Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stress Prevention and Stress Management Techniques VI01:30

Stress Prevention and Stress Management Techniques VI

315
Adopting a healthier lifestyle often requires overcoming significant challenges, but leveraging psychological, social, and cultural resources can facilitate meaningful change. Effective self-change hinges on understanding and applying key tools such as motivation and goal setting, which help sustain efforts toward long-term health benefits.
Motivation and Self-Determination
Motivation, the driving force behind behavior, plays a pivotal role at every stage of the change process. The research...
315
Behavior Modification01:21

Behavior Modification

953
Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
A real-world application of operant conditioning principles is applied...
953
Methods of Medium Optimization01:28

Methods of Medium Optimization

53
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
53
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

3.1K
A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
3.1K
Operant Conditioning Intervention01:24

Operant Conditioning Intervention

657
Operant conditioning serves as a foundational principle in therapeutic interventions aimed at modifying maladaptive behaviors. Central to this approach is the notion that behaviors, both adaptive and maladaptive, are learned through reinforcement. By analyzing the environmental factors that reinforce problematic behaviors, clinicians can design interventions to weaken these reinforcements and replace maladaptive behaviors with healthier alternatives.
In operant conditioning, behaviors that are...
657
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

3.7K
Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
3.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Promoting Problem-Solving Among Low-Income Adults With Type 2 Diabetes: Cluster-Randomized Controlled Trial of a Mobile Health Intervention With SMS Text Messaging (Mobile Diabetes Detective).

Journal of medical Internet research·2026
Same author

A Multi-Modular Human-AI Workflow for LLM-Assisted Thematic Analysis: Application to COPD Telerehabilitation Interviews.

Studies in health technology and informatics·2026
Same author

Association of Remote Patient Monitoring with Care Utilization in Patients with Chronic Cardiopulmonary Conditions.

Studies in health technology and informatics·2026
Same author

Machine Learning Approaches for Mortality Prediction in ARDS.

Studies in health technology and informatics·2026
Same author

Estimating Mosaic Loss of the Y Chromosome in Male Bladder Cancer Participants Using "All of Us" Data.

Studies in health technology and informatics·2026
Same author

Early Prediction of Delirium in ICU Patients Using Machine Learning Analysis of Admission Data.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Apr 5, 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

779

Optimizing Decision Support for Tailored Health Behavior Change Applications.

Rita Kukafka1, In cheol Jeong2, Joseph Finkelstein2

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Studies in Health Technology and Informatics
|August 12, 2015
PubMed
Summary

Identifying key health behavior determinants like smoking status and self-efficacy can streamline lifestyle change apps. This helps reduce user data entry while improving tailored health interventions.

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Related Experiment Videos

Last Updated: Apr 5, 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

779
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Area of Science:

  • Health behavior change
  • Digital health interventions
  • Personalized medicine

Background:

  • The Tailored Lifestyle Change Decision Aid (TLC DA) system supports individuals with multiple unhealthy behaviors.
  • Current data collection methods for TLC DA are extensive, posing a user burden.
  • Optimizing data input is crucial for enhancing user engagement and adherence.

Purpose of the Study:

  • To identify the primary determinants influencing health behavior choices within the TLC DA system.
  • To reduce the data entry burden for users by pinpointing essential self-reported parameters.
  • To enhance the personalization and effectiveness of digital health interventions.

Main Methods:

  • Discriminant analysis was employed to identify key predictors of health behavior choices.
  • The study analyzed self-reported data collected through the TLC DA system.
  • Predictive accuracy was assessed for various health behaviors.

Main Results:

  • An optimal set of predictors was identified, including smoking status, smoking cessation success estimate, self-efficacy, body mass index, and diet status.
  • Smoking cessation choice prediction demonstrated the highest accuracy.
  • Weight management prediction was the second most accurate.
  • Physical activity and diet choices were best identified collectively.

Conclusions:

  • Specific self-reported parameters significantly determine health behavior choices in digital interventions.
  • Reducing data input requirements through identified key determinants can improve user experience.
  • The findings support the development of more efficient and personalized digital tools for lifestyle modification.