Profile of Physical Activity Levels in Community-Dwelling Older Adults

Abstract

Purpose: 

To examine relationships between selected sociodemographic, health-related and environmental factors and levels of physical activity in older adults across three age groups.

Methods: 

Seven hundred sixty-four older adults (mean age = 77.4 ± 8.6 yr) from a midsize Canadian city completed a self-administered questionnaire under researcher supervision. Level of physical activity was determined using the Physical Activity Scale for the Elderly (PASE). Correlates of physical activity were examined using previously validated questionnaires. The findings pertaining to personal and environmental factors are presented.

Results: 

Overall, significantly higher mean PASE scores were seen in those individuals in the following categories: male (P < 0.001), married or common-law (P < 0.001), not living alone (P < 0.001), not living in senior's housing (P < 0.001), higher levels of education (P < 0.001) and higher incomes (P < 0.001). Better physical health showed significant positive associations (P < 0.001) with PASE score. Individuals reporting at least four or more chronic health conditions had significantly lower PASE scores than those reporting no chronic conditions (P < 0.001). Significantly lower PASE scores were also reported in those using domestic services (P < 0.001). Higher PASE scores were related to the presence of hills, biking and walking trails, street lights, various recreation facilities, seeing others active and unattended dogs (P < 0.001 to P < 0.05).

Conclusion: 

An understanding of the factors that influence physical activity behavior in older adults is critical to developing effective intervention strategies that will address the problem of physical inactivity in this population, and in doing so, improve the health status and quality of life of the older adult, while having a significant impact on healthcare expenditures.

Physical inactivity is widely recognized as a risk factor in the development of numerous chronic diseases including coronary heart disease (CHD), type 2 diabetes mellitus (T2DM), obesity, stroke, and certain types of cancer (⁵). Despite the efforts of various international health organizations to increase public awareness of the health benefits of physical activity, levels of physical activity remain low in industrialized nations, particularly among older adults. In the United States, at least 60% of adults are not active enough to achieve health benefits (⁵). Similarly, in Canada, 55% of men and 67% of women over the age of 50 are not active enough to achieve reduction in disease risk (²).

Currently, persons aged 50 yr and older account for 29.3% of the Canadian population and it is estimated that by 2026, this segment of the population will account for 41% of the total Canadian population (²⁸). The high prevalence of lifestyle-related chronic diseases, such as CHD and T2DM, among this growing population of older adults has significant implication for the economic costs and burden of treating these diseases. Because physical inactivity is a modifiable risk factor, there is significant potential to increase the health and quality of life of older adults, as well as improve the economic health of the nation through physical activity intervention strategies. An understanding of the factors that influence physical activity behavior in older adults is critical to developing effective intervention strategies that will address the problem of physical inactivity in this population. In doing so, the health status and quality of life of the older adult will be improved, while having a significant impact on healthcare expenditures (⁹).

The associations between physical activity behavior and certain personal (age, gender, education, socioeconomic status, stage of change, self-efficacy, barriers to physical activity participation such as lack of time and bad weather) and social factors (social support) in the adult population (aged 18 yr or older), have been well-established (³⁰). More recently the role of the environment (e.g. recreation facilities and green spaces) in determining physical activity behavior has been examined (²³). Despite the extensive work done in the area of physical activity correlates in the general adult population, the relation between these environmental factors and the physical activity behavior of older-aged adults (i.e. those over the age of 50) are still less explored (³⁰). Although in recent years the older adult population as a group has been increasingly studied in the physical activity context, most of the published literature has been gender specific (i.e., female) (¹²), limited to those aged 60 and older (¹) or have been narrowly focused on a few select correlates (i.e., age, psychosocial factors) (¹²). While some research has been conducted on the correlates of physical activity in older adults, little has been done to examine these correlates across different age groups within the older adult population, and thus our understanding remains incomplete. As the term “older adult” can encompass a large age range (i.e., 50 yr and older), it is important to identify the possible differences in correlates of physical activity so that effective intervention programs can be designed.

