Assessing the Diets of Young Children and Adolescents in India: Challenges and Opportunities

The ABCDs of Nutritional Assessment in Infectious Diseases Research

Malnutrition is the most common acquired cause of immunodeficiency worldwide. Nutritional deficiencies can blunt both the innate and adaptive immune response to pathogens. Furthermore, malnutrition is both a cause and consequence of infectious diseases. The bidirectional relationship between infectious diseases and undernutrition, as well as the inflammatory milieu of infectious diseases, can complicate nutritional assessment. This article aims to provide clinicians and researchers with an overview of commonly used tools to assess nutritional status, with a particular emphasis on their use in the context of infectious diseases. These tools include anthropometric, biochemical, clinical/physical, and dietary assessments to screen and evaluate undernutrition, diet quality, and food insecurity effectively.

Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications

Introduction

Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) technologies have opened new avenues for their applications in dietary assessments. Conventional dietary assessment methods are time-consuming, labor-driven, and have high recall bias. AI-assisted tools can be user-friendly and provide accurate dietary data. Hence, this review aimed to explore the applications of AI-assisted dietary assessment tools in real-world settings that could potentially enhance Next-Gen nutrition care delivery.

Materials and methods

A total of 17,613 original, full-text articles using keywords such as "artificial intelligence OR food image analysis OR wearable devices AND dietary OR nutritional assessment," published in English between January 2014 and September 2024 were extracted from Scopus, Web of Science, and PubMed databases. All studies exploring applications of AI-assisted dietary assessment tools with human participation were included; While methodological/developmental research and studies without human participants were excluded as this review specifically aimed to explore their applications in real-world scenarios for clinical purposes. In the final phase of screening, 66 articles were reviewed that matched our inclusion criteria and the review followed PRISMA-ScR reporting guidelines.

Results

We observed that existing AI-assisted dietary assessment tools are integrated with mobile/web-based applications to provide a user-friendly interface. These tools can broadly be categorized as "Image-based" and "Motion sensor-based." Image-based tools allow food recognition, classification, food volume/weight, and nutrient estimation whereas, Motion sensor-based tools help capture eating occasions through wrist movement, eating sounds, jaw motion & swallowing. These functionalities capture the dietary data regarding the type of food or beverage consumed, calorie intake, portion sizes, frequency of eating, and shared eating occasions as real-time data making it more accurate as against conventional dietary assessment methods. Dietary assessment tools integrated with AI and ML could estimate real-time energy and macronutrient intake in patients with chronic conditions such as obesity, diabetes, and dementia. Additionally, these tools are non-laborious, time-efficient, user-friendly, and provide fairly accurate data free from recall/reporting bias enabling clinicians to offer personalized nutrition.

Conclusion

Therefore, integrating AI-based dietary assessment tools will help improve the quality of nutrition care and navigate next-gen nutrition care practices. More studies are required further to evaluate the efficacy and accuracy of these tools.

Energy and nutrient intake by 11-13-year-old young adolescents attending private schools in Delhi, India

There are no high-quality data on dietary behaviour of adolescents in India. This study aimed to assess the intake of energy (E), macronutrients and selected micronutrients in a sample of 11-13-year-old schoolchildren in Delhi, India. Participants from private schools (n=10) recorded dietary intake using a 3-d food diary. Information was entered into the dietary assessment tool, Intake24, to ascertain portion size and convert data into nutrient intake through integrated food tables. Of the 514 consenting participants, 393 (76·4 %) (169 girls, 224 boys) aged 11·4 (±1·8) years completed the study. The median (interquartile range (IQR) daily E intake was 2580 (2139·3-2989·8) kcal (10·8 (9·0 - 12·5) MJ) for girls and 2941·5 (2466·7-3599·3) kcal (12·3 (10·3-15·2) MJ) for boys. The median (IQR) daily nutrient intakes for girls and boys respectively were protein 64·6 (54·8-79·3) g, 74·4 (61·4; 89·4) g; carbohydrate 336·5 (285·3-393·6) g, 379·6 (317·8-461·8) g; and saturated fat 45·6 (34·8-58·3) g, 54·6 (41·9-69·5) g. There were no significant between-gender differences in percentage E from protein (10·2 (9·2-11·4)), or carbohydrate (52·4 (48·7-56·7)). Girls obtained less percentage E from saturated fat (16·1 (11·0-18·2) compared with boys 16·3 (14·2-19·1) (P < 0·05). E from saturated fat was above FAO recommendations in >74 % of participants. The estimated average requirement for iron was achieved by < 40 % of girls. In conclusion, strategies to optimise the dietary intake of adolescents in India should focus on preventing excess intakes of E and saturated fat and improving iron intake in girls.

