Physical Activity Monitoring
69
Self-monitoring of Physical
Activity
Effects on Self-efficacy and Behavior in
People With Type 2 Diabetes
Purpose
The purpose of this study was to test the effect of keeping daily activity records on physical activity levels and
self-efficacy for physical activity in adults with type 2
diabetes, and to examine the feasibility and acceptability of this intervention from the perspective of the participants.
Methods
This intervention study included 58 individuals with
type 2 diabetes aged 40 to 65 years. Participants were
randomly assigned: individuals in the intervention group
kept daily activity records for 6 weeks, mailed to the
researcher every 2 weeks. Data collection was completed at the beginning of the study and 6 weeks later, using
the habitual physical activity index and the self-efficacy
for exercise scale. Participants in the intervention group
also completed the perceived feasibility checklist.
Results
The intervention resulted in enhanced self-efficacy.
Physical activity improved in both the intervention and
control groups. Activity recording was judged to be
acceptable and feasible.
Conclusions
Daily activity recording can be used as part of a program
to increase physical activity self-efficacy levels. Focused
interactions between health care providers and patients
Gleeson-Kreig
JoAnn M. Gleeson-Kreig, PhD, RN
From Plattsburgh State University, Plattsburgh, New York.
Correspondence to JoAnn M. Gleeson-Kreig, PhD, RN,
Plattsburgh State University, 101 Broad Street, Plattsburgh,
NY 12901 ([email protected]).
Acknowledgment: This research was supported by the
University of Connecticut Research Foundation and the
Gamma Delta Chapter of Sigma Theta Tau International.
DOI: 10.1177/0145721705284285
The Diabetes EDUCATOR
70
may be enough to motivate people to higher levels of
physical activity. The relationship between self-efficacy
and behavior is complex and should be the subject of further research.
L
ack of adequate physical activity is a contributing factor in the rising prevalence of type
2 diabetes. Higher levels of physical activity,
even of moderate intensity, are associated with
a substantial reduction in risk.1 Although physical activity is a recommended treatment of diabetes, levels in
adults with type 2 diabetes are low. In one study, 31% of
the people with diabetes surveyed reported no regular
physical activity, and an additional 38% reported less
than recommended levels.2 Physical inactivity in people
with type 2 diabetes has been found to be a significant
predictor of higher overall mortality.3 Numerous studies
show that metabolic parameters can be improved
through increased levels of physical activity.4
Recommendations abound for the development of
interventions that aim to increase the level of physical
activity of adults5 and thereby reduce diabetes-related
risks. The challenge, however, is to help individuals with
well-ingrained habitual patterns create healthy lifestyle
modifications. Interventions that encourage selfresponsibility and promote self-efficacy could enhance
attempts to change health habits. Self-monitoring activities, such as keeping logs or journals, provide cues to
enact desired behavior yet are simple to administer, low
in cost, and require minimal time from the health care
professional.
Theoretical Framework
Social cognitive theory6 provides one approach to
understanding human action and was the guiding framework for this study. The theory is based on the idea that
behavior is influenced by a transaction between behavior, cognitive thoughts, and environmental stimuli.
Perceived self-efficacy describes the belief a person has
about his or her personal capabilities to accomplish a
task and is commonly thought of as a person’s confidence regarding the behavior.6 Efficacy beliefs affect
what people will try, motivating them to choose skills
with which they believe they will be successful.
According to the theory, self-efficacy beliefs are influenced by 4 main sources: enactive attainment, vicarious
experience, social persuasion, and physiological/emotional
states.6 Enactive attainment is based on a person’s actual
past performance, giving an authentic measure of a person’s capability that can encourage further attempts.
Vicarious experience comes from the confidence gained by
seeing other people judged to be similar to self perform the
behavior. Social persuasion encourages action through verbal reinforcement of the ability to succeed. Positive subjective states, whether physical or emotional, can increase
the likelihood of repeated action.
