Activity and Well-Being of Older Adults

Abstract

This analysis assesses the activity level of 324 older adults and the relationship of activity to quality of life with a specific emphasis on the role of cognitive ability. Although the number of older adults with cognitive impairment continues to grow, few studies have examined the variation in activity and quality of life based on the older adults’ cognitive status. Results indicated that cognitively impaired older adults were less active than their nonimpaired peers; however, correlations revealed that regardless of impairment status, more activity was related to a higher quality of life. There was no support for the hypothesis that impaired older adults who have more cognitive ability will have a higher rated quality of life. These results should be considered in the development of programs for older adults. Regardless of impairment level, activity is paramount to maintaining quality of life.

Keywords

activity theory cognitive ability quality of life

By 2030, one in five Americans born during the “Baby Boom” will be 65 years of age or older (Vincent & Velkoff, 2010). With this population surge, many new concerns and situations will arise that affect the well-being of older adults. Increasing health care costs, the affordability of government entitlement programs such as Medicare and Social Security and safe affordable housing, are just a few of the many issues facing older adults that have been very much debated in recent years. Along with these very real social concerns, interpersonal issues such as life satisfaction and well-being are also critical issues facing our aging population. These social and interpersonal concerns are important to older adults of all cognitive and physical abilities. Yet, very little is known about how these factors affect older adults with cognitive impairment. This study fills the literature gap by taking a further look into the activity of older adults and its impact on well-being.

Background

The role of social engagement in maintaining optimal life satisfaction has been, and continues to be, a very important topic of inquiry within the field of gerontology (Adams, Leibbrandt, & Moon, 2011). For more than five decades, gerontological researchers have attempted to determine how leisure and social activities affect the quality of life for older adults as they transition along their life course. Understanding the relationship between social engagement, well-being, and quality of life is of increasing importance as people live longer and healthier lives due to medical advances, and as the first of the Baby Boomers (Longino, 2005) transition into older adulthood.

Many studies have examined the relationship between life satisfaction and older adults’ social activity level. Specifically, activity theory’s basic premise is that older adults who continue to stay more active will have better well-being and life satisfaction compared to older adults who are not as active (Havighurst, 1961). Activity theory adherents argue that it is important to maintain activity levels through older adulthood and replace activities when necessary as people age (Iso-Ahola, 1994).

Lemon, Bengtson, and Peterson (1972) conducted one of the first studies that attempted to provide evidence for activity theory. The researchers hypothesized that for older adults who were considering moving into a retirement community, life satisfaction would be associated with levels of formal (e.g., volunteering), informal (e.g., being with friends), and solitary activities (e.g., hobbies). They also hypothesized that older adults with higher levels of both formal and informal activities would have higher ratings of life satisfaction compared to older adults who took part in more solitary activities. Their results found no significant support for any of their hypotheses, nor for activity theory. Limitations of the study included, most notably, that the sample was mostly homogeneous in regard to social class, race, and religion. Also, the fact that participants were drawn from a single retirement community in Southern California was another critique. An additional limitation of the study is that the measures for activity were single-item ordinal level questions that measured the frequency of different activities, such as interaction with friends and neighbors or participation in organizations. Overall, the lack of significant findings may have reflected methodological short comings.

A second noteworthy study was conducted by Longino and Kart (1982) who attempted to repeat the Lemon et al. (1972) study and to test the merits of activity theory. Unlike the original study, which included 411 individuals from one retirement community, Longino and Kart’s replication included over 1,200 older adults from rural, suburban, and urban settings in the Midwest United States. Their sample was also much more ethnically diverse than the original study. In addition, rather than using single-item questions to measure frequency levels of activity, they created three different scales to examine informal, formal, and solitary activity level. Findings indicated that informal activity was more positively associated with life satisfaction, and formal activity was more highly related to life satisfaction than solitary activity. With better measures and a more diverse sample, Longino and Kart did provide support for activity theory.

More recent studies have tested the concept of activity theory in groups of older adults facing physical and mental health disorders. Depression in older adults is of extreme importance because of its negative association with well-being. Older adults who suffer from depression have more functional impairments, higher medical costs, and increased risks of suicide (Hong, Hasche, & Bowland, 2009). Hong, Hasche, and Bowland (2009) examined the association between social activities and late life depression over a 6-year period for adults age 70 and older. Using a nationally representative sample of 5,294 older adults and controlling for variables such as sociodemographics, health, and health insurance coverage, results indicated that individuals who were more socially engaged in activities were less depressed initially. Moreover, depression also decreased for these more socially engaged individuals over the three waves of the study.

