Environmental Factors Associated With Social Participation of Older Adults Living in Metropolitan, Urban, and Rural Areas: The NuAge Study

Abstract

Objectives. We compared the social participation of older adults living in metropolitan, urban, and rural areas, and identified associated environmental factors.

Methods. From 2004 to 2006, we conducted a cross-sectional study using an age-, gender-, and area-stratified random sample of 1198 adults (aged 67–82 years). We collected data via interviewer-administered questionnaires and derived from Canadian censuses.

Results. Social participation did not differ across living areas (P = .09), but after controlling for potential confounding variables, we identified associated area-specific environmental variables. In metropolitan areas, higher social participation was associated with greater proximity to neighborhood resources, having a driver’s license, transit use, and better quality social network (R² = 0.18). In urban areas, higher social participation was associated with greater proximity to neighborhood resources and having a driver’s license (R² = 0.11). Finally, in rural areas, higher social participation was associated with greater accessibility to key resources, having a driver’s license, children living in the neighborhood, and more years lived in the current dwelling (R² = 0.18).

Conclusions. To enhance social participation of older adults, public health interventions need to address different environmental factors according to living areas.

Social participation, which is defined as the involvement of the person in activities that provide interactions with others in the community,¹ is a key element of successful² and healthy^3,4 aging that ensures survival and development of people in society throughout their existence.⁵ As a modifiable target of health interventions, social participation is conceptualized by the Human Development Model and Disability Creation Process to be the result of bidirectional interaction between personal and environmental factors.⁵ Some personal factors,⁶ including age, gender, and health, are recognized as being related to social participation.² Environmental factors (i.e., aspects that are extrinsic to individuals and generate a reaction from them)⁷ relate to the immediate social and physical environment to which individuals, especially older adults, are exposed. Environmental factors may act as facilitators or barriers to the accomplishment of social and community activities.⁵ Environmental factors are also important because interventions targeting the environment may have a greater impact on an individual’s social participation than those targeting individual factors.⁸

To date, some theoretical and empirical evidence supports associations between specific environmental factors and social participation.⁹ For example, the Human Development Model and Disability Creation Process showed that support, attitude, services, systems, policies, and accessibility of the physical environment can be associated with social participation.⁵ Another study demonstrated that user-friendliness of the physical environment and access to transport facilities promote older adults’ social participation in both urban and rural areas.¹⁰ Favorable characteristics, such as proximity to resources and services, including access to food shopping, health services, banking, and social or sports clubs, are also important factors.^11,12 Moreover, independently of individual demographic and socioeconomic characteristics, older adults living in affluent areas are less likely to have lower social participation.¹³ Support from the social environment¹⁴ and resource accessibility in the physical environment¹¹ may be seen as imperatives to help individuals with disabilities living in the community.¹⁵ The presence of local resources may have an impact on the likelihood of initiating and maintaining social links with community members.¹⁶ However, little is known about which environmental factors are associated with social participation of older adults according to living area. Living in metropolitan, urban, or rural areas can have an impact on many personal factors, such as health and well-being, as well as on several environmental factors (e.g., neighborhood socioeconomic status or access to services and transportation). For example, access to public transport for people living in rural areas may be limited, which can be a challenge.¹⁷ To our knowledge, only 1 study¹⁸ compared social participation of older people living in metropolitan, urban, and rural areas. Despite area differences in income, access to public transportation, services and resources, automobile use, satisfaction with social support, and sense of security, no significant difference was found in social participation and its associated factors.¹⁸ In our study, which involved 350 older adults, we operationalized social participation by the level of difficulty and assistance required in targeted daily activities and social roles. Because having a better understanding of older adults’ social participation according to their living environment could improve the development of public health services, further studies operationalizing social participation by the frequency of involvement in social activities and considering other environmental factors are needed. We aimed to compare social participation of older adults living in metropolitan, urban and rural areas, and identified associated environmental factors.

