Theoretical views on the role of activities in later life

Rewarding daily activities are seen as an essential component of living a ‘good life’ in old age. Their importance has been acknowledged in the activity theory of ageing (Havighurst, Reference Havighurst1961; Lemon et al., Reference Lemon, Bengtson and Peterson1972), the studies on the quality of life of older adults (Raphael et al., Reference Raphael, Brown, Renwick and Rootman1997), and reflected in the classic version of the concept of successful ageing which lists good physical functioning and ‘active engagement with life’ as key indicators of successful ageing (Rowe and Kahn, Reference Rowe and Kahn1987, 1997). Noteworthy, the types of activities seen as contributing to good ageing are not fixed and vary depending on the theoretical approach. An alternative perspective on successful ageing offered by the psychosocial approach (Baltes and Baltes, Reference Baltes, Baltes, Baltes and Baltes1990; Kahana and Kahana, Reference Kahana, Kahana and Bengtson1996) frames it in terms of a positive adaptation to increasing limitations and challenges related to ageing. In this case, good ageing would translate to the ability to maintain social roles, compensate for losses and safeguard wellbeing in the face of increasing functional limitations. In behavioural terms, the psychosocial approach emphasises selecting appropriate leisure and meaningful social activities (Adams, Reference Adams2004).

The concept of the Valuation of Life (Lawton et al., Reference Lawton, Moss, Hoffman, Grant, Have and Kleban1999) is another take on the foundations of a good life and mental resilience in late adulthood. It postulates that there is a strong link between activities and positive health and wellbeing outcomes, but does not emphasise particular activities (Moss et al., Reference Moss, Hoffman, Mossey and Rovine2007). Studies on the Valuation of Life conclude that there is no one-size-fits-all approach. Different aspects of life may be endorsed by different older adults to maintain a positive valuation of life while adapting to changes associated with ageing (Jopp et al., Reference Jopp, Rott and Oswald2008). Activity participation is seen as a way to fulfil personal goals and, regardless of the nature of a particular activity, add meaning to life and boost wellbeing (Lawton et al., Reference Lawton, Moss, Winter and Hoffman2002). Review studies on activities and wellbeing generally support this view and state that not all activities are equally appealing to all older adults (Adams et al., Reference Adams, Leibbrandt and Moon2011). There are various ways of living which can promote wellbeing in late adulthood, and they depend on individuals’ characteristics, their health and preferences.

Figure 1. Predictive margins per class.

Notes: Margins at 95 per cent confidence intervals. Product.: productive.

Differentiation and the significance of daily activities

Everyday time-use patterns of older adults are socially differentiated. Differentiation runs along the lines of the basic socio-demographic characteristics, primarily individuals’ age and gender (Gauthier and Smeeding, Reference Gauthier and Smeeding2003), marital status (Chen et al., Reference Chen, Waite and Lauderdale2015), educational attainment (Zhu et al., Reference Zhu, Qiu, Zeng and Li2017) and social status (Chen et al., Reference Chen, Covinsky, Cenzer, Adler and Williams2012). Living arrangements and area of residence may also affect daily behaviours. Living with others, as opposed to living alone, may facilitate social activities, as it generates opportunities for meaningful social interaction (Ayalon, Reference Ayalon2009). Area of residence affects daily lives, in particular for individuals with functional limitations. Older adults living in rural areas face greater obstacles to mobility and activity choices, which results in less travel and overall more monotonous lifestyles (Li, Reference Li2006).

Daily activities are also closely linked with an individual's health condition, which has been seen as both a determinant and an outcome. On the one hand, a high share of passive leisure may be indicative of living with a disability or having long-term health problems (Espinel et al., Reference Espinel, Chau, van der Ploeg and Merom2015). On the other, greater physical activity has been linked with better long-term physical health and wellbeing (Mullen et al., Reference Mullen, Olson, Phillips, Szabo, Wójcicki, Mailey, Gothe, Fanning, Kramer and McAuley2011), and predicted lower rates of cognitive decline among older adults (Yaffe et al., Reference Yaffe, Barnes, Nevitt, Lui and Covinsky2001). Overall, studies have analysed a broad range of different activities and found different associations with both positive or negative health outcomes. Involvement in productive activities, such as work, informal help, care work or volunteering, has been associated with better health and wellbeing (Luo et al., Reference Luo, Pan and Zhang2019). Socialising (Talmage et al., Reference Talmage, Coon, Dugger, Knopf, O'Connor and Schofield2020), child care (Drew and Silverstein, Reference Drew and Silverstein2007) or pursuing diverse types of leisure (Cho et al., Reference Cho, Post and Kim2018) have been associated with older adults’ psychosocial wellbeing, and have affected their future outlooks, including mortality rates (Agahi and Parker, Reference Agahi and Parker2008). Conversely, some activities have been associated with negative health outcomes. In particular, time spent watching television has been associated with declining cognitive functioning in the older population (Kesse-Guyot et al., Reference Kesse-Guyot, Charreire, Andreeva, Touvier, Hercberg, Galan and Oppert2012).

