a dietary intervention in primary care practice: the eating patterns study

A Dietary Intervention in Prinmary CarePractice: The Eating Patterns Study

Shirley A. A. Beresford, PhD, Susan J. Curry, PhD, Alan R. Kristal, DrPH,DeAnn Lazovich, PhD, MPH, Ziding Feng, PhD, and Edward H. Wagner, MD,MPH

IntroductionA high intake of dietary fat and a low

intake of fiber are associated with anincreased risk of serious diseases, includ-ing digestive system cancers and cardio-vascular disease.' The scientific evidencesupporting a causal relationship is not yetdefinitive, but there is reasonable consen-sus that public health interventions arenow warranted.

Most dietary intervention researchhas targeted individuals at high risk fordisease. In general, these interventions arebased on intensive individual or groupcounseling, with goals of significant andrapid dietary behavior change.24 Al-though such intensive interventions areeffective among individuals highly moti-vated to change their diet, their applica-tion to the general population is relativelycostly and may be less successful amongcomparatively healthy individuals. Onlyrecently have studies evaluated publichealth approaches designed both to beaccessible to a wide audience of healthyindividuals, and to encourage small di-etary changes in everyone, regardless ofdisease risk.7-"

Low-intensity interventions, such asself-help materials, may be an importantpublic health strategy for changing diet.Self-help diet-change interventions havebeen shown to be as effective as moreintensive, interpersonal interventions forthe control of diabetes,12 hypercholesterol-emia,4"13 and weight loss'4 and in promot-ing a very low-fat diet.'5 Further, it hasbeen found that many people preferself-help materials as an approach to thepromotion of changes in health behav-ior.1-18 The importance of these low-intensity interventions lies in their poten-tial to affect large numbers of the

population.7 Although the resulting changein behavior may be quite small at theindividual level, its impact at the popula-tion level can be quite dramatic.

This study follows a small study19using the health care setting to deliver thedietary intervention, conducted by one ofus (S. Beresford) in North Carolina. TheEating Pattems Study is a larger random-ized controlled trial in the Seattle area, toevaluate the effectiveness of self-helpmaterials in decreasing fat and increasingfiber among individuals visiting theirprimary care physician.

MethodsWe conducted the study in primary

care clinics of Group Health Cooperative,a large health maintenance organization inthe Puget Sound area, starting in April1990. Six clinics were recruited within theGroup Health System through presenta-tions to the leadership in the primary carenetwork. A presentation was made in each

Shirley A. A. Beresford and Alan R. Kristal arewith the Department of Epidemiology, Univer-sity of Washington, and the Fred HutchinsonCancer Research Center, Seattle, Wash. Susan J.Curry and Edward H. Wagner are with theDepartment of Health Services, University ofWashington and the Center for Health Studies,Seattle, Wash. DeAnn Lazovich was with theFred Hutchinson Cancer Research Center, Se-attle, Wash, at the time of the study and is nowwith the School of Public Health, University ofMinnesota, Minneapolis. Ziding Feng is with theFred Hutchinson Cancer Research Center, Se-attle, Wash.

Requests for reprints should be sent toShirley A. A. Beresford, PhD, Department ofEpidemiology, Box 357236, School of PublicHealth and Community Medicine, University ofWashington, Seattle, WA 98195-7236.

This paper was accepted August 2, 1996.Editor's Note. See related editorial by

Green (p 541) in this issue.

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The Eating Patterns Study

interested clinic to recruit physician prac-tices, and between 4 and 6 physicianpractice units participated from each.Within each clinic, an equal number ofpractices were randomized to interventionand to control status. Specifically, withineach clinic, all possible combinations ofintervention and control practices consis-tent with a balanced design were enumer-ated, and one combination was chosen bythe use of a table of random numbers.Altogether, we randomized 28 physicianpractice units, each consisting of 1 full-time or 2 to 3 part-time family practicephysicians, to deliver either the self-helpdietary intervention or no intervention(usual care) to patients with a nonacute,nonurgent doctor's visit.

