Abstract

Chronic low back pain (CLBP) is a nonspecific and persistent ailment that entails many physiological, psychological, social, and economic consequences for individuals and societies. Although there is a plethora of treatments available to treat CLBP, each treatment has varying efficacy for different patients, and it is currently unknown how to best link patients to their ideal treatment. However, it is known that biopsychosocial influences associated with CLBP affect the way that we move. It has been hypothesized that identifying phenotypes of spinal motion could facilitate an objective and repeatable method of determining the optimal treatment for each patient. The objective of this research was to develop an array of high deflection strain gauges to monitor spinal motion, and use that information to identify spinal-motion phenotypes. The high deflection strain gauges used in this endeavor exhibit highly nonlinear electrical signal due to their viscoelastic material properties. Two sub-models were developed to account for these nonlinearities: the first characterizes the relationship between quasistatic strain and resistance, and the second accounts for transient electrical phenomena due to the viscoelastic response to dynamic loads. These sub-models are superimposed to predict and interpret the electrical signal under a wide range of applications. The combined model accurately predicts sensor strain with a mean absolute error (MAE) of 1.4% strain and strain rate with an MAE of 0.036 mm/s. Additionally, a multilayered architecture was developed for the strain gauges to provide mechanical support during high strain, cyclic loads. The architecture significantly mitigates sensor creep and viscoplastic deformation, thereby reducing electrical signal drift by 74%. This research also evaluates the effects of CLBP on patient-reported outcomes. An exploratory factor analysis revealed that there are five primary components of well-being: Pain and Physical Limitations, Psychological Distress, Physical Activity, Sleep Deprivation, and Pain Catastrophizing. The presence of CLBP has adverse effects on all these components. It was also observed that different patient reported outcomes are highly correlated with each other, and the presence of CLBP is a significant moderating factor in many of these relationships. Arrays of high-deflection strain gauges were used to collect spinal kinematic data from 274 subjects. Seven phenotypes of spinal motion were identified among study participants. Statistical analyses revealed significant differences in the patient-reported outcomes of subjects who exhibited different phenotypes. This is a promising indication that the phenotypes may also provide important information to clinicians who treat patients suffering from CLBP. Future research will be conducted to develop and identify the optimal treatments for patients according to their phenotypes, which has the potential to reduce medical costs, expedite recovery, and improve the lives of millions of patients worldwide.

Degree

PhD

College and Department

Ira A. Fulton College of Engineering; Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2024-04-12

Document Type

Dissertation

Handle

http://hdl.lib.byu.edu/1877/etd13131

Keywords

nanocomposites, high-deflection strain gauges, modeling, patient-reported outcomes, phenotyping, machine learning, chronic low back pain

Language

english

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