With regards to the papers that focused exclusively on segmentation, Haq et al. [26] used shape-aware models on 3D MRI of 21 patients for the segmentation of IVDs, achieving an average DICE of 91.7%. In addition, in a successive article Haq et al. [25] utilized a shape statistical deformable model for the segmentation of IVDs on CT images of 18 subjects from the SpineWeb dataset, achieving DICE scores ranging from 91.7 to 95.4%. Li et al. [28] applied a threshold to the results of a Gaussian Mixture Model for segmenting vertebrae on a total of 115 CT images from the SpineWeb and the Microsoft Research datasets, with an average DICE of 92.1%. Ibragimov et al. [29] used landmark detection and deformable models for segmenting 30 vertebrae on CT images, with a DICE of 84.7%. Yu et al. [30] utilized bone-sheet assisted grid cut to segment vertebrae from 21 CT images, achieving an average DICE of 93.9%. Korez et al. [31] applied a shape-constrained deformable model for vertebrae segmentation from CT images of 220 patients, with a DICE of 94.6%. Al-Helo et al. [32] combined Active-shape models and GVF-snake for the segmentation of vertebrae from CT images of 50 subjects, assessing the segmentation quality by visual evaluation. Ruiz-España et al. [33] used a Selective Binary Gaussian Filtering Regularized Level Set to segment vertebrae on CT images of 10 subjects, achieving an average DICE of 95%. Huang et al. [34] exploited Otsu thresholding, Edge- and Region-based level sets to segment vertebrae on CT images of 56 subjects, with a 94% DICE. Mastmeyer et al. [38] utilized volume growing and morphological operations to segment vertebrae on CT images of 41 subjects, achieving DICE scores greater than 98.6%. Zhang et al. [53] applied Hough transform and Fourier descriptors for vertebrae segmentation on one fluoroscopic image, assessing the segmentation quality by visual evaluation. Michopoulou et al. [45] used an Atlas-robust-fuzzy C-Means for segmenting IVDs on MRI of 34 subjects, achieving a 90% DICE. Fallah et al. [46] exploited Hierarchical Conditional Random Fields and a Random Forest for the segmentation of IVDs and vertebrae, respectively, on MRI of 34 subjects, achieving a DICE of 92.5 and 91.4%, respectively. Ghosh et al. [47] combined Random Forest and context features for the segmentation of IVDs and vertebrae, respectively, on MRI of 212 subjects, achieving a DICE of 87 and 84%, respectively. Kim et al. [48] used graph-based and line-based segmentation algorithms for segmenting vertebrae on MRI of 19 patients, achieving a 90% DICE. Gaonkar et al. [49] applied a multi-parametric ensemble to segment vertebrae on MRI of 63 subjects, with an average DICE of 83%. Gawel et al. [50] combined a cascade classifier and an Active Appearance Model to segment vertebrae on 50 MRI, achieving a DICE of 91.4%. Engstrom et al. [51] used a Statistical Shape model for the segmentation of the quadratus lumborum muscle on MRI of 20 patients, achieving a DICE of 87%. Baum et al. [52] exploited an Average Shape model and a Dual Feature model for paraspinal muscle segmentation on MRI of 10 subjects, with a DICE of 83%. Jurcak et al. [54] applied Probabilistic atlases and Geodesic Active Contours for the segmentation of quadratus lumborum muscle on MRI of 20 subjects with a 77% DICE. Ribeiro et al. [61] used Gabor Filters and an ANN to segment vertebrae on X-ray images of 41 patients, achieving a DICE of 91.7%.

With regards to the articles that performed localization following segmentation, Mahdy et al. [35] used a threshold method followed by an adaptive K-Means for the segmentation and localization of lumbar vertebrae on CT images of 10 subjects in order to identify degenerated IVDs, and evaluated the performance by visual evaluation. Courbot et al. [36] exploited a Hidden Markov Chain for semi-automated segmentation of vertebrae on CT images of 15 subjects, achieving a localization accuracy of 89.4%. Rasoulian et al. [37] developed a multi-object shape model for vertebrae localization on 32 CT images, correctly localizing the centers of mass with a MAE of 2 mm with the aim of identifying the optimal location for spinal needle injection. Štern et al. [42] performed an analysis of the geometry of the spinal structures to localize the centers of IVDs and vertebrae on 13 MRI and 29 CT images, respectively, with a localization error of 2.8 and 1.8 mm, respectively. Neubert et al. [56] used an Active Shape model to segment IVDs on MRI of 44 subjects achieving a DICE of 92.3%, and an AUC of 0.98 for localization of degenerated IVDs using Linear Discriminant Analysis and SVM. Kim et al. [59] exploited Fuzzy C-Means Clustering

for the localization of lumbar multifidus muscle on ultrasound images of 50 subjects, with a 2 mm localization discrepancy. Lui et al. [60] utilized Decoupled Active Contour for the localization of lumbar multifidus muscle on ultrasound images of 10 subjects, achieving an F1-Score of 90.9%. Sa et al. [62] used Gradient Vector Flow Snake and SVM for the localization of vertebrae on X-ray images of 30 subjects, achieving a True Positive Rate of 75%.