Participant Recruitment

We identified potential study partici-pants by abstracting the names of patientswho had routine appointments with partici-pating physicians, 1 to 2 weeks prior totheir scheduled visit. We requested thatthis list be reviewed by the physicians andthat names of patients be removed if theyhad a cognitive impairment or werecritically ill. Very few names were re-moved as a result of this review. Weattempted to reach the remaining individu-als to check their eligibility and invitethem to participate in the study, providedno more than 9 patients were recruitedfrom any one physician practice in any 1day. Individuals who were unable to speakEnglish, were pregnant, or were likely toleave the area within the year wereexcluded. Table 1 gives the response ratesto this screening interview for interven-tion and control groups. Both the inter-viewer and the potential participant wereblind to the group assignment. At thescreening interview, we set up an appoint-ment for a 45-minute telephone interviewprior to the doctor's visit with those whoagreed to participate. About 21% of eachgroup refused to make an appointment,and a further 11.2% and 9.6%, in theintervention and control groups respec-tively, indicated a willingness in principleto join the study but a lack of time beforetheir appointment. A further 5.9% and5.2%, respectively, were unavailable forthe interview at the time that had beenscheduled. About 75 patients per physi-cian practice unit were recruited to thestudy, and a total of 2121 individualscompleted the baseline interview. Theeffective interview rate is the product ofthe screening rate and the interview rate,which was 50.9% for the interventiongroup and 53.4% for the control group,

TABLE 1-Eligibility and Participation Rates in the Eating Patterns Study,Seattle, 1990 to 1992

Intervention Control

No. (%) No. (%)

No. names abstracted (A)Quota reached, staff shortage (B)Unable to contact (disconnected,

wrong no., etc.)Refused screening, including multiple

requests to call backScreenedAdjusted screening rate (screened x

100/(A- B))IneligibleEligible (C)Reached quota after screening

(D)Refused interviewNo timeUnavailable at scheduled inter-view time (passive refusal)

Completed interviews (com-pleted x 100/(C - D))

Corrected interview rate (screen-ing rate times completed inter-view rate)

Adjusted interview rate (adjustedscreening rate times completedinterview rate)

with adjustment made for names removedfrom the pool when the quota per practiceday had been reached (Table 1).

The Intervention

The intervention consisted of twocomponents: a self-help booklet andphysician endorsement. We developed thebooklet, Help Yourself: A Guide to Health-ful Eating,20 on the basis of behavior-change principles derived from sociallearning theory,21 and the dietary recom-mendations of the National ResearchCouncil.' We presented motivations fordietary change such as improving health,following the changing social norm to eatlower fat, higher fiber foods, and doingsomething positive for oneself. Currentdietary behavior was assessed through theuse of a brief self-test at the beginning ofthe book. We presented specific behav-ioral skills in an easy-to-follow format,beginning by identifying current behav-iors and suggesting sequential changes insmall, simple steps. No extemal goalswere included: rather, individuals wereencouraged to set their own goals. Weorganized the materials around meals (i.e.,breakfast, lunch, dinner) and, for each

2351125124

2427116

(5.3) 143 (5.9)

269 (11.4) 255 (10.5)

1833 (78.0)(82.4)

182

165116

346 (21.2)183 (11.2)96 (5.9)

1913 (78.8)(82.8)

172

174119

356 (20.7)166 (9.6)89 (5.2)

1010 (61.8) 1111 (64.5)

61.8 x 78.0 = 48.2% 64.5 x 78.8 = 50.8%

61.8 x 82.4 = 50.9% 64.5 x 82.8 = 53.4%

eating pattem (e.g., bacon and eggs forbreakfast), displayed both small (e.g.,eggs with Canadian bacon) and large(e.g., poached egg with fruit) changestoward a low-fatlhigh-fiber diet. We in-cluded self-assessment questionnaires andsections for recording short- and long-term goals. Finally, we included sectionson eating out, shopping, and socialactivities.

