Journal of Aging and Physical Activity, 2006, 14, 423-438
© 2006, Human Kinetics, Inc.
Correlates of Physical Activity
in a Community Sample
of Older Adults in Appalachia
Sam Zizzi, Dave Goodrich, Ying Wu, Lindsey Parker,
Sheila Rye, Vivek Pawar, Carol Mangone, and Irene Tessaro
Although much has been learned about the global determinants of physical activity
in adults, there has been a lack of specific focus on gender, age, and urban/rural
differences. In this church-based community sample of Appalachian adults (N =
1,239), the primary correlates of physical activity included age, gender, obesity,
and self-efficacy. Overall, 42% of all participants and 31% of adults age 65
years or older met recommended guidelines for physical activity, which suggests
that most participants do not engage in adequate levels of physical activity. Of
participants who met physical activity guidelines, the most common modes of
moderate and vigorous activity were walking briskly or uphill, heavy housework
or gardening, light strength training, and biking. These particular activities that
focus on building self-efficacy might be viable targets for intervention among
older adults in rural communities.
Key Words: church members, colorectal cancer, rural, self-efficacy
Given that the negative health and economic consequences of sedentary
behavior have been well documented (Colditz, 1999), considerable effort is now
being devoted to primary and secondary prevention, with increased physical activity as the focus of many interventions (Adams, Der Ananian, DuBose, Kirtland, &
Ainsworth, 2003; Macera et al., 2005). One of the primary thrusts for prevention
efforts in communities includes a focus on physical activity as a means to improve
health, prevent disease, and manage weight. Although much has been learned about
the global determinants of physical activity in adults, the generalizability of the
findings from previous epidemiological studies has been criticized for a lack of
specific focus on gender, age, and urban/rural differences (Plotnikoff, Mayhew,
Birkett, Loucaides, & Fodor, 2004). In many rural communities, where the population of older adults is rapidly growing, it is important to develop ecologically
valid physical activity interventions that address important social, cultural, and
environmental factors (Satorino & McAuley, 2003).
Zizzi is with the Sport and Exercise Psychology Program, School of Physical Education, West Virginia
University, Morgantown, WV, 26506-6116. All other authors are with the School of Nursing at West
Virginia University.
423
424
Zizzi et al.
Some research has established the prevalence of older adultsʼ meeting guidelines for physical activity, yet less is known about the specific aspects of the activities
used to meet these objectives (e.g., duration, time, and intensity). Most estimates
suggest that physical activity levels decrease with age and that less than 30% of
adults over 65 years of age meet CDC guidelines (Centers for Disease Control and
Prevention, 2004; Conn, Minor, Burks, Rantz, & Pomeroy, 2003; Park, Houseman,
& Brownson, 2003). Recent research that incorporated lifestyle physical activity
assessment, however, revealed that estimates are slightly higher (Macera et al.,
2005). An integrated review of physical activity interventions with aging adults
reveals that few studies have focused on lifestyle physical activity (Conn et al.,
2003), even though it is the most appropriate type of physical activity for older
adults. In this population, suggestions have been made for research and intervention
efforts to identify age-specific barriers (OʼNeill & Reid, 1991), include activities of
daily living, and study alternative settings within communities (Conn et al.; Morgan,
2001). Other researchers recommend modifying language and exercise prescription appropriately to increase self-efficacy, enjoyment, and adherence (Brawley,
Rejeski, & King, 2003; Ory, Kinney-Hoffman, Hawkins, Sanner, & Mockenhaupt,
2003). Moreover, it is becoming clear that the reciprocal interaction between the
environmental context of an individual (i.e., urban, suburban, or rural setting), local
cultural attitudes, and attitudes toward physical activity might have a significant
effect on an individualʼs physical activity behavior (Adams et al., 2003; Eyler &
West, 2002; Satorino & McAuley, 2003).
