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Risk and resilience processes in daily mood fluctuations: Unique and joint contributions in a large-scale EMA study
Vera E Heininga1, Nienke C Jonker2, Peter de Jonge3
1Department of Pedagogical and Educational Sciences, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands.
Background:
Daily affect fluctuates in response to multiple co-occurring daily-life processes, yet these factors are often studied in isolation, limiting insight into their collective and relative contributions.
Methods:
We analyzed ecological momentary assessment data from 1396 adults who completed three assessments per day for 30 days (up to 90 observations per person). Multilevel models examined the unique within-person associations of five daily-life processes assessed over the preceding six hours (negative events, sleep quality, feeling emotionally off balance, appraisal of social company, and physical activity) with negative affect (NA) and positive affect (PA).
Results:
Collectively, the five processes explained ∼15% of within-person variance in NA and ∼ 20% in PA. Feeling emotionally off balance showed the largest unique associations with both higher NA and lower PA, while sleep quality also showed unique associations with both affective dimensions. More favorable appraisals of social company and greater physical activity were associated with higher subsequent PA, whereas negative events were not uniquely associated with either NA or PA in the full multivariable models.
Conclusions:
In multivariable models of daily life, feeling emotionally off balance and sleep quality showed relatively large unique associations with within-person affective fluctuations across a six-hour interval. These findings underscore the value of multivariable EMA approaches for identifying which daily-life processes retain prospective associations with affect over time. However, conclusions should be interpreted with caution given the six-hour assessment interval, and the conditional nature of estimates from joint multivariable models.
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