- Email: [email protected]
Efficacy of a minimal intervention to reduce fat intake
Social Science & Medicine 52 (2001) 1517–1524
Efficacy of a minimal intervention to reduce fat intake Christopher J. Armitagea,*, Mark Connerb a
Centre for Research in Social Attitudes, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK b School of Psychology, University of Leeds, Leeds, UK
Abstract Effective dietary interventions must be developed to reduce fat intake in whole populations, rather than clinical subgroups. This study tested the effects of personalised feedback on fat intake in a general population. Hospital workers (n ¼ 801) were randomised to receive personalised feedback or no personalised feedback. Personalised feedback consisted of one sentence expressing current fat intake as a percentage of total calorific intake. Changes in fat intake from baseline to five months post-intervention were evaluated. The personalised intervention produced significant decreases in total and saturated fat intake, compared with the control group. Total-fat decreased by 8.6% (versus 0.2% in the control group); saturated fat decreased by 9.3% (versus 1.7% in the control group). Fat intake as a proportion of total calorific intake did not decrease significantly in either condition. Findings also revealed differential effects of feedback on high- versus low-fat consumer groups. Personalised feedback significantly reduced fat intake in high-fat consumers, and prevented low-fat consumers from increasing their fat intake. Personalised fat feedback therefore represents an efficacious and low-intensity approach to the reduction of fat intake in the general population. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Fat intake; Diet; Health promotion; Randomised controlled trial; Health risk
Introduction Excessive fat intake has been linked with increased health risk from coronary heart disease and several forms of cancer (Anon, 1993; Temple, 1996; Trichopoulos & Willett, 1996). The link between fat intake and the major causes of death and health expenditure in industrialised countries has led several governments to set specific targets for nutrition. For example, the UK government recommends that individuals should derive no more than 35% of food energy from fat in the diet, and 11% from saturated fat (Department of Health, 1992). Recent trends suggest this target is unlikely to have been reached by the year 2000 (Ministry of Agriculture, Fisheries and Food, 1992). Moreover, given that US government recommendations are considerably *Corresponding author. Tel: +44-114-222-6626; fax: +44114-276-6515. E-mail address: c.j.armitage@sheffield.ac.uk (C.J. Armitage).
lower than those for the UK (30% of food energy from fat), fat intake represents an important target for intervention (Department of Health and Human Services, 1991). Intensive dietary interventions targeted at individuals deemed to be ‘‘at risk’’ have generally been successful in reducing fat intake (e.g., Schapira, Kumar, Lyman, & Baile, 1991). Attempts to reduce fat intake in the general population have typically been focused on multi-media campaigns and large-scale intensive interventions. Evidence for the utility of these approaches has been equivocal (Family Heart Study Group, 1994; Maccoby, Farquhar, Wood, & Alexander, 1977; Ministry of Agriculture, Fisheries and Food, 1992; OXCHECK, 1994). Recently, however, several studies have reported that self-help materials (plus a motivational message) and tailored feedback significantly reduce fat intake in general populations (Beresford et al., 1997; Brug, Steenhuis, van Assema, & de Vries, 1996; Campbell et al., 1994). The implication is that dietary interventions
0277-9536/01/$ - see front matter# 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 2 6 5 - 3
1518
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
designed to reduce fat intake in the general population will be most effective when they are tailored (see Brug, Campbell, & van Assema, 1999, for a recent review). There are three principal reasons why one would expect personalised feedback to reduce fat consumption. First, individuals are more likely to attend to, and remember personally relevant information (Brug et al., 1996, 1999; Campbell et al., 1994). Thus, tailored feedback provided in conjunction with self-help materials is likely to engender more dietary change than no tailored feedback. Second, general information may be dismissed as irrelevant by the recipient, through psychological mechanisms such as unrealistic optimism (e.g., Weinstein, 1987). However, if one presents accurate information about current levels of fat consumption, the effect of unrealistic optimism may be diminished. Third, recent evidence suggests that individuals have inaccurate perceptions of their fat intake (e.g., Armitage & Conner, 1999a; Lechner, Brug, de Vries, van Assema, & Mudde, 1998). Presenting individuals with accurate personal feedback may challenge this misperception and engender behaviour change. Previous research supports the utility of tailored interventions (see Brug et al., 1999). However, previous ‘tailoring’ studies can also be criticised. First, the experimental materials often exert demand characteristics that are not controlled for. For example, Campbell and colleagues (1994) report that, ‘‘The tailored messages were personalised and provided more information than did the non-tailored group’’ (p. 787). Second, the outcome of interest (i.e., reduction in fat intake) has typically focused on reductions in absolute fat intake (see Brug et al., 1999 for a review and Beresford et al., 1997 for an exception). Whilst it is clearly important to reduce individuals’ total-fat intake, this might simply reflect a decrease in total calorific intake, rather than a specific reduction in fat intake. Expressing fat intake as a proportion of total calorific intake allows the researcher the opportunity to evaluate whether the intervention reduces fat intake specifically, or whether it reduces total calorific intake. The present paper seeks to extend previous work by presenting a randomised controlled trial, designed to assess the efficacy of providing individuals with personalised dietary feedback and general information, as opposed to providing them with general information only. In an attempt to control for the demand characteristics that have threatened the validity of previous studies, the present study provided only a minimal intervention. Following work by Brug and colleagues, the materials given to the intervention group differed from the control group with respect to only one sentence, which informed them of their current dietary fat intake (see Methods; Brug et al., 1996). The approach is attractive because it is cost-effective and could be targeted at diverse populations. Beyond this,
the present study also reports multiple indicators of dietary fat intake, specifically: total-fat intake, saturated-fat intake, and fat intake expressed as a proportion of total calorific intake. It is hypothesised that the provision of personalised feedback will significantly decrease fat intake. More specifically, it is predicted that the personalised feedback will exert greatest impact upon total- and saturated-fat intake because they are more salient and therefore more amenable to change. Moreover, it is expected that the intervention will exert a significant positive effect upon fat intake specifically. That is, the personalised feedback should reduce the proportion of calories derived from fat. However, it is anticipated that such effects will be smaller because of the in-depth knowledge of nutrition that would be required to achieve this.
Methods Study population The study was conducted on a sample of 801 hospital workers, a population whose shift patterns are likely to exert a negative impact upon their diets. Baseline (n ¼ 801) and follow-up (n ¼ 517) measures of dietary intake were taken in order to ascertain intervention effects. The reported analyses are based on the 517 participants who responded at both time points (a response rate of 64.5%). Responders did not differ from non-responders in terms of total-fat intake (F [1, 798]=1.74, p ¼ 0:19), percent food energy from fat (F [1, 798]=3.53, p ¼ 0:06), or saturated-fat intake (F [1, 798]=2.10, p ¼ 0:15). It was concluded that the 517 respondents were a representative sample of the original 801 people we questioned. Participants were told that they would receive dietary information, but not that we expected changes in their diet, nor that some of the feedback would be personalised.
Procedure The study was a randomised controlled trial, testing the efficacy of personalised feedback versus no personalised feedback. Following baseline assessment of current diet, individuals were randomised to feedback or nofeedback conditions. Both groups received general information leaflets and covering letters. The intervention group differed from the control group in only one respect: their covering letters included an additional sentence informing them of their current fat intake. Assessment of the impact of the intervention took place five months post-intervention, using the same measures as at baseline.
