Expanding the Capabilities of Nutrition Research and Health Promotion Through Mobile-Based Applications

Nutritional Profiles and Their Links to Insulin Resistance and Anthropometric Variables in a Female Cohort

This study investigates the relationship between dietary habits and metabolic health among women, emphasizing the role of anthropometric parameters as proxies for insulin resistance. We analyzed data from 443 women categorized into two groups based on the presence or absence of clinically diagnosed insulin resistance. Our assessments included dietary quality, socio-demographic characteristics, and a series of anthropometric measurements such as body weight, Body Mass Index (BMI), Waist-Hip Ratio (WHR), Abdominal Volume Index (AVI), and Body Adiposity Index (BAI). The results indicated significant disparities in these parameters, with the insulin-resistant group exhibiting higher average body weight (78.92 kg vs. 65.04 kg, p < 0.001), BMI (28.45 kg/m2 vs. 23.17 kg/m2, p < 0.001), and other related measures, suggesting a strong influence of dietary patterns on body composition and metabolic risk. The study underscores the importance of dietary management in addressing insulin resistance, advocating for personalized dietary strategies to improve metabolic health outcomes in women. This approach highlights the need for integrating dietary changes with lifestyle modifications and socio-demographic considerations to combat metabolic risks effectively.

Effect of using the snackability app on snack quality among US college students with overweight and obesity: A randomized controlled trial

OBJECTIVE

To determine if using the Snackability app improves snack intake among college students.

PARTICIPANTS

US college students with overweight/obesity were recruited in June 2020-April 2021.

METHODS

A randomized controlled trial was conducted to test the Snackability app use for 12 wk on snack scores (calculated from the app) compared to controls. We also explored the effect on diet quality (Healthy Eating Index-2015) and weight. Outcomes were compared between the intervention and the control groups at 4, 8, and 12 wk using repeated measures ANOVA.

RESULTS

Participants in the app group significantly increased snack score at week 4 (p < 0.001) and week 8 (p = 0.015) and increased HEI-2020 score (p < 0.001) at week 4 compared to controls. The first 4 wk had the highest app usage. No significant differences were seen in body weight.

CONCLUSIONS

The Snackability app can be used as a tool to help improve snack and diet quality in this group.

Exploring preliminary dietary intake results using a novel dietary assessment tool with pregnant participants enrolled in a birth cohort

OBJECTIVE

We aimed to describe preliminary dietary intake results using DietID™ for dietary assessment during pregnancy. A sub-sample of participants in the Research Enterprise to Advance Children's Health (REACH) prospective birth cohort from Detroit, MI received a unique web link to complete the DietID™ assessment multiple times during pregnancy. We present results for the first dietary assessment completed during pregnancy by each participant. DietID™ uses an image-based algorithm to estimate nutrient intake, dietary patterns, and diet quality and provides immediate results to participants. Descriptive statistics were used to summarize participant characteristics, nutrient intakes, dietary patterns, diet quality, and participant-rated accuracy of individual dietary assessment results. Differences in diet parameters were assessed by participant race with an independent t-test.

RESULTS

Participants (n = 84) identified as majority Black (n = 47; 56%), reflective of the source population. Mean (SD) maternal age and gestational age at dietary assessment were 32 (5.6) years and 14.3 (4.8) weeks, respectively. Mean dietary quality, as reported in the DietID™ data output as the Healthy Eating Index (HEI), was 68 (range 12-98; higher scores indicate higher diet quality) and varied significantly between Black (mean [SD] 61 [23]) and White (mean [SD] 81 [19]) race (p < 0.01). Mean participant-rated accuracy of individual dietary assessment results was high at 87% on a scale of 0-100% ("not quite right" to "perfect"; range 47-100%).

