College student sleep and executive functioning: An examination of potential moderators - Cifre, Anthony B.; Walters, Kenneth S.; Budnick, Christopher J. (Translational Issues in Psychological Science, 2020)
Approximately 60% of college students report sleep disturbances. Sleep disturbances, such as insomnia, negatively influence physical energy, cognitive resources, and affective states that might inhibit executive functioning. To better delineate the variables that alter the college student insomnia and executive functioning relationship we examined sleepiness, sleep debt, and attention-deficit/hyperactivity disorder (ADHD) symptomatology. We expected insomnia to predict executive dysfunction, with a stronger relationship observed at higher levels of the focal moderator (i.e., sleepiness, sleep debt, or ADHD symptoms). Undergraduate participants (n = 472) completed a cross-sectional survey assessing insomnia, state sleepiness, sleep debt, ADHD symptomatology (inattention, hyperactivity, and impulsivity), and executive dysfunction. Hierarchical linear regressions showed that poor sleep had a negative influence on executive function when college students also had high levels of impulsivity, state sleepiness, or sleep debt. These results partially support our expectations and further the academic sleep-related literature while providing insight for counselors, academic advisors, or other professionals working with college student populations. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
3 citations (Crossref) [2023-10-31] tex.ids: cifreCollegeStudentSleepinpress place: US
Keywords: Attention Deficit Disorder with Hyperactivity; College Students; Executive Function; Financial Strain; Insomnia; Sleep; Sleepiness; Symptoms; dept.psy; peer-reviewed

College student Fear of Missing Out (FoMO) and maladaptive behavior: Traditional statistical modeling and predictive analysis using machine learning - McKee, Paul C.; Budnick, Christopher J.; Walters, Kenneth S.; Antonios, Imad (PLOS ONE, 2022)
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.
4 citations (Crossref) [2023-10-31]
Keywords: Alcohol consumption; Behavior; Cannabis; Drug interactions; Forecasting; Machine learning; Machine learning algorithms; Principal component analysis; dept.csc; dept.psy; peer-reviewed

The role of combat exposure on drinking behavior and subjective well-being: A Rubin Causal Model approach. - McKee, Paul; Budnick, Christopher J.; Walters, Kenneth S.; Bind, Marie-Abèle (Psychological Trauma: Theory, Research, Practice, and Policy, 2024)
Keywords: dept.psy; peer-reviewed

Substance use, anxiety, and depressive symptoms among college students - Walters, Kenneth S.; Bulmer, Sandra Minor; Troiano, Peter F.; Obiaka, Uzoma; Bonhomme, Rebecca (Journal of Child & Adolescent Substance Abuse, 2018)
Research on college substance use and mental illness is limited and inconsistent. Measures of substance use, and anxiety and depressive symptoms, were completed by 1,316 undergraduates within a major drug transportation corridor. Hierarchical linear regressions were used to test associations between anxious and depressive symptoms and substance use (i.e., alcohol, cannabis, tobacco, cocaine, other amphetamines, sedatives, hallucinogens, and designer drugs). Depressive symptoms were associated with use of cannabis, tobacco, amphetamines, cocaine, sedatives, and hallucinogens. Anxiety symptoms were unrelated to substance use. These findings support the need for education and prevention at universities, emphasizing tobacco, cannabis, and certain “harder” drugs.
58 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/158-625-507-666-259, pop00148 tex.type: [object Object]
Keywords: anxiety; college students; depression; dept.hhs; dept.psy; mental health; peer-reviewed; substance use