METHODS

From 2004 to 2006, we conducted a cross-sectional secondary study within the Nutrition as a determinant of successful aging: the Quebec Longitudinal Study (NuAge)^19,20 research initiative. The NuAge study was a 5-year observational study of 1793 older adults, aged 67 to 82 years, who were in good general health at recruitment in 2003. The cohort was created from an age-, gender-, and area-stratified random sample drawn from the Québec Medicare database for Montréal, Laval, and Sherbrooke in Québec, Canada. Because of universal health care coverage, all residents of the province are included in this Medicare database. Community-dwelling older adults were included in the study if they spoke French or English, were free of disabilities in activities of daily living, not suspected of having moderate or severe cognitive impairment (Modified Mini-Mental State > 79), able to walk 1 block or climb 1 floor without rest, and were willing to commit to a 5-year study period (2003–2008). Those who had class II or greater heart failure according to the Canadian Cardiovascular Society Functional Classification of Angina, chronic obstructive pulmonary disease requiring oxygen therapy or oral steroids, inflammatory digestive diseases, or cancer treated by radiation therapy, chemotherapy, or surgery in the past 5 years were excluded. The numbers of participants recruited in each age and gender stratum were as follows: 67 to 72 years (337 women, 329 men); 73 to 77 years (305 women, 289 men); and 78 to 82 years (298 women, 235 men).

NuAge participants were tested annually using a series of nutritional, functional, medical, biological, and social measurements. At the second (time 2) or third (time 3) follow-up, a series of questions about the perceived presence of services and amenities, perceived housing and social environment, perceived quality of the walking environment and transportation services, and perceived access to services and amenities in the neighborhood were added to the measurement protocol. We based our present investigation on a subgroup of participants still in the cohort at time 3 or time 4 and who completed this additional questionnaire. Because no subsequent exclusion for health reasons occurred, participants included in this study had various levels of physical disabilities, but had good cognitive function. However, of the 1599 potential participants at time 2 or time 3, some respondents had to be excluded from our analysis because they had incomplete self-reported data (n = 344) or addresses (n = 20), or moved to another residence during the year preceding data collection (n = 37). The nonparticipants were not clinically different, although statistically they were older (1.2 year; P < .01), had higher disability levels (2.8/87; P < .001), and presented with greater depressive symptoms (1.1 of 30; P = .04). Moreover, the nonparticipants reported a lower family income (< $ 11 600 Canadian dollars; P < .001) and had a lower level of education (< 1.0 year; P = .01). These differences between participants and nonparticipants were not unexpected and represented an issue common to all longitudinal studies of older adults.²¹ Our analysis thus involved 1198 participants residing at the same location and provided social participation and environmental data at the time 3 or time 4 annual follow-ups. All participants signed an informed consent form. Computer-assisted interviews (WilliamMD, ©Multispectra, 1997–2004 data capture software) were carried out at the research centers by trained research dieticians and nurses following rigorous standardized procedures.

Variables and Measurement Tools

Social participation.

We estimated the frequency of monthly involvement in 10 social activities with a tool that combined the social portion of the Elderly Activity Inventory Questionnaire²² and Statistics Canada’s Participation and Activity Limitation Survey.²³ The social activities, such as attending cultural events, taking courses, and volunteering,¹¹ could be carried out inside or outside the neighborhood. We converted response options into frequency of engagement per month for each activity (almost every day: 20; at least once a week: 6; at least once a month: 2; less than once a month: 1; and never: 0). Summing frequencies over all 10 activities resulted in a total social participation score representing the number of social activities per month. Internal consistency of the scale established through application of the principles of item-response theory was high (Chronbach α = 0.85).

Environmental factors.

Based on their residential address and Statistics Canada classification, we classified participants as living in metropolitan (≥ 150 000 habitants), urban (< 150 000 and ≥ 10 000), or rural (< 10 000) areas.²⁴ Based on information from Canadian censuses, the address also served to estimate material and social deprivation of the neighborhood, as well as residential density. Developed by Pampalon et al.,²⁵ material and social deprivation indexes considered the proportion of persons without a high school diploma, the proportion employed, and average personal income, as well as the proportion of persons living alone, separated, divorced or widowed, and single-parent families. Indexes of deprivation were available for every small area unit (i.e., composed of ≥1 dissemination blocks and had a population between 400–700 persons)²⁶ and were classified in population-weighted quintiles (i.e., groups of 20%) ranging from least (1) to most (5) deprived. Residential population density was defined as thousands of residents per square kilometer.

