the effects of providing personalized dietary feedback.: a semi-computerized approach

Patient Education and Counseling 37 (1999) 177–189

The effects of providing personalized dietary feedback.

A semi-computerized approach

a ,b , a a a*Monique M. Raats , Paul Sparks , Moira A. Geekie , Richard ShepherdaInstitute of Food Research, Earley Gate, Reading, RG6 6BZ, United Kingdom

bHealth Education Authority, Trevelyan House, 30 Great Peter Street, London, SW1P 2HW, United Kingdom

Received 7 February 1997; received in revised form 23 July 1998; accepted 16 August 1998

Abstract

There not only seems to be a trend for people to underestimate the dietary risks that they face, but it appears that thisunderestimation may be related to the difficulties they encounter when trying to assess their own dietary intake. A study(n 5 118) examining the effects of providing people with information about their own dietary fat intake on their attitudestowards dietary change and their subsequent fat consumption is described. Participants in a group receiving feedback abouttheir fat intake did not decrease their consumption of fat more than did those in a control group. No effects on subsequent fatconsumption were observed in a group who had higher than average levels of fat consumption, higher perceived fatconsumption than actual fat consumption and who received feedback information about their fat consumption. The resultsare discussed in relation to their implications for health promotion strategies that focus on the motivational effects ofproviding people with information about their fat consumption. 1999 Elsevier Science Ireland Ltd All rights reserved.

Keywords: Feedback; Fat consumption; Risk; Diet; Computer tailored

1. Introduction over the past 10 years in the UK suggests that newavenues for delivery of health information need to be

To achieve the government targets of a reduction investigated [3].in the average fat intake in the UK population and to In a number of behavioural domains beneficialcomply with recommendations in the recent Commit- effects have been demonstrated for interventions thattee on Medical Aspects of Food Policy report [1,2], have provided people with feedback informationeffective dietary interventions need to be developed about their behaviour. Such effects have been ob-and evaluated. The negligible reduction in fat intake served, for example, in relation to heart attack risks

[4], water use [5] driving [6] and electricity con-sumption [7]. Positive effects of feedback are also*Corresponding author: Tel.: 1 44-171-413-2607; fax: 1 44-apparent in research addressing fat intake in, for171-413-2046.

E-mail address: [email protected] (M.M. Raats) example, two US studies involving patients in Gen-

0738-3991/99/$ – see front matter 1999 Elsevier Science Ireland Ltd All rights reserved.PI I : S0738-3991( 98 )00114-1

178 M.M. Raats et al. / Patient Education and Counseling 37 (1999) 177 –189

eral Practitioner practices [8,9], a study involving tive norm’) and an assessment of people’s percep-high school students [10] and in the Netherlands tions of the existence of behavioural constraints andinvolving employees of large company [11]. Another facilitators (‘perceived behavioural control’). All thedietary fat feedback study [12] found no differences components of the model can be assessed by suitableamong three feedback groups in the reduction in fat questionnaire items. The thesis has been widelyintake (although there was a decrease with time, applied to health-related behaviours [17], althoughthere was no control group). The authors noted the there have been few applications of this type ofimportance of the emotional and cognitive conse- model to the assessment of behaviour change [18].quences of feedback: participants who received high There have been a number of suggestions regard-fat feedback showed greater negative emotional ing extensions to the Theory of Planned Behaviour:distress in response to the feedback and stated that these include perceived need [19] and anticipatedthey knew less about high-fat foods than participants affect [20]. Affective factors are thought to be highlyreceiving lower feedback [12]. influential in people’s behavioural choices [21–24],

In the previously mentioned dietary studies, the including those relating to health behaviour [25].assessment of food intake was made using food In the study reported here, we included com-frequency questionnaires of varying length: 443-item ponents from an extended Theory of Planned Be-[12], 30-item [11], 28-item (18 of which fat-related) haviour model since we believed these to be po-[8], 18-item [9] and ten-item (seven of which fat- tential psychological mediators of the effects foundrelated) [10]. The data collected with these tools can in feedback research. We also felt that relatingbe converted into ‘feedback information’ very rapid- feedback effects to broad theoretical structures ex-ly. Campbell et al. [8] noted that their fat intake plaining social behaviour would help pave the way tomeasure could not permit an assessment of the a more concerted discussion about the processespercentage energy coming from fat. A recent study involved in the aetiology of those effects.[13] looking at the extent to which dietary assess- Perceived need is a measure of the extent to whichment instruments are susceptible to intervention-as- it is felt necessary to carry out the behaviour insociated response set bias, warned that the assess- question. Anticipated affect is a measure of antici-ment of dietary change recommendations, when pated, postbehavioural, affective reactions (e.g., anbased on dietary self-report, can be overestimated. anticipated positive feeling about ‘eating less fat’). InThe study reported here makes use of a more the study reported here, we sought to make anelaborate tool, similar to that recommended by assessment of the inclusion of measures of perceivedBingham et al. [14] in their comparison of dietary need and anticipated affect.assessment methods. Another theoretical perspective that has been

The purpose of this study was to examine the frequently brought to bear on health-related behav-effects of providing people with personalized in- iours is that of ‘unrealistic optimism’ [26], this refersformation about the fat content of their diets. It was to the tendency of people to report that they are lessexpected that participants who learn that their fat likely than the average person of their gender andintake is (i) higher than they expected and (ii) higher age to experience a hazard (or more likely than thethan expert recommendations would be motivated to average person to experience ‘positive life events’).reduce their fat consumption. Many early descriptions of this phenomenon in-

In this study, we also incorporate components of cluded nutrition-related hazards such as sufferingthe Theory of Planned Behaviour [15,16], an ex- from diabetes, heart attacks, tooth decay and foodtremely popular model of attitude–behaviour rela- poisoning [26–29]. Unrealistic optimism refers to ationships which comprises several interrelated com- group tendency: not every person is unrealisticallyponents. This theory suggests that behaviour is optimistic but the tendency is for more people to saypredicted by intention to perform the behaviour, that they are at below average risk than to say thatwhich is in turn predicted by a person’s own attitude they are at above average risk.towards the behaviour, the social pressure perceived The implications of unrealistic optimism in health-by the individual to perform the behaviour (‘subjec- related domains of behaviour have long been of

