AI Applications to Reduce Loneliness Among Older Adults: A Systematic Review of Effectiveness and Technologies.

Loneliness among older adults is a prevalent issue, significantly impacting their quality of life and increasing the risk of physical and mental health complications. The application of artificial intelligence (AI) technologies in behavioral interventions offers a promising avenue to overcome challenges in designing and implementing interventions to reduce loneliness by enabling personalized and scalable solutions. This study systematically reviews the AI-enabled interventions in addressing loneliness among older adults, focusing on the effectiveness and underlying technologies used.

Impact of hurricane Maria on physical activity, smoking, and alcohol use among Puerto Ricans.

Hurricane Maria devastated Puerto Rico in September 2017. We examined it's impact on physical activity, smoking, and alcohol use. Data was from 2015-2019 Behavioral Risk Factor Surveillance System.

Artificial Intelligence Holds Promise for Transforming Public Health Nutrition.

The intersection of artificial intelligence (AI) and public health nutrition is rapidly evolving, offering transformative potential for how we understand, assess, and improve population health [...

Effect of Oral Nutritional Supplementation on Health-Related Outcomes and Nutritional Biomarkers in Children and Adolescents with Undernutrition: A Systematic Review and Meta-Analysis.

This systematic review aims to synthesize scientific evidence on the effects of oral nutritional supplementation (ONS) on health-related outcomes and nutritional biomarkers among children and adolescents with undernutrition. The review protocol was reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A comprehensive keyword and reference search was conducted in seven electronic bibliographic databases: PubMed, Academic Search Complete, Academic Search Premier, CINAHL, Global Health, Web of Science, and Scopus.

Quality and Accountability of ChatGPT in Health Care in Low- and Middle-Income Countries: Simulated Patient Study.

Using simulated patients to mimic 9 established noncommunicable and infectious diseases, we assessed ChatGPT's performance in treatment recommendations for common diseases in low- and middle-income countries. ChatGPT had a high level of accuracy in both correct diagnoses (20/27, 74%) and medication prescriptions (22/27, 82%) but a concerning level of unnecessary or harmful medications (23/27, 85%) even with correct diagnoses. ChatGPT performed better in managing noncommunicable diseases than infectious ones.

Use of Deep Neural Networks to Predict Obesity With Short Audio Recordings: Development and Usability Study.

Background: The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated with obesity, suggesting the feasibility of using voice as a noninvasive biomarker for obesity detection.

Objective: This study aims to use deep neural networks to predict obesity status through the analysis of short audio recordings, investigating the relationship between vocal characteristics and obesity.

Are Supplemental Nutrition Assistance Program Restrictions on Sugar-Sweetened Beverages Effective in Reducing Purchase or Consumption? A Systematic Review.

The participants in the Supplemental Nutrition Assistance Program (SNAP) consume greater amounts of sugar and sweetened beverages (SSBs) compared to non-eligible individuals, which could result in potential negative health outcomes. This can be attributed to the lack of restrictions on SSB purchases with SNAP benefits. In view of the increasing calls from advocates and policymakers to restrict the purchase of SSBs with SNAP benefits, we performed a systematic review to assess its impact towards SSB purchases and consumption.

Build Deep Neural Network Models to Detect Common Edible Nuts from Photos and Estimate Nutrient Portfolio.

Nuts are nutrient-dense foods and can be incorporated into a healthy diet. Artificial intelligence-powered diet-tracking apps may promote nut consumption by providing real-time, accurate nutrition information but depend on data and model availability. Our team developed a dataset comprising 1380 photographs, each in RGB color format and with a resolution of 4032 × 3024 pixels.

Effectively communicating with local policymakers: a randomized trial of policy brief dissemination to address obesity.

Introduction: Evidence-based policies are a powerful tool for impacting health and addressing obesity. Effectively communicating evidence to policymakers is critical to ensure evidence is incorporated into policies. While all public health is local, limited knowledge exists regarding effective approaches for improving local policymakers' uptake of evidence-based policies.

Using Mobile Phone Data to Assess Socio-Economic Disparities in Unhealthy Food Reliance during the COVID-19 Pandemic.

