Improving adherence to a cholesterol-lowering diet: a behavioral intervention study

Patient Education and Counseling 57 (2005) 134–142

Improving adherence to a cholesterol-lowering diet: a behavioral intervention study Lora E. Burkea,∗ , Jacqueline Dunbar-Jacoba , Trevor J. Orchardb , Susan M. Sereikaa a

University of Pittsburgh School of Nursing, 415 Victoria Building, Pittsburgh, PA 15261, USA b University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA 15261, USA

Received 27 November 2003; received in revised form 31 March 2004; accepted 17 May 2004

Abstract Less than 50% of US adults follow dietary recommendations. Despite these figures, little research has focused on improving adherence to a therapeutic eating plan. The research utilizing self-efficacy theory has shown promise for improving behavior change and treatment adherence. This study evaluated the efficacy of a telephone-delivered, self-efficacy based intervention designed to improve adherence to a cholesterol-lowering diet among those self-reporting nonadherence. Sixty-five men and women diagnosed with hypercholesterolemia were randomized to usual care or treatment, which consisted of six intervention sessions delivered every 2 weeks by telephone and focused on how to manage eating behavior in challenging situations. There were significant between group differences post intervention in the consumption of saturated fat (P < .001) and cholesterol (P = .040) with the intervention group improving their dietary adherence. Significant change (P = .013) occurred over time in low-density lipoprotein-cholesterol (LDL-C) in the intervention group. No changes were observed in self-efficacy between groups, suggesting that self-efficacy was not a mediator of the improved adherence. The study’s findings confirm that the telephone is a useful tool to deliver adherence-enhancing interventions. © 2004 Elsevier Ireland Ltd. All rights reserved. Keywords: Dietary adherence/compliance; Hypercholesterolemia; LDL-C; Self-efficacy; Telephone-delivered intervention

1. Introduction The Adult Treatment Panel’s third report (ATP III) advocated dietary treatment as the cornerstone of therapy in lowering low density lipoprotein cholesterol (LDL-C), reserving pharmacological therapy for those at high risk for coronary heart disease (CHD) as a supplemental treatment modality and not a substitute for dietary therapy [1]. The guidelines assert that the goal of dietary therapy is not a temporary “diet,” but rather a permanent, life-long change in eating behavior. Multiple factors may adversely impact an individual’s ability to maintain the behavioral changes required to adhere to the therapeutic eating plan [2–5]. According to the ATP III guidelines, approximately one third of all adults in the US are candidates for lipid-lowering ∗

Corresponding author. Tel.: +1 412 624 2305; fax: +1 412 383 7293. E-mail address: [email protected] (L.E. Burke).

0738-3991/$ – see front matter © 2004 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pec.2004.05.007

diets [1]. However, fewer than half follow the recommended dietary guidelines [6]. Despite these statistics, intervention research specifically targeting enhancement of adherence to a cholesterol-lowering diet has been limited. Large primary prevention trials have focused on instruction augmented by individual counseling [7] or the use of behavioral techniques [8]. These studies demonstrated improvement in adherence; however, the findings are limited by the absence of a comparison or control group. This has precluded determining whether the intervention alone or other factors were determinants of the outcome. Hyman and colleagues delivered a 4-week diet and behavioral cholesterol reduction program followed by 6 months of automated telephone follow-up but failed to show a difference between the intervention and control groups, which may have been due to the limited intervention period or the absence of a professional care provider with whom the patient could interact on the phone session [9].

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

More recently, McCann et al. reported that changes in selfefficacy were related to lipid lowering and dietary change [4]. Although these results are promising, the findings are limited by a small sample size and lack of attention to inadequate adherence and strategies to remediate nonadherence. However, the findings are consistent with what others have reported, that self-efficacy may be a mediator for behavior change and improved maintenance of treatment adherence [10–12]. Thus, the next step is the empirical testing of an intervention guided by self-efficacy theory and designed to improve dietary adherence among those who have reported nonadherence to a cholesterol-lowering eating plan. Self-efficacy is described as the perception of one’s capabilities to mobilize the motivation, cognitive resources, and courses of action required to meet given situational demands [13]. Outcome expectancy, one’s perception of whether actually performing the behavior will lead to the desired outcome, represents the second component of the self-efficacy construct [13]. Individuals may believe that a specific course of action (e.g., altering eating patterns), will ensure a desirable outcome. However, if they doubt their capability to perform the requisite behavior, their belief in the therapeutic efficacy will not influence their behavior [13]. The two components of self-efficacy, efficacy expectancy and outcome expectancy, have seldom been examined concurrently in examining behavior maintenance [14]. The purpose of the study was to evaluate the efficacy of a nurse-delivered intervention designed to improve adherence to a cholesterol-lowering diet. We hypothesized that compared to subjects in the usual care group, subjects who received the intervention would improve their adherence to the cholesterol-lowering diet, which would result in reduced low density lipoprotein (LDL-C). The intervention, based on self-efficacy theory, was delivered as an adjunct to usual care among patients treated for hypercholesterolemia.

