OBJECTIVE:Chronic low back pain (cLBP) is a common condition that impacts quality of life and function. There are many evidence-based treatments to address cLBP; however, treatment effects are modest, perhaps in part due to individual variation in treatment response. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed as the collaborative centerpiece of the Back Pain Consortium (BACPAC) research program. This consortium was sponsored by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) as part of the Helping to End Addiction Long-term (HEAL) Initiative. DESIGN:The BEST trial was a sequential multiple assignment randomized trial (SMART) designed with the primary goal of identifying in whom different treatments show optimal response. The primary focus of the study was to use patient features, including biomarkers and phenotypic measures, to identify subsets of persons with cLBP who respond best to specific common treatments. METHODS:Four interventions were chosen for the trial: Enhanced Self-Care, Acceptance and Commitment Therapy, Duloxetine, and Evidence-Based Exercise and Manual Therapy. Following a run-in period and baseline assessment, participants were randomized to 1 of the 4 treatments for the first 12-week intervention period. Participants were reassessed and based on their self-reported response to initial treatment, continued that initial treatment, were augmented with an additional randomly assigned treatment, or were switched to a new treatment. CONCLUSION:This trial was designed to deliver rich phenotypic data that will both potentially aid in the discovery of phenotypic characteristics that predict treatment response and provide a greater mechanistic understanding of cLBP. CLINICAL TRIAL REGISTRATION NUMBER:The Biomarkers for Evaluating Spine Treatments (BEST) trial is registered on ClinicalTrials.gov (Registration number: NCT05396014; https://clinicaltrials.gov/study/NCT05396014).

Orthopedic implant-associated infections are a growing problem. These infections are often associated with bacterial biofilms, such as those formed by Staphylococcus aureus. Nanotextured surfaces can reduce or prevent the development of bacterial biofilms and could help reduce infection rates and severity. Previous work has shown that a carbon-infiltrated carbon nanotube (CICNT) surface reduces the growth of S. aureus biofilms. This work expands on previous experiments, showing that the topography of the CICNT, rather than its surface chemistry, is responsible for the reduction in biofilm growth. Additionally, the CICNT surface does not reduce biofilm growth by killing the bacteria or by preventing their attachment. Rather it likely slows cell growth, resulting in fewer cells and reduced biofilm formation.

Wearable nanocomposite stretch sensors are an exciting new development in biomaterials for biomechanical motion-tracking technology, with applications in the treatment of low back pain, knee rehabilitation, fetal movement tracking, and other fields. When strained, the resistance of the low-cost sensors is reduced, enabling human motion to be monitored using a suitable sensor array. However, current sensor technologies have exhibited significant drift, in the form of increased electrical resistance, if left stored in typical room conditions. The purpose of the present work was to evaluate the influence of several environmental factors, including temperature, humidity, oxygen levels, and light exposure, that could impact the change in electrical properties of these sensors. These physiological conditions are present during use of the sensors on human subjects as well as during sensor storage, making it vital to understand their effects on sensor properties. The electromechanical performance of the sensors stored under a range of conditions was monitored over a period of several weeks. The observations obtained indicate that the presence of oxygen and humidity in the environment where the sensors are stored is the primary contributor to drift in the sensor response. Sensors that are kept in de-oxygenated or desiccated environments do not display an increase in electrical resistance over time. This understanding allows for long-term storage of the sensors without degradation. It also assists in identifying the internal processes at work within the nanoparticle-polymer matrix that cause changes in electrical properties.

Recent advancements in wearable data measurement technologies have allowed for real-time collection of biosignals related to spinal function and back pain. These data also have the potential to completely transform back pain treatment paradigms, to improve diagnostic movement phenotyping and to track treatment effectiveness longitudinally. The primary objective of the present scoping review was to investigate the status of development and trends in the use of wearable sensor technologies employed to measure biosignals related to spinal function and back pain, to identify the major developments and future trends for this field.Until recently, much of the wearable sensor data related to spinal function and back pain have come from a relatively small number of technologies, were sampled by a judiciously placed single device, and were analyzed using traditional statistical modeling techniques. However, based on the state of the literature, the field of wearable sensors for spine appears to have reached an inflection point where the previous limiting factors are no longer significant barriers. The growing number of wearable sensor types, combined with real-time interpretation using machine-learning algorithms, is paving the way for objective and comprehensive evaluations of spinal movements that can guide both research and clinical practice.Literature Search: PubMed, Web of Science and EMBASE, all articles prior to 9 April 2025.

