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

You might also read

Related Articles

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

Sort by
Same author

Effect of Branched-Chain Amino Acid Supplementation Alone or Combined With Tryptophan or Methionine on Appetite Control and Related Health Outcomes in Older Adults: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Health literacy of adolescents' responses to a workshop focusing on food, nutrition, climate change and digital technology solutions in Oceania: a multi-site pilot study in Vanuatu.

BMC public health·2025
Same author

Self-reported and accelerometry measures of sleep components in adolescents living in Pacific Island countries and territories: Exploring the role of sociocultural background.

Child: care, health and development·2024
Same author

A mixed-methods study exploring women's perceptions and recommendations for a pregnancy app with monitoring tools.

NPJ digital medicine·2023
Same author

Pregnancy Apps for Self-Monitoring: Scoping Review of the Most Popular Global Apps Available in Australia.

International journal of environmental research and public health·2023
Same author

Unsupervised Early Detection of Physical Activity Behaviour Changes from Wearable Accelerometer Data.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 8, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review.

Claudio Diaz1, Corinne Caillaud2, Kalina Yacef1

  • 1School of Computer Science, The University of Sydney, Sydney, Australia.

JMIR Medical Informatics
|March 6, 2023
PubMed
Summary

This review explores data mining techniques for analyzing physical activity changes from sensor data in health interventions. It highlights opportunities for personalized feedback and the need for standardized data preprocessing and mining methods.

Keywords:
activity trackerartificial intelligencebehavior changedata miningeducationfitness trackershealthhealth educationphysical activitysensor datatrackerswearable deviceswearable electronic devices

More Related Videos

Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.8K
Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

6.8K

Related Experiment Videos

Last Updated: Aug 8, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

6.8K
Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity
05:59

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

6.8K

Area of Science:

  • Health Informatics
  • Wearable Technology
  • Data Mining

Background:

  • Sensors in health interventions capture continuous physical activity data.
  • Sensor data offers potential for analyzing physical activity behavior patterns and changes.
  • Machine learning and data mining techniques are increasingly used to analyze this data.

Purpose of the Study:

  • To systematically review data mining techniques for analyzing physical activity behavior changes from sensor data in health education and promotion.
  • To identify current techniques used for mining physical activity sensor data.
  • To explore challenges and opportunities in this field.

Main Methods:

  • Systematic review conducted in May 2021 following PRISMA guidelines.
  • Searched multiple literature databases (ACM, IEEE Xplore, Scopus, etc.).
  • Included 19 peer-reviewed articles after screening 4388 references.

Main Results:

  • Accelerometers were the primary sensors used, often combined with others.
  • Data preprocessing typically involved proprietary software, yielding step counts and activity time.
  • Common data mining methods included classifiers, clusters, and decision-making algorithms, focusing on personalization and behavior analysis.

Conclusions:

  • Sensor data mining offers opportunities for analyzing behavior changes and personalized feedback.
  • Larger sample sizes and longer recording times enhance analysis.
  • Improvements in transparency, standardization, and reproducibility of data preprocessing and mining are needed.