Physical Activity in Rural Settings
In the United States, 75% of the counties, which contain 20% of the total population,
are classified as rural settings (Glasser et al., 2003). Research has documented that
residents of rural settings in the United States are more likely than urban residents
to be obese and inactive and suffer from higher rates of other chronic diseases
(Eberhardt & Pamuk, 2004; Glasser et al.; Park et al., 2003; Wilcox, Castro, King,
Houseman, & Brownson, 2000). Common problems emerging in rural settings
that might contribute to health disparities include living farther from health-care
resources, lower income, and less education. It is possible, however, that increased
community involvement in rural areas (Greiner, Li, Kawachi, Hunt, & Ahluwalia,
2004) could help facilitate the effectiveness of physical activity programs that are
designed to take advantage of the stronger social resources in some rural areas to
develop a subjective norm that promotes physical activity.
Although walking is the most common form of physical activity for adults
across all age groups, promoting this activity in rural settings poses unique challenges. Urban neighborhoods often facilitate walking, but many rural communities
would likely be considered “unwalkable” environments (Saelens, Sallis, & Frank,
2003), characterized by poorly connected streets, a lack of sidewalks, inadequate
street lighting, and an emphasis on car travel. These perceived and objective
environmental barriers might negatively affect the viability of physical activity
interventions in rural communities. Evidence indicates that older adults might
show greater adherence to activities of shorter duration and lower intensity such
as walking and biking (Dishman & Buckworth, 1996; Lim & Taylor, 2005). Thus,
a continued focus on identifying the “purposeful” activities engaged in by active
Physical Activity in Appalachia
425
adults in specific settings (Morgan, 2001) is needed to develop tailored strategies
for intervention.
This article describes the correlates of physical activity in a church-based
sample of Appalachian adults. The substantial percentage of adults older than 65
in this sample reflects regional demographics (U.S. Census Bureau, 2000) and
might help provide insight into the activity patterns in this specific subgroup and
other older adults. Analyses explore the degree to which participants met national
guidelines for physical activity and the common modes and intensities of activity
used to meet these guidelines. Identifying the unique factors related to physical
activity patterns and the activities used to meet guidelines might help with matching future intervention programs in these communities.
Methodology
Design
Baseline data on 1,239 men and women were collected as part of a church-based
intervention focusing on colorectal cancer control. Using a factorial research design,
this project (Focus on Health) evaluates the independent and combined effects
of two intervention strategies on primary and secondary prevention of colorectal
cancer among members of 16 churches.
Churches in the Ohio Valley region of West Virginia were identified and
recruited during 2002 and 2003. Of 708 churches contacted, 74 were eligible to
participate and were subsequently randomized for recruitment. Sixteen churches
with a history of conducting health activities were successfully recruited into the
study. Increasing physical activity and fruit and vegetable intake, reducing fat
intake, and age-appropriate colorectal cancer screenings are the priority health
behaviors.
Data Collection
Before data collection, a church advisory group was set up in each church to provide
participatory guidance on data collection, the educational training sessions, and
implementation of interventions. Project staff worked with each church advisory
group to arrange data collection. In each church, all consenting individuals (18
and older) were eligible to participate in the study. Participants completed the
questionnaire at the church after signing consent forms. This study was approved
by the institutional review board at West Virginia University.
Participant Characteristics
Demographic characteristics were assessed as follows: age (date of birth), educational level (eighth grade or less, some high school, high school graduate or
GED, some college or postsecondary, college degree, postgraduate or professional
degree), and household income (six levels from less than $10,000 to $75,000 and
more). Weight and height were self-reported. Body-mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Participants
were categorized by BMI as normal (BMI ≤25), overweight (25 < BMI < 30), or
obese (BMI ≥ 30).
426
Zizzi et al.
Measures
Physical Activity. Detailed measurement of physical activity patterns was cal-
culated based on participantsʼ responses to a modified CHAMPS physical activity
questionnaire designed for older adults (Stewart et al., 2001). This frequency-based
checklist included 30 activities with metabolic-equivalent (MET) values ranging
from 2 to 6, including recreational and leisure-time activity, occupation-related
activity, and home-related activity. Minor modifications included omitting a few
of the sedentary activities and adding items related to occupational activity. The
participants were first asked whether they had done each of these activities in a
typical week during the last month (yes/no). Those who answered no were referred
to the next item. Those who answered yes then responded to the frequency question
“how many times a week?” and the duration question “how many hours a week?”
with six response options (<1, 1–2, 3–4, 5–6, 7–8, and ≥9 hr). These duration values
were coded based on the midpoint of each range. For example, <1 hr was coded as
30 min, and 1–2 hr was coded as 90 min. Thus, the minimum duration of activity
coded for any activity was 30 min/week.
