Abstract
Introduction
Hip arthroplasty is considered common to patients aged 65 and over however, both Jennings, et al., (2012) and Bergmann (2016) found THA patients are substantially younger with more patients expecting to return to preoperative activity levels. With heavier, younger, and often more active patients, devices must be able to support a more demanding loading-regime to meet patient expectations. McClung (2000) demonstrated that obese patients can display lower wear-rates with UHMWPE bearing resulting from post-operative, self-induced reduced ambulatory movement, thus questioning if obese kinematics and loading are indeed the worst-case.
Current loading patterns used to test hip implants are governed by ISO 14242-1 (2014). This study aimed to characterize a heavy and active population (referred to as HA) and investigate how the gait profile may differ to the current ISO profile.
Method
A comprehensive anthropometric data set of 4082 men (Gordon, CC., et.al., 2014) was used to characterize a HA population. Obese and HA participants were classed as BMI ≥30 however HA participants were identified by applying anthropometric ratios indicative of lower body fat, namely “waist to height” (i.e. WHtR <0.6) and “waist to hip” (i.e. WHpR <0.9).
Findings
Of 491 obese participants 61 were identified as HA (i.e. BMI> 30, WHpR<0.9; WHtR<0.6) (Figure 1). These characteristics were validated against a population of elite rugby players that were found to be a true reflection of HA patients (Figure 2). Combining the Army and Rugby populations resulted in a weight of 123kg for the 95th percentile, which based on 3× body weight (as referenced in ISO14242-1) would equate to a peak simulator load of 3620N.
Conclusion
Characterization of a HA population was successfully defined as clinically obese by BMI, but with WHtR and WHpR associated with lower body fat. The author was unable to identify gait characteristics of a HA population through existing literature.
Future Work
A gait-lab based study will be used to compare literature-based kinematics of obese subjects to those of HA subjects. A worst-case gait cycle can then be established for standard walking and other activities and translated into hip simulator parameters for HA patients.