Physicians randomized to the inter-vention were asked to introduce thebooklet to the subject in a standardizedfashion, taking less than 3 minutes, andreceived training for this either individu-ally or as a group. The purpose of thephysician endorsement of dietary changewas to add a motivational element to theself-help booklet. Just prior to the partici-pant's appointment, a self-help bookletand a script to introduce it were placed inthe patient's medical chart. The physicianintroduced the booklet to the participant ata convenient time during the encounter.After the visit, the physician recorded thedisposition of the intervention on a track-ing form. About 2 weeks later, a reminderletter signed by the physician was sent to

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Beresford et al.

participants who had received the interven-tion.

Evaluation Measures

As described above, we conductedbaseline telephone interviews with studyparticipants after physician randomiza-tion, but before their scheduled visit. Wecompleted follow-up interviews at 3 and12 months after randomization. The mainoutcome measures were change in fat andfiber intake from baseline (from a food-frequency questionnaire) and change frombaseline in scales that measure dietaryhabits related to reducing fat or increasingfiber (from the fat- and fiber-related dietbehavior questionnaire). We also mea-

sured stage of change in adopting a

low-fat and high-fiber diet, autonomy inmeal preparation, and descriptive demo-graphic and medical information. Themain diet-related measures are describedbelow.

Food-frequency questionnaire. Wedeveloped a food-frequency questionnairebased on a previously validated instru-ment22 that was extensively modified bothfor ease of telephone administration andto be sensitive to fat-modified foods andfood-preparation methods. This question-naire consisted of 94 food items or foodgroups, with 13 introductory questionsand 2 summary questions used to refinenutrient calculations. Frequency responses

allowed participants to specify number oftimes per day, week, or month. At thebeginning of each section, interviewersexplicitly asked about portion sizes inrelation to a specified medium portionsize. Particular attention was paid in thewording of the interview to elicit portionsize for those foods contributing a largeamount of fat or fiber to the diet. We usedverbal cues such as comparisons with thesize of a slice of bread for meats and fish.The nutrient database, from the Universityof Minnesota Nutrition Coordinating Cen-ter's Nutrient Data System and algorithmsfor analyzing this food-frequency question-naire are described in detail elsewhere.23Some respondents to a food-frequencyquestionnaire tend to exaggerate the fre-quency of consumption of food items andsome respondents underestimate the fre-quency. To correct for systematic underre-porting or overreporting of foods, we

expressed fat intake in terms of thepercentage of energy from fat and fiber interms of grams of fiber per 1000 kilocalo-ries.

Fat- andfiber-related behavior ques-tionnaire. This questionnaire, modifiedfor telephone interview and expandedfrom a previously validated self-adminis-tered instrument,24 consisted of 41 itemsthat assessed food choices made in the lastmonth. The items provided informationon five factors related to selecting low-fat

diets and three factors related to selectingdiets high in fiber. Item responses were

usually, sometimes, or rarely/never, andwere coded 1, 2, and 3, respectively.Responses were rescaled so that an item inthe fat score correlated positively with thepercentage of energy obtained from fat,and an item in the fiber score correlatedpositively with fiber intake. The summary

fat-related and fiber-related diet-behaviorscores were the means of their relatedfactors. Detailed results on the validityand reliability of the fat- and fiber-relatedbehavior questionnaire have been pub-lished.25 The correlation of the fat score

with the percentage of energy obtainedfrom fat from this questionnaire was 0.53,and between the fiber score and grams offiber per 1000 kcal was 0.49.