In the current study, we examined a comprehensive set of personal, social and environmental correlates of physical activity in a diverse, community-living older adult population and investigated whether these relationships were consistent across three age categories (50–64, 65–79, and ≥80 yr). These 15-yr age groups were based on the existing gap in the literature on younger older adults (50–65 yr), the age of pension eligibility (65+ yr), and the representation of the old older adults (80+ yr) (⁴). This paper presents the findings relative to selected sociodemographic, health-related, and environmental factors and levels of physical activity in the three age stratified groups of older individuals.

METHODS

Subject recruitment.

This study used a convenience sampling approach as it has been argued that convenience sampling is often the norm in health behavior research (⁷), particularly in the older adult population (¹⁶). The sample consisted of 764 community-dwelling older adults in a midsized Canadian city (total population approximately 200,000, with individuals over age 50 comprising approximately 20% of the population). Individuals were recruited for the study from seniors' organizations, service clubs, and seniors' housing units. A citywide senior's directory and community housing authority were used to identify retirement communities and older adult housing units, while service organizations and clubs were identified through the local council on aging and the city telephone directory. Senior's housing included self-contained units (apartment complexes where residents are responsible for meals, housekeeping, etc., with some limited support services) and supported independent residences (apartment complexes where residents have their own suite, but have access to services such as meals and housekeeping). Of the total 41 community groups, retirement communities and housing units contacted, 30 agreed to participate.

Following approval from the tenants' associations, study recruitment posters and information letters were distributed to the retirement communities and housing units one week before the arranged day of data collection. The posters were displayed in the common areas of the facility and letters were hand-delivered to each resident. For interested service organizations and clubs, the information was given to a contact person for distribution. As an incentive, participants were informed that one dollar would be donated back to the tenants' association, service organization or club, or to a charity of their choice for each questionnaire completed.

Questionnaire administration.

Completion of the questionnaire at the retirement communities and housing units was typically arranged around an event or meeting and administered in a small group setting of six to eight participants per research assistant. The purpose of the study and procedures for completing the questionnaire were outlined to the group and participants were encouraged to complete the questionnaire at their own pace. Informed written consent according to the guidelines established by the university advisory committee on ethics in behavioral science research was obtained from all participants. Although participants completed the questionnaires independently, the research assistants provided clarification as necessary. Research assistants reviewed questionnaires for completeness and attempted to obtain responses for all items. Data were collected from May to August 2002 to avoid the impact of possible seasonal variations in activity levels.

Questionnaire items.

The complete survey instrument consisted of previously validated and reliable questionnaires (^{12,14,18,20,22,24,25,32}). The questionnaire items focused on the dependent variable, physical activity and its relationship with independent variables thought to be associated with physical activity across three domains: interpersonal, environmental, and social (²¹).

Physical activity.

The Physical Activity Scale for the Elderly (PASE), a 12-item questionnaire, was used to assess self-reported physical activity. The PASE has previously been shown to be a valid and reliable instrument, and is easy to administer and score. It has been noted that the PASE more reasonably represents the types of activities that older adults commonly participate in such as housework, gardening and caring for others, as opposed to other instruments that focus only on sport or recreational activities (³⁴). The PASE score is based on 12 leisure, household, and occupational activities that have been performed in the previous seven days. The 12 activities include light, moderate and strenuous sport/recreational activities, muscle strength/endurance exercises, light and heavy housework, home repairs, lawn work or yard care, caring for another person, and work for pay or as a volunteer. As outlined by Washburn et al., (³³) the PASE score is computed by multiplying duration of activity (h·wk⁻¹) or participation in an activity (yes/no) by empirically derived weights, based on Caltrac counts, daily energy expenditures (metabolic equivalents; METs) and self-reported physical activity, and then summing the product for all 12 items (¹⁵).

Sociodemographic characteristics.

Items from a Canadian census survey (²⁴) were modified and used to develop questions related to sociodemographic characteristics. Information on age, gender, marital status, residence status, ethnicity, education, income, and employment status was collected.

Self-reported health and chronic health conditions.

