Contour of the Day: Social Patterning of Time in Later Life and Variation in Reported Well-Being in Activities

Abstract

Objective: To contextualize experiences of activities during the day and investigate whether the contour of the day is correlated with well-being during activities. Methods: Drawing on American Time Use Surveys, we employ sequence and cluster analyses to create distinct typologies of daily life patterns, and bivariate analyses to describe whether well-being across activities varies by these typologies. Results: We identified four typologies characterized by different primary activity of the day: leisure (22.7%), TV (22.4%), housework (47.5%), and work (7.5%). Individuals in the work and leisure clusters tend to report more positive well-being and individuals in the housework and TV clusters tend to report more negative well-being in experiences of activities during the day. We also found that well-being experiences in the same activity differed across individuals in the different typologies. Conclusion: Understanding the daily life patterns of older adults may be important, given its correlation with well-being during activities.

Keywords

activity levels successful aging health-related quality of life time use

Introduction

Later life is a period during which individuals may engage in a wide range of activities, given the diversity of social roles and forms of engagement (Burr et al., 2007; Morrow-Howell et al., 2014). As evidence suggests, much of the structuring of time of working-age adults centers on the social institutions of work and family (Vagni & Cornwell, 2018). As such, older adults may develop more heterogeneous time use patterns than younger or middle-aged adults as they exit the labor market and complete the child-rearing stage of the life course. Activity and engagement theories have, therefore, been predominant in gerontology (Havinghurst 1961; Johnson & Mutchler, 2014; Lemon et al., 1972), emphasizing the importance of ongoing engagement. This approach has provided a broad overview of individuals’ activities, empirically demonstrating the diversity and complexity of social engagement among older adults (Chen et al., 2019) and their links with health and well-being.

Existing knowledge on activity engagement later in life has typically been developed by drawing on broad measures of social roles and activities (Burr et al., 2007; Morrow-Howell et al., 2014). These studies have utilized measures in datasets such as the Americans’ Changing Lives survey (Burr et al., 2007) or the Health and Retirement Study (Chen et al., 2019; Morrow-Howell et al., 2014). Due to the nature of the measures, however, the operationalization of the activities has been necessarily decontextualized. For instance, existing studies have drawn on indicators that captured how many hours individuals may engage in an activity per week or per month (Chen et al., 2019; Morrow-Howell et al., 2014), or in some cases, considering activity engagement through a dichotomous variable indicating whether a participant may engage in an activity at all (Burr et al., 2007). Arguably, these indicators are vague and give little sense of whether the context upon which these activities are performed matters. This is the case even as recent research has shown that the context upon which activities are performed indeed helps provide further explanations for understanding variations in well-being, made possible in recent years by the availability of time use datasets (Cornwell, 2011, Cornwell et al., 2019; Musick et al., 2016). For example, investigations of the enjoyment of activities have been made possible through reports of momentary well-being in activities, in which respondents indicate how happy, tired, sad, stressed, or in pain they felt while engaged in specific activities during the day in a time diary. Importantly, these findings show how the experience of the same activity may vary across individuals.

In the current study, we draw on time diary information from the American Time Use Survey (ATUS), utilizing sequence and cluster analyses to identify and describe patterns of daily activities over the course of a single day for a large sample of older Americans aged 65 years or older. This builds on and extends current knowledge on the diversity and multiplicity of social roles and forms of engagement in later life. Our article makes three contributions to the existing literature. First, to our knowledge, we do not have information about the daily activities of older Americans. The only study that we are aware of that depicts the life of older adults uses time use data from South Africa (Grapsa & Posel, 2016). Drawing on a rich, national survey, we, therefore, provide for first time an overall view of the different types of days older Americans might have. This fills an important knowledge gap and extends prior literature on the activity profiles of older adults. This also begins to build an evidence base, and provides a benchmark for cross-national or historical comparisons, against older adults in other countries or perhaps future cohorts of older Americans, to observe how daily life might differ or change over time. Second, we contribute by highlighting how the experiences of activities vary across the types of days. This is an advancement of the current knowledge on how active engagement is related to well-being, showing how the contour of the day correlates with experiences of activities throughout the day. Furthermore, drawing on measures that captures various dimensions of well-being, we provide a more nuanced account of the implications of being active for older adults, to show that being active could also relate to different components of well-being during activities (such as in meaningfulness, stress, etc.). Our third contribution is by shifting the framing underpinned by the unit of analysis, away from types of individuals toward a focus on types of days. Previous research, focused on the individual level, necessarily typecasts or reifies individuals into types of individuals. Our focus on days, however, focuses to the varying types of days individuals might have, and how this correlates with well-being. Not only is it a more realistic account of daily life but also may have more usefulness for practitioners for informing and educating older adults of the well-being associations of the different types of days, contributing to informing and enhancing older adults’ health.

