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

Naturalistic Observations02:30

Naturalistic Observations

15.2K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.2K
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

524
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
524
Nursing Assessment01:29

Nursing Assessment

8.3K
The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
The nurse collects all aspects of the patient's health in the initial assessment, establishing priorities for ongoing focused assessments...
8.3K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

955
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
955

You might also read

Related Articles

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

Sort by
Same author

Development and usability testing of a mindfulness-based smoking cessation app for adults with low income: "It's helped me to not react and grab a cigarette".

Journal of health psychology·2026
Same author

Sociodemographic Paradoxes and Enrollment Differences in In-Person Versus Online Recruitment to a Mobile Health Smoking Cessation Intervention for Food-Insecure Adults: Secondary Analysis of a Randomized Controlled Trial.

Journal of medical Internet research·2026
Same author

Modes of cannabis administration, cannabis and tobacco co-use, and associations with respiratory and cannabis use outcomes in a permissive medical cannabis state.

Addictive behaviors·2026
Same author

A Preliminary Study of Smoking Abstinence Effects on Acquisition and Reversal Learning in a Probabilistic Learning Task.

Substance use & misuse·2026
Same author

Mobile Health Technology for Personalized Tobacco Cessation Support in Laos (Project Support Laos): Protocol for a Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Effects of Ecological Momentary Assessment Prompting Schedule on Affect Measurement Variability and Associations With Next-Day Health Behaviors.

Assessment·2026
Same journal

When Do Interaction/Moderation Effects Stabilize in Linear Regression?

Advances in methods and practices in psychological science·2026
Same journal

Multilab Direct Replication of Flavell, Beach, and Chinsky (1966): Spontaneous Verbal Rehearsal in a Memory Task as a Function of Age.

Advances in methods and practices in psychological science·2025
Same journal

Tutorial: Power analyses for interaction effects in cross-sectional regressions.

Advances in methods and practices in psychological science·2025
Same journal

A Delphi Study to Strengthen Research-Methods Training in Undergraduate Psychology Programs.

Advances in methods and practices in psychological science·2025
Same journal

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models.

Advances in methods and practices in psychological science·2025
Same journal

Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction.

Advances in methods and practices in psychological science·2024
See all related articles

Related Experiment Video

Updated: May 5, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

2.6K

Time-Related Considerations for Modeling Event-Based Data Collected via Ecological Momentary Assessment.

Lizbeth Benson1, Emily T Hébert2,3, Nicholas Hartman4,5

  • 1Institute for Social Research, University of Michigan, Ann Arbor, Michigan.

Advances in Methods and Practices in Psychological Science
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

Ecological momentary assessments (EMAs) and wearable devices collect real-time event data. Survival analysis can model this data to answer questions about when and why events occur in daily life.

Keywords:
affectdrug/substance useemotionhealthindividual differencesintensive longitudinal datasurvival analysis

More Related Videos

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

6.1K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

10.6K

Related Experiment Videos

Last Updated: May 5, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

2.6K
A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
08:33

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Published on: September 4, 2019

6.1K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

10.6K

Area of Science:

  • Psychology
  • Behavioral Science
  • Data Science

Background:

  • Ecological momentary assessments (EMAs) and wearable devices enable real-time data collection on daily life events.
  • Dense sampling approaches allow for rapid event measurement, expanding temporal modeling possibilities.

Purpose of the Study:

  • To provide an overview of survival analysis for psychosocial and behavioral scientists.
  • To describe time-based considerations for applying survival analysis to event-based EMA data.
  • To illustrate the application of survival analysis with examples and spark future research.

Main Methods:

  • Overview of survival analysis principles.
  • Discussion of time-based considerations for EMA data.
  • Illustrative examples of survival analysis applied to event data.

Main Results:

  • Survival analysis offers a powerful framework for analyzing event-based EMA data.
  • Specific time-based considerations are crucial for accurate modeling.
  • The approach can address novel research questions in psychological and behavioral sciences.

Conclusions:

  • Survival analysis is underutilized but highly relevant for EMA data.
  • Understanding temporal dynamics in event data can be enhanced with survival analysis.
  • This method can uncover new insights into the timing and occurrence of daily life events.