Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables
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 meas...
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2025-01-01
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| author | Mostafa Rezapour Rachel B. Seymour Suman Medda Stephen H. Sims Madhav A. Karunakar Nahir Habet Metin Nafi Gurcan |
| author_facet | Mostafa Rezapour Rachel B. Seymour Suman Medda Stephen H. Sims Madhav A. Karunakar Nahir Habet Metin Nafi Gurcan |
| author_sort | Mostafa Rezapour |
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| description | 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 (<i>p</i> < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes. |
| format | Article |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Bioengineering |
| spelling | doaj-art-8178b3cb37e34ff1aa11483f9ef6341a2025-01-24T13:23:09ZengMDPI AGBioengineering2306-53542025-01-011216710.3390/bioengineering12010067Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis VariablesMostafa Rezapour0Rachel B. Seymour1Suman Medda2Stephen H. Sims3Madhav A. Karunakar4Nahir Habet5Metin Nafi Gurcan6Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USADepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USADepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USADepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USADepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USADepartment of Orthopaedic Surgery, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, Charlotte, NC 28210, USACenter for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USAIn 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 (<i>p</i> < 0.001). Pelvis fractures had the highest readmission rates, while Tibia-Diaphyseal and Tibia-Distal fractures were more prone to infections, highlighting the need for enhanced infection control strategies. Femur fractures showed moderate readmission and infection rates, indicating a mixed risk profile. In conclusion, our findings emphasize the importance of fracture-specific rehabilitation strategies, focusing on infection prevention and individualized treatment plans to optimize recovery outcomes.https://www.mdpi.com/2306-5354/12/1/67gait analysisrecovery trajectorylower extremity fractureslinear mixed models (LMM) |
| spellingShingle | Mostafa Rezapour Rachel B. Seymour Suman Medda Stephen H. Sims Madhav A. Karunakar Nahir Habet Metin Nafi Gurcan Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables Bioengineering gait analysis recovery trajectory lower extremity fractures linear mixed models (LMM) |
| title | Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables |
| title_full | Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables |
| title_fullStr | Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables |
| title_full_unstemmed | Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables |
| title_short | Analyzing Gait Dynamics and Recovery Trajectory in Lower Extremity Fractures Using Linear Mixed Models and Gait Analysis Variables |
| title_sort | analyzing gait dynamics and recovery trajectory in lower extremity fractures using linear mixed models and gait analysis variables |
| topic | gait analysis recovery trajectory lower extremity fractures linear mixed models (LMM) |
| url | https://www.mdpi.com/2306-5354/12/1/67 |
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