Exploring the gut microbiota: lifestyle choices, disease associations, and personal genomics

The gut microbiota is a rich and dynamic ecosystem that actively interacts with the human body, playing a significant role in the state of health and disease of the host. Diet, exercise, mental health, and other factors have exhibited the ability to influence the gut bacterial composition, leading to changes that can prevent and improve, or favor and worsen, both intestinal and extra-intestinal conditions. Altered gut microbial states, or 'dysbiosis', associated with conditions and diseases are often characterized by shifts in bacterial abundance and diversity, including an impaired Firmicutes to Bacteroidetes ratio. By understanding the effect of lifestyle on the gut microbiota, personalized advice can be generated to suit each individual profile and foster the adoption of lifestyle changes that can both prevent and ameliorate dysbiosis. The delivery of effective and reliable advice, however, depends not only on the available research and current understanding of the topic, but also on the methods used to assess individuals and to discover the associations, which can introduce bias at multiple stages. The aim of this review is to summarize how human gut microbial variability is defined and what lifestyle choices and diseases have shown association with gut bacterial composition. Furthermore, popular methods to investigate the human gut microbiota are outlined, with a focus on the possible bias caused by the lack of use of standardized methods. Finally, an overview of the current state of personalized advice based on gut microbiota testing is presented, underlining its power and limitations.

Nutrition and Diet Apps: Brazilian Panorama before and during the COVID-19 Pandemic

In the last decade, we have seen a substantial increase in the development and use of mobile technology to improve diet and healthy eating behaviors.

OBJECTIVE

To describe the characteristics of nutrition and diet apps before and after the COVID-19 pandemic available in Brazil.

METHODS

Nutrition and diet apps were identified using the official Apple and Google stores. The search occurred in January 2020 and May 2022 in Brazil. We extracted the nutritional content and standard indicators (e.g., being developed before or after 2020, number of languages, target population, investment, prices, seller, number of reviews and downloads, consumer rating).

RESULTS

280 apps were launched before and 411 during the COVID-19 period. Most apps were available in at least ten languages (96.6%), with no indication of age (95.6%) or partial or full cost (59%). As for the contents, 18.9% addressed personal diet suggestions; 73.4%, nutritional education; 48.8%, revenues; 35.9%, physical activity with a nutritional guide; 2.3%, nutritional recommendation for eating out; 23.9%, grocery shopping with a scan code; 32.4%, food diary; 18.9%, water intake; and 4.6%, nutrition/diseases. The data show an evolution that may have been boosted by the pandemic and that reveals a trend towards the development of apps with educational content.

CONCLUSION

During the pandemic, there was a positive qualitative and quantitative movement in e-health regarding the promotion of education.

A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study

BACKGROUND

The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG.

OBJECTIVE

This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time.

METHODS

The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15).

RESULTS

The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was "acceptable," with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype.

CONCLUSIONS

A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

Integration of USDA Food Classification System and Food Composition Database for Image-Based Dietary Assessment among Individuals Using Insulin

New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.

Stance4Health Nutritional APP: A Path to Personalized Smart Nutrition

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article, we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Skin optical properties in the obese and their relation to body mass index: a review

SIGNIFICANCE

Obesity is a worldwide epidemic contributing directly to several cardiovascular risk factors including hypertension and type 2 diabetes. Wearable devices are becoming better at quantifying biomarkers relevant for the management of health and fitness. Unfortunately, both anecdotal evidence and recent studies indicate that some wearables have higher levels of error when utilized by populations with darker skin tones and high body mass index (BMI). There is an urgent need for a better evaluation of the limits of wearable health technologies when used by obese individuals.

AIMS

(1) To review the current know-how on changes due to obesity in the skin epidermis, dermis, and subcutis that could affect the skin optical properties; (2) for the green wavelength range, to evaluate the difference in absorption and scattering coefficients from the abdominal skin between individuals with and without elevated BMI. The changes include alterations in layer thickness and cell size, as well as significant differences in chromophores and scatterer content, e.g., water, hemoglobin, collagen, and lipids.

APPROACH

We have summarized literature pertaining to changes in skin and its components in obesity and report the results of our search using articles published between years 1971 and 2020. A linear model was used to demonstrate the absorption and reduced scattering coefficient of the abdominal skin of individuals with and without elevated BMI in the green wavelength range (530 to 550 nm) that is typically found in most wearables.

RESULTS

The general trends indicate a decrease in absorption for both dermis and subcutis and an increase in reduced scattering for both epidermis and dermis. At 544-nm wavelength, a typical wavelength used for photoplethysmography (PPG), the absorption coefficient's relative percentage difference between high and low BMI skin, was 49% in the subcutis, 19% in the dermis, and negligible in the epidermis, whereas the reduced scattering coefficient relative difference was 21%, 29%, and 165% respectively.

CONCLUSIONS

These findings suggest that there could be significant errors in the output of optical devices used for monitoring health and fitness if changes due to obesity are not accounted for in their design.