Evidence map›Paper›PMID 34636887›Full record

ArticlePhysical therapy2021

Classification of Geriatric Low Back Pain Based on Hip Characteristics With a 12-Month Longitudinal Exploration of Clinical Outcomes: Findings From Delaware Spine Studies.

Gregory E Hicks, Ryan T Pohlig, Peter C Coyle, J Megan Sions, Debra K Weiner, Jenifer M Pugliese, Teonette O Velasco, Victoria A O'Brien

Open access · bronzeAbstract read
In one paragraph

Article in Physical therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% 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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Clinical Characterization of Hip Subgroups in Geriatric Chronic Low Back Pain.Archives of physical medicine and rehabilitation · 2025
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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

8 authors at 4 institutions in 1 country.

Gregory E HicksDepartment of Physical Therapy, University of Delaware, Newark, Delaware, USA.
Ryan T PohligDepartment of Epidemiology, University of Delaware, Newark, Delaware, USA.
Peter C CoyleDepartment of Physical Therapy, University of Delaware, Newark, Delaware, USA.ORCID 0000-0003-3954-2563
J Megan SionsDepartment of Physical Therapy, University of Delaware, Newark, Delaware, USA.
Debra K WeinerClinical and Translational Sciences Institute, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
Jenifer M PuglieseDepartment of Physical Therapy, University of Delaware, Newark, Delaware, USA.ORCID 0000-0003-0119-2158
Teonette O VelascoNational Capital Region, The Geneva Foundation, Bethesda, Maryland, USA.
Victoria A O'BrienChristianaCare Health System, Newark, Delaware, USA.
University of Delaware · USChristiana Care Health System · USThe Geneva Foundation · USUniversity of Pittsburgh · US

Funding

Chronic Low Back Pain in Older Adults: The Role of Co-Existing Hip ImpairmentsR01AG041202 · NIA · UNIVERSITY OF DELAWARE · PI Gregory E Hicks · 2012 to 2026
$8.4M
NIA NIH HHS R01 AG041202
6 · The paper itself

Abstract

objectiveThe purpose of this study is to identify geriatric chronic low back pain (LBP) subgroups based on the presence of potentially modifiable hip impairments, using Latent Variable Mixture Modeling (LVMM), and to examine the prospective relationship between these subgroups and key outcomes over time.

methodsBaseline, 3-month, 6-month, and 12-month data were collected from a prospective cohort of 250 community-dwelling older adults with chronic LBP. Comprehensive hip (symptoms, strength, range of motion, and flexibility), LBP (intensity and disability), and mobility function (gait speed and 6-Minute Walk Test) examinations were performed at each timepoint. Baseline hip measures were included in LVMM; observed classes/subgroups were compared longitudinally on LBP and mobility function outcomes using mixed models.

resultsRegarding LVMM, a model with 3 classes/subgroup fit best. Broadly speaking, subgroups were differentiated best by hip strength and symptom presence: subgroup 1 = strong and nonsymptomatic, subgroup 2 = weak and nonsymptomatic, and subgroup 3 = weak and symptomatic (WS). Regarding longitudinal mixed models, all subgroups improved in most outcomes over time. Specifically, over 12 months, the nonsymptomatic subgroups had lower LBP intensity and disability levels compared with the WS subgroup, whereas the strong and nonsymptomatic subgroup had better mobility function than the 2 "weak" subgroups.

conclusionThese subgroup classifications may help in tailoring specific interventions in future trials. Special attention should be given to the WS subgroup given their consistently poor LBP and mobility function outcomes. IMPACT: Among older adults with chronic low back pain, there are 3 hip subgroups: "strong and nonsymptomatic," "weak and nonsymptomatic," and "weak and symptomatic." People in these subgroups demonstrate different outcomes and require different treatment; proper identification will result in tailored interventions designed to benefit individual patients. In particular, people in the WS subgroup deserve special attention, because their outcomes are consistently poorer than those in the other subgroups.

Indexed as

Geriatric AssessmentAgedAged, 80 and overAlgorithmsChronic DiseaseCohort StudiesDisability EvaluationFemaleHip JointHumansIndependent LivingLongitudinal StudiesLow Back PainMaleMiddle AgedPain MeasurementAgingArthritisBack PainChronic PainGeriatricsHipHip OsteoarthritisLow Back PainMobility LimitationModelsStatistical

Identifiers

PMID34636887
PMCPMC8801002
OpenAlexW3205477707

What OpenQuestion holds

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Registered trials

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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.