The Whitehall II study (Marmot & Brunner, 2005) is another longitudinal study that examined the relationship between activity and well-being. Beginning in 1985, data were collected from office staff of various civil service departments in London. Singh-Manoux, Richards, and Marmot (2003) examined data collected during the fifth wave (1997–1999) to test whether participating in leisure activities affected cognitive functioning later in life. Information on frequency of 13 different activities was collected and then categorized by whether the activity entailed low or high cognitive effort. The researchers found that increased social activity was strongly related to better cognitive ability. These findings provide further evidence of the positive relationship between activity and well-being for older adults, also introduce the question of whether one’s level of cognitive ability effects his or her activity level.

Research Questions

In summary, there are few research studies that specifically examine the relationship between activity and well-being with a specific emphasis on how cognitive ability plays a role in the relationship between activity and well-being. Understanding this phenomenon is especially important given the significant increase in the number of older adults and older adults with cognitive impairment (blinded for review). Based on the principles of “Activity Theory” this article investigates two important areas:

Activity and Quality of Life For Older Adults

Hypothesis 1: Individuals who are more active will report a higher quality of life compared to those that participate in fewer activities during the week.

Activity and Quality of Life For Cognitively Impaired Older Adults

Hypothesis 2a: Cognitively impaired individuals will participate in fewer weekly activities compared to cognitively intact individuals.

Hypothesis 2b: Cognitively impaired individuals who report participating in a greater number of activities will report a higher quality of life compared to cognitively impaired individuals who participate in fewer weekly activities.

Hypothesis 2c: Cognitively impaired individuals who have more cognitive functioning will report a higher quality of life compared to cognitively impaired individuals with less cognitive functioning.

Method

Data

This study involved in-person interviews between December 2005 and September 2008 with 324 older adults who were over the age of 50 years old. The original study sought to recruit 50 cognitively intact and 25 cognitively impaired individuals within each of four age groups. Recruitment sites included the blinded for review (blinded for review), long-term care settings such as assisted living facilities and nursing homes and apartment buildings with a large percentage of older adults. Also, a large number of participants were recruited through community fairs and forums, advertisements on local radio stations, newsletters, as well as word of mouth from earlier study participants. The majority of sample participants were from Northeast Ohio, while a very small percentage lived out of state.

Sample Characteristics

Most participants were women (71.6%), and the average age of all participants was 76 years, with a range from 53 to 101 years of age. Over half (59.2%) had more than a high school degree. Participants were White (69%), African Americans (30%), and Hispanics/Latinos, American Indians/Alaskan Natives (1%). Fifty-eight percent reported having a household income below $30,000 a year.

Table 1 depicts that 104 of the participants were classified as having cognitive impairment. A participant was considered cognitively impaired if they reported being diagnosed with a cognitive impairing condition (e.g., Alzheimer’s disease), and/or they scored 26 or lower on the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975; Tombaugh & McIntyre, 1992). The decision to use the MMSE score as a classification characteristic (i.e., cognitively impaired vs. no cognitive impairment) was based on research indicating that minority populations and persons with low socioeconomic status have less access to diagnostic procedures (Manton, Patrick, & Johnson, 1987). The average MMSE score for persons with cognitive impairment was 23.4 (SD = 4.24), with a range of 8–30.

Table 1.

Demographic Description of Sample.

	Full sample (n = 324)	Cognitively intact sample (n = 220)	Cognitively impaired sample (n = 104)
Gender (% female)	71.6	72.3	70.2
Average age (SD)	76.5 (9.3)	75.9 (9.0)	77.8 (9.8)
Race (% White)**	69.0	74.1	57.7
Education (% more than high school)**	59.2	65.9	45.2
Marital status (% married/living wife/partner)**	34.3	40.0	22.1
Income (% less than $30,000)**	58.5	51.8	73.6
Average MMSE (SD)**	27.1 (3.6)	28.9 (1.0)	23.4 (4.2)
Weekly activity count average (SD)**	31.6 (8.8)	32.7 (8.1)	29.3 (9.6)
Quality of life score average (SD)**	39.1 (5.6)	39.8 (5.2)	37.6 (6.2)
Health conditions (SD)	3.6 (1.8)	3.5 (1.8)	3.6 (1.8)

Note. MMSE = Mini-Mental State Examination; SD = standard deviation.