Literature Review

Existing studies have shown that the enjoyment of activities varies across activities (Cornwell, 2011, Cornwell et al., 2019). On average, housework and paid work tend to be the least pleasant, while leisure activities tend to be the most pleasurable. Other studies have sought to explain other potential sources of variation, such as the importance of social roles. In their study, Musick et al. (2016) observed that parents report better subjective well-being during activities with their children than without them, with some variation between mothers and fathers. People also may engage in activities differently, with implications for well-being. In their study, Yamashita et al. (2019) found that compared to passive leisure, physically active leisure was linked to higher levels of subjective well-being among older adults, including higher levels of reported happiness and meaningfulness during the activity and lower levels of sadness.

Nevertheless, as described above, the majority of the current research that examines the activity engagement of older adults draws on broad measures of activity engagement (Burr et al., 2007; Morrow-Howell et al., 2014). Furthermore, individuals may also vary in their involvement in tasks and activities across days of the week, given that our daily life is structured by social institutions. For instance, a full-time worker may, nevertheless, have days in which they are engaged in active leisure, while a retiree may similarly have days where they are predominantly engaged in housework or active versus passive leisure activities. With innovative data from time use diary information, the first contribution of this article is to identify and describe the experiences of older adults at the daily level. The current study aims to identify and describe clusters of activity patterns among older adults, moving beyond aggregate time statistics and broad measures of social roles and activities. While individuals may follow different patterns of activities, there may also be variation across groups of individuals. Prior research has demonstrated that patterning of time is a reflection on household and social structures in later life (Chen et al., 2019). Existing research has shown variations in the experiences of activities and time engagement by class and gender (Lesnard, 2008; Sullivan, 1997; Sullivan & Katz-Gerro, 2007). We, therefore, seek to explore associations between different activity profiles and individual characteristics.

Further, we investigate variations in the experiences of activities across activity clusters, drawing on reported measures on momentary well-being across activities. We examine whether the structure and sequence of daily life may be associated with reports of well-being across the same activities. Given research around activity theory, we expect that daily profiles that depicts the respondents as being more active to also report more positive well-being. While current research drawing on activity theory proposes that individuals experience better well-being if they remain active (Menec, 2003), and while the concept of “successful aging” argues that older adults who remain actively engaged receive benefits through mental stimulation, a sense of routine and greater self-esteem and efficacy, our study further investigates whether another potential pathway may exist through nuanced contextual variation in the experiences of activities as related to the broader structure of the day. This extends prior research in the social sciences about the temporal regularity of daily life and social time as a tool for analysis (Lewis & Weigert, 1981; Zerubavel, 1981) and demonstrates whether and how the structure and temporality of daily life are linked to individual well-being (Zisberg et al., 2010). While the patterning of time has been of interest to scholars in recent years (Chen et al., 2019), the current study also aims to advance existing knowledge by highlighting the extent to which the degree of enjoyment of activities might vary, given the structure of the day. The study thus underscores how daily-level experiences might be associated with emotional valence at the activity level.

Research Design

To conduct our study, we draw on data from the ATUS. We use data from the ATUS Data Extract Builder (ATUS-X) from IPUMS Time Use, which provides harmonized data over the survey period (Hofferth et al., 2020). The ATUS is a nationally representative time diary survey in the United States. Households that have previously participated in the Current Population Survey (CPS) are selected to join the ATUS. The CPS is a monthly US household survey conducted jointly by the US Census Bureau and the Bureau of Labor Statistics (BLS) and whose main purpose is to collect information about the labor market and labor force participation. For each selected household, sociodemographic information of the household and its members is gathered, and one member of the household aged 15 years or older completes a 24-hour diary of all activities.