Review of the Literature
The current guidelines for exercise recommend at
least 30 minutes of moderate physical activity on most
days of the week.7 Moderate activity holds fewer risks
than vigorous activity, may be more acceptable to sedentary individuals, and has been shown to have positive
effects for people with diabetes.4 There is also evidence
that multiple short bouts of physical activity lasting 10
minutes or more accumulated to 30 minutes per day
resulted in similar improvements in cardio respiratory
fitness as longer bouts.8
Many barriers to obtaining adequate physical activity
have been identified, including lack of time,9 lack of role
models,10 and bad weather, especially in northern climates.11,12 Perceived barriers13 and benefits14 are influential determinants of exercise behavior. Self-efficacy
research focuses on confidence in one’s ability to meet
activity goals, despite barriers. Multiple studies have
found support for self-efficacy as a significant correlate
of exercise behavior across varying populations.15
Longitudinal designs in experimental-type research have
also found self-efficacy to be a strong predictor of adherence to exercise goals following an intervention. The
relationship of baseline self-efficacy to postintervention
physical activity levels has found wide support.16-18
Multivariate techniques have further demonstrated the
direct role of self-efficacy in explaining variations in
behavior.14,19 There have been exceptions to the strong positive relationship purported to exist between self-efficacy
and physical activity. In some studies, self-efficacy was a
weak predictor of physical activity change.20 There have
also been reports that self-efficacy decreased when an
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Physical Activity Monitoring
71
activity program was started21 and that self-efficacy was
predictive during the initiation phase of an exercise program but not during maintenance.19,22
Although only roughly 25% to 28% of individuals
with diabetes reported receiving specific guidelines for
exercise from their physicians,23 research has shown that
recommendations and individual counseling can provide
an impetus for exercise behavior. People who received
professional advice to modify physical activity levels
were more likely to meet activity recommendations.24
Successful counseling sessions may be as short as 1
hour25 and can include generic rather than individualized
information.26 Individual counseling by nurses has also
been found to have positive effects on physical activity.18
Strategies that encourage self-management, empowerment, and self-motivation have enjoyed success in
improving metabolic parameters.27 Home-based programs have been found to enlist better adherence with
exercise prescriptions than programs that required the
participant to attend a class28 and do so at a cost savings.
Ongoing self-monitoring has been found to be an effective moderator of change in exercise behavior.29 The act
of keeping a daily record requires conscious thought
about activity levels and serves as a reminder to exercise.
In a study of healthy, working women, keeping activity
records was found to have a significantly large effect on
improving physical activity levels; however, no benefit
to self-efficacy was found.21
A self-monitoring intervention could influence selfefficacy primarily through two avenues. First, activity
recording provides an individual with information about
his or her actual behavior and may increase self-efficacy
through focusing on enactive attainment. A visual documentation of performance is created, highlighting
progress and needs. Second, activity recording creates an
opportunity for social persuasion, allowing the health
care professional to reinforce the client’s effort and to
discuss how activity fits into daily life. The document
also may provide self-persuasion. The use of activity
records as a means to improve physical activity levels
has not been tested in a clinical setting with people with
diabetes. The purpose of this study was to test the effect
of keeping daily activity records on physical activity levels and self-efficacy for physical activity in adults with
type 2 diabetes, and to examine the feasibility and
acceptability of this intervention from the perspective of
the participants.
Gleeson-Kreig
There were 3 research questions addressed in the
study: (1) Is there a difference in the outcome of physical activity levels between individuals who performed
the 6-week self-monitoring intervention of keeping
activity records and those who did not, after controlling
for initial physical activity levels? (2) Is there a difference in the outcome of self-efficacy for physical activity
between individuals who performed the 6-week self-monitoring intervention of keeping activity records and those
who did not, after controlling for initial self-efficacy levels? and (3) What is the perceived feasibility and acceptability of keeping activity records?
Method
Sample
Inclusion criteria required participants to be English
speaking, aged 40 to 65 years, with self-reported type 2
diabetes, and under a physician’s care. People were
excluded if they had restrictions that prohibited them
from engaging in 30 minutes of low- to moderate-intensity physical activity on most days of the week, did not
have the cognitive ability to keep simple activity records,
or could not give informed consent to participate.