Two separate guideline criteria were used in the current study based on MET
values. Participants were considered to meet physical activity guidelines if they
accumulated five or more activities per week of moderate or vigorous intensity (150
min of MET ≥3) or they completed three or more activities per week of vigorous
intensity (60 min of MET ≥4). The number of activities they engaged in at each
intensity level was recorded based on the question “how many times a week?” from
the CHAMPS. Additional analyses targeted meeting duration guidelines (>150
min of accumulated moderate- or vigorous-intensity activity). Duration for each
activity was computed by multiplying weekly frequency by the midpoint values
for time on each activity and then summing all endorsed activities. Preliminary
descriptive analyses indicated that this method might have resulted in overreporting of physical activity duration, so results related to duration guidelines should
be interpreted with caution.
Psychosocial Variables. Participants who reported they had not exercised for
20 min or more at least three times per week during the preceding month were
classified according to stages of exercise readiness (Prochaska & Velicer, 1997) as
follows: precontemplation (not thinking of changing), contemplation (thinking of
changing), or preparation (planning on changing). Those who were exercising for
20 min or more at least three times per week were classified as follows: action (for
6 months or less) or maintenance (for more than 6 months). Factors that prevented
participants from starting or continuing to exercise were assessed with respect to
the following: “donʼt like to exercise,” “willpower,” “time,” “canʼt stick with it,”
“physical problem,” “no support,” “no one to exercise with,” and “donʼt want to
change” (yes/no). To assess exercise self-efficacy, participants were asked how
sure or confident they were that they could start or continue to exercise for 20 min
or more at least three times per week, with response options ranging from 1 (very
unsure) to 4 (very sure). Participants were also asked how much they could count
on those close to them for support and help if they wanted to become more physically active, with options ranging from 1 (not at all) to 4 (a lot).
Physical Activity in Appalachia
427
Results
The final sample (N = 1,239) was 99.1% White, 66% female, and 39% over the
age of 64 years. For data analyses, participants were classified according to the
following age groups: 18–39 (n = 145), 40–64 (n = 596), and ≥65 years (n = 470).
Thirty-nine percent (n = 473) of participants had completed a college education.
Across obesity categories, participants were characterized as either normal weight
(37%), overweight (38%), or obese (24%). Thirty-two percent of the sample reported
family incomes less than $30,000 per year. A large majority of respondents (85%)
self-reported either good or excellent health. Participants were classified across the
stages of readiness for exercise accordingly: precontemplation (10%), contemplation (24%), preparation (15%), action (12%), and maintenance (39%). See Table
1 for additional demographic information on the sample.
Table 1
Sample Characteristics of the 1,239 Participants
Age, years
<40
40–64
≥65
Gender
female
male
Education
some high school or less
high school or some college
college or higher
Body-mass index
<25 (normal weight)
25–30 (overweight)
≥30 (obese )
Household income
<$20,000
$20,000–29,999
$30,000–49,999
≥$50,000
Health status
poor
fair
good
excellent
Meeting physical activity guidelinea
n
%
145
596
470
12.0
49.2
38.8
814
423
65.8
34.2
57
687
473
4.7
56.5
38.9
441
456
290
37.2
38.4
24.4
164
171
270
442
15.7
16.3
25.8
42.2
17
161
519
530
520
1.4
13.1
42.3
43.2
42.0
Accumulated five or more activities with MET value ≥3 and for a total of ≥150 min or three or more
activities with MET value >4 and a total of ≥60 min weekly. Using the “times per week” category from
the CHAMPS.
