The effectiveness of a brief online dietary feedback intervention on reducing adults’ discretionary choice intake

The effectiveness of a brief online dietary feedback intervention on reducing adults’ discretionary choice intake | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effectiveness of a brief online dietary feedback intervention on reducing adults’ discretionary choice intake Joyce Haddad, Gilly A Hendrie, Rebecca K Golley This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7647393/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study evaluated the effectiveness of a brief online dietary feedback intervention designed to reduce adults’ intake of discretionary choices by incorporating tailored nutrition message frames and behaviour change techniques (BCTs). A total of 3,453 adults enrolled online, and 1,441 completed the follow-up surveys. Participants were randomised to receive either two emails with tailored nutrition message frames and enhanced behavioural support (delivered through nine embedded BCTs, such as goal setting and action planning), or two emails with generic nutrition messages without additional support. The primary outcome was daily servings of discretionary choices (energy-dense, nutrient-poor foods and beverages), and secondary outcomes included predictors of intake reduction. No significant difference in discretionary choice intake was observed between the groups (3.2 ± 0.13 vs 3.1 ± 0.12 servings, p = 0.49). However, lower baseline diet quality was a significant predictor of a one serving or more reduction in discretionary choice intake (OR 1.57, 95% CI [1.47, 1.68], p < 0.001). These findings suggest that tailoring message framing based on intention, even when combined with established BCTs, may not enhance dietary outcomes in motivated populations. Future interventions may be more effective if they focus on less motivated individuals with lower baseline diet quality and explore alternative approaches to message tailoring. eHealth discretionary foods and beverages nutrition behavior change Figures Figure 1 Figure 2 Introduction Unhealthy foods and beverages are generally described as those that are energy dense, nutrient poor, high in fat, added sugars and/or salt. ( 1 ) The intake of these foods is associated with greater risk of non-communicable diseases such as cardiovascular disease, ( 2 ) obesity ( 3 ) and all-cause mortality. ( 4 ) The terminology used to describe these foods is different around the world, but the pattern of consumption is similar. Reports show that adults in the United States of America ( 5 ) , Scotland ( 6 ) and Mexico, ( 7 ) consume between 19 and 28% of their energy intake from these foods. In Australia, the dietary guidelines use the term “discretionary choices”. Data show discretionary choice intake contributes up to 35% of Australian adults’ daily energy intake ( 8 ) and is the poorest performing area of compliance with the national dietary guidelines for nearly 80% of Australians. ( 9 ) Overconsumption of discretionary choices is negatively impacting diet quality and as such is a priority for intervention. ( 9 ) Online diet quality assessment tools have been developed worldwide ( 10 – 14 ) with some providing brief feedback ( 15 ) or behavioral support ( 16 ) to improve overall diet quality or diet scores at scale. In many of these tools, feedback is generated from participants’ dietary intake data, typically targeting the lowest-scoring components of their diet. For example, individuals with low fruit intake may receive messages encouraging fruit consumption. While these tools are increasingly used, the effectiveness of the feedback messages themselves is often not evaluated ( 10 , 11 ) or on evaluation, had a modest effect on diet quality. ( 13 , 17 – 19 ) For the purpose of this study, the focus will be on feedback given about discretionary choices, as this is the dietary component known to be poorly aligned with national guidelines in the general population. ( 9 ) Current dietary feedback interventions typically provide generic messaging based on national guidelines, focusing on what dietary behaviors need to change to improve diet quality. ( 10 , 11 ) However, how to effectively communicate nutrition feedback to promote change in dietary behavior has not been thoroughly explored. Message framing, which involves stressing either positive or negative health outcomes, has been linked to behavior change in broader health contexts ( 20 , 21 ) but remains underexplored in the context of dietary behavior. ( 22 , 23 ) Additionally, providing social comparisons in feedback, such as highlighting the dietary behaviors of people with similar characteristics, could further influence dietary behaviors. ( 24 , 25 ) Despite the potential of these approaches, no studies have compared the effect of framing a message positively, negatively or by using social comparisons, on diet quality. ( 26 ) Given that message framing has been associated with behavior change at a population level, ( 27 ) there is scope to understand whether delivering feedback using tailored nutrition message frames ( 28 , 29 ) could improve the effects of existing interventions. The message framing literature suggests that the effectiveness of a message frame may depend on individual characteristics, including emotional response, ( 30 ) motivational orientation, ( 31 ) or baseline intention to change behavior ( 31 – 34 ) . While intention is a known predictor of dietary change ( 35 ) , it can also serve as a practical proxy for assessing message responsiveness. As intention is easily measurable, tailoring feedback based on intention ratings offers a feasible approach for large-scale delivery. ( 33 ) There is merit in testing whether tailoring message framing based on participants’ immediate intention response to different messages could improve the effect of dietary feedback. Specific behavior change techniques (BCTs) have been recommended to enhance the effectiveness of online dietary interventions. ( 15 , 16 , 19 , 36 ) The most common BCTs—such as information about health consequences, instruction on how to perform a behavior, action planning, feedback on behavior, and social comparison—have been linked to improved dietary behaviors in systematic reviews. ( 15 , 19 ) Incorporating tailored nutrition message frames within a brief online dietary feedback intervention, alongside BCTs for enhanced behavioral support, could be a novel strategy to improve diet quality outcomes. While many could benefit from interventions to reduce discretionary intake, ( 37 ) some individuals respond better than others to nutrition advice. ( 38 ) Studies show that sex and age predict dietary behavior ( 39 – 43 ) but identifying other characteristics that predict responses to such interventions could provide greater support for those who need it the most. In summary, well-designed feedback messaging interventions, tested using randomised controlled trials (RCT) with more support for behavior change, are needed. ( 19 ) Therefore, this study aimed to design and test the effectiveness of an online dietary feedback intervention with tailored nutrition message frames and enhanced behavioral support to reduce adults’ discretionary choice intake; and determine the demographic, anthropometric, behavioral and psychosocial characteristics that predict an improvement in discretionary choice intake. Methods This section was prepared using the CONsolidated Standards of Reporting Trials (CONSORT) 2010 statement for reporting parallel group randomized trials. ( 44 ) STUDY DESIGN This study was a 28-day two-armed parallel RCT, designed to deliver a tailored nutrition message frame in a brief online intervention and to test its effectiveness against that of a generic nutrition message (control intervention). Ethics approval was received from the CSIRO Low Risk Health & Medical Research Ethics Committee (2019_051_LR) and reciprocal ethics was approved by the Flinders University Social and Behavioural Research Ethics Committee (OH-00224) in August 2019. The trial was registered on the Australian New Zealand Clinical Trials Registry (ACTRN12619001202156) and approved on 28 August 2019. PARTICIPANTS Participants were included in the study if they reported they were at least 18 years old; residing in Australia; not purposely avoiding major food groups (whole grains, fruit, vegetables, dairy and/or alternatives, and meat and/or alternatives); having internet access; and having good spoken/written English language skills. Data for the study were collected from 8 September to 23 December 2019 using the online software program Alchemer ( https://www.alchemer.com/ ), previously known as SurveyGizmo. Recruitment for the study was conducted through paid advertisements using social media and via a database of volunteers. An incentive was offered in the form of a draw to win one of 30 gift vouchers to the value of AU $ 100. RANDOMIZATION AND BLINDING Individuals who were eligible to participate and provided consent online were randomised into the RCT through a survey-generated randomisation sequence using an A/B block design with a 1:1 allocation. Participants were blinded to intervention group allocation. INTERVENTION DESIGN At baseline, all participants reported their intention to change discretionary choice intake for the next 28 days and self-reported baseline dietary intake through a short food survey ( 45 ) . All participants were asked to report demographic and anthropometric measures, and their best contact email address to receive intervention content. Participants assigned to the tailored intervention group completed a pre-intervention message preference task, which involved viewing 4 different nutrition message frames in random order and rating their intention to reduce discretionary choice intake after each. The 4 intention scores for each individual were ranked, and the message frame with the highest intention score was then used as the tailored message in the RCT. This method assumed that all participants had some intention to change behaviour; and even if there were low-intention participants, the task still allowed for identification of the most persuasive message relative to others, which could serve as a starting point for engagement. Figures outlining the process of the pre-intervention message preference task which was followed by the RCT are found in the Supplementary File. Based on the Regulatory Focus Theory, ( 46 ) the Social Norms Theory, ( 24 , 25 ) and evidence from message framing literature, ( 25 , 47 – 49 ) four nutrition message frames were selected for testing in the pre-intervention message preference task. These frames were: (1) positive gain-framed (emphasising benefits of reducing discretionary choices), (2) negative loss-framed (highlighting risks of not reducing discretionary choices), (3) descriptive majority norm (what most people like you do), and (4) descriptive minority norm (what fewer people like you do). Full message texts can be found in the Supplementary File. The choice to test these messages was informed by prior experimental studies that reported differential effects of message framing based on behavioural outcomes and participant-level moderators such as intention. ( 31 – 34 ) This may indicate a certain type of nutrition message frame may be effective for one individual but not another. Further, no studies have examined the effects of positive, negative alongside descriptive norm messages in a single design, underscoring the value of comparing these approaches within the same intervention context. In addition to the type of message frame delivered, the content of the emails differed between the intervention groups. The tailored intervention group received the tailored nutrition messages with enhanced behavioural support using extra BCTs. These included goal-setting (reporting the intention), self-monitoring (reporting dietary data), prompts/cues (emailing the message again half way through the intervention); while the following BCTs were delivered through the extra tips inside the intervention emails: social support, instruction on how to perform a behaviour, information about emotional consequences, monitoring of emotional consequences, behavioural substitution, and avoidance/reducing exposure to cues for the behaviour (Supplementary File). BCTs were used according to the techniques and definitions listed in the 93-item Behaviour Change Taxonomy v1 ( 50 ) . The number and type of BCTs delivered were informed from recommendation of previous systematic reviews in the field of online nutrition interventions ( 15 , 