135

subjects completed the DHS at least 6 weeks following completion of the original study. 2.3. Subjects

The study utilized a randomized controlled two-group pretest, post-test design to enroll only subjects who were considered nonadherent to the cholesterol-lowering diet. The Institutional Review Board approved the study protocol.

Eligibility criteria included: (a) age 20 years or older; (b) stabilized on cholesterol-lowering dietary and/or pharmacological therapy for a minimum of 2 weeks; and (c) a score on the Connor DHS indicating that the individual was consuming a diet with >30% of calories from fat, and was therefore nonadherent to the Step I Diet. Patients were excluded if they had secondary hyperlipidemia or complex medical and dietary regimens, e.g., patients with type 1 diabetes; were unable to comprehend and write English, showed signs of mental confusion, or would be unavailable for the 3-month follow-up. If the Connor DHS score indicated dietary nonadherence and the patient met other eligibility criteria, the individual was invited to participate in the intervention study. For subjects on hypolipidemic pharmacological therapy, a minimum LDL-C was not set since the person had established hypercholesterolemia. If the person was not on cholesterollowering drug therapy, the ATP II algorithm was followed, e.g., without CHD and with two or more risk factors, dietary therapy would be initiated for a serum LDL-C ≥130 mg/dL (3.4 mmol/L). Of the 333 patients screened for nonadherence to a cholesterol-lowering diet, 163 (49%) were determined to be eligible, of whom 121 (74%) participated in the next phase and provided baseline data. Seventy-nine of these participants (65%) expressed interest in the intervention study, of which 65 (82%) consented to participate in the trial and were randomized. The 14 subjects who expressed interest but were not randomized experienced subsequent changes in lipid management during the baseline assessment period. Table 1 details the socio-demographic data of the sample by treatment group, which was well balanced across the two groups. Ninety-four percent were white, while over half were married (72%), employed full time (62%), and male (63%). The mean age was 55 ± 11 years. Using a computer-based randomization program, subjects were stratified by gender and site to ensure comparability of these factors between the two treatment groups. Subjects were randomized with equal allocation to usual care or usual care plus the intervention.

2.2. Recruitment and screening

2.4. Usual care and behavioral intervention

Screening for the study occurred at three academic medical center practice and research sites: a lipid clinic, a cardiology clinic, and a research site. At the lipid and cardiology clinics, patients completed the Connor Diet Habit Survey (DHS), a screening questionnaire that assessed fat and cholesterol consumption [15]. Subjects from the research site were former participants in a study examining the behavioral correlates of cholesterol reduction. These

Usual care, which was similar across the sites, consisted of follow-up visits with the physician and/or lipid measurements every 3–6 months. Self-efficacy theory guided the intervention. Performance attainment and verbal persuasion are two sources of information that contribute to perceived selfefficacy [16]. The process of facilitating performance attainment and mastery is achieved through the development of specific skills, referred to as self-regulation [17]. These skills

2. Methods 2.1. Design

136

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

Table 1 Descriptive statistics for participants by treatment group (N = 65)

Table 2 Description of self-efficacy theory based intervention

Characteristic

Theory-based strategy

Intervention strategy

Goal setting

Participants (Ps) chose a behavior they wished to change, starting with an easy behavior If necessary, target behavior was divided into easily managed components that progressed in a step-wise manner to facilitate success Goals were: (a) specific, tailored to individual; (b) difficult enough to provide challenge, yet attainable; (c) had temporal proximity – able to attain within 2-week interval, which permitted reinforcement at next session Ps recorded behavior in relation to specific goal in daily log, described circumstances that made behavior change difficult or easy Ps used recording to develop self-evaluation skills by comparing performance to pre-set goal Ps read diary at each phone session to evaluate their progress and match behavior to goal Interventionist focused on long-term progress and any positive changes Contingent self rewards consisted of positive statements regarding attainment or efforts to attain inter-session goal Ps read diary, were assisted to make positive statements regarding achievements in attaining behavior change; attribution given to Ps Interventionist verbally encouraged Ps through use of statements designed to help Ps see themselves as efficacious when positive aspects of behavior identified (progress made, person persisted longer than in previous attempts) Interventionist convinced Ps they were capable of achieving the next selected goal

Intervention (n = 31) % (n)

Usual care (n = 34)% (n)

Site Lipid clinic Cardiology Research site

19 (6) 23 (7) 58 (18)

21 (7) 27 (9) 53 (18)

Gender Male Female

65 (20) 36 (11)

62 (21) 38 (13)

Race/ethnicity White Non-white

94 (29) 6 (2)

94 (32) 6 (2)

71 (22) 26 (8)

74 (25) 18 (6)

3 (1)

9 (3)

Marital status Married Widowed/separated/ divorced Never married Employment Full-time Part-time/other

64.5 (20) 35.5 (11)

58.8 (20) 41.2 (14)

Income ≤$ 29,999 $ 30,000–63,999 ≥$ 64,000

27.6 (8) 51.7 (15) 20.7 (6)

36.4 (12) 27.3 (9) 36.4 (12)

Age (in years) M S.D.