We select data for 2010, 2012, and 2013, which were the years when a well-being module was included in the survey. In addition to diary and sociodemographic information, the well-being module collects information on momentary well-being in three randomly selected activity episodes during the diary day. For each selected episode, individuals are asked to report their perceived pain, happiness, sadness, fatigue, stress, and meaningfulness during the activity on a scale from 0 to 6. As this study targets the older adult population, we select people aged 65 years and over, which yields a sample size of 7326 respondents.

We apply sequence analysis techniques to the selected dataset in order to establish clusters of individuals with similar reported activities. These methods are appropriate to categorical data that consist of sequentially linked categorical states and our overall goal is to create a finite set of clusters made of homogenous sequences (Fasang & Liao, 2014; Vagni, 2020). Although there are other techniques such as latent class analysis that can be applied for the same purposes, there is not a clear superiority of one method over another and sequence analysis has been widely used in time use studies (Barban & Billari, 2012; Lesnard, 2010; Minnen et al., 2016; Vagni & Cornwell, 2018; Vagni, 2020). We use TraMineR, which is an R package for mining and visualizing sequences of categorical data (Gabadinho et al., 2011). To create the clusters, we identify and select the reported activity at minutes 0, 10, 20, 30, 40, and 50 of each hour.¹ We only consider main activities reported by the respondents in their diary of activities. Although the dataset contains predefined activities, for simplicity and in order to estimate our models, we recoded the activities disseminated by ATUS-X into 11 groups of activities that are detailed in Appendix Table A1. For example, “grooming,” “health-related activities,” and “personal care” were predefined as distinct activities in the data, but we combined these together to create the category of “personal care.” Note that the BLS also applied data imputation methods to nonresponses and missing values in order to create a dataset with “complete” cases (BLS, 2020). The data we used, therefore, do not contain any missing values. In total, each individual contributes a sequence of 144 observations that are the inputs for the analysis. From these inputs, the package computes the optimal matching distances using substitution costs based on the transition rates observed in the data and an indel cost of 1.² The output is a distance matrix between the sequences of each individual that is used to create the cluster in the following step. Using the cluster library in R, we build a ward hierarchical cluster of the sequences from the optimal matching distances and retrieve the cluster membership from each individual’s sequence. Using the dendrogram of the cluster procedure (Appendix Figure A1), we select the most meaningful number of typologies of the trajectories. Three statistics suggested by Han et al. (2017: 321) were used to determine the quality of clusters in terms of their size (see Appendix Table A2). Each individual is then assigned to one of the clusters.

According to each individual’s cluster assignment, we first explore time use by groups by considering the average time spent in each activity for each group and visually displaying the daily life patterns over a 24-hour period. The average time is computed as the mean of the total time spent for each individual in the cluster in the selected activities. Patterns are displayed by tempo graphs that represent the proportion of individuals performing a select activity for each moment of the day.

Second, we report the sociodemographic characteristics of individuals in each cluster and test whether there are significant differences across the typologies. The analysis consists of the distribution of the individuals in each cluster according to their gender, living arrangement, race, age, level of education, employment status, and self-reported health.

Finally, we examine momentary well-being during the activities of individuals across each cluster to investigate potential differences, drawing on information collected from the well-being module. This enables us to consider how the structure of the day is related to the enjoyment of activities throughout the day. In this case, for every activity, we compute averages of the different measures of well-being reported by each individual during three random activity episodes.

Results

Clustering Daily Time Use Patterns of The Elderly Population

After applying the TraMineR package to create the distance matrix and the cluster algorithm, we choose to classify individuals into four different clusters according to the dendrogram (Appendix Figure A1). These four groups are clearly identified in the dendrogram, and the distances decrease considerably in the following step (see Appendix Table A2). In Table 1, we show the number of cases and percentages of each cluster. As shown, the largest cluster comprises slightly less than half of the respondents (47.5%), while the smallest cluster contains approximately 7.5% of the sample.

Table 1.

Distribution of Cases by Clusters.

	Clu_1	Clu_2	Clu3	Clu_4	Total
N	1660	1642	3477	547	7326
%	22.7	22.4	47.5	7.5

Source. Own calculations from Hofferth et al., 2020.