16 , 19 , 36 ) . These BCTs were operationalised via tailored email messages sent on Day 1 and Day 14 of the 28-day intervention period. The control group did not conduct the pre-intervention message preference task and received a generic nutrition message based on the standard practice of current dietary feedback interventions (see Supplementary File for the full message). The control emails included three BCTs inherently present in the intervention design. These were goal setting, because participants reported an intention to reduce discretionary choice intake over a set period; self-monitoring of behaviour with self-reported diet intake data; and the use of prompts/cues in the half-way email reminder. All participants received an email on day one and day 14. On day 28, all participants received an email with a unique link to complete the follow-up survey. This consisted of follow-up measures of intention to change discretionary choice intake and dietary intake using the short food survey. MEASURES Discretionary choice intake using the Short Food Survey. The short food survey was used to collect dietary consumption data on discretionary choice intake. In brief, the survey is a 38-item self-reported measure of individual dietary intake, developed for the Australian population, and provides estimates of diet quality for adults ( 45 ) . The survey asks individuals to report their usual dietary consumption patterns, such as the frequency and quantity of core food group servings (grains, fruit, vegetables, meat and alternatives, and dairy and alternatives) and discretionary choices (e.g. cakes and biscuits, chocolate and confectionary, takeaway foods, savoury pies and pastries, sugar-sweetened beverages, and alcohol) consumed. Individuals are also asked to report the quality of core foods (frequency of whole grain and reduced fat dairy) and the variety of intake within core food groups. These components were individually scored and summed to provide an overall diet score, ranging from 0–100, where a higher score reflects higher diet quality defined as greater compliance with the Australian Dietary Guidelines ( 10 ) . Within this survey, participants reported the frequency and amount of discretionary choice intake (11 items) in servings, by each day, each week or each month. The total servings of discretionary choices was calculated and adjusted in order to address self-report bias, ( 51 ) and was reported at baseline and at follow-up. In the present study, although participants completed a validated short food survey, the intervention feedback was not tailored based on individual responses. Instead, messages focused exclusively on discretionary choice intake, based on existing evidence that this is a consistently low-scoring component of diet quality in the general Australian population. ( 8 , 9 ) Intention to reduce discretionary choice intake questionnaire. Participants were asked to complete a questionnaire with 3 items at baseline and follow-up. Using a visual analogue scale, participants rated, from ‘strongly disagree’ (= 1) to ‘strongly agree’ (= 100), the following statements: ‘I expect to—’, ‘I want to—’ and ‘I intend to—’ followed by ‘eat less discretionary choices at meal and snack times, each day for the next month’. This questionnaire was informed by theory and research ( 33 , 35 , 52 ) . Demographic and anthropometric characteristics. Demographic and anthropometric measures were self-reported at baseline. Information on gender (male or female), birth year, height (cm), weight (kg) and postcode were collected. Participants’ age was calculated, based on which they were categorised into 4 groups. Body Mass Index (BMI) was calculated, and participants were categorised into 4 weight status groups. Socio-economic status was assessed using the Socio-Economic Indexes for Areas (SEIFA) which is a measure of geographical socio-economic status derived using postcode of residence ( 53 ) . Area-level disadvantage was divided into quintiles, ranging from the most disadvantaged (quintile 1) to the least disadvantaged (i.e. most affluent—quintile 5). Where there were category sample numbers that comprised less than 2% of the overall sample, the results were not shown. STATISTICAL ANALYSIS Based on prior studies evaluating brief dietary interventions with tailored feedback, a small but meaningful reduction in discretionary choice intake, ranging from 0.25 to 0.30 servings, was considered a plausible effect size between intervention and comparison groups. ( 15 , 54 ) Accordingly, a priori power calculations indicated that a sample range of 732 to 1,430 participants would give 80% power to detect a small effect size at a significance level of 0.05. An additional 25% accounted for potential participant attrition, resulting in a sample size estimate of 915 to 1,788 participants. Means and standard deviations (SD) were presented for normally distributed data, whereas median ( Mdn ) and interquartile ranges (IQR) were presented for data not normally distributed. Categorical data were presented as percentages. Descriptive analysis, chi-square tests for categorical variables and t -tests for continuous variables were used to check for differences between intervention groups. Significance was set at p < 0.05. To evaluate the main effects of the intervention and between-group effects, analysis of covariance (ANCOVA) was used ( 55 ) . Baseline discretionary choice intake was used as a covariate. Logistic regression analyses were employed to examine the participant characteristics that predicted the two exploratory outcomes. The chi-square values from the Omnibus Tests of Model Coefficients, together with the 2-log likelihood, Cox and Snell R Square and Nagelkerke R squared values were examined to determine the model goodness of fit ( 56 ) . Sensitivity analyses were conducted to determine the level of impact of extreme outliers on the results. Extreme outliers were identified and removed if the change in the reported discretionary choice intake from baseline to post-intervention was 3 or more standard deviations from the mean, or if they were deemed biologically implausible (i.e. a value larger than a 10-serving decrease or increase in intake). All analyses were conducted using SPSS statistical software package, Version 25 (IBM SPSS Statistics [computer program]. Version 25. Armonk, NY: IBM Corp; 25 August 2017). Results PARTICIPANT CHARACTERISTICS Of the 5,353 individuals who enquired about the study, 3,453 (64.5%) consented and were randomly assigned to either the tailored intervention group (n = 1,745) or the control group (n = 1,708). After excluding those who did not complete the baseline questionnaire, 2,750 participants were eligible for follow-up. Among these, 1,174 participants were lost to follow-up, with higher attrition in the intervention group (n = 630) compared to the control group (n = 544). After excluding participants who did not complete all follow-up and process evaluation measures, the final analytic sample comprised 1,441 participants, representing a complete case retention rate of 52.4% from the eligible baseline sample (1,441/2,750), and 41.7% retention from the total consented and randomized sample (1,441/3,453). The final participant sample was 77.3% female, with a mean age of 50.8 ± 16.0 years (Table 1 ). According to their BMI, participants were evenly distributed across the healthy weight, overweight and obesity groups (33.3, 33.7 and 31.9% respectively). All Australian states and territories were represented in the sample. Between the intervention groups, there were no statistically significant differences in the distribution of gender, weight status, age and socio-economic (SEIFA) groups. Baseline intention to change discretionary choice intake had a median score of 80.7 out of 100 (IQR = 66.3–92.2), and the mean diet score for the total sample was 54.4 ± 10.5, out of 100. Table 1 Baseline characteristics of participants ( N = 1,441) who finished the intervention presented as n (%) unless otherwise indicated. Characteristic Total sample ( N = 1,441) Control group ( n = 784) Intervention group ( n = 657) Sex Male 327 (22.7) 183 (23.3) 144 (21.9) Female 1114 (77.3) 601 (76.7) 513 (78.1) Age (years) a 50.8 (16.0) 51.6 (16.0) 49.9 (15.8) Age group (years) 18–30 187 (13) 99 (12.6) 88 (13.4) 31–50 484 (33.6) 250 (31.9) 234 (35.6) 51–70 628 (43.6) 349 (44.5) 279 (42.5) 71+ 142 (9.9) 86 (11.0) 56 (8.5) BMI (kg/m 2 ) a 28.2 (6.3) 28.5 (6.6) 27.8 (5.9) Weight status category Underweight - - - Healthy weight 480 (33.3) 248 (31.6) 232 (35.3) Overweight 485 (33.7) 264 (33.7) 221 (33.6) Obesity 460 (31.9) 262 (33.4) 198 (30.1) State of residence New South Wales 166 (11.5) 90 (11.5) 76 (11.6) Queensland 92 (6.4) 51 (6.5) 41 (6.2) Australian Capital Territory 25 (1.7) 15 (1.9) 10 (1.5) Northern Territory 7 (0.5) 5 (0.6) 2 (0.3) Tasmania 28 (1.9) 18 (2.3) 10 (1.5) Victoria 121 (8.4) 67 (8.5) 54 (8.2) Western Australia 54 (3.7) 29 (3.7) 25 (3.8) South Australia 948 (65.8) 509 (64.9) 439 (66.8) Socio-economic status 1 (most disadvantaged) 170 (11.8) 101 (12.9) 69 (10.5) 2 189 (13.1) 96 (12.2) 93 (14.2) 3 313 (21.7) 179 (22.8) 134 (20.4) 4 350 (24.3) 185 (23.6) 165 (25.1) 5 (least disadvantaged) 419 (29.1) 223 (28.4) 196 (29.8) Intention (1–100) b 80.7 (66.3–92.2) 80.7 (65.3–93.3) 80.7 (67.0–91.7) Diet score (out of 100) a 54.4 (10.5) 54.5 (10.4) 54.2 (10.6) Notes: a Reported as mean (SD). b Reported as median and interquartile range. Age , calculated by subtracting year reported (2019) from participant-reported birth year. BMI , Body Mass Index (kg/m 2 ) calculated from participant-reported height (cm) and weight (kg). Weight status category is according to Body Mass Index (BMI) (kg/m2); Underweight: 30 kg/m 2 . Socio-economic status as indicated by national Socio-Economic Indexes For Areas (SEIFA) of relative advantage and disadvantage represented by matching participant-reported postcode 49 . INTERVENTION EFFECT The mean reported discretionary choice intake at baseline for the whole sample was 4.2 ± 3.9 servings. The tailored intervention group had a significantly higher reported discretionary choice intake at baseline than did the control (4.5 ± 4.4 and 4.0 ± 3.5, p = 0.015). Within the tailored intervention group ( n = 657), 30.3% received the positively framed message as their intervention, 27.7% received the negatively framed message, 18.3% received the majority norm message and 23.7% received the minority norm message. A one-way ANOVA showed no significant differences between the different tailored message groups for discretionary choice intake post-intervention ( p = 0.695). When adjusted for baseline discretionary choice intake, the ANCOVA model showed that the intervention did not have a significant effect on discretionary choice intake. The adjusted discretionary choice intake mean was 3.2 servings for the tailored intervention group and 3.1 servings for the control group (adjusted mean serving difference between groups = 0.13, p = 0.49) (Fig. 2 ) PREDICTORS OF INTERVENTION EFFECT As there were no significant differences between intervention and control groups, the sample was combined to determine the demographic, anthropometric, behavioural and psychosocial characteristics that predict an improvement in discretionary choice intake. The proportion of participants who reduced their discretionary choice intake by one serving or more was described relative to the observed proportion of participants who did not achieve this reduction. The different proportions of participants who reduced (or not) their discretionary choice intake, by characteristic, are found in the supplementary file. The associated odds ratios (OR) for reducing discretionary choice intake by one serving or more after the intervention are shown in Table 2 . The odds of reducing discretionary choice intake were higher for participants who had a higher baseline discretionary choice intake. For every additional serving of discretionary choices consumed at baseline, participants were 57% more likely to reduce their intake by one serving or more (OR 1.57, 95% CI [1.47, 1.68], p < 0.001). Table 2 Multivariate adjusted odds ratios of the sample (N = 1,441) who reduced discretionary choice (DC) intake by one serving or more after the brief online 28-day intervention Characteristics Odds of reducing DC intake by one serving or more OR 95% CI P Baseline DC intake a 1.57 1.47, 1.68 < 0.001 Intervention Group % Control (ref) 1 - - Tailored 0.86 0.67, 1.11 0.260 Sex % Male (ref) 1 - - Female 1.29 0.945, 1.759 0.108 Age group % 18–30 (ref) 1 - - 31–50 0.83 0.55, 1.25 0.375 51–70 1.03 0.68, 