It seems that different activities may influence different aspects of physical or mental health. For example, productive activities seem to have a better effect on physical functioning, whereas personal hobbies may increase happiness (Menec, Reference Menec2003). Likewise, the same activities may affect different individuals in different ways. For example, productive activities, such as housework or shopping, were shown to have the greatest impact on reducing the risk of cognitive decline for urban women compared to other groups of respondents (Luo et al., Reference Luo, Pan and Zhang2019). Differences in the benefits derived from the same activities suggest that their effects on individuals might be moderated by certain individual-level characteristics, e.g. activity preferences.

The complex association between daily behaviours, health and subjective wellbeing

Having rewarding daily activities may promote healthy ageing and help maintain meaningful social relationships (Steptoe and Fancourt, Reference Steptoe and Fancourt2019), but at the same time it is conceivable that being healthy or having fulfilling social relations may make daily life better. While numerous observational studies report links between selected activities and wellbeing in later life (e.g. Warr et al., Reference Warr, Butcher and Robertson2004; Litwin and Shiovitz-Ezra, Reference Litwin and Shiovitz-Ezra2006; Nimrod, Reference Nimrod2008; Grønning et al., Reference Grønning, Espnes, Nguyen, Rodrigues, Gregorio, Sousa, Canhão and André2018), determining the direction of this association is not straightforward, even in research using longitudinal data (Adams et al., Reference Adams, Leibbrandt and Moon2011). Overall, most articles assume that an activity promotes wellbeing (Menec, Reference Menec2003), however, some provide strong evidence for the opposite to be the case, i.e. wellbeing being the causal factor influencing activity participation (Janke et al., Reference Janke, Nimrod and Kleiber2008).

Intervention studies targeting physical health, psychological functioning, or both, add detail to the picture of complex associations between activities, health and wellbeing. Studies describing interventions targeting both physical and mental health report improved wellbeing post-intervention (Clark et al., Reference Clark, Jackson, Carlson, Chou, Cherry, Jordan-Marsh, Knight, Mandel, Blanchard, Granger, Wilcox, Lai, White, Hay, Lam, Marterella and Azen2012). However, this improvement may be due to better physical functioning. Conversely, reports from studies targeting mental health alone do not find lifestyle intervention to be effective in improving or sustaining mental wellbeing (Mountain et al., Reference Mountain, Windle, Hind, Walters, Keertharuth, Chatters, Sprange, Craig, Cook, Lee, Chater, Woods, Newbould, Powell, Shortland and Roberts2017). As physical and mental health are closely linked, in particular in the population of older adults (Fillenbaum and World Health Organization, Reference Fillenbaum1984), analysing individuals’ health profile in more detail might help better understand the association between these variables and daily activities.

Lastly, there are also confounding factors that may affect the relationship between what older adults do and how they feel about their lives or themselves. Additional activity characteristics including, its social context and location, may change the meaning and effect of a given activity, yet those are rarely included in research on wellbeing (Adams et al., Reference Adams, Leibbrandt and Moon2011). For example, eating typically brings greater joy when it is done with others (Vailas and Nitzke, Reference Vailas and Nitzke1998), and particular eating locations may improve appetite (Stroebele-Benschop et al., Reference Stroebele-Benschop, Depa and de Castro2016). While older adults spend most of their time at home, their overall time spent out of the home has been associated with better physical, cognitive and emotional functioning (Petersen et al., Reference Petersen, Austin, Mattek and Kaye2015). The broader lifestyle context may also shape the effect of specific activities. For example, the proportion of time individuals spent alone may have an effect on how much enjoyment they derive from their social activities (Klumb, Reference Klumb2004). Likewise, even though being out of the home is seen as generally beneficial for wellbeing, older adults report much higher satisfaction with home-based activities compared to younger individuals (Fortuijn et al., Reference Fortuijn, van der Meer, Burholt, Ferring, Quattrini, Hallberg, Weber and Wenger2006).

