Development of a collaborative model of low back pain: report from the 2017 NASS consensus meeting

The effects of osteopathic manipulative treatment on pain and disability in patients with chronic low back pain: a single-blinded randomized controlled trial

CONTEXT

The evidence for the efficacy of osteopathic manipulative treatment (OMT) in the management of low back pain (LBP) is considered weak by systematic reviews, because it is generally based on low-quality studies. Consequently, there is a need for more randomized controlled trials (RCTs) with a low risk of bias.

OBJECTIVES

The objective of this study is to evaluate the efficacy of an OMT intervention for reducing pain and disability in patients with chronic LBP.

METHODS

A single-blinded, crossover, RCT was conducted at a university-based health system. Participants were adults, 21-65 years old, with nonspecific LBP. Eligible participants (n=80) were randomized to two trial arms: an immediate OMT intervention group and a delayed OMT (waiting period) group. The intervention consisted of three to four OMT sessions over 4-6 weeks, after which the participants switched (crossed-over) groups. The primary clinical outcomes were average pain, current pain, Patient-Reported Outcomes Measurement Information System (PROMIS) 29 v1.0 pain interference and physical function, and modified Oswestry Disability Index (ODI). Secondary outcomes included the remaining PROMIS health domains and the Fear Avoidance Beliefs Questionnaire (FABQ). These measures were taken at baseline (T0), after one OMT session (T1), at the crossover point (T2), and at the end of the trial (T3). Due to the carryover effects of OMT intervention, only the outcomes obtained prior to T2 were evaluated utilizing mixed-effects models and after adjusting for baseline values.

RESULTS

Totals of 35 and 36 participants with chronic LBP were available for the analysis at T1 in the immediate OMT and waiting period groups, respectively, whereas 31 and 33 participants were available for the analysis at T2 in the immediate OMT and waiting period groups, respectively. After one session of OMT (T1), the analysis showed a significant reduction in the secondary outcomes of sleep disturbance and anxiety compared to the waiting period group. Following the entire intervention period (T2), the immediate OMT group demonstrated a significantly better average pain outcome. The effect size was a 0.8 standard deviation (SD), rendering the reduction in pain clinically significant. Further, the improvement in anxiety remained statistically significant. No study-related serious adverse events (AEs) were reported.

CONCLUSIONS

OMT intervention is safe and effective in reducing pain along with improving sleep and anxiety profiles in patients with chronic LBP.

An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research

Chronic low back pain (LBP) is influenced by a broad spectrum of patient-specific factors as codified in domains of the biopsychosocial model (BSM). Operationalizing the BSM into research and clinical care is challenging because most investigators work in silos that concentrate on only one or two BSM domains. Furthermore, the expanding, multidisciplinary nature of BSM research creates practical limitations as to how individual investigators integrate current data into their processes of generating impactful hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for organizing knowledge, but the practical aspects for how AI may advance LBP research and clinical are beginning to be explored. The goals of the work presented here are to: (1) explore the current capabilities of knowledge integration technologies (large language models (LLM), similarity graphs (SGs), and knowledge graphs (KGs)) to synthesize biomedical literature and depict multimodal relationships reflected in the BSM, and; (2) highlight limitations, implementation details, and future areas of research to improve performance. We demonstrate preliminary evidence that LLMs, like GPT-3, may be useful in helping scientists analyze and distinguish cLBP publications across multiple BSM domains and determine the degree to which the literature supports or contradicts emergent hypotheses. We show that SG representations and KGs enable exploring LBP's literature in novel ways, possibly providing, trans-disciplinary perspectives or insights that are currently difficult, if not infeasible to achieve. The SG approach is automated, simple, and inexpensive to execute, and thereby may be useful for early-phase literature and narrative explorations beyond one's areas of expertise. Likewise, we show that KGs can be constructed using automated pipelines, queried to provide semantic information, and analyzed to explore trans-domain linkages. The examples presented support the feasibility for LBP-tailored AI protocols to organize knowledge and support developing and refining trans-domain hypotheses.

Using medical storytelling to communicate problems and solutions in the low back pain conundrum: an evidence-based tale of twins

OBJECTIVES

Low back pain (LBP) is the number one cause of disability world-wide. It is also the most expensive area in healthcare. Patient-centered innovations are needed. This paper uses medical storytelling to illustrate the common problems that often lead to unnecessary suffering for patients, and costs to society. We present innovative solutions, including narrative interventions.

METHODS

We use medical storytelling to present a scenario in which hypothetical twin patients with identical LBP episodes enter the healthcare system, with one twin managed in an appropriate manner, and the other inappropriately.

