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The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2022 Jul 1;77(12):2157–2169. doi: 10.1093/geronb/gbac084

Exploring the Association Between Affect and Leisure Activity Engagement in Black Adults

Angie L Sardina 1,✉, Christa T Mahlobo 2, Alyssa A Gamaldo 3, Jason C Allaire 4, Keith E Whitfield 5
Editor: Elizabeth Munoz
PMCID: PMC9923799  PMID: 35772778

Abstract

Objectives

This study examined engagement levels across various domains of leisure activities in community-dwelling Black adults (age range = 50–80 years) and variability in daily leisure activity engagement and positive affect (PA; positive emotions or mood) and negative affect (NA; negative emotions or mood). Additionally, we explored whether PA and NA were associated with leisure activity engagement and whether these associations varied by sociodemographics.

Methods

Fifty adults (78% women; mean education = 11.62 years, standard deviation = 2.4) reported affect and leisure activity engagement over 8 occasions (2–3 weeks).

Results

Participants averaged 3 leisure activities/day with more engagement in watching television (news), walking, reading, and visiting others. Multilevel models identified significant within-person variation across domains of leisure activity engagement. A significant main effect was observed between daily NA and reduced social activity engagement. A significant interaction between NA and education was further illustrated on those occasions when NA was higher than usual, social and total leisure activity engagement tended to be lower, particularly for adults with ≤10 years of education. A significant interaction between NA and education was observed for entertainment activities. However, results indicated adults with ≥14 years of education, and a mean NA above the sample average, tended to engage in more entertainment activities. Finally, a significant interaction between PA and age was observed indicating adults aged ≥73 had a greater social engagement, particularly when daily PA was heightened.

Discussion

Results demonstrate within-person changes in the types of leisure engagement among Black adults. Potential factors related to these changes may result from interconnections between affect and demographic factors (age and education).

Keywords: Daily variability, Positive affect, Race, Socioeconomic status

Adults aged 65–74 spent approximately 7.1 hr/day engaged in leisure activity, with engagement increasing with advancing age (U.S. Bureau of Labor Statistics, 2020). Leisure engagement, defined as activities individuals choose to engage in during their free time (e.g., gardening, painting, etc.; Kuykendall et al., 2015), often comprises passive, or sedentary, activity engagement (e.g., watching TV) averaging 5 of 7 hr/day (Chang et al., 2021; U.S. Bureau of Labor Statistics, 2020). Burgeoning empirical evidence suggests regular participation in leisure activities is associated with physical, cognitive (Sala et al., 2019), and mental health benefits (Bone et al., 2022); and has supported ongoing social networks in older adulthood (Toepoel, 2013). Consistent with the activity theory (Knapp, 1977), older adults who regularly participate in leisure activities will demonstrate better psychosocial well-being over time (Michèle et al., 2019). Thus, understanding the types of leisure activities and nature of engagement in these leisure activities is imperative to promoting healthy aging. This is particularly relevant in middle-aged and older Black adults, who are at greater risk of experiencing disparate leisure opportunities with age (Floyd, 2014; Philipp, 1995; Shinew et al., 2004).

Leisure Engagement Over Time

Individuals are relatively stable in their leisure engagement over time; however, they tend to transition from physically active (physical leisure and sports or exercise at moderate- to high-intensity levels) to more passive activities (sedentary leisure; TV watching and reading), with advancing age (Janke et al., 2006; Michèle et al., 2019). These patterns of engagement, when viewed from the lifecourse perspective, vary as individuals experience physical declines and/or changes in mental health (e.g., depression and/or positive/negative affective states; Janke et al., 2006), and navigate life-changing events (e.g., retirement and/or losses; Lee et al., 2018). While the lifecourse perspective of leisure engagement has enriched our understanding of intraindividual and interindividual changes in leisure patterns, these design measurements may mask daily intraindividual fluctuations between assessments (Rast et al., 2012). Additionally, they may not capture information related to types of leisure activity engagement. Leisure engagement varies daily in adolescence (Zhang & Zheng, 2017); yet, this phenomenon, as well as mechanisms increasing variability (e.g., socioeconomic indices, race, and/or affect) in adult populations, are understudied. Given the benefits of regular leisure engagement on health in adulthood (Bone et al., 2022; Sala et al., 2019; Toepoel, 2013), it is imperative to understand daily variability, and factors associated with variability.

Leisure, Sociodemographic and Health Characteristics

Sociodemographic factors (e.g., age, sex, race, and education) are in/directly correlated to leisure engagement levels (Michèle et al., 2019; Sardina et al., 2021). Age is of interest to leisure researchers, given older adults may focus social relationships and interactions on those in which they derive the greatest levels of satisfaction and feelings of emotional well-being, as consistent with the socioemotional selectivity theory (Carstensen et al., 1999). However, among studies that examined sociodemographic factors of leisure participation, race/ethnicity, and education are unique predictors of leisure interests, engagement, and constraints (Sardina et al., 2021). Indeed, Black adults and/or adults who report lower levels of education experience greater disparities in leisure experiences (Philipp, 1995; Sardina et al., 2021; Shinew et al., 2004). Thus, the current study expanded upon these prior observations by further exploring leisure activity engagement, specifically within Black adults. The current study’s within-racial group approach has been suggested to better understand heterogeneity of health behaviors and outcomes often observed within Black adults (Whitfield et al., 2008).