A common approach to resolving discogenic low back pain involves replacing one or more degenerated spinal discs with a total disc replacement device. However, existing solutions for intervertebral disc replacement are unable to fully capture the kinetic and kinematic characteristics (i.e., the quality of motion) of the intact spinal disc. In the present work, a novel single-piece compliant mechanism driven, motion-preserving lumbar intervertebral implant design is described. Prototypes were manufactured from Ti6Al4V and evaluated using benchtop mechanical and in vitro biomechanical testing. ASTM F2346 static testing procedures were followed to assess the design's compressive, shear, and torsional properties. Similarly, the forces necessary to cause the device to be ejected from the interbody space and the force required to cause subsidence were tested. in vitro testing was conducted with fresh-frozen human cadaveric lumbar spinal segments to analyze the quality of motion of the intact segments and after they were instrumented with the compliant interbody device prototype. The design was robust in static compressive, shear, and torsional loading. Expulsion and subsidence test results were comparable to devices currently in use. in vitro testing indicated that when appropriately placed in the intervertebral space, the instrumented segment's quality of motion closely replicated the intact segment.

Background Context Since the early 2000s, various expandable spinal fusion cages have been developed to facilitate less invasive procedures, however, expandable cages have often been evaluated as a homogeneous group, neglecting differences in shape, size, material, expandability and lordotic adjustability. This systematic review aimed to comprehensively survey the literature on expandable spinal fusion cages, discuss their differentiating factors, and identify gaps in the literature regarding these devices. Purpose To demonstrate the range of design features included in expandable interbody devices and identify which of these features are associated with improved surgical outcomes. Study Design Systematic review. Methods The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. An electronic search of MEDLINE and Embase using the search terms “lumbar” AND “fusion” AND (“expandable cage” OR “expandable interbody”) including only English language articles that contained sufficient detail to correlate a specific expandable cage design to patient outcomes. Relevant elements, including device design parameters, patient population information, details of the intervention, comparison data, outcome variables, and the timeframe were extracted. Statistical analysis was conducted to correlate patient outcomes with different device features. Results While 387 different articles were initially identified, 49 met all the criteria for inclusion. Design differences contributed to disparate outcomes, with rectangular titanium cages featuring medial-lateral and vertical expansion and continuous lordotic adjustability being correlated with significantly improved patient-reported outcomes. The surgical approach and location were also found to be correlated with patient outcomes, indicating that confounding factors are present. Conclusions We recommend that expandable cage technologies not be considered a homogenous group, as long-term outcomes likely are dependent upon specific design characteristics. Categorizing devices based on design features such as material composition, shape, vertical expandability, horizontal expandability, and restoration of segmental lordosis may allow for more rapid identification of device characteristics associated with better outcomes.

This work introduces an interior contact-aided rolling element (I-CORE) compliant mechanism that draws upon the concepts used for the contact-aided rolling element, cross-axis flexural pivot, and pre-curved flexible beam. The I-CORE incorporates a bilinear compressive stiffness response with an initial tailorable stiffness governed by the flexural geometry, followed by a stiffness curve governed by the material stiffness at the contact point. The I-CORE mechanism can achieve one or two degrees of rotational freedom as well as a single degree of translational freedom. The purpose of the present work was to introduce the I-CORE mechanism, as well as a pseudo-rigid-body replacement model (PRBM) of the I-CORE mechanism which was subsequently validated using both finite element analysis and benchtop mechanical testing. A pseudo-rigid body model was created for the I-CORE to simplify the rapid adaptation of this mechanism to different design applications. This model was validated using both finite element analysis and benchtop mechanical testing under both compression and rotation loading conditions. Additionally, multiple configurations of the device were created and evaluated in order to test its sensitivity to certain design features including the flexure width, flexure thickness, and the radius of the rounded contact surfaces. It was found that the model is sensitive to the thickness of the flexures and that despite some limitations, the pseudo-rigid body model is sufficiently accurate for initial design work. Some possible applications of the mechanism are proposed.

Objective Chronic low back pain (cLBP) is a significant public health problem in the United States. A method to identify treatments that are most likely effective for an individual patient based on their unique characteristics is needed.Methods The Biomarkers for Evaluating Spine Treatments (BEST) Trial is a sequential, multiple assignment, randomized trial designed to estimate an optimal treatment or combination of treatments to reduce pain intensity and interference at 24 weeks in individuals with cLBP.Results We describe the patient-reported characteristics of the BEST Trial at the Baseline visit. Data collection for extensive required phenotyping is reported. We analyzed the run-in period of the BEST Trial to evaluate predictors of run-in failure. The BEST Trial enrolled 1019 participants and randomized 805 participants (61.6% female, mean age 50.4, 12.5% Black or African American) to the first stage of treatment. We collected extensive required phenotyping on all 805 randomized BEST Trial participants, and additional optional phenotyping on 510 (63.4%) participants.Conclusions The BEST Trial successfully enrolled a racially and geographically diverse sample of chronic low back pain patients and completed rich phenotypic assessments to inform our primary goal of identifying in whom different treatments show optimal response. We demonstrated the feasibility of collecting extensive phenotypic assessments in a multi-site clinical trial of cLBP.Clinical trial registration number The Biomarkers for Evaluating Spine Treatments (BEST) Trial is registered on ClinicalTrials.gov. Registration number: NCT05396014 (https://clinicaltrials.gov/study/NCT05396014).

