Development and evaluation of a community-based approach to promote health-related
behaviour among older adults in a socioeconomically disadvantaged community
Luten, Karla
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2
Correlates of physical activity
among older adults
in a socioeconomically disadvantaged
rural area in the Netherlands
Karla A. Luten Andrea F. de Winter Arie Dijkstra Sijmen A. Reijneveld
Abstract
Objective: To identify sociodemographic, health-related, cultural, and psychological
correlates of physical activity (total and per type of physical activity) in older adults in a socioeconomically disadvantaged rural area, and to assess whether their socioeconomic status (SES) affected the correlates of physical activity.
Methods: We conducted a cross-sectional study among 244 adults. Physical activity, total
and transport-related, household-related, and leisure-time, was measured with the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH).
Results: We found significant correlates from different clusters of variables per type of
physical activity. Having a partner and higher self-efficacy were related to more total physical activity. Younger age, better physical fitness, and being less happy to be a person from this region were associated with more transport-related physical activity. Being female, having no (paid) work, less physical fitness, and feeling less connected to the region were related to more household-related physical activity. Being male, having a partner, better physical fitness, better overall health, being born and having lived in the region, being happy to be a person from this region, and feeling connected to the region were associated with more leisure-time physical activity. Associations between low and higher older SES adults hardly differed.
Conclusions: Our findings imply that in general strategies to improve physical activity are
Introduction
It is well known that regular physical activity can reduce the risks of health problems like cardiovascular diseases, type 2 diabetes, and obesity.1-3 However, despite these beneficial
effects of physical activity on general health, less than half of the adult population engages in sufficient physical activity.2,4 The prevalence of physical inactivity is especially high among
older people,4,5 people with a low socioeconomic status (SES),6-8 and those living in rural
areas.7 Thus older adults with a low SES living in a rural area represent a major risk group
for problems resulting from physical inactivity.
For targeted public health prevention, insight into the correlates of physical activity is needed to effectively promote an active lifestyle.9 The number of older adults is growing
and physical activity is essential for them to maintain physical independence and preserve health as they age.10 Correlates of physical activity have been investigated among various
populations in socioeconomically disadvantaged parts of cities. For people from socioeconomically disadvantaged rural areas few data are available, but rural populations seem to show lower levels of physical activity when compared to urban populations.11 That
implies that correlates of physical activity for older adults in socioeconomically disadvantaged rural areas are likely to differ from those from urban areas, due for example to differences in physical or social environment.12 Correlates of physical activity have indeed
been shown to differ between geographical areas,7 although results among (older) women
were mixed.11,12 A lower physical activity is in particular likely when people are socially and
culturally embedded in a socioeconomically disadvantaged rural area in which structural neighbourhood factors influence activity behaviour negatively. This may for instance regard a lack of care and physical activity facilities, and the interacting social and cultural norms against physical activity.13 Correlates of physical activity can also vary among socioeconomic
subgroups as a result of differences in financial circumstances and perceived health.14
However, so far hardly any study has specifically assessed correlates of physical activity among older adults living in a socioeconomically disadvantaged rural area.
We need insight into the correlates of physical activity among these older adults in order to develop effective, segmented, and tailored strategies to increase their physical activity.15 We have therefore chosen for our research the I-Change model, which integrates
concepts of various cognitive models including the Theory of Reasoned Action,16 Bandura’s
Social Learning Theory,17 the Transtheoretical Model,18 the Health Belief Model19 and the
Precaution Adoption Model.20 The I-Change model provides a useful framework for
understanding behaviours like physical activity.21 It distinguishes between different types
and levels of correlates of health-related behaviours, such as predisposing, preceding, and motivation factors. In the present study we focus concretely on sociodemographic, health-related, cultural, and psychological variables in relation to physical activity.