a
Moderate intensity
play golf, carrying or pulling
your equipment
play doubles tennis
heavy work around the house
heavy gardening
work on car or other machinery
walk fast or briskly for exercise
ride a bicycle or stationary
cycle
water exercises
swim gently
aerobics or aerobic dancing
light strength training
manual labor or loading trucks
at work
Physical activity
8.2
1.0
52.3
39.8
22.4
41.8
15.8
6.0
9.0
4.5
15.2
7.9
4
3
3
3.5
3
3
Overall %
3
4
3
4
3
3.5
METs
13.8
18.2
4.1
11.7
8.3
20.2
8.4
0.7
46.5
31.5
29.2
50.0
<40
10.7
14.4
4.4
8.6
4.7
12.7
9.1
1.0
55.5
43.6
24.6
45.2
40–64
2.2
16.8
8.7
8.4
2.9
16.6
6.9
1.1
49.9
37.4
17.1
34.9
65+
Age, years
****
*
*
*
***
***
p
2.6
14.4
7.2
9.1
6.7
17.2
4.6
1.4
46.8
34.3
5.1
42.8
F
18.4
18.7
3.9
9.0
0.7
11.2
15.2
0.7
63.1
51.0
57.0
39.7
M
Gender
<25
****
5.2
14.3
6.3
8.8
**** 7.2
*
20.4
*
8.4
1.6
**** 48.7
**** 39.1
**** 18.7
52.8
****
p
11.5
18.9
5.1
8.5
2.9
12.3
10.0
0.9
60.1
44.4
29.9
40.3
25–30
7.3
13.3
6.3
9.4
2.1
11.2
5.3
0.4
47.2
34.3
19.9
29.3
30+
Body-Mass Index
**
***
***
***
*
***
****
p
4.0
9.0
4.3
6.4
2.2
7.4
4.6
0.7
42.4
30.7
15.0
26.3
No
13.4
25.1
8.4
13.6
8.0
25.6
13.0
1.7
65.5
52.2
33.0
62.4
Yes
p
****
****
**
***
****
****
****
****
****
****
****
PA Group
Table 2 Percentages of Participation in Specific Activities by Age, Gender, Body-Mass Index, and Physical
Activity (PA) Group
428
Zizzi et al.
5.4
1.4
1.5
10.1
29.9
10.1
4.7
16.9
4.7
4.5
6
4.5
7
6
5
5
4.5
5
13.1
20.8
11.0
5.5
8.5
4.9
2.8
20.1
34.0
5.6
20.0
9.0
4.4
4.60
1.10
1.90
11.5
34.8
0.9
11.5
11.3
4.7
5.4
0.7
0.7
4.9
22.2
****
***
2.4
12.6
9.7
4.0
5.9
***
1.5
*
1.6
**** 7.5
**** 27.4
9.7
25.2
11.0
6.1
4.9
1.7
1.7
15.5
35.8
****
6.1
**** 22.0
13.2
5.2
6.8
3.0
1.6
**** 14.2
** 35.8
4.7
17.8
8.5
4.3
5.4
0.0
1.6
11.8
32.3
3.5
9.1
7.7
3.8
3.6
1.1
1.4
2.1
20.1
****
**
****
****
***
1.6
4.9
4.8
2.8
3.5
0.3
1.0
3.8
16.4
9.3
33.0
17.4
7.4
8.3
3.3
2.5
18.9
48.7
****
****
****
***
***
****
*
****
****
Note. Chi-square test: *p < .05, **p < .01, ***p < .001, ****p < .0001, indicating significance of a linear trend. For PA group: no = did not meet either of the physical
activity guidelines (5 days × 30 min or 3 days × 20 min per week); yes = met one of the guidelines.