Chronic low back pain (CLBP) is a prevalent condition with significant physiological, psychological, social, and economic impacts. A range of patient-reported outcomes (PROs) are used to collect self-reported perceptions of patient health and well-being relating to the condition. Correlations between different types of PROs have previously been demonstrated – for example, between PROs that measure pain, and those that measure anxiety. Furthermore, PROs have evolved over time, and there exist strong correlations between more recently developed outcomes, such as PROMIS PROs, and legacy PROs. However, most studies in this area focus only on subjects with CLBP. It would be enlightening to determine whether the same correlations between PROS exist in healthy subjects, and whether and how the presence of CLBP moderates the relationships between these PROs. This comparative cross-sectional study hypothesizes that: We compared outcomes of legacy PROs with PROMIS PROs collected from participants aged 35–65 with (n = 133) and without (n = 100) CLBP. Welch t-tests compared PRO scores between groups. Linear regressions evaluated the relationship between legacy and PROMIS PROs, accounting for CLBP as a binary variable. An exploratory factor analysis identified latent factors summarizing variance in the PROs. Cases reported significantly lower scores than controls across all PROs except for activity level. Strong correlations emerged between several PROMIS metrics and two legacy PROs measuring pain intensity and disability. CLBP significantly moderated these relationships. Moderate correlations were noted between PROMIS metrics and pain catastrophizing and anxiety, with weaker correlations for activity level. Five latent factors were identified, capturing key characteristics that influence variance. Legacy and PROMIS PROs performed similarly in terms of correlation with CLBP, suggesting they capture overlapping information. However, latent factor analysis indicates potential for designing more focused PROs, targeting characteristics in these factors, to better capture variance in outcomes across individuals with and without CLBP. Not applicable.

Flexible strain sensors, fabricated from high-elongation polymers and conductive filler particles, are proving an essential tool in the study of biomechanics using wearable technology. It has been previously shown that the resistive response of such composites, relative to the amount of conductive filler material, can be reasonably modeled using a standard percolation-type model. Once a certain critical fraction of filler material is reached, a conductive network across the sample is established and resistance rapidly decreases. However, modeling the more subtle resistance changes that occur while deforming the sensors during operation is more nuanced. Conductivity across the network of particles is dominated by tunneling mechanisms at the interfaces between the filler materials. Small changes in strain at these interfaces lead to relatively large, but nevertheless continuous, changes in local resistance. By assigning some arbitrary value of resistance as a dividing line between ‘low’ and ‘high’ resistance, one might model the piezoresistive behavior using a standard percolation model. But such an assumption is likely to lead to low accuracy. Our alternative approach is to divide the range of potential resistance values into several bins (rather than the usual two bins) and apply a relatively novel multi-state percolation theory. The performance of the multi-state percolation model is assessed using a random resistor model that is assumed to provide the ground truth. The model is applied to predict resistance response with both changes in relative amount of conductive filler (i.e., to help design the initial unstrained sensor) and with applied strain (for an operating sensor). We find that a multi-state percolation model captures the behavior of the simulated composite sensor in both cases. The multicomponent percolation theory becomes more accurate with more divisions/bins of the resistance distribution, and we found good agreement with the simulation using between 10 and 20 divisions.

There has been an influx of skin-adhered wearables that have begun to show promise for their ability to continuously monitor spinal kinematics based on skin deformation for assessing spine-related problems including low-back pain. However, a lack of information regarding the amount of stretch (or strain) that lumbar skin experiences when wearers perform uniplanar or multiplanar movements makes the designing of these wearables difficult. In this study, skin motion was measured using a relatively dense grid of small reflective markers during 6 uniplanar, 4 multiplanar, and 1 activity of daily living (ADL) movements of increasing functionality. These data were used to compute dynamic, inhomogeneous, anisotropic strain fields of lumbar skin based on large deformation strain theory. Of particular note, macroscopic principal strains were highest in Flexion, reaching averages as high as 103 %, with strain rates up to 151 % per second. Principal strain orientations were movement dependent. Males exhibited higher principal strains than females during Flexion (p = 0.0027) and Sit To Stand (p = 0.0453) motions. Repeatability was high between repetitions, ranging from 71.1 % (extension) to 97.2 % (Sit To Stand motion). Skin strain fields were sensitive to both underlying spinal geometry and dermal collagen fiber orientations. The results of this study are relevant to the precision of spinal-specific wearables when placed on different regions of the lumbar skin and may also have clinical relevance to choice of surgical incision orientation and wound care in the lumbar region.