Recruitment was through local primary care offices, the
office of a certified diabetes educator, a diabetes support
group, and advertisements. Fifty-eight people completed
the initial data collection. Three participants did not
complete the second data collection, leaving 27 people in
the control group (14 women and 13 men), and 28 in the
intervention group (14 women and 14 men; total N =
55). The sample ranged in age from 39 to 67 years, with
a mean age of 53 years. The time since diagnosis with
diabetes ranged from 1 month to 10 years, with a mean
of 78 months. Sixty-six percent were married, and 34%
were single. Yearly household income ranged from
$6700 to more than $200 000, with a median income of
$50 000. Three individuals had less than a high school
degree, and 4 held doctoral degrees; the mean number of
years of education was 15. All were Caucasian, residing
in midsized towns in northern New York State. This
region is mostly rural, with snowy, cold winters. The
local hospital offers only sporadic diabetes education
programs; there is no coordinated center for diabetes
care.
The Diabetes EDUCATOR
72
Procedures
Sun
Mon
Tue
Wed
Thu
Fri
Sat
Participants were randomly
assigned to intervention (kept
activity records) or control (no
records) groups, blocked by gender to ensure near equal numbers
of men and women in each
group. Interviews took place in
the participant’s home, the
researcher’s office in a university
setting, or another location chosen by the participant. At the initial interview, subjects signed an
informed consent form approved
by appropriate institutional
review boards. Demographic
information was gathered, and
the client was weighed. Moderate
physical activity was defined for
Intensity Codes
For each day, record activities you Activity Codes
WALK = walking, treadmill
did for 10 minutes or more:
the participant as “activities that
ACTIVITY MINUTES INTENSITY
SW = swimming
SL = Slow
use large muscle groups and are
JOG = jogging
MOD = Moderate
at least equivalent to brisk walkExample
EX = calisthenics, aerobics
BR = Brisk, vigorous
STEPS = climbing stairs
ing,”30 and the researcher
WALK 15 BR
LG = lawn, garden
reviewed a list of examples that
LG
20 MOD
DAN = dance
GOLF 120 SL
STR = stretching exercises
represented moderate amounts of
CC = child care
activity. The study questionnaires
YOG = yoga
were administered in random
HH = household
LIFT = lift moderate weight
order. The researcher explained
ROW = rowing machine
how to keep daily activity records
SPORT = (specify)
on the calendar supplied to particOTHER = (specify)
ipants in the intervention group.
Codes were provided that reflect- Figure 1. Physical activity records.
ed moderate activities most likely to be performed by people
with type 2 diabetes,4 and participants were instructed to create
Participants in the intervention group also completed an
codes if needed. For each day, participants were asked to
instrument to measure perceptions regarding the acceptnote the activity code, the duration of the activity in
ability and feasibility of keeping daily activity records.
minutes, and a rating of intensity as slow, moderate, or
In lieu of a stipend, individuals were given a pedometer,
brisk for any activity performed for 10 minutes or more
valued at $15.
(see Figure 1). They were instructed to send the records
Measures
to the researcher every 2 weeks, using self-addressed,
stamped envelopes. This initial meeting lasted between
Physical activity was defined conceptually to include
30 minutes and 1 hour. After approximately 6 weeks,
both planned activity designed to exercise large muscles
another meeting occurred, lasting about 15 to 30 minand movement of the body performed as part of daily livutes. Subjects again completed questionnaires about
ing. The Habitual Physical Activity Index (HPAI)31 was
physical activity and self-efficacy for physical activity.
Volume 32, Number 1, January/February 2006
Physical Activity Monitoring
73
used at both pretest and posttest to measure physical
activity. The HPAI was chosen as it was designed to
assess habitual patterns of light-, moderate-, and higher
intensity activities. The original HPAI had 3 subscales,
occupational, leisure, and sport; however, after factor
analysis with a sample from the United States, it was
modified to include only 2 subscales, occupational and
leisure.32 It was also adjusted to be more sensitive to
activities traditionally performed by women through the
addition of a household/family care subscale.33 The
HPAI, in its various forms, has shown adequate reliability. Burns and Froman’s32 2-index scale resulted in α
coefficients of .84 and .78 and 2-week test-retest stability estimates of .89 and .92 for the occupational and
leisure indexes, respectively. Evaluation of the added
household/family care subscale revealed a 1-month testretest reliability of .79 to .91.33 Validity has also been
supported with significant comparisons between index
scores and corresponding activities in an activity log, as
well as between index scores and an objective measure
of physical activity using an accelerometer.33 This study
used 3 indexes: (1) household/family care, (2) occupational, and (3) leisure, for a total of 30 items. The indexes were summed to create a total score. Possible total
scores ranged from 3 to 15, with a higher score indicating higher levels of physical activity. Baseline internal
consistency of the occupational and leisure subscales in
the present study was acceptable at α = .84 and .78,
respectively. The household/family care subscale was
only α = .54. Result for the total scale was α = .76.