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Measures Food frequency: A validated food frequency instrument consisting of 63 items was used to assess initial diet. This instrument was developed by Margetts, Cade, and Osmond (1989), based upon data from Cade and Margetts (1988). The reliability and validity of the instrument has been demonstrated in a number of studies. For example, Armitage and Conner (1999b) report that it possesses good test-retest reliability (r [411]=0.62, p50.01, across a three-month time period). Congruent with this, Margetts et al. (1989) assessed the validity of the instrument, by comparing it with a 24-hour dietary record: even across a three-year period, all correlations between the two measures were significant (see also Thompson & Margetts, 1993). Response options (i.e., the 63 food products) were presented as a checklist with six response choices: ‘‘two or more times a day’’, ‘‘every day’’, ‘‘three to five times per week’’, ‘‘one to two times per week’’, ‘‘one to three times per month’’, and ‘‘rarely or never’’. Daily use of food was calculated by multiplying frequency of consumption (calculated as servings per day) by portion data (calculated separately for males and females). Dietary- and saturated-fat scores were obtained by multiplying daily use of food by the average fat content for each item, and summing the items. Average fat content was derived from data provided by the UK Ministry of Agriculture, Fisheries and Food (1992). Average portion sizes were derived from Cade and Margetts (1988). Stage of change: Stage of change with respect to eating a low-fat diet was assessed using a series of questions derived from Prochaska and DiClemente’s transtheoretical model (e.g., Prochaska & DiClemente, 1983). At baseline, participants were categorised as being in one of the following stages: precontemplation (not thinking about eating a low-fat diet), contemplation (thinking about eating a low-fat diet), preparation (some attempt to eat a low-fat diet), action (currently eating a low-fat diet, but for less than 6 months), or maintenance (currently eating a low-fat diet, having done so for 6 months). Whilst this measure differs somewhat from measures utilised in previous research (e.g., Brug, Hospers, & Kok, 1997), it has been widely employed in the domain of health psychology (e.g., Courneya, 1995), and provides a parsimonious way to classify individuals into transtheoretical stages. Perhaps more importantly, unlike many of the algorithms that have been employed in the literature, this method of classification has been shown to possess adequate test-retest reliability (Donovan, Jones, D’Arcy, Holman, & Corti, 1998). Intervention: All participants received a general information leaflet with a covering letter. The general information leaflet took the form of 4-page A5-size (i.e.,
1519
148 mm 105 mm) leaflets. The leaflet provided basic dietary information concerning the differences between saturated and unsaturated fat; the current UK government recommendations concerning fat intake (i.e., 35% of food energy from fat in diet); epidemiological data on current UK levels of fat consumption; and morbidity and mortality from diet-related conditions. The covering letter thanked participants for their support, and explained the demands the study would make of them. The covering letter sent to participants in the intervention condition differed in one respect. In this condition, letters included an additional sentence that stated, ‘‘Currently you are deriving __% of calories from fat in your diet.’’ This was the minimal intervention.1 The estimate of percentage of calories from fat was calculated from the food frequency data collected at baseline. Food frequency questionnaires designed to measure dietary change were distributed five-months post-intervention.
Results Characteristics of participants The study sample was primarily female (81.4%), with an average age of 37.00 years (see Table 1). Across the whole sample, mean initial fat intake was 65.16 g/day (SE=1.23); mean saturated-fat intake was 24.59 g/day (SE=0.49); and overall, participants derived 34.69% of their calories from fat (SE=0.27). Note that UK government recommendations are set at no more than 35% of calories from fat; 254 individuals in our sample consumed a greater proportion of fat than this. As expected, males consumed significantly more fat than females (on all indicators, see Table 1), and gender was used as a covariate in all subsequent analyses. There were no effects of age on fat intake. Analysis of stage of change data revealed that participants were spread across the groups. Of the total sample, 8.9% were precontemplators, 12.0% were contemplators, 38.3% were preparators, 10.8% were in the action stage, and 28.4% were maintainers. Analysis of covariance (controlling for age and gender) revealed a significant effect of stage on all indicators of fat intake. These data provide descriptive validity for the model, as precontemplators consumed the most fat, and action and maintenance stage participants consumed the least. 1 By ‘minimal’, we mean that the questionnaire takes less than 5 min to complete, less than 4 min to analyse and } potentially } the results could be fed back to the participants instantaneously. This approach is also cost-effective, because feedback can be provided by individuals with no knowledge of nutrition and with very little training.
1520
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Table 1 Hospital workers’ baseline dietary intake, by demographic group and stage of change Total fat (g/day)
Fat intake (%)
Saturated fat (g/day)
N
Mean (SE)
P
Mean (SE)
P
Mean (SE)
Pa
Gender Male Female
96 421
83.63 (2.93) 60.95 (1.27)
50.001
36.85 (0.58) 34.19 (0.30)
50.001
31.17 (1.19) 23.08 (0.52)
50.001
Age, years 528 29–34 35–43 44+
106 111 138 141
62.54 66.74 63.67 66.99
Consumer group Low fat ( 4 35%) High fat (>35%)
263 254
51.07 (1.19) 79.74 (1.78)
50.001
29.79 (0.24) 39.76 (0.22)
50.001
18.86 (0.47) 30.52 (0.71)
50.001
Stage Precontemplation Contemplation Preparation Action Maintenance
46 62 198 56 147
92.14 77.14 64.78 55.88 56.39
50.001
40.99 37.94 34.75 32.21 32.31
50.001
37.09 29.37 24.61 19.95 20.66
50.001
a
(3.02) (2.65) (2.22) (2.35)
(4.39) (4.46) (1.86) (2.59) (1.84)
a
0.52
34.10 34.34 34.29 35.66
(0.61) (0.58) (0.56) (0.49)
(0.63) (0.56) (0.39) (0.82) (0.52)
a
0.15
23.56 25.68 23.95 24.95
(1.12) (1.08) (0.94) (0.93)
(1.86) (1.56) (0.73) (0.94) (0.77)
0.47
P values for dietary differences are based on F tests after adjustment for effects in the model.
However, because participants were not evenly distributed across stages, subsequent analyses were restricted to comparisons of high- and low-fat consumers. Intervention effects The intervention group did not differ significantly from the control group on any baseline measures (all t’s [515]51.47, all p’s>0.14). Analysis of covariance revealed a significant impact of the tailored feedback on dietary intake (see Table 2). These were shown by significant within-subjects interactions between time and feedback for measures of total fat (F [1, 512]=6.82, p50:01) and saturated fat (F [1, 512]=5.00, p50:05). There was no impact of the intervention on fat intake expressed as a percentage of total calorific intake (F [1, 512]=0.11, p ¼ 0:74). Congruent with the analyses of covariance, post-hoc t-tests revealed that feedback significantly reduced total and saturated fats; the control group showed no effects between baseline and follow-up. More specifically, feedback significantly reduced total-fat intake (t [271]= 3.79, p50:001); whereas baseline and followup measures did not differ significantly in the control condition (t [244]= 0.11, p ¼ 0:91). Similarly, the feedback group showed a significant decrease in saturated-fat intake (t [271]= 4.06, p50:001), in contrast to those who did not receive feedback (t [244]= 0.73, p ¼ 0:47). Therefore, in spite of the fact that both groups received the same general information,
provision of personalised feedback had a significant impact on fat intake. Percentage fat intake was not reduced in either the experimental (t [271]=1.00, p ¼ 0:32) or control condition (t [244]=1.37, p ¼ 0:17). These analyses provide some support for the role of personalised feedback in reducing fat consumption. Controlling for initial fat intake We noted earlier that our participants were eating within current UK government recommendations. Baseline measures indicated that participants were deriving a mean of 34.69% of calories from fat in the diet, while current UK government recommendations suggest that 35% is adequate to protect health. We, therefore, divided the sample into high-fat (i.e., >35% of calories from fat) consumers (n ¼ 254) and low-fat (i.e., 4 35%) consumers (n ¼ 263), to control for initial fat intake (see Table 3). As expected, there were differences between high- and low-fat consumers across time and across feedback groups. These were shown by significant time by consumer group and time by feedback interactions for total fat (F [1, 513]=29.46, p50:001; F [1, 513]=7.68, p50:01, respectively), and saturated fat (F [1, 513]=34.07, p50:001; F [1, 513]=5.81, p50:05, respectively). In terms of the impact on percentage fat intake, there was a significant time by consumer group interaction (F [1, 513]=56.51, p50:001), but no time by feedback interaction (F [1, 513]=0.15, p ¼ 0:70).