Existent research exploring patterns of leisure activity engagement has focused on leisure-time physical activities in adulthood (Boulton et al., 2018). Little is known, however, about other types of everyday leisure preferences (e.g., cognitive and psychosocial activities), as well as the extent of daily variability in engagement in preferred leisure activities amongst Black adults of varying sociodemographic and health characteristics. These gaps exist, despite a call by Floyd (2014) to enhance awareness of interindividual factors existing within racial/ethnic groups versus across groups, thus, enriching our understanding of intersecting factors influencing the leisure experience. Engagement in leisure activities is linked with individual responses to daily stressors and negative life events (Chen et al., 2020), which Black adults may be more likely to experience (e.g., discrimination, financial strain, and/or disadvantaged physical environments; Brown et al., 2018). Thus, this research has significant implications for understanding leisure activities of interest and variability in engagement amongst Black individuals.

Leisure, Sociodemographic and Health Characteristics, and Positive and Negative Affect

In addition to sociodemographic factors, intersectionality may exist between race, education, and other individual-level factors, such as positive affect (PA; positive emotions or mood) and negative affect (NA; negative emotions or mood). PA and NA are emotional reactions to daily events experienced by individuals (Diener et al., 1999). Affect is associated with leisure engagement (Barnett, 2006; Lawton, 1994) and may shape the leisure experience. Indeed, PA and NA can occur simultaneously (Watson & Tellegen, 1985) and are subject to momentary fluctuations throughout the course of a day (Dunton et al., 2014; Röcke & Brose, 2013). Affect improves due to participation in leisure activities (Chen et al., 2020), particularly physical activity engagement (Dunton et al., 2014). PA and NA are of particular interest, as they have been found to be a powerful predictor of health behaviors (e.g., alcohol use and smoking, participation in exercise, and consumption of fruits; Conner et al., 2015) regardless of age. Furthermore, PA and NA may be differentially experienced by Blacks, when compared with Whites, such that Blacks are more likely to experience higher levels of PA concurrent with high levels of NA (Lankarani & Assari, 2017). This may vary by indices of socioeconomic status (SES). Specifically, lower levels of education are associated with lesser PA and elevated NA, which negatively influence health behaviors and outcomes in Black adults (Brondolo et al., 2008). Thus, it is plausible that PA and/or NA may predict other health-related behaviors (e.g., daily leisure activity engagement). Indeed, prior studies observed that individuals with higher PA were more inclined to participate in physically and/or socially stimulating leisure activities (Mannell et al., 2014; Tsai, 2007). Conversely, individuals with higher NA participated in more passive leisure activities (e.g., watching TV; Tsai, 2007). Limited studies have explored affect as a predictor for variability in daily leisure engagement, specifically across a variety of leisure activities (e.g., social, cognitive, physical, and entertainment) within Black adults.

Therefore, the purposes of the current study are to (a) examine the types of daily leisure activities in which Black adults tend to engage; (b) investigate whether intraindividual variability exists in daily leisure activity engagement and affect (positive and negative); and (c) explore whether PA and NA are associated with types of leisure activities, and whether these relationships vary by sociodemographics (e.g., age and education levels), and comorbidities. We hypothesized that participants would engage predominantly in cognitive and entertainment-based activities. Additionally, we hypothesized that intraindividual variability would be observed between leisure activities and affect, and the relationship between these constructs would vary significantly by age, education, and presence of comorbidities. Finally, it was hypothesized that individuals who demonstrated higher NA would engage more in passive activities (e.g., entertainment) versus social and/or physical activities, whereas those with higher PA would engage more in social and physical activities.

Method

Participants and Study Procedure

The current study was a supplemental project of the Baltimore Study of Black Aging: Patterns of Cognitive Aging (BSBA: PCA; NIA#24108), originally designed to examine cognition, health, and other critical factors in Black adults (Whitfield & Baker-Thomas, 1999). More details regarding the current study are described in published literature (Gamaldo et al., 2010, 2012). The current study recruited 50 (39 women and 11 men) community-dwelling Black adults, aged 50–80 years, from similar senior housing facilities within Baltimore, Maryland that were identified by the main BSBA: PCA study to have a large and heterogeneous population of older Black adults in terms of sociodemographics (e.g., education and income; Aiken-Morgan et al., 2015).

Participants were assessed on eight occasions over a 2–3-week period within a private space (i.e., office or library) of a senior high-rise facility (Gamaldo et al., 2010). All participants consented and completed baseline assessments in-person with a trained research interviewer. The initial testing session lasted approximately 2–2.5 hr, and the daily sessions lasted 1.5–2 hr. Participants were compensated $120 for the completion of all eight waves in the study. Data collection for the study occurred from February 2008 to September 2008.

Measures

Affect

The Positive and Negative Affect Schedule (PANAS; Watson et al., 1988) comprises two 10-item subscales measuring PA and NA, and can measure these constructs within various time frames (e.g., within 1 day or over the past week). Each item on the PANAS includes one word describing a feeling (e.g., NA subscale includes: “upset,” “irritable,” and “nervous”; and the PA subscale includes: “excited,” “strong,” and “determined”). Participants selected the extent to which they experienced specific feelings in the last 12 hr in a response range of 1 (“very slightly or not at all”) to 5 (“extremely”). PA and NA sum scores were estimated from responses across the two subscales (range = 10–50 for each subscale). Higher scores reflect higher PA or NA, respectively.