Physical activity-specific self-efficacy was measured
using the 9-item Self-efficacy for Exercise (SEE) scale,14
developed by previous modification of the Self-efficacy
Barriers to Exercise scale.34 The original scale was found
to have α coefficients of .9034 and .84.16 The SEE also
demonstrated internal consistency, with an α coefficient
of .92, and validity by finding a significant correlation
between scores on the SEE and exercise behavior (r =
0.56, P < .05).14 For this study, the SEE was modified to
reflect current activity recommendations30 by changing
the stem from “How confident are you right now that you
could exercise 3 times per week for 20 minutes if . . .” to
“How confident are you right now that you could exercise
for 30 minutes on most days of the week if . . . .” One
item of the SEE was also modified from the degree of
confidence “if you felt pain when exercising” to “if you
felt minor muscle aches when exercising” to prevent
Gleeson-Kreig
awarding higher points to a person who would inappropriately exercise through pain that may indicate a serious
risk. The scoring was collapsed from a 10-point response
format to a 5-point format to make it simpler and to be
consistent with other instruments in the study. The total
score was the mean of the 9 items, making potential
scores range from 1 to 5, with higher scores indicating a
higher level of self-efficacy. A high degree of internal
consistency was demonstrated at baseline by an α score
of .91.
The Perceived Feasibility Checklist was developed
specifically for use in this study. The 9 items addressed
understandability and usability of the records, time
requirements to complete the records, influence the
records had on motivation, and possible benefits the
records may have conferred. Two nurses with experience
with people making lifestyle changes were consulted
regarding the content. The checklist was pilot tested for
clarity and readability prior to use, with minor alterations
made. The checklist used a 5-point Likert-type response
format. Scores on each item were averaged to create a
total score ranging from 1 to 5, with higher scores
reflecting a more positive attitude toward the activity
records. Initial reliability was supported with α = .81.
The instrument also included an open-ended item that
gave the participants the opportunity to provide additional information.
Data Analysis
The data were analyzed using SPSS, version 11.5.
Descriptive statistics for the demographic variables of
age, gender, length of time with diabetes, income, educational level, and marital status were examined, with no
significant differences between groups. Weight at baseline, however, was significantly different between the
groups (control group mean = 235 lb, intervention group
mean = 206 lb, t = 2.79, df = 56, P = .01) and was considered as a covariate in later analyses. Study variables
of physical activity and self-efficacy were found to be
normally distributed. Physical activity and self-efficacy
for physical activity at baseline (see Table 1) did not
significantly differ between groups as confirmed by
independent-samples t test (t = 0.52, df = 56, P =.60,
and t = –.72, df = 56, P = .48, respectively).
The effect of keeping daily physical activity records
was addressed through the use of analysis of covariance
(ANCOVA). The 2 dependent variables were physical
The Diabetes EDUCATOR
74
activity and self-efficacy as measured at the end of the
6-week period, and the independent variable was the
intervention. The dependent variables were uncorrelated to each other (r = 0.23, P = .10), allowing each
to be analyzed separately. Pretest physical activity and
pretest self-efficacy were correlated with the respective dependent variables but were uncorrelated with
each other, therefore becoming appropriate covariates
(see Table 2). Initial weight was also included as a
covariate to account for individual differences
between groups. To assess the effect of time on each
group, repeated-measures ANCOVA was performed
for each of the dependent variables.
Results
Table 1
Mean (SD) of Study Variables at Pretest and Posttest
Physical Activity
Pretest
Posttest
Control group 7.38 (1.32) 7.93 (1.50)
Intervention 7.05 (1.46) 7.69 (1.32)
group
Self-efficacy
Pretest
Posttest
3.35 (1.05) 3.23 (1.00)
3.55 (0.97) 3.69 (0.91)
Control group n = 27, intervention group n = 28.
