Medicare Part B covers manual manipulation of the spine if it is determined to be medically necessary. Tell the chiropractor about any medications prescription or over-the-counter and dietary supplements you take. If the chiropractor suggests a dietary supplement, ask about potential interactions with your medications or other supplements.
Take charge of your health—talk with your health care providers about any complementary health approaches you use. Together, you can make shared, well-informed decisions. For More Information.
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How low back pain sufferers differ from normals. Implications for outcome measures research. Part I: kinematic assessments of lumbar function. Download references.
You can also search for this author in PubMed Google Scholar. Correspondence to Mario Millan. All authors instigated this review. MM and CLY designed the check-lists. MM and BB searched in the databases. MM and CLY reviewed the literature and wrote the first draft. BB, MDC and MAA provided expertise on the topic, assisted with the literature review and provided critical comments to the first draft. All authors reviewed the final manuscript and approved the final version.
This article is published under license to BioMed Central Ltd. Reprints and Permissions. Millan, M. The effect of spinal manipulative therapy on spinal range of motion: a systematic literature review. Chiropr Man Therap 20, 23 Download citation. Received : 03 June Accepted : 18 July Published : 06 August Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Abstract Background Spinal manipulative therapy SMT has been shown to have an effect on spine-related pain, both clinically and in experimentally induced pain.
Design A systematic critical literature review. Results Fifteen articles were retained reporting on experiments on the neck, lumbar spine, hip and jaw. Introduction It is well known that back pain is highly prevalent in the general population, with serious economic consequences both on an individual and societal level. Methods Design : systematic critical literature review.
Humans or animals. ROM had to be one of the outcome variables. The following studies were excluded: Studies of subjects with specific pathologies, such as inflammatory diseases, tumors or severe spinal degeneration, because these might influence the spinal structures in such a way as to limit ROM permanently and therefore confuse the results.
Literature reviews Data extraction Two authors MM and CLY extracted data from each article, independently of each other, into two check-lists, one descriptive and one qualitative. Table 2 Quality check-list of articles reviewed Full size table. Results Study selection Figure 1 shows a flow diagram of the study selection process. Figure 1. Full size image. Why such small effects- if any? Significant effects only in the cervical spine In this review, SMT only showed discernible effects in the cervical spine.
Post hoc analysis In order to investigate this matter further, all articles that tested the effect of SMT specifically in the upper cervical spine were re-analyzed, in relation to the size of the effect. Technical challenges in measuring ROM There are also some technical issues that have to be taken into account in interpreting the results presented herein.
Methodological considerations of this review As with all systematic literature reviews, it is likely that not all relevant articles were found, and the review process itself has a subjective element even with the systematic approach of using checklists. Implications of findings for future research It has been shown that it is difficult to evaluate ROM if there is pain[ 55 ]. Implications of findings for clinical practice The results do not support the concept that SMT has an immediate, strong and obvious effect on ROM in the human spine.
References 1. Article PubMed Google Scholar 2. Fourth, Google Scholar 3. Article Google Scholar 4. Article PubMed Google Scholar 5. Article PubMed Google Scholar 6. Article PubMed Google Scholar 8. Secondary outcomes were return-to-work and quality of life. Two review authors independently conducted the study selection, risk of bias assessment and data extraction.
GRADE was used to assess the quality of the evidence. Sensitivity analyses and investigation of heterogeneity were performed, where possible, for the meta-analyses. In general, there is high quality evidence that SMT has a small, statistically significant but not clinically relevant, short-term effect on pain relief MD: Sensitivity analyses confirmed the robustness of these findings.
There is varying quality of evidence ranging from low to high that SMT has a statistically significant short-term effect on pain relief and functional status when added to another intervention.
There is very low quality evidence that SMT is not statistically significantly more effective than inert interventions or sham SMT for short-term pain relief or functional status. Data were particularly sparse for recovery, return-to-work, quality of life, and costs of care. No serious complications were observed with SMT. Your browser does not support the audio element. Categorical data was summarized by frequencies and percentages.
We have summarized the statistical methods used for data analysis in Fig 1. An initial exploratory analysis demonstrated that the collected variables at the baseline were associated with the relative changes in ODI.
An optimal scaling analysis was also performed to address the problem of too few observations for some of the categorical variables. Optimal scaling is a general approach to treat multivariate data through the optimal transformation of qualitative scales to quantitative values.
Using this approach, both nominal and ordinal variables can be optimally transformed into numerical values to reduce multicollinearity among predictors and maximize the homogeneity or internal consistency among variables. As a result nonlinear relationships between transformed variables can be modeled [ 48 , 49 ]. Finally, a multiple logistic regression model was built using a forward Wald procedure to explore those baseline variables that could predict overall outcome response status at 1-week reassessment [ 6 ].
An alpha value of 0. Function was evaluated using Oswestry Disability Index on a 0— scale, with lower numbers indicating better function [ 36 ]. The short form of the University of Washington concerns about pain UWCAP is a measure of pain catastrophizing including 8-items, with each item rated on a 5-point scale: 1 Never to 5 always.