Figures 1 and 2 plot the daily life pattern of each cluster, and Table 2 shows the mean time spent in selected activities for individuals in each cluster. Figure 1 plots the percentage of respondents in each cluster performing a certain activity for every moment of the day, at each ten-minute interval. Figure 2 shows the sequence of activities for a random selection of 250 cases for every cluster. As illustrated in the figures, the daily patterns of each cluster are very different, not only in the activities performed but also in the rhythm of how they are performed, as shown through the timing of activities throughout the day. Taking into account the most characteristic activity in each cluster, we label the clusters as leisure, TV, housework, and work. The other activities are less relevant, probably because individuals generally spend less time in those activities. We note that care for others is, however, likely an underreported main activity, which is one of the limitations of the time use surveys (Durán and Rogero-García, 2009), and it can be reported as a secondary activity while our analyses is based only on the primary activity. We mention this as a limitation in the Discussion.

Figure 1.

Tempo graph showing the proportion of members in each cluster performing certain activities at each time point throughout the day (from 4 a.m. to 4 p.m.).

Figure 2.

Sequence index plots showing time-specific activities for 250 random cases in each cluster (from 4 a.m. to 4 p.m.).

Table 2.

Mean Time Spent in Selected Activities by Cluster. Minutes Per Day.

Notes. Reported values are computed using analytic weights. Lines denote significant differences at p_value = 0.05 using Scheffé test.

We conduct ANOVA tests to determine whether there are significant differences in the average time spent in each of the activities across the clusters, although results show that differences are significant for all activities. Thus, we apply Scheffe’s method (Salkind, 2010) as a post hoc test to determine which pairs of means are significantly different. As shown in Tables 2 and 4, we constructed lines to denote which cluster differences are significant at p value = 0.05.

We find that the main defining time use characteristic of individuals in the first cluster is time engaged in non-TV leisure activities (light red). Respondents in this cluster spend an average of six hours and 36 minutes in this type of activity. Such activities might include socializing, relaxing, engaging in hobbies, practicing or attending sports, participating in religious activities, volunteering, and studying. These activities are more common during the middle of the day, with short breaks for eating. According to Figure 1, more than half of the individuals in this group perform leisure activities between 8 a.m. and 8 p.m. They also spend a considerable amount of time in housework, with slightly more time spent in the morning and more time watching TV between 8 p.m. and 11 p.m.

Respondents in the second cluster are characterized by the large amount of time they spend watching TV (red), an average of approximately 8 hours and 40 minutes per day. Watching TV is especially common in the evening, as almost 75% of individuals in this group watch TV at approximately 8 p.m. In the other clusters, the proportion of individuals watching TV at 8 p.m. is slightly less than 50%. Other activities that are clearly identified in cluster two are non-TV leisure and housework, to which they dedicate a total average of almost three and a half hours.

Regarding cluster 3, it is the largest group in our sample (47.5%). We find that the defining characteristic of this group is time spent in housework (yellow). Individuals in this group spend an average of 4 hours and 35 minutes doing housework. Housework is more prevalent between 9 a.m. and 4 p.m., when almost 40% of the individuals in this cluster engage in this activity, with a peak of approximately 50% at around 10 a.m. In the evening, this group changes their activities to watching TV, which is the most common activity at 9 p.m. This group spends more time in personal care (58 minutes), and eating (87 minutes) than the other groups. They also spend more time in childcare than the other groups, although the total time in this activity is low (13 minutes).

Finally, regarding the fourth and last cluster, the main defining characteristic is the time in paid work (violet), that is, an average of 7 hours and 32 minutes over the observed day. As a point of comparison, the other groups spend practically 0 minutes per day in paid work.

As shown in Figure 1 around 9 a.m. approximately, 75% of individuals in this cluster are performing some paid work activity. This proportion decreases after 4 p.m., when a larger proportion of the individuals in this group are engaged in housework or watching television, which also becomes the most popular activity by 9 p.m. This group spends the least amount of time sleeping and engaging in leisure activities, but these respondents also travel more than those in the other clusters.

Through the sequence analysis and cluster algorithm, we created clusters to define typologies that describe different patterns of daily life. In the next section, we examine and report the sociodemographic characteristics of each cluster. In contrast to the activities, these characteristics are not used to create the cluster; rather, the bivariate analysis will allow us to explore and describe differences in activities across sociodemographic characteristics.

Sociodemographic Characteristics of The Clusters

Table 3 presents information across various sociodemographic characteristics of the individuals in each cluster. For all characteristics, we perform chi-square tests to examine whether the distributions observed are independent of the cluster, and in all cases, we find that they are not. This suggests that characteristics are not equal across the clusters, and as we describe below, some characteristics are overrepresented and some are underrepresented across the different clusters.