Stage of change. Using a constructbased on the transtheoretical model ofchange,26 we measured participants' readi-ness to adopt new dietary behavior. Thismeasure has been validated.27 Accordingto this model, we expected greater dietarybehavior change among respondents inthe action or maintenance stage of readi-ness for behavior change than amongthose just contemplating change. Weclassified respondents along a con-

tinuum-precontemplation, contempla-tion, decision, action, and maintenance-related to reducing fat. A similar set of

612 American Journal of Public Health

TABLE 2-Nutrient Intake and Fat- and Fiber-Related Diet-Behavior Scale Scores at Baseline, among Those Completing1-Year Follow-Up in the Eating Patterns Study, Seattle, 1990 to 1992

Meana (SE of Mean)

Fat, g Fat, % Energy Fiber, g Fiber, g/1000 kcal Fat Score Fiber Score

Full groupIntervention (n = 859) 71 (2) 37.6 (0.3) 14.9 (0.2) 10 (0.1) 1.95 (0.006) 1.85 (0.01)Control (n = 959) 70 (2) 37.5 (0.3) 15.3 (0.2) 10 (0.1) 1.95 (0.006) 1.85 (0.01)

Complete autonomyIntervention (n = 501) 69 (3) 37.3 (0.4) 14.9 (0.3) 10 (0.2) 1.95 (0.01) 1.84 (0.02)Control (n = 536) 67 (3) 36.8 (0.3) 15.4 (0.2) 10 (0.2) 1.95 (0.01) 1.85 (0.02)

Partial or no autonomyIntervention (n = 357) 73 (3) 38.0 (0.4) 14.9 (0.3) 9.9 (0.2) 1.95 (0.01) 1.85 (0.02)Control (n = 423) 73 (3) 38.3 (0.4) 14.9 (0.3) 9.7 (0.2) 1.94 (0.01) 1.84 (0.02)

Action or maintenance stageIntervention (n = 632) 64 (2) 36.2 (0.3) 13.3 (0.2)b 11 (0.2)b 1.87 (0.012) 1.95 (0.019)Control (n = 679) 65 (2) 36.1 (0.3) 13.4 (0.2)c 11 (0.2)c 1.88 (0.012) 1.96 (0.019)

Earlier stageIntervention (n = 226) 91 (4) 41.4 (0.5) 16.4 (0.2)d 8.9 (0.2)d 2.18 (0.020) 1.72 (0.020)Control (n = 279) 81 (4) 40.9 (0.4) 16.8 (0.2)e 8.9 (0.2) 2.15 (0.018) 1.73 (0.019)

aAdjusted for clinic and practice effects, age, and gender.bStaged for fiber change; n = 461.CStaged for fiber change; n = 512.dn = 397.en = 445.

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questions measured stage of change forincreasing fiber.

Autonomy in meal preparation. Wedefined autonomy as the extent to which a

participant had control over his or her dietvia responsibility in planning, shopping,and preparing meals. Each item hadresponses of little or none, about half, andall or most. Participants with all or most

responsibility for all three were defined as

having complete autonomy. To explorethe hypothesis9 that this group madelarger changes, we compared changes in

those who had complete autonomy withchanges in those who had partial or no

autonomy at baseline.Plasma cholesterol. At the time of

the clinic visit, for both intervention andcontrol groups, a study laboratory techni-cian collected 5 mL of blood usingvenipuncture techniques. Blood was cen-

trifuged, frozen, and analyzed for totalplasma cholesterol in batches containingvials from both intervention and controlpatients.

Social desirability. We included a

measure of social desirability trait atbaseline because we were concemed thatthe self-report of dietary intake might bebiased in the intervention group at follow-up. We hypothesized that interventionparticipants would be better able todiscem what answers would reflect desir-able dietary behavior and that those withthe trait of high social desirability wouldshow a larger intervention effect thanthose with a low social desirability trait.This line of reasoning is supported bystudies suggesting that social approvalneeds may influence the reporting ofsocially desirable foods.28 We classifiedindividuals into low and high socialdesirability using the median value.

