Investigating proximal predictors of intraindividual affect variability in older adults
McGlynn, Sean Andrew
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The aging process is often coupled with major life changes such as retirement, death of friends and family members, and declines in physical and psychological functioning. Intuitively, any one or a conjunction of these events might be expected to lead to decreases in positive affect (PA) and increases in negative affect (NA). However, older adults tend to be emotionally positive and stable even late in life. Thus, it is possible that emotion-based strategies for coping with the challenges presented in later life can be used effectively by older adults, even amidst potential vulnerabilities in other domains. The design of effective interventions and technologies aimed at facilitating this coping process, will depend on understanding that emotions can influence health in different ways. Affect level and intraindividual variability (IIV) are independently related to distal factors such as personality and health-related outcomes such as immune functioning and mortality, among others. By nature, emotions are subject to daily fluctuations that cannot be captured by investigation of mean affect levels alone. Research on affect IIV has focused primarily on whether there are stability differences in younger and older adults. In general, older adults tend to be more stable, perhaps because the failure to regulate emotions is particularly detrimental for older adults’ physiological health. It is therefore important to understand how proximal factors in everyday life lead to intraindividual emotional changes. The primary goal of this study was to identify the factors occurring within older adults’ daily lives that predicted emotional deviations and to determine whether individuals differed in the types of factors that were emotionally-relevant. As such, it was imperative to employ a methodology that could differentiate the factors that evoked consistent emotional responses across all individuals from the factors whose impact on affect were person-dependent. Specifically, participants were given online surveys three times per day for 20 consecutive weekdays that included assessments of their current positive and negative emotional states and questions (at least once per day) about their stress, pain, sleep quality, life space, physical activity, and social activity. Multilevel modeling (MLM) was used to determine if there was significant affect IIV for these older adults and how much IIV could be explained by these proximal predictors. This analysis approach was used because it is well-suited for nested data (in this case, observations nested within-persons) and does not assume independence of observations (which is a concern when individuals receive repeated assessments). Additionally, MLM analyzes the complete dataset rather than complete cases (individuals), which allowed for comparison of fixed effects regression models and random effects regression models. Random effects models, which are the hallmark of MLM, enabled the analysis of potential individual differences in the within-person relationships between the predictors and affect. As expected, there was significant affect IIV in these older adults for both PA and NA. The predictors of PA and NA were analyzed first in isolation (referred to as “isolated models”) and then when controlling for the other proximal variables (referred to as “full models”). The random effects isolated models were generally better fitting than the fixed effects isolated models, indicating that the models that did not constrain individual predictor-affect slopes to be the same across persons (random) were more accurate representations of the observed data than models that constrained individuals’ slopes to be the same (fixed). Full fixed slopes and full random slopes models were built in stepwise fashion based on the results of the isolated models. Again, the random effects full models better fit the observed data than the fixed effects models for both PA and NA, providing strong evidence in favor of the hypothesis that a larger percentage of affect IIV would be explained when allowing individual differences in the within-person predictor-affect relationships. The full random models accounted for 32% of the PA IIV, and 45% of the NA IIV. These were both better fitting than their respective null models, indicating that overall, the proximal predictors accounted for significant proportions of the within-person PA and NA variance. Certain factors accounted for larger percentages of the IIV than others and in general, there were differences between the PA and NA model in terms of which factors led to emotional fluctuations. Subjective health accounted for the largest percentage of PA IIV and stress accounted for the largest percentage of NA IIV. Additionally, subjective health, life space, stress, and pain were significant unique predictors of PA, NA, or both. However, there were specific unique effects across both PA and NA, namely, the slope variances for stress and pain. Follow-up analyses were unable to account for these slope variances using person-level predictors. In essence, an individual’s emotional reactivity to pain and stress did not depend on his or her overall mean level of those factors, or of the other daily predictors. This provided further evidence that PA and NA should be treated as separable variables (e.g., it is possible for a daily event to decrease older adult’s positivity without necessarily increasing their negativity) but also highlighted factors that have pervasive influences on emotion regardless of valence, which is harmonious with models of affect that propose a dynamic relationship between PA and NA. The results from this study have theoretical and practical implications. Theories on emotional stability often focus on if and why older adults are more stable than younger adults. Findings of the present study both support and expand upon these theories by identifying within an older adult population, which proximal factors were likely to cause emotional deviations after partialling out the effects of other daily variables, including factors that were previously unstudied in this domain. The analysis methodology implemented in the present research allowed for direct investigation of whether certain individuals were more prone to the influences of these factors than others. These results are discussed in the context of coping and resiliency theories that posit individual differences in emotional responses to stimuli based on these capabilities. From a practical perspective, these results highlight that the design of interventions and technologies intended to provide older adults with effective skills and resources to maintain or improve their emotional well-being should be tailored to individuals’ affective profiles.