Types of leisure activities engagement

A daily leisure activity checklist assessed everyday activity engagement (e.g., social, cognitive, physical, and entertainment), and was based on prior research (Baltes & Baltes, 1990; Hultsch et al., 1999) concerning activity engagement over 12 hr. Participants checked “yes” or “no” to engagement in 49 leisure activities. Twenty four of the 49 activities were included in four domains: (a) social (six activities), (b) cognitive (six activities), (c) physical (four activities), and (d) entertainment (eight activities; see Table 1 for comprehensive activity list). Given the small participant sample, factor analyses were not conducted. Rather, categorizations of activities were developed based upon classifications within prior research (Hultsch et al., 1999; Katz et al., 1963; Klumb & Baltes, 1999; Lawton & Brody, 1969). For example, playing games was classified as social, given the games include social interactions with other individuals. Additionally, pet care was classified as physical given prior research connecting pet care with increased levels of physical activity, irrespective of the type of animal (Peacock et al., 2020). While activities within domains may overlap, we classified activities to the primary domain on which prior research has focused. Domains comprised leisure activities that reflected ≥10% engagement in ≥1 of the eight waves. This was a cut-point established by the research team to promote greater degrees of freedom within the final analyses, and to maximize the ability to detect variability in day-to-day participation within each domain (Supplemental Table 1 provides complete list and frequencies of all assessed activities by wave). Total leisure activity engagement sum score was estimated for each wave of data, which is consistent with a published study that observed significant associations between education quality and total leisure interests (Sardina et al., 2021).

Table 1.

Leisure Activity Engagement at Each Wave (Occasion) of Data Collection

Activities Wave 0 Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Wave 6 Wave 7
Social activities (n [%])
Church 8 (16.0) 7 (14.0) 8 (16.3) 4 (8.2) 4 (8.3) 5 (10.4) 6 (12.8) 6 (13.0)
Eating out 12 (24.0) 8 (16.0) 14 (28.6) 8 (16.3) 9 (18.8) 7 (14.6) 11 (23.4) 10 (21.7)
Playing cards 4 (8.0) 5 (10.0) 7 (14.3) 5 (10.2) 5 (10.4) 3 (6.3) 5 (10.6) 5 (10.9)
Playing games 6 (12.0) 7 (14.0) 7 (14.3) 6 (12.2) 10 (20.8) 7 (14.6) 7 (14.9) 5 (10.9)
Visiting 20 (40.0) 17 (34.0) 22 (44.9) 19 (38.8) 14 (29.2) 17 (35.4) 16 (34) 18 (39.1)
Volunteering 4 (8.0) 6 (12.0) 9 (18.4) 3 (6.1) 6 (12.5) 6 (12.5) 8 (17.0) 6 (13.0)
Cognitive activities (n [%])
Calculator 7 (14.0) 6 (12.0) 8 (16.3) 4 (8.2) 4 (8.3) 4 (8.3) 5 (10.6) 5 (10.9)
Doing math 12 (24.0) 7 (14.0) 5 (10.2) 3 (6.1) 1 (2.1) 3 (6.3) 0 (0.0) 1 (2.2)
Puzzles 9 (18.0) 13 (26.0) 7 (14.3) 10 (20.4) 9 (18.8) 9 (18.8) 8 (17.0) 5 (10.9)
Reading 27 (54.0) 26 (52.0) 18 (36.7) 20 (40.8) 20 (41.7) 21 (43.75) 18 (38.3) 18 (39.1)
Self-study 14 (28.0) 14 (28.0) 15 (30.6) 10 (20.4) 10 (20.83) 10 (20.8) 8 (17.0) 10 (21.7)
Writing letters/e-mail 8 (16.0) 4 (8.0) 1 (2.0) 3 (6.1) 2 (4.2) 3 (6.3) 4 (8.5) 4 (8.7)
Physical activities (n [%])
Gardening 5 (10.0) 3 (6.0) 5 (10.2) 0 (0.0) 2 (4.2) 3 (6.3) 1 (2.1) 1 (2.2)
Pet care 4 (8.0) 5 (10.0) 4 (8.2) 2 (4.1) 3 (6.3) 4 (8.3) 4 (8.5) 5 (10.9)
Stretching 5 (10.0) 8 (16.0) 6 (12.2) 6 (12.2) 6 (12.5) 8 (16.7) 9 (19.2) 7 (15.2)
Walking 32 (64.0) 26 (52.0) 31 (63.3) 33 (67.4) 28 (58.3) 30 (62.5) 30 (63.8) 31 (67.4)
Entertainment activities (n [%])
Radio 25 (50.0) 21 (42.0) 21 (42.9) 22 (44.9) 20 (41.7) 24 (50.0) 19 (40.4) 20 (43.5)
Watching TV―comedy 22 (44.0) 24 (48.0) 24 (49.0) 21(43.9) 23 (48.9) 22 (45.8) 20 (42.6) 18 (39.1)
Watching TV―drama 26 (52.0) 22 (44.0) 26 (53.1) 23 (47.9) 22 (45.8) 29 (60.4) 26 (55.3) 24 (52.2)
Watching TV―game shows 21 (42.0) 16 (32.0) 24 (49.0) 18 (36.7) 19 (39.6) 19 (39.6) 24 (51.1) 23 (50.0)
Watching TV―news 45 (90.0) 46 (92.0) 45 (91.9) 41 (84.7) 42 (87.5) 42 (87.5) 42 (89.7) 44 (95.7)
Watching TV―soaps 19 (38.0) 17 (34.0) 18 (36.7) 20 (40.8) 20 (41.7) 20 (41.7) 18 (38.3) 20 (43.5)
Watching TV―sports 6 (12.0) 5 (10.0) 5 (10.2) 3 (6.1) 4 (8.3) 2 (4.2) 4 (8.5) 4 (8.7)
Watching TV―TV films 18 (36.0) 20 (40.0) 21 (42.9) 18 (36.7) 22 (45.8) 17 (35.4) 13 (27.7) 19 (41.3)
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Covariates: Sociodemographic characteristics and total comorbidities

The current analyses included linear and quadratic, demographic variables, and total comorbidities, as covariates. Total number of comorbidities was measured by subjective assessment of any diagnosed illnesses or diseases (e.g., high blood pressure, cardiovascular disease, and diabetes). Comorbidities were summed for analyses (range = 0–12).