53.74 11.60

55.90 10.64

Education (in years) M S.D.

14.68 2.86

15.41 3.15

Self-monitoring

Self-reinforcement

Verbal persuasion

No significant differences between groups (P-values = .320–.910).

include: (a) monitoring the behavior one seeks to change; (b) setting short-range attainable sub-goals to motivate and direct one’s efforts; and (c) enlisting incentives and social supports to sustain the effort needed to succeed [18]. The strategies were used to facilitate successful achievement of goals. Verbal persuasion was used to convince individuals that they could successfully perform or cope with dietary adherence when they had previously failed. The individually tailored, telephone intervention, detailed in Table 2, included the following components: setting a goal for each 2-week period between sessions, the subject reviewing the self-monitoring notes of the previous 2 weeks, the interventionist providing reinforcement and encouraging the subject’s self-reinforcement, and the interventionist providing verbal persuasion directed at achievement of the next goal. The self-monitoring was performed for behavior relevant to the specific goal. The intervention was conducted during pre-scheduled telephone appointments. 2.5. Adherence to the intervention protocol Overall, participants were adherent to the intervention protocol: 77% (n = 24) completed all six treatment sessions, 10%

(n = 3) completed five, 6% (n = 2) completed four sessions, 3% (n = 1) completed only one treatment session, and 3% (n = 1) no session. Goals were met for 92% (n = 153) of the sessions. Participants recorded in their self-monitoring logs for 92% (n = 152) of the inter-session periods. Sixty-six percent (n = 114) of the calls were completed on the first attempt. On average, the intervention call lasted 22 min. 2.6. Measures All measures were completed at baseline pre-randomization and 14-weeks post-randomization, unless otherwise noted. 2.6.1. Eating behavior and clinical history and risk profile This 26-item questionnaire assessed food-related habits, medical history, and coronary risk factors. This survey was administered only at baseline. 2.6.2. Dietary adherence For screening purposes, dietary adherence was assessed through the administration of the 38-question Connor DHS, which is comprised of five sub-scales. The

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

Cholesterol-Saturated Fat and the Carbohydrate Sub-scales were used to screen for adherence to the cholesterollowering eating plan because carbohydrate intake as well as reduction of high fat foods must be considered in evaluating one’s diet for cholesterol-reduction purposes [15]. For the study’s outcome, a Three-Day Food Record at baseline and at 14 weeks was used to assess adherence to total fat, saturated fat, and cholesterol guidelines. Participants recorded total food intake on 3 days, with one of the days a leisure or nonworking day. A dietitian, blinded to treatment, contacted each participant to fill in any missing information or clarify ambiguous entries. Adherence was examined by comparing the actual intake of total and saturated fat and cholesterol to the prescribed amount. In the absence of a specific recommendation for fat and cholesterol allowance on the subject’s medical record, a conservative approach was taken and the ATP II guidelines were applied for fat and cholesterol, as well as for caloric allowance (2500 kcal/day for males, 1800 kcal/day for females) [1]. Therefore, if a subject was not on drug therapy, the dietary prescription was the Step I Diet. If the subject did not have a documented dietary prescription and was on drug therapy, the prescription was the Step II Diet. For the measurement of adherence, these percentages were converted to absolute values for each subject. Adherence scores for each nutrient were computed as the amount consumed divided by the amount prescribed per day × 100. This calculation related the consumption of the nutrient to the amount prescribed, rather than as a proportion of the total amount of calories consumed each day. Unlike other health behavior regimens where adherence is a problem of under performance, eating adherence is an issue of “over-doing,” or behavioral excess. Therefore, nonadherence was defined as when the 100% level was exceeded, or when intake of total fat, saturated fat, or cholesterol was in excess of the dietary prescription. The underlying assumption of this computation scheme is that under-consumption of fat is not a problem in this population. Thus, anyone having an adherence score of less than 100 was considered adherent to the recommended eating plan. 2.6.3. Self-efficacy Self-efficacy was measured by the Cholesterol-Lowering Diet Self-Efficacy Scale (CLDSES), a 33-item scale. Using a scale of 0–100, respondents were asked to rate their level of confidence in their capability to carry out specific eatingrelated activities, e.g., selecting a low fat food or resisting a high fat food. The CLDSES has long-term (14 weeks) temporal stability (r = .76, P < .01) and high internal consistency (Cronbach’s alpha = .97) [19]. 2.6.4. Outcome expectancy Outcome expectancy was measured by the Perceived Therapeutic Efficacy Scale, a 10-item scale on which par-

