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Background: Overconsumption of energy-dense, nutrient-poor "discretionary" foods and beverages can indicate poor diet quality, which is a risk factor for obesity and chronic disease. With 60% of Australians exceeding the recommended intake of discretionary foods, there is a need for interventions to reduce their consumption., Objective: The objective was to model the impact of 26 specific scenarios to limit discretionary food intake on energy and nutrient intake., Design: The scenario modeling analysis was based on reduction strategies targeting portion size, frequency, or variety of discretionary food and beverage items consumed., Participants/setting: Data from 12,153 respondents aged 2 to 85 years from the 2011-12 Australian National Nutrition and Physical Activity Survey were used., Outcome Measures: The outcome measures were change in servings of discretionary foods and key nutrients (energy, total fat, saturated fat, sugar, alcohol, sodium, trans fat) for the population, and by sex, age group, weight status, and socioeconomic status., Statistical Analyses Performed: Descriptive and inferential statistical analysis were conducted., Results: Scenarios reduced total energy intake by up to 26% across the population as a whole. The removal of discretionary foods (not beverages) resulted in the greatest reduction in servings and energy (74.8% and 25.6%, respectively), followed by the removal of discretionary items in portions greater than 143 kcal (59.1% and 24.3%) and not consuming discretionary items at main meals (51.2% and 17.8%). Targeting single categories of discretionary foods reduced energy intake by an average of 5.6% for the removal of cakes and biscuits, 4.4% for alcohol, and 3.9% for sugar-sweetened beverages. Strategies reduced total fat, saturated fat, and sugar by up to 35%, 38%, and 40% respectively., Conclusions: Strategies that are specific to discretionary food and beverage intake targeting reductions in portion size, frequency, or variety have the potential to reduce energy intake and improve diet quality. These findings have implications for designing interventions with potential to tailor messages to current dietary habits. Exploration of how these strategies could be effectively implemented will be the focus of further research., (Copyright © 2021 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.)

Background: Obesity is a global public health challenge, and there is a need for more evidence-based self-management programs that support longer-term, sustained weight loss., Objective: This study used data from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) Total Wellbeing Diet Online program to determine the reach and weight loss results over its first 5 years., Methods: Participants were adults who joined the commercial weight loss program of their own volition between October 2014 and September 2019 (N=61,164). Information collected included year of birth, sex, height, weight, and usage data (eg, entries into the food diary, views of the menu, and program content). Weight loss and percentage of starting body weight lost were calculated. Members were divided into 2 groups for analysis: "stayers" were members who signed up for at least 12 weeks of the program and recorded a weight entry at baseline and at the end of the program, while "starters" began the program but did not record a weight after 12 weeks. Descriptive statistics and multiple linear regression were used to describe weight loss and determine the member and program characteristics associated with weight loss., Results: Data were available from 59,686 members for analysis. Members were predominately female (48,979/59,686, 82.06%) with an average age of 50 years (SD 12.6). The average starting weight was 90.2 kg (SD 19.7), and over half of all members (34,195/59,688, 57.29%) were classified as obese. At week 12, 94.56% (56,438/59,686) of the members had a paid program membership, which decreased to 41.48% (24,756/59,686) at 24 weeks. At week 12, 52.03% (29,115/55,958) of the remaining members were actively using the platform, and by week 24, 26.59% (14,880/55,958) were using the platform. The average weight loss for all members was 2.8 kg or 3.1% of their starting body weight. Stayers lost 4.9 kg (5.3% of starting body weight) compared to starters, who lost 1.6 kg (1.7% of starting body weight). Almost half (11,082/22,658, 48.91%) the members who stayed on the program lost 5% or more of their starting body weight, and 15.48% (3507/22,658) achieved a weight loss of 10% or more. Of the members who were classified as class 1 obese when they joined the program, 41.39% (3065/7405) who stayed on the program were no longer classified as obese at the end, and across all categories of obesity, 24% (3180/13,319) were no longer classified as obese at the end of the program. Based on multiple linear regression, platform usage was the strongest predictor of weight loss (β=.263; P<.001), with higher usage associated with greater weight loss., Conclusions: This comprehensive evaluation of a commercial, online weight loss program showed that it was effective for weight loss, particularly for members who finished the program and were active in using the platform and tools provided. If the results demonstrated here can be achieved at an even greater scale, the potential social and economic benefits will be extremely significant., (©Gilly A Hendrie, Danielle L Baird, Emily Brindal, Gemma Williams, Jennie Brand-Miller, Beverly Muhlhausler. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.06.2021.)