With regards to the papers that performed identification following segmentation, Neubert et al. [27] used a Statistical Shape model on 3D MRI of 28 subjects to segment and identify IVDs and vertebrae, achieving segmentation DICE of 89 and 91%, respectively, and 98.3% specificity and 100% sensitivity for the identification of degenerated IVDs. Castro-Mateos et al. [58] described an Active Contour Model for the segmentation and a Feedforward NN for the identification and classification of IVDs on MRI of 48 subjects, achieving 87% Sensitivity.

With regards to the papers that performed both localization and identification, Jimenez-Pastor et al. [39] used a Decision Forest and morphological image processing to localize and identify vertebrae on 272 CT images, achieving a localization error of 13.7 mm and an accuracy of 74.8%. Lee et al. [40] exploited threshold and thinning-based integrated cost on CT images of 19 subjects, for the localization and identification of lumbar pedicles in order to increase accuracy and safety during transpedicular screw placement, with a localization error of 0.14 mm and 93.2% accuracy. Klinder et al. [41] used a Triangulated Shape model on CT images of 64 subjects, achieving a vertebrae localization error of 1.1 mm and 92% accuracy. Oktay et al. [57] combined a Probabilistic model with an SVM to localize and detect IVDs on MRI of 40 subjects, achieving a localization rate of 95.4% and an accuracy of 97%.

In addition, Wong et al. [43] used Wavelets and a Shape-Active Contour-Based model for vertebrae segmentation and Tracking on 2 videos of fluoroscopic images, evaluating the performance by visual evaluation. Zheng et al. [44] utilized Statistical Shape models for vertebrae segmentation and 3D reconstruction on 4 fluoroscopic images, achieving a mean reconstruction error of less than 1.6 mm. Finally, Fortin et al. [55] used a threshold algorithm for segmentation and quantification of paraspinal muscle composition with a reliability coefficient ranging between 97 and 99%.

### 4.2.2. Deep Learning

Deep learning is a class of AI algorithms based on Artificial Neural Networks. More in detail, an NN is said to be "deep" if it is composed of more than 2 hidden layers. Deep learning techniques for segmentation take as an input the whole original image, and perform feature extraction, feature selection, segmentation and any further step (e.g., classification, regression) in one single model. In this review, we identified a total of 23 papers that performed deep learning segmentation, and their main characteristics are reported in Table 3. In detail:

Vertebrae were the most investigated lumbar structures (16 papers), followed by IVDs (11 papers), spinal canal (7 papers), and muscles (5 papers). In total, 9 articles focused exclusively on segmentation without further tasks. Among the others, 5 manuscripts performed successive structure identification, 3 carried out a regression task, 3 performed successive structure reconstruction, 1 work performed classification, 1 performed structure localization, and 1 carried out both structure localization and identification. It is worth noting that the vast majority of the works included in this section exploited Convolutional Neural Networks (CNNs) or models that derive from them.

With regards to the articles that focused exclusively on segmentation, Iriondo et al. [63] used a Coarse-to-fine context memory NN to segment IVDs on 3D MRI of 31 subjects, achieving a DICE greater than 85%. Malinda et al. [67] utilized Generative Adversarial Networks (GANs) for vertebrae segmentation on CT images of 120 subjects, achieving a DICE of 94.2%. Kim et al. [71] exploited a BSU-net for IVDs segmentation on 20 MRI from the SpineWeb dataset, achieving a DICE of 89.4%. Shen et al. [72] used a Feedforward NN on MRI of 120 subjects, achieving a Jaccard index for the segmentation of IVDs, spinal canal and muscles of 87, 82 and 85%, respectively. Gaonkar et al. [73] applied a U-net to segment IVDs on 39295 MRI images, achieving an 88% DICE; they also combined an SVM with a Regression Tree to segment the spinal canal with a DICE of 87%. Huang et al. [74] used a U-net to segment IVDs and vertebrae on 100 MRI achieving a Jaccard index of 92.6 and 94.7%, respectively. Li et al. [75] utilized a CNN to segment vertebrae and spinal canal on MRI of 120 patients achieving an overall DICE of 92.5%. Moreover, they used a deformed U-net [76] for the segmentation of paraspinal muscles on 120 MRI achieving an overall DICE greater than 91.3%. Zhou et al. [77] utilized a U-net for vertebrae segmentation on MRI of 57 subjects, achieving a DICE of 84.9%.

With regards to the papers that performed structure identification following segmentation, Siemionow et al. [68] used a CNN to identify vertebrae on CT images of 45 subjects, with an overall accuracy ranging from 96 to 99%. Zhou et al. [80] combined a CNN and similarity with a beforehand lumbar image for vertebrae identification on MRI images of 1318 healthy and unhealthy subjects, achieving an accuracy of 98.9%. Forsberg et al. [81] combined a CNN and graph-based graphical models on MRI enriched with clinical notes to identify vertebrae of 475 patients, achieving an accuracy of 97%. Baka et al. [82] utilized a CNN and a matching strategy for vertebrae identification on ultrasound images from 19 datasets, achieving an accuracy of 92%. Li et al. [84] were the only to perform vertebrae identification on X-ray images. They applied a CNN on 110 images, achieving an 80.4% accuracy.

