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

15.4K
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...
15.4K
Social Loafing01:37

Social Loafing

34.8K
Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
34.8K
Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

4.0K
The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent...
4.0K
Introduction to Biological Bases of Psychology01:30

Introduction to Biological Bases of Psychology

3.2K
Biopsychology serves as a vital bridge connecting the intricate domains of biology and psychology, shedding light on how biological systems influence psychological phenomena. This field scrutinizes the biological substrates of behavior and mental processes, emphasizing the nervous system along with the roles of neurotransmitters, hormones, and genetics. It also incorporates evolutionary perspectives to explain the adaptive nature of mental functions.
The nervous system, the cornerstone of...
3.2K

You might also read

Related Articles

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

Sort by
Same author

A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection.

Physiological measurement·2026
Same author

Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients.

Cancer medicine·2026
Same author

Emotion-Adaptive Large Language Model-Driven Clinical Decision Support: User Evaluation of the Empathic Clinical Decision Support System Framework for Trust and Explainability.

JMIR human factors·2026
Same author

"I Don't Trust it, but I Use it": Navigating Trust, Privacy, and Identity in Disabled People's Use of Generative AI.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2026
Same author

Building courage, strength, and knowledge: Mindfulness training reduces psychological threat and increases engagement in college physics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

MatplotAlt: A Python Library for Adding Alt Text to Matplotlib Figures in Computational Notebooks.

Computer graphics forum : journal of the European Association for Computer Graphics·2026
Same journal

Supporting Radiology Resident Education and Clinical Decision-Making With Large Language Models: Comparative Study of Reasoning Models DeepSeek-R1 and ChatGPT-o1.

JMIR AI·2026
Same journal

Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study.

JMIR AI·2026
Same journal

Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

JMIR AI·2026
Same journal

Correction: Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study.

JMIR AI·2026
Same journal

AI-Assisted Systematic Literature Review of the Economic Burden of Pneumococcal Disease: Development and Validation Study.

JMIR AI·2026
Same journal

Knowledge-Augmented Large Language Model for Multimodal Electronic Health Record-Based Risk Prediction: Development and Validation Study.

JMIR AI·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 2025

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
10:16

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

8.5K

Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory

Runze Yan1, Xinwen Liu2, Janine M Dutcher2

  • 1University of Virginia, Charlottesville, VA, United States.

JMIR AI
|June 14, 2024
PubMed
Summary
This summary is machine-generated.

Stable biobehavioral rhythms are linked to higher productivity. This study modeled rhythms from mobile data, finding that greater rhythm stability correlates with increased productivity, offering insights for cyber-human systems.

Keywords:
biobehavioral rhythmscomputational modelingmobile phonemobile sensingproductivity

More Related Videos

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

12.0K
Recording and Analysis of Circadian Rhythms in Running-wheel Activity in Rodents
05:46

Recording and Analysis of Circadian Rhythms in Running-wheel Activity in Rodents

Published on: January 24, 2013

21.3K

Related Experiment Videos

Last Updated: Jun 23, 2025

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World
10:16

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

8.5K
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

12.0K
Recording and Analysis of Circadian Rhythms in Running-wheel Activity in Rodents
05:46

Recording and Analysis of Circadian Rhythms in Running-wheel Activity in Rodents

Published on: January 24, 2013

21.3K

Area of Science:

  • Human-computer interaction
  • Chronobiology
  • Digital phenotyping

Background:

  • Biobehavioral rhythms, encompassing biological, behavioral, and psychosocial cycles, are crucial for health.
  • Disruptions in these rhythms are associated with health issues like sleep disorders, obesity, and depression.

Purpose of the Study:

  • To investigate the relationship between productivity and biobehavioral rhythms.
  • To model these rhythms using passively collected mobile data streams.

Main Methods:

  • Utilized a multimodal mobile sensing dataset from 188 college students over 16 weeks.
  • Collected self-evaluated productivity scores and sensor data from smartphones and Fitbits.
  • Modeled cyclic behavior patterns and analyzed rhythm stability in relation to productivity.

Main Results:

  • Students with greater biobehavioral rhythm stability reported higher productivity.
  • A negative correlation was found between productivity and the standard error of the phase (SE) for the 24-hour period, indicating lower stability with higher SE.

Conclusions:

  • Biobehavioral rhythm modeling can quantify and predict productivity.
  • Findings support the development of cyber-human systems that synchronize with human rhythms to enhance well-being and performance.