137

ticipants rated from 0 to 10 their confidence that the therapeutic diet will prevent or reduce the risk of heart disease. The scale demonstrated good temporal stability over 14 weeks in the usual care group (r = .84) and high internal consistency (Cronbach’s alpha = .93) [20]. 2.6.5. Lipid levels Serum lipids were measured following a 12-h fast on two occasions at least 1 week apart. If the LDL-C value of the two measures differed by more than 30 mg/dL (0.8 mmol/L), a third assay was performed and the average of the three values was used. Lipids were analyzed enzymatically in a Center for Disease Control standardized laboratory that used the Abbott VP Supersystem. Total cholesterol was determined using the enzymatic method of Allain [21]. The coefficient of variation between runs was 1.3%. LDL-C concentration was calculated from the Friedewald formula [22]. In the presence of a triglyceride value exceeding 400 mg/dL (4.5 mmol/L), direct quantitative determination of LDL-C in the serum was performed. 2.6.6. Participants’ evaluation of the intervention components Two months following the study, a questionnaire was sent to intervention group participants asking them to evaluate the intervention. Participants were asked to rank order the helpfulness of the three major components of the intervention: telephone contacts, self-monitoring, and goal setting. 2.7. Management of Three-Day Food Record data Three-Day Food Record data were coded and entered into the computer. Nutrient calculations were performed using the Minnesota Nutrition Data System (NDS) software developed by the Nutrition Coordinating Center (NCC) at the University of Minnesota [23]. 2.8. Data analysis Pearson product moment correlations were used to examine the inter-relationships between self-efficacy, outcome expectancy, and adherence at baseline and between baseline and 14 weeks. We examined the difference or change scores (post score minus baseline score) and the percent difference scores (amount of change that occurred relative to the baseline score) of the four outcome variables, dietary adherence, self-efficacy, outcome expectancy, and LDL-C. The Mann–Whitney U-test, a nonparametric procedure, was used for analyses of the change scores, which were nonnormally distributed. The two sample t-test was used for comparison of means between treatment groups, and the Chi-square test or Fisher’s exact test was used for comparison of categorical responses between treatment groups. The level of significance was set at .05. The intention-to-treat model was used for handling protocol deviations and the baseline values of outcomes

138

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

Table 3 Cardiovascular risk factor profile of sample by treatment group (N = 65)

3.2. Dietary adherence

Risk factor

Table 4 reports descriptive statistics for the baseline and post-intervention assessments as well as the difference scores and percent difference scores for dietary adherence. Overall, based on the Three-Day Food Record, subjects in both groups were adherent at baseline, except the usual care group to saturated fat. Those in the intervention group reduced their intake of total fat, saturated fat and cholesterol following the intervention, while the usual care increased their consumption. Changes between the groups, as well as the difference scores were statistically significant for all variables except mean adherence to total fat post-intervention (intervention group 81.06 versus usual care group 100.86, P = .111).

Intervention (n = 31) % (n)

Usual care (n = 34) % (n)

History of heart diseasea Yes

38.7 (12)

35.3 (12)

Hypertensiona Yes

22.6 (7)

11.8 (4)

Cigarette smoker Currently Former

19.4 (6) 29.0 (9)

23.5 (8) 41.2 (14)

Activity level – general Sedentary Moderately active Active

16.1 (5) 35.5 (11) 48.4 (15)

17.6 (6) 44.1 (15) 38.2 (13)

Activity level – occupationa Sitting Walking Heavy physical

58.1 (18) 35.5 (11) 6.5 (2)

61.3 (18) 32.3 (10) 6.5 (2)

Body mass indexa Males ≥27 Females ≥27

45.0 (9) 45.5 (5)

38.1 (8) 38.5 (5)

Waist-to-hip ratioa Males >.90 Females >.80

84.2 (16) 100 (11)

68.4 (13) 92.3 (12)

63.7 (7)

61.6 (8)

87.5 (7)

44.4 (4)

41.9 (13)

50.0 (17)

Females – menopausal Menopausal females on HRT Hypolipidemic drugs prescribed

No significant differences between groups. a Comparison based on Fisher’s exact test, two-tailed, P = .203–1.000.

were carried forward to represent the values at 14-weeks postrandomization for the one subject in the intervention group who withdrew before any intervention session was conducted [24].

3. Results

3.3. Self-efficacy and outcome expectancy Scores for self-efficacy and for outcome expectancy for the usual care and treatment groups at baseline were very similar, 72.27 ± 18.00 and 6.61 ± 1.97 for the usual care subjects and 75.59 ± 14 and 6.57 ± 1.90 for the intervention group subjects, respectively. The difference and percentage difference scores showed that each group changed little in the self-efficacy scores between baseline and end of study (percentage difference score for the intervention group 1.62 versus usual care group −3.22, P = .159). For outcome expectancy, there was a significant change in the difference scores (P = .007) and percent difference scores (percentage difference score for the intervention group 15.67 versus usual care group 7.99, P = .008). Examination of the relationship between self-efficacy and adherence scores from the Three-Day Food Record revealed statistically significant inverse relationships between baseline self-efficacy and concurrent adherence scores within the intervention group for total fat (r = −.42, P < .05), saturated fat (r = −.52, P < .01) and cholesterol (r = −.40, P < .05); there were significant inverse relationships between baseline self-efficacy and adherence within the usual care group only for cholesterol (r = −.36, P < .05). At the end of the study, the only adherence scores that were related to self-efficacy measured at baseline were saturated fat (r = −.42, P < .05) for the intervention group, and saturated fat (r = −.53, P < .01) and cholesterol (r = −.40, P < .05) for the usual care group.