The 12-item Short Form Health Survey (SF-12) was used to assess self-reported health. It has been reported to be a reliable and valid estimate of individual levels of health when compared to the SF-36 (Medical Outcomes study 36-item Short-Form Health Survey) (³²), which has been validated for use in the Canadian population (⁸).

Single item responses in the SF-12 are combined into 8 subscales. The physical health component score (PCS) is based on four subscales: physical functioning, role-physical, bodily pain and general health. The mental health component score (MCS) is based on the vitality, social functioning, role-emotional and mental health subscales. Using norm-based methods developed on the American general population outlined by Ware et al. (³²) the PCS and MCS components are derived by creating indicator variables (scored 1/0) for the item response categories. The indicator variables are then weighted and aggregated. The PCS-12 and MSC-12 are computed by multiplying their respective regression weights and summing the products to produce the two scores (³¹).

The descriptions for questions related to defining chronic health conditions were based on items from the National Population Health Survey (²⁵). Participants were asked to identify from a listing any medically diagnosed conditions that have affected their overall health for longer than six months. These conditions included: musculoskeletal problems, breathing problems, heart and circulation problems, digestive system problems, kidney, bladder or urinary problems, neurological problems, mental or emotional problems, cancer, blood problems, eye problems, high blood pressure, diabetes, and others unspecified above.

Services used.

Two questions were asked regarding domestic services and activities available and used by the participants. Services and activities considered included housekeeping, meals, nursing (registered nurse), personal care, home care, social activities, and physical activities/exercise time.

Neighborhood characteristics.

Neighborhood environment, convenient facilities, and neighborhood safety were determined using a modified environmental questionnaire developed by Sallis et al. (²³). Defined neighborhood characteristics included hills, enjoyable scenery, sidewalks, biking lanes or trails, walking/hiking trails, water fountains, benches to sit, streetlights, heavy traffic, dogs unattended, frequently seeing people walking or exercising, and high crime. The presence of neighborhood physical activity facilities was also assessed. Participants were asked to indicate which items were found in their neighborhood (within a five minute walk or drive); to indicate the safety of their neighborhood (1 = very unsafe to 5 = very safe) and to define the type of neighborhood in which they resided (residential, mixed commercial and residential, or mainly commercial).

Statistical analysis.

The independent variables included sociodemographics, self-reported health and chronic health conditions, services used, and neighborhood characteristics. Due to small sample sizes within various levels of sociodemographic characteristics, these independent variables were collapsed, resulting in the following categories: living situation was recoded from living “alone,” “with a spouse/common-law partner/partner,” “children (daughter, son, son-in-law, daughter-in-law),” “other family member,” “friend,” or “other” to either living “alone” or “not alone.” Marital status was recoded from “married/common law,” “living with a partner,” “divorced,” “single (never married),” “widowed,” “separated,” or “other” into four categories: “married/common law/living with a partner,” “divorced/separated,” “widowed,” or “single.” Education level was recoded from “no formal schooling,” “elementary only,” “some secondary (without graduation diploma),” “secondary or high school graduation diploma,” “some trade, technical, vocational school, business college, community college, nursing school, or university,” “diploma/ certificate from trade, technical, vocational school, business college, community college, or nursing school,” “degree/certificate from university of teacher's college,” “master's degree, degree in medicine, dentistry, veterinary medicine, optometry, or doctorate,” or “other” into three categories: “less than secondary education,” “some or completed secondary education,” and “postsecondary education.” Employment was recoded from “full-time employment,” “part-time employment,” “retired,” “unemployed,” or “other” to “employed,” “unemployed,” or “retired.” Type of dwelling was recoded from “own home,” “own apartment,” “senior's housing,” “family member's home,” or “other” to a dichotomous variable of “living in senior's housing” or “not living in senior's housing.” Total annual household income was recoded from “less than $20,000,” “$20,000 to less than $30,000,” “$30,000 to less than $40,000,” “$40,000 to less than $50,000,” “$50,000 to less than $60,000,” or “greater than $60,000” into three levels: “<$20,000,” “≥$20,000 to <$30,000,” and “≥$30,000.” The low-income cutoff of $20,000 was based on that employed by Statistics Canada (²⁷). Age groups were categorized as 50–64, 65–79, and ≥80 yr, and were based on the existing gap in literature (i.e., those younger than 65), the age of retirement (65+) years, and to represent the old older adult (80+) (¹). Chronic disease status was considered by examining each chronic illness, as well as looking at the total number of chronic illnesses reported by each participant.