This study assumes that it is not a single activity that could make one's life good in later adulthood, but multiple factors operating at the level of daily living, which are reflected in the different things individuals do, as well where and with whom they spend their time. These should be analysed together to get a better understanding of the everyday reality of older adults and how it affects them. This study assumes that the main drivers behind individuals’ daily behaviours are structural factors, on the one hand, and their health profile, on the other. The assumed influence of structural factors is in line with structural constructivism and the related theoretical approaches, and it is supported by the empirical evidence cited earlier. Health status has been shown to be one of the major predictors of wellbeing in later life, and the degree to which poor health affects daily functioning is seen as the main factor negatively impacting happiness (Angner et al., Reference Angner, Ghandhi, Williams Purvis, Amante and Allison2013).

Data

The data used in the study came from a sample of 200 non-institutionalised adults aged 65 and above collected at the beginning of 2020 in Poland. Individuals were randomly sampled with quotas for age (using two categories: 65–74, and 75 and over), gender and the size of their place of residence. They were contacted using the Polish telephone directory or approached in public spaces by trained interviewers. A screening questionnaire was used to select eligible respondents. The overall response rate was 27 per cent. The main reason for refusing to participate in the study was due to concerns about sharing one's telephone number for the purpose of data collection; the length of fieldwork was also discouraging. The final sample characteristics are given in Table S1 in the online supplementary material.

Respondents’ background information was collected using a standard survey questionnaire. Activity data were collected during five days of fieldwork using the Experience Sampling Method. The Experience Sampling Method records activities in real time, removing memory bias and allowing for a greater accuracy of measures (Csikszentmihalyi and Hunter, Reference Csikszentmihalyi and Hunter2003). Each respondent received a phone call twice a day, at random times, and was asked about an activity he or she was doing at that time. The calls were distributed across time and days to ensure that all parts of the day were covered for each respondent and that equal representation was achieved for the whole sample. The interviewers asked about their current activity including what it was, where it was happening and who else was present. The dataset is available at Zenodo.org (Jarosz, Reference Jarosz2020). All respondents provided written informed consent to participate in the study. The project received ethical clearance from the University of Warsaw Rector's Committee for the Ethics of Research Involving Human Participants.

Measures

All activities were recorded verbatim and then collapsed into broader categories for the purpose of the study. The information on daily activities was used to construct count variables representing the number of reported activities that fell within each of the categories: (a) necessary, (b) passive, (c) productive, (d) active, (e) social, and (f) leisure pursuits. Necessary activities largely correspond to the conceptualisation of ‘necessary time’ by Ås (Reference Ås and Straikov1982), and include sleeping, eating or drinking, and self-care. Passive activities represent sedentary restful activities requiring little cognitive effort, such as passive rest or relaxation, and watching television, with the latter being the most commonly reported primary activity in the dataset. Productive activities represent unpaid work (housework, care work), informal help, running errands (shopping, using services), as well as related travel. Active time denotes any form of physical activity, the most common of which was walking. Social time represents any form of meaningful social interaction, both in person and over the phone or online. Leisure pursuits include leisure activities involving cognitive effort such as hobbies, handicrafts, reading, crosswords and computer use.

The detailed location code was recoded into a count variable representing the number of times a respondent was away from their home. Respondents’ own homes were the most frequent location for the reported activities. Two binary variables were constructed to denote whether a respondent reported at least one episode when he or she was accompanied by (a) a family member or (b) by a friend/acquaintance. The binary variables were used in this case due to the fact that a high share of respondents reported only solitary activities throughout the fieldwork. As the Experience Sampling Method does not allow for the reconstruction of individuals’ entire time-use sequences, all of these variables indicate the likelihood of particular events occurring during the day rather than provide exact estimates of their frequency.