In addition, many variables related to neighborhood living conditions,¹¹ considering perceived housing, social environment, and quality of walking environment and transportation services were collected by interviewer-administered questionnaires (Table 1). We estimated quality of the social network using the social resources section of the Duke Older Americans Resources and Services (OARS) Multidimensional Functional Assessment Questionnaire (OMFAQ).^27–29 Answered on a 6-point scale ranging from 1 (excellent functioning) to 6 (functioning totally impaired), the social resources section includes 7 questions related to marital status, living arrangements, extent and type of contact with others, availability of confidant, perception of loneliness, and availability, duration, and source of help. The OMFAQ scale demonstrated high correlations with physical therapist measures of self-care capacity (Pearson correlation coefficient = 0.89) as well as, for the social resources section, high inter-rater reliability (intraclass correlation coefficient = 0.82).³⁰

TABLE 1—

Participants’ Characteristics: NuAge Study, Quebec, 2004–2006

Variables	Total (n = 1198; 100%), Mean (SE) or %	Metropolitan (n = 338; 28.2%), Mean (SE) or %	Urban (n = 634; 52.9%), Mean (SE) or %	Rural (n = 226; 18.9%), Mean (SE) or %	P for Comparison Across Areasa
Continuous variables
Age, y	73.7 (0.1)	73.9b (0.1)	73.2 (0.1)	72.9 (0.1)	< .001
Disabilityc	6.1 (0.2)	6.3d (0.3)	5.5 (0.2)	5.4 (0.3)	< .05
Depressive symptomse	5.0 (0.2)	5.2 (0.3)	4.4 (0.2)	4.5 (0.2)	.05
Family income, in thousands of Can $	45.1 (1.2)	47.4b (1.6)	38.3 (1.0)	37.0 (1.2)	< .001
Social participation (no. of activities/mo)	26.0 (0.7)	26.3 (0.9)	24.6 (0.6)	26.9 (1.1)	.09
Visit family members/friends	4.9 (0.2)	4.8 (0.3)	5.2 (0.2)	5.5 (0.3)	.23
Engage in a hobby outside of home	3.5 (0.2)	3.4 (0.3)	4.0 (0.2)	4.4 (0.4)	.08
Attend activities at a community/leisure center	2.4 (0.2)	2.4 (0.3)	2.1 (0.2)	2.2 (0.2)	.49
Go shopping	5.0 (0.2)	5.0 (0.3)	4.8 (0.3)	4.7 (0.2)	.76
Go to restaurant/pub/café	3.8 (0.2)	3.8f (0.2)	3.7 (0.1)	4.7 (0.3)	< .05
Attend a sports or cultural event	1.5 (0.1)	1.6b (0.1)	1.1 (0.1)	1.1 (0.1)	< .001
Take lessons or courses	0.8 (0.1)	0.9 (0.1)	0.6 (0.1)	0.6 (0.1)	.12
Participate in a self-help or discussion group	0.5 (0.1)	0.6 (0.1)	0.4 (0.1)	0.6 (0.1)	.06
Go to a public library or cultural center	1.2 (0.1)	1.3g (0.1)	0.9 (0.1)	0.6 (0.1)	< .001
Do some volunteer work	2.4 (0.2)	2.5 (0.3)	1.8 (0.2)	2.5 (0.3)	< .05
Accessibility to key resourcesh	3.2 (0.0)	3.3 (0.1)	3.1 (0.0)	2.0i (0.1)	< .001
Proximity to neighborhood resourcesj	3.4 (0.1)	3.7b (0.2)	2.6f (0.1)	2.0 (0.2)	< .001
Quality of social networkk	2.7 (0.1)	2.8 (0.1)	2.6 (0.0)	2.6 (0.1)	< .05
Availability of helpl	2.3 (0.0)	2.3 (0.1)	2.4 (0.0)	2.5 (0.1)	.09
Perception of lonelinessm	1.8 (0.0)	1.8 (0.0)	1.9 (0.0)	1.9 (0.0)	.06
No. of in-person contactsn	2.0 (0.0)	2.0f (0.0)	2.0 (0.0)	2.2 (0.0)	< .05
Happiness about frequency of contact with others	69.4	69.3	68.9	73.5	.76
No. of phone contactso	2.7 (0.0)	2.7b (0.0)	2.6 (0.0)	2.5 (0.0)	< .01
Availability of confidant	91.6	92.0	90.8	88.6	.54
No. of relatives visitingp	2.8 (0.0)	2.8 (0.0)	2.8 (0.0)	2.9 (0.0)	.06
Residential density of population, in thousands of residents/km2	8.5 (0.3)	10.4b (0.5)	3.5e (0.1)	0.5 (0.1)	< .001
Material deprivationq	2.2 (0.1)	2.1b (0.1)	2.6e (0.1)	3.3 (0.1)	< .001
Social deprivationr	3.6 (0.1)	3.7b (0.1)	3.3e (0.1)	2.8 (0.1)	< .001
Categorical variabless
Women	60.4	60.5	60.7	56.8	.18
Education, y					< .001
≤ 11	37.3	31.2	54.2	62.0
12–13	18.1	17.8	19.8	14.1
≥ 14	44.6	50.9	26.0	23.9
Marital status					< .001
Married/common-law	50.5	47.0	60.0	67.6
Single	14.8	17.3	7.3	6.7
Separated, divorced, or widowed	34.7	35.7	32.7	25.7
No. of diseases					< .001
< 2	18.9	21.0	12.8	11.3
2–4	51.4	51.2	52.4	48.2
> 4	29.7	27.7	34.8	40.5
Lives alone	38.4	41.6	30.1	23.0	< .001
Neighborhood living conditions
≥ 20 y lived in current dwelling	53.5	51.9	58.1	60.0	.07
≥ 20 y lived in current neighborhood	70.4	70.7	68.7	72.9	.68
Quite or very strong sense of belonging to neighborhood	79.7	78.8	80.8	90.1	.07
Children living in neighborhood	59.8	55.6	73.1	68.3	< .001
Quite or very easy user-friendliness of walking environment	95.7	96.9	93.0	87.2	< .001
Has a driver’s license	75.8	73.4	81.9	90.4	< .001
Uses transit	31.9	40.4	7.4	0	< .001