M.M. Raats et al. / Patient Education and Counseling 37 (1999) 177 –189 179

concern. Van der Pligt [30] has suggested that an following statement: ‘‘From your personal food andillusion of relative invulnerability to hazards might drink diary, we have calculated that X% of themean that people are less likely to adopt health- energy (calories) in your diet comes from fat. Currentpromoting behaviours: ‘‘If health risks primarily recommendations are that, on average, people shouldconcern other people and not oneself there is no not get more than 35% of their energy (calories)reason to adapt one’s behaviour’’ (p.139). Weinstein from fat.’’ The letter also contained a bar chart([31], p.1232) also stated: ‘‘Optimistic biases in depicting the same information. The letters werepersonal risk perceptions are important because they prepared using a combination of computer softwaremay seriously hinder efforts to promote risk-reducing packages (word processing [MSWord], spreadsheetbehaviours’’. In this study, we also make an assess- [Excel] and database [ORACLE]). Participants werement of unrealistic optimism in relation to diet- told they would be receiving a second Diary in 4related risks, along with an assessment of people’s weeks time. Control group participants received aperceptions of their diets. letter only stating that they would be receiving a

second Diary in 4 weeks time. All participantsreceived a second and third Diary at 4 and 18 weeks

2. Method after receiving the feedback information. Upon re-turn of each completed Diary, participants were sent

2.1. Participants a second and third ‘Behaviour Changes’ ques-tionnaire. The questionnaires were again returned by

One hundred and seventy-one participants were mail.volunteers recruited from the staff of OxfordBrookes University. Participants responded to being 2.3. Materialssent a personal introductory letter describing thestudy, along with a ‘Food and Drink Diary’ [32] 2.3.1. ‘Seven-Day Food and Drink Diary’developed for this study (copies are available from A self-administered, validated diary was used tothe first author), 1211 letters were sent out. Particip- record food intake [32]. The diary consisted of twoants were randomly allocated to the study or control pages of instructions, an example day record, ninegroup. One hundred and fifteen participants (67%; pages for recording foods or drinks and amountsfemales: n 5 71; males: n 5 44) completed all phases eaten and five pages of photographs depictingof the study within the required time period, no medium portion sizes of commonly eaten foods. Thesignificant differences were found at baseline be- diary also contained two foldout flaps with descrip-tween these participants and those who dropped out tions of medium portion sizes. Participants are askedat some point during the study. Participants partici- to describe ‘amount eaten’ in terms of portion sizespated on an unpaid, voluntary basis, though on the based on the photographs and list provided, in termsunderstanding that they would receive personalized of household measures or in terms of weights takendietary feedback upon completion of the study. from food packaging. The data from the diaries was

coded using the food codes from the UK Nutrient2.2. Procedure Databank (maintained by The Royal Society of

Chemistry and the Ministry of Agriculture, FisheriesParticipants recorded their entire food intake for a and Food) and nutrient intakes were calculated using

seven-day period using a ‘Seven-Day Food and the nutrient values in the database.Drink Diary’ [32] developed especially for thisstudy. Upon return by mail, participants were sent a 2.3.2. ‘Behaviour Changes’ questionnaire‘Behaviour Changes’ questionnaire, which was also The ‘Behaviour Changes’ questionnaire containedto be returned by mail. Twelve weeks after being questions concerning ten dietary changes (viz. eatingsent the first diary, participants in the feedback group less fat, eating more fruit, eating fewer cakes, eatingwere sent feedback information about their diet in more bread, eating less cheese, eating fewer biscuits,the form of a personal letter which included the eating less red meat, eating less butter and mar-


garine, eating more vegetables, and eating fewer dietary message relevance. In addition participantschips). With respect to each of these changes, the were asked to (i) estimate the percentage of energyquestionnaire assessed, inter alia, central components (calories) coming from fat in their diet, both onof the Theory of Planned Behaviour (attitudes, average and during the preceding week in which theyperceptions of social pressure, perceived control, had kept a record of what they were eating and (ii)intentions, expectations), perceived need to change, comment on whether that particular week was typicaland risk perceptions (Table 1). In addition, particip- of their ‘normal’ eating habits. We also took aants were asked to estimate the percentage of energy measure of their stage of change relating to fatcoming from fat in their diet. consumption (the question was adapted from ques-

A number of the questions were also phrased in tions of Prochaska et al. [33]), these data will beterms of the ‘average other of your sex and age in reported elsewhere. Participants were also asked toGreat Britain’, these included: perceived need to indicate their age, gender, weight, height and educa-change, behaviour, health risk susceptibility and tional qualifications.

Table 1The ‘Behaviour Changes’ questionnaire

Affective attitude How unenjoyable /enjoyable would it be for you to eat the following (less fat, more fruit, fewer cakes,more bread, less cheese, fewer biscuits, less red meat, less butter and margarine, more vegetables, andfewer chips) in the next 6 months? [‘extremely unenjoyable’ (24) to ‘extremely enjoyable’ (4)]

Cognitive attitude How harmful /beneficial would it be for you to eat the following (less fat, more fruit, fewer cakes,more bread, less cheese, fewer biscuits, less red meat, less butter and margarine, more vegetables, andfewer chips) in the next 6 months? [‘extremely harmful’ (24) to ‘extremely beneficial’ (4)]

Subjective norm Most people who are important to me think I should eat the following (less fat, more fruit, fewercakes, more bread, less cheese, fewer biscuits, less red meat, less butter and margarine, morevegetables, and fewer chips) in the next 6 months [‘extremely unlikely’ (1) to ‘extremely likely’ (9)]

Perceived control How easy or difficult would it be for you to eat the following (less fat, more fruit, fewer cakes, morebread, less cheese, fewer biscuits, less red meat, less butter and margarine, more vegetables, andfewer chips) in the next 6 months? [‘extremely difficult’ (1) to ‘extremely easy’ (9)]

Anticipated affect How displeased or pleased would you be if you managed to eat the following (less fat, more fruit,fewer cakes, more bread, less cheese, fewer biscuits, less red meat, less butter and margarine, morevegetables, and fewer chips) in the next 6 months? [‘extremely displeased’ (1) to ‘extremely pleased’(9)]

Intention I intend to eat the following (less fat, more fruit, fewer cakes, more bread, less cheese, fewer biscuits,less red meat, less butter and margarine, more vegetables, and fewer chips) in the next 6 months[‘definitely do not’ (1) to ‘definitely do’ (9)]

Expectation How unlikely or likely is it that you will eat the following in the next 6 months? [‘extremely unlikely’(1) to ‘extremely likely’ (9)]