Vigorous intensity
dance
play singles tennis
skate
jog or run
walk uphill or hike uphill
using aerobic machines (i.e.,
rowing, stepper)
swim moderately or fast
moderate to heavy strength
training
play basketball, soccer, or racquetball
Physical Activity in Appalachia
429
512.4
430.9
.0010***
590.7
523.2
396.8
.0300****
489.3
497.1
449.8
457.6
482.8
513.5
510.5
.0020
11.0
7.9
5.4
.0600****
8.5
7.1
6.2
.0200****
5.3
6.9
8.1
8.7
.0300****
Duration
(min)
7.6
6.7
.0030*
Frequency
(times)
Low
2.3
3.4
3.8
4.7
.0400****
4.2
4.0
2.7
.0200****
4.9
4.1
2.9
.0300****
3.3
4.7
.0200****
Frequency
(times)
Moderate
237.8
325.4
329.9
354.0
.0100**
298.8
381.1
288.1
.0200****
346.8
351.9
254.8
.0200****
248.2
431.8
.0600****
Duration
(min)
Activity Intensity
Frequency and Duration Across the Three Types of Activities (N = 1,239), M
Gender
female
male
ES
Age, years
18–39
40–64
65+
ES
Body-mass index
normal
overweight
obese
ES
Income
<$20,000
$20,000–29,999
$30,000–49,999
≥$50,000
ES
Table 3
1.3
1.2
1.6
2.6
.0350****
2.5
1.7
0.9
.0400****
3.0
2.0
1.1
.0400****
1.5
2.4
.0200****
Frequency
(times)
Vigorous
88.0
80.0
70.4
118.90
.0140**
109.40
94.6
48.5
.0200****
118.00
98.1
63.1
.0200****
76.1
116.80
.0100****
Duration
(min)
430
Zizzi et al.
Education
some high school or less
4.1
334.3
high school or some college
6.8
488.5
college or higher
8.5
501.4
ES
.0020****
.0070*
Stage of readiness
precontemplation
3.6
350.0
contemplation
6.1
441.7
preparation
6.6
452.1
maintenance
9.0
542.5
ES
.0600****
.0300****
Self-efficacy
unsure
4.5
393.7
sure
8.1
508.9
ES
.0400****
.0100****
Time barrier
yes
6.9
458.8
no
8.4
540.4
ES
.0010***
.0010***
Safety/Injury concerns
yes
6.6
438.2
no
7.5
386.0
ES
.0020
.0030
Note. ES = effect size of group-mean difference from one-way ANOVA.
*p < .05. **p < .01. ***p < .001. ****p < .0001.
270.6
311.8
317.1
.0007
197.7
244.8
212.0
393.5
.0600****
217.7
335.8
.0200****
324.2
304.9
.0007
207.1
332.9
.0180****
2.8
3.6
4.2
.0070*
1.9
2.2
2.3
5.3
.1200****
1.8
4.3
.0500****
3.9
3.7
.0002
2.6
4.0
.0150****
1.7
1.8
.0004
1.7
1.9
.0005
0.3
2.2
.0600****
0.3
0.6
0.6
3.0
.1500****
1.7
1.4
2.5
.0300****
69.3
93.9
.0030
92.5
84.0
.0005
30.0
106.10
.0300****
16.6
29.3
25.6
148.70
.1300****
108.00
70.7
116.80
.0200****
Physical Activity in Appalachia
431
432
Zizzi et al.
Regarding sedentary behavior, approximately 48% of respondents reported
not exercising regularly on a weekly basis, and 12% (n = 145) reported that poor
health prevented them from being physically active. Overall perceived barriers to
physical activity were low, with only 33% of participants reporting a lack of time
to be physically active. Other less common barriers included lack of willpower
(19%), physical problems or injury (17%), and not liking exercise (15%). A small
percentage of respondents (2.5%) reported no access to places for exercise. The
most common physical activities engaged in by this sample included light work
around the house (85%), light gardening (63%), walking leisurely (60%), heavy
work around the house (52%), walking briskly for exercise (42%), heavy gardening
(40%), walking to do errands (39%), and stretching or flexibility exercises (38%;
see Table 2 for a summary of moderate and vigorous activities). The least common
activities among participants were singles (2%) and doubles tennis (1%), skating
(2%), yoga or Tai Chi (4%), aerobics or aerobic dancing (5%), swimming moderately fast (5%), and various sports (5%; basketball, soccer, racket ball). These data
suggest that low-intensity activities are most commonly engaged in, followed by
moderate, and then vigorous activities.