**p < .01. *p < .05.

Instrumentation/Measures

Data were collected in a 20–30-min in-person interview that included demographic items, measures of cognitive impairment, quality of life, daily activity involvement, and general preferences. Demographic and background information of the participants were collected using single-item measures based on the self-report of the participant. Demographic questions included gender, date of birth, highest educational level achieved, race/ethnicity, current marital status, household income level, and religious affiliations.

The Weekly Activity Count (WAC) was created for this study to calculate participants’ level of activity (see Table 2). The WAC estimates activity level by calculating the number of days the participant reports engaging in 12 different activities (e.g., exercise, volunteer activities, watch television). For example, if a participant reported “going out to eat at a restaurant” 5 times a week and “exercising” 3 times a week he or she would have a WAC sum score of eight activity occurrences. The WAC items were asked using responses from “0” to “7” (i.e., 0 = zero days, 7 = seven days). The mean WAC sum score for the full sample was 31.6 (possible range 0–84). Research demonstrates that persons with cognitive impairment can provide consistent and accurate responses to fact-based questions such as weekly activities (Clark, Tucke, & Whitlatch, 2008), thus supporting the decision to rely on data from persons with this level of cognitive impairment.

Table 2.

Average Weekly Activity Count by Impairment Status.

WAC Items (M/SD)
	Full sample (n = 324)	Cognitively intact sample (n = 220)	Cognitively impaired sample (n = 104)
In a typical 7-day week, how many days do you participate in the following activities?
1. Drink alcohol?*	.78 (1.82)	.94 (2.00)	.43 (1.28)
2. Go out to eat at a restaurant?*	1.27 (1.48)	1.44 (1.48)	.91 (1.42)
3. Exercise?	3.83 (2.63)	3.87 (2.59)	3.75 (2.73)
4. Go to church or participate in religious activities?	1.26 (1.59)	1.23 (1.53)	1.31 (1.74)
5. Participate in group or organized activities?	1.40 (1.74)	1.48 (1.72)	1.23 (1.78)
6. Go to movies or see a show?	.36 (.89)	.34 (.80)	.40 (1.05)
7. Participate in a hobby or other interest?*	3.00 (2.69)	3.23 (2.60)	2.52 (2.81)
8. Read a book, newspaper, or magazine?*	6.02 (2.13)	6.21 (1.88)	5.62 (2.54)
9. Go grocery shopping?*	1.52 (1.53)	1.64 (1.47)	1.26 (1.63)
10. Visit with friends or talk to friends on phone?	4.64 (2.48)	4.69 (2.37)	4.55 (2.71)
11. Participate in volunteer activities?	1.15 (1.93)	1.26 (1.91)	.90 (1.97)
12. Watch television?	6.34 (1.64)	6.36 (1.56)	6.28 (1.80)

Note. SD = standard deviation; WAC = weekly activity count.

**p < .01. *p < .05.

The Quality of Life-Alzheimer's Disease measure (QOL-AD; Logsdon, Gibbons, McCurry, & Teri, 2002) was used to assess participants’ quality of life. The QOL-AD is a 13-item measure designed for individuals with memory loss that focuses on quality-of-life domains identified as important for memory-impaired older adults. The QOL-AD measure, while designed for persons with memory loss, includes broad concepts of quality of life applicable to individuals without memory loss. It has been used with both persons with memory loss and caregivers (Logsdon, Gibbons, McCurry, & Teri, 1999, 2002). In the current study, the QOL-AD mean score for the entire sample was 39.1 (possible range 13–52).

Analysis

Statistical Package for Social Sciences (SPSS 17.0) was used for data management and analysis. First, in order to identify any possible relationships among the dependent variables, Pearson bivariate correlations were conducted with quality of life, WACs, and participant cognitive status (0 = cognitively intact sample, 1 = cognitively impaired sample). Independent samples t-tests and ordinary least squares (OLS) regression analyses were performed to understand any possible relationships between quality of life, activity, and impairment status.

Results

Hypothesis 1: Activity and Quality of Life For Older Adults

Participants had an average WAC sum score of 31.63 (SD = 8.76) and reported a range of 0–65 activity occurrences, indicating that on average participants were engaged in nearly 32 occurrences of the 12 activities in the WAC. The activities that were most often engaged in during the week were “watching television” (M = 6.34, SD = 1.64) and “reading a book, newspaper or magazine” (M = 6.02, SD = 2.12). Activities that were least likely to be engaged in were “going to see a movie or show” (M = .37, SD = .88) and “drinking alcohol” (M = .78, SD = 1.82).