Perceived need to change To what extent do you feel that you need to eat the following (less fat, more fruit, fewer cakes, morebread, less cheese, fewer biscuits, less red meat, less butter and margarine, more vegetables, andfewer chips) in the next 6 months? [‘definitely do not need to’ (1) to ‘definitely need to’ (9)]

Perceived intake How low or high do you think your consumption is of the following (fat, fruit, cakes, bread, cheese,biscuits, red meat, butter and margarine, vegetables, and chips)? [‘extremely low’ (1) to ‘extremelyhigh’ (9)]

Dietary message relevance How irrelevant or relevant, to you, do you consider health promotion messages (reducing fatconsumption, increasing vegetable consumption, increasing fruit consumption) about the following tobe? [‘extremely irrelevant’ (1) to ‘extremely relevant’ (9)]

Health risk susceptibility How likely do you think you are to experience or get the following (heart disease, cancer, ill health,indigestion and weight gain) at some time in the future, because of the amount of fat in your diet?[‘extremely unlikely’ (1) to ‘extremely likely’ (9)]

Link between diet and health risks There is a strong link between fat consumption and . . . [‘disagree very strongly’ (1) to ‘agree verystrongly’ (9)]

Nine-point response scales were used unless otherwise indicated: end-point response options are indicated in parentheses.


3. Results in relation to reducing fat consumption, revealednonsignificant time effects and group 3 time interac-

3.1. Effect of the intervention on dietary intake tions (see Table 3).

The sample revealed an average intake of 34.3% 3.3. Relationship between Theory of Plannedenergy from fat (calories from fat as a percentage of Behaviour variablestotal nonalcohol calories) at baseline (see also Table2). The number of participants that underestimated Multiple regressions of behaviour change (differ-their fat intake (their actual percentage energy from ence between percentage energy from fat intake atfat intake exceeded their estimated fat intake) was 41 baseline and 18 weeks) on perceived behavioural(35.7%). Fifty-one participants (44.3%) had a fat control and intention (both measured at baseline)intake greater than the recommended 35% energy revealed nonsignificant effects for both perceived

2from fat. When asked whether they needed to eat less behavioural control and intention (R 5 0.02; seefat, 14 participants (12.4%) selected the ‘definitely Table 4). Multiple regressions of intention to eat lessdo not need to’ option. fat on cognitive attitude, affective attitude, subjective

A 2 3 2 [(Group: Intervention, Control) 3 (Time: norm, perceived behavioural control, anticipated4 weeks postfeedback, 18 weeks postfeedback)] affect and perceived need to change (all variables

2repeated measures ANOVA (with the baseline mea- measured at baseline; R 5 0.39) revealed significantsurement as a covariate) to assess fat intake revealed independent effects only for affective attitude (b 5

nonsignificant time effects and group 3 time interac- 0.27, P , 0.001), anticipated affect (b 5 0.22, P ,

tions (see Table 2). 0.05) and perceived need to change (b 5 0.42, P ,

Similar analyses of variance (ANOVAs) of other 0.001; see Table 4). Similar regressions of expecta-2variables only revealed significant group 3 time in- tion (all variables measured at baseline, R 5 0.27)

teractions for participants’ estimated fat intake revealed significant independent effects only for(F(1,96) 5 10.43, P , 0.01) (see Table 2). anticipated affect (b 5 0.32, P , 0.001) and per-

For those participants who underestimated their fat ceived need to change (b 5 0.22, P , 0.05; seeintake (their actual percentage energy from fat intake Table 4).exceeded their estimated fat intake) and had a fat In fact, affective attitude, anticipated affect andintake greater than the recommended 35% energy perceived need to change were most important infrom fat, a repeated measures ANOVA (with the predicting intention to make the ten dietary changesbaseline measurement as a covariate) did not reveal a studied (see Table 5). Other components, includingsignificant group 3 time interaction (F(1,27) 5 0.49, cognitive attitude, subjective norm and perceivedP . 0.10). For this group, average fat intake was behavioural control were found to be less influential.39.4% energy from fat at baseline (see Table 3).Similar ANOVAs of other dietary intake-related 3.4. Comparisons between ‘self’ and ‘others’variables also revealed nonsignificant time effectsand group 3 time interactions (see Table 3). In this A multivariate analysis of variance (MANOVA)study therefore, we did not find support for our carried out on difference scores between ‘personalhypothesis that those participants who consumed intake’ and ‘others’ intake’, in relation to the threemore than the recommended 35% energy from fat behaviours relating to foods people are encouragedand underestimated their fat consumption would to eat more of, indicated a significant multivariatereduce their intake after receiving feedback infor- effect (F(3,107) 5 33.63, P , 0.001), with significantmation. univariate effects for ‘eating fruit’ and ‘eating veget-

ables’ (see Table 6). A similar MANOVA in relation3.2. Effect of the intervention other variables to the seven behaviours relating to sources of fat in

the diet, also revealed a significant multivariateRepeated measures ANOVAs (with the baseline effect (F(7,101) 5 45.58, P , 0.001), with significant

measurement as a covariate) of attitudinal variables univariate effects for all the behaviours (see Table


Table 2Dietary intake-related variables and Theory of Planned Behaviour variables relating to ‘eating less fat’ (mean and standard deviations) for allparticipants

Group Baseline Four weeks 18 Weeks F Ftime group3time

postfeedback postfeedback

Energy from fat (%) 0.05 0.78Intervention (n 5 59) 33.4 (5.3) 34.0 (5.5) 33.7 (5.8)Control (n 5 54) 35.3 (5.3) 34.8 (4.5) 35.2 (4.9)

Energy from carbohydrate (%) 0.26 0.33Intervention (n 5 59) 51.4 (5.2) 51.0 (5.5) 51.0 (5.5)Control (n 5 54) 50.3 (5.2) 50.7 (4.6) 50.3 (4.7)

Energy from protein (%) 0.02 0.02Feedback (n 5 59) 15.2 (2.6) 15.1 (2.5) 15.1 (2.5)Control (n 5 54) 14.5 (2.2) 14.5 (1.8) 14.5 (2.3)

Energy (kilocalories) 0.22 0.10Intervention (n 5 59) 1974 (427) 1942 (396) 1966 (509)Control (n 5 54) 2045 (468) 1995 (491) 2000 (468)