Although COVID-19 has disproportionately affected socio-economically vulnerable populations, research on its impact on socio-economic disparities in unhealthy food reliance remains scarce. This study uses mobile phone data to evaluate the impact of COVID-19 on socio-economic disparities in reliance on convenience stores and fast food. Reliance is defined in terms of the proportion of visits to convenience stores out of the total visits to both convenience and grocery stores, and the proportion of visits to fast food restaurants out of the total visits to both fast food and full-service restaurants.

Leveraging machine learning for effective child maltreatment prevention: A case study of home visiting service assessments.

Background: Early identification of children and families who may benefit from support is crucial for implementing strategies that can prevent the onset of child maltreatment. Predictive risk modeling (PRM) may offer valuable and efficient enhancements to existing risk assessment techniques.

Objective: To evaluate the PRM's effectiveness against the existing assessment tool in identifying children and families needing home visiting services.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.

Background: The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms.

Influence of Pokémon GO on Physical Activity and Psychosocial Well-Being in Children and Adolescents: Systematic Review.

Background: Pokémon GO, an augmented reality game with widespread popularity, can potentially influence players' physical activity (PA) levels and psychosocial well-being.

Objective: This review aims to systematically examine the scientific evidence regarding the impact of Pokémon GO on PA and psychosocial well-being in children and adolescents.

Methods: Using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework, we conducted keyword and reference searches in the PubMed, CINAHL, Web of Science, and Scopus databases.

Artificial Intelligence Applications to Public Health Nutrition.

Public health nutrition occupies a paramount position in the overarching domains of health promotion and disease prevention, setting itself apart from nutritional investigations concentrated at the individual level [...

Sentiment Analysis of Tweets on Menu Labeling Regulations in the US.

Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news.

A scoping review of methodologies for applying artificial intelligence to physical activity interventions.

Purpose: This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence (AI) applications in physical activity (PA) interventions; introduce them to prevalent machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms; and encourage the adoption of AI methodologies.

Methods: A scoping review was performed in PubMed, Web of Science, Cochrane Library, and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes. AI methodologies were summarized and categorized to identify synergies, patterns, and trends informing future research.

Assessing the longitudinal relationship between whole egg consumption and cognition in Chinese older adults.

Background: Given China's fast-growing aging population, cognitive decline is a leading public health concern. Eggs are an affordable food rich in several shortfall nutrients that may benefit cognitive health.

Aim: This study assessed the longitudinal relationship between whole egg consumption and cognition among older adults in China.

Healthy Diet Promotion through Evidence-Informed Policies.

The Special Issue entitled "The Impact of Policy and Food Environment on Food Purchase and Dietary Behavior" comprises 13 articles that collectively provide valuable insights into the complex interplay between policy, food environment, and individual food purchase and consumption [...

Building Machine Learning Models to Correct Self-Reported Anthropometric Measures.

Monitoring population obesity risk primarily depends on self-reported anthropometric data prone to recall error and bias. This study developed machine learning (ML) models to correct self-reported height and weight and estimate obesity prevalence in US adults. Individual-level data from 50 274 adults were retrieved from the National Health and Nutrition Examination Survey (NHANES) 1999-2020 waves.

Choline Intake and Cognitive Function Among U.S. Older Adults.

Choline is an essential nutrient affects brain development in early life. However, evidence is lacking regarding its potential neuroprotective effects in later life from community-based cohorts. This study assessed the relationship between choline intake and cognitive functioning in a sample of older adults 60 years + from the National Health and Nutrition Examination Survey 2011-2012 and 2013-2014 waves ( = 2,796).

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies.

Background: Obesity is a leading cause of preventable death worldwide. Artificial intelligence (AI), characterized by machine learning (ML) and deep learning (DL), has become an indispensable tool in obesity research.

Objective: This scoping review aimed to provide researchers and practitioners with an overview of the AI applications to obesity research, familiarize them with popular ML and DL models, and facilitate the adoption of AI applications.

The Safety and Efficacy of Clonidine in Hemodialysis Patients: A Systematic Review and Meta-Analysis.

Background: Clonidine is a frequently prescribed long-term antihypertensive medication in hemodialysis (HD) patients in the USA, but its safety and efficacy has not been clearly established in the HD population.

Objective: To evaluate, we conducted a systematic review and meta-analysis on the safety and efficacy of clonidine in HD patients.

Methods: Keyword search of "clonidine" and "dialysis" was conducted through April 2021 in PubMed, Cochrane Library, Web of Science, Scopus, and ClinicalTrials.