Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence

Nutrients, Год журнала: 2025, Номер 17(1), С. 190 - 190

Опубликована: Янв. 5, 2025

Background: Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at intricate connection between food and health in both an individual a community context. AI also helps tracing offering solutions dietary assessment, personalized clinical nutrition, well disease prediction management, such cardiovascular diseases, diabetes, cancer, obesity. This review aims investigate assess different applications roles understand potential future impact. Methods: We used PubMed, Scopus, Web Science, Google Scholar, EBSCO databases for our search. Results: Our findings indicate that is reshaping field ways were previously unimaginable. By enhancing how we diets, customize plans, manage complex conditions, has become tool. Technologies like machine learning models, wearable devices, chatbot revolutionizing accuracy tracking, making it easier than ever provide tailored individuals communities. These innovations proving invaluable combating diet-related illnesses encouraging healthier eating habits. One breakthrough been where significantly reduced errors common traditional methods. Tools use visual recognition, deep learning, mobile have made possible analyze nutrient content meals with incredible precision. Conclusions: Moving forward, collaboration tech developers, healthcare professionals, policymakers, researchers will be essential. focusing on high-quality data, addressing ethical challenges, keeping user needs forefront, can truly revolutionize science. The enormous. set make not only more effective but equitable accessible everyone.

Язык: Английский

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Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development

Food Research International, Год журнала: 2025, Номер unknown, С. 116306 - 116306

Опубликована: Март 1, 2025

Язык: Английский

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The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations

Medicina, Год журнала: 2025, Номер 61(2), С. 358 - 358

Опубликована: Фев. 19, 2025

Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap leading comorbidities such heart disease, innovative solutions necessary to improve risk prediction management strategies. In recent years, artificial intelligence (AI) machine learning (ML) have emerged powerful tools in healthcare, offering novel approaches chronic disease prevention. This narrative review explores the role of AI/ML obesity management, a special focus on childhood obesity. We begin by examining multifactorial nature obesity, including genetic, behavioral, environmental factors, limitations traditional predict treat morbidity Next, we analyze techniques commonly used risk, particularly minimizing risk. shift application comparing perspectives from healthcare providers versus patients. From provider's perspective, offer real-time data electronic medical records, wearables, health apps stratify patient customize treatment plans, enhance clinical decision making. patient's AI/ML-driven interventions personalized coaching long-term engagement management. Finally, address key challenges, determinants embracing while our recommendations based literature review.

Язык: Английский

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Identifying Dietary Triggers Among Individuals with Overweight and Obesity: An Ecological Momentary Assessment Study

Nutrients, Год журнала: 2025, Номер 17(3), С. 481 - 481

Опубликована: Янв. 29, 2025

Background/Objectives: Excess adiposity, affecting 43% of the global adult population, is a major contributor to cardiometabolic diseases. Lifestyle behaviours, specifically dietary habits, play key role in weight management. Real-time assessment methods such as Ecological Momentary Assessment (EMA) provide context-rich data that reduce recall bias and offer insights into triggers lapses. This study examines among adults with excess adiposity Singapore using EMA, focusing on factors influencing adherence Methods: A total 250 participants BMI ≥ 23 kg/m2 were recruited track habits for one week, at least three times day, Eating Behaviour Lapse Inventory Survey (eBLISS) embedded within Trigger Response Inhibition Program (eTRIP© V.1) smartphone app. Logistic regression analysis was used identify predictors adherence. Results: Of 4708 responses, 76.4% responses indicative plans. Non-adherence primarily associated food accessibility negative emotions (stress, nervousness, sadness). Factors meals prepared by domestic helpers self-preparation significantly Negative premenstrual syndrome identified significant Conclusions: EMA offers valuable behaviours identifying real-time Future interventions can utilise technology-driven approaches predict prevent lapses, potentially improving management outcomes.

Язык: Английский

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A validity and reliability study of the artificial intelligence attitude scale (AIAS-4) and its relationship with social media addiction and eating behaviors in Turkish adults

BMC Public Health, Год журнала: 2025, Номер 25(1)