Bodymass index 0.26 0.11Intervention (n 5 53) 23.5 (3.3) 23.5 (3.6) 23.4 (3.5)Control (n 5 49) 22.9 (3.8) 23.0 (3.7) 23.0 (3.8)

**Estimated % energy from fat (%) 2.77 10.43Intervention (n 5 54) 34.4 (9.0) 34.6 (6.2) 35.3 (5.7)Control (n 5 43) 35.0 (10.5) 36.3 (8.2) 34.1 (7.6)

Affective attitude 0.55 2.27Intervention (n 5 56) 2 0.1 (1.5) 2 0.4 (1.8) 2 0.3 (1.8)Control (n 5 51) 0.0 (1.4) 0.1 (1.7) 2 0.3 (1.8)

Cognitive attitude 0.20 1.85Intervention (n 5 58) 2.1 (1.5) 2.2 (1.6) 2.0 (1.3)Control (n 5 51) 2.0 (1.6) 2.0 (1.5) 2.3 (1.0)

Subjective norm 0.00 0.10Intervention (n 5 52) 5.4 (1.8) 5.7 (1.3) 5.7 (1.4)Control (n 5 50) 5.8 (1.6) 5.6 (1.6) 5.7 (1.5)

Perceived control 1.29 0.59Intervention (n 5 58) 5.3 (2.0) 5.2 (1.9) 4.8 (1.7)Control (n 5 50) 5.1 (1.9) 5.2 (1.7) 5.1 (1.8)

Anticipated affect 2.45 0.08Intervention (n 5 58) 6.7 (1.7) 6.8 (1.7) 6.7 (1.7)Control (n 5 50) 6.5 (1.6) 6.8 (1.5) 6.6 (1.5)

Perceived need to change 0.02 0.44Intervention (n 5 57) 5.5 (2.7) 5.8 (2.3) 5.7 (2.2)Control (n 5 51) 5.7 (2.1) 5.7 (2.0) 5.9 (1.7)

Intention 1.61 0.09Intervention (n 5 56) 5.8 (2.4) 6.1 (1.9) 5.8 (2.0)Control (n 5 48) 5.7 (2.0) 5.9 (1.7) 5.7 (1.4)

Expectation 0.17 2.71Intervention (n 5 58) 5.4 (1.5) 5.5 (1.4) 5.3 (1.5)Control (n 5 50) 5.3 (1.7) 5.0 (1.5) 5.3 (1.4)

Perceived behaviour 0.32 0.32Intervention (n 5 58) 4.6 (1.5) 5.3 (1.6) 5.1 (1.5)Control (n 5 53) 4.8 (1.8) 4.9 (1.7) 4.9 (1.5)

Personal relevance fat messages 0.12 0.30Intervention (n 5 58) 6.5 (2.1) 6.5 (2.3) 6.5 (2.3)Control (n 5 54) 6.5 (2.0) 6.5 (1.9) 6.6 (2.0)

** P , 0.01.


Table 3Dietary intake-related variables and Theory of Planned Behaviour variables relating to ‘eating less fat’ (mean and standard deviations) forparticipants who underestimated their fat intake and had a fat intake greater than 35% energy from fat as a percentage of total nonalcoholenergy

Group Baseline Four weeks 18 Weeks F Ftime group3time

postfeedback postfeedback

Energy from fat (%) 1.44 0.49Intervention (n 5 12) 38.9 (3.4) 36.0 (4.6) 37.5 (3.8)Control (n 5 17) 39.8 (2.9) 36.9 (4.7) 37.3 (4.5)

Energy from carbohydrate (%) 0.27 0.07Intervention (n 5 12) 46.3 (3.5) 48.5 (5.8) 47.8 (4.3)Control (n 5 17) 46.2 (3.7) 48.5 (5.3) 48.3 (4.5)

Energy from protein (%) 2.35 1.03Feedback (n 5 12) 14.8 (1.7) 15.6 (2.5) 14.7 (1.5)Control (n 5 17) 14.0 (2.4) 14.6 (2.0) 14.4 (2.1)

Energy (kilocalories) 1.97 1.96Intervention (n 5 12) 1984 (488) 1935 (365) 2113 (520)Control (n 5 17) 2127 (500) 1982 (574) 1982 (569)

Bodymass index 0.00 0.04Intervention (n 5 10) 22.8 (2.2) 22.9 (2.4) 22.9 (2.3)Control (n 5 17) 23.0 (2.6) 23.1 (2.6) 23.1 (2.6)

Estimated % energy from fat (%) 0.72 2.29Intervention (n 5 12) 30.8 (7.0) 37.2 (4.2) 37.7 (6.8)Control (n 5 14) 30.4 (8.9) 34.8 (8.6) 32.9 (7.8)

Affective attitude 0.15 0.15Intervention (n 5 11) 2 0.1 (1.3) 2 1.2 (1.2) 2 1.0 (1.2)Control (n 5 16) 2 0.3 (1.3) 2 0.9 (1.5) 2 0.9 (1.6)

Cognitive attitude 0.76 2.26Intervention (n 5 12) 2.1 (1.4) 2.3 (1.3) 2.2 (0.8)Control (n 5 16) 1.2 (1.9) 1.6 (1.8) 2.3 (0.9)

Subjective norm 0.03 0.28Intervention (n 5 10) 5.2 (1.9) 5.9 (1.5) 5.8 (1.0)Control (n 5 16) 5.6 (1.4) 5.4 (1.7) 5.6 (1.3)

Perceived control 0.74 0.15Intervention (n 5 12) 5.3 (2.2) 4.8 (2.6) 4.6 (1.8)Control (n 5 16) 5.6 (2.0) 5.3 (1.6) 4.9 (1.6)

Anticipated affect 1.31 0.61Intervention (n 5 12) 7.3 (1.8) 7.2 (1.3) 7.3 (1.4)Control (n 5 16) 6.5 (1.7) 6.1 (1.6) 6.5 (1.2)

Perceived need to change 0.14 0.14Intervention (n 5 11) 6.1 (0.8) 6.3 (1.3) 6.3 (1.1)Control (n 5 17) 5.9 (2.5) 5.9 (2.0) 6.3 (1.6)

Intention 0.20 1.97Intervention (n 5 12) 5.2 (1.5) 5.3 (0.7) 5.8 (1.1)Control (n 5 14) 6.0 (2.0) 5.5 (1.3) 5.3 (1.1)

