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

How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.2K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.2K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

43.3K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
43.3K
Classifying Matter by Composition03:35

Classifying Matter by Composition

90.0K
Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
90.0K
Longitudinal Research02:20

Longitudinal Research

13.2K
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...
13.2K
Classifying Matter by State02:49

Classifying Matter by State

102.9K
Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
102.9K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

545
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
545

You might also read

Related Articles

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

Sort by
Same author

Presurgical immune biomarkers associated with pain intensity and pain interference recovery after total knee arthroplasty: findings from the PRIME-KNEE study.

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

Comparing two methods to quantify physical resilience.

The Journal of frailty & aging·2026
Same author

Drug Burden Index and Its Association With Functional Outcomes in Patients Receiving Hemodialysis.

Kidney medicine·2026
Same author

Examining Quality-of-Life Priorities of Older Adults Receiving Hemodialysis: A Q-Methodology Study.

Kidney medicine·2026
Same author

Feasibility and acceptability of remote ischemic conditioning combined with low-intensity resistance training in older adults with mobility impairments: A randomized controlled pilot trial protocol.

Experimental gerontology·2026
Same author

Supporting family care partners during physical therapy care.

Physical therapy·2026

Related Experiment Video

Updated: Jan 26, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

487

Two Approaches to Classifying and Quantifying Physical Resilience in Longitudinal Data.

Cathleen Colón-Emeric1,2,3, Carl F Pieper2,3, Kenneth E Schmader1,2,3

  • 1Division of Geriatrics, Duke University, Durham, North Carolina.

The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences
|April 18, 2019
PubMed
Summary
This summary is machine-generated.

Quantifying physical resilience in older adults is now possible using two novel approaches: recovery phenotype and expected recovery differential. These methods help identify resilient individuals and understand the factors contributing to their recovery after stressors.

Keywords:
AgingBiomarkersPhenotypes

More Related Videos

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement
12:22

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

Published on: July 1, 2015

24.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Related Experiment Videos

Last Updated: Jan 26, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

487
Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement
12:22

Mindfulness in Motion MIM: An Onsite Mindfulness Based Intervention MBI for Chronically High Stress Work Environments to Increase Resiliency and Work Engagement

Published on: July 1, 2015

24.4K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

Area of Science:

  • Gerontology
  • Biostatistics
  • Rehabilitation Medicine

Background:

  • Quantifying physical resilience in older adults remains an underexplored area.
  • Existing longitudinal data sets provide opportunities to develop and test new resilience measurement approaches.

Purpose of the Study:

  • To apply and evaluate two conceptual approaches for defining and quantifying physical resilience in older adults.
  • To assess the utility of recovery phenotype and expected recovery differential methods in existing longitudinal datasets.

Main Methods:

  • Two conceptual approaches were applied: a "recovery phenotype" approach using statistical methods (e.g., factor analysis, latent class profile analysis) to assess recovery speed and completeness, and an "expected recovery differential" approach comparing actual outcomes to predicted outcomes.
  • These methods were applied to longitudinal data from a viral respiratory cohort (n=186) and a hip fracture cohort (n=541).

Main Results:

  • Application of the approaches identified different participants as most or least physically resilient, with moderate agreement between methods (kappa=0.37).
  • The expected recovery differential approach identified individuals with more comorbidities and lower baseline function as highly resilient in the viral respiratory cohort.
  • Preliminary evidence suggests a latent, whole-person level resilience trait in the hip fracture cohort.

Conclusions:

  • Recovery phenotypes can be valuable in clinical prediction models by summarizing multi-measure outcomes.
  • Expected recovery differentials provide insights into resilience mechanisms beyond age and comorbidities, aiding in understanding physical resilience.