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Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality

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A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".

Deadline for manuscript submissions: closed (30 July 2020) | Viewed by 17440

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Dr. Maya Vadiveloo

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Department of Nutrition and Food Sciences, University of Rhode Island, 41 Lower College Rd, Kingston, RI, 02881, USA
Interests: dietary quality; novel diet assessment methods; eating behavior; environmental determinants of diet; diet and cardiometabolic health

Special Issue Information

Dear Colleagues,

Eating decisions and dietary quality are driven by myriad interwoven personal and environmental factors. These factors are dynamic and shape our health, making diet one of the most challenging and complex exposures to understand and modify. Despite the strong evidence base demonstrating the role of plant-based and Mediterranean dietary patterns in promoting health, diet remains a leading risk factor for chronic disease morbidity and mortality in the US and globally. Nevertheless, as technological advancements are more broadly integrated into society, compelling opportunities exist to innovate within dietary assessment and within behavioral interventions designed to modify personal and environmental determinants of diet. Routine and accurate dietary assessment is paramount to understand dynamic changes in diet quality and developing just-in-time interventions applicable for dissemination at the population-level to improve diet quality and associated health parameters.

This Special issue invites original research and review articles that focus on novel dietary assessment methods, including new assessment technologies, combining assessment methods to enhance accuracy and validity, machine learning and artificial intelligence approaches, and just-in-time or adaptive intervention approaches to modify determinants of dietary intake. Potential topics include but are not limited to:

Development and/or validation of novel dietary assessment methods;
Mobile applications to monitor and modify diet;
Household- and environmental-level diet quality measures;
Supervised learning/artificial intelligence in diet assessment;
Combining dietary assessment methods to enhance accuracy and validity.

Dr. Maya Vadiveloo
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Dietary quality
Eating behavior
Novel diet assessment methods
just-in-time/adaptive interventions to improve diet quality and cardiometabolic health
Supervised learning/artificial intelligence

Published Papers (5 papers)

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Research

18 pages, 1319 KiB

Open AccessArticle

Sociodemographic Differences in the Dietary Quality of Food-at-Home Acquisitions and Purchases among Participants in the U.S. Nationally Representative Food Acquisition and Purchase Survey (FoodAPS)

by Maya K. Vadiveloo, Haley W. Parker, Filippa Juul and Niyati Parekh

Nutrients 2020, 12(8), 2354; https://doi.org/10.3390/nu12082354 - 7 Aug 2020

Cited by 13 | Viewed by 3835

Abstract

Insufficient research has explored whether sociodemographic differences in self-reported, individual-level diet quality are similarly reflected by grocery purchase quality. This cross-sectional analysis of n = 3961 U.S. households from the nationally representative Food Acquisition and Purchase Survey (FoodAPS) compared Healthy Eating Index (HEI)-2015 scores from 1 week of food-at-home acquisitions across self-reported demographic factors (race/ethnicity, Supplemental Nutrition Assistance Program (SNAP) participation, food security, and household-level obesity status). Multivariable-adjusted, survey-weighted regression models compared household HEI-2015 scores across sociodemographic groups. Respondents were primarily White and female, with a mean age of 50.6 years, 14.0% were food insecure, and 12.7% were SNAP-participating. Mean HEI-2015 scores were 54.7; scores differed across all sociodemographic exposures (p < 0.05). Interactions (p < 0.1) were detected between SNAP participation and (1) food insecurity and (2) household-level obesity, and race/ethnicity and (1) household-level obesity. HEI-2015 scores were higher among food secure, non-SNAP households than among food insecure, SNAP-participating households (53.9 ± 0.5 vs. 50.3 ± 0.7, p = 0.007); non-SNAP households without obesity had significantly higher HEI-2015 scores than other households. Household-level obesity was associated with lower HEI-2015 scores in White (50.8 ± 0.5 vs. 52.5 ± 0.7, p = 0.046) and Black (48.8 ± 1.5 vs. 53.1 ± 1.4, p = 0.018) but not Hispanic households (54.4 ± 1.0 vs. 52.2 ± 1.2, p = 0.21). Sociodemographic disparities in household HEI-2015 scores were consistent with previous research on individual-level diet quality. Full article