Table 3.

Sociodemographic Characteristics of the Individuals in Each Cluster.

	Leisure	TV	Housework	Work	All
N	1660	1642	3477	547	7326
%	22.7	22.4	47.5	7.5
Male	47.9	51.7	36.1	59.4	43.9
Female	52.1	48.3	63.9	40.6	56.1
Living alone	31.9	32.3	28.1	22.8	29.2
Only with partner	49.6	42.8	50.9	60.6	49.9
Partner and others	8.0	8.8	9.2	11.9	9.2
Others, without partner	10.5	16.0	11.8	4.6	11.7
White	86.8	82.3	89.0	88.6	87.1
Non-white	13.2	17.7	11.0	11.4	12.9
65–74	52.1	49.4	55.8	81.7	56.2
75–84	36.9	37.8	35.7	16.8	34.6
85+	11.0	12.8	8.4	1.5	9.2
Less than college	71.7	85.3	74.3	61.8	74.9
Some college or more	28.3	14.7	25.7	38.2	25.1
Not employed	88.9	93.2	88.1	3.7	81.4
Employed	11.1	6.8	11.9	96.3	18.6
Poor health	22.3	34.0	23.7	9.5	24.1
Good health	77.7	66.0	76.3	90.5	75.9

Note. Reported values are computed using analytic weights.

Gender is one of the most differential characteristics between the clusters. Women are more prevalent in the housework cluster, in which they represent approximately two out of three individuals, or 63.9% of the cases. As a point of comparison, women constitute only 40.6% of the work cluster. On the whole, the leisure and TV clusters look very similar to one another in terms of their sociodemographic characteristics. In terms of characteristics examined, we also find that individuals in the housework cluster mirror mostly closely to the characteristics of the full sample overall.

Focusing on the work cluster, we find individuals in this cluster have characteristics that are the most different from those of the sample, on average. Respondents in this cluster are more likely to be men (15.5 percentage points higher than the sample average), live only with a partner (10.7 points percentage higher), have an educational attainment of “some college or more” (13.1 points percentage higher), and report good health (14.6 points percentage higher). They are also younger, with 81.7% of the members in the group being aged 65–74 years, as compared with 56.2% of the full sample belonging to this age group.

In contrast, individuals in the TV cluster are more likely to have “less than college” (10.4 percentage points higher than the sample average), be non-white (4.8 percentage points higher than the sample average), and have poor health (9.9 percentage points higher than the sample average).

Wellbeing During Activities Across Clusters

In this section, we describe the average reported well-being of individuals in each of the clusters during three randomly selected activities.

As shown in Table 4, we observe salient differences across the clusters. For example, individuals in the leisure cluster tend to report higher levels of happiness and lower levels of pain, sadness and stress than individuals in the TV and housework clusters during their activities. As a point of comparison, individuals in the TV cluster tend to report higher reported levels of pain and sadness and lower levels of meaningfulness and happiness than individuals in the leisure and work clusters during their activities. Individuals in the housework cluster report higher levels of fatigue than those in the leisure and TV clusters during activities, but not as compared with those in the work cluster. The housework cluster is also in a middling position in terms of sadness during activities, with lower levels than that reported by individuals in the TV cluster but higher levels than that reported by individuals in the leisure and work clusters. Individuals in the housework cluster also report higher levels of stress during activities than individuals in the leisure cluster, but lower levels of stress than individuals in the work cluster. At the same time, they report higher levels of pain during activities than individuals in the leisure and work clusters.

Table 4.

Average Well-Being Measures by Cluster.

Notes. Reported values are computed using analytic weights. Lines denote significant differences at p_value = 0.05 using Scheffé test.

As for experiences of activities for individuals in the work cluster, this is likely shaped by the fact that they are in paid work during the observed day. As such, individuals in the work cluster report higher levels of fatigue and stress in their activities than individuals in the leisure and TV clusters. They, however, also report lower levels of sadness in their activities as compared with those in the TV and housework clusters, and higher levels of meaningfulness in their activities as compared with individuals in the other three clusters.

In Table 5, we present measures of well-being for the same activity, in instances where we have at least 30 cases observing the same activity across each of the clusters. This provides a more nuanced understanding of whether reported well-being in the same activity also varies across the observed clusters.

Table 5.