Purpose: There is conflicting advice about the inclusion of dairy foods in a lower greenhouse gas (GHG) emission dietary pattern. Our purpose was to assess the prevalence of dairy food intake among higher diet quality and lower GHG emission diets in Australia and within these diets assess the association between level of dairy food intake and adequate intake of a broad range of nutrients., Methods: Dietary intake data collected using a 24-h recall process were sourced from the most recent Australian Health Survey. Diet quality was assessed by level of compliance with the food group-based Australian Dietary Guidelines. A subgroup of 1732 adult (19 years and above) daily diets was identified having higher diet quality score and lower GHG emissions (HQLE). Intake of core dairy foods (milk, cheese, yoghurt) was assessed and nutrient profiling was undertaken for 42 macro- and micronutrients., Results: The HQLE subgroup had 37% higher diet quality score and 43% lower GHG emissions than the average Australian adult diet (P < 0.05). Intake of dairy foods was very common (90% of HQLE diets) and greatly exceeded the intake of non-dairy alternatives (1.53 serves compared to 0.04 serves). HQLE daily diets in the highest tertile of dairy food intake were more likely to achieve the recommended intake of a wide range of nutrients, including calcium, protein, riboflavin, vitamin B12, folate, phosphorous, magnesium, iodine and potassium compared to other HQLE daily diets., Conclusion: Core dairy foods have an important role for achieving adequate nutrient intakes in a healthy and lower GHG emission dietary pattern in Australia.

Background: Large-scale initiatives to improve diet quality through increased vegetable consumption have had small to moderate success. Digital technologies have features that are appealing for health-related behavior change interventions., Objective: This study aimed to describe the implementation and evaluation of a mobile phone app called VegEze, which aims to increase vegetable intake among Australian adults., Methods: To capture the impact of this app in a real-world setting, the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework was utilized. An uncontrolled, quantitative cohort study was conducted, with evaluations after 21 and 90 days. The app was available in the Apple App Store and was accompanied by television, radio, and social media promotion. Evaluation surveys were embedded into the app using ResearchKit. The primary outcomes were vegetable intake (servings per day) and vegetable variety (types per day). Psychological variables (attitudes, intentions, self-efficacy, and action planning) and app usage were also assessed. Descriptive statistics and multiple linear regression were used to describe the impact of the app on vegetable intake and to determine the characteristics associated with the increased intake., Results: Data were available from 5062 participants who completed the baseline survey; 1224 participants completed the 21-day survey, and 273 completed the 90-day survey. The participants resided across Australia and were mostly women (4265/5062, 84.3%) with a mean age of 48.2 years (SD 14.1). The mean increase in intake was 0.48 servings, from 3.06 servings at baseline to 3.54 servings at the end of the 21-day challenge (t 1223 =8.71; P<.001). The variety of vegetables consumed also increased by 0.35 types per day (t 1123 =9.59; P<.001). No changes in intake and variety were found from day 21 to the 90-day follow-up. Participants with the highest app usage increased their vegetable intake by 0.63 (SD 2.02) servings per day compared with 0.32 (SD 1.69) servings per day for those with the lowest app usage. On the basis of multiple linear regression, gender; age; BMI; psychological variables of self-efficacy, attitudes, intentions, and action planning specific to vegetable intake; baseline vegetable intake; and active days of app usage accounted for 23.3% of the variance associated with the change in intake (F 9,1208 =42.09; P<.001). Baseline vegetable intake was the strongest predictor of change in intake (beta=-.495; P<.001), with lower baseline intake associated with a greater change in intake. Self-efficacy (beta=.116; P<.001), action planning (beta=.066; P=.02), BMI (beta=.070; P=.01), and app usage (beta=.081; P=.002) were all significant predictors of the change in intake., Conclusions: The VegEze app was able to increase intake by half a serving in a large sample of Australian adults. Testing the app in a real-world setting and embedding the consent process allowed for greater reach and an efficient, robust evaluation. Further work to improve engagement is warranted., (©Gilly A A Hendrie, M Sazzad Hussain, Emily Brindal, Genevieve James-Martin, Gemma Williams, Anna Crook. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 17.04.2020.)

