Evidence map›Paper›PMID 39851341›Full record

ArticleBioengineering (Basel, Switzerland)2025

Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables.

Mostafa Rezapour, Rachel B Seymour, Suman Medda, Stephen H Sims, Madhav A Karunakar, Nahir Habet, Metin Nafi Gurcan

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Mostafa RezapourCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0001-9569-118X
Rachel B SeymourDepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.
Suman MeddaDepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.ORCID 0000-0002-7765-1173
Stephen H SimsDepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.
Madhav A KarunakarDepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.
Nahir HabetDepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USA.ORCID 0000-0003-3389-8323
Metin Nafi GurcanCenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA.ORCID 0000-0002-2421-8229

Funding

AO North America via the AO Strategy Fund NA
6 · The paper itself

Abstract

In a prospective study, we examined the recovery trajectory of patients with lower extremity fractures to better understand the healing process in the absence of complications. Using a chest-mounted inertial measurement unit (IMU) device for gait analysis and collecting patient-reported outcome measures, we focused on 12 key gait variables, including Mean Leg Lift Acceleration, Stance Time, and Body Orientation. We employed a linear mixed model (LMM) to analyze these variables over time, incorporating both fixed and random effects to account for individual differences and the time since injury. This model also adjusted for varying intervals between assessments. Our study provided insights into gait recovery across different fracture types using data from 318 patients who experienced no complications or readmissions during their recovery. Through LMM analysis, we found that Tibia-Distal fractures demonstrated the fastest recovery, particularly in terms of mobility and strength. Tibia-Proximal fractures showed balanced improvements in both mobility and stability, suggesting that rehabilitation should target both strength and balance. Femur fractures exhibited varied recovery, with Diaphyseal fractures showing clear improvements in stability, while Distal fractures reflected gains in limb strength but with some variability in stability. To examine patients with readmissions, we conducted a Chi-squared test of independence to determine whether there was a relationship between fracture type and readmission rates, revealing a significant association (

Indexed as

gait analysislinear mixed models (LMM)lower extremity fracturesrecovery trajectory

Identifiers

PMID39851341
PMCPMC11762351

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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.