With regards to the articles that performed a regression task, Watanabe et al. [70] used a CNN to estimate spinal alignment on 1996 Moire images, with a Cobb angle MAE of 3.42°. Natalia et al. [79] combined a SegNet and a Contour Evolution Algorithm to measure anteroposterior diameter and foraminal widths on MRI of 515 patients suffering from lumbar spinal stenosis with a mean error of 0.9 mm. Cho et al. [83] used a U-net for the automated segmentation and measurement of lumbar lordosis on X-ray images of 629 patients, achieving a DICE of 82.1% and a MAE of 8.06°.

With regards to the articles performing a Reconstruction task, Staartjes et al. [64] developed a CNN to segment and reconstruct the lumbar structures from 3D MRI of 3 patients, evaluating the performance by visual evaluation. Lee et al. [65] used GANs to generate synthetic spine lumbar structures MRI from 280 CT images, with a MAE of 21 pixels. Fan et al. [66] axploited a U-net to reconstruct lumbar structures from CT images of 108 subjects, with a Kambin triangle of 161 mm2.

With regards to the articles performing a classification task, Jamuladin et al. [78] used a CNN for classification of IVDs and vertebrae on MRI of 2009 subjects achieving an accuracy of 95.6%.

In addition, Sa et al. [85] fine-tuned a Faster Region-based CNN (R-CNN) for IVD localization on 1081 X-ray images with a 90.5% precision. Finally, Netherton et al. [69] used an X-net ensemble to localize and identify vertebrae on 330 CT images, achieving a localization error of 2.2 mm and an accuracy of 94%.

## **5. Discussion**

Due to the extensive use of advanced imaging modalities and the complexity of anatomical structures involved in the development of LBP and its sequelae, a vast body of research has been investigating the utilization of AI in the elaboration of digital images for different purposes. The vast majority of the works in the literature exploit MRI or CT imaging, whereas a minority of works exploit X-ray, fluoroscopic or ultrasound imaging. It is worth noting that vertebral structures are the main focus of articles performing segmentation, both with DIP and deep learning techniques; conversely, articles performing feature extraction are mainly focused on IVDs.

With regards to feature extraction, which is the capacity of a system to recognize a specific set of relevant features, all included studies collectively showed an accuracy > 80% in identifying the location of vertebrae [24] and IVDs [8,20–24], with the ability to even detect annular tears and lumbar disc herniation [21–23]. Although the majority of the studies were conducted on MRI images [8,20–24], one study utilized X-ray imaging [10] and another study built a 3D model of patients' backs using a noninvasive surface acquisition technology [19]. Moreover, some of these studies also reported the capacity of the described systems to perform classification and regression tasks on extracted data, such as estimating the degree of IVD degeneration [8,22–24], scoliosis curve type classification [19] and prediction of curve progression [10], the presence of spinal stenosis [23] and to explore the correlation between degenerative changes and the presence of LBP [24].

However, most studies focused on segmentation, which is the differentiation of specific subregions of an image based on distinct parameters. Traditionally, segmentation tasks have been performed by DIP systems via subdivision of elements within an image based on gradient thresholding or statistical shape models, which fall under the definition of semantic segmentation [86]. However, recent research has been exploring the use of deep learning-based AI systems which are able to perform multiple tasks at the basic and advanced level in a single model [1]. Vertebrae are by far the most investigated structure, with AI systems reaching > 90% DICE and > 90% accuracy in the majority of studies included in our review, both using DIP [28–41,43,44,48–50,53,61,62] and deep learning models [67,69,77,80–84]. In particular, a study from Lee et al. [40] proposed a model to obtain an automated segmentation of lumbar pedicles from CT images in order to increase accuracy and safety during transpedicular screw placement. On the other hand, a study from Watanabe and colleagues [70] described a CNN able to estimate spinal alignment, vertebral rotation and Cobb angle with a mean absolute error of 3.6 pixels for vertebral position, 2.9° for vertebral rotation and 3.42° with regards to the estimated Cobb angle. Similarly, Cho et al. [83] presented a CNN capable of segmenting lumbar vertebrae and subsequently calculate lumbar lordosis, with a mean absolute error of 8.055°. In this manuscript, Several AI systems for automated segmentation of IVDs have been described as well [25,26,45,48,56–58,63] with a reported DICE > 90% in nearly all studies. Besides, performance of systems developed for the segmentation of paraspinal muscles have reported a higher variability compared to other structures [51,52,54,55,60,71], with higher DICE values for systems based on deep learning models [76]. In addition, some studies evaluated the simultaneous segmentation of multiple structures, in particular IVDs and vertebrae [27,42,46,56,74,78], with a DICE > 90% in DIP-based systems [27,42,46,56] and a reported accuracy > 95% in most deep learning-based systems [68,71,74,78,85]. Furthermore, some of the latter have been used in order to synthesize CT images from MRI and vice versa. For example, Staartjes et al. [64] introduced a CNN-based system able to generate synthetic CT images from spine MRI, so as to acquire more precise information about osseous structures compared to traditional MRI without the need to expose patients to additional radiation. On the other hand, Lee and colleagues [65] presented a model based on GANs capable of producing a synthetic MRI from spine CT scans, which resulted in a mean overall similarity with real MRI scans of 80.2%. This study demonstrated the possibility to extract accurate information about soft tissues from spine CT without the necessity to order an MRI, which is often expensive and time-consuming. Other studies have also shown the possibility to automatically calculate the spinal canal area [73] as well as segmenting and reconstructing multiple structures at the same time [47,66,72,75,79] with an elevate degree of accuracy.