The contribution of each PASE item to the overall PASE score was derived from the product of the PASE item weight and the sample mean for each question, as outlined by Washburn et al. (³⁴).

All analyses were completed using the Statistical Package for the Social Sciences (SPSS) version 11.0. Level of significance was defined as P < 0.05. For categorical independent variables, the PASE mean scores were compared through the use of independent samples t-tests or one way analysis of variance (ANOVA) if the assumptions for these tests were met. In cases where the ANOVA was used and significant differences were found, pairwise comparisons using Scheffe's post hoc procedure was completed. When a continuous variable exhibited a nonnormal distribution or when there was no clear pattern of the distribution, the Mann–Whitney test or Kruskal–Wallis test was used in place of the independent samples t-test and ANOVA, respectively. Where the result from the Kruskal–Wallis test was significant, pairwise comparisons were completed through the use of multiple Mann–Whitney tests with a Bonferonni correction. In cases where the independent variable was continuous, correlation was assessed through the use of Pearson's correlation coefficient when the assumptions were met, or Spearman's correlation coefficient if either of the variables were nonnormally distributed. Sample size calculations based on the comparison of means for a t-test were conducted. Based on these calculations, an effect size of 0.50 in the older two age groups (65–79 yr: N = 351; 80+ yr: N = 307) was sufficient to detect differences with 80% power. In the youngest age group, however, the effect size required to detect differences was near 0.80.

RESULTS

Population Characteristics

Sociodemographics.

Table 1 provides an overview of the study population overall and stratified by age, and includes demographic characteristics and number of chronic health conditions. Briefly, participants ranged in age from 50 to 99 yr (mean = 77.4 ± 8.6 yr). Most participants were female (79.8%) and over the age of 65 (92%). The majority of the study population lived alone (43.7%), were widowed (46.1%), lived in senior's housing (55.2%), were retired (95.8%), earned less than $20,000 annually (33.6%), and had completed at least some secondary education (75.8%). Not shown in Table 1 is ethnic background, which revealed that most of the population was Caucasian (95.8%).

TABLE 1: Description of the study population by age group.

A gender-stratified analysis showed that a greater proportion of females compared with males lived alone (66.5 vs 18.8%, respectively, P < 0.001), were never married (13.6 vs 6.1%, respectively, P < 0.05) or widowed (55.4 vs 12.8%, respectively, P < 0.05), and lived in senior's housing (59.0 vs 41.6%, respectively, P < 0.05). A greater proportion of males compared with females had completed at least some postsecondary education (53.7 vs 36.7%, respectively, P = 0.001) and earned more than $20,000 annually (79.8 vs 44.3%, respectively, P < 0.001).

Self-reported health and chronic conditions.

Overall, 86% of the study population reported one or more chronic health problems, with 27.6% reporting at least four chronic health problems (Table 1). The distribution of chronic conditions in our sample is similar to Canadian data in which arthritis, hypertension, and heart disease are reported to be among the most common conditions in adults aged 65 and older (²⁶).

The SF-12 mean physical health score of 40.3 ± 12.3 for the sample population was below the standardized score of 50 (³¹). The SF-12 mean mental health score of 51.9 ± 10.3 was slightly above the standardized score of 50 (³¹). Males had significantly higher scores than females on the physical (43.6 ± 11.5 vs 39.5 ± 12.4; P < 0.001) and mental (53.6 ± 9.3 vs 51.4 ± 10.5; P < 0.05) scales. Both the physical scale scores and mental health scores decreased with age. On the physical composite scale, the 50–64 and 65–79 age groups had higher mean scores (41.6 ± 12.8 and 42.0 ± 12.5, respectively) compared with the 80+ age group (38.1 ± 11.8), with significant differences observed between the 65–79 and 80+ age groups (P = 0.001). Higher mental composite scores were seen in the youngest age group (54.7 ± 8.7) compared with the 65–79 and 80+ age groups (52.1 ± 10.4 and 51.0 ± 10.3, respectively). There was a significant difference in the mental health composite score between the youngest and oldest age groups (P < 0.05).