RESULTS

One twin becomes a chronic LBP sufferer, while the other experiences quick resolution, despite identical conditions. Recommendations are made to de-implement inappropriate action and to implement a more productive approach.

CONCLUSIONS

Many patients with LBP descend into chronic pain. This is rarely inevitable based on clinical factors. Much of chronic LBP results from how the condition is handled within the healthcare system. Medical narrative may be one innovation to illustrate the problem of current LBP management, recommend solutions and foster changes in clinical behavior.

PRACTICAL IMPLICATIONS

The starkly different outcomes for each identical twin are illustrated. Recommendations are made for reframing the situation to de-implement the inappropriate and to implement a more appropriate approach.

Effect of integrated exercise therapy and psychosocial interventions on self-efficacy in patients with chronic low back pain: A systematic review

OBJECTIVE

Investigate if integrated exercise and psychosocial (EP) interventions effect self-efficacy to manage pain and self-efficacy for physical functioning compared to alternate interventions, usual care, waitlists and attention controls for individuals with chronic low back pain (CLBP).

METHODS

MEDLINE, Embase, CINAHL, Web of Science, PsychINFO, PEDro, and Cochrane Library were searched. Included randomized controlled trials utilized an EP intervention for CLBP and measured self-efficacy. Independent reviewers screened abstracts, reviewed full-texts, extracted data, and assessed risk of bias. GRADE, synthesis without meta-analysis, and ranges of effects (Hedges' g) were used.

RESULTS

2207 Participants were included (22-studies). EP interventions positively effected self-efficacy to manage pain short-term compared to usual care (range of effects: -0.02, 0.94) and controls (range of effects: 0.69, 0.80) and intermediately compared to usual care (range of effects: 0.11, 0.29); however, no differences were found when compared to alternate interventions. EP interventions positively effected self-efficacy for physical functioning short-term compared to alternate interventions (range of effects: 0.57, 0.71), usual care (range of effects: -0.15, 0.94), and controls (range of effects: 0.31, 0.56), and intermediately compared to alternate interventions (1-study, effect: 0.57) and controls (1-study, effect: 0.56). Conclusions were limited by low to very low-quality-evidence often from risk of bias, imprecision, and clinical/statistical heterogeneity.

CONCLUSIONS

EP interventions may be more effective short-term for self-efficacy to manage pain than usual care and waitlists, but not alternate interventions. EP interventions may be effective for self-efficacy for physical functioning at short- and intermediate-term compared to alternate interventions, usual care, waitlist and attention controls. Considerations for future research include methods for blinding and measurement of self-efficacy for physical functioning.

Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.

How Individuals With Low Back Pain Conceptualize Their Condition: A Collaborative Modeling Approach

Low back pain (LBP) is complex. This study aimed to use collaborative modeling to evaluate conceptual models that individuals with LBP have of their condition, and to compare these models with those of researchers/clinicians. Twenty-eight individuals with LBP were facilitated to generate mental models, using "fuzzy cognitive maps," that represented conceptualization of their own LBP and LBP "in general." "Components" (ie, causes, outcomes and treatments) related to pain, disability and quality of life were proposed, along with the weighted "Connections" between Components. Components were classified into thematic categories. Weighting of Connections were summed for each Component to judge relative importance. Individual models were aggregated into a metamodel. When considering their own condition, participants' models included 19(SD = 6) Components and 43(18) Connections with greatest weight on "Biomechanical" components. When considering LBP in general, models changed slightly. Patient models contrasted the more complex models of researchers/clinicians (25(7) Components; 77(42) Connections), with most weight on "Psychological" components. This study provides unique insight into how individuals with LBP consider their condition, which is largely biomedical and narrower than clinician/researcher perspectives. Findings highlight challenges for changing public perception of LBP, and provide a method with potential utility to understand how individuals conceptualize their condition. PERSPECTIVE: Collaborative modeling was used to understand how individuals with low back pain conceptualize their own condition, the condition in general, and compare this with models of expert researchers/clinicians. Data revealed issues in how individuals with back pain conceptualize their condition, and the method's potential utility for clinical evaluation of patients.

A vision for the future of wearable sensors in spine care and its challenges: narrative review

OBJECTIVE

This review aimed to: (I) provide a brief overview of some topical areas of current literature regarding applications of wearable sensors in the management of low back pain (LBP); (II) present a vision for a future comprehensive system that integrates wearable sensors to measure multiple parameters in the real world that contributes data to guide treatment selection (aided by artificial intelligence), uses wearables to aid treatment support, adherence and outcome monitoring, and interrogates the response of the individual patient to the prescribed treatment to guide future decision support for other individuals who present with LBP; and (III) consider the challenges that will need to be overcome to make such a system a reality.