Average Well-Being Measures by Cluster and Activity.

Notes. Reported values are computed using analytic weights. Letters denote significant differences using Scheffé test.

a: significant difference between clusters leisure and TV at the 0.05 level.

b: significant difference between clusters leisure and housework at the 0.05 level.

c: significant difference between clusters leisure and work at the 0.05 level.

d: significant difference between clusters TV and housework at the 0.05 level.

e: significant difference between clusters TV and work at the 0.05 level.

f: significant difference between clusters housework and work at the 0.05 level.

We find that TV, travel, housework, and eating are the activities that present with the most significant differences across the clusters. This is also more often observed for measures that capture negative emotional valence during the activities (i.e., pain, sadness, fatigue, and stress). For instance, when engaging in the activity “eating,” respondents in the TV cluster report higher levels of sadness than individuals in the other three clusters engaging in the same activity. They also report lower levels of meaningfulness in their leisure and travel activities, as compared with individuals in the leisure and housework clusters while engaged in the same activities. Perhaps, due to the nature of the day and the activities they engaged in during other parts of the day, individuals in the TV cluster at the same time report lower levels of fatigue when engaged in leisure and travel.

As a point of comparison, individuals in the leisure cluster report lower scores on many of the negative indicators. They report lower levels of fatigue when engaged in housework, as compared with individuals in the housework and work clusters during the same activity. They also report higher levels of happiness when they travel, as compared with individuals in the TV and housework clusters during the same activity. We note that this may be related to the fact that they likely travel to participate in more appreciated activities (i.e., leisure). In contrast, for individuals in the work cluster, we observe significant differences during travel in terms of fatigue and stress. This is likely explained by the fact that they are commuting for work and as shown in Table 2, commuting for a longer period of time. Overall, our findings from Table 5 show individuals do not experience the same activity in the same way. Rather, the structure of the day correlates and contextualizes experience, enjoyment, and reported well-being during activities.

Discussion

With time use diaries, researchers have collected detailed information on the daily lives of individuals, including data on all activities engaged over a 24-hour period. However, existing studies have typically focused their efforts on detailing the lives of working-age adults (Vagni & Cornwell, 2018). In this study, we extend this research to explore and describe meaningful clusters of daily activities among older adults aged 65 years and older. We aimed to extend the research on the activity engagement of older adults (Chen et al., 2019). Although a stream of research has produced knowledge on the activity profiles of older adults (Burr et al., 2007), this research has been somewhat limited, as it has drawn on crude measures of activity engagement. Utilizing sequence and cluster analyses of daily activities drawing on time use diaries, we identified four distinct clusters of activities among a sample of older adults from a national survey that characterize four different types of days.

To distinguish the identified clusters, we also visually show both the sequence and duration of different activities throughout the day, suggesting important differences in the time use of the older adult population. We go beyond summary statistics of the time engaged in select activities to highlight the rhythm and temporality of daily activities across the clusters. In addition, we show that these groups are heterogeneous, in terms of not only their time use but also their sociodemographic characteristics. Finally, regarding the extent to which individuals across clusters vary in their reported well-being during their activities, we find that individuals in certain clusters report more positive well-being based on their reported experiences of their activities during the day. Furthermore, we show that even in experiences of the same activity, some variation in experiences emerges and could be linked to the broader contour of the day.

Although our study has advanced the research in this area by identifying and describing salient activity clusters in later life, it is not without limitations. First, our analysis focused only on individuals’ main activities. In other words, individuals might engage in multiple activities simultaneously. For example, caring activities may be reported in the ATUS as a secondary activity, and some older adults may care for family members as a secondary activity. Nevertheless, due to our analytic approach, we were restricted to only examining the primary activity. Further, due to our interests in examining correlates of well-being as linked to the activity clusters, we limited our analysis to 3 years of available data, restricting the size of our analytic sample. Future research using a larger sample and more years of available data may be able to explicate nuances by also considering respondents’ secondary activities. A second limitation of our study is our use of cross-sectional data, which prevented us from disentangling how health may similarly be an antecedent of activity engagement. Future research using longitudinal data may be better positioned to adjust for selection into activity engagement while considering its well-being correlates.