Services used.

Respondents reported the following services being used (percent using): housekeeping (20.3%), meals (33.0%), nursing (2.0%), personal care (5.9%), home care (6.3%), physical activities (23.2%), and social activities (49%). A greater proportion of females reported participating in social (54.8%) and physical (26.0%) activities compared with their male counterparts (35.1 and 14.2%, respectively, P < 0.05). Comparing age groups, domestic services (housekeeping and meals) were used less frequently in the 50–64 age group (housekeeping: 5%; meals: 16.7%) compared with the 65–79 and ≥80 age groups (housekeeping: 13.4 and 31.6%; meals: 24.8 and 46.8%, respectively; P < 0.05). Personal care services and home care services were used more frequently in the ≥80 age group (10.3 and 9.0%, respectively) compared with the two younger age groups (4.3% or less, P < 0.05). Use of all other services (e.g., registered nurse, social, and physical activities) were not statistically different across the age groups.

Neighborhood characteristics.

The findings related to neighborhood characteristics for the overall sample population are presented in Table 6. Most respondents reported living in neighborhoods with attractive scenery, sidewalks, benches, streetlights, parks, and people frequently seen walking. In addition, neighborhoods were most often described as being without hills, bike trails, fountains, golf courses, skating rinks, pools, tennis courts, dance studios, recreation centers, unattended dogs, or high crime.

TABLE 6: Mean PASE score by neighborhood characteristics for each age group.

PASE

Sociodemographics.

A wide range in the level of physical activity, as measured by PASE, was reported (Table 2). PASE scores were significantly higher in the 50–64 (141.7 ± 77.2, P < 0.001) and 65–79 (128.2 ± 65.7, P < 0.001) age groups compared with the ≥80 age group (81.4 ± 47.5). Overall, significantly higher mean PASE scores were seen in the following categories: male (P < 0.001), married or common-law (P < 0.001), not living alone (P < 0.001), not living in senior's housing (P < 0.001), higher levels of education (P < 0.001), and higher incomes (P < 0.001). Within each age group, similar patterns emerged with two notable exceptions. In the 50–64 age group, significant differences in PASE score were associated with only level of education (P < 0.05), whereas in the ≥80 age group there were no differences in PASE score related to living arrangement or level of education.

TABLE 2: Mean PASE scores by demographic variables for each age group.

Overall, PASE scores increased as education level increased. When stratified into age groups, significant differences in PASE scores by level of education were apparent in only the two youngest age groups. In the 65–79 age group, significantly lower PASE scores were observed among those with less than secondary education and those who had at least some postsecondary education (P < 0.05). As well, there were significantly lower PASE scores between those who had completed some secondary school and those who had completed some postsecondary school (P < 0.05). In the 50–64 age group, the highest PASE scores were seen in those who had attended and/or completed secondary school.

With regard to employment status, significant differences were noted only in the 65–79 age group, with higher mean PASE scores reported by those who were employed (208.8 ± 91.2) compared with those who were retired (127.8 ± 64.7; P < 0.05; data not shown). As noted previously, 95.8% of our study participants were retired.

Self-reported health and chronic health conditions.

General physical health, measured by the SF-12 physical component score, showed statistically significant positive associations with the PASE score. The oldest age group had a higher correlation (Pearson's r = 0.40; P < 0.001) followed by the youngest age group (Spearman's ρ = 0.36; P < 0.01), with the lowest correlation observed in the 65–79 age group (Pearson's r = 0.35; P < 0.001). The association between PASE and the SF-12 mental health score was also significant, although the correlations were weaker when compared with the physical scale. The highest correlation was in the 65–79 age group (Pearson's r = 0.23; P < 0.001), with the oldest (Pearson's r = 0.13; P < 0.05) and youngest (Spearman's ρ = 0.13) age groups being lower.