Background: Few people successfully maintain lost weight over the longer term. Mobile phones have the potential to deliver weight loss management programs that can encourage self-monitoring while also providing some behavioral therapy to assist users in developing personal skills that may be necessary for improved longer term weight loss maintenance., Objective: The aim of this study was to evaluate a program supporting weight maintenance, which uses a behaviorally based mobile phone app to manage weight, food, exercise, mood, and stress., Methods: In a randomized controlled trial over 24 weeks, the full version of the app (MotiMate) was compared with a control app (monitoring only; excluding mood and stress) for its effect on weight, diet, and psychological well-being. Both apps had the same visual appearance and were designed to deliver all intervention content without face-to-face contact. The control version included features to track weight, food intake, and exercise with limited feedback and no encouraging/persuasive features. The intervention app included more persuasive and interactive features to help users track their weight, food intake, and physical activity and prompted users to enter data each day through notifications and included a mood and stress workshopping tool. Participants were recruited through advertising and existing databases. Clinic visits occurred at baseline, 4 weeks, 8 weeks, 12 weeks, and 24 weeks. At all visits, the clinical trial manager recorded body weight, and participants then completed a computer-delivered survey, which measured psychological and lifestyle outcomes. Objective app usage data were recorded throughout the trial., Results: A total of 88 adults who had lost and maintained at least 5% of their body weight within the last 2 years were randomized (45 MotiMate and 43 control). Overall, 75% (66/88) were female, and 69% (61/88) completed week 24 with no differences in dropout by condition (χ 2 1,87 =0.7, P=.49). Mixed models suggested no significant changes in weight or psychological outcomes over 24 weeks regardless of condition. Of 61 completers, 53% (32/61) remained within 2% of their starting weight. Significant increases occurred over 24 weeks for satisfaction with life and weight loss self-efficacy regardless of app condition. Diet and physical activity behaviors did not vary by app or week. Negative binomial models indicated that those receiving the full app remained active users of the app for 46 days longer than controls (P=.02). Users of the full version of the app also reported that they felt more supported than those with the control app (P=.01)., Conclusions: Although some aspects of the intervention app such as usage and user feedback showed promise, there were few observable effects on behavioral and psychological outcomes. Future evaluation of the app should implement alternative research methods or target more specific populations to better understand the utility of the coping interface., Trial Registration: Australia New Zealand Clinical Trials Registry ACTRN12614000474651; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366120., (©Emily Brindal, Gilly A Hendrie, Jill Freyne, Manny Noakes. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 04.09.2019.)

As "taste" is a primary driver of food choice, the objective of this study was to understand how the sensory properties of diets relate to energy intake (EI). A database of 720 frequently consumed foods, described by a trained panel for basic tastes (sweetness, saltiness) and fatty mouthfeel, was systematically applied to all foods reportedly consumed in 24hr recalls as part of the 2011-2012 Australian National Nutrition and Physical Activity Survey (n = 12,153 adults and children). Food groups were classified according to the Australian Guide to Healthy Eating, and their contribution to total nutrient and sensory intake estimated. There were significant positive correlations between the nutrient and sensory properties of diets, for example, for adults, EI and fatty mouthfeel r = 0.740; EI and saltiness r = 0.623 and EI and sweetness r = 0.517 (all p < 0.01). Core foods (e.g. fruits, vegetables, grains, dairy) can provide similar sensory stimulation whilst being of lower energy content than discretionary foods (e.g. confectionary, snacks). Regression models in adults, controlling for age, sex and BMI, revealed that fatty mouthfeel (β = 0.492), saltiness (β = 0.161) and sweetness (β = 0.138) were significant predictors of EI, explaining 56% variance (p < 0.01). Similar results were found for children. Fatty mouthfeel was the primary driver of energy intake but such sensory stimulation can be derived from core (e.g. dairy foods), rather than discretionary foods, at lower energy content., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Methods to address misreporting associated with short dietary assessment instruments are needed., Objective: Our objective was to develop and evaluate the direct and indirect validity of adjustment factors applied to a short dietary assessment instrument to improve estimates of usual consumption of core and discretionary food and beverage intake., Design: Validation of the Short Food Survey relative to 24-hour recalls was performed. The Short Food Survey requires individuals to report their usual intake of fruit, vegetables, grains, meat, dairy, and discretionary choices in multiples of standard servings. Adjustment factors were developed based on a ratio (usual portion size estimated from national data to standard serving size). The estimates of food group intakes (unadjusted and adjusted) were compared to 24-hour recalls., Participants/setting: Three population samples were used in this study. The direct validation used data from 61 Australian adults (collected 2013-2014). The indirect validation compared data from the 2011-2013 Australian National Nutrition and Physical Activity Survey (n=9,435) to a sample of 145,975 who completed the Short Food Survey in a format that is freely available online (2015-2016)., Main Outcome Measures: Food group intake (in servings) was measured., Statistical Analyses Performed: Descriptive and inferential statistical analyses were conducted., Results: Direct validation showed the adjustment factors improved the survey-derived estimates of intake for all food groups except grain foods. For grains, the mean difference went from -0.6 servings to +1.2 to 1.5 servings. The absolute difference in food group intake between the adjusted Short Food Survey and recalls remained statistically significant for fruit, meat, dairy, and grains, but was not different for vegetables and discretionary foods. The indirect validation showed that the adjusted estimates of intake from the online Short Food Survey were closer to the population estimates reported by 24-hour recall for all food groups except meat., Conclusions: Adjustment factors can improve estimates of food group intake assessed using a short dietary assessment instrument for some but not all food groups., (Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.)