1521
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524 Table 2 Effects of intervention on dietary intake Total fat (g/day) Mean (SE) Feedback group (n=272) Baseline Follow-up Differenceb
65.67 (1.77) 60.02 (1.57) 5.62
No feedback group (n=244) Baseline Follow-up Differenceb
64.47 (1.63) 64.34 (1.56) 0.13
Fat intake (%) P
a
Mean (SE)
50.001
34.31 (0.39) 34.65 (0.40) +0.34
0.91
35.10 (0.36) 35.58 (0.37) +0.48
Saturated fat (g/day) a
P
Mean (SE)
Pa
0.32
24.60 (0.69) 22.32 (0.60) 2.28
50.001
0.17
24.53 (0.68) 24.10 (0.64) 0.43
0.47
a
P values for dietary differences are based on t-tests after analyses of covariance, comparing baseline with follow-up. Difference is computed by subtracting follow-up adjusted mean from baseline adjusted mean. Plus/Minus signs show direction of change. b
Table 3 Effects of intervention on dietary intake: high- versus low-fat consumers
Condition Feedback group Baseline Follow-up Difference No feedback group Baseline Follow-up Difference a b
High Fat Consumers: Mean Values (SE)
Low Fat Consumers: Mean Values (SE)
Total Fat (g/day)
Fat Intake (%)
Saturated Fat (g/day)
Total Fat (g/day)
Fat Intake (%)
Saturated Fat (g/day)
83.31 (2.56) 71.60 (2.59) 11.71a
39.65 (0.31) 38.36 (0.49) 1.29
31.66 (0.99) 26.81 (0.98) 4.85b
49.29 (1.42) 49.26 (1.28) 0.03a
29.34 (0.35) 31.22 (0.47) +1.88
18.04 (0.53) 18.14 (0.51) +0.10
75.94 (2.41) 70.43 (2.26) 5.51
39.87 (0.32) 38.45 (0.43) 1.42
29.29 (1.02) 26.63 (0.94) 2.66
53.13 (1.96) 58.37 (2.24) +5.24
30.31 (0.31) 32.66 (0.52) +2.35
19.80 (0.80) 21.59 (0.90) +1.79
Differs significantly (p50:01) from no feedback group. Differs significantly (p50:05) from no feedback group.
These findings, therefore, suggest differential effects of feedback on high- and low-fat consumers. Post-hoc analysis of difference scores between baseline and follow-up revealed differential effects of the intervention on high- and low-fat consumers (see Table 3). For high-fat consumers, the intervention significantly decreased total- and saturated-fat intake (t [253]=8.52, p50:01; t [253]=3.01, p50:05, respectively). Although the intervention had no significant impact on percentage fat intake (t [253]= 0.18, p > 0:05), overall, we managed to reduce the percentage of calories derived from fat by more than 1% in all high-fat consumers. In general, all high-fat consumers reduced their fat intake over time, but the effects were significantly stronger for those who had received feedback.
The intervention produced different effects in low-fat consumers. Those low-fat consumers who did not receive dietary feedback significantly increased their total-fat intake at follow-up (t [262]= 7.23, p50:01). The implication is that the feedback prevented low-fat consumers from increasing their fat intake post-intervention. There were no other significant effects of feedback on low-fat consumers, although the trends suggest that provision of feedback to low-fat consumers protects them from increasing their fat intake. The implication is that the provision of general nutrition information may encourage greater fat consumption in low-fat consumers, unless they are also provided with personalised feedback.
1522
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Discussion Summary of findings This randomised controlled trial demonstrated the positive effects of providing minimal personalised nutrition messages in conjunction with a general information leaflet. Five months post-intervention, participants in the intervention condition had significantly reduced their total- and saturated-fat intakes, compared with participants in the control group. These dietary improvements were over and above the fact that, on average, individuals were already consuming fat at a level currently endorsed by the UK government (35% of calories from fat). Controlling for initial fat intake, the intervention had different } but beneficial } effects on both high-fat consumers and low-fat consumers. The findings are therefore encouraging for promotion of dietary change efforts for disease prevention in general populations. In addition, the descriptive findings provided support for the application of the transtheoretical model to dietary behaviour: self-reported dietary data were associated with stage of change. Congruent with previous research, precontemplators consumed the most fat, action and maintenance groups consumed the least (Campbell et al., 1994). Comparisons with previous work In recent years, a number of studies have shown the efficacy of providing tailored feedback to reducing fat intake (Beresford et al., 1997; Brug et al., 1996; Campbell et al., 1994, for a review see Brug et al., 1999). Congruent with these, the present study showed significant reductions in fat intake following a minimal intervention. However, the effects were somewhat smaller than those found in previous research. For example, Campbell and colleagues reported a 23% reduction in total-fat intake, compared with 8.6% in the present study. There are two related explanations for this finding. It seems likely that minimal interventions will have a smaller impact on behaviour; in addition, previous intervention studies may have exerted additional influence through demand characteristics (see Campbell et al., 1994, p. 787). In general, the present findings corroborate those from other studies of tailored dietary information provision (e.g., Brug et al., 1999). However, as expected, the impact of the interventions of proportion of calories derived from fat was modest and failed to reach statistical significance. It seems likely that whilst it is relatively easy to monitor one’s total intake of fat (or saturated fat), monitoring fat intake as a proportion of total dietary intake is much more difficult. Clearly, a more sophisticated intervention would be required to change the proportion of calories derived from fat in the diet (cf. Beresford et al., 1997).