3.1. Clinical characteristics and risk profile of the sample

3.4. LDL-cholesterol

Table 3 summarizes risk factor information in the total sample and the treatment groups. Approximately 37% (n = 24) of the sample had heart disease for an average of 10 years, 17% reported having hypertension, and 46% were taking lipid-lowering drugs. These and other risk factor data show that the treatment groups did not differ significantly in any aspect of their medical history or cardiovascular risk profile.

Table 5 contains the descriptive statistics as well as the difference scores and percent difference scores from baseline to 14-weeks post-randomization for LDL-C. There was a nonsignificant difference between the treatment groups in the serum LDL-C at baseline (usual care 150 ± 37 mg/dL [3.88 ± 0.95 mmol/L]) versus intervention (173 ± 70 mg/dL [4.47 ± 1.81 mmol/L]), t(63) = −1.694, P = .095). One subject in the treatment group had very high values, skewing the data positively. Analyses performed with and without the subject

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

139

Table 4 Baseline and post-intervention adherence and difference () scores by Three-Day Food Record (N = 65) Dietary adherencea

Treatment (n = 31)

Usual care (n = 34)

Test statistic

P-valueb

Total fat Baseline M ± S.D. Post M ± S.D.  score ± S.D.  percentage score ± S.D.

90.03 ± 45.3 81.06 ± 61.04 − 10.41 ± 50.36 −7.63 ± 50.87

98.00 ± 35.6 100.86 ± 31.58 2.35 ± 36.76 11.39 ± 43.43

t(62) = 0.785 t(60) =1.619 Z = −2.106 Z = −2.310

.436 .111 .035 .021

Saturated fat Baseline M ± S.D. Post M ± S.D.  score ± S.D.  percentage score ± S.D.

97.15 ± 50.90 75.08 ± 41.92 −22.96 ± 49.33 −10.08 ± 57.31

109.46 ± 47.80 110.93 ± 41.25 .18 ± 46.15 10.36 ± 47.73

t(62) =.998 t(60) =3.392 Z = −2.007 Z= −2.282

.322 .001 .045 .022

Cholesterol Baseline M ± S.D. Post M ± S.D.  score ± S.D.  percentage score ± S.D.

84.42 ± 40.32 65.46 ± 27.28 −19.96 ± 38.54 −2.80 ± 77.39

82.85 ± 43.40 86.71± 48.79 3.44 ± 43.17 16.67 ± 64.05

t(62) =−.150 t(60) = 2.097 Z= −2.437 Z= −2.155

.881 .040 .015 .031

a b

Food record adherence scores: <100 indicates adherence, >100 indicates nonadherence. Two-tailed P-values.

did not affect the results. Five subjects had changes made in their drug therapy during the course of the study, involving the initiation or discontinuation of a drug or alteration of dose. All five participants were in the treatment group. The mean serum LDL-C at the end of the study was 150 ± 41 mg/dL (3.88 ± 1.06 mmol/L) in the usual care group versus 155 ± 58 mg/dL (4.00 ± 1.50 mmol/L) in the intervention group in the full sample (n = 65), and 150 ± 41 mg/dL (3.88 ± 1.06 mmol/L) in the usual care group versus 158 ± 62 mg/dL (4.09 ± 1.60 mmol/L) in the intervention group when the five subjects were excluded (n = 60). Analyses performed with the full sample (n = 65) revealed significant change occurred over time in the LDL-C by treatment group based on comparisons of difference scores (P = .013). When the five subjects with hypolipidemic drug dosage changes during the study were excluded from the analysis, the findings were similar (P = .027).

one, and 15% ranked self-monitoring as first in helping them make the eating behavior changes.

4. Discussion This study focused on improving inadequate adherence among individuals with hypercholesterolemia to the Step I Diet, the eating plan that is recommended as a hygienic measure for the population and a first step in the dietary treatment of this disorder. All participants had been advised previously to make dietary changes, and 46% were being treated with hypolipidemic drugs. Therefore, adherence to the 30% fat eating plan was not an extreme criterion for dietary adherence. When compared to those receiving usual care, there was a significant difference for total and saturated fat and dietary cholesterol adherence and for serum LDL-C in the intervention group. Thus, the behavioral intervention demonstrated a positive effect in improving adherence to the prescribed cholesterol-lowering eating pattern. The improved adherence, which translates into reduced energy from total and saturated fats, was also reflected in the serum LDL-C value, which was reduced by 6% in the intervention group as compared to a 1.3% increase in

3.5. Intervention process evaluation There was a 68% (n = 21) response to the follow up questionnaire that was sent to participants 2 months after the completion of the study requesting them to rank order the three intervention components. Sixty percent ranked goal setting as number one, 25% ranked the phone contacts as number

Table 5 Baseline and post-intervention LDL-C values and difference () scores (N = 65) LDL-C

Treatment (n = 31)

Usual care (n = 34)

Test statistic

P-valuea

Baseline M ± S.D. Post M ± S.D.  score ± S.D.  percentage score ± S.D.