Expectation 0.50 1.08Intervention (n 5 12) 5.6 (0.7) 5.2 (1.3) 5.1 (1.1)Control (n 5 16) 5.1 (1.4) 4.6 (1.6) 5.0 (0.9)

Perceived behaviour 0.13 1.33Intervention (n 5 12) 4.8 (1.0) 5.9 (0.8) 5.6 (1.3)Control (n 5 17) 4.7 (1.8) 5.0 (1.9) 5.2 (1.3)

Personal relevance fat messages 0.01 0.43Intervention (n 5 12) 6.0 (2.1) 6.2 (2.3) 6.4 (2.6)Control (n 5 17) 5.8 (2.1) 6.5 (1.7) 6.4 (1.9)


Table 4Regressions of intentions, expectations and behaviour (difference between % energy from fat intake at baseline and 18 weeks post feedback)on affective attitude, cognitive attitude, subjective norm, perceived behavioural control, anticipated affect and need to change (n 5 99)

2Step Predictor R F Final beta-coefficientchange

Behaviour (difference between fat intake at baseline and at 18 weeks)1 Intention 0.02 2.03 2 0.142 Perceived behavioural control 0.02 0.05 0.02

Intention (at baseline)*** **1 Affective attitude 0.15 16.72 0.27*2 Cognitive attitude 0.18 4.33 0.03

3 Subjective norm 0.19 1.31 2 0.064 Perceived behavioural control 0.20 0.64 0.08

** *5 Anticipated affect 0.26 7.82 0.22*** ***6 Perceived need to change 0.39 19.64 0.42

Expectation (at baseline)*1 Affective attitude 0.05 4.58 0.02U2 Cognitive attitude 0.08 3.79 2 0.01*3 Subjective norm 0.13 5.19 0.14

4 Perceived behavioural control 0.14 1.24 0.10** **5 Anticipated affect 0.23 11.19 0.32* *6 Perceived need to change 0.27 4.47 0.22

† * ** ***P , 0.10; P , 0.05; P , 0.01; P , 0.001.

Table 5Mean scores and standard deviations for Theory of Planned Behaviour variables and beta-coefficients from multiple regressions of intentionson cognitive attitude, affective attitude, subjective norm, perceived control, anticipated affect and perceived need (all variables measured atbaseline)

2R Behavioral Affective Cognitive Subjective Perceived Anticipated Perceived need

intention attitude attitude norm behavioural affect to change

control

Mean scores and standard deviations

Less fat (n 5 99) 5.7 (2.3) 2.1 (1.4) 0.0 (1.5) 5.6 (1.7) 5.2 (2.0) 6.6 (1.7) 5.7 (2.3)

More bread (n 5 98) 4.8 (2.3) 0.3 (1.4) 0.8 (1.3) 5.1 (1.5) 5.9 (1.8) 5.4 (1.3) 4.4 (2.2)

Fewer biscuits (n 5 95) 5.7 (2.4) 2 0.8 (1.6) 1.7 (1.4) 5.5 (1.6) 5.6 (2.0) 6.2 (1.5) 5.3 (2.6)

Less butter /margarine (n 5 97) 5.1 (2.3) 1.5 (1.2) 2 0.6 (1.3) 5.1 (1.4) 5.0 (1.8) 5.7 (1.5) 5.0 (2.2)

Fewer cakes (n 5 89) 5.4 (2.3) 1.8 (1.6) 2 0.5 (1.7) 5.4 (1.7) 5.7 (1.9) 6.0 (1.6) 5.0 (2.5)

Less cheese (n 5 98) 4.8 (2.2) 1.3 (1.4) 2 1.2 (1.4) 5.4 (1.5) 5.0 (1.8) 5.1 (1.6) 5.2 (2.3)

Fewer chips (n 5 81) 5.2 (2.6) 1.4 (1.6) 2 0.3 (1.5) 5.2 (1.8) 5.6 (2.1) 5.6 (1.7) 4.8 (2.5)

More fruit (n 5 105) 6.4 (2.3) 2.5 (1.4) 1.8 (1.5) 6.1 (1.7) 6.8 (1.8) 7.0 (1.3) 6.5 (2.1)

Less red meat (n 5 85) 4.9 (2.5) 1.1 (1.5) 2 0.6 (1.9) 4.8 (1.6) 5.5 (2.2) 5.2 (1.7) 4.7 (2.4)

More vegetables (n 5 103) 6.2 (2.3) 2.5 (1.3) 1.4 (1.7) 6.1 (1.7) 6.6 (1.9) 7.0 (1.5) 6.1 (2.3)

Beta-coefficients from multiple regressions of intentions to eat . . .

Less fat (n 5 99) 0.39 0.27** 0.03 2 0.06 0.08 0.22* 0.42***More bread (n 5 98) 0.41 2 0.10 0.02 0.24** 0.09 0.33** 0.29**Fewer biscuits (n 5 95) 0.48 0.31** 0.13 0.10 2 0.01 0.27** 0.41***Less butter /margarine (n 5 97) 0.53 0.32*** 0.28** 0.04 0.01 0.18* 0.37***Fewer cakes (n 5 89) 0.40 0.27* 0.04 0.03 0.04 0.28** 0.35***Less cheese (n 5 98) 0.36 0.22* 0.04 0.15 2 0.00 0.26* 0.31**Fewer chips (n 5 81) 0.57 0.27** 2 0.07 0.18* 0.04 0.37*** 0.38***More fruit (n 5 105) 0.49 0.06 0.12 2 0.02 0.19* 0.32*** 0.33**Less red meat (n 5 85) 0.63 0.12 0.19* 0.06 0.09 0.32** 0.36***More vegetables (n 5 103) 0.48 0.21* 0.14 2 0.14 0.09 0.31** 0.25**

†*** ** *P , 0.001; P , 0.01; P , 0.05; P , 0.10.