Taken together, findings to date suggest that not only is personalised nutrition information the most efficacious way of improving diet, but that such interventions need to be combined with an effectively delivered message. The present findings are also congruent with those from other areas. The significant impact of personalised feedback corroborates psychological work on increased attention to health-relevant materials, unrealistic optimism, and misperception of one’s fat intake (Armitage & Conner, 1999a; Brug et al., 1996; Campbell et al., 1994; Lechner et al., 1998; Weinstein, 1987). Limitations This study was conducted in a general population based in a hospital. A potential limitation of the present study is that hospital workers may be more amenable to dietary change. Although participants were spread evenly across the transtheoretical stages, this only implies that the findings may be extended to more general populations. Similarly, the majority (circa 80%) of participants were women, meaning that the present study requires replication in different samples and different settings. However, it is worth noting that in spite of the majority of participants conforming to UK government recommendations at baseline, we still produced dietary change. This suggests that the approach is robust and worthy of further study in a population where fat consumption exceeds government recommendations. Implications for practice The findings have implications for practice. To date, the principal approaches to providing nutrition advice have been intensive, typically based on individual patient counselling provided by general practitioners, nurses or dieticians. Not only is this service usually offered exclusively to patients with a high risk of dietrelated morbidity, it is also time consuming and costly (Beresford et al., 1997; Schapira et al., 1991). Similarly, high-profile campaigns initiated by government-funded bodies focus on provision of general information. Evidence for the efficacy of this approach to changing dietary behaviour is minimal (Ministry of Agriculture, Fisheries and Food, 1992). The present study shows that presenting individual dietary feedback in combination with educational materials is more effective than standard educational materials alone in promoting behaviour change. Perhaps more importantly, the present intervention is relatively quick and inexpensive. More specifically, from a baseline of no information, personal feedback can be provided in under 10 min; the intervention could be administered by someone with little relevant training;
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
and printing costs would be reduced because ‘glossy’ leaflets are not required. Contrast this with (for example) a dietician’s analysis of a 24-h dietary record. Thus, the present intervention suggests a way in which health-related dietary change may be encouraged on a large scale, without incurring the costs of (for example) individual counselling. As such, it can be provided to both low- and high-risk individuals who are interested in improving their health, with minimal impact on the time of health professionals. The personalised feedback intervention was shown to have different } but beneficial } effects on both highand low-fat consumers. Across both the experimental and control groups, the general information reduced fat intake in high-fat consumers, and increased the fat intake of low-fat consumers. However, provision of personalised feedback further reduced fat intake in highfat consumers, and ameliorated the tendency for low-fat consumers to increase their fat intake post-intervention. The implication is that the personalised intervention prevented individuals from ignoring, dismissing or misperceiving health-relevant materials, regardless of current dietary practice (Armitage & Conner, 1999a; Brug et al., 1996; Campbell et al., 1994; Lechner et al., 1998; Weinstein, 1987). These findings mirror the lack of support for more general public health interventions: provision of general information is clearly insufficient to produce dietary change in a general population (Ministry of Agriculture, Fisheries and Food, 1992). The present study, therefore, shows that both high- and lowfat consumers benefited from receiving a personalised intervention. While the full generalisability of the intervention is still to be established, a low-intensity dietary intervention could have important public health implications. The technique has the potential to reach large sections of the population. In particular, we managed to reduce percentage of calories derived from fat by more than 1% in high-fat consumers. Whilst this did not achieve statistical significance, in public health terms, this finding is of great significance. If one considers that even a 1% reduction in dietary calories from fat could result in 10,000 deaths saved in the US alone; when applied at the population level, the present intervention may have an important impact on morbidity and mortality (Rose, 1985, 1990).
Conclusions This study has demonstrated the efficacy of providing personalised feedback to reduce fat intake. The intervention was shown to be relatively durable, with effects being sustained over a period of five months. Moreover, given that positive effects were observed in both highand low-fat consumers, the intervention is clearly both
1523
generalizable and robust. This approach, if adopted on a large scale, could result in large improvements in public health across the range of current dietary practice.
Acknowledgements The authors wish to thank Pam Gardner (St James’ University Hospital, Leeds) for her help in setting up the study.
References Anon. (1993). International conference on nutrition. World Health Forum, 14, 207–209. Armitage, C. J., & Conner, M. (1999a). Distinguishing perceptions of control from self-efficacy: Predicting consumption of a low fat diet using the theory of planned behavior. Journal of Applied Social Psychology, 29, 72–90. Armitage, C. J., & Conner, M. (1999b). The theory of planned behaviour: Assessment of predictive validity and ‘‘perceived control’’. British Journal of Social Psychology, 38, 35–54. Beresford, S. A. A., Curry, S. J., Kristal, A. R., Lazovich, D., Feng, Z., & Wagner, E. H. (1997). A dietary intervention in primary care practice: The eating patterns study. American Journal of Public Health, 87, 610–616. Brug, J., Campbell, M., & van Assema, P. (1999). The application and impact of computer-generated personalized nutrition education: A review of the literature. Patient Education and Counseling, 36, 145–156. Brug, J., Hospers, H. J., & Kok, G. (1997). Differences in psychosocial factors and fat consumption between stages of change for fat reduction. Psychology and Health, 12, 719–727. Brug, J., Steenhuis, I., van Assema, P., & de Vries, H. (1996). The impact of a computer-tailored nutrition intervention. Preventive Medicine, 25, 236–242. Cade, J. E., & Margetts, B. M. (1988). Nutrient sources in the English diet: Quantitative data from three English towns. International Journal of Epidemiology, 17, 844–848. Campbell, M. K., DeVellis, B. M., Strecher, V. J., Ammerman, A. S., DeVellis, R. F., & Sandler, R. S. (1994). Improving dietary behavior: The effectiveness of tailored messages in primary care settings. American Journal of Public Health, 84, 783–787. Courneya, K. S. (1995). Understanding readiness for regular physical activity in older individuals: An application of the theory of planned behavior. Health Psychology, 14, 80–87. Department of Health. (1992). The health of the nation: A strategy for health in England. London: HMSO. Department of Health and Human Services. (1991). Health people 2000: National health promotion and disease prevention objectives. Washington DC: DHHS publication, PHS 91-50212. Donovan, R. J., Jones, S., D’Arcy, C., Holman, J., & Corti, B. (1998). Assessing the reliability of a stage of change scale. Health Education Research, 13, 285–291. Family Heart Study Group. (1994). Randomised controlled trial evaluating cardiovascular screening and intervention in
1524
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
general practice: principle results of the British Family Heart study. British Medical Journal, 308, 313–320. Lechner, L., Brug, J., de Vries, H., van Assema, P., & Mudde, A. (1998). Stages of change for fruit, vegetable and fat intake: consequences of misconception. Health Education Research, 13, 1–11. Maccoby, N., Farquhar, J. W., Wood, P. D., & Alexander, J. (1977). Reducing the risk of cardiovascular disease: Effects of a community-based campaign on knowledge and behavior. Journal of Community Health, 3, 100–114. Margetts, B. M., Cade, J. E., & Osmond, C. (1989). Comparison of a food frequency questionnaire with a diet record. International Journal of Epidemiology, 18, 868–873. Ministry of Agriculture, Fisheries and Food. (1992). Manual of Nutrition. London: HMSO. OXCHECK Study Group. (1994). Effectiveness of health checks conducted by nurses in primary care: results of the OXCHECK study after one year. British Medical Journal, 308, 308–312. Prochaska, J., & DiClemente, C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395.
Rose, G. (1990). Future of disease prevention: British perspectives on the US preventive services task force guidelines. Journal of General Internal Medicine, 5(Suppl), S128–S132. Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14, 32–38. Schapira, D. V., Kumar, N. B., Lyman, G. H., & Baile, W. F. (1991). The effect of duration of intervention and locus of control on dietary change. American Journal of Preventive Medicine, 7, 341–347. Temple, N. J. (1996). Dietary fats and coronary heart disease. Biomedicine and Pharmacotherapy, 50, 261–268. Thompson, R. L., & Margetts, B. M. (1993). Comparison of a food frequency questionnaire with a 10-day weighed record in cigarette smokers. International Journal of Epidemiology, 22, 824–833. Trichopoulos, D., & Willett, D. C. (1996). Nutrition and cancer [Special issue]. Cancer Causes and Control, 7, 3–180. Weinstein, N. D. (1987). Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample. Journal of Behavioral Medicine, 10, 481–500.