1.56 0.889 71+ 1.15 0.66, 2.01 0.622 Weight status category % Healthy weight (ref) 1 - - Underweight - - - Overweight 1.14 0.84, 1.55 0.395 Obesity 1.02 0.73, 1.41 0.911 Socio-economic status (SEIFA Quintile) % 1 (most disadvantaged) (ref) 1 - - 2 0.65 0.40, 1.07 0.091 3 0.67 0.43, 1.05 0.078 4 0.90 0.59, 1.38 0.638 5 (least disadvantaged) 0.63 0.41, 0.95 0.030 Intention tertiles (out of 100; range) % Low (1.0–71.7) (ref) 1 - - Med (71.8–88.3) 1.47 1.08, 2.01 0.015 High (88.4–100.0) 1.41 1.02, 1.93 0.035 Diet score quintiles (out of 100; range) % 1 (21.1–45.5) (ref) 1 2 (45.6–51.4) 0.71 0.49, 1.04 0.078 3 (51.5–56.6) 0.74 0.50, 1.08 0.113 4 (56.7–63.2) 0.90 0.60, 1.33 0.581 5 (63.3–90.6) 0.51 0.33, 0.79 0.003 Note: a Continous variable for servings of discretionary choices (DC). Ref i ndicates reference variable. Age group categories consistent with nutrient reference values ( 78 ) . Weight status categories are according to Body Mass Index (BMI) (kg/m2); Underweight: 30 kg/m 2 . Values for underweight sample (n = 16) not shown. Socio-Economic Status is indicated by national Socio-Economic Indexes for Areas (SEIFA) of relative advantage and disadvantage represented by matching participant-reported postcode ( 53 ) . P- values were derived from Wald test. Values in bold font indicate significance at < 0.05. Model fit statistics : X 2 (25, N = 1441) = 427.72, p < 0.001 Cox and Snell R 2 = 25.7% and Nagelkerke R 2 = 34.7% The odds of reducing discretionary choice intake were 41% higher for participants with higher baseline levels of intention compared to those with low intention (OR 1.41, 95% CI [1.02, 1.93], p = 0.035). The odds of reducing discretionary choice intake were lower for participants who reported living in the least disadvantaged areas of socio-economic status—SEIFA quintile 5—than for those participants who reported living in the most disadvantaged areas (OR 0.63, 95% CI [0.41, 0.95], p = 0.030). Participants with a higher diet score at baseline (quintile 5 of diet score) had lower odds reducing discretionary choice intake relative to participants with a lower diet score at baseline (quintile 1) (OR 0.51, 95% CI [0.33, 0.79], p = 0.003). The characteristics of participants reducing their discretionary choice intake by one serving or more did not statistically differ by other demographic variables included in this analysis, such as sex and age, nor by weight status and intervention group allocation. Discussion This study tested the effectiveness of a brief online dietary feedback intervention that incorporated theoretically grounded message framing and tailoring based on participant intention, alongside enhanced behavioral support techniques, to reduce adults’ discretionary choice intake. Drawing on frameworks such as Regulatory Focus Theory and Social Norms Theory, participants were assigned to receive messages framed as positive, negative, majority norm, or minority norm, with tailoring based on their intention response. The study also aimed to identify participant characteristics associated with a greater reduction in discretionary choice intake. Results showed that tailoring brief feedback messages on intention and including enhanced behavioral support, did not lead to greater reductions in discretionary choice intake compared to generic messages. However, certain baseline characteristics predicted more substantial dietary improvement. Participants with lower diet quality, higher baseline intention to reduce discretionary choices, and those living in more socioeconomically disadvantaged areas were more likely to report a reduction of one serving or more in daily discretionary intake. While this intervention combined two evidence-based components—tailored message framing and enhanced behavioral support—the null findings suggest that the additive effect of combining these may not yield greater behavior change in already motivated individuals. The behavioral support elements, such as goal setting, self-monitoring and the use of prompts, may have been equally effective in both groups due to the act of completing the diet survey and receiving any form of feedback. ( 16 , 36 ) Self-monitoring has consistently shown positive effects in brief interventions, ( 15 , 57 ) and completing a dietary questionnaire may, in itself, have resulted in a simple but intensive act of reflection on dietary behavior. ( 57 ) The lack of intervention effect may also be due to the motivated participants in our study. Participating in a nutrition intervention could lead to a drive for behavior change, and when receiving a brief intervention, any message may be just as beneficial as more enhanced messages. ( 58 ) For our motivated sample, a brief intervention might have been sufficient. Future interventions should explore whether greater dose, frequency, or dynamic tailoring of support might enhance the effect of message framing, particularly in populations with lower intention. ( 59 ) The current study’s brief intervention provided tailored nutrition message frames based on participants’ intention ratings in response to different messages, identifying which frame elicited the highest intention to reduce discretionary choice intake. While prior studies suggest that baseline intention can moderate the effectiveness of framed messages, ( 31 – 34 ) the intention ratings in this study served as a message preference tool, not a proxy for readiness to change. Given the high baseline intention in our sample, it is possible that participants were equally responsive to any message, reducing the potential benefit of tailoring. Our findings are consistent with previous research evaluating the effects of brief dietary feedback interventions. The Food4Me study compared generic feedback based on population-level guidelines with feedback personalised to baseline dietary intake and found only a modest reduction in energy intake from discretionary foods in the personalised group after six months (31.2 ± 0.59% vs 32.7 ± 0.59. (18) A more recent study evaluated increasing levels of tailored feedback, where the control group received brief tailored feedback after completing a short dietary assessment survey, and the intervention groups receiving more comprehensive tailored feedback. ( 13 ) Results showed that the within- and between-group results were not significant. ( 13 ) These findings mirror our own, suggesting that tailoring dietary feedback—even when based on individual intake data—may offer limited added benefit in reducing discretionary choice intake. The evidence on the effect of current dietary feedback interventions on discretionary choice intake is also limited. Research from Australia ( 60 ) and the United Kingdom ( 14 ) found no significant differences in energy intake from fats, saturated fats, or alcohol between groups receiving generic or comprehensive dietary feedback. A 2024 meta-analysis on personalised feedback interventions indicated no significant differences in fat intake between intervention and comparator groups. ( 61 ) The mixed evidence for reducing discretionary choice intake across previous dietary feedback interventions may reflect the heterogeneity of foods in this category. ( 62 ) Future research may benefit from specifying particular discretionary foods or beverages in feedback messages to improve salience and relevance. ( 63 ) Regarding key predictors of change in discretionary choice intake, a lower overall diet quality score and a higher intake of discretionary choices at baseline, were the characteristics of participants who achieved a one serving or more reduction in discretionary choice intake after the intervention. This finding is common in previous studies. ( 64 , 65 ) A regression towards the mean could explain our results. Despite statistically adjusting for the baseline dietary intake in this study, ( 55 ) the participants who had higher baseline discretionary choice intake may have had a larger scope to reduce their intake. Our study also found that participants who reported living in more disadvantaged areas were more likely to reduce discretionary choice intake. Cross-sectional studies have consistently found that higher socio-economic status predicts healthier dietary behaviors. ( 37 , 40 , 66 – 68 ) This can be interpreted as those living in low-socio economic areas may have a larger scope to reduce discretionary choice intake after an intervention. More longitudinal research is needed to better understand how socio-economic characteristics interact with intervention exposure to influence change in dietary behavior. IMPLICATIONS FOR RESEARCH, PRACTICE AND POLICY The implications of our findings could be important for future dietary feedback interventions wanting to target specific populations. Future research could explore targeting an intervention with enhanced behavioral support to those with lower baseline diet quality or lower intention. In those groups, different tailoring methods could be compared to further improve the effect of brief online dietary feedback interventions. For example, tailoring could be in relation to the amount and type of behavioral support that is provided, the frequency of brief feedback, and the messages specifying the discretionary food or beverage that needs to be consumed less of. Recruiting a more socioeconomically and motivationally diverse sample, including individuals with lower baseline intention to change and those from more disadvantaged areas, is warranted in future trials. These groups showed greater potential for dietary improvement in the study and may benefit most from targeted support to improve diet quality. STRENGTHS AND LIMITATIONS This study’s key strength is its novel approach of incorporating tailored nutrition message frames based on individual levels of intention, into a brief online feedback intervention, using an RCT design. The robust RCT design was recognised through multiple sensitivity analyses (data not shown), which demonstrated that the pattern of results remained consistent regardless of the removal of extreme outliers, and by adjusting for baseline measures. The aim of the intervention, to reduce discretionary choice intake, also appealed to many people as evidenced by the large number of participants who enquired about the intervention ( N = 5,353) and completed the study ( N = 1,441). The RCT design and the moderate level of retention (30%) also optimised internal validity. Regarding the limitations, having a highly motivated sample was a key limitation of this intervention. ( 59 ) Participants voluntarily signed up to the intervention and may have already had an intention to act on the messages to improve their dietary behaviors ( 69 ) . Volunteer bias could have been a reason for the null findings between intervention groups ( 70 ) . Another important consideration is the potential for false-positive results due to Type I error. The number of participants recruited for this study met the top range of the sample size calculation, increasing the statistical power of the analysis. To minimise statistical bias, effect sizes were calculated, to aid the interpretation of the magnitude of differences ( 71 , 72 ) . Since only the participants who completed the study were considered for analysis, the potential for selection bias must be acknowledged. An intention-to-treat analysis may have yielded an unbiased estimate of the efficacy of the intervention on discretionary choice intake; however, this analytical approach requires complete outcome data ( 73 – 75 ) . Twenty-three participants were not included in the final analysed sample due to being non-completers (Fig. 1 ), and it is unlikely that adding these datapoints would have made a significant difference in the results. Despite recruitment strategies to target more males, the final sample was not representative of the Australian population ( 76 ) . This sample overrepresented females, those in the 31–70 age group, those in higher socio-economic areas of advantage and those residing in South Australia. Therefore, caution must be taken in generalising these results. While message frames messages were developed based on established theoretical constructs and prior framing studies, they were not formally pre-tested or validated to confirm that participants consistently perceived them as intended. As such, it is unclear whether the null findings reflect a true lack of effect from tailoring message frames based on intention, or whether participants did not perceive the framing distinctions strongly enough to influence behavior. Finally, data were self-reported, which may have resulted in reporting bias, social bias and