The first aim of this paper is to identify and describe sociodemographic, health-related, cultural, and psychological correlates of physical activity among older adults in a socioeconomically disadvantaged rural area in the Netherlands. While most studies focus only on total physical activity or leisure-time physical activity, our study distinguishes two additional types: transport-related physical activity and household-related physical activity to provide a broader perspective on physical activity.22 The second aim of this paper is to
assess whether correlates of physical activity in older adults vary between low and higher SES people in a socioeconomically disadvantaged community. Our study sample was recruited from Eastern Groningen, a rural area in the northeastern part of the Netherlands, and one with relatively high percentages of unemployment and social problems (e.g., poverty). This area also has a high percentage of older adults as well as people with low SES, and the reported prevalence of health problems among inhabitants of this area is greater than in other areas in the Netherlands.23
Methods
Procedure and recruitment
We conducted a cross-sectional study among adults aged 55 years and older living in the municipality of Vlagtwedde in Eastern Groningen, in 2010. Six-hundred inhabitants were randomly selected from population registers of the municipality and invited for the study by letter; the letter explained the purpose, content and procedure of the study and offered participants the chance to win a prize (vouchers worth twenty and fifty euros). They received a postal questionnaire; this was returned by a total of 244 respondents (40.6%). The response to the reminder was about 6%. Participants gave consent to participate in the study by completing and returning the questionnaire. After two weeks, non-responders received a reminder by post. From these participants (the total sample) we obtained data on physical activity, sociodemographic, health-related, and cultural variables, using a self-report questionnaire. In addition, half of the sample (the subsample; n=135) answered a supplement to the questionnaire dealing with psychological variables related to physical activity. After evaluating our study protocol the Medical Ethical Committee of the University Medical Centre Groningen found it unnecessary to file it for ethical approval.
Measures
Sociodemographic variables
We assessed sociodemographic variables as gender, age, marital status, employment status, and SES. For marital status we distinguished ‘married’(1), ‘cohabiting’(2), ‘divorced’(3), ‘single’(4), and ‘widowed’(5). We recoded these categories into ‘having a partner’(1-2), and ‘single’(3-5). For employment status the answering options were ‘paid work, 32 hours or more’(1), ‘paid work, between 20 and 32 hours’(2), ‘paid work, between
12 and 20 hours’(3), ‘paid work, less than 12 hours’(4), ‘(early) retired’(5), ‘unemployed/ looking for a job’(6), ‘disabled’(7), ‘social welfare’(8), ‘housekeeping’(9), ‘study’(10). We dichotomised employment status into ‘paid work’(1-4) and ‘no (paid) work’(5-10). We assessed SES according to educational level using the following categories: ‘no education’(1), ‘primary education’(2), ‘lower general or professional education’(3), ‘intermediate general education’(4), ‘intermediate professional education’(5), ‘higher general education’(6), ‘higher professional education’(7), and ‘university’(8). Categories 1 to 3 were recoded as ‘low’ and categories 4 to 8 were recoded as ‘higher’.
Health-related variables
Health-related variables were operationalised as functional health status measured by three items taken from the COOP/WONCA charts: physical fitness, emotional feelings, and overall health.24 Each item was scored on a five-point Likert scale. Physical fitness was
assessed by asking ‘During the past 2 weeks…. what was the hardest physical activity you were able to do for at least 2 minutes?’; answering options went from ‘very light, for example walk at a slow pace or not able to walk’(1) to ‘very heavy, for example run at fast pace’(5). Emotional feelings were assessed by the question ‘During the past 2 weeks…. how much have you been bothered by emotional problems such as feeling anxious, depressed, irritable or downhearted and sad?’; answering options went from ‘not at all’(1) to ‘extremely’(5). Overall health was assessed by asking ‘During the past 2 weeks…. how would you rate your health in general?’; answering options went from ‘poor’(1) to ‘excellent’(5). A higher score on each item indicates a higher level of fitness, more emotional problems, and a better overall health, respectively.