Efficacy of a minimal intervention to reduce fat intake Christopher J. Armitagea,*, Mark Connerb a
Centre for Research in Social Attitudes, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK b School of Psychology, University of Leeds, Leeds, UK
Abstract Effective dietary interventions must be developed to reduce fat intake in whole populations, rather than clinical subgroups. This study tested the effects of personalised feedback on fat intake in a general population. Hospital workers (n ¼ 801) were randomised to receive personalised feedback or no personalised feedback. Personalised feedback consisted of one sentence expressing current fat intake as a percentage of total calorific intake. Changes in fat intake from baseline to five months post-intervention were evaluated. The personalised intervention produced significant decreases in total and saturated fat intake, compared with the control group. Total-fat decreased by 8.6% (versus 0.2% in the control group); saturated fat decreased by 9.3% (versus 1.7% in the control group). Fat intake as a proportion of total calorific intake did not decrease significantly in either condition. Findings also revealed differential effects of feedback on high- versus low-fat consumer groups. Personalised feedback significantly reduced fat intake in high-fat consumers, and prevented low-fat consumers from increasing their fat intake. Personalised fat feedback therefore represents an efficacious and low-intensity approach to the reduction of fat intake in the general population. # 2001 Elsevier Science Ltd. All rights reserved. Keywords: Fat intake; Diet; Health promotion; Randomised controlled trial; Health risk
Introduction Excessive fat intake has been linked with increased health risk from coronary heart disease and several forms of cancer (Anon, 1993; Temple, 1996; Trichopoulos & Willett, 1996). The link between fat intake and the major causes of death and health expenditure in industrialised countries has led several governments to set specific targets for nutrition. For example, the UK government recommends that individuals should derive no more than 35% of food energy from fat in the diet, and 11% from saturated fat (Department of Health, 1992). Recent trends suggest this target is unlikely to have been reached by the year 2000 (Ministry of Agriculture, Fisheries and Food, 1992). Moreover, given that US government recommendations are considerably *Corresponding author. Tel: +44-114-222-6626; fax: +44114-276-6515. E-mail address: c.j.armitage@sheffield.ac.uk (C.J. Armitage).
lower than those for the UK (30% of food energy from fat), fat intake represents an important target for intervention (Department of Health and Human Services, 1991). Intensive dietary interventions targeted at individuals deemed to be ‘‘at risk’’ have generally been successful in reducing fat intake (e.g., Schapira, Kumar, Lyman, & Baile, 1991). Attempts to reduce fat intake in the general population have typically been focused on multi-media campaigns and large-scale intensive interventions. Evidence for the utility of these approaches has been equivocal (Family Heart Study Group, 1994; Maccoby, Farquhar, Wood, & Alexander, 1977; Ministry of Agriculture, Fisheries and Food, 1992; OXCHECK, 1994). Recently, however, several studies have reported that self-help materials (plus a motivational message) and tailored feedback significantly reduce fat intake in general populations (Beresford et al., 1997; Brug, Steenhuis, van Assema, & de Vries, 1996; Campbell et al., 1994). The implication is that dietary interventions
0277-9536/01/$ - see front matter# 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 7 - 9 5 3 6 ( 0 0 ) 0 0 2 6 5 - 3
1518
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
designed to reduce fat intake in the general population will be most effective when they are tailored (see Brug, Campbell, & van Assema, 1999, for a recent review). There are three principal reasons why one would expect personalised feedback to reduce fat consumption. First, individuals are more likely to attend to, and remember personally relevant information (Brug et al., 1996, 1999; Campbell et al., 1994). Thus, tailored feedback provided in conjunction with self-help materials is likely to engender more dietary change than no tailored feedback. Second, general information may be dismissed as irrelevant by the recipient, through psychological mechanisms such as unrealistic optimism (e.g., Weinstein, 1987). However, if one presents accurate information about current levels of fat consumption, the effect of unrealistic optimism may be diminished. Third, recent evidence suggests that individuals have inaccurate perceptions of their fat intake (e.g., Armitage & Conner, 1999a; Lechner, Brug, de Vries, van Assema, & Mudde, 1998). Presenting individuals with accurate personal feedback may challenge this misperception and engender behaviour change. Previous research supports the utility of tailored interventions (see Brug et al., 1999). However, previous ‘tailoring’ studies can also be criticised. First, the experimental materials often exert demand characteristics that are not controlled for. For example, Campbell and colleagues (1994) report that, ‘‘The tailored messages were personalised and provided more information than did the non-tailored group’’ (p. 787). Second, the outcome of interest (i.e., reduction in fat intake) has typically focused on reductions in absolute fat intake (see Brug et al., 1999 for a review and Beresford et al., 1997 for an exception). Whilst it is clearly important to reduce individuals’ total-fat intake, this might simply reflect a decrease in total calorific intake, rather than a specific reduction in fat intake. Expressing fat intake as a proportion of total calorific intake allows the researcher the opportunity to evaluate whether the intervention reduces fat intake specifically, or whether it reduces total calorific intake. The present paper seeks to extend previous work by presenting a randomised controlled trial, designed to assess the efficacy of providing individuals with personalised dietary feedback and general information, as opposed to providing them with general information only. In an attempt to control for the demand characteristics that have threatened the validity of previous studies, the present study provided only a minimal intervention. Following work by Brug and colleagues, the materials given to the intervention group differed from the control group with respect to only one sentence, which informed them of their current dietary fat intake (see Methods; Brug et al., 1996). The approach is attractive because it is cost-effective and could be targeted at diverse populations. Beyond this,
the present study also reports multiple indicators of dietary fat intake, specifically: total-fat intake, saturated-fat intake, and fat intake expressed as a proportion of total calorific intake. It is hypothesised that the provision of personalised feedback will significantly decrease fat intake. More specifically, it is predicted that the personalised feedback will exert greatest impact upon total- and saturated-fat intake because they are more salient and therefore more amenable to change. Moreover, it is expected that the intervention will exert a significant positive effect upon fat intake specifically. That is, the personalised feedback should reduce the proportion of calories derived from fat. However, it is anticipated that such effects will be smaller because of the in-depth knowledge of nutrition that would be required to achieve this.
Methods Study population The study was conducted on a sample of 801 hospital workers, a population whose shift patterns are likely to exert a negative impact upon their diets. Baseline (n ¼ 801) and follow-up (n ¼ 517) measures of dietary intake were taken in order to ascertain intervention effects. The reported analyses are based on the 517 participants who responded at both time points (a response rate of 64.5%). Responders did not differ from non-responders in terms of total-fat intake (F [1, 798]=1.74, p ¼ 0:19), percent food energy from fat (F [1, 798]=3.53, p ¼ 0:06), or saturated-fat intake (F [1, 798]=2.10, p ¼ 0:15). It was concluded that the 517 respondents were a representative sample of the original 801 people we questioned. Participants were told that they would receive dietary information, but not that we expected changes in their diet, nor that some of the feedback would be personalised.
Procedure The study was a randomised controlled trial, testing the efficacy of personalised feedback versus no personalised feedback. Following baseline assessment of current diet, individuals were randomised to feedback or nofeedback conditions. Both groups received general information leaflets and covering letters. The intervention group differed from the control group in only one respect: their covering letters included an additional sentence informing them of their current fat intake. Assessment of the impact of the intervention took place five months post-intervention, using the same measures as at baseline.