Statistical Analysis

We analyzed results using a mixedlinear model,29 in which the design effectswere clinic, arm (intervention or control),and physician practice (nested in clinicand arm). Because the unit of randomiza-tion was the physician practice (withinclinic), the clinic effects and the physicianpractice effects were treated as randomeffects in the model. This model acknowl-edges that the intervention effects varyamong clinic and physician practices andthus allows the results of this trial to begeneralized to the population of similarclinics and physician practices. The maincovariates in the model were baselinedietary intake or behavior and age andgender. Adjustments for other characteris-tics did not affect the results that we

present. For two a priori hypotheses, we

evaluated interaction effects, namely,whether there were differences in interven-tion effects depending on respondents'baseline values of stage of change andautonomy. Because the unit of randomiza-tion was physician practice within clinic,we repeated the main analyses for thepercentage of energy obtained from fatusing group means. We calculated inter-vention and control means within eachclinic by averaging the appropriate prac-tice means and compared them using botha nonparametric signed rank test and a ttest on the six within-clinic differences.

ResultsCharacteristics ofParticipants

Baseline characteristics for interven-tion and control groups were similar: 24%of the intervention group and 27% of thecontrol group were aged 65 years andover; 69% and 67%, respectively, were

female; 92% and 90%, respectively, were

White; 75% and 71%, respectively, had atleast some college education; and 27%and 29%, respectively, had family incomebelow $25 000 per year. Of the partici-pants, 86% completed the 1-year follow-up; the loss to follow-up was similar in theintervention and control groups. Partici-pants who dropped out of the study were

younger, less likely to be White, and morelikely to have lower family income thanthose who completed follow-up. Theyalso had slightly higher fat intake andlower fiber intake.

Table 2 gives the baseline values fornutrient intake and fat- and fiber-relateddiet-behavior scores for the interventionand control groups. The groups were verysimilar in all measures, with the exceptionof a higher baseline intake of fiber ingrams in the control group. The controlgroup also had higher calorie intake, so

that the fiber intake per 1000 kcal was

identical in the two groups. This table alsogives the baseline values by autonomyand stage of change. As expected, the

American Journal of Public Health 613

TABLE 3-Changes from Baseline in Nutrient Intake and Fat- andFiber-Related Diet-Behavior Scale Scores, and InterventionEffects at 3- and 12-Month Follow-Up in the Eating PatternsStudy, Seattle, 1990 to 1992

Fiber,Fat, % Energy g/1000 kcal Fat Score Fiber Score

3-month follow-upIntervention group

(n = 896)Mean change -1.52 0.50 -0.085 0.06295% Cl (-1.98, -1.06) (0.14, 0.86) (-0.105, -0.065) (0.039, 0.085)

Control group(n = 990)Mean change -0.48 0.36 -0.039 0.02495% Cl (-0.91, -0.05) (0.02, 0.70) (-0.058, -0.020) (0.003, 0.046)

Intervention effectMean effect -1.04 0.14 -0.046 0.03895% Cl (-1.67, -0.41)** (-0.35, 0.64) (-0.074, -0.018)" (0.006, 0.069)*

12-month follow-upIntervention group

(n = 859)Mean change -1.54 0.55 -0.084 0.04695% Cl (-1.88, -1.19) (0.27, 0.83) (-0.105, -0.063) (0.028, 0.064)

Control group(n = 959)Mean change -0.34 0.22 -0.040 0.01195% Cl (-0.66, -0.01) (-0.03, 0.49) (-0.059, -0.020) (-0.007, 0.028)

Intervention effectMean effect -1.20 0.32 -0.044 0.03695% Cl (-1.68, -0.73)** (-0.06, 0.70) (-0.073, -0.016)" (0.011, 0.061)*

Note. Data are adjusted for clinic and practice effects, baseline value, age and gender. Cl =confidence interval.

*P < .05; **P < .01.

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Beresford et al.

highest fat intakes were reported byparticipants in precontemplation, contem-plation, or preparation stages of change.Indeed, with the exception of fiber ingrams, those at an early stage of readinessfor change had a less healthy diet.Individuals at an early stage of readinessfor fiber change consumed more calories,somewhat more fiber in grams, but lessfiber per 1000 kcal.