As shown in Table 3, results of bivariate correlations indicated that there was a significant positive relationship between participants’ WAC sum score and their QOL score. For the full sample, individuals who were more active reported higher quality of life (r = .376, p < .01). These results provide support for Hypothesis 1: individuals who are more active report higher quality of life.

Table 3.

Correlations of MMSE, Weekly Activities, and Quality of Life.

		QOL	WAC
Full sample (n = 324)	MMSE	.229**	.261**
Full sample (n = 324)	WAC	.376**	—
Cognitively intact sample (n = 220)	MMSE	.217**	.104
Cognitively intact sample (n = 220)	WAC	.326**	—
Cognitively impaired sample (n = 104)	MMSE	.140	.270**
Cognitively impaired sample (n = 104)	WAC	.399**	—

Note. MMSE = Mini-Mental State Examination; QOL = quality of life; WAC = weekly activity count.

**p < .01. *p < .05.

Hypothesis 2: Activity and Quality of Life For Cognitively Impaired Older Adults

We next examined activity level and quality of life to determine if any major differences existed between cognitively impaired individuals and intact individuals. Results indicated that cognitively impaired individuals participated in significantly fewer weekly activities compared to cognitively intact individuals (see Table 1, M = 29.27, SD = 9.6 vs. M = 32.74, SD = 8.1, p < .05). QOL-AD scores indicated that, on average, cognitively impaired individuals scored significantly lower on the QOL-AD than cognitively intact individuals (M = 37.62, SD = 6.2 vs. M = 39.77, SD = 5.2, p < .01).

Additional analyses were performed to determine if cognitively impaired individuals who reported more participation in weekly activities would also report higher quality of life. Bivariate correlations indicate that for impaired individuals, participating in more weekly activities was significantly related to a higher QOL-AD score (r = .399, p < .01). These findings prompted us to perform OLS regressions predicting quality of life with impairment status and weekly activities as independent variables (see Table 4). For the full sample, individuals who reported participating in more weekly activities (β = .340, p < .05) who were less cognitively impaired (β = .148, p < .05), reported a higher quality of life (Adjusted R ²= .156, p < .05). When looking at only those individuals who were considered cognitively intact, results were similar to the entire sample. Cognitively intact individuals who reported higher QOL scores also reported participating in more weekly activities (β = .307, p < .05) and had higher cognitive ability (β = .185, p < .05). Results were slightly different for the impaired individuals in this sample. More weekly activities was significantly associated with higher QOL scores (β = .388, p < .05), but cognitive impairment was not significantly associated with quality of life. Values for statistics of regression tolerance ranged from .942 to .989 indicating no multicollinearity among the independent variables (Chou & Chi, 1999).

Table 4.

Ordinary Least Squares (OLS) Regressions of Weekly Activity and Cognitive Ability on Quality of Life.

	Full sample (N = 324)		Cognitively intact sample (N = 220)		Cognitively impaired sample (N = 104)
	Weekly Activity	MMSE	Weekly Activity	MMSE	Weekly Activity	MMSE
Unstandardized (SE)	2.16 (.034)*	.251 (.090)*	.195 (.040)*	.945 (.325)*	.248 (.061)*	.082 (.160)
Standardized	.340*	.148*	.307*	.185*	.388*	.049
R ² (adjusted R)	.156*		.132*		.144*

Note. MMSE = Mini-Mental State Examination.

Dependent variable = quality of Life

*p < .05.

Discussion

The results of this study clearly demonstrate a link between activity level and overall quality of life for older adults. Results of Hypothesis 1, which examined the relationship between quality of life and the number of activities during the week, indicated that regardless of impairment status, the more activity occurrences that individuals reported, the higher they rated their quality of life. This finding is not surprising given that one of the main assumptions for better well-being for older adults is staying both socially and cognitively active (Rowe & Kahn, 1997). Similarly, the most common activity reported was “watch television,” which is an activity that the average adult engages in almost 3 hr a day (Bureau of Labor Statistics, 2011). “Going to see a movie or show” was reported as the activity least likely to be engaged in during the week. Although not the main focus of this article, “exercise” was an activity that produced an interesting finding. On average, participants reported engaging in exercise almost 4 days of the week (M = 3.83, SD = 2.63). Even those older adults who were classified as cognitively impaired, reported exercising as often as persons who were not cognitively impaired (M = 3.75, SD = 2.73 vs. M = 3.87, SD = 2.59).