Cultural variables
Cultural variables refer to the extent to which a person is embedded in the culture in a geographically defined area; these consisted of one self-descriptive history item and two identity evaluation items. Self-descriptive history concerns a person’s level of perception of common heritage after living a longer time in a certain area. Answering options ranged from ‘yes, I was born here and have always lived in Eastern Groningen’(1) to ‘no, I was born outside the Netherlands, but I now live in Eastern Groningen’(4). The categories were merged into ‘being born and having lived in Eastern Groningen’(1) and ’not being born or not always having lived in Eastern Groningen’(2-4). Identity evaluation was a person’s perception of his relationship with his sociocultural environment. This consisted of two items scored on a five-point Likert scale: ‘Feeling happy to be a person from Eastern Groningen’ with answers from ‘totally disagree’(1) to ‘totally agree’(5) and ‘feeling connected to Eastern Groningen’ with answers from ‘I don’t feel at all connected’(1) to ‘I feel very connected’(5). A higher score indicates that a person is more regionally embedded.
Psychological variables
Psychological variables related to physical activity included attitude, perceived advantages, perceived disadvantages, self-efficacy, and social norms, based on the I-Change Model representing an integrated framework of individual factors of health-related behaviour.21
Attitude towards physical activity was measured by one item that asked: ‘How
important is sufficient physical activity to you?’ The question was rated on a five-point Likert scale from ‘not important at all’(1) to ‘very important’(5). A higher score indicates a more positive attitude towards physical activity.
Perceived advantages were what a person regarded as benefits of physical activity;
these consisted of nine items: ‘keeping my weight under control’; ‘taking care that I live longer’; ‘feeling more relaxed’; ‘giving me more energy’; ‘giving me a better condition’; ‘keeping me healthy’; ‘getting a disease less soon’; ‘making me feel more fit’; ‘making me feel good about myself’. The items were scored on a five-point Likert scale (α=.89) from ‘totally disagree’(1) to ‘totally agree’(5) and were averaged into one score. A higher score indicates perception of more advantages.
Perceived disadvantages were negative consequences, experienced or expected,
related to physical activity; these were measured by four items: ‘is painful’; ‘makes me too exhausted’; ‘takes a lot of time’; ‘makes me afraid to fall’. The items were scored on a five-point Likert scale (α=.72) from ‘totally disagree’(1) to ‘totally agree’(5) and were averaged in one score. A higher score indicates perception of more disadvantages.
Self-efficacy is a person’s confidence in his or her ability to be physically active in
specified situations. This was assessed using 12 items about expectations to succeed in various difficult situations. The items concerned ‘bad weather circumstances’; ‘having no partner in physical activity’; ‘lacking safety’; ‘not liking physical activity’; ‘feeling tired’; ‘having little time’; ‘not feeling well’; ‘suffering physical complaints’; ‘suffering physical complaints afterwards’; ‘lacking facilities’; ‘lacking contact with people of one’s own age’; ‘lacking acquaintances’. The items were scored on a five-point Likert scale (α=.87) from ‘very difficult’(1) to ‘very easy’(5) and were averaged in one score. A higher score indicates a higher self-efficacy.
The social norm regarding physical activity was measured by one item: ‘How important is physical activity for people in your direct social environment?’ This question was rated on a five-point Likert scale from ‘very unimportant’(1) to ‘very important’(5). A higher score indicates a stronger social norm, thus more social pressure/support to engage in physical activity.
Physical activity
We measured physical activity with the Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH), a validated Dutch questionnaire to measure physical activity in an adult population.25 The questionnaire includes four types of physical activity:
household-related physical activity, and leisure-time physical activity. The overall hours per week of physical activity and the three separate physical activity types (transport-related physical activity, household-related physical activity, and leisure-time physical activity) were calculated. Because only 21% of the participants population had not retired from work, we excluded work-related physical activity in this paper.
Statistical analyses
Three of the four physical activity measures (the subscales transport-related, household-related, and leisure-time physical activity) showed a skewed distribution; only total physical activity was approximately normally distributed. We therefore decided to dichotomise the subscales of physical activity measures to study their associations. We dichotomised household-related physical activity and leisure-time physical activity, using a median-split. Because more than 50% of the participants filled in no transport-related physical activity, we dichotomised this variable into ‘no’ versus ‘yes’. Total physical activity was presented as a continuous-level outcome variable.