(This article belongs to the Special Issue Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality)

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14 pages, 453 KiB

Open AccessArticle

Reduced Screen Time is Associated with Healthy Dietary Behaviors but Not Body Weight Status among Polish Adolescents. Report from the Wise Nutrition—Healthy Generation Project

by Joanna Myszkowska-Ryciak, Anna Harton, Ewa Lange, Wacław Laskowski, Agata Wawrzyniak, Jadwiga Hamulka and Danuta Gajewska

Nutrients 2020, 12(5), 1323; https://doi.org/10.3390/nu12051323 - 6 May 2020

Cited by 12 | Viewed by 3495

Abstract

Screen time (ST) not only affects physical activity but can also be associated with dietary behaviors. Both of these factors determine the health and development of adolescents. The aims of the study were: 1. to analyze the relationship between ST and nutritional behaviors among adolescents; 2. to examine this association in relation to body weight status. Data on the ST duration and nutritional behaviors were collected using a questionnaire. Body mass status was assessed based on weight and height measurements. A total of 14,044 students aged 13–19 years old from 207 schools participated in the study. A significant relationship between ST and gender, age and type of school was observed, but not body weight status. The average ST duration increased with age (from 2.6 h among 13 years old to 3.2 h among 19 years old), and was significantly higher among boys in all age categories (2.7 h vs. 2.5 h in the youngest age group, and 3.5 h vs. 3.0 h in the oldest age group, respectively). The chance for meeting the recommendation for ST in a group of girls (regardless of age) was almost 50% higher compared to boys. Meeting ST recommendation (≤2 h) was associated with a greater odds ratio for favorable nutritional behaviors in the whole group, with exception of drinking milk or milk beverages, and significantly reduced the odds ratio of adverse dietary behaviors (drinking sweet beverages, consumption of sweets and fast food) in the whole group and by gender. More research is needed to clarify the possible cause-and-effect relationships between ST and dietary behaviors. Full article

(This article belongs to the Special Issue Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality)

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16 pages, 270 KiB

Open AccessArticle

Do Where The Elderly Live Matter? Factors Associated with Diet Quality among Korean Elderly Population Living in Urban Versus Rural Areas

by Sohyun Park, Hyun Ja Kim and Kirang Kim

Nutrients 2020, 12(5), 1314; https://doi.org/10.3390/nu12051314 - 5 May 2020

Cited by 13 | Viewed by 2913

Abstract

This study aimed to examine whether there is an area difference on diet quality among the Korean elderly population. The effect of personal factors on diet quality is also estimated and compared between rural and urban areas. A cross-sectional data from the 2013–2015 Korea National Health and Nutrition Examination Survey (KNHANES) was used for this study. The participants were older adults aged ≥ 65 years (n = 3207) who participated in the KNHANES. Urban and rural areas classified the region and the Korean Healthy Eating Index (KHEI) assessed the diet quality. Personal factors that were related to diet quality included socio-demographic factors, health behaviors, and health conditions. This study found that the diet quality was different between urban and rural areas in the Korean elderly population, showing a higher mean of KHEI scores in urban areas than rural areas (67.3 for urban seniors, 63.6 for rural seniors, p < 0.001), and the regional difference was still significant, even after adjusting for the personal factors (p < 0.001). Different sets of personal factors were found to be significant that explain the diet quality of participants between areas, such as economic resources, walking exercise, and perceived oral health status in urban areas, and age and food insecurity in rural areas. In conclusions, this study found that there was a regional disparity in diet quality and some personal factors affecting diet quality were dependent on areas, which implied that regional environment with diverse contexts could influence diet quality. These findings emphasize the need to provide targeted intervention programs that take into account both the characteristics of individuals and local food environments in order to improve the overall diet quality in older adults. Full article

(This article belongs to the Special Issue Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality)

