From Barriers to Tactics: A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching

• URL: http://arxiv.org/abs/2410.14041v1
• Date: Thu, 17 Oct 2024 21:35:07 GMT
• Title: From Barriers to Tactics: A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching
• Abstract summary: This paper introduces a novel LLM-powered agentic workflow designed to provide personalized nutrition coaching. A specialized LLM agent intentionally probes for and identifies the root cause of a patient's dietary struggles. A separate LLM agent delivers tailored tactics designed to overcome those specific barriers with patient context.
• Score: 0.2808786262095875
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while previous attempts aimed at automating nutrition coaching lack the personalization needed to address these diverse challenges. This paper introduces a novel LLM-powered agentic workflow designed to provide personalized nutrition coaching by directly targeting and mitigating patient-specific barriers. Grounded in behavioral science principles, the workflow leverages a comprehensive mapping of nutrition-related barriers to corresponding evidence-based strategies. A specialized LLM agent intentionally probes for and identifies the root cause of a patient's dietary struggles. Subsequently, a separate LLM agent delivers tailored tactics designed to overcome those specific barriers with patient context. We designed and validated our approach through a user study with individuals with cardiometabolic conditions, demonstrating the system's ability to accurately identify barriers and provide personalized guidance. Furthermore, we conducted a large-scale simulation study, grounding on real patient vignettes and expert-validated metrics, to evaluate the system's performance across a wide range of scenarios. Our findings demonstrate the potential of this LLM-powered agentic workflow to improve nutrition coaching by providing personalized, scalable, and behaviorally-informed interventions.