Table 2
Pearson product–moment correlations were perCorrelations of Study Variables in Entire Sample (N = 55)
formed to look for confounding variables. Income was
positively related to education (r = 0.55, P = .000),
Variable
PA 1
SE 1
PA 2
SE 2
and the number of months since being diagnosed with
diabetes was negatively related to income (r = –0.27,
Physical activity T1 (PA 1)
1
0.23
0.79** 0.19
P = .04). There was no relationship between any of the
Self-efficacy T1 (SE 1)
1
0.29* 0.76**
demographic variables and physical activity or selfPhysical activity T2 (PA 2)
1
0.23
efficacy.
Self-efficacy T2 (SE 2)
1
A 1-way between-subjects ANCOVA was calculated with each of the dependent variables of posttest
T1 measures were taken at enrollment in study, T2 approximately 6 weeks later.
*P < .05. **P < .01.
physical activity and posttest self-efficacy, the independent variable of group status, the covariates of
weight, and the respective pretest values. Physical
efficacy scores in either the intervention or control
activity was not significantly different between the intergroup, F(1, 27) = 1.04, P = .32, and F(1, 26) = 1.04, P =
vention and the control group at the study conclusion,
.32,
respectively. The changes undergone by each group
F(1, 51) = 0.06, P = .81. The covariate of weight did not
were
in the opposite direction, with an increase in selfexert a significant effect on differences in physical activefficacy in the intervention group and a decrease in the
ity, F(1, 51) = 0.79, P = .38, while pretest physical activcontrol group (see Table 1).
ity did, F(1, 51) = 76.01, P = .00. On examination of the
Participants in the intervention group were assessed
outcome of self-efficacy, there was, however, a signifiregarding
the feasibility and acceptability of keeping
cant difference between groups, F(1, 51) = 7.23, P = .01,
activity records. Descriptive data related to the individwith the intervention group (mean = 3.69, SD = 0.91)
ual items of the Perceived Feasibility Checklist are
showing greater self-efficacy than the control group
reported in Table 3. The results indicated that the partic(mean = 3.23, SD = 1.00). Both pretest self-efficacy and
ipants felt generally positive about the intervention.
weight were related to posttest self-efficacy, F(1, 51) =
They noted that the records helped them to think about
84.58, P = .00, and F(1, 51) = 5.93, P = .02.
their activity and motivated them to follow activity recRepeated-measures ANCOVA revealed that physical
ommendations. The structure provided enough codes to
activity was significantly increased from the beginning of
describe commonly performed activities, and the docuthe study to the end in both the intervention group, F(1,
mentation was easy to understand. Participants stated
27) = 17.04, P = .00, and the control group, F(1, 27) =
that the intervention was beneficial and was not excep7.98, P = .01. There was not a significant change in selftionally time-consuming. The total scores ranged from
Volume 32, Number 1, January/February 2006
Physical Activity Monitoring
75
similar increase in physical activity in both groups
may reflect that there are benefits to enacting a simple, face-to-face meeting focused solely on physical
Descriptive Data for Perceived Feasibility Checklist
activity, even without formal record keeping.
(Intervention Group Only, N = 28)
Discussion of physical activity with a professional
provides a cue to action that may be an impetus in
Standard
itself for initiation of behavior change. Professionals
Item
Mean Deviation
need to evaluate the messages they send. One individual stated that his physician had not told him any
Easy to understand
4.05
0.58
specifics about exercise; therefore, even though he
Time-consuming (before reverse coding)
2.07
1.12
knows physical activity is beneficial, he has only
Think about activity
4.21
1.07
made minor exercise changes. He further stated that if
Motivated to follow recommendations
3.75
1.14
given a specific exercise recommendation, he would
Enough codes
4.14
0.65
follow it, “absolutely.” More research must test
Do more to have something to write
3.32
1.12
whether interactions with health care providers, as
Talked about records with family/friends
2.75
1.21
well as with peers, either one-on-one or in a group,
Records helped to talk to health professional 3.29
1.27
can be adapted to encourage behavior change.