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Measures Food frequency: A validated food frequency instrument consisting of 63 items was used to assess initial diet. This instrument was developed by Margetts, Cade, and Osmond (1989), based upon data from Cade and Margetts (1988). The reliability and validity of the instrument has been demonstrated in a number of studies. For example, Armitage and Conner (1999b) report that it possesses good test-retest reliability (r [411]=0.62, p50.01, across a three-month time period). Congruent with this, Margetts et al. (1989) assessed the validity of the instrument, by comparing it with a 24-hour dietary record: even across a three-year period, all correlations between the two measures were significant (see also Thompson & Margetts, 1993). Response options (i.e., the 63 food products) were presented as a checklist with six response choices: ‘‘two or more times a day’’, ‘‘every day’’, ‘‘three to five times per week’’, ‘‘one to two times per week’’, ‘‘one to three times per month’’, and ‘‘rarely or never’’. Daily use of food was calculated by multiplying frequency of consumption (calculated as servings per day) by portion data (calculated separately for males and females). Dietary- and saturated-fat scores were obtained by multiplying daily use of food by the average fat content for each item, and summing the items. Average fat content was derived from data provided by the UK Ministry of Agriculture, Fisheries and Food (1992). Average portion sizes were derived from Cade and Margetts (1988). Stage of change: Stage of change with respect to eating a low-fat diet was assessed using a series of questions derived from Prochaska and DiClemente’s transtheoretical model (e.g., Prochaska & DiClemente, 1983). At baseline, participants were categorised as being in one of the following stages: precontemplation (not thinking about eating a low-fat diet), contemplation (thinking about eating a low-fat diet), preparation (some attempt to eat a low-fat diet), action (currently eating a low-fat diet, but for less than 6 months), or maintenance (currently eating a low-fat diet, having done so for 6 months). Whilst this measure differs somewhat from measures utilised in previous research (e.g., Brug, Hospers, & Kok, 1997), it has been widely employed in the domain of health psychology (e.g., Courneya, 1995), and provides a parsimonious way to classify individuals into transtheoretical stages. Perhaps more importantly, unlike many of the algorithms that have been employed in the literature, this method of classification has been shown to possess adequate test-retest reliability (Donovan, Jones, D’Arcy, Holman, & Corti, 1998). Intervention: All participants received a general information leaflet with a covering letter. The general information leaflet took the form of 4-page A5-size (i.e.,
1519
148 mm 105 mm) leaflets. The leaflet provided basic dietary information concerning the differences between saturated and unsaturated fat; the current UK government recommendations concerning fat intake (i.e., 35% of food energy from fat in diet); epidemiological data on current UK levels of fat consumption; and morbidity and mortality from diet-related conditions. The covering letter thanked participants for their support, and explained the demands the study would make of them. The covering letter sent to participants in the intervention condition differed in one respect. In this condition, letters included an additional sentence that stated, ‘‘Currently you are deriving __% of calories from fat in your diet.’’ This was the minimal intervention.1 The estimate of percentage of calories from fat was calculated from the food frequency data collected at baseline. Food frequency questionnaires designed to measure dietary change were distributed five-months post-intervention.
Results Characteristics of participants The study sample was primarily female (81.4%), with an average age of 37.00 years (see Table 1). Across the whole sample, mean initial fat intake was 65.16 g/day (SE=1.23); mean saturated-fat intake was 24.59 g/day (SE=0.49); and overall, participants derived 34.69% of their calories from fat (SE=0.27). Note that UK government recommendations are set at no more than 35% of calories from fat; 254 individuals in our sample consumed a greater proportion of fat than this. As expected, males consumed significantly more fat than females (on all indicators, see Table 1), and gender was used as a covariate in all subsequent analyses. There were no effects of age on fat intake. Analysis of stage of change data revealed that participants were spread across the groups. Of the total sample, 8.9% were precontemplators, 12.0% were contemplators, 38.3% were preparators, 10.8% were in the action stage, and 28.4% were maintainers. Analysis of covariance (controlling for age and gender) revealed a significant effect of stage on all indicators of fat intake. These data provide descriptive validity for the model, as precontemplators consumed the most fat, and action and maintenance stage participants consumed the least. 1 By ‘minimal’, we mean that the questionnaire takes less than 5 min to complete, less than 4 min to analyse and } potentially } the results could be fed back to the participants instantaneously. This approach is also cost-effective, because feedback can be provided by individuals with no knowledge of nutrition and with very little training.
1520
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Table 1 Hospital workers’ baseline dietary intake, by demographic group and stage of change Total fat (g/day)
Fat intake (%)
Saturated fat (g/day)
N
Mean (SE)
P
Mean (SE)
P
Mean (SE)
Pa
Gender Male Female
96 421
83.63 (2.93) 60.95 (1.27)
50.001
36.85 (0.58) 34.19 (0.30)
50.001
31.17 (1.19) 23.08 (0.52)
50.001
Age, years 528 29–34 35–43 44+
106 111 138 141
62.54 66.74 63.67 66.99
Consumer group Low fat ( 4 35%) High fat (>35%)
263 254
51.07 (1.19) 79.74 (1.78)
50.001
29.79 (0.24) 39.76 (0.22)
50.001
18.86 (0.47) 30.52 (0.71)
50.001
Stage Precontemplation Contemplation Preparation Action Maintenance
46 62 198 56 147
92.14 77.14 64.78 55.88 56.39
50.001
40.99 37.94 34.75 32.21 32.31
50.001
37.09 29.37 24.61 19.95 20.66
50.001
a
(3.02) (2.65) (2.22) (2.35)
(4.39) (4.46) (1.86) (2.59) (1.84)
a
0.52
34.10 34.34 34.29 35.66
(0.61) (0.58) (0.56) (0.49)
(0.63) (0.56) (0.39) (0.82) (0.52)
a
0.15
23.56 25.68 23.95 24.95
(1.12) (1.08) (0.94) (0.93)
(1.86) (1.56) (0.73) (0.94) (0.77)
0.47
P values for dietary differences are based on F tests after adjustment for effects in the model.
However, because participants were not evenly distributed across stages, subsequent analyses were restricted to comparisons of high- and low-fat consumers. Intervention effects The intervention group did not differ significantly from the control group on any baseline measures (all t’s [515]51.47, all p’s>0.14). Analysis of covariance revealed a significant impact of the tailored feedback on dietary intake (see Table 2). These were shown by significant within-subjects interactions between time and feedback for measures of total fat (F [1, 512]=6.82, p50:01) and saturated fat (F [1, 512]=5.00, p50:05). There was no impact of the intervention on fat intake expressed as a percentage of total calorific intake (F [1, 512]=0.11, p ¼ 0:74). Congruent with the analyses of covariance, post-hoc t-tests revealed that feedback significantly reduced total and saturated fats; the control group showed no effects between baseline and follow-up. More specifically, feedback significantly reduced total-fat intake (t [271]= 3.79, p50:001); whereas baseline and followup measures did not differ significantly in the control condition (t [244]= 0.11, p ¼ 0:91). Similarly, the feedback group showed a significant decrease in saturated-fat intake (t [271]= 4.06, p50:001), in contrast to those who did not receive feedback (t [244]= 0.73, p ¼ 0:47). Therefore, in spite of the fact that both groups received the same general information,
provision of personalised feedback had a significant impact on fat intake. Percentage fat intake was not reduced in either the experimental (t [271]=1.00, p ¼ 0:32) or control condition (t [244]=1.37, p ¼ 0:17). These analyses provide some support for the role of personalised feedback in reducing fat consumption. Controlling for initial fat intake We noted earlier that our participants were eating within current UK government recommendations. Baseline measures indicated that participants were deriving a mean of 34.69% of calories from fat in the diet, while current UK government recommendations suggest that 35% is adequate to protect health. We, therefore, divided the sample into high-fat (i.e., >35% of calories from fat) consumers (n ¼ 254) and low-fat (i.e., 4 35%) consumers (n ¼ 263), to control for initial fat intake (see Table 3). As expected, there were differences between high- and low-fat consumers across time and across feedback groups. These were shown by significant time by consumer group and time by feedback interactions for total fat (F [1, 513]=29.46, p50:001; F [1, 513]=7.68, p50:01, respectively), and saturated fat (F [1, 513]=34.07, p50:001; F [1, 513]=5.81, p50:05, respectively). In terms of the impact on percentage fat intake, there was a significant time by consumer group interaction (F [1, 513]=56.51, p50:001), but no time by feedback interaction (F [1, 513]=0.15, p ¼ 0:70).