Figure 4 shows a boxplot that summarizes the results for the segmentation of IVDs, vertebrae and lumbar muscles, and the identification accuracy for different lumbar structures. With regards to the segmentation of IVDs and vertebrae, it is worth noting that DIP

and deep learning techniques achieve very similar results, with DIP methods performing slightly better. This is mainly due to the regular and homogeneous surface of such structures, whose well-defined edges can be effectively identified using DIP techniques such as threshold and region-growing methods. Conversely, lumbar muscle segmentation performance of deep learning techniques is sensitively better than that of DIP methods. Indeed, the structure of muscles is irregular and more challenging to detect properly, and deep NNs provide a better tool for such a task. With regards to the identification accuracy, deep learning provides generally better results; nonetheless, DIP methods followed by machine learning techniques are typically faster and less computationally expensive, and, in some cases, provide similar performance.

**Figure 4.** Boxplot summarizing the results for different structures and tasks. The three left columns refer to the DICE scores for the segmentation of IVDs, vertebrae and muscles; the right column refers to the identification accuracy for different structures.

Although the application of computer vision to the elaboration of radiological images of the spine is continuously increasing, some concerns still exist. Indeed, system validation still largely depends on multiple user interventions and cannot replace the human counterpart for obvious reasons, from both clinical and ethical perspectives. Furthermore, the best performing methods are based on the application of NNs, which usually require a large amount of images and computational capacity for training, which are not available to all researchers. However, some DIP techniques provide equal or better performance in the segmentation of regular-shaped structures such as vertebrae and IVDs, while requiring a smaller amount of data for training and limiting the computational burden. Moreover, some methods already exist for the automatic detection and grading of conditions such as spondylolisthesis, disc herniation and scoliosis.

## **6. Conclusions**

In the last decade, the utilization of AI has increased considerably in all fields, and medical research made no exception. Indeed, AI-based computers have already shown the potential to revolutionize the medical field, including spine surgery. In this study, we have systematically reviewed the available literature on the use of AI, and more specifically computer vision, in the prevention, diagnosis, and treatment of LBP. In conclusion, computer vision techniques bear promises for effectively improving clinical practice in coming years, thanks to the availability of public datasets and to the natural upcoming increase of the

computational capacity. Furthermore, steps are being taken towards the interpretability of AI and, in particular, of deep learning models. Such improvements will lead to the development of systems that will not require multiple user interventions, thus providing a valid assessment tool for physicians. LBP diagnosis and treatment often require the utilization and integration of advanced imaging modalities. In addition, several structural alterations, often subtle and nonunivocal to interpret, concur to define the clinical scenario. In this picture, the use of AI and computer vision may effectively assist and implement the diagnostic process, thus possibly improving clinical outcomes and diagnostic accuracy.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/ijerph182010909/s1, Table S1: Summary of the methodological quality of included studies regarding the 4 domains assessing the risk of bias of the QUADAS-2 score; Table S2: Summary of the methodological quality of included studies regarding the 3 domains assessing applicability concerns of the QUADAS-2 score.

**Author Contributions:** Conceptualization, F.D., F.R., M.M.; methodology, F.D., F.R., L.A., L.V., G.V., M.M.; writing—original draft preparation, F.D., F.R., L.A., M.M.;investigation, F.D.; writing—review and editing, F.D., F.R., L.A., L.V., G.V., M.M., R.P., V.D.; visualization, F.D.; supervision, F.R., L.V., G.V., M.M., R.P., V.D.; funding acquisition, F.R., G.V., R.P., V.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Research Grant (BRiC-2018 ID3) of the Italian Workers' Compensation Authority (INAIL).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Review* **Epidural Steroid Injections for Low Back Pain: A Narrative Review**

**Massimiliano Carassiti 1,\*, Giuseppe Pascarella 1, Alessandro Strumia 1, Fabrizio Russo 2, Giuseppe Francesco Papalia 2, Rita Cataldo 1, Francesca Gargano 1, Fabio Costa 1, Michelangelo Pierri 3, Francesca De Tommasi 4, Carlo Massaroni 4, Emiliano Schena <sup>4</sup> and Felice Eugenio Agrò <sup>1</sup>**

