Harnessing SmartPhones to Personalize Nutrition in a Time of Global Pandemic

Precision or Personalized Nutrition: A Bibliometric Analysis

Food systems face the challenge of maintaining adequate nutrition for all populations. Inter-individual responses to the same diet have made precision or personalized nutrition (PN) an emerging and relevant topic. The aim of this study is to analyze the evolution of the PN field, identifying the principal actors and topics, and providing a comprehensive overview. Therefore, a bibliometric analysis of the scientific research available through the Web of Science (WOS) database was performed, revealing 2148 relevant papers up to June 2024. VOSviewer and the WOS platform were employed for the processing and analysis, and included an evaluation of diverse data such as country, author or most frequent keywords, among others. The analysis revealed a period of exponential growth from 2015 to 2023, with the USA, Spain, and England as the top contributors. The field of "Nutrition and Dietetics" is particularly significant, comprising nearly 33% of the total publications. The most highly cited institutions are the universities of Tufts, College Dublin, and Navarra. The relationship between nutrition, genetics, and omics sciences, along with dietary intervention studies, has been a defining factor in the evolution of PN. In conclusion, PN represents a promising field of research with significant potential for further advancement and growth.

Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis

Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.

What Intervention Elements Drive Weight Loss in Blended-Care Behavior Change Interventions? A Real-World Data Analysis with 25,706 Patients

BACKGROUND

Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss.

OBJECTIVES

This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI.

METHODS

Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months.

RESULTS

Overall relative weight loss was -1.63 kg at 1 month, -3.61 kg at 3 months, -5.28 kg at 6 months, and -6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss.

CONCLUSIONS

The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.

Rethinking healthy eating in light of the gut microbiome

Given the worldwide epidemic of diet-related chronic diseases, evidence-based dietary recommendations are fundamentally important for health promotion. Despite the importance of the human gut microbiota for the physiological effects of diet and chronic disease etiology, national dietary guidelines around the world are just beginning to capitalize on scientific breakthroughs in the microbiome field. In this review, we discuss contemporary nutritional recommendations from a microbiome science perspective, focusing on mechanistic evidence that established host-microbe interactions as mediators of the physiological effects of diet. We apply this knowledge to inform discussions of nutrition controversies, advance innovative dietary strategies, and propose an experimental framework that integrates the microbiome into nutrition research. The congruence of key paradigms in the nutrition and microbiome disciplines validates current recommendations in dietary guidelines, and the systematic incorporation of microbiome science into nutrition research has the potential to further improve and innovate healthy eating.

The Effect of COVID-19-Related Lifestyle Changes on Depression

OBJECTIVE

This study aimed to identify the effect of coronavirus disease (COVID-19)-related lifestyle changes on depression.

METHODS

This secondary data analysis study included 229,269 adults from a community health survey conducted in the South Korea in 2020. Data were collected using a structured questionnaire about participants' lifestyle changes related to COVID-19 and the Patient Health Questionnaire-9. The data were analyzed using a complex sample independent t-test, analysis of variance, Pearson's correlation coefficient, and multiple regression analysis.

RESULTS

The mean age of the participants was 48.76; 49.6% were male, and 50.4% were female. The multiple regression showed that depression increased due to COVID-19-related lifestyle changes (physical activity, sleep duration, consumption of convenience foods, alcohol consumption, smoking, and use of public transportation). The explanatory power was 27.3%, and the model was suitable (Wald F=63.75, p<0.001).

CONCLUSION

This study identified the effect of COVID-19-related lifestyle changes on depression, and the results have implications for future depression-relieving interventions.

Multi-Device Nutrition Control

Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human-computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users.

Validating Accuracy of a Mobile Application against Food Frequency Questionnaire on Key Nutrients with Modern Diets for mHealth Era

In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: -5% for total calories, -19% for cobalamin, -33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000-2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.

Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.

Use of digital technologies in the nutritional management of catabolism-prone chronic diseases: A rapid review

BACKGROUND

Diet and nutrition applications (apps) have become more readily accessible as smartphone ownership increases. These apps have the potential to improve nutritional outcomes, but it remains unclear whether they are effective in patients with catabolism-prone conditions and specialized nutritional needs.

AIMS

The primary aim of this rapid review was to determine if delivery of a nutrition intervention via an app was more effective than standard care in improving nutritional outcomes in patients with a selected set of catabolism-prone chronic diseases. Secondary aims included summarizing intervention components and reviewing adherence and acceptance.

METHODS

The research question was developed using the Population, Intervention, Comparison, Outcomes (PICO) framework. Comprehensive literature searches were conducted across three databases. Screening, study selection, extraction, and risk of bias (RoB) assessment were conducted for the included randomized controlled trials (RCTs).

RESULTS

15 articles were included, including 5 RCTs; 3/5 RCTs were judged to be at high RoB. The study aims, measured outcomes, and intervention components were diverse. Adherence and acceptance to the app interventions were encouraging.

CONCLUSIONS

Due to the heterogeneity of study design, nutrition interventions, outcomes, and reporting across studies, we were unable to aggregate data regarding the impact on nutritional outcomes. Reassuringly though, the available evidence suggests high adherence and acceptance, which needs to be interpreted in light of the associated personnel support provided within each study. The use of digital technology to deliver diet and nutrition interventions in catabolism-prone conditions is feasible, easy to adhere to, and well-accepted by participants.