^aP value associated with the 1-way analysis of variance, in which a significant P value (P < .05) indicates that at least 2 areas differ.

^bMetropolitan differs significantly from the other 2 areas (P < .017).

^cOn a scale of 0–87, as measured by SMAF (Functional Autonomy Measurement System) in which higher scores indicate greater disability.

^dMetropolitan differs significantly from urban area (P < .017).

^eAs measured by Geriatric Depression Scale. A score of 10 or lower indicated the absence of depressive symptoms, 11 to 20 referred to mild depressive symptoms, and 21 to 30 was equal to moderate or severe depressive symptoms.

^fUrban differs significantly from rural area (P < .017).

^gMetropolitan differs significantly from rural area (P < .017).

^hOn a scale of 0-4, with higher numbers indicating greater accessibility to key resources.

ⁱRural differs significantly from the other 2 areas (P < .017).

^jOn a scale of 0–12, with higher numbers indicating higher proximity to resources.

^kOn a scale of 1–6, as measured by Older American Resources and Services Multidimensional Functional Assessment Questionnaire, with higher numbers indicating greater impairment.

^lOn a scale of 0–3, with higher numbers indicating greater availability of help.

^mOn a scale of 0–2, with higher numbers indicating less perceived loneliness.

ⁿOn a scale of 0–3, with higher numbers indicating greater number of in-person contacts.

^oOn a scale of 0–3, with higher numbers indicating greater number of phone contacts.

^pOn a scale of 0–3, with higher numbers indicating greater number of relatives visiting.

^qOn a scale of 1–5, with higher numbers indicating greater material deprivation.

^rOn a scale of 1–5, with higher numbers indicating greater social deprivation.

^sP value associated with χ² test, in which a significant P value (P < .05) indicates that at least 2 areas differ.

Finally, 2 scales measured older adults’ perception of the proximity to neighborhood resources. Specifically, 1 scale (i.e., perceived accessibility to key resources) included 4 items that assessed ease or difficulty of accessing resources in the neighborhood: (1) good quality, affordable food; (2) good range of businesses and services (pharmacy, and so on); (3) leisure activities of interest; and (4) facilities to engage in preferred physical activities or sports (reliability coefficient = 0.63). Items rated very or quite easy were summed for a maximum total score of 4. The other scale, perceived proximity to neighborhood resources, was derived from a series of items that assessed perceived walking time (in minutes) between respondents’ residence and the nearest grocery or food store; convenience or corner store; bank; pharmacy; community or leisure center; sports center; restaurant, bistro, or café; library or cultural center; store or shopping center; church or place of worship; local health and social services clinic or medical clinic; or park. Resources perceived by the participants as being located within a 5-minute walk from their residence were summed for a maximum total score of 12. Internal consistency (Cronbach α) of these series of questions was previously reported to be 0.94¹¹ and 0.82.^12,31

Sociodemographic and clinical characteristics.