Background: Mobile phone apps may be acceptable to users and could improve retention and adherence over more traditional methods, but there is mixed literature supporting their efficacy. In the weight management space, very little is known about how a mobile phone app integrating features beyond text messaging (short message service) can affect behavior, particularly when combined with face-to-face support., Objective: The objective of this study was to examine the effectiveness of a mobile phone app when combined with a partial meal replacement program including face-to-face support. This paper compares a static versus supportive app over a 6-month randomized trial for effects on weight loss, weight-related biomarkers, and psychological outcomes., Methods: Overweight and obese adults (71.2% female, 104/146; mean 48.11, SD 11.75 years) were recruited to participate in the weight loss study, and they were randomized on a 1:1 basis using a computer algorithm. The supportive app (n=75) provided information, food intake recording, rewards, prompts for regular interaction through reminders, and the opportunity to review personal compliance with the dietary program. The static app (n=71) included only recipes and weight loss information. Both groups recieved equal amounts of face-to-face support in addition to app., Results: The overall reduction in app usage over 24 weeks was lower for the supportive app in comparison with the static app; approximately 39.0% (57/146) of the users were still using the app at week 24. Despite the promising results for app usage, there were no differences in weight loss between groups (F1,128.12=0.83, P=.36). However, it should be noted that almost 60% (49/84) of all participants lost 5% or more of body weight during the trial. No weight-related biomarkers were significantly different between groups. Both groups experienced an increase in positive mood, but this was significantly higher for those who received the static app (F1,118.12=4.93, P=.03)., Conclusions: Although the supportive app was well received by users, we found little evidence of the added benefit of this versus the static app in combination with face-to-face support in a community-delivered weight loss program. Future versions of the app may incorporate more unique behavioral techniques beyond those provided by the consultant to improve the potency of the app., Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12613000547741; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364187 (Archived by WebCite http://www.webcitation.org/6yivwfMI9)., (©Emily Brindal, Gilly A Hendrie, Jill Freyne, Manny Noakes. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 18.04.2018.)

The food system is a major source of environmental impact, and dietary change has been recommended as an important and necessary strategy to reduce this impact. However, assessing the environmental performance of diets is complex due to the many types of foods eaten and the diversity of agricultural production systems and local environmental settings. To assess the state of science and identify knowledge gaps, an integrative review of the broad topic of environment and diet was undertaken, with particular focus on the completeness of coverage of environmental concerns and the metrics used. Compared with the 14 discrete environmental areas of concern identified in the United Nations Sustainable Development Goals, the located journal literature mainly addressed greenhouse gas (GHG) emissions and, to a lesser extent, land and water use. Some relevant concerns were rarely addressed or not addressed at all. In the case of GHG emissions, changes in land use and soil carbon stocks were seldom considered. This represents a disconnect between the science informing strategic climate action in the agricultural sector and the science informing public health nutrition. In the case of land and water use, few studies used metrics that are appropriate in a life-cycle context. Some metrics produce inherently biased results, which misinform about environmental impact. The limited evidence generally points to recommended diets having lower environmental impacts than typical diets, although not in every case. This is largely explained by the overconsumption of food energy associated with average diets, which is also a major driver of obesity. A shared-knowledge framework is identified as being needed to guide future research on this topic. Until the evidence base becomes more complete, commentators on sustainable diets should not be quick to assume that a dietary strategy to reduce overall environmental impact can be readily defined or recommended., Competing Interests: Author disclosures: BGR has undertaken food systems research related to environmental issues for a variety of private sector organizations and Australian government agencies. GAH and MN undertake contract research for several companies and nongovernmental organizations on a fee-for-service basis unrelated to environmental issues., (© 2017 American Society for Nutrition.)