Age (continuous) and sex (binary) were used as demographic variables in the models explaining latent class membership. Information on respondents’ educational attainment was used in the form of a categorical variable using International Standard Classification of Education-based categories differentiating between individuals with primary and below, secondary and tertiary education. Respondents’ occupational class prior to retirement (or during the fieldwork, if the respondent was working at that time) was used as a proxy indicator of their social position with two categories: having held a managerial or professional position (International Standard Classification of Occupations, ISCO codes 1 or 2), or a different occupational position. A binary variable was generated to represent whether a respondent worked for pay at the time of the survey. Living arrangements were represented by an individual's marital status and household size. The primary purpose of the latter variable was to differentiate between individuals who lived alone and those who did not. The variable describing a respondent's area of residence differentiated between ‘rural’ and ‘urban’.

No objective measures of health were available in the dataset, but it included information on self-reported health. The original variable used a five-point Likert scale, varying from very good (5) to very poor (1). In this study, this variable is recorded as a binary variable in which the ‘poor’ and ‘very poor’ answers are collapsed into the ‘poor health’ category, and all other answers into ‘average and good health’. In addition to that, a set of binary variables were constructed using the items from a short version of the Nottingham Health Profile (NHP) questionnaire (Kaplan, Reference Kaplan, Smelser and Baltes2001) which was also collected in the study. The NHP items included statements pertaining to individuals’ energy levels: ‘Everything is an effort’; and ‘I soon run out of energy’. Emotional response was measured using the following items: ‘The days seem to drag’, ‘I have forgotten what it is like to enjoy myself’, ‘I feel that life is not worth living’ and ‘I feel I am losing control over my life’. Social isolation was measured using the items: ‘I feel lonely’, ‘I feel there is nobody that I am close to’ and ‘I'm finding it hard to make contact with people’. Finally, sleep disturbances were indicated by one item: ‘I sleep badly at night.’ Because the study used a short version of the questionnaire, and the NHP does not provide relative-importance weighting for its different dimensions, using these dimensions separately was more informative and easier to interpret in the context of the present research. The four dimensions (energy levels, emotional response, social isolation and sleep disturbances) were thus constructed as binary variables, with 1 corresponding to at least one of the items being answered with ‘yes’, and 0 meaning the respondent answered ‘no’ to all items within the given dimension. Frequencies for the four composite variables are given in Table 1.

Table 1. Nottingham Health Profile items: distribution of answers, excluding missing

Lastly, individuals’ satisfaction with life in general was used as a proxy for subjective wellbeing. It was measured using the following question: ‘How satisfied are you with your life in general?’, with answers ranging between not satisfied at all (1) to very satisfied (7).

Analytical approach

Data on daily activities (activity type, location and co-presence) were used in a latent class analysis (LCA) by fitting the generalised structural equation models in Stata 16 (StataCorp, College Station, TX). A Poisson link was used for count variables (activities, location), and a logit link for binary indicators (co-presence). There were no constraints on estimated parameters. LCA as a method fitted well with the theoretical assumptions of this study. Compared to a cluster analysis, it allows capturing the latent structure behind the data as opposed to merely categorising individuals based on similarities in the observable variables. An additional benefit of LCA is its probabilistic character which allows for selecting the model with optimal fit to the data.

Four models were estimated using different combinations of activities, and specifying two or three classes. In two models the category of necessary activities was excluded due to its highly non-specific character (everyone engages in those activities). The maximum number of classes was set to three due to the low sample size and the fact that individuals’ class assignment was used for further analyses. The models were not adjusted for gender, age or health status. These variables were later used to examine class belonging. The model with optimal fit to the data was selected based on the fit statistics, the Bayesian Information Criterion (BIC), the Akaike Information Criterion (AIC), the log likelihood ratio and the interpretative criteria.

Individuals’ class membership was then used as an outcome variable in a multinomial logistic regression model. The model used the following explanatory variables: age, gender, self-reported poor health, education, work status, occupational position, marital status, household size, area of residence, and detailed measures of individuals’ subjective health derived from the NHP – emotional response, energy levels, social isolation and sleep.

Individuals’ subjective wellbeing was analysed in relation to their class membership, socio-demographic characteristics and health profile. Nested multivariable linear regression models were used to examine this association. In the first model, wellbeing was regressed on class membership alone. The second model added the rest of the variables. In all of the above models, missing values were retained to preserve the sample size.Footnote ¹