Table 6MANOVAS of difference scores (mean and standard deviations) between the questions assessing perceived intake of ‘self’ and ‘others’ (allvariables measured at baseline)

Difference score ‘self’ 2 ‘others’ F(univariate) F(multivariate)

***Perceived intake (n 5 110) F(3,107) 5 33.63Bread 0.1 (2.3) 0.22

***Fruit 1.8 (2.1) 81.94***Vegetables 2.0 (2.3) 80.05

***Perceived intake (n 5 108) F(7,101) 5 45.58***Biscuits 2 2.5 (2.5) 105.44***Butter and margarine 2 2.0 (2.2) 87.85***Cakes 2 2.7 (2.4) 136.96***Cheese 2 0.9 (2.2) 18.84***Chips 2 3.9 (2.5) 256.77***Fat 2 2.3 (2.1) 136.23***Red meat 2 207 (2.5) 130.09

*** P , 0.001.

6). The results of both these analyses indicate that behaviours (see Table 7). This indicates that peoplethe sample has tended to estimate the healthiness of may see themselves as having less need to changetheir diets as being greater compared to the healthi- than the ‘average other’.ness of others’ diets. A MANOVA carried out on difference scores

A MANOVA carried out on difference scores between perceptions of ‘personal risk’ and ‘others’between ‘personal need to change’ and ‘others’ need risk’ in relation to five diet-related risks indicated ato change’ in relation to the three behaviours relating significant multivariate effect (F(5,105) 5 23.87,to foods people are encouraged to eat more of, P , 0.001), with significant univariate effects forindicated a significant multivariate effect each of the diet-related risks (see Table 8). This(F(3,109) 5 26.43, P , 0.001), with significant uni- indicates that the sample have tended to under-variate effects for all behaviours (see Table 7). A estimate the risks that they face relative to the riskssimilar MANOVA in relation to the seven behaviours they see the ‘average other’ as facing. A similarrelating to sources of fat in the diet, indicated a MANOVA carried out on difference scores betweensignificant multivariate effect (F(7,57) 5 14.81, P , the questions assessing ‘relevance to self’ and ‘rele-0.001), with significant univariate effects for all the vance to others’ in relation to three dietary messages

Table 7MANOVAS of difference scores (mean and standard deviations) between the questions assessing perceived need to change for ‘self’ and‘others’ (all variables measured at baseline)


***Perceived need to change (n 5 112) F(3,109) 5 26.43***Eat more bread 2 1.5 (2.5) 38.33***Eat more fruit 2 1.4 (2.2) 45.22***Eat more vegetables 2 1.7 (2.5) 52.61

***Perceived need to change (n 5 64) F(7,57) 5 14.81***Eat fewer biscuits 2 2.0 (2.5) 41.89***Eat less butter and margarine 2 2.3 (2.5) 53.83***Eat fewer cakes 2 2.2 (2.5) 51.95**Eat less cheese 2 1.0 (2.6) 9.79***Eat fewer chips 2 2.6 (2.8) 57.53***Eat less fat 2 1.9 (2.5) 36.56***Eat less red meat 2 2.3 (2.4) 61.45

** ***P , 0.01; P , 0.001.


Table 8MANOVAS of difference scores (mean and standard deviations) between the health risk susceptibility and dietary message relevancequestions assessing ‘self’ and ‘others’ (all variables measured at baseline)


***Health risk susceptibility (n 5 110) F(5,105) 5 23.87***Cancer 2 1.1 (1.6) 52.31***Heart disease 2 1.9 (1.8) 118.78***Ill health 2 1.6 (2.0) 74.05***Indigestion 2 1.4 (1.9) 64.92***Weight gain 2 1.8 (2.5) 55.72

*Dietary message relevance (n 5 113) F(3,110) 5 2.73Reducing fat consumption 2 0.5 (2.2) 6.83Increasing fruit consumption 2 0.3 (2.2) 2.07Increasing vegetable consumption 2 0.2 (2.1) 1.04

*** *P , 0.001; P , 0.05.

indicated a significant multivariate effect in intervention work. The findings from this research(F(3,110) 5 2.73, P , 0.05) indicating however uni- also indicate the importance of affective factors as anvariate effects nonsignificant for each of the three influence on people’s intentions to instigate dietarydietary messages (see Table 8). change.

Participants seemed to view their own diet morepositively than that of other people, both in terms of

4. Discussion its perceived healthiness and the perceived need tomake health-beneficial dietary changes. These find-

In this study, providing participants with personal- ings might account for some of the failure ofized dietary feedback information about their per- nutritional information, since people may accept thecentage energy intake from fat was not sufficient to general validity of such messages while at the samepromote dietary change in the short term. It should time believing them to be more applicable to othersbe noted that our sample was found to be consuming than to themselves. In this study however, ratings oflow levels of fat ‘at baseline’ of the study. However, ‘relevance to self’ and ‘relevance to others’ inno effects were observed in a group who had higher relation to three dietary messages did not differ.than population average levels of fat consumption, Our intervention was fairly minimal: we simplyhigher perceived than actual fat consumption and provided information about fat consumption levels.who received information about their fat consump- We did not request people to change their consump-tion. Here, we assess some potential reasons for this tion habits or suggest they do so. In this research, welack of effect that might be considered in future were not primarily concerned with persuading peopleresearch. to eat more healthily. Rather, we focused on pro-

Given that perceived need to change has been viding people with information that would enableidentified in previous research [24], as an important them to make more informed choices. If we hadmotivational influence, we consider that this variable taken a more directive approach, we might haveshould be researched further. Especially important observed the effects that we had hoped for. More-are the reasons why people have the perceptions of over, feedback information was only given once:need to change that they do and how these percep- many involved in health promotion would suggesttions might be influenced by subsequent information. that once-only presentations of information are un-However, while dietary habits are an obvious public likely to be effective [34]. Recently, however, Fish-health issue, at an individual level, many people may bein [36] has categorically stated that information insee few health benefits in changing their own dietary and of itself can produce behaviour change, citing ahabits [34,35]. This would indicate that benefits other number of examples [37–39]. The nature of thethan health risk reduction ought also to be promoted information is potentially important: the types of


information cited as being possibly effective include of dietary intake were able to provide feedbackinformation about the consequences of performing within 48 h [10] and 2 to 4 weeks [9].the behaviour, information about groups supporting We should also point out that, as suggested by onethe behaviour, and/or ways to overcome barriers to reviewer, it is possible that the intensiveness of ourbehavioural performance [36]. measure of dietary intake, per se, may have had an

Previous studies demonstrating beneficial dietary impact on dietary behaviour such that feedbackeffects of interventions [8,9,11] have provided per- information itself did not lead to significant addition-sonalised feedback that took account of participants’ al effects. However, we felt it advisable to include adiet as well as psycho–social factors (e.g., perceived detailed measure of food intake in order to bebarriers to change, self efficacy). The information confident that we were measuring food intake accu-provided in these studies was much more extensive rately, that we were giving accurate feedback in-and elaborate than that presented to participants in formation (for both practical and ethical reasons) andthe study reported here. that participants were likely to view the feedback as