Despite these limitations, our findings advance existing knowledge by providing detailed illustrations of the rhythm and patterns of older adults’ daily lives. The study moves beyond single, broad measures of activities to delineate activity engagement over a 24-hour period. By reporting sociodemographic characteristics that correlate with different activity clusters, the study also alerts practitioners and family members to potentially vulnerable groups of older adults and the importance of detecting older adults’ potentially more negative days based on the activities they are performing, which may relate to their experiences of other activities.

Conclusion

Activity and engagement theories have been advanced in the gerontology literature to underscore the importance of remaining active and engaged in later life (Havighurst, 1961; Johnson & Mutchler, 2014). In line with this, a number of empirical studies have begun to articulate the diversity of activity engagement (Burr et al., 2007; Morrow-Howell et al., 2014). These studies, drawing upon available data, have shown the different activity profiles of older adults, and importantly, established links between activity profiles and well-being outcomes (Chen et al., 2019). Nevertheless, due to the nature of the data drawn upon, existing quantitative studies around the activity engagement of older adults have been necessarily decontextualized. Drawing on a rich time use survey, with information on activities over a 24-hour period, we further describe and elaborate on the daily lives of older Americans. Here, we move toward contextualizing the experiences of activities, within the context of a single day, and also empirically test whether this correlates with their well-being during activities. Specifically, we showed that in the aggregate, experienced well-being varies across different activity clusters, replicating prior research linking activity profiles and well-being outcomes (Chen et al., 2019; Freedman et al., 2019). In addition, we showed that the experience of well-being in the same activity also differs across individuals in different activity clusters. This is an advancement of current knowledge, as it suggests that the context of the day also relate to experiences of activities. Further, our findings contribute and extend research on activity theory, moving beyond binary distinction between uniformly positive or negative well-being associations, highlighting nuances in the engagement of activities for older adults, such as in energy depletion.

While our analyses described bivariate associations, they also point to important future directions for research. They highlight that while activities matter, the context in which they are performed may also be salient and worthy of future investigations. Understanding the context in which activities are performed advances activity and engagement theories. In this study, we empirically show that the context of the day is related to the experiences of activities, as well as pointing to the fact that activities are multifaceted. This highlights that future research may wish to investigate other characteristics of activities that may similarly shape the experiences of activities, such as with whom activities are engaged (Lam and Garcia-Roman 2020), where the activities are performed, and how the activity may correspond to an individual’s social role. Future research that aggregates across more waves of data might also be able to tease out whether there might be important differences by types of passive and leisure activities, as well as volunteering or religious activities.

Understanding whether and how various components of activities might relate to older adults’ well-being could open up opportunities to consider how to move different levers to improve the experience of activities. This would advance current knowledge, which heavily focuses on the intensity of activity engagement and whether an activity is engaged in or not, to include other factors that would also be of salience. Our research findings also have implications for research, as well as programs and practice. While prior research may reify individuals as belonging to certain activity clusters, our study moves the focus from individuals to types of days. Potentially, this could alert researchers and practitioners to variations in the types of days older adults may have, as well as how this could shape their well-being. For example, while an employed older adult may spend much of their time engaged in activities related to work, there may also be days when they are predominantly engaged in active leisure or television watching. Shifting the focus of analysis from individuals to days could alert researchers and practitioners to how structures of the day are related to experiences of activities, with implications for well-being. Given advances in available data and statistical modeling, new conceptualizations of activities and their implications could move beyond the ways it has traditionally been operationalized and toward providing richer contexts.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The lead author would like to acknowledge support by the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027). The views expressed herein are those of the author and are not necessarily those of the Australian Research Council.

ORCID iD

Jack Lam

Notes

Appendix

Table A2.

Measures of Statistical Cluster Quality.We use the three statistics suggested by Han et al., 2017:321 to determine the optimal number of clusters. According to this article, and are preferred to be as high as possible, while low value of HC indicates good clustering. The estimates for the clusters 2 to 8 are as follows.

N clusters	Indicators
N clusters	PBC	ASW	HC
Cluster2	0.258	0.122	0.341
Cluster3	0.336	0.131	0.270
Cluster4	0.417	0.152	0.209
Cluster5	0.376	0.116	0.215
Cluster6	0.356	0.104	0.214
Cluster7	0.339	0.094	0.212
Cluster8	0.321	0.086	0.213

Note. HC = Hubert’s C index; ASW = Average Silhouette Width; PBC = Point Bi-serial Correlation.

Appendix

Figure A1.

Dendogram derived from the cluster procedure.

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