16 pages, 1481 KiB

Open AccessArticle

Evaluating Diet Quality of Canadian Adults Using Health Canada’s Surveillance Tool Tier System: Findings from the 2015 Canadian Community Health Survey-Nutrition

by Salma Hack, Mahsa Jessri and Mary R. L’Abbé

Nutrients 2020, 12(4), 1113; https://doi.org/10.3390/nu12041113 - 16 Apr 2020

Cited by 14 | Viewed by 3637

Abstract

The 2014 Health Canada’s Surveillance Tool, Tier System (HCST) is a nutrient profiling model developed to evaluate adherence of food choices to dietary recommendations. With the recent release of the nationally representative Canadian Community Health Survey-Nutrition (CCHS-N) 2015, this study used HCST to evaluate nutritional quality of the dietary intakes of Canadians in the CCHS-N. Dietary intakes were ascertained using 24-hour dietary recalls from Canadians adults ≥19 years (N = 13,605). Foods were categorized into four Tiers based on degree of adherence to dietary recommendations according to thresholds for sodium, total fat, saturated fats, and sugars. Tier 1 and Tier 2 represented “recommended foods”, Tier 3 represents foods to “choose less often”, and Tier 4 represented foods “not recommended”. Across all dietary reference intakes (DRI) groups, most foods were categorized as Tier 1 for Vegetable and Fruits (2.2–3.8 servings/day), Tier 2 for Grain Products (2.9–3.4 servings/day), Tier 3 for Milk and Alternatives (0.7–1 serving/day) or for Meat and Alternatives (1.1–1.6 servings/day). Consumption of foods from Tier 4 and “other foods” such as high fat/sugary foods, sugar-sweetened beverages, and alcohol, represented 24–26% and 21–23% kcal/day, for males and females, respectively. Canadians are eating more foods categorized as Tier 1–3, rather than Tier 4. Adults with the highest intakes of Tier 4 and “other foods” had lower intakes of macronutrients and increased body mass index. These findings can be used by policy makers to assist in identifying targets for food reformulation at the nutrient level and quantitative guidance to support healthy food choices. Full article

(This article belongs to the Special Issue Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality)

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21 pages, 1969 KiB

Open AccessArticle

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults

by Ran Xu, Bruce E. Blanchard, Jeanne M. McCaffrey, Stephen Woolley, Lauren M. L. Corso and Valerie B. Duffy

Nutrients 2020, 12(4), 882; https://doi.org/10.3390/nu12040882 - 25 Mar 2020

Cited by 11 | Viewed by 3150

Abstract

The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically from existing datasets, or a combination of the two. We used these methods to derive diet quality indexes (DQI), generated from a novel dietary assessment, and to evaluate relationships with cardiometabolic risk factors in young adults with (n = 106) or without (n = 106) diagnosed depression (62% female, mean age = 21). Participants completed a liking survey (proxy for usual dietary consumption). Principle component analysis of plasma (insulin, glucose, lipids) and adiposity (BMI, Waist-to-Hip ratio) measures formed a continuous cardiometabolic risk factor score (CRFS). DQIs were created: theoretically (food/beverages grouped, weighted conceptually), empirically (grouping by factor analysis, weights empirically-derived by ridge regression analysis of CRFS), and hybrid (food/beverages conceptually-grouped, weights empirically-derived). The out-of-sample CRFS predictability for the DQI was assessed by two-fold and five-fold cross validations. While moderate consistencies between theoretically- and empirically-generated weights existed, the hybrid outperformed theoretical and empirical DQIs in cross validations (five-fold showed DQI explained 2.6% theoretical, 2.7% empirical, and 6.5% hybrid of CRFS variance). These pilot data support a liking survey that can generate reliable/valid DQIs that are significantly associated with cardiometabolic risk factors, especially theoretically- plus empirically-derived DQI. Full article

(This article belongs to the Special Issue Dietary Assessment, Diet Quality, and Modifying Factors in the Environment to Influence Life Quality)

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