilized to test a mediational model linking interpersonal stress and FoMO with mental health through a mediator of insomnia. We hypothesized that higher levels of interpersonal stress and FoMO would be associated with higher levels of insomnia symptoms, which would in turn be associated with poorer mental health. RESULTS:As predicted, insomnia partially mediated significant associations of interpersonal stress and FoMO with mental health. The association of interpersonal stress with insomnia and mental health was more robust than the association of FoMO with these variables. CONCLUSIONS:The pathway from interpersonal stress and/or FoMO, through insomnia, to compromises in mental health may be modifiable through behavioral interventions focusing on coping skills, sleep hygiene, and even technology-related habit changes. Recommendations to help disrupt this pathway, particularly among college students, are discussed.

SUBMITTER: Adams SK

PROVIDER: S-EPMC7071423 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Sleep in the Social World of College Students: Bridging Interpersonal Stress and Fear of Missing Out with Mental Health.

Adams Sue K SK Murdock Karla K KK Daly-Cano Meada M Rose Meredith M

Behavioral sciences (Basel, Switzerland) 20200206 2

<h4>Introduction</h4>The college years are characterized by psychosocial and biological phenomena that may impact mental health, such as heightened sensitivity to social stressors and compromises in sleep quantity and quality. The current study uses a biopsychosocial approach to examine the associations among interpersonal stress, Fear of Missing Out (FoMO), insomnia, and mental health.<h4>Methods</h4>Survey data were collected from 283 undergraduate students (90% female) with a mean age of 21.4 ...[more]

PMID: 32041120

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Project description:This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO's relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.

Dataset Information

Sleep in the Social World of College Students: Bridging Interpersonal Stress and Fear of Missing Out with Mental Health.

Publications

Sleep in the Social World of College Students: Bridging Interpersonal Stress and Fear of Missing Out with Mental Health.

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