We used a series of self-reported questions to describe participants’ sociodemographic characteristics (Table 1). We estimated depressive symptoms with the Geriatric Depression Scale (GDS), including 30 dichotomous questions (yes/no) in which people answered according to how they felt during the week preceding the interview.³² A score of 10 or lower indicated the absence of depressive symptoms, 11 to 20 referred to mild depressive symptoms, and 21 to 30 was equal to moderate or severe depressive symptoms.³³ The GDS has been widely used in aging populations and was shown to be a valid and reliable indicator of depressive symptoms.^32–34 We used the Functional Autonomy Measurement System (SMAF),³⁵ which is well known and widely used in gerontology literature,³⁶ to estimate disability. The total score represented the sum of all items and ranged from 0 to 87, with higher scores indicating severe disabilities. Psychometric properties of the SMAF were strong; there were high intraclass correlation coefficients for test–retest (0.95) and inter-rater (0.75) reliability, and good discriminant validity.³⁷ Moreover, the SMAF was highly correlated (r = 0.90) with the well-known Functional Independence Measure.³⁷

Analysis

We described participants using means with SEs or percentages, according to the type of variables (continuous or categorical, respectively). We carried out all analyses using SAS (version 9.2; SAS survey procedures; SAS Institute Inc., Cary, NC), which took into account the stratified random sampling strategy. We performed the χ² test and analyses of variance, followed by pairwise comparisons (with Bonferonni adjustment), to identify differences among areas. We simultaneously examined the associations between all environmental factors and their interactions with gender, material and social deprivation, and social participation using multiple regressions. In line with previous studies,^38–42 we used age, gender, living situation, and family income as sociodemographic control variables. Moreover, because they influenced social participation, we considered depressive symptoms (GDS) and disability level (SMAF) to be clinical control variables. To address the study objectives, we created 3 models (1 per area). We reduced the models for parsimony using the all-possible regression procedure.⁴³ We visually verified assumption of normality with histograms and statistically with the Kolmogorov-Smirnov test. Because social participation distribution was positively skewed, we used log transformation for regression analyses. We did not observe any collinearity problems, and we performed residual analyses to verify basic assumptions. P values less than .05 were considered significant.

RESULTS

The majority of participants were women, all aged between 67 and 82 years, and most lived in urban areas and for more than 20 years in their current dwelling (Table 1). The majority had a quite or very strong sense of belonging to their neighborhood, lived with others, had children living in the neighborhood, and reported a family income of more than 35 000 Canadian dollars. Most had 2 to 4 diseases, but they did not have depressive symptoms. Participants’ mean disability scores indicated minor disabilities, with participants living in metropolitan areas having a higher disability mean level than those living in urban and rural areas (Table 1). Although statistically significant, this difference was not clinically meaningful. Compared with those living in other areas, metropolitan participants were less likely to have a driver’s license and more likely to use public transit, which was less available in other areas, especially rural areas. Accessibility to key resources was superior in more populated areas (Table 1). Proximity to neighborhood resources also differed across areas, with participants living in metropolitan and urban areas reporting more services and amenities located within a 5-minute walk of their dwelling. The quality of the social network score indicated mildly impaired social functioning with in-person or phone contacts being greater for rural and metropolitan participants, respectively (Table 1). Finally, although residential density and social deprivation were greater in areas with larger populations, material deprivation was worse elsewhere.