Dietary Outcomes

According to our clinic documenta-tion, 95% of intervention subjects whokept their appointment received the book-let. Regardless of whether or not theyreceived the booklet, participants whokept their doctor's appointment were

included in the analysis. Table 3 gives the

changes from baseline in percentage ofenergy obtained from fat, fiber (grams per1000 kcal), summary fat score, andsummary fiber score at both the 3- and12-month follow-ups. Both groups re-

duced their fat intake (percentage ofenergy from fat), but at both 3 and 12months, changes were significantly largerin the intervention group. The interven-tion effects and their associated 95%confidence intervals (CI) were -1.04(95% CI =-1.67, -0.41) and -1.20(95% CI= -1.68, -0.73), respectively,as shown in Table 3. The correspondingdifferential change in fat score was

-0.046 (95% CI = -0.074, -0.018) at 3months, and -0.044 (95% CI = -0.073,-0.016) at 12 months. Both groupsincreased their fiber by 3 months, but the

control group did not appear to sustain theincrease to 12 months. Although theintervention group increased their fiberintake more than the control group at both3 and 12 months, the differences were notstatistically significant. The interventioneffect in g/1000 kcal was 0.14 (95%CI = -0.35, 0.64) at 3 months and 0.32(95% CI = -0.06, 0.70) at 12 months.On the other hand, the fiber score from thefat- and fiber-related behavior question-naire increased in both groups, with a

significantly larger increase in the interven-tion group at both 3- and 12-monthfollow-up. The differential change was

0.038 (95% CI = 0.006, 0.069) at 3months, and 0.036 (95% CI = 0.011,0.061) at 12 months. When we repeatedthe analyses for percentage of energy

from fat using differences in group means,

we found the mean decrease in fat in theintervention group was larger than in thecontrol group within all six clinics. Thenonparametric signed rank test yieldedP = .031. The corresponding t test on 5 dfwas 7.29, (P = .00076).

The Role ofAutonomy

Table 4 presents results by autonomyand stage of change at baseline, givingonly the differential change (the interven-tion effect) between intervention andcontrol groups. The intervention effectswithin the subgroup with complete au-

tonomy were statistically significant forboth fat measures at 3 and 12 months, butfor fiber only at the 12-month follow-upand only for fiber score. At 3 months forpercentage of energy obtained from fatand fiber intake, and at 12 months for allmeasures, the intervention effects ap-peared to be higher among those withcomplete autonomy for shopping, plan-ning, and preparing meals than amongthose with partial or no autonomy, but theinteraction effect was statistically signifi-cant only for percentage of energy ob-tained from fat at 12 months (P = .019).

The Role ofStage ofChange

Intervention and control participantsin the action or maintenance stage demon-strated a larger change in percentage ofenergy obtained from fat and in fat score

than those in the earlier stages. Further,the intervention effect for percentage ofenergy obtained from fat appeared to belarger in the subgroup at the action or

maintenance stage at both 3 and 12months. The intervention effect for fat

614 American Journal of Public Health

TABLE 4-Intervention Effects at 3 and 12 Months, by Autonomy and Stageof Dietary Change at Baseline, in the Eating Patterns Study,Seattle, 1990 to 1992

Fiber,Fat, % Energy g/1000 kcal Fat Score Fiber Score

3-month follow-upAutonomy

CompleteMean effect -1.28 0.24 -0.044 0.03495% Cl (-2.13, -0.44)* (-0.42, 0.89) (-0.081, -0.007)* (-0.007,0.076)

Partial or noneMean effect -0.74 0.02 -0.049 0.04295% Cl (-1.69,0.22) (-0.71, 0.76) (-0.090, -0.007)* (-0.005,0.089)

Stage of dietary changeAction or

maintenanceMean effect -1.13 0.10 -0.035 0.04195% Cl (-1.80, -0.46)** (-0.59,0.80) (-0.065, -0.005)* (-0.003,0.085)