Statistical Analyses

Descriptive statistics were used to estimate rates of participation in leisure activities at each wave. Calculation of the intra-class correlations (ICC) estimated within- and between-person variability for affect and leisure activity engagement. Multilevel models (MLM) were run to examine the association between (positive/negative) affect and each leisure activity domain (social, cognitive, physical, entertainment, and total activity engagement). For each domain, two models were conducted. In the first model, the “coupling parameters” (level 1 affect predictor), between-person parameters (or level 2 affect predictor), linear time, quadratic time, and covariates (i.e., age, education, and comorbidities) to assess potential main effects. All covariates without a meaningful zero were grand mean centered by the sample’s average score, so the intercept in the model could be interpreted (see Figure 1; e.g., γ 01−γ 04 equation parameters). The coupling parameter (Occasion Affectij−Mean Affecti; i ranges from 1 to 50; j ranges from 1 to 8) was group-mean centered by each individual’s average (positive/negative) affect score across all eight waves (see Figure 1―β1 equation parameter). The coupling parameter was estimated separately for occasion PA (PA Occasion) and occasion NA (NA Occasion). The PA Occasion coupling parameter can be interpreted as the extent an individual’s PA on a particular day/occasion was greater or less than their average PA across the eight-occasions. The NA Occasion coupling parameter can be interpreted as the extent an individual’s NA on a particular day/occasion was greater or less than their average NA across the eight occasions. The quadratic effect of occasion affect was included in the model by squaring the occasion affect variable.

Figure 1.

Figure 1.

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Equation of MLM models explored. MLM = multilevel models.

In addition to the between-person covariate parameters, two level 2 predictors of interest were included in the model. These predictors were grand mean centered by the sample’s average score (Figure 1; e.g., γ 01−γ 04 equation parameters). For example, separate parameters were estimated for mean PA (PA Mean) and mean NA (NA Mean). The PA Mean parameter can be interpreted as the extent an individual’s average PA across all eight occasions was greater or less than the average PA of the participant sample across the eight occasions. The NA Mean parameter can be interpreted as the extent an individual’s average NA across all eight occasions was greater or less than the average NA of the participant sample across the eight occasions. The first level 2 predictor reflected the relationship between leisure activity and an individual’s mean (positive/negative) affect across the eight occasions (γ 01). The quadratic effect of mean (positive/negative) affect was included in the model by squaring the mean affect variable. Level 2 demographic predictors (age, education, and comorbidities) were also included to test their association with leisure activity across the eight occasions (γ 02−γ 04). Several interactions (i.e., between-person interactions and cross-level interactions) were added to the initial model, in a second model, to assess potential moderating relationships (Figure 1).

Results

Descriptives

Participants were predominantly older in age (M = 65.4, standard deviation [SD] = 8.5), female (n = 39, 78%), and noted less than high school education (M = 11.6, SD = 2.4). On average, there was a span of 13.42 days between the baseline testing and the last daily assessment (SD = 3.8). Each testing occasion averaged 61.6 min (SD = 29.3).

Type of Daily Leisure Activity Engagement

Table 1 incorporates descriptive statistics on engagement across each activity within each domain. Within the social domain, visiting others was the most frequently engaged activity, as participation ranged from 14 to 22 occurrences across the eight waves. The next top two reported activities across the eight waves for the social domain included: (a) eating out (7–14 occurrences) and (b) church activities (4–8 occurrences). Within the cognitive domain, reading was the most frequently engaged activity, with participation ranging from 18 to 27 occurrences across the eight waves. The next most frequently engaged activities within this domain included: (a) self-study (8–15 occurrences) and (b) puzzles (5–13 occurrences). Within the physical domain, the most frequently engaged activity was walking, with participation ranging from 26 to 33 occurrences across the eight waves. The next two most frequently engaged activities within this domain include: (a) stretching (5–9 occurrences) and (b) pet care (2–5 occurrences). Within the entertainment domain, the most frequently engaged activity was watching the news, with participation ranging from 41 to 46 occurrences across the eight waves. The next most frequently engaged activities within this domain included: (a) watching TV dramas (22–29 occurrences) and (b) listening to the radio (19–25 occurrences). On average, across all waves, participants engaged in approximately three leisure activities/day. Total occurrences of leisure activity engagement tended to vary across the eight waves, with the average number of activities fluctuating from 2.9 (occasions 1–2), to 3 (occasion 3), to 2.7 (occasions 4–5), and 2.8 (occasions 6–8).

Intraindividual Variability Exists in Daily Leisure Activity Engagement and Affect

To assure there was sufficient within- and between-person variance to conduct our subsequent MLM analyses (Raudenbush & Bryk, 2002), ICCs were also calculated for the leisure activity and affect variables.

Leisure activity

Significant within-person variances were observed for total leisure activities (57%; σ² = 0.55, z = 12.99, p < .0001) and each of the leisure domains: social (70%; σ² = 0.17, z = 12.98, p < .0001), cognitive (64%; σ² = 0.15, z = 12.99, p < .0001), physical (50%; σ² = 0.11, z = 12.99, p < .0001), and entertainment (81%; σ² = 0.02, z = 12.96, p < .0001). There were significant between-person variances for total leisure activities (43%; σ² = 0.40, z = 4.21, p < .0001) and each of the leisure domains: social (30%; σ² = 0.07, z = 3.76, p < .0001), cognitive (36%; σ² = 0.09, z = 4.03, p < .0001), physical (50%; σ² = 0.11, z = 4.38, p < .0001), and entertainment (19%; σ² = 0.00, z = 3.11, p < .001).