**Abstract:** Low back pain represents a significant socioeconomic burden. Several nonsurgical medical treatments have been proposed for the treatment of this disabling condition. Epidural steroid injections (ESIs) are commonly used to treat lumbosacral radicular pain and to avoid surgery. Even though it is still not clear which type of conservative intervention is superior, several studies have proved that ESIs are able to increase patients' quality of life, relieve lumbosacral radicular pain and finally, reduce or delay more invasive interventions, such as spinal surgery. The aim of this narrative review is to analyze the mechanism of action of ESIs in patients affected by low back pain and investigate their current application in treating this widespread pathology.

**Keywords:** epidural steroid injections; low back pain; lumbosacral radicular pain; disk herniation; canal stenosis; review

## **1. Introduction**

Low back pain (LBP) and lumbosacral radicular pain are common causes of physical and mental morbidity and they are also a significant economic burden, causing an expenditure of more than USD 100 billion per year in the United States alone [1,2]. In the medical literature, low back pain is referred to as sciatica, lumbosacral radicular syndrome, lumbar radiculopathy, nerve root pain and nerve root entrapment/irritation, and is commonly described as a pain starting in the back and radiating to the legs. The etiological cause of low back pain is first represented by intervertebral disk disease. The pathophysiological changes involved in the intervertebral disk disease may lead to disk herniation or degenerative diseases, such as canal stenosis or chronic instability of the diseased segments. The most common cause of sciatica is the herniation of the nucleus pulposus, a component of the intervertebral disk in the lumbar region, which causes stenosis and inflammation [3,4]. Some estimate that sciatica caused by herniation of the lumbar disk has a prevalence of 9.8 out of 1000 [5], meaning that of all reported cases of sciatica it appears that 90% are caused by herniation of the lumbar disk [6].

Several nonsurgical medical treatments have been proposed for lumbosacral radicular pain, from lifestyle changes, exercise and physical therapy to analgesic local/oral drugs and epidural steroid injections (ESIs) [7,8]. The conservative management of LBP aims to delay or avoid surgery. As a matter of fact, LBP can improve spontaneously or with

**Citation:** Carassiti, M.; Pascarella, G.; Strumia, A.; Russo, F.; Papalia, G.F.; Cataldo, R.; Gargano, F.; Costa, F.; Pierri, M.; De Tommasi, F.; et al. Epidural Steroid Injections for Low Back Pain: A Narrative Review. *IJERPH* **2022**, *19*, 231. https:// doi.org/10.3390/ijerph19010231

Academic Editor: Paul B. Tchounwou

Received: 3 December 2021 Accepted: 22 December 2021 Published: 26 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

conservative treatment. Cases which do not respond to treatment are candidates for surgical intervention.

An ESI is a common and minimally invasive procedure, performed to successfully treat lumbosacral radicular pain, which has also proved its effectiveness in the treatment of back acute pain and leg symptoms. The injections are used to deliver steroids, and sometimes local anesthetics, to the epidural space, directly to the site that causes the pain using a caudal, interlaminar or transforaminal approach [9]. The epidural injection is a well-founded anesthetic and analgesic technique; moreover, nowadays, new technological devices can help anesthesiologists to learn and to administer it [10–16]. Even though it is still not clear which type of conservative intervention is superior, several studies have proved that an ESI is able to increase patients' quality of life, relieve lumbosacral radicular pain and finally, reduce or delay more invasive interventions, such as spinal surgery. Although ESIs should represent a treatment of choice in the case of acute LBP or leg pain, in our research we focused on the efficacy of ESIs in the treatment of chronic LBP.

The aim of this narrative review is to analyze the mechanism of action of ESIs in chronic lumbar pain patients and to understand their current use, application and success in treating this significant widespread pathology.

## **2. Materials and Methods**

A literature review using online databases was carried out regarding the use of epidural steroid injections for lumbar canal stenosis and disk herniation. Articles were extracted from PubMed, Google Scholar, MEDLINE, UpToDate, Embase and Web of Science, combining the terms "spinal disease," "radicular pain," "spinal stenosis," "canal stenosis," "disk herniation" and "epidural steroid injection" as keywords for the research. Only papers in the English language and regarding human studies were taken into consideration. Non-English language studies were excluded. Scientific publications up to September 2021were included. Only papers focusing on epidural steroid injections for lumbar canal stenosis or disk herniation were included. All reference lists of the relevant studies were then screened to identify any missing publications. The search and the study selection were performed by two investigators (G.P.; A.S.) working independently. At the first level, the titles and abstracts of identified studies were screened. At the second level, the full texts were retrieved and assessed. Ethical approval and patient informed consent were not required because this was a review of previously published studies and did not involve direct contact with patients or alterations to patient care. Any discrepancies were resolved by a third author (M.C.) through consensus. The following data were extracted from each eligible study: first author's name; publication year; study design; intervention protocol type (the type and amount of steroid and local anesthetic used for the ESI and therapies or medication used for conservative treatment); outcome parameters including Visual Analogic Scale (VAS), Numeric Rating Scale (NRS), Oswestry Disability Index (ODI) and successful events; and the summary of findings.