173.47 ± 70.49 155.05 ± 57.97 −18.42 ± 35.29 −6.40 ± 23.90

150.05 ± 37.47 150.30 ± 41.41 0.25 ± 26.11 1.29 ± 18.74

t(63) = −1.694 t(63) = −0.383 Z = −2.226 Z = −2.147

.095 .350 .013 .016

a

Two-tailed P-values.

140

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

the usual care group, a difference that can be clinically significant. Demonstration of nonadherence, through a score below a specified range on the Connor DHS, was a pre-requisite for study entry. The Three-Day Food Record provided a baseline measure of dietary adherence, which yielded higher rates of adherence than the Diet Habit Survey. The differences in reported adherence may be related to the format of the assessment tools. The Diet Habit Survey provides numerous options to select, possibly prompting recall of recently consumed foods. The food record depends on recording what is currently being eaten, and furthermore, may influence the actual foods consumed during the recording period. It is possible that as participants went through the baseline assessment, they began to modify their food intake and thus reported lower fat and cholesterol intake than was assessed through the Diet Habit Survey. Although the Three-Day Food Record data showed that intervention participants were adherent at baseline to some components of the diet, they made significant improvement in their adherence over the course of the 3-month study. This sample was more adherent to the fat and cholesterol restriction guidelines at pre-treatment than has been reported in other studies [12,25], but similar to a study that targeted primary prevention [4]. The adherence for total fat and for saturated fat at post-intervention in the treatment group suggests over-achievement of goal by some subjects. Considering the “backsliding” that has been reported to occur following the termination of a nutrition-targeted intervention, this does not present a clinical concern [4,26]. Comparing the results of our study to the primary prevention study reported by McCann and colleagues, our achieved reduction in saturated fat was similar to their findings following a 4-week intervention which included weekly meetings with a dietitian either alone or in combination with behavioral self-management [4]. In contrast, the intervention shown to be efficacious in increasing adherence in our study, would seem to have a lower participant burden and thus could facilitate long-term adherence to the treatment. Moreover, others have demonstrated that the use of the telephone for inter-visit-interval contact can reduce utilization of health care services [28], be highly acceptable to participants [29], and have a favorable impact on outcome [25,27,30–33]. A follow-up survey revealed which components of the intervention the participants thought influenced the improved adherence. Goal setting, which is central to the self-regulation process, was ranked first by the majority of participants [17]. In this study, attention was given to the properties of the goals, which are key to success, i.e., the specificity, level of difficulty, and temporal proximity of the goal. A study comparing the effectiveness of proximal and distal goals in weight loss showed that those who set proximal goals lost a significant amount of weight compared to those who set distal or long-term goals [34].

Telephone contacts have demonstrated efficacy in promoting behavior change to improve adherence [9,25,30,31,33]. One fourth of the intervention group ranked telephone contacts as the most helpful strategy in promoting behavior change. Being accountable to another individual at regular intervals has also been shown to be effective in promoting behavior change [17,27,35]. Self-monitoring engages the individual in systematically observing his or her behavior and the circumstances surrounding or prompting behavior. Although as a strategy self-monitoring has not been well studied, one report evaluating the effect of self-monitoring on weight loss revealed that the group with the highest overall level of consistency and completeness of monitoring lost significantly more weight than the group with the lowest overall level of self-monitoring [34]. Verbal persuasion, a component of the intervention, lent itself well to the telephone format. This strategy can lead people to make a more concerted effort than they might make without the boost. However, verbal persuasion may be limited in its power to instill increased efficacy beliefs [18], and thus may have been limited in its potential to influence perceived self-efficacy. The intervention in this study was based on self-efficacy theory. Therefore, the lack of a significant change in the level of perceived self-efficacy was unexpected. Moreover, it suggests that self-efficacy was not a mediator of the improved adherence following the intervention. It is possible that a longer period of dietary success is required before selfefficacy changes, and thus the 3-month intervention period was too brief to detect a difference in perceived efficacy. It is also possible that the self-efficacy instrument was not sensitive enough to detect changes. Other investigators have reported an absence of change in self-efficacy in weight management [36], the dietary treatment of hypercholesterolemia [4], and among a lower socioeconomic population in a cardiovascular risk reduction program [37]. The more consistent finding in the literature is that perceived self-efficacy predicts subsequent behavior and that post-intervention levels predict subsequent behavior at follow-up. This study did not include a follow-up. However, the significant correlation between self-efficacy at baseline and the saturated fat adherence score post-treatment indicated that higher self-efficacy pre-treatment predicted lower saturated fat intake at 3-months, which was supported by the change in adherence at 3 months occurring in saturated fat intake. Outcome expectancy, or confidence in a positive outcome from adhering to the eating plan, was significantly related to adherence behavior only at baseline, which is surprising considering that the intervention group showed a significant change in outcome expectancy compared to the usual care group. It is possible that in this 3month intervention the participants were more receptive to the importance of the cholesterol-lowering dietary treatment than to believing in their improved capability of