Forty-two percent of participants (n = 520) met the high-frequency standard
(5 or more days per week for a total of least 150 min), and 20% (n = 250) achieved
the lower frequency, more vigorous standard (3 or more days per week of vigorous
activities for a total of at least 60 min). Among participants age 65 or older, 31%
met one of these standards. To more closely analyze physical activity patterns
in this sample, the frequency and duration of activities were examined across a
variety of demographic and psychosocial variables. Differences were assessed by
one-way ANOVA using a significance level of .01. Means and effect-size estimates
(eta-squared values) are reported in Table 3 across nine independent variables. A
variety of small effects were found in hypothesized directions. For example, physical activity frequency and duration of all intensities decreased significantly as age
and obesity increased. The opposite effect was found for education and income
levels; frequency and duration of low- and moderate-intensity activities increased
with higher education and household income. Exercise readiness and self-efficacy
were highly significant at differentiating physical activity behavior, particularly at
moderate and vigorous intensities (ES range .09–.20). As participants increased in
their self-efficacy and readiness, they reported higher frequency and duration of
physical activity. There was no significant difference between those who perceived
and those who did not perceive time barriers to exercise on the frequency or duration
of low, moderate, or vigorous activities (p > .01 in all cases). Thus, in the present
sample, perceived time barriers did not mediate physical activity behavior.
Common Modes of Activity Used to Meet Guidelines
Specific attention during the analyses focused on the modes of activity across age
group, gender, BMI, and physical activity guidelines (yes/no), using two-way chisquares (See Table 2). The prevalence of the following moderate or vigorous activities decreased significantly as age increased: working on cars or other machinery,
walking briskly or uphill, aerobics, manual labor, strength training, jogging, and
various sports. Between genders, men reported a higher incidence of 10 moderate
or vigorous activities, including large differences in heavy work around the house,
Physical Activity in Appalachia
433
working on cars or other machinery, manual labor, jogging, sports, and moderate or
heavy strength training. Women, conversely, were more likely than men to report
engaging in aerobics or light strength training. As participants increased in BMI,
the prevalence of the following moderate and vigorous activities decreased accordingly: bike riding, strength training, jogging, and walking uphill.
The most common moderate-intensity activities used to meet the guidelines
included the following: heavy work around the house (66%), walking briskly for
exercise (62%), heavy gardening (52%), and light strength training (25%). The most
common vigorous activities used to meet the guidelines were walking or hiking
uphill (49%), moderate or heavy strength training (33%), jogging or running (19%),
and using aerobic machines such as rowers and steppers (17%).
Correlates of Physical Activity
As a final step in the data analysis, two multiple logistic-regression analyses were
used to explore the demographic and psychosocial factors that independently predicted whether or not participants met the frequency or duration guidelines (see
Table 4). Independent variables in the regression models included age group (three
levels), gender (two levels), household income (four levels), obesity (three levels),
time barrier (yes/no), safety or injury barrier (yes/no), and self-efficacy (four levels).
For the frequency guideline, five of the variables emerged with p values <.001.
Compared with the participants age 65 years or older, those in the youngest age
group (18–39 years) were nearly three times as likely to meet the frequency standard
(OR = 2.77). Women were significantly less likely to meet the frequency standard
than were men (OR = 0.37). The likelihood of meeting the frequency guideline
increased as income or self-efficacy levels increased or as body weight decreased.
Perceived time and safety/injury barriers were not independently associated with
physical activity frequency.
The factors associated with meeting the duration guideline included gender
(p < .0001), income (p = .004), safety/injury concerns (p = .008), and self-efficacy
(p < .0001). The following demographic characteristics were independently associated with not meeting duration guidelines: women compared with men (OR =
0.25), those with household income <$20,000 compared with those with income
>$50,000 (OR = 0.55), and those with safety/injury barriers compared with those
without this barrier (OR = 0.58). Finally, self-efficacy was also strongly associated
with meeting the duration guideline (OR = 2.08).
Discussion
In this community sample of Appalachian adults, the primary correlates of physical
activity included age, gender, obesity, and self-efficacy. These determinants closely
resemble those used in previous research on physical activity patterns in American
adults (Conn et al., 2003; Park et al., 2003). Overall, 42% of all participants and 31%
of older adults met recommended guidelines for physical activity, which suggests
that most of the participants sampled did not engage in adequate levels of physical
activity. These prevalence rates, although not ideal, compare favorably with recent
data from a stratified telephone survey of U.S. adults (Macera et al., 2005). Of those
participants who met physical activity guidelines, the most common modes of
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Zizzi et al.