measurement error ( 77 ) . However, the anonymity ensured in online questionnaire completion may have reduced perceived social judgement and may have offset the potential risk of social desirability bias. Conclusions Brief online interventions may support reductions in discretionary choice intake; however, tailoring nutrition message framing based on participants’ preferred message, selected in relation to their intention to reduce intake, while providing additional behavioural support, did not enhance effectiveness in this motivated sample. Given that high baseline intention predicted dietary improvement, future research should prioritise recruiting a less motivated sample, who may be more responsive to intervention support. For these groups, tailoring the type and intensity of behavioral support, as well as providing more specific and actionable feedback (e.g., naming particular discretionary foods to reduce), may improve the effect of brief online dietary feedback interventions. Abbreviations BCTs – Behaviour Change Techniques RCT – Randomised Controlled Trial CONSORT – CONsolidated Standards of Reporting Trials BMI – Body Mass Index SEIFA – Socio-Economic Indexes for Areas SD – Standard Deviation IQR – Interquartile Range ANCOVA – Analysis of Covariance OR – Odds Ratio Declarations Author contributions - JH led the research, under the supervision of GAH and RKG. GAH and RKG developed the questions and scoring algorithm of the validated survey. JH collected and analyzed the data. JH drafted the first version of the manuscript. All authors reviewed and provided critical input into drafts and approved of the final manuscript. Reprint contact – the corresponding author Acknowledgements - JH was supported by a Flinders University Research Scholarship and a top-up scholarships funded by Flinders University, Commonwealth Scientific and Industrial Research Organisation (CSIRO), the HDA & Channel 7 Children's Research Foundation, and the Australian Commonwealth Research Training Program Scholarship. We would like to acknowledge Dr. Kacie Dickinson for her support in the conception of the study and the participants who volunteered their time. Funding/financial disclosures - This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Conflict of interest disclosures - There are no conflicts to report. Consent to Publish declaration - Not applicable. 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1","display":"","copyAsset":false,"role":"figure","size":176671,"visible":true,"origin":"","legend":"\u003cp\u003eCONsolidated Standards of Reporting Trials (CONSORT) flowchart of progress of participants through the randomised controlled trial from recruitment to analysis, by group allocation to either the tailored or the control group. SFS: Short Food Survey.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7647393/v1/99d6255d1c8b7496506b1293.png"},{"id":93063248,"identity":"d745a9f7-4d91-4da1-bb2d-2aa488d2a191","added_by":"auto","created_at":"2025-10-08 16:38:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56886,"visible":true,"origin":"","legend":"\u003cp\u003eMean (SD) and adjusted mean (SE) of discretionary choice intake in servings pre and post intervention, by control (n = 784) and tailored (n = 657) groups. 1 Analysis of covariance with baseline intake as a covariate was used to calculate between-intervention group effect.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7647393/v1/48b8cc46a1d9944568472e57.png"},{"id":93064407,"identity":"377b0995-6d04-42ee-9703-ba839d90366c","added_by":"auto","created_at":"2025-10-08 16:41:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1427451,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7647393/v1/9d190695-9798-4013-8c8e-0417faa9bddb.pdf"},{"id":93062808,"identity":"31afc544-9f78-4c33-84db-a561ca8f2ebf","added_by":"auto","created_at":"2025-10-08 16:38:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":379755,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7647393/v1/2f66ad7e6370ba5c261a5de7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effectiveness of a brief online dietary feedback intervention on reducing adults’ discretionary choice intake","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnhealthy foods and beverages are generally described as those that are energy dense, nutrient poor, high in fat, added sugars and/or salt. \u003csup\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/sup\u003e The intake of these foods is associated with greater risk of non-communicable diseases such as cardiovascular disease, \u003csup\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/sup\u003e obesity \u003csup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/sup\u003e and all-cause mortality. \u003csup\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/sup\u003e The terminology used to describe these foods is different around the world, but the pattern of consumption is similar. Reports show that adults in the United States of America \u003csup\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/sup\u003e, Scotland \u003csup\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/sup\u003e and Mexico, \u003csup\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/sup\u003e consume between 19 and 28% of their energy intake from these foods. In Australia, the dietary guidelines use the term \u0026ldquo;discretionary choices\u0026rdquo;. Data show discretionary choice intake contributes up to 35% of Australian adults\u0026rsquo; daily energy intake\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/sup\u003e and is the poorest performing area of compliance with the national dietary guidelines for nearly 80% of Australians.\u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e Overconsumption of discretionary choices is negatively impacting diet quality and as such is a priority for intervention.\u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOnline diet quality assessment tools have been developed worldwide\u003csup\u003e(\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e with some providing brief feedback \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/sup\u003e or behavioral support\u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/sup\u003e to improve overall diet quality or diet scores at scale. In many of these tools, feedback is generated from participants\u0026rsquo; dietary intake data, typically targeting the lowest-scoring components of their diet. For example, individuals with low fruit intake may receive messages encouraging fruit consumption. While these tools are increasingly used, the effectiveness of the feedback messages themselves is often not evaluated\u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e or on evaluation, had a modest effect on diet quality. \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e For the purpose of this study, the focus will be on feedback given about discretionary choices, as this is the dietary component known to be poorly aligned with national guidelines in the general population. \u003csup\u003e(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e Current dietary feedback interventions typically provide generic messaging based on national guidelines, focusing on what dietary behaviors need to change to improve diet quality. \u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/sup\u003e However, how to effectively communicate nutrition feedback to promote change in dietary behavior has not been thoroughly explored. Message framing, which involves stressing either positive or negative health outcomes, has been linked to behavior change in broader health contexts \u003csup\u003e(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/sup\u003e but remains underexplored in the context of dietary behavior. \u003csup\u003e(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/sup\u003e Additionally, providing social comparisons in feedback, such as highlighting the dietary behaviors of people with similar characteristics, could further influence dietary behaviors. \u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e Despite the potential of these approaches, no studies have compared the effect of framing a message positively, negatively or by using social comparisons, on diet quality.\u003csup\u003e(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eGiven that message framing has been associated with behavior change at a population level, \u003csup\u003e(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/sup\u003e there is scope to understand whether delivering feedback using tailored nutrition message frames \u003csup\u003e(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/sup\u003e could improve the effects of existing interventions. The message framing literature suggests that the effectiveness of a message frame may depend on individual characteristics, including emotional response, \u003csup\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/sup\u003e motivational orientation, \u003csup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/sup\u003e or baseline intention to change behavior \u003csup\u003e(\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e. While intention is a known predictor of dietary change\u003csup\u003e(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/sup\u003e, it can also serve as a practical proxy for assessing message responsiveness. As intention is easily measurable, tailoring feedback based on intention ratings offers a feasible approach for large-scale delivery. \u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/sup\u003e There is merit in testing whether tailoring message framing based on participants\u0026rsquo; immediate intention response to different messages could improve the effect of dietary feedback.\u003c/p\u003e\u003cp\u003eSpecific behavior change techniques (BCTs) have been recommended to enhance the effectiveness of online dietary interventions. \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e The most common BCTs\u0026mdash;such as information about health consequences, instruction on how to perform a behavior, action planning, feedback on behavior, and social comparison\u0026mdash;have been linked to improved dietary behaviors in systematic reviews. \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e Incorporating tailored nutrition message frames within a brief online dietary feedback intervention, alongside BCTs for enhanced behavioral support, could be a novel strategy to improve diet quality outcomes.\u003c/p\u003e\u003cp\u003eWhile many could benefit from interventions to reduce discretionary intake, \u003csup\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/sup\u003e some individuals respond better than others to nutrition advice. \u003csup\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u003c/sup\u003e Studies show that sex and age predict dietary behavior\u003csup\u003e(\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/sup\u003e but identifying other characteristics that predict responses to such interventions could provide greater support for those who need it the most.\u003c/p\u003e\u003cp\u003eIn summary, well-designed feedback messaging interventions, tested using randomised controlled trials (RCT) with more support for behavior change, are needed.\u003csup\u003e(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/sup\u003e Therefore, this study aimed to design and test the effectiveness of an online dietary feedback intervention with tailored nutrition message frames and enhanced behavioral support to reduce adults\u0026rsquo; discretionary choice intake; and determine the demographic, anthropometric, behavioral and psychosocial characteristics that predict an improvement in discretionary choice intake.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis section was prepared using the CONsolidated Standards of Reporting Trials (CONSORT) 2010 statement for reporting parallel group randomized trials. \u003csup\u003e(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSTUDY DESIGN\u003c/h2\u003e\u003cp\u003eThis study was a 28-day two-armed parallel RCT, designed to deliver a tailored nutrition message frame in a brief online intervention and to test its effectiveness against that of a generic nutrition message (control intervention). Ethics approval was received from the CSIRO Low Risk Health \u0026amp; Medical Research Ethics Committee (2019_051_LR) and reciprocal ethics was approved by the Flinders University Social and Behavioural Research Ethics Committee (OH-00224) in August 2019. The trial was registered on the Australian New Zealand Clinical Trials Registry (ACTRN12619001202156) and approved on 28 August 2019.