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

Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

217
Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
217
Longitudinal Research02:20

Longitudinal Research

12.5K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.5K

You might also read

Related Articles

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

Sort by
Same author

Adverse Childhood Experiences and Growth Outcomes in Childhood: A Longitudinal EHR-Based Study.

medRxiv : the preprint server for health sciences·2026
Same author

Suicidality phenotypes reflect both shared and distinct genetic factors.

medRxiv : the preprint server for health sciences·2026
Same author

Variation in <i>SNX29</i> and Acute Vasodilator Response in Pulmonary Arterial Hypertension.

Circulation research·2026
Same author

Life's Essential 8 and Incident Cardiovascular Disease: Validation Using Real World Data from Consumer Devices in the All of Us Research Program.

medRxiv : the preprint server for health sciences·2026
Same author

Multi-scale data improves performance of machine learning model for long COVID identification.

Communications medicine·2026
Same author

Effectiveness and Safety of Low-Sodium Oxybate in Participants with Narcolepsy: Primary Results from the DUET Study.

Neurology and therapy·2026

Related Experiment Video

Updated: Sep 13, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.1K

Capturing Real-World Habitual Sleep Patterns With a Novel User-Centric Algorithm to Preprocess Fitbit Data in the All

Hiral Master1, Jeffrey Annis1, Jack H Ching2

  • 1Vanderbilt University Medical Center, Nashville, TN, United States.

Journal of Medical Internet Research
|July 28, 2025
PubMed
Summary

A new user-centric algorithm improves Fitbit sleep tracking by accounting for individual sleep patterns, offering more accurate sleep duration and efficiency metrics. This approach enhances wearable sleep data analysis for research and clinical applications.

Keywords:
All of Us Research ProgramFitbitR packagealgorithmsmetricssleep

More Related Videos

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.7K
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

Related Experiment Videos

Last Updated: Sep 13, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.1K
Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.7K
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

Area of Science:

  • Sleep Medicine
  • Wearable Technology
  • Digital Health

Background:

  • Commercial wearables like Fitbit use fixed sleep measurement periods, potentially misrepresenting individual sleep patterns.
  • A novel user-centric algorithm was developed to enhance the accuracy of wearable-derived sleep metrics.

Purpose of the Study:

  • Develop and describe a user-centric sleep algorithm.
  • Compare its performance against default calendar-relative algorithms.
  • Provide guidance for analyzing Fitbit sleep data on cloud platforms.

Main Methods:

  • Implemented default and user-centric algorithms on Fitbit data from 8563 All of Us participants.
  • Calculated variations in typical sleep patterns.
  • Used linear mixed-effects models to compare sleep metrics.

Main Results:

  • The user-centric algorithm reclassified 4.75% of sleep logs as primary sleep.
  • Participants with high sleep pattern variability showed increased total sleep time (+17.6 min) and wake after sleep onset (+13.9 min) with the user-centric algorithm.
  • Sleep efficiency was lower by 2.0% with the user-centric algorithm in this group.

Conclusions:

  • The user-centric algorithm effectively captures natural sleep schedule variability.
  • It offers an improved method for preprocessing and evaluating sleep metrics related to schedule, duration, and disturbances.
  • A publicly available R package supports its use in clinical and translational research.