We first described the background characteristics of our sample. Then we assessed the associations of our four measures of physical activity with sociodemographic (gender, age, marital status, employment status, and SES), health-related (physical fitness, emotional feelings, and overall health), cultural (historical area identity and identity evaluation), and psychological variables related to physical activity (attitude, perceived advantages, perceived disadvantages, self-efficacy, and social norms). Because the aim was not to test a conceptual model or mediational relations among variables, only univariate (no multivariate) analyses were conducted. We did so using linear regression analyses for total physical activity and logistic regression analyses for transport-related, household-related, and leisure-time physical activity. Finally, we separately assessed the associations within the two SES groups. To determine whether correlates of physical activity differed between the level of SES, interaction effects between each of the potential correlates on the one hand and level of SES on the other hand were analysed. In the case of a statistically significant interaction, the relation of the interacting variable with physical activity was assessed separately within low SES and within higher SES participants. Statistical tests were considered to be significant when p<0.05. All analyses were performed using SPSS version 20.0.
Results
Sample characteristics
Characteristics of the study participants, including level of SES, appear in Table 1. The mean age of the total sample was 66.7 years and 41% of the sample had a low SES. The data reveal
some clear differences between low and higher SES participants. About 64% of the low SES older adults were born and had remained in Eastern Groningen compared to only 32% of those with a higher SES. Regarding physical fitness, 27% of the low SES but 43% of higher SES older adults had a higher score, meaning that they had engaged in ‘heavy’ or ‘very heavy’ physical activity during the past 2 weeks.
Correlates of physical activity
Table 2 shows the results of regressing total, transport-related, household-related, and leisure-time physical activity on sociodemographic, health-related, cultural, and psychological variables. More total physical activity was associated to having a partner and higher self-efficacy. Higher scores on transport-related physical activity were related to younger age, better physical fitness, and being less happy to be a person from this region. More household-related physical activity was associated to being female, having no (paid) work, less physical fitness, and feeling less connected to this region. Higher scores on leisure-time physical activity were related to being male, having a partner, reporting better physical fitness, reporting better overall health, being born and having lived in the region, being happy to be a person from this region, and feeling connected to the region.
Correlates of physical activity per SES group
The univariate results for the low and higher SES participants separately are presented in Table 3. No significant interaction effects of total physical activity were found. Concerning transport-related physical activity, significant interaction effects were found for feelings and overall health: Only in higher SES participants, a lower score on emotional feelings was significantly associated with more physical activity. In low SES participants, overall health was related negatively to physical activity while in higher SES participants it was related positively to physical activity, but both relations were not significant. Regarding household-related physical activity, a significant interaction effect was found only for emotional feelings: In low SES participants, less emotional feelings was significantly associated with higher physical activity scores. Concerning leisure-time physical activity, only a significant interaction effect was found for being born and having lived in the region: For low SES participants, being born and having lived in the region was significantly associated with more physical activity.
Table 1. Characteristics of the participants.