Earlier stageMean effect -0.69 0.18 -0.067 0.03495% Cl (-1.77,0.38) (-0.54,0.90) (-0.115, -0.020)* (-0.011,0.079)

12-month follow-upAutonomyCompleteMean effect -1.85 0.44 -0.046 0.04595% Cl (-2.50, -1.20)***t (-0.07,0.95) (-0.083, -0.009)* (0.012, 0.078)*

Partial or noneMean effect -0.37 0.17 -0.042 0.02495% Cl (-1.10,0.36) (-0.41, 0.75) (-0.084, -0.000)* (-0.014,0.061)

Stage of dietary changeActon or

maintenanceMean effect -1.28 0.33 -0.034 0.04895% Cl (-1.88, -0.68)** (-0.16, 0.83) (-0.063, -0.005)* (0.013, 0.083)*

Eadier stageMean effect -0.90 0.27 -0.067 0.02195% Cl (-1.86,0.07) (-0.25, 0.79) (-0.113, -0.020)* (-0.016,0.058)

Note. Data are adjusted for clinic and practice effects, baseline value, age and gender. Cl =confidence interval.

*Pfor intervention effects < .05; **Pfor intervention effects < .01; ***Pfor intervention effects< .001; tPfor interaction = .019.

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score, although significant for both stagegroups at each follow-up, appeared some-what smaller among those in the action ormaintenance stage, owing to the largechanges made in the control group. Thedifferential change in fiber g/1000 kcalwas small at both 3 and 12 months andvaried little by stage of readiness for fiberchange. The intervention effect for fiberscore appeared stronger in the subgroup atthe action or maintenance stage and wassignificant at 12 months (P < .05). Noneof the interactions by subgroup of stage ofchange were statistically significant.

Plasma Cholesterol and Body MassIndex

Over the 12-month follow-up period,both groups decreased their total choles-terol levels by a similar and small amount(approximately 3.5 mg/dL). There was nochange in either group in body massindex, as calculated from self-reportedheight and weight.

Social Desirability

We found that the intervention effectfor percentage of energy obtained from fatwas larger (but not significantly so)among those with a low social desirabilitytrait (-1.7%; P < .01) compared withthose with a high social desirability trait(-1.0%; P < .01), contrary to expecta-tion. Similar results were found for fatscore and fiber score, favoring those witha low social desirability trait. Theseresults strengthen the inference that theobserved effect of the intervention re-flected true dietary change and not bias.

DiscussionWe found that a low-intensity nutri-

tion intervention delivered by a physicianresulted in healthful dietary changes in fatand fiber consumption, as measured bythe percentage of energy obtained fromfat, fat score, and fiber score. The effect ofthe intervention was evident at both 3 and12 months postintervention. Indeed, theeffect was somewhat stronger for fatas percentage energy at 1-year follow-up. The intervention effect as assessedby fiber per 1000 kcal at 12 monthswas significant only at P = .09. Thetrend toward improved outcomes overtime with self-help intervention is consis-tent with interventions on other healthbehaviors such as smoking cessation.30

Consistency with Other Studies

The findings from this study areconsistent with other studies in health care

settings that have used a low-intensity,public health approach. Only three studieshave been published that evaluated alow-intensity dietary intervention in ahealth care setting. Baron et al.'9 random-ized 368 patients attending a generalmedical practice in Britain to receiveeither a self-help booklet with individualcounseling from a nurse or no interven-tion. They found significant and sustainedchanges associated with the interventionin some dietary behavior at 1 year.Beresford et al.9 randomized 242 individu-als to receive either a set of self-helpmaterials, introduced in 5 minutes by anurse, or no intervention. At 3 months,there was a small differential reduction infat intake associated with the intervention,which was statistically significant whenthe analysis was restricted to persons withsome responsibility for meal preparation.Campbell et al.31 randomized 558 adultpatients attending family practice clinicsto receive one of the following: a packetof tailored nutritional information, a packetof nontailored nutritional information, andno information. Although the food-fre-quency questionnaire they used containedonly 28 items, and no adjustment wasmade for calories, they found that thetailored intervention was associated witha significant differential reduction in totalfat consumption of 9 g per day at 4months' follow-up.