The relationship between level of physical activity and specific chronic conditions is outlined in Table 3. Overall, significantly lower PASE scores were seen in participants reporting the presence of a chronic condition compared to those who reported no chronic conditions. When at least four chronic conditions were reported, the PASE score was significantly lower than when zero, one, or two conditions were reported (P < 0.001; data not shown). In the 65–79 age group, PASE scores were significantly lower among those reporting breathing (P < 0.001), heart and circulation (P = 0.006), digestive (P = 0.014), kidney/bladder/urinary (P = 0.002), and emotional problems (P = 0.024). In the oldest age group there were significantly lower PASE scores when musculoskeletal (P = 0.004) and digestive (P = 0.014) conditions were present. The youngest group showed no statistically significant differences in PASE score between absence and presence of a chronic condition. However, in most cases this age group had few persons reporting a condition.

TABLE 3: Mean PASE scores by chronic health condition for each age group.

Services used.

Significantly lower PASE scores were reported in respondents using housekeeping (P < 0.001), meals (P < 0.001), personal care (P < 0.001), home care (P < 0.001), and social activity services (P < 0.001). When stratified by age, significantly lower PASE scores were reported in subjects using housekeeping (P < 0.01), meals (P < 0.05), home care (P < 0.001), and/or social activity (P < 0.05) services in the 65–79 age group (Table 4). In the ≥80 age group, PASE scores were significantly lower in those utilizing housekeeping (P < 0.001), meal (P < 0.001), personal care (P < 0.001), and/or home care services (P < 0.01) (Table 4). There were very few people in the 50–64 age group who utilized any of the services we examined.

TABLE 4: Mean PASE scores by services used for each age group.

Neighborhood characteristics.

The PASE scores were evaluated to assess the associations with various neighborhood environmental characteristics (Table 6). Overall, respondents reported significantly higher PASE scores when the following neighborhood characteristics were present: hills (P < 0.05), biking lanes/trails (P < 0.001), walking/hiking trails (P < 0.001), street lights (P < 0.05), seeing active people in their neighborhoods (P < 0.001), unattended dogs (P < 0.01), golf courses (P < 0.001), public parks (P < 0.001), skating rinks (P < 0.001), swimming pools (P < 0.05), and tennis courts (P < 0.01). In addition, the absence of benches (P < 0.01) was associated with a significantly higher mean PASE score than when they were present.

Compared with the overall sample, there was no apparent pattern between levels of physical activity and environment in each age group. In the 50- to 64-yr-old group, significantly higher PASE scores were seen in those living in neighborhoods with enjoyable scenery (P < 0.05), skating rinks (P < 0.05), and swimming pools (P < 0.05), while in the 65- to 79-yr-old group, significantly higher PASE scores were seen with the presence of public parks (P < 0.05), skating rinks (P < 0.05), and the absence of benches (P < 0.01). In the ≥80-yr-old group, significantly higher PASE scores were reported with the presence of walking/hiking trails (P < 0.001), golf courses (P < 0.05), public parks (P < 0.01), skating rinks (P < 0.05), swimming pools (P < 0.05), tennis courts (P < 0.01), dance studios (P < 0.001), unattended dogs (P < 0.05), and frequently seeing active people (P < 0.01).

PASE score.

The contribution of each PASE component to the overall PASE score for each age group is presented in Table 5. PASE components making the largest contribution to the PASE score in all age groups were walking and household-related activities (i.e., housework, home repairs, lawn/yard care, outdoor gardening, caring for others), as opposed to planned and structured exercise (i.e., light, moderate, and strenuous sports, muscle strength/endurance).

TABLE 5: Contribution to total PASE score by PASE component.

DISCUSSION

Previous studies investigating physical activity in older adults have been limited in certain groups such as women and particular age ranges (i.e., predominantly those older than 60 yr). In this study we have examined physical activity levels in a large, inclusive community-dwelling, older adult population across many sociodemographic, health-related, and neighborhood environmental characteristics.

