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Related Concept Videos

Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
Therapeutic Drug Monitoring: Overview and Classification01:16

Therapeutic Drug Monitoring: Overview and Classification

Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...
Community Based Intervention01:30

Community Based Intervention

Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...

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Related Experiment Video

Updated: Jun 4, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Use of the Dynamic Systems Development Method to Inform Technology-Assisted Motivational Interviewing (TAMI) for

Brian Borsari1,2, Joannalyn Delacruz1,2, Ahson Saiyed3

  • 1San Francisco VA Medical Center, 4150 Clement St (116B), San Francisco, CA, 94121, United States, 1 415-221-4810 ext 26078.

JMIR Formative Research
|June 2, 2026
PubMed
Summary

A new chatbot, technology-assisted motivational interviewing (TAMI), uses machine learning to help people quit smoking. User feedback shows engagement is improved by anonymity and reminders, but technical issues remain a challenge.

Keywords:
chatbotmHealthmachine learningmobile healthmotivational interviewingnicotinequalitative

Related Experiment Videos

Last Updated: Jun 4, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
07:14

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models

Published on: December 23, 2025

Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Behavioral Science

Background:

  • Smoking remains a primary cause of preventable death in the U.S., with over 480,000 annual fatalities.
  • Advanced digital interventions leveraging AI and machine learning offer potential for effective smoking cessation support.

Purpose of the Study:

  • To develop a technology-assisted motivational interviewing (TAMI) chatbot using machine learning for tobacco cessation.
  • To incorporate patient and consumer conversational data into the chatbot's design.

Main Methods:

  • Utilized the dynamic systems development method for chatbot creation.
  • Incorporated user-centered design interviews with smokers (n=3) and user experience interviews (n=9) during a pilot trial.
  • Employed existing datasets to guide the integration of motivational interviewing (MI) principles, language recognition, and topic classification.

Main Results:

  • User interviews highlighted a need for engaging and personalized chatbots for smoking cessation.
  • Anonymity, regular reminders, and a humanized interaction style enhanced user engagement with TAMI.
  • Technical glitches, misunderstandings, and rapport issues were identified as barriers to engagement.

Conclusions:

  • The TAMI chatbot, informed by user data, can utilize MI techniques to encourage behavior change and assess cessation readiness.
  • Future development aims to improve user engagement and support underserved populations in achieving health behavior goals.