The higher the score, the more catastrophizing thoughts are present. It is a 9-item scale, with each item rated on a 5-point scale: 0 Not at all to 5 very much.
Higher scores represent higher confidence to function with pain. The mean score of 50 represents a mean of a large sample of people with chronic pain. The STarT Back Tool SBT is a 9-item questionnaire including physical and psychosocial statements that are used to categorize patients into low, medium, or high-risk groups for persistent LBP-related disability [ 39 ]. Principal component analysis identified a three-factor solution for the stiffness values, one-factor solution for ultrasound values, and four-factor solution for the mobility testing results.
Together these factors explained Lumbar spine stiffness values, LM activation values, and mobility testing results were then converted into principal component scores to construct our model. Logistic regression analysis resulted in a model with eight baseline variables Table 7. As seen in Table 7 , the effect of gender is significant but negative, indicating that females were 0.
Participants with peripheralized pain during extension and those with more frequent pain in the past six month were 1. Table 8 presents the degree to which predicted probabilities agree with actual outcomes in a classification table. The overall correct prediction, Identification of SMT responders and non-responders prior to application of the SMT has received increasing attention in the conservative treatment of patients with LBP; however, the evidence for the effectiveness of this approach is mixed.
To determine if the baseline prediction of SMT responders can be improved through the use of a restricted, non-pragmatic methodology, established definitions of responder status, and newly developed physical measures observed to change with SMT, we investigated the predictive values of 20 history and demographic variables, 6 patient-reported outcome measures, 22 physical measures, and 28 instrumented measures as unique domains and in combination.
Our results suggest that it is possible to predict SMT response in a specific group of patients with To our knowledge, this is the first investigation to achieve prediction results of this magnitude for responder group although the model has yet to be validated. Prior studies that have generated successful predictions of SMT response have tended to arise from pragmatic designs.
In contrast, prior studies that have chosen to provide SMT alone or with minimal additional interventions have not achieved successful predictions. While it is possible that the prior success of pragmatic studies in this regard is because a pragmatic design more closely mimics clinical practice, our results do not support that idea. Specifically, our methodology applied fewer SMTs over a shorter time frame using a pre-defined technique for SMT application.
Therefore, one explanation for our non-congruent results is that our hypothesis is tenable; that is, predicting SMT response is best assessed in a short-time frame and in isolation of other interventions. Although this previous model consisted of fewer variables i. While at first glance it may appear unwieldy to use an 8-variable model including a 9-item questionnaire in a future clinical situation, 7 of the 8 variables can be collected in advance of the examination.
The remaining one variable can be collected by clinicians with relative ease and expediency extension status. Although previous studies showed illness beliefs and beliefs about rehabilitation make a significant contribution to the prediction of different rehabilitation outcome indicators, the reason for this association remains unexplained [ 51 — 56 ]. However, it would be worthwhile to address the power of treatment expectations in comparison to other psychosocial factors in this group of patients.
Importantly, none of the clinical measures included in our final model involved newly described physical measures involving special equipment and training ultrasonic evaluation of muscle contraction, evaluation of spinal stiffness evaluation with a mechanical device. The strengths of our study include a multi-site design which would tend to mitigate the possibility of our results arising from a specific population.
Given this and considering the high sensitivity and specificity of our prediction results, we propose that a future validation study of this model is warranted. If found to be valid, these 8 variable models could provide clinicians with the opportunity to construct a more focused intervention plan after only 1 week of care.
This would benefit both patients and clinicians by reducing more traditional re-evaluation periods of an initial trial of care that may extend into multiple weeks with many more treatment sessions.
As with all experiments, our study had limitations. First, our sample was heterogeneous in terms of pain duration. Although most participants in this study could be classified as having chronic LBP, our inclusion criteria were not limited to chronicity. Since the original primary study was designed to assess therapeutic effects in a wide range of participants, it did not restrict enrollment to a specific duration of low back pain.
Therefore, the usability of the proposed model cannot be easily extrapolated to populations that may be highly homogeneous in pain duration. The 8 variable model presented here was able to predict SMT response with a sensitivity of Given these results, and that 7 model variables can be collected prior to clinician engagement, future validation of the model is warranted.
Should the model be valid, it may benefit both patients and clinicians by reducing the time needed to re-evaluate an initial trial of care. The authors would like to thank Dr.
Randy Vollrath, Dr. Shannon Wandler, Dr. Moe Gebara, Dr. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
Abstract Background Spinal manipulative therapy SMT is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others.
Materials and methods We conducted a secondary analysis of a prior study that provided two applications of standardized SMT over a period of 1 week. Results Two hundred and thirty-eight participants completed the 1-week reassessment age Conclusion It is possible to predict response to treatment before application of SMT in low back pain patients.
Introduction Spinal manipulative therapy SMT is among the nonpharmacologic interventions for low back pain LBP recommended as a second-line or adjunctive treatment option after exercise or cognitive behavioral therapy [ 1 ]. Download: PPT. Table 1.
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