As expected, a shift in certain demographic variables with increased age was found including an increase in the proportion of persons who lived alone and were widowed, and a decrease in the proportion that were married. Similarly, the decrease seen in the proportion of persons living in their own home and the increase in the proportion living in seniors' housing reflects a trend among older adults over the age of 80 yr. The inverse relationship between age and level of education is also reflective of past societal trends in that those over 65 yr tended to have lower levels of education. This may have implications with regards to understanding and acting upon health-related information or advice (i.e., lower level of literacy). For example, the ability to evaluate the benefits (or risks) of lifestyle choices such as engaging in physical activity (¹⁶). Overall, our findings support previous studies specifically showing physical activity levels are higher in younger older adults (³³), males (^1,19), individuals reporting higher levels of education (^12,19), and those with higher household incomes (¹⁹). As expected, physical activity levels were found to vary between age groups, showing an age-related decline. The associations between physical activity and age, gender, education, and income were evident in each age group, highlighting the diversity within the older adult population. As this study focuses on the descriptive nature of the population, the interaction between these variables has not been investigated, and thus we are unable to fully identify the underlying factors associated with levels of physical activity in this sample population.

The results further showed that lower levels of physical activity were associated with several chronic conditions, namely those involving the musculoskeletal, respiratory, heart and circulatory, digestive, and kidney/bladder/urinary systems as well as mental/emotional conditions. This was most apparent in the 65–79 age group among those reporting breathing, heart and circulation, digestive system, kidney, urinary and bladder, and mental or emotional problems. Intuitively, one would think that older adults with more health problems would be less physically active. Therefore, it was somewhat surprising that there were no significant differences in physical activity scores, apart from those reporting musculoskeletal and digestive conditions, in the oldest age group. This is particularly interesting given that one third of the respondents in this age category reported four or more chronic conditions. This begs the question of what has a greater influence on levels of physical activity: the total number of conditions or the specific nature of the condition. For example, an individual may report several chronic conditions that are less likely to influence physical activity levels (e.g., cataracts, constipation, bronchitis), whereas another may report few chronic conditions that result in severe limitations with respect to physical activity (e.g., COPD, Parkinson's, etc.). It is interesting to note that of the clinical conditions (i.e., musculoskeletal, breathing, heart and circulation, diabetes) associated with significantly lower PASE scores, the majority are exacerbated by a sedentary lifestyle.

The positive associations between physical activity and physical and mental health observed in the present study was expected, given the abundance of literature supporting the physical and psychological benefits of physical activity (²⁹). Although in our study the association between physical activity and overall mental health, derived from the SF-12, was low across all age groups, the results suggest that individuals who are more physically active tend to have higher levels of psychological well-being. These findings are supported by previous literature, which showed that older adults aged 50–64 who adopted or maintained physical activity experienced better emotional health (^10,13) and improved self-rated mood (¹¹) Interestingly, the positive association between physical activity and mental health was strongest in the 65−79 age group. Often, this is a time of transition for older adults, when events such as such as retirement, the death of a spouse, reduced incomes, and changes in living arrangements are commonly experienced. These changes play an important role in emotional health, as such stressors may have a negative impact on well-being in the absence of good mental health and adequate coping mechanisms. Therefore, our results suggest that physical activity may serve as an important coping mechanism during this period of change for the older adult.

The results emphasizing that adults living in senior's housing had significantly lower activity levels than those living in self-supporting environments (i.e., own home or apartment) were not surprising. The physical activities required for maintenance of one's home, such as housework, home repairs, and lawn care, would contribute to the higher PASE score observed in this group compared with those living in senior's housing complexes where such services are often provided. The lower physical activity levels observed in those who utilize domestic services such as housecleaning and meal preparation further support the hypothesis that household tasks contribute significantly to increased physical activity levels in older adults, regardless of age. Previous studies have shown that a significant proportion of physical activities undertaken by older adults are in the form of household-related activities (³³). Although the PASE scores provide an assessment of physical activity levels in older adults, it is important to recognize the nature of activities contributing to the PASE scores (i.e., caregiving duties, housework, and home repairs). Although the ability to perform activities of daily living is an important component for maintaining functional independence, these types of activities may be insufficient to achieve health benefits as suggested by Health Canada (³) and the U.S. Surgeon General (⁵).