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePARTICIPANTS\u003c/h3\u003e\n\u003cp\u003eParticipants were included in the study if they reported they were at least 18 years old; residing in Australia; not purposely avoiding major food groups (whole grains, fruit, vegetables, dairy and/or alternatives, and meat and/or alternatives); having internet access; and having good spoken/written English language skills. Data for the study were collected from 8 September to 23 December 2019 using the online software program Alchemer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.alchemer.com/\u003c/span\u003e\u003cspan address=\"https://www.alchemer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), previously known as SurveyGizmo. Recruitment for the study was conducted through paid advertisements using social media and via a database of volunteers. An incentive was offered in the form of a draw to win one of 30 gift vouchers to the value of AU\u003cspan\u003e$\u003c/span\u003e100.\u003c/p\u003e\n\u003ch3\u003eRANDOMIZATION AND BLINDING\u003c/h3\u003e\n\u003cp\u003e Individuals who were eligible to participate and provided consent online were randomised into the RCT through a survey-generated randomisation sequence using an A/B block design with a 1:1 allocation. Participants were blinded to intervention group allocation.\u003c/p\u003e\n\u003ch3\u003eINTERVENTION DESIGN\u003c/h3\u003e\n\u003cp\u003eAt baseline, all participants reported their intention to change discretionary choice intake for the next 28 days and self-reported baseline dietary intake through a short food survey \u003csup\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/sup\u003e. All participants were asked to report demographic and anthropometric measures, and their best contact email address to receive intervention content.\u003c/p\u003e\u003cp\u003eParticipants assigned to the tailored intervention group completed a pre-intervention message preference task, which involved viewing 4 different nutrition message frames in random order and rating their intention to reduce discretionary choice intake after each. The 4 intention scores for each individual were ranked, and the message frame with the highest intention score was then used as the tailored message in the RCT. This method assumed that all participants had some intention to change behaviour; and even if there were low-intention participants, the task still allowed for identification of the most persuasive message relative to others, which could serve as a starting point for engagement. Figures outlining the process of the pre-intervention message preference task which was followed by the RCT are found in the Supplementary File.\u003c/p\u003e\u003cp\u003eBased on the Regulatory Focus Theory, \u003csup\u003e(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/sup\u003e the Social Norms Theory, \u003csup\u003e(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/sup\u003e and evidence from message framing literature, \u003csup\u003e(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e)\u003c/sup\u003e four nutrition message frames were selected for testing in the pre-intervention message preference task. These frames were: (1) positive gain-framed (emphasising benefits of reducing discretionary choices), (2) negative loss-framed (highlighting risks of not reducing discretionary choices), (3) descriptive majority norm (what most people like you do), and (4) descriptive minority norm (what fewer people like you do). Full message texts can be found in the Supplementary File. The choice to test these messages was informed by prior experimental studies that reported differential effects of message framing based on behavioural outcomes and participant-level moderators such as intention. \u003csup\u003e(\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e This may indicate a certain type of nutrition message frame may be effective for one individual but not another. Further, no studies have examined the effects of positive, negative alongside descriptive norm messages in a single design, underscoring the value of comparing these approaches within the same intervention context.\u003c/p\u003e\u003cp\u003eIn addition to the type of message frame delivered, the content of the emails differed between the intervention groups. The tailored intervention group received the tailored nutrition messages with enhanced behavioural support using extra BCTs. These included goal-setting (reporting the intention), self-monitoring (reporting dietary data), prompts/cues (emailing the message again half way through the intervention); while the following BCTs were delivered through the extra tips inside the intervention emails: social support, instruction on how to perform a behaviour, information about emotional consequences, monitoring of emotional consequences, behavioural substitution, and avoidance/reducing exposure to cues for the behaviour (Supplementary File). BCTs were used according to the techniques and definitions listed in the 93-item Behaviour Change Taxonomy v1 \u003csup\u003e(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e)\u003c/sup\u003e. The number and type of BCTs delivered were informed from recommendation of previous systematic reviews in the field of online nutrition interventions \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e. These BCTs were operationalised via tailored email messages sent on Day 1 and Day 14 of the 28-day intervention period.\u003c/p\u003e\u003cp\u003eThe control group did not conduct the pre-intervention message preference task and received a generic nutrition message based on the standard practice of current dietary feedback interventions (see Supplementary File for the full message). The control emails included three BCTs inherently present in the intervention design. These were goal setting, because participants reported an intention to reduce discretionary choice intake over a set period; self-monitoring of behaviour with self-reported diet intake data; and the use of prompts/cues in the half-way email reminder.\u003c/p\u003e\u003cp\u003eAll participants received an email on day one and day 14. On day 28, all participants received an email with a unique link to complete the follow-up survey. This consisted of follow-up measures of intention to change discretionary choice intake and dietary intake using the short food survey.\u003c/p\u003e\n\u003ch3\u003eMEASURES\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eDiscretionary choice intake using the Short Food Survey.\u003c/b\u003e The short food survey was used to collect dietary consumption data on discretionary choice intake. In brief, the survey is a 38-item self-reported measure of individual dietary intake, developed for the Australian population, and provides estimates of diet quality for adults \u003csup\u003e(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/sup\u003e. The survey asks individuals to report their usual dietary consumption patterns, such as the frequency and quantity of core food group servings (grains, fruit, vegetables, meat and alternatives, and dairy and alternatives) and discretionary choices (e.g. cakes and biscuits, chocolate and confectionary, takeaway foods, savoury pies and pastries, sugar-sweetened beverages, and alcohol) consumed. Individuals are also asked to report the quality of core foods (frequency of whole grain and reduced fat dairy) and the variety of intake within core food groups. These components were individually scored and summed to provide an overall diet score, ranging from 0\u0026ndash;100, where a higher score reflects higher diet quality defined as greater compliance with the Australian Dietary Guidelines \u003csup\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/sup\u003e. Within this survey, participants reported the frequency and amount of discretionary choice intake (11 items) in servings, by each day, each week or each month. The total servings of discretionary choices was calculated and adjusted in order to address self-report bias,\u003csup\u003e(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/sup\u003e and was reported at baseline and at follow-up.\u003c/p\u003e\u003cp\u003eIn the present study, although participants completed a validated short food survey, the intervention feedback was not tailored based on individual responses. Instead, messages focused exclusively on discretionary choice intake, based on existing evidence that this is a consistently low-scoring component of diet quality in the general Australian population.\u003csup\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIntention to reduce discretionary choice intake questionnaire.\u003c/b\u003e Participants were asked to complete a questionnaire with 3 items at baseline and follow-up. Using a visual analogue scale, participants rated, from \u0026lsquo;strongly disagree\u0026rsquo; (=\u0026thinsp;1) to \u0026lsquo;strongly agree\u0026rsquo; (=\u0026thinsp;100), the following statements: \u0026lsquo;I expect to\u0026mdash;\u0026rsquo;, \u0026lsquo;I want to\u0026mdash;\u0026rsquo; and \u0026lsquo;I intend to\u0026mdash;\u0026rsquo; followed by \u0026lsquo;eat less discretionary choices at meal and snack times, each day for the next month\u0026rsquo;. This questionnaire was informed by theory and research \u003csup\u003e(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographic and anthropometric characteristics.\u003c/b\u003e Demographic and anthropometric measures were self-reported at baseline. Information on gender (male or female), birth year, height (cm), weight (kg) and postcode were collected. Participants\u0026rsquo; age was calculated, based on which they were categorised into 4 groups. Body Mass Index (BMI) was calculated, and participants were categorised into 4 weight status groups. Socio-economic status was assessed using the Socio-Economic Indexes for Areas (SEIFA) which is a measure of geographical socio-economic status derived using postcode of residence \u003csup\u003e(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e. Area-level disadvantage was divided into quintiles, ranging from the most disadvantaged (quintile 1) to the least disadvantaged (i.e. most affluent\u0026mdash;quintile 5). Where there were category sample numbers that comprised less than 2% of the overall sample, the results were not shown.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e\u003cp\u003eBased on prior studies evaluating brief dietary interventions with tailored feedback, a small but meaningful reduction in discretionary choice intake, ranging from 0.25 to 0.30 servings, was considered a plausible effect size between intervention and comparison groups. \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/sup\u003e Accordingly, \u003cem\u003ea priori\u003c/em\u003e power calculations indicated that a sample range of 732 to 1,430 participants would give 80% power to detect a small effect size at a significance level of 0.05. An additional 25% accounted for potential participant attrition, resulting in a sample size estimate of 915 to 1,788 participants.\u003c/p\u003e\u003cp\u003eMeans and standard deviations (SD) were presented for normally distributed data, whereas median (\u003cem\u003eMdn\u003c/em\u003e) and interquartile ranges (IQR) were presented for data not normally distributed. Categorical data were presented as percentages. Descriptive analysis, chi-square tests for categorical variables and \u003cem\u003et\u003c/em\u003e-tests for continuous variables were used to check for differences between intervention groups. Significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eTo evaluate the main effects of the intervention and between-group effects, analysis of covariance (ANCOVA) was used \u003csup\u003e(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003c/sup\u003e. Baseline discretionary choice intake was used as a covariate.\u003c/p\u003e\u003cp\u003eLogistic regression analyses were employed to examine the participant characteristics that predicted the two exploratory outcomes. The chi-square values from the Omnibus Tests of Model Coefficients, together with the 2-log likelihood, Cox and Snell R Square and Nagelkerke R squared values were examined to determine the model goodness of fit \u003csup\u003e(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/sup\u003e. Sensitivity analyses were conducted to determine the level of impact of extreme outliers on the results. Extreme outliers were identified and removed if the change in the reported discretionary choice intake from baseline to post-intervention was 3 or more standard deviations from the mean, or if they were deemed biologically implausible (i.e. a value larger than a 10-serving decrease or increase in intake). All analyses were conducted using SPSS statistical software package, Version 25 (IBM SPSS Statistics [computer program]. Version 25. Armonk, NY: IBM Corp; 25 August 2017).