Table 4 Multiple Logistic Regressions for Frequency and Duration
of Physical Activity (N = 994)
Physical Activity
Durationb
Physical Activity
Frequencya
Odds
(95% CI)
p
Odds
(95% CI)
p
Age, years
18–39
40–64
65+
2.77
1.51
1.00
(1.70–4.56)
(1.05–2.17)
<.001
.534
1.90
1.27
1.00
(1.08–3.36)
(0.86–1.86)
.04
.64
Gender
female
male
0.37
1.00
(0.27–0.50)
<.0001
0.25
1.00
(0.17–0.37)
<.0001
Household income
<$20,000
$20,000–29,999
$30,000–49,999
≥$50,000
0.30
0.53
0.62
1.00
(0.18–0.49)
(0.34–0.82)
(0.44–0.89)
<.001
.713
.398
0.55
0.95
1.14
1.00
(0.34–0.90)
(0.59–1.55)
(0.76–1.22)
.004
.62
.07
Body-mass index
obese
overweight
normal
0.34
0.57
1.00
(0.23–0.50)
(0.41–0.80)
<.0001 0.62
0.864 0.79
1.00
(0.41–0.92)
(0.54–1.14)
.04
.99
Time barrier
yes
no
0.98
1.00
(0.72–1.35)
.91
1.06
1.00
(0.75–1.51)
.74
Safety/Injury concerns
yes
no
1.42
1.00
(0.93–2.11)
.10
0.58
1.00
(0.39–0.87)
.008
Self–efficacyc
2.04
(1.72–2.46)
<.0001 2.08
(1.76–2.47)
<.0001
At least five times on the activities with MET value ≥3 or at least three times on the activities with
MET value >4 per week (yes/no). Using the “times per week” category from the CHAMPS. bAt least
150 min on the activities with MET value ≥3 or at least 60 min on the activities with MET value >4
per week (yes/no). Using the “for how long each week” category from the CHAMPS. cOrdinal score
ranged from 0 (very unsure) to 3 (very sure).
a
moderate and vigorous activity were walking briskly or uphill, heavy housework or
gardening, strength training, and biking. These particular activities might be viable
targets for intervention among adults in Appalachia. A brief discussion of the key
results is offered, with the understanding that the external validity of results might
be limited to church-based populations in rural communities.
Along with heavy work around the house, brisk walking was the most common
mode of activity used to achieve physical activity guidelines. Two pieces of data
Physical Activity in Appalachia
435
on the physical activity patterns of adults over 65 are somewhat disconcerting.
First, 31% of this age group engaged in adequate physical activity to confer health
benefits, and only 35% reported engaging in brisk walking for exercise. Several
factors are likely contributing to these less than ideal prevalence rates, including
perceptions of physical activity, barriers, and environmental issues. It is possible that
older adults still perceive “exercise” as including only vigorous-intensity activities
engaged in for a specific duration of time and that walking briskly “doesnʼt count,”
thus leading to a lower prioritization of physical activity behavior. Some older adults
might be hesitant to begin exercise programs because of the unrealistic expectation that they might worsen their health. The preceding misperceptions have been
identified in previous research as potential barriers to adopting and maintaining
physical activity (Eyler & West, 2002; Tudor-Locke et al., 2003).
In rural communities, where access to health education is often limited, perceptions might be further complicated by environmental barriers. In a well-designed,
stratified survey study by Park and colleagues (2003), rural low-income residents
were less likely to meet physical activity recommendations than other subgroups,
and rural groups were not influenced by access to walking or jogging trails or parks.
The current data support this finding in that perceived access was not related to
physical activity patterns, but income and education levels were higher in the present
sample than in typical rural populations. In Park et al.ʼs study, the only predictive
factor in rural low-income settings was the walkability of neighborhood streets,
whereas the rural, high-income participants preferred access to an indoor gym.