For the second group of hypotheses, we compared cognitively intact older adults to older adults with cognitive impairment and found support for two of the three hypotheses. One significant group difference was the number of activities participated in during the week. Older adults who were cognitively impaired were significantly less active. Although cognitive impairment may play a role, demographic variations between the groups may also explain the significant differences in activity level. Compared to the cognitively intact sample, impaired older adults were less likely to be married or live with a partner (40.0% vs. 22.1%) and more likely to reside in a household with an annual income under $30,000 (51.8% vs. 73.6%). Subsequent analyses with larger samples need to consider how these demographic differences contribute to the activity level of impaired older adults.

For the full sample, there was a positive relationship between engaging in more weekly activities and higher quality of life. This finding reinforces the notion that older adults, regardless of impairment status, can benefit from maintained physical and social activity levels. In addition, these study findings support the inclusion of impaired older adults in activity research and programs.

We found no support for Hypothesis 2c, which examined whether cognitively impaired individuals with higher cognitive functioning would report better quality of life compared to those with lower cognitive functioning. Level of cognitive impairment, as measured by the MMSE, had no significant relationship with the reported quality of life of the cognitively impaired sample. Although this finding was somewhat unexpected, it again highlights the overall importance of activity level of older adults of all cognitive abilities. No matter how cognitively impaired older adults were, if they were more active, they were likely to report high quality of life. This finding has relevance for practioners who work with individuals with mild to moderate cognitive loss because it reinforces the importance of maintaining activity in older adulthood.

Study Limitations

There are a few limitations to this analysis. First, the original purpose of the study was not to examine the activity level of participants, but rather to examine consistency of responses by cognitively intact individuals and cognitively impaired individuals after 1 week. Thus, measures were not originally designed to assess specific issues related to activity and well-being. Another shortcoming was that the measure of activity level (WAC) does not measure all potential activities. In future studies, a much more sensitive activity scale should be used or created to capture more accurately the activity level of older adults.

Another limitation is that most participants lived in Northeast Ohio, thus our findings may not be generalizable to the broader population of older adults. There may be potential socioeconomic factors in Northeast Ohio that could have affected some of the outcomes. While, Ohio’s climate may play a role in determining what activities individuals take part in during the week, it is important to recall that interviews were conducted across all four seasons of the year. Another question to explore is whether Northeast Ohio offers similar programs for older adults that facilitate one’s activity level. Unfortunately, without a much more geographically diverse sample these particular questions will remain unanswered.

In the future, we suggest that it would be important to include a larger and more generalizable sample as a way to look more closely at the relationship between activity level and well-being. Including a more comprehensive measure of activity would also be necessary to test the importance of staying active and its impact on the ongoing well-being of older adults.

Conclusion

Our overall findings indicate that the greater number of activity occurrences older adults participated in during the week, the higher they rated their quality of life. This major finding strongly reinforces the idea that supporting older adults to engage in activities, regardless of their cognitive status, is very important for their well-being. Early stage Alzheimer’s disease or dementia programs that provide socialization and education, as well as activity, should be available within agencies that work with older adults who are showing signs of memory loss. Increasingly, Alzheimer’s Association chapters across the country are offering early stage programs where individuals visit museums, zoos, and other types of places in a group setting with family members and other people with dementia (Mittleman & Epstein, 2008; Rhoades, 2009). These types of programs should be expanded to ensure that all older adults suffering from dementia are able to attend if they are interested and able to participate.

These findings point to the importance of programs that engage older adults regardless of cognitive loss, range of abilities, or geographic location. It is clear that staying active is beneficial. It is essential to ensure that a range of programs are available for older adults regardless of their cognitive status. These programs could include helping older adults find volunteer opportunities of interest or nonvolunteer activities they enjoy. Moreover, to insure participation, programs must be affordable and reliable transportation services must be provided for older adults unable to drive. By removing barriers to participation, it is more likely that older adults would be able to engage in meaningful activities and, in turn, enhance their quality of life. Determining the most optimal level of activity will also be important to investigate. Overall, these findings show great promise for improving the lives of older adults regardless of the presence of cognitive loss.

Footnotes

Acknowledgments

We thank the OAR study interviewers and participants for their contributions to this study.

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: This study was supported by a grant from the Retirement Research Foundation (2005-088).

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