Perceptions of carotenoid and melanin colouration in faces among young Australian adults

Objective:  Human skin colour is influenced by three pigments: haemoglobin, carotenoids, and melanin. Carotenoids are abundant in fruits and vegetables, and when consumed accumulate in all layers of the skin, predominantly imparting yellowness (b*). This study investigated the effect of the manipulation of carotenoid-based skin colour, relative to the skin colour conferred by melanin on the perceptions of health amongst a group of Australian adults. Method:  Fifty-seven participants (n = 4 male; mean age 27.9 ± 7.5 years) completed three computer-based experiments on 50 trial faces. In the first two experiments, face image colour was manipulated along one or two independent single carotenoid or melanin axes on each trial to ‘make the face appear as healthy as possible’. In the third trial, face colour was manipulated on both the carotenoid and melanin axes simultaneously. Results:  For the single axis, participants significantly increased melanin colouration and added carotenoid colouration to facial images that were initially low in skin yellowness (b*). When carotenoid and melanin axes were simultaneously manipulated, carotenoid colouration was raised (ΔE  = 3.15 ( SE  ±0.19)) and melanin colouration was lowered (ΔE  = −1.04 ( SE  ±0.1)). Conclusions:  Young Australian adults perceive facial skin colouration, associated with both carotenoid intake from fruit and vegetables and melanin due to sun exposure as conveying the appearance of health in young adults. However, carotenoid colouration was more important to health perception.PostprintPeer reviewe

Investigating the Efficacy and Cost-Effectiveness of Technology-Delivered Personalized Feedback on Dietary Patterns in Young Australian Adults in the Advice, Ideas, and Motivation for My Eating (Aim4Me) Study: Protocol for a Randomized Controlled Trial

https://doi.org/10.2196/15999Background: Web-based health interventions may be easier to access and time efficient relative to face-to-face interventions and therefore may be the most appropriate mode to engage young adults. Objective: This study aims to investigate the impact of 3 different levels of personalized web-based dietary feedback and support on changes in diet quality. Methods: The Advice, Ideas, and Motivation for My Eating (Aim4Me) study is a 12-month assessor-blinded, parallel-group randomized controlled trial evaluating the impact of 3 levels of web-based feedback on diet quality, measured using the Australian Recommended Food Score (ARFS). Participants (N=2570) will primarily be recruited via web-based methods and randomized to 1 of 3 groups. Group 1 (control) will receive the Healthy Eating Quiz, a web-based dietary assessment tool that generates a brief feedback report on diet quality. Individuals randomized to this group can use the brief feedback report to make positive dietary changes. Group 2 will receive the Australian Eating Survey, a web-based dietary assessment tool that generates a comprehensive feedback report on diet quality as well as macro- and micronutrient intake. Group 2 will use the comprehensive feedback report to assist in making positive dietary changes. They will also have access to the Aim4Me website with resources on healthy eating and tools to set goals and self-monitor progress. Group 3 will receive the same intervention as Group 2 (ie, the comprehensive feedback report) in addition to a tailored 30-min video consultation with an accredited practicing dietitian who will use the comprehensive feedback report to assist individuals in making positive dietary changes. The self-determination theory was used as the framework for selecting appropriate website features, including goal setting and self-monitoring. The primary outcome measure is change in diet quality. The completion of questionnaires at baseline and 3, 6, and 12 months will be incentivized with a monetary prize draw. Results: As of December 2019, 1277 participants have been randomized. Conclusions: The web-based delivery of nutrition interventions has the potential to improve dietary intake of young adults. However, the level of support required to improve intake is unknown

Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial

Advances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report generated using the Australian Eating Survey® (AES) food frequency questionnaire data; and (2) high personalization, involving structured video calls with a dietitian using the AES report plus dietary self-monitoring with text message feedback. Intake was measured at baseline and 12 weeks using the AES and diet quality using the Australian Recommended Food Score (ARFS). Fifty participants (aged 39.2 ± 12.5 years; Body Mass Index 26.4 ± 6.0 kg/m2; 86.0% female) completed baseline measures. Significant (p < 0.05) between-group differences in dietary changes favored the high personalization group for total ARFS (5.6 points (95% CI 1.3 to 10.0)) and ARFS sub-scales of meat (0.9 points (0.4 to 1.6)), vegetarian alternatives (0.8 points (0.1 to 1.4)), and dairy (1.3 points (0.3 to 2.3)). Additional significant changes in favor of the high personalization group occurred for proportion of energy intake derived from energy-dense, nutrient-poor foods (−7.2% (−13.8% to −0.5%)) and takeaway foods sub-group (−3.4% (−6.5% to 0.3%). Significant within-group changes were observed for 12 dietary variables in the high personalization group vs. one variable for low personalization. A higher level of personalized support combining the AES report with one-on-one dietitian video calls and dietary self-monitoring resulted in greater dietary change compared to the AES report alone. These findings suggest nutrition-related web and mobile technologies in combination with personalized dietitian delivered advice have a greater impact compared to when used alone