On average, and with small variations, older adults participated in 26.0 social activities per month, and this mean did not differ across living areas (P = .09; Table 1). The 3 activities most frequently engaged in by older adults were also the same across areas (i.e., visiting, shopping, and going to the restaurant, pub, or café). However, some differences in the type of activity were identified across areas, including attending sports or cultural events, going to a public library or cultural center, going to a restaurant, pub, or café, and volunteering (Table 1). For example, compared with rural participants, metropolitan participants attended sports or cultural events more often and visited a public library or cultural center more frequently (P < .001). Although we controlled for age, gender, living situation, family income, depressive symptoms, and disability, different environmental variables were associated with social participation according to the area. In metropolitan areas, higher social participation was associated with greater proximity to neighborhood resources, having a driver’s license, transit use, and a better quality social network (Table 2). In urban areas, higher social participation was associated with greater proximity to neighborhood resources and having a driver’s license. Finally, in rural areas, higher social participation was associated with greater accessibility to key resources, having a driver’s license, children living in the neighborhood, and more years lived in the current dwelling (Table 2). These environmental factors explained a higher percentage of the variance in social participation in metropolitan and rural areas compared with urban areas. We did not find any interaction with gender, material, or social deprivation that significantly modified the associations between environmental factors and social participation.

TABLE 2—

Multivariate Models Estimating the Association Between Environmental Factors and Social Participation According to Living Area: NuAge Study, Quebec, 2004–2006

	Metropolitan (n = 338)		Urban (n = 634)		Rural (n = 226)
Variables	b (SE)	P	b (SE)	P	b (SE)	P
Proximity to neighborhood resourcesa	0.04 (0.01)	< .01	0.03 (0.01)	< .01
Accessibility to key resourcesb					0.09 (0.02)	< .001
Has driver’s license	0.33 (0.10)	< .01	0.17 (0.07)	< .05	0.31 (0.14)	< .05
Uses transit	0.28 (0.08)	< .001
Quality of social networkc	−0.10 (0.04)	< .01
Children living in neighborhood					0.15 (0.07)	< .05
≥ 20 y lived in current dwelling					0.17 (0.07)	< .05
Cumulative unadjusted R2	0.12*		0.03*		0.13*
Age, y	0.01 (0.01)	0.42	−0.01 (0.01)	< .05	0.00 (0.01)	.94
Gender	−0.19 (0.07)	< .01	−0.07 (0.06)	.29	0.06 (0.08)	.42
Living situation	0.13 (0.08)	0.09	0.19 (0.06)	< .01	0.18 (0.10)	.07
Family income	0.00 (0.00)	0.95	−0.00 (0.00)	.13	0.00 (0.00)	.18
Depressive symptomsd	−0.01 (0.01)	0.32	−0.02 (0.01)	< .01	−0,03 (0.01)	< .05
Disabilitye	−0.02 (0.01)	< .05	−0.00 (0.01)	.61	−0.01 (0.01)	.5
Cumulative adjustedf R2	0.18*		0.11*		0.18*

^aMeasured on a scale of 0–12, with higher numbers indicating greater proximity to neighborhood resources.

^bMeasured on a scale of 0–4, with higher numbers indicating greater accessibility to key resources.

^cMeasured using the Duke Older Americans Resources and Services Multidimensional Functional Assessment Questionnaire.

^dMeasured using the Geriatric Depression Scale.

^eMeasured using the SMAF, or Functional Autonomy Measurement System.

^fR² adjusted for age, gender, living situation, family income, depressive symptoms, and disability.

*P < .001.

DISCUSSION

We aimed to compare social participation of older adults living in metropolitan, urban, and rural areas, and to identify associated environmental factors. As in the study by Therrien et al.,¹⁸ we did not identify any difference in social participation according to living area. In addition, environmental factors explained only small percentages of the variance in social participation. Such results might reflect the limited influence of specific environmental factors, such as neighborhood living conditions and resources available, on older adults’ social participation. This small total variance might also be the result of the small variation in social participation scores and lack of consideration of other factors, such as individual preferences, which include meaningful activities,⁴⁴ stability of disabilities, stressful events, well-being,⁴⁵ fewer environmental barriers,^10,45–47 and mobility assistive technologies,⁴⁶ which were shown to be associated with older adults’ social participation. Nevertheless, in all areas and in agreement with previous studies,^11,12,31 greater proximity or accessibility to resources was associated with social participation. Access to key resources, such as good quality, affordable food, businesses and services (pharmacy, health services, banking), leisure activities of interest, and preferred physical activities or sports made it easier to meet people and interact with them in the community.^16,48 Moreover, minimal differences in social participation might reflect older adults’ adaptation to their environment.⁴⁶ In our investigation, for some social activities, disparities in frequency of engagement were noticed across living areas, indicating that older adults chose activities that were available and adjusted their engagement in other activities accordingly. Finally, because having a driver’s license allowed people to engage in activities in any area, the high percentage of participants with a driver’s license might also explain the lack of difference in social participation in metropolitan, urban, and rural areas. User-friendliness of transport,¹⁰ and especially of automobile driving,^17,18 was previously demonstrated to be important for older adults’ social participation in both urban and rural areas.