Related papers

• A Multi-Agent LLM Framework for Rating the Quality of Surgical Feedback [52.053200111780626]
  Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident skill acquisition.<n>We introduce a two-stage LLM-based framework that discovers interpretable feedback quality criteria grounded in the context of surgical training.<n>Our method uses multi-agent and surgical domain knowledge injection to discover a small set of human interpretable scoring criteria.
  arXiv  Detail & Related papers  (2026-05-25T05:31:44Z)
• Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model [57.78184285979881]
  We introduce SepsisAgent, a world model-augmented LLM agent for sepsis treatment recommendation.<n>SepsisAgent uses a learned Clinical World Model to simulate patient responses under candidate fluid--vasopressor interventions.
  arXiv  Detail & Related papers  (2026-05-14T11:50:00Z)
• LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment [59.095718698828286]
  The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments.<n>We propose an LLM-powered method, ours, for automated and personalized therapeutic game design.<n>Inspired by the Dungeons & Dragons, LETGAMES generates an open-world interactive narrative game.
  arXiv  Detail & Related papers  (2026-02-18T15:32:23Z)
• A Unified XAI-LLM Approach for EndotrachealSuctioning Activity Recognition [0.1794226570005898]
  This study proposes a unified framework for video-based activity recognition benchmarked against conventional machine learning and deep learning approaches.<n>Within this framework, the Large Language Model (LLM) serves as the central reasoning module, performing bothtemporal activity recognition and explainable decision analysis from video data.<n> Experimental results demonstrate that the proposed LLM-based approach outperforms baseline models, achieving an improvement of approximately 15-20% in both accuracy and F1 score.
  arXiv  Detail & Related papers  (2026-01-29T14:46:48Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarization [1.877929053436765]
  This study employs a mixed-methods approach to evaluate the efficacy of large language models (LLMs) in psychotherapy.<n>We use LLMs to generate precise summaries of motivational interviewing (MI) dialogues and design a two-stage annotation scheme.<n>Using expert-annotated MI dialogues as ground truth, we formulate multi-class classification tasks to assess model performance under progressive prompting techniques.
  arXiv  Detail & Related papers  (2025-11-28T00:37:58Z)
• Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis [11.025486717604972]
  DSM5AgentFlow is the first LLM-based agent workflow designed to autonomously generate DSM-5 Level-1 diagnostic questionnaires.<n>By simulating therapist-client dialogues with specific client profiles, the framework delivers transparent, step-by-step disorder predictions.<n>This workflow serves as a complementary tool for mental health diagnosis, ensuring adherence to ethical and legal standards.
  arXiv  Detail & Related papers  (2025-08-15T11:08:32Z)
• LLM-as-a-Supervisor: Mistaken Therapeutic Behaviors Trigger Targeted Supervisory Feedback [27.944400312023404]
  Large language models (LLMs) hold significant promise in psychotherapy, but their direct application in patient-facing scenarios raises ethical and safety concerns.<n>This work shifts towards developing an LLM as a supervisor to train real therapists.
  arXiv  Detail & Related papers  (2025-08-12T16:03:36Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Bridging Expertise Gaps: The Role of LLMs in Human-AI Collaboration for Cybersecurity [17.780795900414716]
  This study investigates whether large language models (LLMs) can function as intelligent collaborators to bridge expertise gaps in cybersecurity decision-making.<n>We find that human-AI collaboration improves task performance,reducing false positives in phishing detection and false negatives in intrusion detection.
  arXiv  Detail & Related papers  (2025-05-06T04:47:52Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions. We propose a novel approach utilizing structured medical reasoning. Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• MADP: Multi-Agent Deductive Planning for Enhanced Cognitive-Behavioral Mental Health Question Answer [7.738135970011351]
  We propose a framework named Multi-Agent Deductive Planning (MADP) MADP is based on the interactions between the various psychological elements of Cognitive Behavioral Therapy (CBT) We construct a new dataset based on the MADP framework and use it to fine-tune Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2025-01-27T07:18:47Z)
• Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives [19.462374723301792]
  Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing scenarios. By mimicking human behavior, LLMs can anticipate responses based on concrete demographic or professional profiles. We evaluate the effectiveness of LLMs in simulating individuals with diverse backgrounds and analyze the consistency of these simulated behaviors.
  arXiv  Detail & Related papers  (2025-01-12T22:49:32Z)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
  Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
  arXiv  Detail & Related papers  (2024-10-17T22:04:32Z)
• Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy [6.952909762512736]
  We examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy session via text. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods.
  arXiv  Detail & Related papers  (2024-08-27T17:25:16Z)
• Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts [4.403408362362806]
  We introduce the Chain-of-Interaction prompting method to contextualize large language models for psychiatric decision support by the dyadic interactions. This approach enables large language models to leverage the coding scheme, patient state, and domain knowledge for patient behavioral coding.
  arXiv  Detail & Related papers  (2024-03-20T17:47:49Z)
• Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions [17.405080523382235]
  We propose a novel AI-driven patient monitoring framework using multi-agent deep reinforcement learning (DRL) Our approach deploys multiple learning agents, each dedicated to monitoring a specific physiological feature, such as heart rate, respiration, and temperature. We evaluate the performance of the proposed multi-agent DRL framework using real-world physiological and motion data from two datasets.
  arXiv  Detail & Related papers  (2023-09-20T00:42:08Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations [79.49929463310588]
  We show that KL-regularized reinforcement learning with behavioral reference policies can suffer from pathological training dynamics. We show that the pathology can be remedied by non-parametric behavioral reference policies.
  arXiv  Detail & Related papers  (2022-12-28T16:29:09Z)
• Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing [53.096237570992294]
  Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Standardized fidelity assessment is used to measure adherence to treatment principles. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
  arXiv  Detail & Related papers  (2022-09-14T15:33:30Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Personalized Rehabilitation Robotics based on Online Learning Control [62.6606062732021]
  We propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user. We evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
  arXiv  Detail & Related papers  (2021-10-01T15:28:44Z)
• Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment [0.2399911126932526]
  There is no universally agreed upon strategy for vasopressor and fluid administration. Sepsis is the leading cause of mortality in the ICU, responsible for 6% of all hospitalizations and 35% of all in-hospital deaths in USA. We propose a novel approach, exploiting and unifying complementary strengths of Mathematical Modelling, Deep Learning, Reinforcement Learning and Uncertainty Quantification.
  arXiv  Detail & Related papers  (2021-01-21T07:32:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.