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePARTICIPANT CHARACTERISTICS\u003c/h2\u003e\u003cp\u003eOf the 5,353 individuals who enquired about the study, 3,453 (64.5%) consented and were randomly assigned to either the tailored intervention group (n\u0026thinsp;=\u0026thinsp;1,745) or the control group (n\u0026thinsp;=\u0026thinsp;1,708). After excluding those who did not complete the baseline questionnaire, 2,750 participants were eligible for follow-up. Among these, 1,174 participants were lost to follow-up, with higher attrition in the intervention group (n\u0026thinsp;=\u0026thinsp;630) compared to the control group (n\u0026thinsp;=\u0026thinsp;544). After excluding participants who did not complete all follow-up and process evaluation measures, the final analytic sample comprised 1,441 participants, representing a complete case retention rate of 52.4% from the eligible baseline sample (1,441/2,750), and 41.7% retention from the total consented and randomized sample (1,441/3,453).\u003c/p\u003e\u003cp\u003eThe final participant sample was 77.3% female, with a mean age of 50.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0 years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to their BMI, participants were evenly distributed across the healthy weight, overweight and obesity groups (33.3, 33.7 and 31.9% respectively). All Australian states and territories were represented in the sample. Between the intervention groups, there were no statistically significant differences in the distribution of gender, weight status, age and socio-economic (SEIFA) groups. Baseline intention to change discretionary choice intake had a median score of 80.7 out of 100 (IQR\u0026thinsp;=\u0026thinsp;66.3\u0026ndash;92.2), and the mean diet score for the total sample was 54.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5, out of 100.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1,441) who finished the intervention presented as \u003cem\u003en\u003c/em\u003e (%) unless otherwise indicated.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTotal sample (\u003cem\u003eN\u0026nbsp;=\u003c/em\u003e\u0026nbsp;1,441)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eControl group (\u003cem\u003en\u0026nbsp;=\u003c/em\u003e\u0026nbsp;784)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eIntervention group (\u003cem\u003en\u0026nbsp;=\u003c/em\u003e\u0026nbsp;657)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(21.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(77.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(76.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(78.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(15.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(13.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(35.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(43.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(42.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e71+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(8.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(5.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight status category\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(35.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(33.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(33.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(31.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(30.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eState of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew South Wales\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(11.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQueensland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAustralian Capital Territory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern Territory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTasmania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(1.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVictoria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(8.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWestern Australia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouth Australia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(65.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(66.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-economic status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1 (most disadvantaged)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(14.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(22.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(20.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(25.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5 (least disadvantaged)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(29.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntention (1\u0026ndash;100)\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(66.3\u0026ndash;92.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(65.3\u0026ndash;93.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(67.0\u0026ndash;91.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet score (out of 100)\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(10.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNotes:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eReported as mean (SD). \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003eReported as median and interquartile range.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge\u003c/em\u003e, calculated by subtracting year reported (2019) from participant-reported birth year.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBMI\u003c/em\u003e, Body Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e) calculated from participant-reported height (cm) and weight (kg).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWeight status category\u003c/em\u003e is according to Body Mass Index (BMI) (kg/m2); Underweight: \u0026lt;18.5\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e; Healthy weight: 18.5\u0026ndash;24.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e; Overweight: 25\u0026ndash;29.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e; Obesity\u0026thinsp;\u0026gt;\u0026thinsp;30\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSocio-economic status\u003c/em\u003e as indicated by national Socio-Economic Indexes For Areas (SEIFA) of relative advantage and disadvantage represented by matching participant-reported postcode\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eINTERVENTION EFFECT\u003c/h2\u003e\u003cp\u003eThe mean reported discretionary choice intake at baseline for the whole sample was 4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 servings. The tailored intervention group had a significantly higher reported discretionary choice intake at baseline than did the control (4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4 and 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.015).\u003c/p\u003e\u003cp\u003eWithin the tailored intervention group (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;657), 30.3% received the positively framed message as their intervention, 27.7% received the negatively framed message, 18.3% received the majority norm message and 23.7% received the minority norm message. A one-way ANOVA showed no significant differences between the different tailored message groups for discretionary choice intake post-intervention (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.695).\u003c/p\u003e\u003cp\u003eWhen adjusted for baseline discretionary choice intake, the ANCOVA model showed that the intervention did not have a significant effect on discretionary choice intake. The adjusted discretionary choice intake mean was 3.2 servings for the tailored intervention group and 3.1 servings for the control group (adjusted mean serving difference between groups\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.49) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePREDICTORS OF INTERVENTION EFFECT\u003c/h2\u003e\u003cp\u003eAs there were no significant differences between intervention and control groups, the sample was combined to determine the demographic, anthropometric, behavioural and psychosocial characteristics that predict an improvement in discretionary choice intake. The proportion of participants who reduced their discretionary choice intake by one serving or more was described relative to the observed proportion of participants who did not achieve this reduction. The different proportions of participants who reduced (or not) their discretionary choice intake, by characteristic, are found in the supplementary file.\u003c/p\u003e\u003cp\u003eThe associated odds ratios (OR) for reducing discretionary choice intake by one serving or more after the intervention are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The odds of reducing discretionary choice intake were higher for participants who had a higher baseline discretionary choice intake. For every additional serving of discretionary choices consumed at baseline, participants were 57% more likely to reduce their intake by one serving or more (OR 1.57, 95% CI [1.47, 1.68], \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate adjusted odds ratios of the sample (N\u0026thinsp;=\u0026thinsp;1,441) who reduced discretionary choice (DC) intake by one serving or more after the brief online 28-day intervention\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eOdds of reducing DC intake by one serving or more\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBaseline DC intake\u003c/b\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47, 1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntervention Group\u003c/b\u003e %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eControl (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTailored\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67, 1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMale (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.945, 1.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group %\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e18\u0026ndash;30 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e31\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55, 1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e51\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68, 1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e71+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66, 2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight status category\u003c/b\u003e %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHealthy weight (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84, 1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73, 1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocio-economic status (SEIFA Quintile)\u003c/b\u003e %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e1 (most disadvantaged) (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.40, 1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43, 1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59, 1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e5 (least disadvantaged)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41, 0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntention tertiles (out of 100; range)\u003c/b\u003e %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLow (1.0\u0026ndash;71.7) (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMed (71.8\u0026ndash;88.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08, 2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHigh (88.4\u0026ndash;100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.02, 1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiet score quintiles (out of 100; range)\u003c/b\u003e %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e1 (21.1\u0026ndash;45.5) (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e2 (45.6\u0026ndash;51.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49, 1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e3 (51.5\u0026ndash;56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50, 1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e4 (56.7\u0026ndash;63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60, 1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e5 (63.3\u0026ndash;90.