Affect

Results indicated that 23% of the total variance in PA was within-person (σ² = 23.58, z = 12.96, p < .0001), while 77% was between-person (τ 00 = 78.89, z = 4.75, p < .0001). In contrast, 35% of the total variance in NA was within-person (σ² = 13.11, z = 12.89, p < .0001), while 65% was between-person (τ 00 = 24.73, z = 4.59, p < .0001).

Associations Between Affect and Leisure Activity Domains

A significant main effect was observed for within-person NA for social activities (Table 2). On those occasions when an individual’s NA increased above their average across the eight waves, the number of social activity engagements tended to be lower than on average. No other significant main effects were observed for the within-person affect (PA/NA) variables or the between-person affect (PA/NA) variables across other leisure activity parameters (Tables 2–3). No significant cross-level interactions were observed between within-person affect and between-person affect variables.

Table 2.

Negative Affect (NA) Unstandardized Coefficients (and Standard Errors) of Between-Person Effects and Daily Within-Person Effects for All Activity Domains

Total leisure activities Social activities Cognitive activities Physical activities Entertainment activities
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Within-person
 Occasion NA −0.016 (0.015) −0.022 (0.017) −0.018* (0.008) −0.015 (0.009) 0.008 (0.008) 0.002 (0.009) −0.001 (0.007) −0.006 (0.008) −0.001 (0.002) 0.001 (0.003)
 Quadratic occasion NA 0.001 (0.002) 0.002 (0.002) 0.002 (0.001) 0.002 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.000) −0.000 (0.000)
Between-person
 Mean NA 0.030 (0.038) 0.035 (0.040) −0.006 (0.017) −0.004 (0.019) 0.001 (0.017) 0.005 (0.018) 0.027 (0.017) 0.027 (0.018) 0.004 (0.004) 0.006 (0.004)
 Quadratic mean NA 0.000 (0.003) −0.000 (0.003) 0.001 (0.001) 0.001 (0.001) 0.000 (0.001) 0.000 (0.001) −0.001 (0.001) −0.001 (0.001) −0.000 (0.000) −0.000 (0.000)
 Age −0.009 (0.013) −0.008 (0.013) 0.003 (0.006) 0.002 (0.006) 0.000 (0.006) 0.000 (0.006) −0.013* (0.006) −0.012 (0.006) −0.001 (0.001) −0.001 (0.001)
 Education −0.016 (0.045) −0.003 (0.048) −0.001 (0.021) −0.001 (0.022) 0.008 (0.020) 0.017 (0.021) −0.022 (0.021) −0.022 (0.022) −0.006 (0.004) −0.003 (0.004)
 Comorbidities 0.008 (0.067) 0.006 (0.070) 0.001 (0.030) 0.001 (0.032) 0.033 (0.030) 0.032 (0.031) −0.032 (0.031) −0.031 (0.031) −0.000 (0.006) −0.001 (0.006)
Interactions
 Occasion NA × Mean NA 0.002 (0.002) −0.001 (0.001) 0.002 (0.001) 0.002 (0.001) −0.000 (0.000)
 Occasion NA × Education 0.011*(0.005) 0.005*(0.003) 0.003 (0.002) 0.002 (0.002) 0.001 (0.001)
 Mean NA × Education 0.015 (0.015) 0.000 (0.007) 0.010 (0.006) 0.002 (0.007) 0.003*(0.001)
 Occasion PA × Age 0.001 (0.001) 0.001 (0.001) 0.000 (0.001) 0.000 (0.000) −0.000 (0.000)
 Mean PA × Age 0.000 (0.001) −0.000 (0.001) 0.006 (0.001) 0.000 (0.001) −0.000 (0.000)
 Occasion PA × Comorbidities −0.009 (0.005) −0.005 (0.003) −0.001 (0.003) −0.002 (0.002) −0.001 (0.001)
 Mean PA × Comorbidities 0.007 (0.009) 0.000 (0.004) 0.002 (0.004) 0.006 (0.004) −0.000 (0.001)
Random effects
 Variance intercept 0.4418 0.4818 0.0843 0.0937 0.06452 0.0700 0.0942 0.09793 0.0027 0.0025
 Residual variance 0.5438 0.5260 0.1709 0.1662 0.1385 0.1370 0.1106 0.1102 0.0137 0.0137
Model fit statistics
 −2LL 963.0 1006.8 534.8 593.3 485.1 548.8 399.3 466.6 −388.0 −299.4
 AIC 967.0 1010.8 538.8 597.3 493.1 556.8 403.3 470.6 −384.0 −295.4
Pseudo R2 between 0% 3% 1% 3% 26% 19% 15% 12% 38% 42%
Pseudo R2 within 0% 3% 1% 4% 9% 10% 7% 6% 26% 27%
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Notes: Model 1 represents model with only main effects. Model 2 represents model with the inclusion of the two-way interaction. Occasion NA = negative affect on a particular day. Mean NA = average negative affect across all testing days. Covariates also included linear time and quadratic time. LL represents log-likelihood. AIC represents Akaike information criterion. Significant findings are bolded.

*p < .05.

Table 3.