Опубликована: Апрель 2, 2025

In recent years, there has been a rapid increase in the use of internet and social media. Billions people worldwide media spend an average 2.2 h day on these platforms. At same time, artificial intelligence (AI) applications have become widespread many fields, such as health, education, finance. While AI potential to monitor eating behaviors provide personalized health support, excessive can lead negative effects. These include addiction reduced quality life. It is important examine attitude toward its relationship with addiction, behavior, life satisfaction. Research connection between attitudes habits lacking, which emphasizes necessity validating AIAS-4 Turkish order ensure efficacy this context. The first stage study aimed adapt Grassini's (2023) Artificial Intelligence Attitude Scale (AIAS-4) into assess validity reliability. second stage, it was This cross-sectional methodological conducted two stages Türkiye. 172 adult individuals underwent reliability (43% them were men 57% women), involved adapting Turkish. relationships attitude, satisfaction 510 evaluated age 24.88 ± 7.05 years (30.8% male, 69.2% female). Using snowball sampling technique, survey carried out adults by reaching staff their families from both universities (Gazi University Tokat Gaziosmanpaşa University) well students relatives. A face-to-face approach (delivered interviewer) used for study. study, Social Media Addiction Scale-Adult Form(SMAS-AF) Effects Eating Behavior (SESMEB) measure impact Contentment Life Assessment evaluate satisfaction, Disorder Examination Questionnaire (EDE-Q total) disorder symptoms. Pearson Correlation Spearman according normality Linear regression analysis analyse variables. valid reliable instrument Türkiye (Cronbach's alpha = 0.90 McDonald's omega 0.89). Individuals 3.7 1.99 per All participants WhatsApp, while 89.8% Instagram. correlation found AIAS EDE-Q total, (r=-0.119 p < 0.05). BMI correlated positively total (r 0.391, 0.01). Higher scores associated increased time spent 0.129, 0.001). Conversely, higher lower (r= -0.119, SESMEB 0.169; model showed that (β 0.311; 0.001), =-0.157, 0.005), SMAS-AF 0.036; 0.002) 0.022; 0.038) affected (p 0.001 R2 0.198). revealed (AIAS) adults. results show BMI, positive, behaviors. findings emphasize importance multidisciplinary approaches awareness programs prevention management disorders.

Язык: Английский

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Systematic review exploring human, AI, and hybrid health coaching in digital health interventions: trends, engagement, and lifestyle outcomes

Frontiers in Digital Health, Год журнала: 2025, Номер 7

Опубликована: Апрель 24, 2025

Digital Health Interventions (DHIs) have been identified as a solution to the United Nations Sustainable Development Goals (SDG3) for health promotion and prevention. However, DHIs face criticism shallow transactional engagement retention challenges. Integrating with coaching represents promising that might address these issues by combining scalable accessible nature of meaningful engaging coaching. This systematic review aims synthesise existing peer-reviewed research on coach-facilitated understand how digital is being used in impact it has lifestyle outcomes. Studies examining component addressing outcomes were included. A search APA PsychINFO, Medline, Web Science, Scopus was performed from inception February 2025. Three authors conducted study selection, quality appraisal using Mixed Methods Appraisal Tool (MMAT), data extraction. Data extraction captured characteristics, features, participant engagement, Thirty-five studies synthesised narrative synthesis approach. highlights three modalities DHIs: human coaching, Artificial Intelligence (AI) hybrid (human-AI) All demonstrated feasibility acceptability. While both AI shown positive outcomes, approaches need further refinement harness AI's scalability depth variability metrics protocols limited comparability. Standardising delivery are measured contextualised crucial advancing evidence-based followed PRISMA guidelines registered PROSPERO (Registration number: CRD42022363279). The Irish Research Council supported this work. https://www.crd.york.ac.uk/PROSPERO/view/CRD42022363279, identifier: CRD42022363279.

Язык: Английский

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The effect of eHealth-based guided self help interventions for binge eating disorder : a meta-analysis of randomized controlled trials

Eating Disorders, Год журнала: 2025, Номер unknown, С. 1 - 23

Опубликована: Май 5, 2025

With rapid technological advancements, eHealth-based guided self-help interventions have become accessible, flexible, cost-effective, and stigma-reducing treatment options for binge eating disorder (BED). This meta-analysis evaluated the effectiveness of these in individuals diagnosed with BED or showing symptoms, based on eight randomized controlled trials 1,575 participants. Intervention length varied between a single session to four months. Six studies focused solely web-based interventions, one study implemented hybrid approach combining face-to-face online components, another employed two distinct methods. The included psychoeducational modules, therapist feedback, behavior monitoring, self-assessments. significantly reduced psychopathology (SMD: 0.53; 95% CI: 0.20-0.86) objective (OBE) days 0.49; 0.12-0.85) compared controls. These offer effective solutions facing barriers traditional access.

Язык: Английский

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Effectiveness of digital technologies for remote monitoring of behavioral risk factors in students

CARDIOVASCULAR THERAPY AND PREVENTION, Год журнала: 2025, Номер 24(4), С. 4368 - 4368

Опубликована: Май 16, 2025

Aim . To evaluate the effectiveness of digital technologies for remote monitoring modifying behavioral risk factors excess body weight among students without chronic diseases. Material and methods The study included 38 Pskov State University medical diseases with a mass index >25 kg/m 2 who underwent preventive examination. Behavioral (unhealthy diet, insufficient exercise) were modified using Doctor PM mobile application involvement professionals. Questionnaires (active links in app) used to assess attitude opinion users towards technology. follow-up period was 6 months. Results Dietary habits corrected 77,7% participants, including decrease consumption fats, simple carbohydrates, salt, as well an increase frequency vegetables fruits. Increased physical activity noted by 71,4% students. Bo­dy decreased 65,8% which 31,6% achieved target indicators. majority (86,8%) rated positively conveni­ence utility personalized recommendations application. Conclusion first experience practical pre­ventive technology eating activity, support reducing is presented cohort example. It important note that modification occurred support. Further indepth analysis results are required scaling this

Язык: Английский

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AI-Based Health Assistant for Young Adults

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 263 - 280

Опубликована: Янв. 1, 2025

Язык: Английский

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Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation (Preprint)

Опубликована: Май 30, 2024

Язык: Английский

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