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33, 0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eNote:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003cem\u003eContinous variable for servings of\u003c/em\u003e discretionary choices (DC).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRef i\u003c/em\u003endicates reference variable.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAge group\u003c/em\u003e categories consistent with nutrient reference values \u003csup\u003e(\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWeight status\u003c/em\u003e categories are according to Body Mass Index (BMI) (kg/m2); Underweight: \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e; Healthy weight: 18.5\u0026ndash;24.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e; Overweight: 25\u0026ndash;29.9\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e; Obesity: \u0026gt;30\u0026nbsp;kg/m\u003csup\u003e2\u003c/sup\u003e. Values for underweight sample (n\u0026nbsp;=\u0026nbsp;16) not shown.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSocio-Economic Status\u003c/em\u003e is indicated by national Socio-Economic Indexes for Areas (SEIFA) of relative advantage and disadvantage represented by matching participant-reported postcode \u003csup\u003e(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalues were derived from Wald test. \u003cb\u003eValues in bold font indicate significance at \u0026lt;\u0026thinsp;0.05.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eModel fit statistics\u003c/em\u003e: X\u003csup\u003e2\u003c/sup\u003e (25, \u003cem\u003eN\u0026nbsp;=\u003c/em\u003e\u0026nbsp;1441)\u0026thinsp;=\u0026thinsp;427.72, \u003cem\u003ep\u0026nbsp;\u0026lt;\u003c/em\u003e\u0026nbsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eCox and Snell R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;=\u0026nbsp;25.7% and Nagelkerke R\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;=\u0026nbsp;34.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe odds of reducing discretionary choice intake were 41% higher for participants with higher baseline levels of intention compared to those with low intention (OR 1.41, 95% CI [1.02, 1.93], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.035).\u003c/p\u003e\u003cp\u003eThe odds of reducing discretionary choice intake were lower for participants who reported living in the least disadvantaged areas of socio-economic status\u0026mdash;SEIFA quintile 5\u0026mdash;than for those participants who reported living in the most disadvantaged areas (OR 0.63, 95% CI [0.41, 0.95], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030).\u003c/p\u003e\u003cp\u003eParticipants with a higher diet score at baseline (quintile 5 of diet score) had lower odds reducing discretionary choice intake relative to participants with a lower diet score at baseline (quintile 1) (OR 0.51, 95% CI [0.33, 0.79], \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003eThe characteristics of participants reducing their discretionary choice intake by one serving or more did not statistically differ by other demographic variables included in this analysis, such as sex and age, nor by weight status and intervention group allocation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study tested the effectiveness of a brief online dietary feedback intervention that incorporated theoretically grounded message framing and tailoring based on participant intention, alongside enhanced behavioral support techniques, to reduce adults\u0026rsquo; discretionary choice intake. Drawing on frameworks such as Regulatory Focus Theory and Social Norms Theory, participants were assigned to receive messages framed as positive, negative, majority norm, or minority norm, with tailoring based on their intention response. The study also aimed to identify participant characteristics associated with a greater reduction in discretionary choice intake. Results showed that tailoring brief feedback messages on intention and including enhanced behavioral support, did not lead to greater reductions in discretionary choice intake compared to generic messages. However, certain baseline characteristics predicted more substantial dietary improvement. Participants with lower diet quality, higher baseline intention to reduce discretionary choices, and those living in more socioeconomically disadvantaged areas were more likely to report a reduction of one serving or more in daily discretionary intake.\u003c/p\u003e\u003cp\u003eWhile this intervention combined two evidence-based components\u0026mdash;tailored message framing and enhanced behavioral support\u0026mdash;the null findings suggest that the additive effect of combining these may not yield greater behavior change in already motivated individuals. The behavioral support elements, such as goal setting, self-monitoring and the use of prompts, may have been equally effective in both groups due to the act of completing the diet survey and receiving any form of feedback. \u003csup\u003e(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/sup\u003e Self-monitoring has consistently shown positive effects in brief interventions, \u003csup\u003e(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/sup\u003e and completing a dietary questionnaire may, in itself, have resulted in a simple but intensive act of reflection on dietary behavior. \u003csup\u003e(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/sup\u003e The lack of intervention effect may also be due to the motivated participants in our study. Participating in a nutrition intervention could lead to a drive for behavior change, and when receiving a brief intervention, any message may be just as beneficial as more enhanced messages. \u003csup\u003e(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)\u003c/sup\u003e For our motivated sample, a brief intervention might have been sufficient. Future interventions should explore whether greater dose, frequency, or dynamic tailoring of support might enhance the effect of message framing, particularly in populations with lower intention.\u003csup\u003e(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe current study\u0026rsquo;s brief intervention provided tailored nutrition message frames based on participants\u0026rsquo; intention ratings in response to different messages, identifying which frame elicited the highest intention to reduce discretionary choice intake. While prior studies suggest that baseline intention can moderate the effectiveness of framed messages, \u003csup\u003e(\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/sup\u003e the intention ratings in this study served as a message preference tool, not a proxy for readiness to change. Given the high baseline intention in our sample, it is possible that participants were equally responsive to any message, reducing the potential benefit of tailoring.\u003c/p\u003e\u003cp\u003eOur findings are consistent with previous research evaluating the effects of brief dietary feedback interventions. The Food4Me study compared generic feedback based on population-level guidelines with feedback personalised to baseline dietary intake and found only a modest reduction in energy intake from discretionary foods in the personalised group after six months (31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59% vs 32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59.\u003csup\u003e(18)\u003c/sup\u003e A more recent study evaluated increasing levels of tailored feedback, where the control group received brief tailored feedback after completing a short dietary assessment survey, and the intervention groups receiving more comprehensive tailored feedback.\u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e Results showed that the within- and between-group results were not significant. \u003csup\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/sup\u003e These findings mirror our own, suggesting that tailoring dietary feedback\u0026mdash;even when based on individual intake data\u0026mdash;may offer limited added benefit in reducing discretionary choice intake.\u003c/p\u003e\u003cp\u003eThe evidence on the effect of current dietary feedback interventions on discretionary choice intake is also limited. Research from Australia\u003csup\u003e(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/sup\u003e and the United Kingdom \u003csup\u003e(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/sup\u003e found no significant differences in energy intake from fats, saturated fats, or alcohol between groups receiving generic or comprehensive dietary feedback. A 2024 meta-analysis on personalised feedback interventions indicated no significant differences in fat intake between intervention and comparator groups. \u003csup\u003e(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e)\u003c/sup\u003e The mixed evidence for reducing discretionary choice intake across previous dietary feedback interventions may reflect the heterogeneity of foods in this category. \u003csup\u003e(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/sup\u003e Future research may benefit from specifying particular discretionary foods or beverages in feedback messages to improve salience and relevance.\u003csup\u003e(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e)\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eRegarding key predictors of change in discretionary choice intake, a lower overall diet quality score and a higher intake of discretionary choices at baseline, were the characteristics of participants who achieved a one serving or more reduction in discretionary choice intake after the intervention. This finding is common in previous studies. \u003csup\u003e(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/sup\u003e A regression towards the mean could explain our results. Despite statistically adjusting for the baseline dietary intake in this study, \u003csup\u003e(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003c/sup\u003e the participants who had higher baseline discretionary choice intake may have had a larger scope to reduce their intake. Our study also found that participants who reported living in more disadvantaged areas were more likely to reduce discretionary choice intake. Cross-sectional studies have consistently found that higher socio-economic status predicts healthier dietary behaviors. \u003csup\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e)\u003c/sup\u003e This can be interpreted as those living in low-socio economic areas may have a larger scope to reduce discretionary choice intake after an intervention. More longitudinal research is needed to better understand how socio-economic characteristics interact with intervention exposure to influence change in dietary behavior.