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

Weighted Mean00:57

Weighted Mean

5.9K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
5.9K
Prediction Intervals01:03

Prediction Intervals

2.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.6K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

243
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
243
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Survival Tree01:19

Survival Tree

216
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
216
Regression Analysis01:11

Regression Analysis

6.8K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.8K

You might also read

Related Articles

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

Sort by
Same author

Ranking-Aware Multiple Instance Learning for Histopathology Slide Classification: Development and Validation Study.

JMIR medical informatics·2026
Same author

Shadow-Induced Forgetting in a Game-Based Paradigm on Nonclinical Adults and Its Effects on Consciousness, Emotional Valence, and Temporal Dynamics: Crossover Study.

JMIR serious games·2025
Same author

Multimodal deep learning model for prediction of prognosis in central nervous system inflammation.

Brain communications·2025
Same author

Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

Clinical and translational gastroenterology·2024
Same author

A digital phenotyping dataset for impending panic symptoms: a prospective longitudinal study.

Scientific data·2024
Same author

A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study.

JMIR formative research·2024
Same journal

Effects of Telerehabilitation Based on Motion Recognition Technology on Exercise Endurance of Patients With Non-Small Cell Lung Cancer After Surgery: Single-Center, Prospective, Open-Label, Randomized Controlled Trial.

JMIR mHealth and uHealth·2026
Same journal

Feasibility and Acceptability of mPallCare<i>,</i> a Digital Health Intervention for People Living With Advanced Cancer in a Refugee Settlement in Uganda: Mixed Methods Study.

JMIR mHealth and uHealth·2026
Same journal

Feasibility and Preliminary Efficacy of a "Sit Less" Program Leveraging Fitbit Tracking and Tailored Text Messages in Cardiometabolic Disease: Findings from Two Parallel Randomized Controlled Trials in Coronary Artery Disease and Type 2 Diabetes.

JMIR mHealth and uHealth·2026
Same journal

Long-Term Effectiveness of Unguided Internet-Based Cognitive Behavioral Therapy on Major Depressive Disorder in Chinese Adults: Randomized Controlled Trial With a 12-Month Follow-Up.

JMIR mHealth and uHealth·2026
Same journal

Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.

JMIR mHealth and uHealth·2026
Same journal

Enhancing Cognitive Functions of Older Adults With Software Robot: Longitudinal Exploratory Field Study.

JMIR mHealth and uHealth·2026
See all related articles

Related Experiment Videos

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation

Ho Heon Kim1, Youngin Kim2, Yu Rang Park1

  • 1Department of Biomedical Systems Informatics, College of Medicine, Yonsei University, Seoul, Republic of Korea.

JMIR Mhealth and Uhealth
|March 29, 2021
PubMed
Summary
This summary is machine-generated.

This study developed an interpretable AI model to predict weight loss using mobile app data, achieving 3.50% error. Key factors influencing weight change include user engagement and biological sex, offering insights for personalized obesity management.

Keywords:
artificial intelligencebehavior modificationdevelopmentexplainable AIinterpretable AIinterventionmHealthobesityvalidationweight

Related Experiment Videos

Area of Science:

  • * Digital Health
  • * Artificial Intelligence
  • * Obesity Management

Background:

  • * Mobile-based interventions are increasingly used for obesity management.
  • * Existing mobile interventions lack predictive models for optimizing outcomes.
  • * Noom app data offers potential for developing such predictive models.

Purpose of the Study:

  • * To develop a weight change predictive model using interpretable AI for mobile interventions.
  • * To identify factors contributing to weight loss or gain within these interventions.

Main Methods:

  • * Developed an interpretable recurrent neural network model using 16-week weight loss data from Noom app users in the US.
  • * Utilized both time-variant and time-fixed variables for prediction.
  • * Validated the model using 5-fold cross-validation, measuring performance with mean absolute percentage error.

Main Results:

  • * The model achieved an overall mean absolute percentage error of 3.50%, with error decreasing over the 16-week program.
  • * Identified key contributing factors to weight loss, including usage patterns, input frequency, adherence, exercise, and rapid weight decrease.
  • * Male sex was identified as a significant time-fixed variable influencing weight change.

Conclusions:

  • * An interpretable AI model precisely predicts weight loss using mobile health data while maintaining transparency.
  • * This week-to-week prediction model can enhance weight loss outcomes.
  • * The model provides a global explanation of contributing factors, enabling better personalized interventions.