The studies that demonstrate the greatest changes credible information. The basic research issue was toin dietary behaviour are those studies in which assess the impact of feedback information and not (atparticipants are highly motivated to make the this stage) to develop tools for larger-scale interven-changes and where the interventions have been tions.relatively intensive [40]. One might therefore expect Improvements might also be made to the way inconsiderably less dramatic effects with intervention which we provided information about fat intakeprogrammes (possessing much more limited per although it should be noted that we provided par-capita resources) that are directed at the general ticipants with current fat intake recommendations.population. The work reported in this paper was not For example, participants may not have perceivedcarried out in a clinical or health promotion setting, much difference between, for example, 39% energyboth of which might engender more compliance with from fat and the on average, no more than 35% thatthe perceived purpose of the study. A recent meta- is recommended. In a study in which informationanalysis [41] concluded that individual dietary inter- about triglyceride and cholesterol levels was pro-ventions in primary prevention can achieve modest vided [43], it was demonstrated that patients hadand sustainable improvements in diet and car- great difficulty in interpreting decontextualized num-diovascular disease risk status. However, Hopper and bers. Presenting information in different ways mayBarker [42] found that although the primary health well lead to different effects on people’s motivationscare team is in a primary position to offer dietary and subsequent behaviour. Of course, decisions haveadvice, it is evident that there is a need for improved to be made to present information in an objectivenutrition education and counselling. form (as we have done here) or in a form that may be

While we are confident that the behavioural more subjective and value-laden (as when fat con-measures developed for this study are accurate and sumption is described as ‘high’ or ‘low’, for exam-superior to most others used in this type of research, ple). In a study of twelve men with a heightenedany advances in the accuracy of intake measures level of cholesterol [44], it was found the men didand/or in the ease with which data is gathered would not talk about their condition in terms of exactbe welcome. Of course, behaviour measures in this numbers, although their ‘risk information’ had beensort of study need to be accurate and every care presented to them in such terms.needs to be taken that ‘demand characteristics’ in We also found evidence of unrealistic optimism:experimental conditions do not lead to response this may be of practical importance for health-relatedbiases. The main disadvantage of the tool used in the behaviours [31] although actual evidence concerningstudy reported here was the time needed to interpret the consequences of unrealistic optimism is, to date,the data and convert it into ‘feedback information’. in rather short supply [45]. Additionally, our TheoryThe time between completing the first diary and of Planned Behaviour analysis would seem to con-receiving the feedback information was as much as firm our expectation that affective and perceived10 weeks. Other studies using much cruder measures need factors are likely to be important in people’s


attempts to instigate and maintain changes to their changes they should make in order to comply withdietary behaviour; the nonsignificant relationship expert recommendations, we would expect to seebetween intentions and changes in fat intake might behaviour change. Ideally, dietary changes sub-be interpreted as suggesting that feedback infor- sequent to interventions should be monitored over amation about fat intake could have an important role longer period. Studies need to be carefully designedto play in helping people identify the (nutritional) so that influential factors in dietary change can beoutcomes of their dietary choices. readily identified: it is difficult to generalize from

those studies which show beneficial effects of inter-ventions but give no indication of where those

5. Practical implications effects are coming from.

People need to be able and willing to make dietarychanges if changes are to occur via intentional Acknowledgementsbehaviours. Information provision may serve toeither instill a positive motivation to change or to The U.K. Ministry of Agriculture, Fisheries andenable people to act on already existing positive Food supported the production of this paper throughmotivations. Thus, it might be worth combining their funding of the research project ‘Communicationpersonalized information about how much fat people strategies for the effective promotion of dietaryare consuming with information about the sources of change.’ We would like to thank Peter Harris for hisfat in their diet (akin to the role of a dietitian, see advice on the development of the ‘BehaviorBrug et al. [11]). As Kreuter and Strecher [9] put it: Changes’ questionnaire; and Sabine Hansen, Miles‘‘What is the effect of heightening perceived risk Ellison, Gretel Finch, Sarah Grugeon and Margueritewithout addressing the barriers to reducing the risk?’’ Fazey for their assistance with data processing.(p. 98).

Modifying dietary behavior is complex in a waythat modifying other health-related behaviours (e.g. Referencestobacco and alcohol consumption) may not be. Thisis because many dietary recommendations are [1] Department of Health. The health of the nation. London:

H.M. Stationary Office, 1992.framed in terms of macronutrients, and most people[2] Department of Health. Nutritional aspects of cardiovascularare not aware of their intake of macronutrients in the

disease, report on health and social participants 46. London:way that they are likely to be aware of, for example, H.M. Stationary Office, 1994.their tobacco or alcohol intake. Moreover, for some [3] Ministry of Agriculture, Fisheries and Food. National foodpeople, there are health benefits to be achieved by survey 1996. London: The Stationary Office, 1997.

[4] Avis NE, Smith KW, McKinlay JB. Accuracy of perceptionsadopting unambiguous rules of refraining from allof heart-attack risk—what influences perceptions and canalcohol or tobacco consumption. However, there isthey be changed. Am J Public Health 1989;79:1608–12.

no zero-calorie option with food consumption [46]: [5] Aitken CK, McMahon TA, Wearing AJ, Finlayson BL.an appropriate level of consumption has to be Residential water-use—predicting and reducing consump-achieved. tion. J Appl Soc Psychol 1994;24:136–58.

[6] van Houten R, Nau PA. Feedback interventions and drivingAlthough it was not within the remit of thespeed: a parametric and comparative analysis. J Appl Behavresearch, beneficial effects on behavioral change areAnal 1983;1:253–81.

likely to be achieved if this kind of personalized [7] Becker LJ. Joint effect of feedback and goal setting oninformation is disseminated in conjunction with preformance: a field study of residential energy conservation.persuasive advice on the benefits of change or J Appl Psychol 1978;63:428–33.

[8] Campbell MK, DeVellis BM, Strecher VJ, Sandler R. Im-admonitions /exhortations to change (one could en-proving dietary behavior: the effectiveness of tailored mes-visage the latter in the context of doctor–patientsages in primary care settings. Am J Public Health

relationships, for example). With a more proactive 1993;84:783–7.research brief, in which information provision is [9] Kreuter MW, Strecher VJ. Do tailored behavior changesupplemented with advice to people on what dietary messages enhance the effectiveness of health risk appraisal?


