Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial

Smartphone applications for nutrition Support: A systematic review of the target outcomes and main functionalities

INTRODUCTION

A proper nutrition is essential for human life. Recently, special attention on this topic has been given in relation to three health statuses: obesity, malnutrition and specific diseases that can be related to food or treated with specific diets. Mobile technology is often used to assist users that wish to regulate their eating habits, and identifying which fields of application have been explored the most by the app developers and which main functionalities have been adopted can be useful in view of future app developments.

METHODS

We selected 322 articles mentioning nutrition support apps through a literature database search, all of which have undergone an initial screening. After the exclusion of papers that were already reviews, not presenting apps or not focused on nutrition, not relevant or not developed for human subjects, 100 papers were selected for subsequent analyses that aimed at identifying the main treated conditions, outcome measures and functionalities implemented in the Apps.

RESULTS

Of the selected studies, 33 focus on specific diseases, 24 on obesity, 2 on malnutrition and 41 on other targets (e.g., weight/diet control). Type 2 diabetes is the most targeted disease, followed by gestational diabetes, hypertension, colorectal cancer and CVDs which all were targeted by more than one app. Most Apps include self-monitoring and coaching functionalities, educational content and artificial intelligence (AI) tools are slightly less common, whereas counseling, gamification and questionnaires are the least implemented. Body weight and calories/nutrients were the most common general outcome measures, while glycated hemoglobin (HbA1c) was the most common clinical outcome. No statistically significant differences in the effectiveness of the different functionalities were found.

CONCLUSION

The use of mobile technology to improve nutrition has been widely explored in the last years, especially for weight control and specific diseases like diabetes; however, other food-related conditions such as Irritable Bowel Diseases appear to be less targeted by newly developed smartphone apps and their related studies. All different kinds of functionalities appear to be equally effective, but further specific studies are needed to confirm the results.

Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors

Children, adolescents, and young adult cancer survivors (CAYAs) constitute a growing population requiring a customized approach to mitigate the incidence of severe complications throughout their lifetimes. During cancer treatment, CAYAs cancer survivors undergo significant disruptions in their nutritional status, elevating the risks of mortality, morbidity, and cardiovascular events. The assessment of nutritional status during cancer treatment involves anthropometric and dietary evaluations, emphasizing the necessity for regular assessments and the timely identification of risk factors. Proactive nutritional interventions, addressing both undernutrition and overnutrition, should be tailored to specific age groups and incorporate a family-centered approach. Despite encouraging interventions, a notable evidence gap persists. The goal of this review is to comprehensively examine the existing evidence on potential nutritional interventions for CAYAs cancer survivors. We explore the evidence so far collected on the nutritional intervention strategies elaborated for CAYAs cancer survivors that should target both undernutrition and overnutrition, being age-specific and involving a family-based approach. Furthermore, we suggest harnessing artificial intelligence (AI) to anticipate and prevent malnutrition in CAYAs cancer survivors, contributing to the identification of novel risk factors and promoting proactive, personalized healthcare.

Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis

OBJECTIVES

Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public's views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public's understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice.

DESIGN

This was a meta-synthesis of qualitative studies.

METHOD

A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public's perception of the application of AI in healthcare.

RESULTS

Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public's perspective, ethical and legal concerns about medical AI from the public's perspective, and public suggestions on the application of AI in medical field.

CONCLUSION

Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public's perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice.

PROSPERO REGISTRATION NUMBER

CRD42022315033.