The findings from the present study highlight the importance of informing older adults of the need to engage in additional types of leisure-time physical activity beyond those required within self-supporting environments to attenuate the age-related decline in physiological function and reduced activity associated with upkeep of household work. This may be particularly pertinent for those living in senior housing complexes who have many activities of daily living services provided, and therefore are likely not receiving the functional benefits associated with these tasks. This is reflected in our results as the use of domestic (e.g., housekeeping, meals) and health-related (e.g., registered nurse, personal care, home care) services were associated with lower levels of physical activity.

The environment is an emerging area of study in physical activity correlates research, encompassing a broad range of disciplines including environmental design, urban planning, health promotion, and behavioral medicine (^1,17). Overall findings in this study reflect the results of previous research in this area (^1,12,17,19). Notably, the overall results indicated that the presence of hills, unattended dogs, and the absence of benches were associated with increased PASE scores, whereas high crime and heavy traffic were not associated with physical activity status. Because these environmental variables have typically been regarded as barriers to physical activity, these results seem counterintuitive. However, previous literature has reported similar findings, with the suggestion that perhaps individuals who are active outdoors may be more cognizant of their environmental surroundings (i.e., hills, dogs, etc.) simply due to “getting out more often in their neighborhood,” whereas inactive individuals may be unaware of their environmental surroundings and therefore do not report the presence of these neighborhood characteristics (¹²).

However, when stratified by age, we highlight some differences between the age groups. With respect to the age-related differences in physical activity levels associated with environmental factors, it is possible that convenience and access to formal (i.e., golf courses, skating rinks, swimming pools, etc.) and informal (i.e., parks, walking and biking trails, enjoyable scenery) physical activity facilities may act as enablers to physical activity. However, personal and social influences of the older adult may outweigh the role of an individual's environmental surroundings, particularly given the personal and social changes that accompany aging. Another explanation could be that environmental influences on physical activity are context-specific, and therefore are more relevant to those participating in outdoor activities compared with those who participate in indoor physical activity programs. Currently, research in this area is somewhat limited by the self-reported nature of the data. To fully investigate the contribution of the physical environment to the physical activity status of older adults, further research combining objective measures of the environment with self-report data is required. The application of Geographic Information Systems (GIS) is one such method that would broaden our understanding in this area (⁶).

Certain limitations are inherent in cross-sectional study designs such as the present study, including sample representativeness, potential selection, and recall bias. Despite efforts to minimize these, certain demographic groups remain underrepresented (males, persons aged 50–64 yr, and persons of non-Caucasian ethnic background). Given the small sample sizes in these groups, and in particular within certain variables in the youngest age group, the relationships identified in this study may not reflect those present in these underrepresented groups. The small number of respondents in these groups will reduce the power of the statistical tests and may therefore limit some comparisons. As well, given the focus of the survey, respondents may have tended to overreport their levels of physical activity. As disease status and physical and mental health were assessed at a single point in time, one cannot discount the possibility that the relationships were due to factors which prompted physical activity or contributed to low levels of physical activity, resulting in disease, reduced function, and poor mental health. Finally, it should be noted that the volume of information provided in this paper precluded the inclusion of a gender main effect analysis. Given the apparent differences in physical activity levels between males and females, future research will be directed towards this question.

CONCLUSIONS

Our results have supported previous findings in other groups, and in many cases have shed further light on the associations between physical activity and a variety of lifestyle and health factors. The findings of the present study highlight the heterogeneity within the older adult population, and underscore the importance of resisting the temptation to create “one size fits all” programs for older adults, but rather design and implement innovative strategies to increase physical activity that target specific segments of the older adult population and their changing needs. Effective solutions to the problems associated with sedentary older adults requires multifaceted approaches that consider the collective influences of the personal, social, and environmental factors associated with physical inactivity. These findings are particularly important for community programmers and healthcare practitioners to ensure the appropriateness and effectiveness of physical activity programs and interventions.