Implant-associated infections caused by Staphylococcus aureus are a growing problem for healthcare systems. Implant materials that resist bacterial colonization may help reduce infection rates and severity. This research examined the effect of a copper-coated carbon-infiltrated carbon nanotube surface (Cu-CICNT). We have previously shown that CICNT without copper has an anti-biofilm effect, and copper has long been known to have anti-bacterial properties. Bacterial biofilms were grown in a droplet on the Cu-CICNT surface, and a control consisting of copper deposited on a relatively flat, non-nanotube-structured surface. The Cu-CICNT surface was highly effective at reducing biofilm formation, reducing recoverable S. aureus bacteria by 99.9999% in 12 hours (a 6.3-log reduction). This effect was confirmed in both a methicillin-resistant and a methicillin-sensitive isolate of S. aureus. The Cu-CICNT surface was also highly effective against Pseudomonas aeruginosa, resulting in a 6.9-log reduction in adherent bacteria. The Cu-CICNT surface was more effective at inhibiting biofilm formation than the flat copper-coated titanium, indicating a synergistic effect between the CICNT topography and copper. The concentration of copper ions in growth media was low after exposure to Cu-CICNT (6.2 ppm), and media with this amount of supplemented copper had only a small effect on biofilm reduction, as did conditioned media previously exposed to Cu-CICNT. Our findings suggest that the antibacterial effect is likely due to contact killing of bacteria on the textured copper surface.IMPORTANCEOrthopedic implants and devices are becoming increasingly common. Unfortunately, as their use increases, so does the prevalence of implant-associated infections. These infections are most commonly caused by the bacterium Staphylococcus aureus. S. aureus infections are particularly difficult to treat because they form biofilms resistant to antibiotics and the host immune system. In this research, we used a carbon nanotube-based surface combined with a thin film of copper to produce a surface coating that could be used on implants to prevent bacterial infection. The combination of the surface topography with the copper coating resulted in over a 6-log reduction in the number of adherent bacteria, preventing the formation of a bacterial biofilm. This reduction in adherent bacteria is likely due to the surface killing effects of the bacteria on contact. The potential applications of such a surface could help reduce infection burden, improve patient quality of life, and reduce stress on healthcare systems.

Flexible high-deflection strain gauges have been demonstrated to be cost-effective and accessible sensors for capturing human biomechanical deformations. However, the interpretation of these sensors is notably more complex compared to conventional strain gauges, particularly during dynamic motion. In addition to the non-linear viscoelastic behavior of the strain gauge material itself, the dynamic response of the sensors is even more difficult to capture due to spikes in the resistance during strain path changes. Hence, models for extracting strain from resistance measurements of the gauges most often only work well under quasi-static conditions. The present work develops a novel model that captures the complete dynamic strain–resistance relationship of the sensors, including resistance spikes, during cyclical movements. The forward model, which converts strain to resistance, comprises the following four parts to accurately capture the different aspects of the sensor response: a quasi-static linear model, a spike magnitude model, a long-term creep decay model, and a short-term decay model. The resulting sensor-specific model accurately predicted the resistance output, with an R-squared value of 0.90. Additionally, an inverse model which predicts the strain vs. time data that would result in the observed resistance data was created. The inverse model was calibrated for a particular sensor from a small amount of cyclic data during a single test. The inverse model accurately predicted key strain characteristics with a percent error as low as 0.5%. Together, the models provide new functionality for interpreting high-deflection strain sensors during dynamic strain measurement applications, including wearables sensors used for biomechanical modeling and analysis.