Results from a randomized trial. Health Educ Res [29] Weinstein ND. Unrealistic optimism about susceptibility to1996;11:97–105. health problems: conclusions from a community-wide sam-

[10] Burnett KF, Magel PE, Harrington S, Barr Taylor C. ple. J Behav Med, 1987;10:480–99.Computer-assisted behavioral health counseling for high [30] Van der Pligt J. Healthy thoughts about unhealthy behavior.school students. J Counsel Psychol 1989;36:63–7. Psychol Health 1994;9:187–90.

[11] Brug J, Steenhuis I, van Assema P, DeVries H. The impact of [31] Weinstein ND. Optimistic biases about personal risks. Sci-a computer-tailored nutrition intervention. Prev Med ence 1989;246:1232–3.1996;25:236–42. [32] Geekie M, Raats M, Sparks P, Shepherd R. Validation of a

[12] Bowen DJ, Fries E, Hopp HP. Effects of dietary fat feedback 7-day food and drink diary: a tool for the assessment ofon behavioral and psychological variables. J Behav Med nutrient intake. Proc Nutr Soc 1997;56(1A):120A.1994;17:589–604. [33] Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus

[13] Kristal AR, Andrilla HA, Koepsell TD, Diehr PH, Cheale A. BH, Rakowski W, Fiore C, Harlow LL, Redding CA,Dietary assessment instruments are susceptible to interven- Rosenbloom D, Rossi SR. Stages of change and decisionaltion-associated set bias. J Am Diet Assoc 1998;98:40–3. balance for 12 problem behaviors. Health Psychol

[14] Bingham SA, Gill C, Welch A, Day K, Cassidy A, Khaw 1994;13:39–46.KT, Sneyd MJ, Key TJA, Roe L, Day NE. Comparison of [34] Rose G. Sick individuals and sick populations. Int J Epi-dietary assessment methods in nutritional epidemiology: demiol 1985;14:32–8.weighed records v. 24 h recalls, food frequency ques- [35] Jeffrey RW. Risk behaviors and health: contrasting individualtionnaires and estimated diet records. Br J Nutr and population perspectives. Am Psychol 1989;44:1194–1994;72:619–43. 202.

[15] Ajzen I. Attitudes, personality and behaviour. Milton [36] Fishbein M. Predicting, understanding and changing sociallyKeynes: Open University Press, 1988. relevant behaviors: lessons learned. In: McGarty C, Haslam

[16] Ajzen I. The theory of planned behaviour. Org Behav Hum SA, editors. The message of social psychology, Oxford:Decision Processes 1991;50:179–211. Blackwell, 77–91.

[17] Conner MT, Sparks P. Applying the theory of planned [37] Lhotka DC. The seat belt story. Traffic Safety 1964;64:22–behaviour. In: Conner MT, Norman P, editors. Social cogni- 35.tion in health: predicting behaviour. Milton Keynes: Open [38] Ball-Rokeach SJ, Rokeach M, Grube JW. The great Ameri-University Press, 1996:121–61. can values test: influencing behavior and belief through

[18] Eagly AH, Chaiken S. The psychology of attitudes. San television. New York: The Free Press, 1984.Diego: Harcourt, Brace and Jonanovich, 1993. [39] Fishbein M, Ajzen I, McArdle J. Changing the behavior of

[19] Paisley CM, Sparks P. Expectations of reducing fat intake: alcoholics: effects of persuasive communication. In: Ajzen I,the role of perceived need within the Theory of Planned Fishbein M, editors. Understanding attitudes and predictingBehaviour. Psychol Health 1998;13:341–53. social behavior. Englewood Cliffs, NJ: Prentice–Hall,

[20] Richard R, van der Pligt J, de Vries N. Anticipated affective 1980:217–42.reactions and prevention of AIDS. Br J Soc Psychol [40] Winett RA, Southard DR, Rankin JW. Nutrition promotion1995;34:9–21. and dietary change: framework to meet year 2000 goals. Med

[21] Rosenberg MJ, Hovland CI. Cognitive, affective, and be- Exerc Nutr Health 1993;2:7–26.havioral components of attitudes. In: Hovland CI, Rosenberg [41] Brunner E, White I, Thorogood M, Bristow A, Curle D,MJ, editors. Attitude organization and change. New Haven: Marmot M. Can dietary interventions change diet andYale University Press, 1960:1–14. cardiovascular risk factors? A meta-analysis of randomized

[22] Fishbein M, Ajzen I. Beliefs, attitude, intention, and be- controlled trials. Am J Public Health 1997;87:1415–22.havior. Reading MA: Addison–Wesley, 1975. [42] Hopper D, Barker ME. Dietary advice, nutritional knowledge

[23] Breckler SJ, Wiggens EC. Affect versus evaluation in the and attitudes towards nutrition in primary care. J Hum Nutrstructure of attitudes. J Exp Soc Psychol 1989;25:253–71. Diet 1995;8:279–86.

¨[24] Wilson TD, Dunn DS, Kraft D, Lisle DJ. Introspection, [43] Adelsward V, Sachs L. The meaning of 6.8: numeracy andattitude change and attitude–behavior consistency: the dis- normality in health information talks. Soc Sci Medruptive effects of explaining why we feel the way we do. In: 1996;43:1179–87.Berkowitz L, editor. Advances in social psychology, New [44] Sachs L. Is there a pathology of prevention? Implications ofYork: Academic Press, 1989;22:287–343. visualizing in screening programmes. Cult Med Psychiatry

[25] Ajzen I, Timko C. Correspondence between health attitudes 1995;19:503–25.and behavior. Basic Appl Soc Psychol 1986;7:259–76. [45] Weinstein ND, Klein WM. Unrealistic optimism: present and

[26] Weinstein ND. Unrealistic optimism about future life events. future. J Soc Clin Psychol 1996;15:1–8.J Pers Soc Psychol 1980;39:806–20. [46] Schelling TC. Self-command: a new discipline. In: Loewens-

[27] Weinstein ND. Reducing unrealistic optimism about illness tein G, Elster J, editors. Choice over time. New York: Sage,susceptibility. Health Psychol 1983;2:11–20. 1992.

[28] Weinstein ND. Why it won’t happen to me: perceptions ofrisk factors and susceptibility. Health Psychol 1984;3:431–57.

the effects of providing personalized dietary feedback.: a semi-computerized approach

Documents