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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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For solids whose cross-sectional areas vary in a predictable way, volume can be determined by integrating these areas along an axis perpendicular to the slices. This approach is particularly useful for polyhedral solids, where classical geometric formulas may not be immediately applicable. A tetrahedron provides a clear example of how cross-sectional integration can be applied to a three-dimensional object with continuously changing geometry.Consider a tetrahedron with height h and a base that...
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Updated: Feb 4, 2026

Behavioral Phenotyping of Murine Disease Models with the Integrated Behavioral Station INBEST
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Digital phenotyping and ASCVD risk: An exploratory cross-sectional analysis using online behavioral data.

Lauren Southwick1,2, Neil K R Sehgal3,4, Dena Torrente1

  • 1Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

American Heart Journal Plus : Cardiology Research and Practice
|February 3, 2026
PubMed
Summary

Social media data may offer insights into cardiovascular disease (CVD) risk, potentially complementing traditional clinical assessments. Further research is needed to confirm the association between online behaviors and heart health outcomes.

Area of Science:

  • Digital Health
  • Cardiovascular Medicine
  • Social Epidemiology

Background:

  • Cardiovascular disease (CVD) risk is traditionally assessed using clinical factors.
Keywords:
Cardiovascular healthDigital dataSocial media

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  • Social media platforms generate vast amounts of user data reflecting behaviors and social determinants.
  • The utility of social media data for predicting CVD risk beyond established models is largely unexplored.