What Is Nutritious Snack Food? A Comparison of Expert and Layperson Assessments

The term “nutritious” is being increasingly used by product manufacturers but the term is not currently regulated as a nutrition claim. It is unclear how lay consumers and experts define and interpret the term or how they evaluate the “nutritiousness” of various foods. To address this evidence gap, a mixed methods design was applied and both nutrition experts (n = 206) and lay participants (n = 269) provided definitions of the term “nutritious” and evaluated the “nutritiousness” of 20 different snack foods in a cross-sectional survey. Definitions were analysed using Leximancer and snack evaluations were compared both between groups and with nutrient profile scores (UK Ofcom and Australian Health Star Rating). Expert and lay definitions differed considerably, with experts using terms such as nutrient-density, macro- and micronutrients, kilojoules/Calories, while lay consumers used descriptions such as fuel, fresh, natural, body needs, and functioning. Snack evaluations were highly correlated between groups (Rs > 0.89, p < 0.001) and between nutrient profile scores (Rs > 0.75, p < 0.001). However, mean perceptions significantly differed for 18 out of 20 foods with the largest difference for yoghurts (p < 0.05). There are discrepancies between expert and lay perceptions of snack foods and the definition of the term “nutritious”. The results highlight the need for an agreed definition and the potential regulation of the term “nutritious” in food marketing

Diet Quality Scores of Australian Adults Who Have Completed the Healthy Eating Quiz

Higher scores obtained using diet quality and variety indices are indicators of more optimal food and nutrient intakes and lower chronic disease risk. The aim of this paper is to describe the overall diet quality and variety in a sample of Australian adults who completed an online diet quality self-assessment tool, the Healthy Eating Quiz. The Healthy Eating Quiz takes approximately five minutes to complete online and computes user responses into a total diet quality score (out of a maximum of 73 points) and then categorizes them into the following groups: ‘needs work’ (<33), ‘getting there’ (33–38), ‘excellent’ (39–46), or ’outstanding’ (47+). There was a total of 93,252 first-time respondents, of which 76% were female. Over 80% of respondents were between 16–44 years of age. The mean total score was 34.1 ± 9.7 points. Females had a higher total score than males (p < 0.001) and vegetarians had higher total scores than non-vegetarians (p < 0.001). Healthy eating quiz scores were higher in those aged 45–75 years compared to 16–44 years (p < 0.001). When comparing Socioeconomic Indices for Areas deciles, those most disadvantaged had a lower total score than those least disadvantaged (p < 0.001). Repeat measures showed that those who scored lowest (needs work) in their first completion increased their total score by 3.2 ± 7.4 at their second completion (p < 0.001). While the Healthy Eating Quiz data indicates that individuals receiving feedback on how to improve their score can improve their diet quality, there is a need for further nutrition promotion interventions in Australian adults

ServAR: An augmented reality tool to guide the serving of food

Abstract Background Accurate estimation of food portion size is a difficult task. Visual cues are important mediators of portion size and therefore technology-based aids may assist consumers when serving and estimating food portions. The current study evaluated the usability and impact on estimation error of standard food servings of a novel augmented reality food serving aid, ServAR. Methods Participants were randomised into one of three groups: 1) no information/aid (control); 2) verbal information on standard serving sizes; or 3) ServAR, an aid which overlayed virtual food servings over a plate using a tablet computer. Participants were asked to estimate the standard serving sizes of nine foods (broccoli, carrots, cauliflower, green beans, kidney beans, potato, pasta, rice, and sweetcorn) using validated food replicas. Wilcoxon signed-rank tests compared median served weights of each food to reference standard serving size weights. Percentage error was used to compare the estimation of serving size accuracy between the three groups. All participants also performed a usability test using the ServAR tool to guide the serving of one randomly selected food. Results Ninety adults (78.9% female; a mean (95%CI) age 25.8 (24.9–26.7) years; BMI 24.2 (23.2–25.2) kg/m2) completed the study. The median servings were significantly different to the reference portions for five foods in the ServAR group, compared to eight foods in the information only group and seven foods for the control group. The cumulative proportion of total estimations per group within ±10%, ±25% and ±50% of the reference portion was greater for those using ServAR (30.7, 65.2 and 90.7%; respectively), compared to the information only group (19.6, 47.4 and 77.4%) and control group (10.0, 33.7 and 68.9%). Participants generally found the ServAR tool easy to use and agreed that it showed potential to support optimal portion size selection. However, some refinements to the ServAR tool are required to improve the user experience. Conclusions Use of the augmented reality tool improved accuracy and consistency of estimating standard serve sizes compared to the information only and control conditions. ServAR demonstrates potential as a practical tool to guide the serving of food. Further evaluation across a broad range of foods, portion sizes and settings is warranted