Evidence map›Paper›PMID 30993327›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2020

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

Cathleen Colón-Emeric, Carl F Pieper, Kenneth E Schmader, Richard Sloane, Allison Bloom, Micah McClain, Jay Magaziner, Kim M Huffman, Denise Orwig, Donna M Crabtree and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Examining the relevance of gait speed resiliencies to different stressors: the ARIC Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  4. Article
  5. Article
  6. Clinical predictors of resilience following total knee arthroplasty: the PRIME-KNEE study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Observational
  11. Article
  12. Article
  13. Trajectories of Physical Resilience Among Older Veterans With Stage 4 CKD.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024
    Article
  14. Article
  15. Review
  16. Ageing and physical resilience after health stressors.Stress and health : journal of the International Society for the Investigation of Stress · 2023
    Article
  17. Article
  18. Article
  19. Observational
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Cathleen Colón-EmericDivision of Geriatrics, Duke University, Durham, North Carolina.
Carl F PieperGeriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, North Carolina.
Kenneth E SchmaderDivision of Geriatrics, Duke University, Durham, North Carolina.
Richard SloaneGeriatric Research Education Clinical Center, Durham Veteran Affairs Medical Center, North Carolina.
Allison BloomCenter for Applied Genomics and Precision Medicine, Duke University Medical Centre, Durham, North Carolina.
Micah McClainCenter for Applied Genomics and Precision Medicine, Duke University Medical Centre, Durham, North Carolina.
Jay MagazinerDepartment of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore.
Kim M HuffmanDuke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina.
Denise OrwigDepartment of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore.
Donna M CrabtreeDuke Office of Clinical Research, School of Medicine, Duke University Medical Center, Durham, North Carolina.
Heather E WhitsonDivision of Geriatrics, Duke University, Durham, North Carolina.
Duke Medical Center · USDuke University · USGeriatric Research Education and Clinical Center · USUniversity of Maryland, Baltimore · US

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
University of Maryland Claude D. Pepper Older Americans Independence Center (UM-OAIC)P30AG028747 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI ALICE S. RYAN · 2006 to 2026
$28.9M
Resource Core 3 - Metabolomics CoreP30AG028716 · NIA · DUKE UNIVERSITY · PI CATHLEEN S COLON-EMERIC · 2006 to 2026
$24.6M
Sequelae of Hip Fracture in Men: An Epidemiologic StudyR37AG009901 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI MAGAZINER, JAY · 1992 to 2018
$11.3M
Physical Resiliencies: Indicators and Mechanisms in the Elderly CollaborativeUH3AG056925 · NIA · DUKE UNIVERSITY · PI COLON-EMERIC, CATHLEEN S · 2019 to 2021
$4.6M
Physical Resiliencies: Indicators and Mechanisms in the Elderly CollaborativeUH2AG056925 · NIA · DUKE UNIVERSITY · PI COLON-EMERIC, CATHLEEN S, WHITSON, HEATHER E. · 2017 to 2018
$2.0M
Mentoring Intervention Development in Fall and Fracture PreventionK24AG049077 · NIA · DUKE UNIVERSITY · PI COLON-EMERIC, CATHLEEN S · 2015 to 2019
$361k
NCATS NIH HHS UL1 TR002553NIA NIH HHS K24 AG049077NIA NIH HHS P30 AG028716NIA NIH HHS P30 AG028747NIA NIH HHS R37 AG009901NIA NIH HHS UH2 AG056925NIA NIH HHS UH3 AG056925
6 · The paper itself

Abstract

backgroundApproaches for quantifying physical resilience in older adults have not been described.

methodsWe apply two conceptual approaches to defining physical resilience to existing longitudinal data sets in which outcomes are measured after an acute physical stressor. A "recovery phenotype" approach uses statistical methods to describe how quickly and completely a patient recovers. Statistical methods using a recovery phenotype approach can consider multiple outcomes simultaneously in a composite score (eg, factor analysis and principal components analysis) or identify groups of patients with similar recovery trajectories across multiple outcomes (eg, latent class profile analysis). An "expected recovery differential" approach quantifies how patients' actual outcomes are compared to their predicted outcome based on a population-derived model and their individual clinical characteristics at the time of the stressor.

resultsApplication of the approaches identified different participants as being the most or least physically resilient. In the viral respiratory cohort (n = 186) weighted kappa for agreement across resilience quartiles was 0.37 (0.27-0.47). The expected recovery differential approach identified a group with more comorbidities and lower baseline function as highly resilient. In the hip fracture cohort (n = 541), comparison of the expected recovery differentials across 10 outcome measures within individuals provided preliminary support for the hypothesis that there is a latent resilience trait at the whole-person level.

conclusionsWe posit that recovery phenotypes may be useful in clinical applications such as prediction models because they summarize the observed outcomes across multiple measures. Expected recovery differentials offer insight into mechanisms behind physical resilience not captured by age and other comorbidities.

Indexed as

Stress, PhysiologicalAgedAged, 80 and overAgingCohort StudiesFemaleHip FracturesHumansLongitudinal StudiesMaleModels, BiologicalPhenotypePhysical FitnessRecovery of FunctionRespiratory Tract InfectionsVirus DiseasesAgingBiomarkersPhenotypes

Identifiers

PMID30993327
PMCPMC7328208
OpenAlexW2936957834

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.