Keeping records was beneficial
3.96
1.07
Analysis revealed that the intervention of keeping
activity records did enhance feelings of self-efficacy.
Scores range from 1 to 5. Higher scores note more positive ratings.
Although physical activity significantly increased in
both groups over time, self-efficacy dropped in the
2.22 to 4.89, with a mean score of 3.76 (SD = 0.67).
control group but increased in the intervention group.
Written comments concurred that the records helped
This is in line with the work of researchers who have
reveal developing patterns of predictable activity and
found that self-efficacy can be improved through interthat continuation of self-monitoring could help in workventions directed by professionals.22 Some researchers,
ing toward activity goals.
however, have disagreed. Speck and Looney21 found that
their 12-week intervention of activity recording and
pedometer-assisted monitoring did not encourage selfDiscussion
efficacy, noting a drop in self-efficacy in the intervention
The lack of difference between groups in regard to
group, even with an increase in physical activity. They
physical activity at the end of the study could be
speculated that perceived self-efficacy becomes lower
explained in a number of ways. Since both groups
when people begin a behavior and are faced with actual
improved their physical activity, it seems likely that peobarriers.21 This was not evident in the current study.
ple who agreed to participate may have been interested
Perhaps the shortened time frame may not have providin physical activity and its effect on health to start with.
ed adequate opportunity for participants to encounter
It also may indicate that just by being in the study, peobarriers, avoiding a negative effect on self-efficacy.
ple in both groups may have perceived near equal levels
Other researchers have found that self-efficacy is not
of verbal persuasion. There was only limited additional
strongly related to behavior change before it has begun
contact between the researcher and individuals in the
but takes on a more potent role as modifications in
intervention group. It is likely that to be successful, the
lifestyle are attempted.22 Previous cross-sectional
intervention must provide stronger incentives, ongoing
research seems to have established a positive relationsupport, and problem-solving assistance. More frequent
ship between self-efficacy and behavior17,18; however, in
interaction between participant and professional using
this study, the relationship appears to be more complex.
mail and telephone contact has been shown to improve
Self-efficacy was not consistently related to physical
functional capacity.35 Many participants in this study
activity. Changes in self-efficacy did not consistently
were disappointed to learn that the research protocol did
mirror differences in physical activity. Because lifestyle
not provide more structure of a prescriptive nature. The
change takes time to develop, enhancing self-efficacy
Table 3
Gleeson-Kreig
The Diabetes EDUCATOR
76
could have long-range, beneficial effects. Although the
effects may not be immediate, improved self-efficacy
could boost confidence toward later enactment.
Interventions directed at enhancing self-efficacy seem
especially important for individuals who are overweight.
In light of results that did not reflect a clear link
between self-efficacy and behavior, the role of measurement error must be considered. Although overall, the
HPAI was adequate, poor reliability of the
household/family care subscale requires research into its
psychometric properties, including revision of items and
subsequent factor analysis. The social desirability of
physical activity requires considering the role of potential bias. It is unclear whether self-report data accurately
portray physical activity or whether participants did not
want to appear sedentary. While the use of objective
indicators such as pedometers or accelerometers could
help, these devices are not without problems. The SEE as
a measure of self-efficacy was also open to error. In
administering the scale, it seemed difficult for participants to make the distinction between hypothetical ability and actual intention. Some participants commented
that they had high confidence that none of these constraints “would” hold them back from getting 30 minutes
of physical activity on most days of the week but also
related that they did not have any real plans for engaging
in that much activity. The disparity between what a person can do and what a person will do is not made clear
in this scale and was interpreted uniquely by participants. The SEE seemed more a measure of perceived
barriers than of intention for planned enactment. The
change in scoring format from a 10-point to a 5-point
scale may also have contributed to a low sensitivity to
change. Further refinement of measurement techniques
is needed.
Based on results, the use of self-monitoring of physical activity should be encouraged as a tool to increase
self-efficacy and potentially long-term changes in
behavior. To be successful, health care professionals
must attend to the effort the person has made. Frustration
has been expressed in previous research36 and in this
study, with the experience of keeping detailed records of
blood glucose, physical activity, or food intake, only to
have the health care provider pay no attention to it at the
office visit. Diabetes educators or nurses in outpatient
settings could recommend self-monitoring, take an
active role in the review of client data, and provide sub-
sequent teaching, counseling, and follow-up. Ongoing
counseling directed specifically at physical activity has
been found to assist with long-term improvement.18
Although the client meetings for this study lasted up to 1
hour in some cases, most of the time was spent in consent and data collection. The actual time explaining and
reviewing activity records was well under 15 minutes,
making it time efficient.
