Evidence map›Paper›PMID 35072659›Full record

ArticleMedicine and science in sports and exercise2022

Linking Gait Biomechanics and Daily Steps After ACL Reconstruction.

Caroline Lisee, Hope C Davis-Wilson, Alyssa Evans-Pickett, W Zachary Horton, J Troy Blackburn, Jason R Franz, Louise M Thoma, Jeffrey T Spang, Brian G Pietrosimone

Open access · greenAbstract read
In one paragraph

Article in Medicine and science in sports and exercise, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
2.6field-weighted citation impact, top 11% 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Article
  4. Review
  5. Aberrant Gait Biomechanics Linked to Cartilage Changes After ACL Reconstruction in Those With High Body Mass Index.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
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

9 authors at 4 institutions in 1 country.

Caroline LiseeMOTION Science Institute, Department of Exercise and Sports Science, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Hope C Davis-WilsonDepartment of Physical Medicine and Rehabilitation, University of Colorado, Aurora, CO.
Alyssa Evans-Pickett
W Zachary HortonDepartment of Statistics, University of California Santa Cruz, Santa Cruz, CA.
J Troy Blackburn
Jason R FranzJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC.
Louise M ThomaDepartment of Allied Health Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Jeffrey T SpangDepartment of Orthopaedics, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Brian G Pietrosimone
University of North Carolina at Chapel Hill · USNorth Carolina State University · USUniversity of California, Santa Cruz · USUniversity of Colorado Denver · US

Funding

In vivo Manipulation of Mechanical Loading: Using Real-time Biofeedback to Strategically Understand the Acute Biomechanical, Biochemical and Structural Changes Induced by Lower Extremity LoadingR21AR074094 · NIAMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRANZ, JASON R, PIETROSIMONE, BRIAN · 2018 to 2019
$357k
NIAMS NIH HHS R21 AR074094
6 · The paper itself

Abstract

purposeAberrant biomechanics and altered loading frequency are associated with poor knee joint health in osteoarthritis development. After anterior cruciate ligament reconstruction (ACLR), individuals demonstrate underloading (lesser vertical ground reaction force (vGRF)) with stiffened knee gait biomechanics (lesser knee extension moment (KEM) and knee flexion angle) and take fewer daily steps as early as 6 months after surgery. The purpose of this cross-sectional laboratory study is to compare gait biomechanics throughout stance between individuals 6-12 months after ACLR who take the lowest, moderate, and highest daily steps.

methodsIndividuals with primary, unilateral history of ACLR between the ages of 16 and 35 yr were included (n = 36, 47% females; age, 21 ± 5 yr; months since ACLR, 8 ± 2). Barefoot gait biomechanics of vGRF (body weight), KEM (body weight × height), and knee flexion angle during stance were collected and time normalized. Average daily steps were collected via a waist-mounted accelerometer in free-living settings over 7 d. Participants were separated into tertiles based on lowest daily steps (3326-6042 daily steps), moderate (6043-8198 daily steps), and highest (8199-12,680 daily steps). Biomechanical outcomes of the ACLR limb during stance were compared between daily step groups using functional waveform gait analyses.

resultsThere were no significant differences in sex, body mass index, age, or gait speed between daily step groups. Individuals with the lowest daily steps walk with lesser vGRF and lesser KEM during weight acceptance, and lesser knee flexion angle throughout stance in the ACLR limb compared with individuals with highest and moderate daily steps.

conclusionsAfter ACLR, individuals who take the fewest daily steps also walk with lesser vGRF during weight acceptance and a stiffened knee strategy throughout stance. These results highlight complex interactions between joint loading parameters after ACLR.

Indexed as

Anterior Cruciate Ligament InjuriesBiomechanical PhenomenaBody WeightChild, PreschoolCross-Sectional StudiesFemaleGaitHumansInfantKnee JointMale

Identifiers

PMID35072659
PMCPMC9255696
OpenAlexW4206947243

What OpenQuestion holds

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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.