Total Low SES Higher SES
Total samplea n=244 n=100 n=143
Gender (% male) 41.4 38.0 44.1
Age (mean (SD) in years) 66.7 (8.6) 67.7 (9.7) 66.4 (7.7)
Marital status (% having a partner) 74.6 70.0 77.6
Employment status (% paid work) 20.6 16.0 23.9
Physical fitness (% (very) heavy) 36.8 27.1 42.8
Emotional feelings (% not at all/ slightly) 88.2 87.6 88.7 Overall health (% excellent/ very good) 29.0 19.6 35.5 Being born and having lived in region (% yes) 44.4 63.6 31.5 Feeling happy to be a person from EG (% (very) happy) 75.8 79.0 74.1 Feeling connected to EG (% feeling (very connected) 80.7 82.0 79.7 Total PA (mean (SD))b 29.2 (17.2) 27.6 (17.0) 30.3 (17.3) Low (n=112) 15.9 (7.1) 15.1 (7.6) 16.5 (6.6) High (n=111) 42.6 (13.6) 43.5 (11.5) 42.1 (14.7) Transport-related PA (mean (SD))b 2.5 (4.7) 2.6 (5.0) 2.4 (4.5) No (n=123) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) Yes (n=102) 5.5 (5.7) 6.9 (6.2) 4.8 (5.4) Household-related PA (mean (SD))b 11.0 (10.7) 10.1 (9.5) 11.6 (11.3) Low (n=113) 2.8 (2.9) 2.3 (2.8) 3.2 (3.0) High (n=111) 19.4 (9.1) 18.2 (7.0) 20.1 (10.2) Leisure-time PA (mean (SD))b 10.3 (8.3) 9.5 (8.3) 10.8 (8.3) Low (n=114) 3.9 (2.7) 3.5 (2.7) 4.2 (2.7) High (n=113) 16.7 (7.0) 16.9 (6.7) 16.6 (7.1) Subsample n=137 n=54 n=82
Attitude (mean; range 1-5) 3.83 3.87 3.80
Social norm (mean; range 1-5) 3.73 3.74 3.72
Advantages (mean; range 1-5) 3.73 3.66 3.78
Disadvantages (mean; range 1-5) 2.39 2.48 2.33
Self-efficacy (mean; range 1-5) 2.97 2.84 3.07
SES: socioeconomic status; SD: standard deviation; EG: Eastern Groningen; PA: physical activity in hours/week; a For one respondent the SES classification was missing; b PA outcome missing: Total PA: n=21,
Table 3. Associations of health-related and cultural variables with transport-related
physical activity (PA), household-related PA, and leisure-time PA (hours/week) for older adults with low and with higher socioeconomic status (SES): odds ratios (95% confidence intervals), resulting from logistic regression analyses.
Transport-related PA Household-related PA Leisure-time PA
Low SES Higher SES Low SES Higher SES Low SES Higher SES Health-related Emotional feelings 1.38 (0.85-2.22) 0.58 (0.36-0.93)* 0.55 (0.32-0.95)* 1.24 (0.79-1.94) - - Overall health 0.58 (0.31-1.07) 1.23 (0.84-1.82) - - - - Cultural Being born and having lived in region (vs. not) - - - - 5.11 (1.91-13.70)** 1.25 (0.60-2.60) * p<0.05; ** p<0.01
Discussion
This study aimed to identify correlates of physical activity among older adults in a socioeconomically disadvantaged rural area in the Netherlands, and to assess whether the correlates of physical activity differed by SES. We found significant correlates from different clusters of variables of different types of physical activity: gender, age, marital status, social situation, physical fitness, overall health, self-efficacy, and cultural variables. However, the associations hardly differed between older adults with low and higher SES.
Among older adults in a socioeconomically disadvantaged rural area correlates vary for the different types of physical activity; this is in line with studies made of other target populations.26,27 Lumping all physical activities together may have blurred some
relations: for example, higher total physical activity score was related only to having a partner and higher self-efficacy, while some specific types of physical activity had more significant associations. More transport-related and household-related physical activity were both significantly related to sociodemographic variables, a health-related, and a cultural variable. However, more leisure-time physical activity was related to seven variables: male gender, having a partner, better physical fitness and overall health, and three cultural variables.
First of all, these findings show that the type of physical activity is important: although using total physical activity may indicate physical importance, its use may obscure the relations with potential predictors of physical activity. Leisure-time physical activity, on the other hand, showed several meaningful associations. This could reflect that people have more freedom in how they spend their leisure time than in how they travel (transport).
Second, in our target population we found gender and health-related variables to be associated to household-related and leisure-time physical activity: women engaged in more physical activity in the household, and men during their leisure time. This may represent classical gender role differences in this older population: women take care of the household while men tend to the garden and do odd jobs. Physical fitness was positively related to transport-related physical activity and leisure-time physical activity. The negative relationship between physical fitness and household-related physical activity could be due to confounding by gender.