In addition, pedometers should be tested as a motivator of behavior. At the conclusion of the study, subjects
expressed an eagerness to monitor their level of physical
activity using the device they received for participating.
The feedback from daily use of a pedometer intuitively
makes sense as a motivator; however, very little research
has tested this assumption. The use of a pedometer, worn
continuously, could help measure physical activity that is
integrated into an active lifestyle throughout the day.
Research must continue to address barriers people
have to adequate physical activity. Previous research has
found that fear of physical injuries, negative emotional
states, conflicts with work schedule, lack of enjoyment,23
and inclement weather12 present serious barriers to physical activity. People in this study echoed these sentiments, either on the questionnaires or through verbal
comments. Future research must develop interventions
that focus on overcoming these barriers. Communitybased interventions should be developed and tested that
support safe indoor activity in geographic locations that
experience extremes of temperature. Access to high
school and college gyms, malls, and other facilities
should be made available to the public. Other interventions should be developed that help people create strategies to cope with particularly vulnerable situations, such
as social gatherings and holiday celebrations. Many participants discussed that engaging in physical activity
required a certain mind-set to make it a goal and priority. Some related that emotional states, such as depression, affected whether they exercised. Future research
exploring coping and motivation could further substantiate the knowledge base in this area.
References
1.
2.
Diabetes Prevention Program Research Group. The Diabetes
Prevention Program (DPP): description of lifestyle intervention.
Diabetes Care. 2002;25:2165-2171.
Nelson KM, Reiber G, Boyko EJ. Diet and exercise among adults
with type 2 diabetes: findings from the Third National Health and
Volume 32, Number 1, January/February 2006
Physical Activity Monitoring
77
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
Nutrition Examination Survey (NHANES III). Diabetes Care.
2002;25:1722-1728.
Wei M, Gibbons LW, Kampert JB, Nichaman MZ, Blair SN. Low
cardiorespiratory fitness and physical inactivity as predictors of
mortality in men with type 2 diabetes. Ann Intern Med.
2000;132:605-611.
Irwin ML, Mayer-Davis EJ, Addy CL, et al. Moderate-intensity
physical activity and fasting insulin levels in women: the crosscultural activity participation study. Diabetes Care. 2000;23:449454.
US Department of Health and Human Services. Healthy People
2010. Washington, DC: US Department of Health and Human
Services; 2002.
Bandura A. Social Foundations of Thought and Action: A Social
Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.
Zinman B, Ruderman N, Campaigne BN, Devlin JT, Schneider
SH. Diabetes mellitus and exercise: American Diabetes
Association. Diabetes Care. 2002;25(suppl 1):S64-S68.
Jakicic JM, Wing RR, Butler BA, Robertson RJ. Prescribing
exercise in multiple short bouts versus one continuous bout:
effects on adherence, cardiorespiratory fitness, and weight loss in
overweight women. Int J Obes. 1995;19:893-901.
Eyler AA, Vest JR, Sanderson B, et al. Environmental, policy, and
cultural factors related to physical activity in a diverse sample of
women: the women’s cardiovascular health network project—
summary and discussion. Women Health. 2002;36:123-134.
King AC, Castro C, Eyler AA, Wilcox S, Sallis JF, Brownson RC.
Personal and environmental factors associated with physical
inactivity among different racial-ethnic groups of U.S. middleaged and older-aged women. Health Psychol. 2000;19:354-364.
US Department of Health and Human Services. Monthly estimates of leisure-time physical inactivity—United States, 1994.
MMWR Morb Mortal Wkly Rep. 1997;46:393.
Choudhry UK. Health promotion among immigrant women from
India living in Canada. J Nurs Sch. 1998;30:269-274.
Conn VS. Older adults and exercise: path analysis of self-efficacy related constructs. Nurs Res. 1998;47:180-189.
