Duquesne
Ground Reaction Forces Are Predicted With Functional and Clinical Tests in a Healthy Collegiate Population
Abstract
dc:description.abstract<p>Purpose: This study aimed to generate models predicting Ground Reaction Forces (GRFs), an established predictor of ACL injury incidence, from practical functional and clinical tests. Participants: Forty-two healthy, active college age individuals (21 females, age 20.667±1.461; 70.702±2.363cm; 82.202±7.606kg; 21 males, age 21.571±1.28; 65.524±1.874cm; 64.190±9.059kg) participated.</p> <p>Methods and Materials: After assuring all participants met inclusion criteria and provided consent, lower extremity (LE) dominance was determined with drop landings. Individuals then had Fat Free Mass [FFM] determined from skinfolds, ankle joint dorsiflexion passive range of motion taken with a standard goniometer [DPROM], and performed the overhead deep squat test [ODS]. A warm-up on a bicycle ergometer then preceded determination of vertical [GRFz] and posterior ground reaction forces [GRFy] with five, signal-averaged LE drop landings from 35cm height onto a forceplate. Participants then performed the following tests in a counterbalanced order: Margaria-Kalamen [MK], Single Leg Triple Hop [SLTH], isometric peak force for lateral hip rotation [HipLR], knee flexion and knee extension. The knee flexion and extension peak force data was used to calculate a flexion:extension peak force ratio [H:Q] while GRFz and GRFy values were normalized to the participant’s FFM [nGRFz and nGRFy]. Stepwise linear regression models to predict the GRFs were calculated using FFM, DPROM, ODS, MK, SLTH, HipLR, H:Q and sex as the predictors. Alpha levels for all analyses were set a-priori at P≤ .05.</p> <p>Results: Step-wise linear regression analysis indicated that a significant nGRFz model occurred utilizing all independent variables (Adjusted R2= .197, P= .048), but was most parsimonious with only SLTH and DPROM as predictor variables (Adjusted R2= .274; P=.001). Use of all eight-predictor variables for nGRFy also resulted in a statistically significant result (P= .001) but the most parsimonious model occurred with only H:Q, FFM and DPROM (Adjusted R2= .476; P< .001).</p> <p>Conclusions: Two models significantly predicted GRFs from practical clinical measures and functional tests. One model predicted vertical ground reaction force from SLTH and DPROM, while one model predicted nGRFy from H:Q, FFM and DPROM. Clinical Relevance: If validated, a practical method of predicting nGRFy would be available to identify those at elevated ACL injury risk.</p>
Degree
thesis:*- Name thesis:degree_name
- PhD
- Level thesis:degree_level
- One-year Embargo
- Discipline thesis:degree_discipline
- Rehabilitation Science
- Year dc:date.available
- 2015
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Cacolice, Paul
- Contributors dc:contributor
-
- Christopher Carcia
- Jason Scibek
- Amy Phelps
Subjects
dc:subject × 6Rights
- Language dc:language
- English
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://dsc.duq.edu/etd/1519
- OAI identifier oai:identifier
- oai:dsc.duq.edu:etd-2531