Piezoresistive sensors composed of nickel nanostrands, nickel-coated carbon fibers, and silicone can be used to measure large physical deflections but exhibit viscoelastic properties and creep, leading to a complex and nonlinear electrical response that is difficult to interpret. This study considers the impact of modifying the geometry and architecture of the sensors on their mechanical and electrical performance. Varying the sensor thickness leads to potentially significant differences in conductive fiber alignment, while adding external layers of pure silicone provides elastic support for the sensors, potentially reducing their extreme viscoelastic nature. The impact of such modifications on both mechanical and electrical behavior was assessed by analyzing strain to failure, the magnitude of hysteresis with cycling, the repeatability of the electro-mechanical response, the strain level at which resistance begins to monotonically decrease, and the drift in electrical response with cycling. The results indicate that thicker single-layer sensors have less electrical drift. Sensors with a multilayered architecture exhibit several improvements in behavior, such as increasing the range of the monotonic region by approximately 52%. These improvements become more significant as the thickness of the pure silicone layers increases.

As spinal fusion surgery continues to transition to less invasive techniques, there remains an unmet need for ever smaller and more complex interbody cages to meet the unique needs of this difficult surgery. This work focuses on the hypothesis that this need can be met using the inherent advantages of compliant mechanisms. Deployable Euler spiral connectors (DESCs), optimized using a gradient based optimization algorithm, were used as the foundation for a device that can stow to a very small size for device insertion then bilaterally deploy to a substantially larger device footprint. Additionally, a continuously adjustable lordotic angle was achieved using the same device so as to result in a customized anatomical fit. Several tests, including finite element analysis (FEA), compression testing, shear testing, and deployment in a cadaver, were performed as initial verification and validation that the concept device performs well under typical testing paradigms used for interbody cages. While further device testing and refinements are necessary prior to clinical use, the present work demonstrates the promise of this approach and highlights the potential of compliant mechanism devices for advancing minimally invasive (MIS) lumbar fusion.

OBJECTIVE:One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses. METHODS:Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and nontabular data (eg, imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium. RESULTS:Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected by use of questionnaires across projects. Other nonrequired domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed on the basis of the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging, and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC. CONCLUSIONS:BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs.

Polymeric foams, embedded with nano-scale conductive particles, have previously been shown to display quasi-piezoelectric (QPE) properties; i.e., they produce a voltage in response to rapid deformation. This behavior has been utilized to sense impact and vibration in foam components, such as in sports padding and vibration-isolating pads. However, a detailed characterization of the sensing behavior has not been undertaken. Furthermore, the potential for sensing quasi-static deformation in the same material has not been explored. This paper provides new insights into these self-sensing foams by characterizing voltage response vs frequency of deformation. The correlation between temperature and voltage response is also quantified. Furthermore, a new sensing functionality is observed, in the form of a piezoresistive response to quasi-static deformation. The piezoresistive characteristics are quantified for both in-plane and through-thickness resistance configurations. The new functionality greatly enhances the potential applications for the foam, for example, as insoles that can characterize ground reaction force and pressure during dynamic and/or quasi-static circumstances, or as seat cushioning that can sense pressure and impact.

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-Term(SM) (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science.

Chronic low back pain (cLBP) is a prevalent and multifactorial ailment. No single treatment has been shown to dramatically improve outcomes for all cLBP patients, and current techniques of linking a patient with their most effective treatment lack validation. It has long been recognized that spinal pathology alters motion. Therefore, one potential method to identify optimal treatments is to evaluate patient movement patterns (ie, motion-based phenotypes). Biomechanists, physical therapists, and surgeons each utilize a variety of tools and techniques to qualitatively assess movement as a critical element in their treatment paradigms. However, objectively characterizing and communicating this information is challenging due to the lack of economical, objective, and accurate clinical tools. In response to that need, we have developed a wearable array of nanocomposite stretch sensors that accurately capture the lumbar spinal kinematics, the SPINE Sense System. Data collected from this device are used to identify movement-based phenotypes and analyze correlations between spinal kinematics and patient-reported outcomes. The purpose of this paper is twofold: first, to describe the design and validity of the SPINE Sense System; and second, to describe the protocol and data analysis toward the application of this equipment to enhance understanding of the relationship between spinal movement patterns and patient metrics, which will facilitate the identification of optimal treatment paradigms for cLBP.

Carbon Infiltrated Carbon Nanotubes (CICNTs) show promise as a surface modification for medical devices and implants due to their potential structural resistance to bacterial colonization. However, when 316L stainless steel is used as the substrate for CICNT growth, the steel loses its passivating layer and experiences oxidative corrosion when placed in an aqueous physiological environment. This effect, confirmed by both energy dispersive x-ray analysis and electrochemical potentiokinetic reactivation, may be attributed to carburization of the alloy during CICNT production. One potential solution to this problem was investigated by employing an indirect CICNT growth method that utilized protective thin films under the CICNT surface and a nitrocellulose-based coating on other exposed edges. Samples that had been thus treated exhibited no significant corrosion over a 48-hour testing period.