Resnick B, Palmer MH, Jenkins L, Spellbring A. Path analysis of
efficacy expectations and exercise behaviour in older adults. J
Adv Nurs. 2000;31:1309-1315.
Sternfeld B, Ainsworth BE, Quesenberry CP. Physical activity
patterns in a diverse population of women. Prev Med.
1999;28:313-323.
Resnick B, Spellbring A. Understanding what motivates older
adults to exercise. J Gerontol Nurs. 2000;26:34-42.
Wilbur J, Miller AM, Chandler P, McDevitt J. Determinants of
physical activity and adherence to a 24-week home-based walking program in African American and Caucasian women. Res
Nurs Health. 2003;26:213-224.
Steptoe A, Rink E, Kerry S. Psychosocial predictors of changes
in physical activity in overweight sedentary adults following
counseling in primary care. Prev Med. 2000;31:183-194.
Oman RF, King AC. Predicting the adoption and maintenance of
exercise participation using self-efficacy and previous exercise
participation rates. Am J Health Promot. 1998;12:154-161.
Gleeson-Kreig
20. Bottorff JL, Johnson JL, Ratner PA, Hayduk L. The effects of
cognitive-perceptual factors on health promotion behavior maintenance. Nurs Res. 1996;45:30-36.
21. Speck BJ, Looney SW. Effects of a minimal intervention to
increase physical activity in women: daily activity records. Nurs
Res. 2001;50:374-378.
22. McAuley E, Courneya KS, Rudolph DL, Lox CL. Enhancing
exercise adherence in middle-aged males and females. Prev Med.
1994;23:498-506.
23. Krug LM, Haire-Joshu D. Exercise habits and exercise relapse in
persons with non-insulin-dependent diabetes mellitus. Diabetes
Educ. 1991;17:185-188.
24. Glasgow RE, Eakin EG, Fisher EB, Bacak SJ, Brownson RC.
Physician advice and support for physical activity: results from a
national survey. Am J Prev Med. 2001;21:189-196.
25. Pinto BM, Lynn H, Marcus BH, DePue J, Goldstein MG.
Physician-based activity counseling: intervention effects on
mediators of motivational readiness for physical activity. Ann
Behav Med. 2001;23:2-10.
26. Bull FC, Jamrozik K, Blanksby BA. Tailored advice on exercise:
does it make a difference? Am J Prev Med. 1999;16:230-239.
27. Anderson RM, Funnel MM, Butler PM, Arnold MS, Fitzgerald
JT, Feste CC. Patient empowerment: results of a randomized controlled trial. Diabetes Care. 1995;18:943-949.
28. Oman RF, King AC. The effect of life events and exercise program format on the adoption and maintenance of exercise behavior. Health Psychol. 2000;19:605-612.
29. Lauzon N, Tudor-Locke C, Myers A, et al. Increased physical
activity and improved health measures with a pedometer-based
physical activity intervention for type 2 diabetes. Diabetes.
2003;52:A236.
30. Shalala DE. Physical activity and health: a report of the surgeon
general, 1999 [Centers for Disease Control and Prevention Web
site]. Available at: http:www.cdc.gov/nccdphp/sgr/ataglan.htm.
31. Baeke JAH, Burema J, Frijters JER. A short questionnaire for the
measurement of habitual physical activity in epidemiological
studies. Am J Clin Nutr. 1982;36:936-942.
32. Burns KJ, Froman RD. Refinement of the Habitual Physical
Activity Index for use with American adults. J Nurs Meas.
1997;5:17-32.
33. Ainsworth BE, Sternfeld B, Richardson MT, Jackson K.
Evaluation of the Kaiser Physical Activity Survey in women.
Med Sci Sports Exerc. 2000;32:1327-1334.
34. McAuley E. Self-efficacy and the maintenance of exercise participation in older adults. J Behav Med. 1993;16:103-113.
35. DeBusk RF, Miller NH, Superko R, et al. A case-management
system for coronary risk actor modification after acute myocardial infarction. Ann Intern Med. 1994;120:721-729.
36. Rayman KM, Ellison GC. When management works: an organizational culture that facilitates learning to self-manage type 2 diabetes. Diabetes Educ. 1998;24:612-617.