## **3. Results**

## *3.1. Mechanism of Lumbosacral Radicular Pain*

Low back pain and radicular pain are caused by interrelated biomechanical and biochemical factors. With the advancement of age and the presence of chronic diseases such as diabetes, obesity, smoking and overload, a series of degenerative processes occur inside the intervertebral disk [17]. The intervertebral disk is approximately 7 to 10 mm thick and 4 cm in diameter and is formed of two different components: the nucleus pulposus, rich in water and glycopeptides, and the anulus fibrosus, constituted of a series of 15 to 25 rings, or lamellae, with collagen fibers parallel to the lamellae in addition to elastin fibers. A thin hyaline cartilage endplate is the interface between the disk and the superior and inferior vertebrae bodies. When the nucleus becomes less elastic and the anulus less continent due to aging, dehydration, inflammatory conditions and/or prolonged misusage of the back, a part of the nucleus can herniate, usually backward. This causes an inflammatory

state in the epidural space and the increase in cytokines and other inflammation mediators. This condition, on the one hand favors the ulterior herniation of the nucleus pulposus and on the other, it compresses and stimulates the spinal nerve roots, resulting in back and radicular pain [18,19].

Generally, sciatica from lumbar disk herniation is a self-limiting condition that improves in weeks or months without medical intervention; in some cases, rest, analgesic drugs and a structured exercise program may be needed. Usually, the inflammatory state is more important than the mechanical compression in the pathogenesis and the chronicity of the disease, unless there are no neurological deficits [20]. However, in patients who are refractory to conservative treatment, surgery is usually recommended.

Lumbar spinal canal stenosis is a process that could be part of the aging process and can be related to herniation of an intervertebral disk. Other common causes of stenosis are: congenital deformities; spondylolisthesis; osteophytes; arthritic degeneration; synovial cysts; hypertrophy of the facet joints; hypertrophy of the ligamentum flavum; epidural lipomatosis; spondylosis of the intervertebral disk margins; previous surgery; and neoplastic diseases. All these factors could cause lumbar nerve root compression with microvascular ischemia, axonal injury, intraneural fibrosis and an inflammatory state, leading to chronic back pain [21].

## *3.2. Rationale of Epidural Steroid Injections*

Epidural injections are performed using a Tuohy needle with the tip placed inside the epidural space, which is located between the ligamentum flavum and the dura mater. Usually, the epidural space is localized thanks to the loss of resistance (LOR) technique, where the needle is advanced between the spinal processes of the vertebras with the help of a syringe full of air or saline solution, which is used to continuously test the pressure on the piston of the syringe. The needle passes through the ligamentum flavum and, when the epidural space is reached, a loss of resistance is felt by the operator on the syringe piston. Moreover, epidural injections can also be performed rapidly under CT and navigation guidance (Figure 1). These techniques can be used to precisely guide needle placement, allowing for the visualization of the optimal needle path and identification of potential problems, such as narrow intralaminar spaces and spinal stenosis, before needle insertion (Figure 2).

**Figure 1.** Representative images of (**A**) intraoperative setting for CT and navigation guided epidural injection and (**B**) navigated needle insertion.

**Figure 2.** Representative images of (**A**,**B**) navigated planning of needle path to the epidural space and (**C**,**D**) evidence of injection in the epidural space by the use of a contrast agent.

Corticosteroids are widely used in regional anesthesia and chronic pain procedures, such as epidural injections, intraarticular injections and nerve blocks. Corticosteroids have a similar structure and activity to the endogenous produced hormone cortisol, which has an anti-inflammatory, immunosuppressive, vasoconstrictive and antiproliferative effect. They work by preventing the enzyme PLA2 from liberating arachidonic acid from the cells. This inhibits the cyclo-oxygenase and lipoxygenase production, which is responsible for the level of prostaglandins, thromboxanes and leukotrienes, before finally decreasing the inflammatory state [22]. They also inhibit the nerve transmission in nociceptive C fibers and reduce vasal permeability, which decreases intraneural and perineural oedema. Local anesthetics have been administered in the epidural space since 1901; however, the epidural use of corticosteroids has only been documented since 1952 [23]. Their efficacy when administrated via epidural injections has been demonstrated in various studies and a stronger effect has been proven in patients with a higher protein count in the cerebrospinal fluid, which is usually associated with an inflammatory state [24]. The prolonged use of corticosteroids at high doses has many systemic side effects and can also result in iatrogenic adrenal gland suppression; however, the epidural administration limits the systemic side effects because a smaller dose is necessary to reach the pharmacological target and its diffusion into systemic circulation is more difficult than in other types of administration [25]. In the Yang et al. [26] meta-analysis regarding lumbosacral radicular pain due to any cause, the use of ESIs resulted in the more effective in control of lumbosacral pain compared to pure conservative treatment, both in short and intermediate terms. However, two other recent meta-analyses have shown a similarity in efficacy and duration, in terms of pain

reduction and functional gain, between local anesthetic alone or local anesthetic with a corticosteroid epidural injection [27,28].