The aforementioned neighborhood factors might become even more salient
as people age and spend less time in work environments. Thus, the older adults
surveyed in the church sample might have perceived greater barriers to walking
related to safety of neighborhood streets or community trails or sidewalks. Safety
and injury concerns were predictive of lower duration of physical activity, after
age had been controlled for. Interpreting this effect along with lack of significance
for predicting the frequency guideline suggests that participants with safety and
injury concerns might delay decisions to become physically active despite having
positive intentions. The present data suggest that adults with these barriers might
be more likely to have a higher frequency of activity bouts that are shorter in
duration. Finally, it is important to note that although perceived lack of time is
commonly cited as the number one barrier to adopting and maintaining physical
activity in adults (Dishman & Buckworth, 1996), among participants of this sample
of church-going older adults, this factor was not associated with the frequency or
duration of physical activity behavior.
When community-based interventions are designed, therefore, it will be
important to assess these perceptions beforehand while helping participants identify
safe locations for physical activity. In addition, emphasizing accumulated lifestyle
activity as a means to achieve recommended levels of physical activity might be
particularly useful. These adaptations will allow health professionals to modify
exercise programs and prescriptions to account for both personal and cultural
factors (Brawley et al., 2003; Ory et al., 2003). These approaches might allow
participants to build self-efficacy in their ability to engage in an active lifestyle.
Thus, with physical activity decreasing progressively as people age, research with
older adults will need to consider lifestyle activities in community settings such
as retirement homes, senior centers, and churches. These settings are particularly
436
Zizzi et al.
promising in rural communities, given research suggesting that residents are more
likely to be involved in their communities than are urban residents (Greiner et
al., 2004). If physical activity programs can be infused into existing community
organizations, perhaps the social norm to lead an active lifestyle can be transmitted to a greater number of older adults. Particular emphasis should be placed on
promoting brisk walking, biking, and light strength training in brief durations to
allow participants to feel efficacious in their efforts. Heavy housework, however,
should be promoted with caution because it might pose a greater risk for older
adults—intensity of these activities would need to be monitored to ensure a match
with participant fitness levels. Overall, these tailored intervention efforts might
translate into a greater number of older adults meeting physical activity guidelines,
which has been associated with higher physical functioning and less psychological
distress (Lim & Taylor, 2005).
Limitations
There are several limitations in the present design and sample that warrant caution when interpreting and applying these data to other communities. First of all,
the demographic characteristics of the participants do not match the state norms
for West Virginia and Eastern Ohio although most of the churches were located in
rural counties that are classified as medically underserved areas (U.S. Department
of Health and Human Resources, 2005). The church-based sample showed higher
education and income levels and a higher percentage of women compared with state
norms (U.S. Department of Agriculture, 2003). Because of these limitations, the
physical activity patterns and factors associated with meeting guidelines might
not be generalizable to other rural populations. Next, self-report measures of
physical activity are subject to problems of recall bias and overreporting. More
specifically, those designing future research with the CHAMPS might consider
adding a shorter duration category (e.g., <30 min) in addition to using “in the last
week” instead of “in a typical week in the last month” to reduce problems with
recall that might have occurred in the present study. These suggested modifications to the CHAMPS might reduce error and improve reliability of results in
future research.
Conclusion
To help understand how adults achieve the recommended quantity of physical
activity, more specific research is needed to establish when, with whom, and where
people are active, particularly those who live in rural areas with limited access to
health-care resources. Clearly, one positive message to emerge from this study is
that despite potential misperceptions and walkability barriers in their communities,
some older adults are successfully engaging in regular physical activity, primarily in the form of brisk walking, biking, heavy housework, and strength training.
Studying these patterns more closely and treating these active older adults as the
“experts” will help health professionals develop ecologically sound communitybased interventions.
Physical Activity in Appalachia
437
Acknowledgment
This article was supported by Grant Number U57/CCU320638 from the Centers for Disease Control and Prevention to West Virginia University. Its contents are solely the
responsibility of the authors and do not necessarily represent the official views of the
Centers for Disease Control and Prevention.
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