Third, a novel finding was that one’s regional background was related to physical activity: being born and having lived in the region, feeling happy about coming from the region, and feeling more connected to the region were associated with more leisure-time physical activity. This shows the relevance of a person’s embedding in the physical and social environment of his region.28 We must, however, still establish the direction and meaning of
these relationships. While leisure-time physical activity was related to feeling more connected to the region, household physical activity was related to feeling less connected, and transport-related physical activity was related to feeling less happy to be from the region. “Regional identity” would therefore be an interesting topic for further study.
The second aim of our study was to test whether the correlates of physical activity differed between participants with low or with higher SES; we found only a few differences in correlates of physical activity between these participants. Regarding the total physical activity measure, the lack of a significant interaction between any of the variables and SES indicates that there were no SES differences. Concerning household-related and leisure-time physical activity, participants with low versus higher SES differed only regarding one association. Regarding transport-related, we found differing associations of two variables. Associations thus mostly do not differ between low and higher SES participants in this socioeconomically disadvantaged area. A lower statistical power might explain this finding if looking at subsamples of people with low and higher SES. However, the socioeconomically disadvantaged nature of the total area might offer an alternative explanation; that context may affect all residents, independent of their own SES.
Although most guidelines only concern moderate or high intensive activities, in the present study light physical activity was also included in the physical activity measures. This led to relatively high mean scores regarding minutes of physical activity. Evidence is increasing that not only moderate and heavy exercise are beneficial. For example, Buman et al. showed light-intensity physical activity to be associated with physical health and well-being in older adults.29 This effect may partially be caused by less sedentary behaviour;
recent research has shown that sedentary behaviour (i.e., prolonged sitting) increases the risk of mortality.30-32 In addition, light-intensity physical activity may empower people to
stay socially active and able to live independently longer. Thus, maintaining light-intensity physical activity might be a meaningful component of an active lifestyle.
Strengths and limitations
A first strength of our study is that, based on the I-Change model, we assessed a wide range of variables potentially associated with physical activity. We identified significant correlates from all four clusters: sociodemographic, health-related status, cultural, and psychological variables. Another strength of our study was that, besides testing different clusters of variables, we included and compared different types of physical activity, providing more insights into these complex associations. Finally, older people and people with a low SES are subgroups hard to reach for prevention; 33 our study adds to the hitherto limited knowledge
of an important health-related behaviour of older people from a relatively disadvantaged rural area.
We must also acknowledge some limitations of the present study. First, the low response rate of subjects may have influenced the representativeness of the study. The adults aged 75 years and older in our sample were slightly less than representative when compared to objective figures taken from the municipality of Vlagtwedde.34 In addition,
over 40% of our study sample had a low SES (from no education to lower professional education), suggesting that the educational level of the sample was representative for the region. However, we cannot fully rule out some selection bias. A second limitation is the cross-sectional design, which limits the potential for inferences on causality. However, the I-Change model provides a theoretical background against which relationships can be interpreted. For example, sociodemographic variables (e.g., age) are themselves not causes of physical activity but may be related to causes, whereas perceived advantages may be a primary cause of leisure-time physical activity. Third, our measurement of SES was based on educational level, which represents just one aspect of SES.35 However, this regards the
most frequently used indicator of SES in the Netherlands.36 Finally, we used short measures
to assess the psychological variables. Although even one-item measures are often valid, 37-39 our psychological measures may have lacked sufficient reliability.
Conclusions
This study contributes to the understanding of different clusters of correlates of types of physical activity among older adults in a socioeconomically disadvantaged rural area. We found relevant correlates of physical activity in our sample. These findings can help in the development of effective intervention strategies to promote physical activity among this target population. They may be valued as calling for a multi-component perspective for intervention development, taking into account different types of physical activity. In the end, we hope that such well-informed interventions will help to stimulate people in socioeconomically disadvantaged areas to engage in more physical activity.
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