BACKGROUND

Advances in wearable sensor technologies are opening new opportunities for the assessment and management of spinal conditions. Although evidence of improvements in outcomes for individuals with LBP from the use of sensors is limited, there is enormous future potential.

METHODS

Narrative review and literature synthesis.

CONCLUSIONS

Substantial research is underway by groups internationally to develop and test elements of this system, to design innovative new sensors that enable recording of new data in new ways, and to fuse data from multiple sources to provide rich information about an individual's experience of LBP. Together this system, incorporating data from wearable sensors has potential to personalise care in ways that were hitherto thought impossible. The potential is high but will require concerted effort to develop and ultimately will need to be feasible and more effective than existing management.

Cognitive Maps Across Multiple Social Sectors: Shared and Unique Perceptions on the Quality of Agricultural Soils in Mexico

Incorporating the views and perceptions of local farmers and other actors with stakes in agricultural production is critical for better-informed decision making and tackling pressing issues, such as soil degradation. We conducted a study that sought to integrate and analyze perceptions regarding the quality and degradation of agricultural soils across different social sectors in Mexico, including producers of two annual crops (maize and beans) and two perennial crops (coffee and avocado), members of civil society organizations and members of the Federal Government. We analyzed the community perception using Cognitive Maps and network metrics. Our fully documented method to formally gather and analyze local and regional perceptions can be used in future efforts toward the collective design of sustainable food systems. Our results highlighted common and potentially conflicting aspects among the different perceptions and allowed us to identify and discuss drivers and processes of special interest in different regions in Mexico. This study also contributes to a deeper understanding of the current situation of agricultural soils in Mexico and seeks to inform the decision-making process regarding agricultural management in the country.

Using artificial intelligence algorithms to identify existing knowledge within the back pain literature

PURPOSE

Artificial intelligence algorithms can now identify hidden data patterns within the scientific literature. In 2019, these algorithms identified a thermoelectric material within the pre-2009 chemistry literature; years before its discovery in 2012. This approach inspired us to apply this algorithm to the back pain literature as the cause of back pain remains unknown in 90% of cases.

METHODS

We created a subset of all PubMed abstracts containing "back" and "pain" and then trained the Word2vec algorithm to predict word proximity. We then identified word pairings having high vector proximities between three spinal domains: anatomy, pathology and treatment. We plotted both between-domain and within-domain proximities then used the highest proximity pairs as ground truths in analogy testing to identify known associations (e.g., Canal is to Stenosis as Multifidus is to ?) RESULTS: We found  50,038 abstracts resulting in 27,984 unique words and 108,252 instances of "back pain". Ground truth pairings ranged in proximity from 0.86 to 0.70. Plotting revealed unique proximity representations between the three spine domains. From analogy testing, we identified 13 known word associations (pars_interarticularis is to stress_reaction as nerve_root is to compression).

CONCLUSIONS

Artificial intelligence algorithms can successfully extract complex concepts from back pain literature. While use of AI algorithms to discover potentially unknown word associations requires future validation, our results provide investigators with a novel tool to generate new hypotheses regarding the origins of LBP and other spine related topics. To encourage use of these tools, we have created a free web-based app for investigator-driven queries.

Assessing (Social-Ecological) Systems Thinking by Evaluating Cognitive Maps

Systems thinking (ST) skills are often the foundation of sustainability science curricula. Though ST skill sets are used as a basic approach to reasoning about complex environmental problems, there are gaps in our understanding regarding the best ways to promote and assess ST learning in classrooms. Since ST learning provides Science, Technology, Engineering, and Mathematics (STEM) students’ important skills and awareness to participate in environmental problem-solving, addressing these gaps is an important STEM learning contribution. We have created guidelines for teaching and measuring ST skills derived from a hybrid of a literature review and through case study data collection. Our approach is based on semi-quantitative cognitive mapping techniques meant to support deep reasoning about the complexities of social–ecological issues. We begin by arguing that ST should be evaluated on a continuum of understanding rather than a binary of correct/incorrect or present/absent. We then suggest four fundamental dimensions of teaching and evaluating ST which include: (1) system structure, (2) system function, (3) identification of leverage points for change, and (4) trade-off analysis. Finally, we use a case study to show how these ideas can be assessed through cognitive maps to help students develop deep system understanding and the capacity to propose innovative solutions to sustainability problems.