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIMPLICATIONS FOR RESEARCH, PRACTICE AND POLICY\u003c/h2\u003e\u003cp\u003eThe implications of our findings could be important for future dietary feedback interventions wanting to target specific populations. Future research could explore targeting an intervention with enhanced behavioral support to those with lower baseline diet quality or lower intention. In those groups, different tailoring methods could be compared to further improve the effect of brief online dietary feedback interventions. For example, tailoring could be in relation to the amount and type of behavioral support that is provided, the frequency of brief feedback, and the messages specifying the discretionary food or beverage that needs to be consumed less of. Recruiting a more socioeconomically and motivationally diverse sample, including individuals with lower baseline intention to change and those from more disadvantaged areas, is warranted in future trials. These groups showed greater potential for dietary improvement in the study and may benefit most from targeted support to improve diet quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSTRENGTHS AND LIMITATIONS\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s key strength is its novel approach of incorporating tailored nutrition message frames based on individual levels of intention, into a brief online feedback intervention, using an RCT design. The robust RCT design was recognised through multiple sensitivity analyses (data not shown), which demonstrated that the pattern of results remained consistent regardless of the removal of extreme outliers, and by adjusting for baseline measures. The aim of the intervention, to reduce discretionary choice intake, also appealed to many people as evidenced by the large number of participants who enquired about the intervention (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5,353) and completed the study (\u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1,441). The RCT design and the moderate level of retention (30%) also optimised internal validity. Regarding the limitations, having a highly motivated sample was a key limitation of this intervention. \u003csup\u003e(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e)\u003c/sup\u003e Participants voluntarily signed up to the intervention and may have already had an intention to act on the messages to improve their dietary behaviors \u003csup\u003e(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/sup\u003e. Volunteer bias could have been a reason for the null findings between intervention groups \u003csup\u003e(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e)\u003c/sup\u003e. Another important consideration is the potential for false-positive results due to Type I error. The number of participants recruited for this study met the top range of the sample size calculation, increasing the statistical power of the analysis. To minimise statistical bias, effect sizes were calculated, to aid the interpretation of the magnitude of differences \u003csup\u003e(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e)\u003c/sup\u003e. Since only the participants who completed the study were considered for analysis, the potential for selection bias must be acknowledged. An intention-to-treat analysis may have yielded an unbiased estimate of the efficacy of the intervention on discretionary choice intake; however, this analytical approach requires complete outcome data \u003csup\u003e(\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e)\u003c/sup\u003e. Twenty-three participants were not included in the final analysed sample due to being non-completers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and it is unlikely that adding these datapoints would have made a significant difference in the results. Despite recruitment strategies to target more males, the final sample was not representative of the Australian population \u003csup\u003e(\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e)\u003c/sup\u003e. This sample overrepresented females, those in the 31\u0026ndash;70 age group, those in higher socio-economic areas of advantage and those residing in South Australia. Therefore, caution must be taken in generalising these results. While message frames messages were developed based on established theoretical constructs and prior framing studies, they were not formally pre-tested or validated to confirm that participants consistently perceived them as intended. As such, it is unclear whether the null findings reflect a true lack of effect from tailoring message frames based on intention, or whether participants did not perceive the framing distinctions strongly enough to influence behavior. Finally, data were self-reported, which may have resulted in reporting bias, social bias and measurement error \u003csup\u003e(\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e)\u003c/sup\u003e. However, the anonymity ensured in online questionnaire completion may have reduced perceived social judgement and may have offset the potential risk of social desirability bias.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBrief online interventions may support reductions in discretionary choice intake; however, tailoring nutrition message framing based on participants\u0026rsquo; preferred message, selected in relation to their intention to reduce intake, while providing additional behavioural support, did not enhance effectiveness in this motivated sample. Given that high baseline intention predicted dietary improvement, future research should prioritise recruiting a less motivated sample, who may be more responsive to intervention support. For these groups, tailoring the type and intensity of behavioral support, as well as providing more specific and actionable feedback (e.g., naming particular discretionary foods to reduce), may improve the effect of brief online dietary feedback interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBCTs \u0026ndash; Behaviour Change Techniques\u003c/p\u003e\n\n\u003cp\u003eRCT \u0026ndash; Randomised Controlled Trial\u003c/p\u003e\n\n\u003cp\u003eCONSORT \u0026ndash; CONsolidated Standards of Reporting Trials\u003c/p\u003e\n\n\u003cp\u003eBMI \u0026ndash; Body Mass Index\u003c/p\u003e\n\n\u003cp\u003eSEIFA \u0026ndash; Socio-Economic Indexes for Areas\u003c/p\u003e\n\n\u003cp\u003eSD \u0026ndash; Standard Deviation\u003c/p\u003e\n\n\u003cp\u003eIQR \u0026ndash; Interquartile Range\u003c/p\u003e\n\n\u003cp\u003eANCOVA \u0026ndash; Analysis of Covariance\u003c/p\u003e\n\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions -\u0026nbsp;\u003c/strong\u003eJH led the research, under the supervision of GAH and RKG. GAH and RKG developed the questions and scoring algorithm of the validated survey. JH collected and analyzed the data. JH drafted the first version of the manuscript. All authors reviewed and provided critical input into drafts and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eReprint contact \u0026ndash;\u0026nbsp;\u003c/strong\u003ethe corresponding author\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements -\u0026nbsp;\u003c/strong\u003eJH was supported by a Flinders University Research Scholarship and a top-up scholarships funded by Flinders University, Commonwealth Scientific and Industrial Research Organisation (CSIRO), the HDA \u0026amp; Channel 7 Children\u0026apos;s Research Foundation, and the Australian Commonwealth Research Training Program Scholarship. We would like to acknowledge Dr. Kacie Dickinson for her support in the conception of the study and the participants who volunteered their time. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/financial disclosures -\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflict of interest disclosures -\u0026nbsp;\u003c/strong\u003eThere are no conflicts to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration -\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003eEthics approval was received from the CSIRO Low Risk Health \u0026amp; Medical Research Ethics Committee (2019_051_LR) and reciprocal ethics was approved by the Flinders University Social and Behavioural Research Ethics Committee (OH-00224) in August 2019. Online consent was sought from all eligible participants. The trial was registered on the Australian New Zealand Clinical Trials Registry (ACTRN12619001202156) and approved on 28 August 2019\u003cstrong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational Health and Medical Research Council. Eat for Health. Educator Guide. Canberra: NHMRC; 2013. p. 59.\u003c/li\u003e\n\u003cli\u003eSacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, et al. Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association. Circulation. 2017;136(3):e1-e23.\u003c/li\u003e\n\u003cli\u003eBrand-Miller JC, Barclay AW. Declining consumption of added sugars and sugar-sweetened beverages in Australia: a challenge for obesity prevention. 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Int J Epidemiol. 2015;44(6):2006-19.\u003c/li\u003e\n\u003cli\u003eCohen J, Cohen Ventura J, Cohen J. Statistical power analysis for the behavioral sciences. 2 ed. New York, NY: L. Erlbaum Associates; 1988. 567 p.\u003c/li\u003e\n\u003cli\u003eLakens D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol. 2013;4:863.\u003c/li\u003e\n\u003cli\u003eMcCoy CE. Understanding the Intention-to-treat Principle in Randomized Controlled Trials. West J Emerg Med. 2017;18(6):1075-8.\u003c/li\u003e\n\u003cli\u003eGupta SK. Intention-to-treat concept: A review. Perspect Clin Res. 2011;2(3):109-12.\u003c/li\u003e\n\u003cli\u003eMontori VM, Guyatt GH. Intention-to-treat principle. CMAJ. 2001;165(10):1339-41.\u003c/li\u003e\n\u003cli\u003eAustralian Bureau of Statistics. 2016 Census QuickStats [Internet] Canberra: ABS; 2016 [updated 11 July 2018; cited 2019 Jan 8]. Available from: https://quickstats.censusdata.abs.gov.au/census_services/getproduct/\ncensus/2016/quickstat/036\n#:~:text=The%20median%20a\nge%20of%20people,up%2015.7%\n25%20of%20the%20population.\u003c/li\u003e\n\u003cli\u003eHebert JR, Clemow L, Pbert L, Ockene IS, Ockene JK. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. Int J Epidemiol. 1995;24(2):389-98.\u003c/li\u003e\n\u003cli\u003eNational Health and Medical Research Council, New Zealand Ministry of Health. Nutrient Reference Values for Australia and New Zealand. Canberra: NHMRC; 2016. p. 320.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-social-science-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diss","sideBox":"Learn more about [Discover Social Science and Health](https://www.springer.com/journal/44155)","snPcode":"","submissionUrl":"","title":"Discover Social Science and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"eHealth, discretionary foods and beverages, nutrition, behavior change","lastPublishedDoi":"10.21203/rs.3.rs-7647393/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7647393/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the effectiveness of a brief online dietary feedback intervention designed to reduce adults\u0026rsquo; intake of discretionary choices by incorporating tailored nutrition message frames and behaviour change techniques (BCTs). A total of 3,453 adults enrolled online, and 1,441 completed the follow-up surveys. Participants were randomised to receive either two emails with tailored nutrition message frames and enhanced behavioural support (delivered through nine embedded BCTs, such as goal setting and action planning), or two emails with generic nutrition messages without additional support. The primary outcome was daily servings of discretionary choices (energy-dense, nutrient-poor foods and beverages), and secondary outcomes included predictors of intake reduction. No significant difference in discretionary choice intake was observed between the groups (3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 vs 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 servings, p\u0026thinsp;=\u0026thinsp;0.49). However, lower baseline diet quality was a significant predictor of a one serving or more reduction in discretionary choice intake (OR 1.57, 95% CI [1.47, 1.68], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that tailoring message framing based on intention, even when combined with established BCTs, may not enhance dietary outcomes in motivated populations. Future interventions may be more effective if they focus on less motivated individuals with lower baseline diet quality and explore alternative approaches to message tailoring.\u003c/p\u003e","manuscriptTitle":"The effectiveness of a brief online dietary feedback intervention on reducing adults’ discretionary choice intake","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 16:04:31","doi":"10.21203/rs.3.rs-7647393/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T07:15:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T04:27:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T19:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139721061399359188235588188831656411462","date":"2025-09-28T11:55:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60548766937358689717902150301343569709","date":"2025-09-26T22:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-26T06:57:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T07:24:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-23T07:24:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Social Science and Health","date":"2025-09-18T08:49:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-social-science-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diss","sideBox":"Learn more about [Discover Social Science and Health](https://www.springer.com/journal/44155)","snPcode":"","submissionUrl":"","title":"Discover Social Science and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3701d753-bdec-49d7-bc44-4ec1ba1cdb1c","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T14:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 16:04:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7647393","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7647393","identity":"rs-7647393","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}