1521
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524 Table 2 Effects of intervention on dietary intake Total fat (g/day) Mean (SE) Feedback group (n=272) Baseline Follow-up Differenceb
65.67 (1.77) 60.02 (1.57) 5.62
No feedback group (n=244) Baseline Follow-up Differenceb
64.47 (1.63) 64.34 (1.56) 0.13
Fat intake (%) P
a
Mean (SE)
50.001
34.31 (0.39) 34.65 (0.40) +0.34
0.91
35.10 (0.36) 35.58 (0.37) +0.48
Saturated fat (g/day) a
P
Mean (SE)
Pa
0.32
24.60 (0.69) 22.32 (0.60) 2.28
50.001
0.17
24.53 (0.68) 24.10 (0.64) 0.43
0.47
a
P values for dietary differences are based on t-tests after analyses of covariance, comparing baseline with follow-up. Difference is computed by subtracting follow-up adjusted mean from baseline adjusted mean. Plus/Minus signs show direction of change. b
Table 3 Effects of intervention on dietary intake: high- versus low-fat consumers
Condition Feedback group Baseline Follow-up Difference No feedback group Baseline Follow-up Difference a b
High Fat Consumers: Mean Values (SE)
Low Fat Consumers: Mean Values (SE)
Total Fat (g/day)
Fat Intake (%)
Saturated Fat (g/day)
Total Fat (g/day)
Fat Intake (%)
Saturated Fat (g/day)
83.31 (2.56) 71.60 (2.59) 11.71a
39.65 (0.31) 38.36 (0.49) 1.29
31.66 (0.99) 26.81 (0.98) 4.85b
49.29 (1.42) 49.26 (1.28) 0.03a
29.34 (0.35) 31.22 (0.47) +1.88
18.04 (0.53) 18.14 (0.51) +0.10
75.94 (2.41) 70.43 (2.26) 5.51
39.87 (0.32) 38.45 (0.43) 1.42
29.29 (1.02) 26.63 (0.94) 2.66
53.13 (1.96) 58.37 (2.24) +5.24
30.31 (0.31) 32.66 (0.52) +2.35
19.80 (0.80) 21.59 (0.90) +1.79
Differs significantly (p50:01) from no feedback group. Differs significantly (p50:05) from no feedback group.
These findings, therefore, suggest differential effects of feedback on high- and low-fat consumers. Post-hoc analysis of difference scores between baseline and follow-up revealed differential effects of the intervention on high- and low-fat consumers (see Table 3). For high-fat consumers, the intervention significantly decreased total- and saturated-fat intake (t [253]=8.52, p50:01; t [253]=3.01, p50:05, respectively). Although the intervention had no significant impact on percentage fat intake (t [253]= 0.18, p > 0:05), overall, we managed to reduce the percentage of calories derived from fat by more than 1% in all high-fat consumers. In general, all high-fat consumers reduced their fat intake over time, but the effects were significantly stronger for those who had received feedback.
The intervention produced different effects in low-fat consumers. Those low-fat consumers who did not receive dietary feedback significantly increased their total-fat intake at follow-up (t [262]= 7.23, p50:01). The implication is that the feedback prevented low-fat consumers from increasing their fat intake post-intervention. There were no other significant effects of feedback on low-fat consumers, although the trends suggest that provision of feedback to low-fat consumers protects them from increasing their fat intake. The implication is that the provision of general nutrition information may encourage greater fat consumption in low-fat consumers, unless they are also provided with personalised feedback.
1522
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
Discussion Summary of findings This randomised controlled trial demonstrated the positive effects of providing minimal personalised nutrition messages in conjunction with a general information leaflet. Five months post-intervention, participants in the intervention condition had significantly reduced their total- and saturated-fat intakes, compared with participants in the control group. These dietary improvements were over and above the fact that, on average, individuals were already consuming fat at a level currently endorsed by the UK government (35% of calories from fat). Controlling for initial fat intake, the intervention had different } but beneficial } effects on both high-fat consumers and low-fat consumers. The findings are therefore encouraging for promotion of dietary change efforts for disease prevention in general populations. In addition, the descriptive findings provided support for the application of the transtheoretical model to dietary behaviour: self-reported dietary data were associated with stage of change. Congruent with previous research, precontemplators consumed the most fat, action and maintenance groups consumed the least (Campbell et al., 1994). Comparisons with previous work In recent years, a number of studies have shown the efficacy of providing tailored feedback to reducing fat intake (Beresford et al., 1997; Brug et al., 1996; Campbell et al., 1994, for a review see Brug et al., 1999). Congruent with these, the present study showed significant reductions in fat intake following a minimal intervention. However, the effects were somewhat smaller than those found in previous research. For example, Campbell and colleagues reported a 23% reduction in total-fat intake, compared with 8.6% in the present study. There are two related explanations for this finding. It seems likely that minimal interventions will have a smaller impact on behaviour; in addition, previous intervention studies may have exerted additional influence through demand characteristics (see Campbell et al., 1994, p. 787). In general, the present findings corroborate those from other studies of tailored dietary information provision (e.g., Brug et al., 1999). However, as expected, the impact of the interventions of proportion of calories derived from fat was modest and failed to reach statistical significance. It seems likely that whilst it is relatively easy to monitor one’s total intake of fat (or saturated fat), monitoring fat intake as a proportion of total dietary intake is much more difficult. Clearly, a more sophisticated intervention would be required to change the proportion of calories derived from fat in the diet (cf. Beresford et al., 1997).
