Complex affect dynamics offer limited incremental value for cross-sectional prediction of psychological and behavioural variables in cross-validated models.

Abstract

Background: There is growing interest in the use of end-of-day dairies (EODD) as a means for quantifying how individuals' affective experiences fluctuate over time, and how such dynamics relate to mental health variables. A plethora of methods exist for precisely quantifying these affect dynamics, but recent work pooling data from multiple studies has suggested that most of the variance in outcome measures of depression, borderline symptoms, and life satisfaction is captured by simple measures, such as the mean (M) and standard deviation (SD) of affect ratings over time. Ever-more sophisticated approaches for measuring affect dynamics may offer little value for understanding mental health. Here, we examined a broad array of mental health variables and affect dynamic measures within a single cohort to comprehensively evaluate whether EODD-derived measures of affect dynamics are associated with specific psychopathological experiences.

Methods: A total of 314 adults (97 males; aged 18-45 years) first completed a 2-hour online questionnaire comprising a comprehensive battery of psychometrically validated instruments assessing personality traits, mental health symptoms, life satisfaction, and various psychological factors, and subsequently completed 28 days of EODD assessments (M_{interval days} = 21.4 days, SD_{interval days} = 31.0 days), including once-daily ratings on the Positive and Negative Affect Schedule (PANAS-10) and daily measures of stress, sleep, and alcohol use. We calculated 16 established affect dynamics measures (M, SD, relative SD, mean-squared successive differences, autoregression, intraclass correlation, and Gini coefficient for positive affect (PA) and negative affect (NA), as well as emotion network density and PA-NA correlation) in addition to six additional measures derived using dynamic network analyses of participant responses (promiscuity and flexibility for the entire network, PA, and NA). Predictive power was assessed using cross-validated linear regression models predicting 117 variables spanning five cross-sectional psychometric questionnaires and EODD-based longitudinal behavioral measures. We compared models that included each complex measure against baseline models using only M or M + SD scores quantifying PA and NA.

Results: Across all 117 variables, no complex affect dynamics measures improved cross-validated R² by more than 5.3 % beyond the M and SD of PA and NA.

Conclusions: Elaborate measures of affect dynamics, as indexed by the PANAS-10, offer minimal incremental explanatory power in predicting psychopathology beyond basic summary statistics of daily affect. These findings question the added value of increasingly complex measures of affect dynamics for predicting standard psychological and behavioral variables.