Objective: This study sought to determine the simultaneous effect of immediate eating environment variables; portion size, plate size, proximity to food, variety of food, side serves of salad/vegetables and presence of distraction on dietary energy intake (EI), of a reported evening meal, in the participants' real world setting., Design: A retrospective computer assisted telephone interview collected data on ten immediate eating environment variables, covariates and a 24-hour dietary recall of EI at an evening meal. Multiple regression analysis was conducted to determine relationships of these variables with EI. Significant (P<0.05) standardised beta coefficients (β) are reported., Setting: Australia wide, participants' homes., Subjects: Australian adult males and females (n 150) aged 18-65years., Results: Of the immediate eating environment variables, the absence of salads/vegetables (β=0.237), increased food variety (β=0.208), presence of music (β=0.207), and the consumption of pre-plated (as opposed to self-served) meals (β=0.195) had a positive association with EI, explaining 16.3% of the variance. Of the covariates, being male was the strongest predictor of EI (β=0.242); and hunger score also had a positive relationship with EI (β=0.190), explaining 17.5% of the variance., Conclusion: This study provides evidence that some immediate food environment variables at an evening eating occasion are associated with EI. Findings suggest there are several strategies that people could implement to assist in controlling their EI and help address over consumption., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objective: To explore factors associated with sugar-sweetened beverage (SSB) consumption in Australia., Methods: Pooled data from Western Australian (WA) and South Australian (SA) 2009 and 2012 nutrition monitoring survey series interviews of 2,832 WA and 10,764 SA adults aged 18 to 64 years. Demographic data were collected and independent samples t-test, analysis of variance, multiple logistic regression performed., Results: Obese participants were more likely to consume SSB than healthy weight participants (SA: OR=1.77; 95% CI 1.56-2.02; WA: OR=1.53; 1.05-2.24). SA obese participants consumed more SSB per day (152.0 mL; 140.7-163.5) than healthy weight (80.1 mL; 73.2-88.2; p<0.001) and overweight participants (106.9 mL; 99.0, 114.8; p<0.001). Males were more likely to consume SSB than females (SA: OR 1.80; 1.35-2.40; WA: 1.81; 1.64-2.00). WA participants who didn't think about the healthiness of food (4.55; 2.71-7.64) and bought meals away from home the day prior (1.55; 1.15-2.09) were more likely to consume SSB. SA adults rating their health highest were less likely to consume SSB (0.62; 0.54-0.72)., Conclusions: SSB consumers are more likely to be male, have little interest in health, or have purchased a meal away from home., Implications: Increasing awareness of the adverse health effects of consumption may be a first step in curbing SSB intake., (© 2015 Public Health Association of Australia.)

Objective: Functional data analysis (FDA) is a forecasting approach that, to date, has not been applied to obesity, and that may provide more accurate forecasting analysis to manage uncertainty in public health. This paper uses FDA to provide projections of Body Mass Index (BMI), overweight and obesity in an Australian population through to 2019., Methods: Data from the South Australian Monitoring and Surveillance System (January 2003 to December 2012, n=51,618 adults) were collected via telephone interview survey. FDA was conducted in four steps: 1) age-gender specific BMIs for each year were smoothed using a weighted regression; 2) the functional principal components decomposition was applied to estimate the basis functions; 3) an exponential smoothing state space model was used for forecasting the coefficient series; and 4) forecast coefficients were combined with the basis function., Results: The forecast models suggest that between 2012 and 2019 average BMI will increase from 27.2 kg/m(2) to 28.0 kg/m(2) in males and 26.4 kg/m(2) to 27.6 kg/m(2) in females. The prevalence of obesity is forecast to increase by 6-7 percentage points by 2019 (to 28.7% in males and 29.2% in females)., Conclusions: Projections identify age-gender groups at greatest risk of obesity over time. The novel approach will be useful to facilitate more accurate planning and policy development., (© 2015 Public Health Association of Australia.)