Limitations

The findings from this study are allbased on self-report. Although we in-cluded plasma total cholesterol as anobjective measure, the amount of dietarychange in this study was too small toaffect it. Self-report is obviously liable toexaggeration and misrepresentation inways that might be influenced by theintervention. Our examination of theeffect of social desirability was one waywe tried to test the robustness of ourself-report findings. That a similar de-crease in fat intake was reported in theintervention groups with high and lowsocial desirability, whereas the controlgroup with high social desirability re-ported larger changes than the controlgroup with low social desirability, gave usconfidence in the self-report of the inter-vention group. We also checked theconsistency of the finding using differentmeasures of dietary fat change and withinsubgroups of respondents chosen in ad-vance. All these investigations are consis-tent with the finding of a true dietary fatchange. The evidence is less strong forfiber change.

The generalizability of this study hassome obvious limitations. Although thestudy was designed to be an effectivenessstudy, only people able to be contacted bytelephone and agreeing to be interviewedwithin a week before their clinic visitwere included. Thus, busy people wereunderrepresented in this study. In addi-tion, individuals with no primary carephysician or with no nonurgent visits totheir doctor were excluded by virtue of thestudy design.

Contrast with More IntensiveInterventions

Although the efficacy of the low-intensity intervention of the Eating Pat-tems Study is much smaller than someintensive dietary fat interventions (TheWomen's Health Trial intensive groupintervention was associated with a reduc-tion of 16% in energy obtained from fat at12 months6), the cost of the intervention inboth provider and participant time issubstantially less. Our low-intensity inter-vention required between 1 and 3 minutesof clinician time, followed by discretion-ary time on the part of participants, usingself-help materials at their own conve-nience. In contrast, the intensive interven-tion of the Women's Health Trial calls for18 scheduled group sessions, each lasting1 hour, supplemented with review ofwritten materials by participants at theirown convenience.32

Importance ofPublic HealthInterventions

While the full generalizability of thisintervention is still to be established, alow-intensity dietary intervention couldhave important public health implicationswhen applied at the population level,because of its potential to reach andinvolve large numbers of the population.The public health model, or populationstrategy, consists of shifting the entiredistribution of a risk factor, including themean, down. The diminution in risk for agiven individual is typically small andmay not even be clinically important.Nevertheless, because the entire distribu-tion is affected, the impact on morbidityand mortality can be substantial. This isthe prevention paradox, explained solucidly by Sir Geoffrey Rose.33,34 A 1%reduction in dietary calories from fatmade populationwide could result inabout 10 000 deaths saved in the UnitedStates in a year, on the basis of models fitfrom aggregate data and shown to beconsistent with data from observational

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studies of cancers of the colon, breast, andprostate.35 Both Rose33 and Hitt36 haveargued for a combined approach toprevention, comprising both the publichealth approach and the high-risk strategy,familiar to clinicians, in which intensiveresources are concentrated on individualsat the extreme high end of the distribution.

Conclusions

We demonstrated that this low-intensity intervention was effective andcould be incorporated into routine deliv-ery of primary care. The Eating PattemsStudy confirmed the approach of theNorth Carolina study9 in a very differentsetting, using the participant's own physi-cian and a shorter introduction. It furtherdemonstrated the durability of the interven-tion to 1 year. We conclude that this kindof low-intensity intervention based onpublic health and sound behavioral changeprinciples is efficacious for use in primarycare practice. C]

AcknowledgmentsThis research was supported by the NationalCancer Institute, ROI CA 49643.

We are grateful to the physicians, nurses,and staff of Group Health Cooperative of PugetSound who helped us with the study, and to themany interviewers, programmers, and researchassistants who were members of the studyteam. We extend special thanks to the enrolleesof Group Health Cooperative who participated,without whom this study would not have beenpossible.

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