Taken together, findings to date suggest that not only is personalised nutrition information the most efficacious way of improving diet, but that such interventions need to be combined with an effectively delivered message. The present findings are also congruent with those from other areas. The significant impact of personalised feedback corroborates psychological work on increased attention to health-relevant materials, unrealistic optimism, and misperception of one’s fat intake (Armitage & Conner, 1999a; Brug et al., 1996; Campbell et al., 1994; Lechner et al., 1998; Weinstein, 1987). Limitations This study was conducted in a general population based in a hospital. A potential limitation of the present study is that hospital workers may be more amenable to dietary change. Although participants were spread evenly across the transtheoretical stages, this only implies that the findings may be extended to more general populations. Similarly, the majority (circa 80%) of participants were women, meaning that the present study requires replication in different samples and different settings. However, it is worth noting that in spite of the majority of participants conforming to UK government recommendations at baseline, we still produced dietary change. This suggests that the approach is robust and worthy of further study in a population where fat consumption exceeds government recommendations. Implications for practice The findings have implications for practice. To date, the principal approaches to providing nutrition advice have been intensive, typically based on individual patient counselling provided by general practitioners, nurses or dieticians. Not only is this service usually offered exclusively to patients with a high risk of dietrelated morbidity, it is also time consuming and costly (Beresford et al., 1997; Schapira et al., 1991). Similarly, high-profile campaigns initiated by government-funded bodies focus on provision of general information. Evidence for the efficacy of this approach to changing dietary behaviour is minimal (Ministry of Agriculture, Fisheries and Food, 1992). The present study shows that presenting individual dietary feedback in combination with educational materials is more effective than standard educational materials alone in promoting behaviour change. Perhaps more importantly, the present intervention is relatively quick and inexpensive. More specifically, from a baseline of no information, personal feedback can be provided in under 10 min; the intervention could be administered by someone with little relevant training;
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
and printing costs would be reduced because ‘glossy’ leaflets are not required. Contrast this with (for example) a dietician’s analysis of a 24-h dietary record. Thus, the present intervention suggests a way in which health-related dietary change may be encouraged on a large scale, without incurring the costs of (for example) individual counselling. As such, it can be provided to both low- and high-risk individuals who are interested in improving their health, with minimal impact on the time of health professionals. The personalised feedback intervention was shown to have different } but beneficial } effects on both highand low-fat consumers. Across both the experimental and control groups, the general information reduced fat intake in high-fat consumers, and increased the fat intake of low-fat consumers. However, provision of personalised feedback further reduced fat intake in highfat consumers, and ameliorated the tendency for low-fat consumers to increase their fat intake post-intervention. The implication is that the personalised intervention prevented individuals from ignoring, dismissing or misperceiving health-relevant materials, regardless of current dietary practice (Armitage & Conner, 1999a; Brug et al., 1996; Campbell et al., 1994; Lechner et al., 1998; Weinstein, 1987). These findings mirror the lack of support for more general public health interventions: provision of general information is clearly insufficient to produce dietary change in a general population (Ministry of Agriculture, Fisheries and Food, 1992). The present study, therefore, shows that both high- and lowfat consumers benefited from receiving a personalised intervention. While the full generalisability of the intervention is still to be established, a low-intensity dietary intervention could have important public health implications. The technique has the potential to reach large sections of the population. In particular, we managed to reduce percentage of calories derived from fat by more than 1% in high-fat consumers. Whilst this did not achieve statistical significance, in public health terms, this finding is of great significance. If one considers that even a 1% reduction in dietary calories from fat could result in 10,000 deaths saved in the US alone; when applied at the population level, the present intervention may have an important impact on morbidity and mortality (Rose, 1985, 1990).
Conclusions This study has demonstrated the efficacy of providing personalised feedback to reduce fat intake. The intervention was shown to be relatively durable, with effects being sustained over a period of five months. Moreover, given that positive effects were observed in both highand low-fat consumers, the intervention is clearly both
1523
generalizable and robust. This approach, if adopted on a large scale, could result in large improvements in public health across the range of current dietary practice.
Acknowledgements The authors wish to thank Pam Gardner (St James’ University Hospital, Leeds) for her help in setting up the study.
References Anon. (1993). International conference on nutrition. World Health Forum, 14, 207–209. Armitage, C. J., & Conner, M. (1999a). Distinguishing perceptions of control from self-efficacy: Predicting consumption of a low fat diet using the theory of planned behavior. Journal of Applied Social Psychology, 29, 72–90. Armitage, C. J., & Conner, M. (1999b). The theory of planned behaviour: Assessment of predictive validity and ‘‘perceived control’’. British Journal of Social Psychology, 38, 35–54. Beresford, S. A. A., Curry, S. J., Kristal, A. R., Lazovich, D., Feng, Z., & Wagner, E. H. (1997). A dietary intervention in primary care practice: The eating patterns study. American Journal of Public Health, 87, 610–616. Brug, J., Campbell, M., & van Assema, P. (1999). The application and impact of computer-generated personalized nutrition education: A review of the literature. Patient Education and Counseling, 36, 145–156. Brug, J., Hospers, H. J., & Kok, G. (1997). Differences in psychosocial factors and fat consumption between stages of change for fat reduction. Psychology and Health, 12, 719–727. Brug, J., Steenhuis, I., van Assema, P., & de Vries, H. (1996). The impact of a computer-tailored nutrition intervention. Preventive Medicine, 25, 236–242. Cade, J. E., & Margetts, B. M. (1988). Nutrient sources in the English diet: Quantitative data from three English towns. International Journal of Epidemiology, 17, 844–848. Campbell, M. K., DeVellis, B. M., Strecher, V. J., Ammerman, A. S., DeVellis, R. F., & Sandler, R. S. (1994). Improving dietary behavior: The effectiveness of tailored messages in primary care settings. American Journal of Public Health, 84, 783–787. Courneya, K. S. (1995). Understanding readiness for regular physical activity in older individuals: An application of the theory of planned behavior. Health Psychology, 14, 80–87. Department of Health. (1992). The health of the nation: A strategy for health in England. London: HMSO. Department of Health and Human Services. (1991). Health people 2000: National health promotion and disease prevention objectives. Washington DC: DHHS publication, PHS 91-50212. Donovan, R. J., Jones, S., D’Arcy, C., Holman, J., & Corti, B. (1998). Assessing the reliability of a stage of change scale. Health Education Research, 13, 285–291. Family Heart Study Group. (1994). Randomised controlled trial evaluating cardiovascular screening and intervention in
1524
C.J. Armitage, M. Conner / Social Science & Medicine 52 (2001) 1517–1524
general practice: principle results of the British Family Heart study. British Medical Journal, 308, 313–320. Lechner, L., Brug, J., de Vries, H., van Assema, P., & Mudde, A. (1998). Stages of change for fruit, vegetable and fat intake: consequences of misconception. Health Education Research, 13, 1–11. Maccoby, N., Farquhar, J. W., Wood, P. D., & Alexander, J. (1977). Reducing the risk of cardiovascular disease: Effects of a community-based campaign on knowledge and behavior. Journal of Community Health, 3, 100–114. Margetts, B. M., Cade, J. E., & Osmond, C. (1989). Comparison of a food frequency questionnaire with a diet record. International Journal of Epidemiology, 18, 868–873. Ministry of Agriculture, Fisheries and Food. (1992). Manual of Nutrition. London: HMSO. OXCHECK Study Group. (1994). Effectiveness of health checks conducted by nurses in primary care: results of the OXCHECK study after one year. British Medical Journal, 308, 308–312. Prochaska, J., & DiClemente, C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51, 390–395.
Rose, G. (1990). Future of disease prevention: British perspectives on the US preventive services task force guidelines. Journal of General Internal Medicine, 5(Suppl), S128–S132. Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14, 32–38. Schapira, D. V., Kumar, N. B., Lyman, G. H., & Baile, W. F. (1991). The effect of duration of intervention and locus of control on dietary change. American Journal of Preventive Medicine, 7, 341–347. Temple, N. J. (1996). Dietary fats and coronary heart disease. Biomedicine and Pharmacotherapy, 50, 261–268. Thompson, R. L., & Margetts, B. M. (1993). Comparison of a food frequency questionnaire with a 10-day weighed record in cigarette smokers. International Journal of Epidemiology, 22, 824–833. Trichopoulos, D., & Willett, D. C. (1996). Nutrition and cancer [Special issue]. Cancer Causes and Control, 7, 3–180. Weinstein, N. D. (1987). Unrealistic optimism about susceptibility to health problems: Conclusions from a community-wide sample. Journal of Behavioral Medicine, 10, 481–500.