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

Nocturnal Features and Daytime Characteristics in Narcolepsy: Reliability and Diagnostic Relevance for NT1 vs NT2.

Sleep·2026
Same author

Reliable Change of Blood-Based Biomarkers Following Acute Sport-Related Concussion: A CARE Consortium Study.

Sports medicine (Auckland, N.Z.)·2026
Same author

Validation of the Shoebox PureTest audiometry technology for remote data collection in clinical trials.

Scientific reports·2026
Same author

Associations of accelerometry-derived time in major activity intensities with cognitive outcomes: a compositional data analysis approach.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Change in walking cadence as a digital outcome measure of clinically meaningful improvement in gait speed and 6-minute walk test distance after a mobility intervention in older adults.

PloS one·2026
Same author

Reprogrammed apoptotic platelets drive rapid hemostasis through phosphatidylserine and prostaglandin E2 signaling in preclinical models.

Science translational medicine·2026

Related Experiment Video

Updated: Jan 3, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.9K

Accelerometry data in health research: challenges and opportunities.

Marta Karas1, Jiawei Bai2, Marcin Strączkiewicz3

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Tel.: +1-317-665-4551, mkaras2@jhu.edu.

Statistics in Biosciences
|November 26, 2019
PubMed
Summary
This summary is machine-generated.

Wearable accelerometers offer objective physical activity (PA) data but present challenges in data size, variability, and calibration. This study addresses these issues for accurate health research using real-world data.

Keywords:
AccelerometersAccelerometryPhysical activityWearable accelerometersWearable computing

More Related Videos

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

8.6K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.2K

Related Experiment Videos

Last Updated: Jan 3, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
07:24

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

12.9K
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

8.6K
Home-Based Monitor for Gait and Activity Analysis
07:24

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

7.2K

Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Epidemiology

Background:

  • Wearable accelerometers are increasingly used for objective physical activity (PA) measurement in health research.
  • Technological advancements have led to high-throughput, raw acceleration data collection.
  • The growing popularity necessitates addressing data collection and analysis challenges.

Purpose of the Study:

  • To discuss challenges in collecting and analyzing raw accelerometry data.
  • To present solutions for common issues encountered in wearable sensor data.
  • To illustrate these challenges using data from the Developmental Epidemiological Cohort Study (DECOS).

Main Methods:

  • Review of common problems in raw accelerometry data.
  • Discussion of data size, complexity, and variability.
  • Examination of sensor location, sampling frequency, calibration, labeling, and synchronization issues.
  • Illustration with DECOS study data from controlled and free-living environments.

Main Results:

  • Raw accelerometry data presents significant challenges including large data volumes and high variability.
  • Sensor placement, sampling rates, calibration, and synchronization critically impact data quality.
  • The DECOS study provides a practical example of these challenges in a real-world cohort.

Conclusions:

  • Addressing the complexities of raw accelerometry data is crucial for reliable PA assessment.
  • Standardized methods for data collection and analysis are needed for robust health research.
  • Solutions exist to mitigate challenges, enhancing the utility of wearable sensor data.