Positive Affect (PA) Unstandardized Coefficients (and Standard Errors) of Between-Person Effects and Daily Within-Person Effects for All Activity Domains

Total leisure activities Social activities Cognitive activities Physical activities Entertainment activities
Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 Model 1 Model 2
Within-person
 Occasion PA 0.009 (0.008) 0.014 (0.009) 0.002 (0.005) 0.004 (0.005) 0.008 (0.004) 0.009 (0.005) 0.001 (0.004) 0.002 (0.004) −0.002 (0.001) −0.002 (0.001)
 Quadratic occasion PA −0.004 (0.001) −0.000 (0.001) 0.000 (0.001) 0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.000) −0.000 (0.000)
Between-person
 Mean PA 0.028 (0.015) 0.030 (0.015) −0.001 (0.007) −0.000 (0.007) 0.011 (0.007) 0.014* (0.007) 0.013 (0.007) 0.012 (0.007) 0.002 (0.001) 0.002 (0.001)
 Quadratic mean PA 0.001 (0.001) 0.000 (0.001) −0.000 (0.001) −0.000 (0.001) −0.000 (0.000) −0.000 (0.001) 0.001 (0.001) 0.001 (0.001) 0.000 (0.000) −0.000 (0.000)
 Age −0.013 (0.012) −0.014 (0.013) 0.001 (0.006) 0.001 (0.006) −0.001 (0.005) −0.001 (0.005) −0.013* (0.006) −0.013* (0.006) −0.001 (0.001) −0.001 (0.001)
 Education −0.025 (0.046) −0.001 (0.049) −0.003 (0.022) 0.002 (0.024) 0.003 (0.020) 0.015 (0.021) −0.022 (0.022) −0.018 (0.023) −0.008 (0.004) −0.006 (0.005)
 Comorbidities 0.056 (0.069) 0.057 (0.070) −0.002 (0.033) −0.001 (0.034) 0.053 (0.031) 0.056 (0.030) −0.009 (0.032) −0.011 (0.033) 0.004 (0.006) 0.005 (0.006)
Interactions
 Occasion PA × Mean PA 0.001 (0.001) 0.000 (0.001) 0.001 (0.001) 0.000 (0.001) 0.000 (0.000)
 Occasion PA × Education −0.004 (0.003) 0.001 (0.002) −0.003 (0.002) −0.002 (0.002) −0.001 (0.001)
 Mean PA × Education 0.009 (0.006) 0.002 (0.003) 0.005 (0.003) 0.001 (0.003) 0.001 (0.001)
 Occasion PA × Age 0.001 (0.001) 0.001* (0.001) 0.003 (0.005) 0.000 (0.000) −0.000 (0.000)
 Mean PA × Age −0.000 (0.001) −0.000 (0.001) −0.000 (0.001) 0.000 (0.001) −0.000 (0.000)
 Occasion PA × Comorbidities −0.008 (0.005) −0.006 (0.003) −0.000 (0.003) −0.001 (0.002) −0.001 (0.001)
 Mean PA × Comorbidities 0.003 (0.010) −0.001 (0.005) −0.002 (0.004) 0.006 (0.005) −0.001 (0.001)
Random effects
 Variance intercept 0.4315 0.4405 0.0886 0.0961 0.0600 0.06121 0.0948 0.0975 0.0025 0.0025
 Residual variance 0.5395 0.5314 0.1723 0.1695 0.1365 0.1371 0.1102 0.1109 0.0136 0.0135
Model fit statistics
 −2LL 970.5 1028.2 546.6 614.8 482.2 554.7 404.6 480.3 −393.4 −304.9
 AIC 974.5 1032.2 550.6 618.8 490.2 562.7 408.6 484.3 −389.4 −300.9
Pseudo R2 between 1% 2% 0% 2% 31% 30% 15% 12% 43% 43%
Pseudo R2 within 1% 2% 0% 2% 10% 10% 7% 6% 27% 28%
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Notes: Model 1 represents model with only main effects. Model 2 represents model with the inclusion of the two-way interaction. Occasion PA = positive affect on a particular day. Mean PA = average positive affect across all testing days. Covariates also included linear time and quadratic time. LL represents log-likelihood. AIC represents Akaike information criterion. Significant findings are bolded.

*p < .05.

Associations Between Affect and Leisure Activity Vary by Demographics and Comorbidities

Total leisure activities

One significant two-way interaction was observed between the within-person daily NA and years of education (Table 2). For those adults with ≤10 years of education, within-person daily NA was negatively associated with total leisure activity engagement (b = −0.05, p < .05; Figure 2A). On those occasions, an individual’s NA increased above their average level, and their total leisure activity engagement tended to decrease. No significant association between within-person daily NA and total leisure activity engagement was observed for higher levels of education.

Figure 2.

Figure 2.

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(A–D) Interactions across Level 1 and Level 2 affect and demographic predictors across activity domains. SD = standard deviation.

Social activities

Two significant two-way interactions were observed between (a) the within-person daily PA and age (Table 3), as well as (b) the within-person daily NA and years of education (Table 2). For those adults aged ≥73 years of age, within-person daily PA was positively associated with social activity engagement (b = 0.02, p < .05; Figure 2B). Specifically, on those occasions, an individual’s PA increased above their average level, their social activity engagement tended to increase. No significant association between within-person daily PA and social activity engagement was observed for the younger age groups. For those adults with ≤10 years of education, within-person daily NA was negatively associated with social activity engagement (b = −0.03, p < .05; Figure 2C). On those occasions, an individual’s NA increased above their average level, their total social activity engagement tended to decrease. No significant association between within-person daily NA and social activity engagement was observed for higher levels of education.