Although some environmental factors, such as residential density and access to transportation, differed across metropolitan, urban, and rural areas, we identified few differences associated with social participation in our study. Differences found involved transit use, quality of social network, children living in the neighborhood, and years lived in the current dwelling. First, because public transport was less available in urban and often unavailable in rural areas, the importance of transit use for social participation was noticed only in metropolitan areas. Transportation challenges for people living in rural areas were also highlighted in previous studies.^17,18 Even if this mode of transportation was not usually the main or preferred option, transit use allowed people to engage in more social interactions and access various activities, and was a good alternative to automobile driving, reduced pollution, and might be essential for older adults who stop driving. Second, similarly to previous studies,^15,48 our results supported the importance of the social environment, that is, the quality of the social network in metropolitan areas and, perhaps alternatively, the presence of children living in the neighborhood in rural areas. Considering that they were more often separated, divorced, or widowed and had children living in the neighborhood less often, metropolitan older adults might need to rely more on their social network than those living in urban or rural areas. Moreover, for rural older adults, because their proximity or accessibility to resources was lower, children living in the neighborhood might be more important. Although aging in place, intergenerational housing initiatives might be advantageous. Surprisingly, social environment factors were not associated with urban older adults’ social participation, which was probably associated with other variables that were not considered in our study. Finally, consistent with aging in place policies^49,50 and studies,^48,51 living more years in the current dwelling was associated with higher social participation for older adults living in rural areas, who also reported a greater sense of belonging to their neighborhood. People living in a neighborhood for a longer period of time might have a greater social network and knowledge of the social activities available, even if their proximity or accessibility to resources is lower.

Strengths and Limitations

Our cross-sectional study was a first step in increasing the understanding of social participation of older adults living in different areas and identifying associated environmental factors. This knowledge could foster reflection and might guide the decisions of politicians, managers, and clinicians to promote older adults’ social participation and health. However, our study had some limitations. It was carried out with a sample that appeared to be more educated, had higher levels of income, and fewer disabilities than the older population of the province in general⁵²; thus, it was not fully representative of older adults. Although discussed as playing a central role in public health environmental studies,^13,53–55 the majority of measures we used were self-reported. Other measures of the environment, such as those derived from geographic information systems, might be useful. Finally, the classification of metropolitan, urban, and rural areas was based solely on the number of residents living in the area and did not take proximity to other areas into account.

Conclusions

Our cross-sectional study compared social participation of older adults living in metropolitan, urban, and rural areas, and identified associated environmental factors. Similar frequency of participation in social and community activities was found, but after controlling for potential confounding variables, we identified different associated environmental factors according to metropolitan, urban, or rural areas. In all areas, greater proximity or accessibility to resources and having a driver’s license were associated with social participation. However, transit use and quality of the social network were associated only with the social participation of older adults living in metropolitan areas, whereas the presence of children living in the neighborhood and more years lived in the current dwelling were correlates of social participation identified in rural areas. To enhance older adults’ social participation, public health interventions might address different environmental factors specific to each area. Proximity or accessibility to resources and the ability to drive safely should be maintained to foster social participation of older adults in all areas. Moreover, quality of the social network and transit use might be optimized mainly in metropolitan areas. Finally, living in the same neighborhood as one’s children or aging in place could be encouraged, particularly in rural areas. To improve the development of public health interventions, future social participation studies should consider a qualitative design, other measures (e.g., social capital), or additional environmental factors, such as environmental barriers, built environment, transportation (including public transit and difficulty driving to or parking at resource locations), quality and type of housing, mobility assistive technologies, social media, attitudes in the social environment (e.g., ageism, crime, and traffic), public and government services, and political orientations. Moreover, longitudinal studies linking environmental factors and social participation are needed. Because of the potential to increase understanding of the associations between environmental factors and social participation and the complex pattern of findings we observed, replications and further research is warranted.