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Romantic (versus other) events and momentary affect: Immediate and lagged within-person associations among college students.

ABSTRACT: Given that affect is highly responsive to experiences representing current goals and values, and young adulthood reflects a period in which romantic relationships become increasingly important, this study explored the links between everyday romantic relationship events and momentary affect among young adult college students. Romantic events were then directly compared to academic and family events-two other salient life domains for these students-as predictors of current and subsequent momentary affect. Drawn from an ecological momentary sampling study designed to assess substance use, participants in dating relationships (N = 130) completed four reports per day for 28 days (totaling 10,318 reports). Multilevel models tested within-person associations between positive and negative romantic events (broadly defined) as predictors of positive (e.g., happy, excited) and negative (e.g., sad, lonely) affect in the moment and beyond. Analyses included both event occurrence and event intensity models, facilitating event comparison. Models accounted for day-level effects and several relevant individual and relationship controls. Results indicated that positive romantic events were associated with immediate and lasting increases in positive affect and immediate (but not always lasting) decreases in negative affect, whereas negative romantic events were associated with immediate and lasting changes in both positive and negative affect. When significant, direct comparisons indicated that romantic events were associated with larger changes in concurrent and subsequent affect than academic or family events. Findings highlight the powerful role that young adults' romantic relationships play in their emotional well-being, particularly in comparison to other developmentally and environmentally salient life domains. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

SUBMITTER: Blumenstock SM

PROVIDER: S-EPMC8720109 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe COVID-19 pandemic has resulted in pronounced changes for college students, including shifts in living situations and engagement in virtual environments. Although college drinking decreased at the onset of the pandemic, a nuanced understanding of pandemic-related changes in drinking contexts and the risks conferred by each context on alcohol use and related consequences have yet to be assessed.MethodsSecondary data analyses were conducted on screening data from a large parent clinical trial assessing a college student drinking intervention (N = 1669). Participants across six cohorts (from Spring 2020 to Summer 2021) reported on the frequency of drinking in each context (i.e., outside the home, home alone, home with others in-person, and home with others virtually), typical amount of drinking, and seven alcohol-related consequence subscales.ResultsDescriptive statistics and negative binomial regressions indicated that the proportion and frequency of drinking at home virtually with others decreased, while drinking outside the home increased from Spring 2020 to Summer 2021. Limited differences were observed in the proportion or frequency of individuals drinking at home alone or at home with others in-person. Negative binomial and logistic regressions indicated that the frequency of drinking outside the home was most consistently associated with more alcohol-related consequences (i.e., six of the seven subscales). However, drinking at home was not without risks; drinking home alone was associated with abuse/dependence, personal, social, hangover, and social media consequences; drinking home with others virtually was associated with abuse/dependence and social consequences; drinking home with others in-person was associated with drunk texting/dialing.ConclusionThe proportion and frequency of drinking in certain contexts changed during the COVID-19 pandemic, although drinking outside the home represented the highest risk drinking context across the pandemic. Future prevention and intervention efforts may benefit from considering approaches specific to different drinking contexts.

Project description:BackgroundStress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied.ObjectiveWe aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people.MethodsStudent participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations.ResultsWe identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models).ConclusionsOur findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress.

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Romantic (versus other) events and momentary affect: Immediate and lagged within-person associations among college students.

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