## *3.3. Epidural Steroid Injections for Disk Herniation Lumbar Pain*

The epidural administration of corticosteroids is one of the most common miniinvasive medical treatments for chronic spinal pain caused by disk herniation [29,30]. In fact, in the absence of chronic severe pain or neurological deficit, epidural steroid injections may be the treatment of choice for disk herniation. As mentioned before, it reduces the concentration of inflammatory mediators in the epidural space and vascular permeability [31]; it also reduces the damage of C fibers, which diminishes the pain [32,33]. In particular, the anesthetic effect of methylprednisolone over other steroids and non-steroid anti-inflammatory drugs has been proven when injected into the epidural space [34].

In clinical practice, both corticosteroids and local anesthetics are used [35,36]; the former are used to reduce the inflammation for a prolonged time, while the latter are used to mitigate the discomfort of the procedure and immediately decrease pain.

The difference in the use of a local anesthetic alone or local anesthetic with a corticosteroid in the epidural administration to treat disk herniation pain has been indagated in numerous papers without a clear result [37,38], although a meta-analysis written by Lee at al. evidenced a small difference between the epidural injection of lidocaine and lidocaine plus corticosteroids [39].

However, a good number of studies have described significant pain relief and improvement of functional status after an ESI, especially in short–medium terms [40–45] (Table 1). In fact, Kennedy and colleagues have found a high rate of success of ESIs at 6 months in their study, but there was also a recurrence of the symptoms during the 5 years follow-up after the injection [46]. In a similar way, Buchner et al. found a significant improvement in patients treated with epidural steroid injections for a very short period after the treatment but no improvement was seen after 6 weeks and 6 months, compared to the control group who did not receive the injection [47]. A response to the treatment after 1 h of having the procedure has been suggested as predictive for favorable medium-term success [48]. Interestingly, Buttermann et al. suggested that ESIs could be more effective in patients who presented magnetic resonance imaging of inflammatory endplate changes [49].

On the other hand, the Spine Patient Outcomes Research Trial (SPORT) [50], a prospective multi-center study of the operative versus nonoperative treatment of lumbar intervertebral disk herniation, found no improvement in short- or long-term outcomes in patients who received ESIs compared to patients who did not. However, it is important to say that an increased rate of surgical avoidance was observed in the group treated with ESIs; this could underline the role of conservative treatments, also considering the high incidence of the spontaneous reabsorption of lumbar disk herniation (66.66% according to Zhong et al.) [51].

Finally, Kreiner et al. [52], in their guideline for the diagnosis and treatment of disk herniation with radicular pain, stated that an ESI is indicated for a proportion of patients with lumbar disk herniation to provide symptom relief in the short term (2–4 weeks) with a grade A recommendation. Additionally, at the moment, no sufficient evidence exists to make a recommendation regarding the 12-month, or more, efficacy of ESIs.

**Table**

 = lumbar disk herniation; VAS = visual analogue scale; FU = follow-up; ESI = epidural steroid injections.

LDH

## *3.4. Epidural Steroid Injections for Canal Stenosis Lumbar Pain*

The administration of steroids via epidural injection as a nonsurgical treatment for lumbar spinal stenosis (LSS) has been analyzed in various studies but, again, there is not a clear consensus regarding their efficacy in relieving the symptoms, especially in the long-term follow-ups. It is important to notice that a consistent number of studies have reported some degree of benefit, especially regarding short-term improvements [53–59] (Table 2). A more favorable response seems to be associated with relative youth, female sex and patients with single level stenosis, while BMI, MRI severity and the dimension of the spinal canal are probably not predictive [55,60,61]. Additionally, individual pain sensitivity does not seem to influence the outcome of an ESI in the patients affected by LSS [62]. Interestingly, Milburn and colleagues, in a randomized study, suggested that the response to the treatment is maximized when the ESI is performed at the intervertebral level of maximal stenosis [63], and their result was confirmed by the trial conducted a few years later by Bajpai et al. [64].

A randomized, double-blind controlled trial with a 2-year follow-up was conducted by Manchikanti et al., which compared the epidural injection of local anesthetic alone to local anesthetic plus steroids, and the authors found a significant relief of the symptoms in a convincing percentage of the patients treated, but without significant difference between the two groups [65]. Accordingly, another large randomized trial on 400 patients, conducted by Friedly and colleagues, found minimal or no short-term benefits in adding steroids to a local anesthetic epidural injection for the treatment of LSS [66].

Moreover, some other studies did not find any significant improvement in symptoms or quality of life after an ESI for the treatment of LSS [67–70]. Tran et al. wrote a review regarding the nonsurgical treatment of LSS and concluded that the literature could provide only limited evidence to formulate recommendations pertaining to the nonsurgical treatment of LSS [71].