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

following the diet. This should be considered in future studies. The limitations of the study include the absence of followup. Future studies should evaluate the efficacy of the intervention over the long term. A more diverse sample would increase the generalizability of the study’s findings. Work on improving the self-efficacy instrument is ongoing.

[3]

[4]

[5]

5. Conclusions This study introduced an adherence-enhancing intervention in a controlled study and examined its efficacy in remediating insufficient adherence to the existing regimen that was part of usual care, and its effect on LDL-C. The intervention showed improved dietary adherence in the intervention group. A large change in eating behavior occurred in the three dietary components. The change within the treatment group in the serum LDL-C is what could be expected with a change to the Step I Diet. 5.1. Practice implications Results from this study have implications for dietary counseling delivered by health care professionals. Patients often find it difficult to adhere to a therapeutic eating plan. Using the intervention strategies described in this paper with individuals who are responsive to dietary intervention may be sufficient to delay or prevent the use of lipid-lowering drugs. Considering the reported high rates of nonadherence to statin therapy, it is important to identify strategies that can improve adherence to cholesterol-lowering eating plans [38]. Acknowledgements

[6]

[7]

[8]

[9]

[10]

[11]

[12] [13] [14] [15]

This work was supported in part by the following grants National Research Service Award, NIH, NINR 5 F31 NR06793-03, Graduate Student Research Fellowship, American Heart Association, PA Affiliate, Grant R03 HS08891, DHHS, PHS, Agency for Health Care Policy & Research, Enid Goldberg Research Grant Award, Sigma Theta Tau, Eta Chapter, Small Grant Award, Sigma Theta Tau, Gamma Tau Chapter, Eastern Nursing Research Society Scholar Award, American Nurses Foundation, Additional support was provided by the Obesity/Nutrition Research Center (DK46204), and a Postdoctoral Fellowship Award (HL07560).

[16]

[17]

[18]

[19]

References [1] National Cholesterol Education Program. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Betheda, MA: National Institutes of Health, National Heart, Lung, and Blood Institute; 2001. [2] Bovbjerg VE, McCann BS, Brief DJ, Follette WC, Retzlaff BM, Dowdy AA, Walden Carolyn E, Knopp RH. Spouse support

[20]

[21] [22]

141

and long-term adherence to lipid-lowering diets. Am J Epidemiol 1995;41:451–60. Burke LE, Dunbar-Jacob J. Adherence to medication, diet, and activity recommendations: from assessment to maintenance. J Cardiovasc Nurs 1995;9:62–79. McCann BS, Bjovberg VE, Brief DJ, Turner C, Follette WC, Fitzpatrick V, Dowdy A, Retzlaff BM. Relationship of self-efficacy to cholesterol lowering and dietary change in hyperlipidemia. Ann Behav Med 1995;17:221–6. Burke LE, Dunbar-Jacob J, Hill MM. Compliance to cardiovascular risk reduction strategies: review of the research. Ann Behav Med 1997;19:239–63. Kumanyika SK, Van Horn L, Bowen D, Perri MG, Rolls BJ, Czajkowski SM, Schron E. Maintenance of dietary behavior change. Health Psychol 2000;19:42–56. Dolocek TA, Milas NC, Van Horn LV, et al. A long-term nutrition intervention experience: lipid responses and dietary adherence patterns in the Multiple Risk Factor Intervention Trial. J Am Diet Assoc 1986;86:752–8. Walden CE, McCann BS, Retzlaff BM, et al. Alternative fatrestricted diets for hypercholesterolemia and combined hyperlipidemia: feasibility, design, subject recruitment, and baseline characteristics of the Dietary Alternative Study. J Am Coll Nutr 1991;10:429–42. Hyman DJ, Herd JA, Ho KSI, Dunn JK, Gregory KA. Maintenance of cholesterol reduction using automated telephone calls. Am J Prev Med 1996;12:129–33. Taylor CB, Houston-Miller N, Killen JD, DeBusk RF. Smoking cessation after acute myocardial infarction: effects of a nurse-managed intervention. Ann Intern Med 1990;13:18–23. Warnecke RB, Morera O, Turner L, Mermelstein R, Johnson TP, Parsons J, Crittenden K, Freels S, Flay B. Changes in selfefficacy and readiness for smoking cessation among those with high school or less education. J Health Soc Behav 2001;42:97– 109. Allen JK. Coronary risk factor modification in women after coronary artery bypass surgery. Nurs Res 1996;45:260–5. Bandura A. Self-efficacy—The Exercise of Control. New York: Freeman; 1997. Bandura A. Health promotion from the perspective of social cognitive theory. Psychol Health 1998;13:623–49. Connor SL, Gustafson JR, Sexton G, Becker N, Artaud-Wild S, Connor WE. The Diet Habit Survey: a new method of dietary assessment that relates to plasma cholesterol changes. J Am Diet Assoc 1992;92:41–7. Bandura A. Self-efficacy. In: Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs: Prentice Hall; 1986. p. 390–450. Von Korff M, Gruman J, Schaefer J, Curry S, Wagner E. Collaborative management of chronic illness. Ann Intern Med 1997;127:1097–102. Curry SJ, McBride CM. Relapse prevention for smoking cessation: review and evaluation of concepts and interventions. In: Omenn GS, Fielding JE, Lave LB, editors. Annual review of public health. Palo Alto, CA: Annual Reviews; 1994. p. 345–66. Burke LE, Dunbar-Jacob J, Sereika S, Ewart CK. Development and testing of the Cholesterol-Lowering Diet Self-Efficacy Scale. Eur J Cardiovasc Nurs 2003;2:265–73. Burke LE, Dunbar-Jacob J, Sereika S, Rohay JM. Outcome expectancy vs. self efficacy as predictive of dietary adherence in cardiac rehabilitation patients. Ann Behav Med 1996;18:124. Allain CC. Enzymatic determination of total serum cholesterol. Clin Chem 1974;20:470–5. Friedewald WT, Levy RI, Fredickson DA. Estimation of the concentration of low-density cholesterol in plasma, without the use of the preparative ultracentrifuge. Clin Chem 1972;18:499– 502.