Entertainment activities

One significant two-way interaction was observed between the between-person (mean) NA and years of education (Table 2). For those adults with ≥14 years of education, between-person NA was positively associated with entertainment activity engagement (b = 0.01, p < .05; Figure 2D). Individuals who had higher levels of education, and a mean NA above the sample average, tended to engage in more entertainment activities. No significant association between between-person NA and entertainment activity engagement was observed for adults with lower levels of education.

Discussion

Individuals averaged participation in three leisure activities/day over 2–3 weeks, spanning four activity domains, to which entertainment was the primary activity participated. Within-person variation was observed across engagement in leisure domains, as well as PA and NA, and significant main effects were identified between NA and social activity engagement. Furthermore, relationships between affect and social and entertainment domains varied by age and education, but not comorbidities, which partially supports our hypotheses.

Leisure Engagement and Daily Fluctuations in Leisure Engagement Patterns

Within this study, the entertainment domain had the greatest range of observations, followed by cognitive, physical, and social (Table 1). The most frequently engaged activities across each domain were watching the news (entertainment), walking (physical), reading (cognitive), and visiting with others (social). Our findings on leisure preferences within these Black adults are consistent with existing studies (Carr & Weir, 2019; Philipp, 1995; Shinew et al., 2004). Previous studies concluded that activities like walking, visiting with others, eating out, puzzles, and reading were highly preferred (Hicks & Siedlecki, 2017; Lawton, 1994; Sardina et al., 2021). Of particular interest in our findings is the overtly sedentary (passive) nature of the most commonly engaged activities (e.g., watching TV), which is consistent with literature noting sedentary activities may incur a greater level of engagement with age (Carr & Weir, 2019).

Embracing the lifecourse perspective, previous studies examined changes in leisure patterns over time, to which declines in engagement occur with age (Janke et al., 2006). This decline in engagement may be linked with biopsychosocial and/or contextual changes over time (e.g., physical and/or cognitive changes in health, reduced energy levels, shrinking social networks, and/or reduced availability and access to leisure and recreation programs; Carr & Weir, 2019). Leveraging the theory of selection, optimization, and compensation (Baltes & Baltes, 1990, 1993), older adults may benefit more from selecting the highest quality of leisure activities, rather than focus on the quantity of leisure activities. Additionally, as aligned with the socioemotional selectivity theory, this population may also focus or prioritize social relationships and interactions that are most meaningful (Carstensen et al., 1999). However, further research is needed to support whether high-quality and/or socially/emotionally meaningful prioritization of selected types of leisure activity engagement is evident within older Black adults.

Examining leisure engagement from a lifecourse perspective is valuable, but may mask daily variability in leisure experiences (Rast et al., 2012). For example, we identified participants who averaged three different leisure activities/day. Furthermore, engagement in the total number of leisure activities demonstrated significant, daily, intraindividual variation. These findings are novel and serve as preliminary evidence that underlying factors (e.g., affect) influence daily variability in leisure activity engagement, and complement literature focused on the amount of time one engages in leisure within a given day (Stalling et al., 2020). The findings expand upon prior research examining daily variability in leisure engagement in adolescents (Zhang & Zheng, 2017), and is one of the first studies, to the best of our knowledge, that examined the daily variability in Black adults.

Racial/socioeconomic disparities in leisure are receiving increasingly greater attention given the strong associations of regular leisure engagement to health and well-being (Floyd, 2014; Floyd & Stodolska, 2019). Black adults, and those of lower SES, disproportionally report limited availability and access to preferred leisure opportunities and/or recreational spaces (Sardina et al., 2021), disparate neighborhood conditions deterring engagement (e.g., concerns for safety; Ray, 2017), and/or discrimination (Floyd & Stodolska, 2019; Philipp, 1995; Ray, 2017; Shinew et al., 2004). While prior leisure (e.g., preferences, engagement, and constraints) research typically used a cross-racial comparison approach, strictly using this approach and not subsequently applying a within-racial group exploration approach masks heterogeneity and unique experiences of Black adults (Whitfield et al., 2008). For example, we observed significant intraindividual (temporal changes in activity engagement within an individual; 57%) and interindividual variability (changes in activity engagement between individuals that all identify as Black adults; 43%) in total leisure engagement across the four activity domains. Within-person variance was also greater across the four activity domains than between-person variation, which is novel to the literature, yet expected, given what we know about potential factors influencing leisure engagement (e.g., health status, access, and availability of social networks; Floyd & Stodolska, 2019; Janke et al., 2006; Sardina et al., 2021). However, of particular interest is the significant between-person variability across all activity domains, suggesting heterogeneity exists within Black adults (Amuta-Jimenez et al., 2020; Whitfield et al., 2008). As such, the current findings support the need for further exploration of facilitators and barriers related to variability in leisure engagement, using a within-racial-group approach.

