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DOI: 10.1177/0145721705284285
Self-monitoring of Physical ActivityEffects on Self-efficacy and Behavior in People With Type 2 DiabetesFrom Plattsburgh State University, Plattsburgh, New York. Correspondence to JoAnn M. Gleeson-Kreig, PhD, RN, Plattsburgh State University, 101 Broad Street, Plattsburgh, NY 12901 (joann.gleeson-kreig{at}plattsburgh.edu).
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 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.
Lack 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 self-responsibility 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.
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.
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 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 self-efficacy 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. 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?
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.
Procedures
Measures
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
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
Data Analysis
The effect of keeping daily physical activity records was addressed through the use of analysis of covariance (ANCOVA). The 2 dependent variables were physical 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.
Pearson product–moment correlations were performed to look for confounding variables. Income was positively related to education (r = 0.55, P = .000), and the number of months since being diagnosed with diabetes was negatively related to income (r = –0.27, P = .04). There was no relationship between any of the demographic variables and physical activity or self-efficacy. A 1-way between-subjects ANCOVA was calculated with each of the dependent variables of posttest physical activity and posttest self-efficacy, the independent variable of group status, the covariates of weight, and the respective pretest values. Physical activity was not significantly different between the intervention and the control group at the study conclusion, F(1, 51) = 0.06, P = .81. The covariate of weight did not exert a significant effect on differences in physical activity, F(1, 51) = 0.79, P = .38, while pretest physical activity did, F(1, 51) = 76.01, P = .00. On examination of the outcome of self-efficacy, there was, however, a significant difference between groups, F(1, 51) = 7.23, P = .01, with the intervention group (mean = 3.69, SD = 0.91) showing greater self-efficacy than the control group (mean = 3.23, SD = 1.00). Both pretest self-efficacy and weight were related to posttest self-efficacy, F(1, 51) = 84.58, P = .00, and F(1, 51) = 5.93, P = .02. Repeated-measures ANCOVA revealed that physical activity was significantly increased from the beginning of the study to the end in both the intervention group, F(1, 27) = 17.04, P = .00, and the control group, F(1, 27) = 7.98, P = .01. There was not a significant change in self-efficacy scores in either the intervention or control group, F(1, 27) = 1.04, P = .32, and F(1, 26) = 1.04, P = .32, respectively. The changes undergone by each group were in the opposite direction, with an increase in self-efficacy in the intervention group and a decrease in the control group (see Table 1). Participants in the intervention group were assessed regarding the feasibility and acceptability of keeping activity records. Descriptive data related to the individual items of the Perceived Feasibility Checklist are reported in Table 3. The results indicated that the participants felt generally positive about the intervention. They noted that the records helped them to think about their activity and motivated them to follow activity recommendations. The structure provided enough codes to describe commonly performed activities, and the documentation was easy to understand. Participants stated that the intervention was beneficial and was not exceptionally time-consuming. The total scores ranged from 2.22 to 4.89, with a mean score of 3.76 (SD = 0.67). Written comments concurred that the records helped reveal developing patterns of predictable activity and that continuation of self-monitoring could help in working toward activity goals.
The lack of difference between groups in regard to physical activity at the end of the study could be explained in a number of ways. Since both groups improved their physical activity, it seems likely that people who agreed to participate may have been interested in physical activity and its effect on health to start with. It also may indicate that just by being in the study, people in both groups may have perceived near equal levels of verbal persuasion. There was only limited additional contact between the researcher and individuals in the intervention group. It is likely that to be successful, the intervention must provide stronger incentives, ongoing support, and problem-solving assistance. More frequent interaction between participant and professional using mail and telephone contact has been shown to improve functional capacity.35 Many participants in this study were disappointed to learn that the research protocol did not provide more structure of a prescriptive nature. The 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 activity, even without formal record keeping. Discussion of physical activity with a professional provides a cue to action that may be an impetus in itself for initiation of behavior change. Professionals need to evaluate the messages they send. One individual stated that his physician had not told him any specifics about exercise; therefore, even though he knows physical activity is beneficial, he has only made minor exercise changes. He further stated that if given a specific exercise recommendation, he would follow it, "absolutely." More research must test whether interactions with health care providers, as well as with peers, either one-on-one or in a group, can be adapted to encourage behavior change. Analysis revealed that the intervention of keeping activity records did enhance feelings of self-efficacy. Although physical activity significantly increased in both groups over time, self-efficacy dropped in the control group but increased in the intervention group. This is in line with the work of researchers who have found that self-efficacy can be improved through interventions directed by professionals.22 Some researchers, however, have disagreed. Speck and Looney21 found that their 12-week intervention of activity recording and pedometer-assisted monitoring did not encourage self-efficacy, noting a drop in self-efficacy in the intervention group, even with an increase in physical activity. They speculated that perceived self-efficacy becomes lower when people begin a behavior and are faced with actual barriers.21 This was not evident in the current study. Perhaps the shortened time frame may not have provided adequate opportunity for participants to encounter barriers, avoiding a negative effect on self-efficacy. Other researchers have found that self-efficacy is not strongly related to behavior change before it has begun but takes on a more potent role as modifications in lifestyle are attempted.22 Previous cross-sectional research seems to have established a positive relationship between self-efficacy and behavior17,18; however, in this study, the relationship appears to be more complex. Self-efficacy was not consistently related to physical activity. Changes in self-efficacy did not consistently mirror differences in physical activity. Because lifestyle change takes time to develop, enhancing self-efficacy 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 subsequent 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. Community-based 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.
This research was supported by the University of Connecticut Research Foundation and the Gamma Delta Chapter of Sigma Theta Tau International.
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Abstract
Theoretical Framework
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 test-retest reliability of .79 to
.91.