142

L.E. Burke et al. / Patient Education and Counseling 57 (2005) 134–142

[23] Dixon LB. The Minnesota Nutrition Data System—a tool designed for researchers by researchers. In: Paper presented at the Society for Nutrition Education. St. Louis, MO, July 20–24; 1996. [24] Pledger GW. Basic statistics: importance of adherence. J Clin Res Pharmacoepidemiol 1992;6:77–81. [25] Haskell WL, Alderman EL, Fair JM, et al. Effects of intensive multiple risk factor reduction on coronary atherosclerosis and clinical cardiac events in men and women with coronary artery disease. Circulation 1994;89:975–90. [26] Van Horn L, Dolocek TA, Grandits GA, Skweres L. Adherence to dietary recommendations in the special intervention group in the Multiple Risk Factor Intervention Trial. Am J Clin Nutr 1997;65:289S–304S. [27] Kim HS, Oh JA. Adherence to diabetes control recommendations: impact of nurse telephone calls. J Adv Nurs 2003;44:256–61. [28] Wasson J, Gaudette C, Whaley F, Sauvigne A. Telephone care as a substitute for routine clinic follow-up. JAMA 1992;267:1788–93. [29] Wing RR, Jeffery RW, Hellerstedt WL, Burton LR. Effect of frequent phone contacts and optional food provision on maintenance of weight loss. Ann Behav Med 1996;18:172–6. [30] Dunbar-Jacob J, Sereika S, Burke LE, Rosella J, Starz T, Rohay JM, Kwoh CK. Can poor adherence be improved? Ann Behav Med 1995;17:S061 [abstract].

[31] DeBusk RF, Miller NH, Superko HR, et al. A case-management system for coronary risk factor modification after acute myocardial infarction. Ann Intern Med 1994;120:721–9. [32] Kirkman MS, Weinberger M, Landsman PB, et al. A telephonedelivered intervention for patients with NIDDM. Diabet Care 1994;17:840–6. [33] Castro C, King A, Brassington G. Telephone versus mail interventions for maintenance of physical activity in older adults. Am Psychol Assoc 2001;20:438–44. [34] Baker RC, Kirschenbaum DS. Self-monitoring may be necessary for successful weight control. Behav Ther 1993;24:377. [35] Glasgow RE, La Chance PA, Toobert DJ, Brown J, Hampson SE, Riddle MC. Long term effects and costs of brief behavioural dietary intervention for patients with diabetes delivered from the medical office. Pt Educ Couns 1997;32:175–84. [36] Clark MM, Abrams DB, Niaura RS, Eaton CA, Rossie JS. Self-efficacy in weight management. J Consult Clin Psychol 1991;59:739–44. [37] Shannon J, Kirkley B, Ammerman A. Self-efficacy as a predictor of dietary change in a low-socioeconomic-status southern population. Health Educ Behav 1997;24:357–68. [38] Jackevicius CA, Mamdani M, Tu JV. Adherence with statin therapy in elderly patients with and without acute coronary syndromes. JAMA 2002;288:462–7.