Daily behaviours and emotion regulations

Background

This proposal addresses the lack of research on the interplay between daily emotions and self-selected emotion regulation strategies (music listening, exercise, eating). Using computational modelling and ecological momentary assessment, we will examine how real-world daily emotion fluctuates with selection and success of regulation strategies. This study focuses on quantifying self-directed music listening as an affect regulation strategy compared to exercise and eating habits. Given the range of emotion regulation techniques available in the real world but the limited examination of regulation in the lab (i.e., most research is confined to cognitive techniques such as reappraisal), understanding how music, exercise, and eating influence affect regulation is crucial for a full understanding of what strategies are most effective, for whom, and when. Additionally, very little is known about the role of music in emotion regulation. This research also compares the effectiveness, frequency, and context of different strategies, considering the accessibility of exercise and the unresolved questions surrounding emotional eating.

Research Questions / Hypotheses

(1) How do adults use music/exercise /eat to regulate their emotions in daily life?

(2) How does music listening/exercise/eating relate to fluctuations in emotions over time (within day, between days)?

(3) What profiles characterise advantageous versus disadvantageous emotion regulation (i.e., upregulating positive emotion with music but downregulating negative emotion with exercise)?

(4) How effective are music listening / exercise / eating as emotion regulation strategies?

(5) What specific conditions or circumstances prompt individuals to choose different emotion regulation strategies (i.e., social settings, familiar or unfamiliar others present)?

Participants

60 REP participants have completed the study. Only exclusion criteria is not completing the initial survey nor the daily surveys after multiple reminders.

Methods

This study will be listed as part of REP studies of Melbourne School of Psychological Science for first and second year undergraduate students to sign up and participate. In addition, the study will be listed in Prolific website for public recruitment of adult participants in the US, UK, and AUS (English speaking countries). Those who signed up will answer questionnaires using their own mobile device. The questionnaires include two parts. First, an initial one time survey to collect general profile of the participant, including mood, demographic information, habitual emotion regulation strategies, and accessibilities to those strategies. This survey will be sent to participant's signed up email address and data will be collected online via Jspsych in web browser. Second, a daily EMA survey to collect momentary affect, emotion regulation employed, circumstances of employing those strategies (such as location and the social environment). This will be collected via the SEMA3 app downloaded into participants' phone. SEMA3, Smartphone Ecological Momentary Assessment, is a suite of software for intensive longitudinal survey research using iOS and Android smartphones. The daily EMA survey will be administered 5x daily at different times using preset windows that are then randomly mixed across the 5 days (i.e., morning, midday, late afternoon, evening, night but with exact hours differing each day). Methods have been extensively tested by researchers and are not overly burdensome. Questionnaires at the start of the study can be completed in as little as 15 minutes and daily EMA messages take on average 2 minutes.

Results

EMA data will be analysed using Linear Mixed Effects Models in R. First, we will specify fixed and random effects. Fixed effects (variables that have systematic effects on the outcome such as time of day, age of subject) and random effects (variables that introduce variability that is unique to each individual emotional experience throughout the day). Use R with lme4 we will fit the LMM to our EMA data. Once the model is fitted, we will evaluate how well the LMM fits the data using diagnostic plots and statistical criteria like AIC/ BIC. Temporal dependencies will be accounted for using autoregressive terms and time series analysis. Finally, we will perform sensitivity analyses to assess the robustness of results to different modelling assumptions /data transformations. Models will predict emotion change from regulation strategy as well as fixed effects such as time of day and demographic factors.

Implications

This study will highlight important factors that have thus far been minimally studied in psychology. In particular, this project will examine how individuals use different behavioural strategies to regulate emotions. Emotion regulation has been tied to numerous mental, physical, relational, and societal benefits. The association between music listening specifically will be relevant to identify alternative pathways (i.e., enhancing emotion regulation through music listening) to improve emotion regulation. There is no anticipated direct benefit to participant except the contribution to science and compensation for their time. The planned communication of results include: presentation (conferences, seminars), research project publications, and website (https://www.sarahmtashjian.com/)