Daily Variability and Affect and Its Relation to Variability in Activity Domains

Additionally, we expounded upon potential coupling associations between factors, such as affect, which varies daily (Kotter-Grühn et al., 2015; Röcke & Brose, 2013). Watson et al. (1988) previously concluded the PANAS does not demonstrate a high level of temporal stability, as individuals are likely to experience intraindividual variability within a given day. This variability may be linked with emotional reactivity to stressful situations, to which the burst design offers greater precision and reliability in the measurement of PA and NA (Sliwinski, 2008). We observed significant within- and between-person variation in PA (23% and 77%, respectively) and NA (35% and 65%, respectively). These findings are within ranges of prior research examining within-person variability in PA (22%–28%) and NA (43%–61%) and that of between-person variability in PA (72%–78%; Kotter-Grühn et al., 2015; Röcke & Brose, 2013). However, participants demonstrated greater between-person variance (65%) than what has been previously observed, which ranged from 39% to 57% (Kotter-Grühn et al., 2015; Mroczek & Spiro III, 2003; Röcke & Brose, 2013). These findings contradict prior studies concluding older Blacks tend to report lower NA and higher PA, especially when compared to other racial groups (e.g., Whites; Assari et al., 2018; Krok-Schoen & Baker, 2014). One explanation for the greater variance observed in NA may be linked back to socioeconomic disparities in health. Specifically, on average, participants noted less than high school education, placing these individuals at greater risk for experiencing socioeconomic and environmental stressors (e.g., discrimination; Brown, 2004), disparate neighborhood conditions (LaVeist et al., 2011), and reduced access to health and recreational resources, further exacerbating risks for greater levels of NA (Krok-Schoen & Baker, 2014). It is possible the burst design may have elucidated the changing dynamics of NA between persons in a given day, particularly among those with lower education. Thus, our findings illuminate profound between-person differences observed within-racial groups. Future research should continue to explore factors explaining this variation.

Furthermore, we sought to understand whether affective state predicted leisure engagement across four activity domains. We observed that, on the occasions when a participant’s NA increases above their average reported NA, the number of social activity engagement tends to be lower than on average. Additionally, we identified a significant between-person interaction between NA and education, such that individuals with higher levels of education whose NA was higher than their average, were more likely to engage in entertainment-based activities. Regarding PA, we observed that individuals (aged 73 and older), who demonstrated PA greater than their average, increased their level of engagement in social activities. These findings are novel and complement existing works concluding affective state may predict subsequent leisure engagement (Mannell et al., 2014; Tsai, 2007). Such works concluded the individuals demonstrating higher PA were more inclined to seek activities with greater degrees of physical and/or social stimulation. Conversely, NA may coincide with avoidance of activity engagement (Shahar & Herr, 2011), or result in passive activity engagement (e.g., watching TV; Tsai, 2007). Our findings related to demographic differences in observed daily within-person associations, between leisure activity and affect, support the meaningfulness of exploring heterogeneity within Black adults.

We did not observe any significant findings related to PA or NA and their influence on engagement in physical or cognitive activities. These nonsignificant findings may signify that other factors related to contextual factors (e.g., accessible transportation and neighborhood composition), mental health (e.g., stress and/or depressive symptoms), and/or physical health (e.g., pain) should be considered, given their prior associations with leisure engagement (Carr & Weir, 2019; Ray, 2017; Sardina et al., 2021; Shinew et al., 2004). Further research is warranted in other Black adult samples to explore whether an array of factors, measured daily, are associated with daily engagement in leisure activities.

Limitations

Given the small sample, findings may not be generalizable to other Black adult samples and/or other racial/ethnic groups. For example, our sample reported a restricted amount of activities engaged on a daily basis. Within another adult sample, we may observe greater engagement in a wider variety of activities. Specifically, more recently, new types of leisure activities have become available to older adults, particularly activities that utilize technology (e.g., virtual gaming and reality or physical activity; Dermody et al., 2020). Additionally, potential discrepancies, in terms of engagement in a variety of activities, may be attributed to several factors, including the amount of disposable financial income, social support, and/or accessible resources to engage in diverse leisure experiences (Sardina et al., 2021). The current study did not assess the reasons why participants engaged in specific activities. Furthermore, there is potential for overlap between activities and other domains. For example, playing cards may also be categorized within the cognitive domain (Verghese et al., 2003). Future studies with larger samples should consider conducting factor analysis to determine alternative categorizations of activities within domains. A larger sample size may also detect potential relationships that were not detected in the current study possibly due to type 2 error. Finally, the scope of this paper did not test directionality. Given the current findings, the next study should incorporate lag modeling effects to better understand directionality. Thus, future studies should incorporate measurement items exploring reasons for selection/extent of engagement in daily leisure activities. The amount of time engaged in each activity would also be helpful in understanding the leisure experiences. While a strength of the current study is using a burst design, it is limited in determining whether daily observations are consistent over long periods. It would be beneficial to collect bursts of these daily observations across longer time intervals (e.g., a month), which would allow for more accurate conclusions regarding consistent everyday leisure behaviors over time.

Conclusion

This study’s findings offer unique insights into variation in daily leisure engagement and factors associated with daily variations in leisure engagement. Overall, this study reveals promising directions for future research to implement models that estimate both between- and within-person effects of daily leisure engagement, within minority populations and across racial groups. Our study provides preliminary support that daily PA may be beneficial for improving daily leisure activity engagement. Furthermore, our findings provide evidence for a greater need for public health and therapeutic interventions that promote PA and reduce NA. Doing so may positively affect levels of social engagement, and in turn, overall well-being in older adults.

Supplementary Material

gbac084_suppl_Supplementary_Table_1
Click here for additional data file. (113.7KB, pdf)

Acknowledgments

Data, analytic methods, and study materials will be made available to other researchers upon request. This study was not preregistered.

Contributor Information

Angie L Sardina, School of Health and Applied Human Sciences, University of North Carolina Wilmington, Wilmington, North Carolina, USA.

Christa T Mahlobo, Human Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.

Alyssa A Gamaldo, Human Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.

Jason C Allaire, Department of Psychology, North Carolina State University, Raleigh, North Carolina, USA.

Keith E Whitfield, University of Nevada, Las Vegas, Nevada, USA.

Funding

This research was supported by a NIA Supplement Award (AG024108-01A1) from the R01 Parent Grant (AG24108-01). The authors have indicated no financial conflicts of interest.

Conflict of Interest

None declared.

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