Decoding India's Child Malnutrition Puzzle: A Multivariable Analysis Using a Composite Index

BACKGROUND

This study examines the levels and predictors of malnutrition in Indian children under 5 years of age.

METHODS

Composite Index of Anthropometric Failure was applied to data from the India National Family Health Survey 2019-2021. A multivariable logistic regression model was used to assess the predictors.

RESULTS

52.59% of children experienced anthropometric failure. Child predictors of lower malnutrition risk included female gender (adjusted odds ratio (AOR) = 0.881) and average or large size at birth (AOR = 0.729 and 0.715, respectively, compared to small size). Higher birth order increased malnutrition odds (2nd-4th: AOR = 1.211; 5th or higher: AOR = 1.449) compared to firstborn. Maternal predictors of lower malnutrition risk included age 20-34 years (AOR = 0.806), age 35-49 years (AOR = 0.714) compared to 15-19 years, normal BMI (AOR = 0.752), overweight and obese BMI (AOR = 0.504) compared to underweight, and secondary or higher education vs. no education (AOR = 0.865). Maternal predictors of higher malnutrition risk included severe anemia vs. no anemia (AOR = 1.232). Protective socioeconomic factors included middle (AOR = 0.903) and rich wealth index (AOR = 0.717) compared to poor, and toilet access (AOR = 0.803). Children's malnutrition risk also declined with paternal education (primary: AOR = 0.901; secondary or higher: AOR = 0.822) vs. no education. Conversely, malnutrition risk increased with Hindu (AOR = 1.258) or Islam religion (AOR = 1.369) vs. other religions.

CONCLUSIONS

Child malnutrition remains a critical issue in India, necessitating concerted efforts from both private and public sectors. A 'Health in All Policies' approach should guide public health leadership in influencing policies that impact children's nutritional status.

Characteristics of Global Data on Adolescent's Dietary Intake: A Systematic Scoping Review

Data on adolescents' dietary intake are essential to improve their diets and nutrition. However, the availability of (high-quality) data on adolescents' dietary intake is scarce with great global differences. We conducted a systematic scoping review to investigate the availability, characteristics, and gaps in global adolescent dietary data, following the Preferred Reporting Items for Systematic Reviews and Meta Analyses-Extension for Scoping Reviews checklist and guidelines (registered under PROSPERO no. 171170 https://www.crd.york.ac.uk/PROSPERO/). We included peer-reviewed and grey literature articles (2010 onwards) on the dietary intake of male and female adolescents (10-24 y). Studies from all countries and languages and including any information related to types of food consumed, diet composition, dietary diversity, or meal patterns were considered. We excluded studies with insufficient methodological information, unclear description of population, samples sizes <25, school-based data sets containing <6 schools, and studies that focused on pregnant or unhealthy study populations. Data, including year(s) of data collection, age, gender, sample size, dietary assessment methods, number of food items/groups, study design, location, and representativeness, were extracted. A total of 52,889 titles were identified and 722 articles, describing 1,322 data sets, were retained for analysis. Nationally representative, detailed dietary data for adolescents aged 10-24 y are still lacking, particularly in sub-Saharan Africa, South Asia, and low-income countries. Data quality and representativeness remain limited, highlighting the need for data disaggregation by age, gender, locality, comprehensive dietary information, and broader geographic coverage. A notable amount of data was available through grey literature, especially in data-scarce countries. The study underscores the importance of addressing adolescent nutrition, emphasizing the urgent need for more robust, accessible, and representative data on adolescents' dietary intake to support effective nutritional efforts.

Development of mobile phone-based dietary data collection applications in pregnant women and infants for the M-SAKHI trial

In nutritional epidemiological studies, it is imperative to collect high-quality data to ensure accurate dietary assessment. However, dietary data collection using traditional paper forms has several limitations that may compromise data quality. The aim of this study was to propose novel methods to design and develop software applications (Apps) for dietary data collection to assess the nutritional status of pregnant women and infants. This study is part of the M-SAKHI (Mobile-Solutions for Aiding Knowledge for Health Improvement) cluster randomised controlled trial (cRCT) implemented in central India. Three tablet-based software Apps were developed in this study: the ACEC (Automated Coding and Energy Calculation) App to establish a generic cooked food recipe database, the FFQ (Food Frequency Questionnaire), and the IDR (24 h Infant Dietary Recall) Apps to collect dietary data from pregnant women and their infants from rural area of Bhandara and Nagpur districts. Regional food lists, recipes, and portion resource kits were developed to support the data collection using the Apps. In conclusion, the Apps were user-friendly, required minimal prior training, had built-in validation checks for erroneous data entry and provided automated calculations. The Apps were successfully deployed in low-resource rural settings to accurately collect high-quality regional cooked food data and individual-level dietary data of pregnant women and their infants.