Building a Collaborative Model of Sacroiliac Joint Dysfunction and Pelvic Girdle Pain to Understand the Diverse Perspectives of Experts

BACKGROUND

Pelvic girdle pain (PGP) and sacroiliac joint (SIJ) dysfunction/pain are considered frequent contributors to low back pain (LBP). Like other persistent pain conditions, PGP is increasingly recognized as a multifactorial problem involving biological, psychological, and social factors. Perspectives differ between experts and a diversity of treatments (with variable degrees of evidence) have been utilized.

OBJECTIVE

To develop a collaborative model of PGP that represents the collective view of a group of experts. Specific goals were to analyze structure and composition of conceptual models contributed by participants, to aggregate them into a metamodel, to analyze the metamodel's composition, and to consider predicted efficacy of treatments.

DESIGN

To develop a collaborative model of PGP, models were generated by invited individuals to represent their understanding of PGP using fuzzy cognitive mapping (FCM). FCMs involved proposal of components related to causes, outcomes, and treatments for pain, disability, and quality of life, and their connections. Components were classified into thematic categories. Weighting of connections was summed for components to judge their relative importance. FCMs were aggregated into a metamodel for analysis of the collective opinion it represented and to evaluate expected efficacy of treatments.

RESULTS

From 21 potential contributors, 14 (67%) agreed to participate (representing six disciplines and seven countries). Participants' models included a mean (SD) of 22 (5) components each. FCMs were refined to combine similar terms, leaving 89 components in 10 categories. Biomechanical factors were the most important in individual FCMs. The collective opinion from the metamodel predicted greatest efficacy for injection, exercise therapy, and surgery for pain relief.

CONCLUSIONS

The collaborative model of PGP showed a bias toward biomechanical factors. Most efficacious treatments predicted by the model have modest to no evidence from clinical trials, suggesting a mismatch between opinion and evidence. The model enables integration and communication of the collection of opinions on PGP.

Can Biomechanics Research Lead to More Effective Treatment of Low Back Pain? A Point-Counterpoint Debate

SYNOPSIS

Although biomechanics plays a role in the development and perhaps the persistent or recurrent nature of low back pain (LBP), whether biomechanics alone can provide the basis for intervention is debated. Biomechanics, which refers to the mechanics of the body, including its neuromuscular control, has been studied extensively in LBP. But, can gains be made in understanding LBP by research focused on this component of biology in the multifactorial biopsychosocial problem of LBP? This commentary considers whether biomechanics research has the potential to advance treatment of LBP, and how likely it is that this research will lead to better treatment strategies. A point-counterpoint format is taken to present both sides of the argument. First, the challenges faced by an approach that considers biomechanics in isolation are presented. Next, we describe 3 models that place substantial emphasis on biomechanical factors. Finally, reactions to each point are presented as a foundation for further research and clinical practice to progress understanding of the place for biomechanics in guiding treatment of LBP. J Orthop Sports Phys Ther 2019;49(6):425-436. Epub 15 May 2019. doi:10.2519/jospt.2019.8825.

Model Simulations Challenge Reductionist Research Approaches to Studying Chronic Low Back Pain

BACKGROUND

Traditionally, low back pain (LBP) is studied using a reductionist approach, in which the factors contributing to the clinical presentation of LBP are studied in isolation to identify the primary pathology or condition linked to LBP. We argue that reductionism may not be suitable for studying LBP, considering the complex, multifactorial nature of this condition.

OBJECTIVES

To quantify the likelihood of successfully subclassifying patients with LBP and effectively targeting treatment based on a single dominant factor contributing to LBP.

METHODS

Both analytical and numerical simulations (Monte Carlo) of 1 million patients with LBP were performed. Several factors contributing to LBP were randomly assigned to each individual. The following outcome measures were computed, as a function of the number of factors: the percentage of individuals who could be subclassified by identifying a single factor exceeding a certain threshold, and the average reduction in LBP when treatment eliminates the largest contributing factor versus a multimodal treatment that eliminates a number of the randomly selected factors.

RESULTS

With an increasing number of factors, the probability of subclassifying an individual to a subgroup based on a single factor tends toward zero. A multimodal treatment arbitrarily addressing any 2 or more factors was more effective than diagnosing and treating a single factor that maximally contributed to LBP.

CONCLUSION

Results suggest that reductionism is not appropriate for subclassifying patients with LBP or for targeting treatment. The use of reductionist approaches may explain some of the challenges when creating LBP classification systems and designing effective treatment interventions. J Orthop Sports Phys Ther 2019;49(6):477-481. Epub 15 May 2019. doi:10.2519/jospt.2019.8791.