Finally, Liu et al., in their systematic review and metanalysis, also concluded that there is minimal evidence to show that epidural steroids are better than local anesthetic alone in the treatment of LSS patients [72].

**Table 2.** Epidural steroid injections for canal stenosis studies.

### *3.5. Image-Guided Epidural Injections*

Special consideration should be given to image-guided techniques that may help the clinician in performing epidural injections, although no significant difference has been shown regarding outcomes. The use of ultrasound in performing an interlaminar approach could help to estimate the distance from the skin to the epidural space and the optimal needle direction [73,74]. However, although it is a radiation-free technique, ultrasound is limited by the operator's experience and the real-time visualization of needle tip advancement could be challenging, especially in obese patients [75]. Fluoroscopy (x-rays) and computerized tomography (CT) have both proved to be effective and safe techniques for guiding transforaminal epidural injections, although the former provides less radiation exposure for patients [76,77]. Moreover, new generation CT devices may integrate neuronavigation systems that are able to perform a computerized analysis in order to best define the needle's path towards the epidural space. Its use has been described for spinal surgery but it may be expanded to transforaminal epidural injections as well, although the high cost of these devices should be considered [78]. Future studies are expected to determine the best technique in terms of efficacy and safety for both patients and clinicians.

#### **4. Discussion**

Chronic lumbar pain is a widespread problem, which affects a large part of population at some point of their life. Disk herniation and canal stenosis are the most common causes and they need to be treated due to the high impact of the symptoms on patients' quality of life, especially because they could affect walking and ability to work [18].

Surgical intervention has been proven to be effective but is not usually considered as the first option [7]. On the other hand, nonsurgical treatments, such as epidural steroid injections, do not have clear literature consensus. Fully understanding whether ESIs would be able to relieve symptoms and delay or prevent surgery could be a crucial step, especially because chronic back pain patients are typically elderly and multimorbid who could be more affected by the impact of surgery.

In this narrative review, we tried to analyze the existing literature regarding the use ESIs for these kinds of patients, considering randomized controlled studies as well as reviews, meta-analyses and guidelines.

Overall, ESIs seem to be safe and quite effective in relieving the main symptoms, especially in short-term follow-ups, and in delaying surgery, according to a consistent number of studies and to the guideline written by Kreiner and colleagues [52]. Moreover, ESIs could be more powerful in the case of patients with disk herniation than patients with canal stenosis [61]. Attention should also be paid to the technique and to the vertebral level of the injection, at least for spinal stenosis [63,64].

As mentioned before, literature consensus is still missing and numerous studies did not find significative improvements, especially in long-term follow-ups. In addition, it seems to be difficult to find significative differences between using local anesthetics alone or local anesthetics plus steroids in the injection.

Due to the anti-inflammatory action of steroids, patients with high local inflammatory status could probably benefit more from using steroids [49]. However, epidural steroid injections have also been associated with potential adverse effects, including acute neurological symptoms [79,80], in addition to other possible complications related to the epidural technique, i.e., inadvertent dural puncture, hematomas and infection. [81]

In this context, the identity of the patients who could benefit the most from this procedure has not been completely established yet and could be a crucial future goal.

This narrative review has some limitations. Firstly, the studies taken into consideration did have different epidural injection approaches. Secondly, the heterogeneity of the enrolled patients, analyzed parameters and data collection in the studies taken into consideration could be an important bias. Lastly, the heterogeneity of the purposes of the studies, such as the comparison of steroid injections versus nothing or local anesthetic injections versus local anesthetic plus steroids, could make global analysis difficult.

Surely, more randomized studies with larger numbers of patients are needed to fully understand the efficacy of ESIs and define which patients could benefit more from the procedure, especially in order to delay or prevent surgery.

## **5. Conclusions**

According to the literature analyzed in this narrative review, there is no consensus on the use of ESIs for patients with chronic lumbar pain. ESIs seem to be effective in relieving symptoms in the short term and delaying surgery, while evidence of any long-terms benefits is still lacking. More studies are needed to better understand which patients could benefit more from epidural steroid injections.

**Author Contributions:** Conceptualization, M.C. and G.P.; methodology, G.F.P. and M.P.; software, C.M.; validation, A.S., G.P. and M.C.; formal analysis, F.D.T.; investigation, A.S. and E.S.; resources, R.C.; data curation, F.R.; writing—original draft preparation, A.S.; writing—review and editing, F.C. and G.F.P.; visualization, F.G. and F.R.; supervision, M.C.; project administration, F.E.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Review* **Psychological Approaches for the Integrative Care of Chronic Low Back Pain: A Systematic Review and Metanalysis**

**Giorgia Petrucci 1, Giuseppe Francesco Papalia 1, Fabrizio Russo 1,\*, Gianluca Vadalà 1, Michela Piredda 2, Maria Grazia De Marinis 2, Rocco Papalia <sup>1</sup> and Vincenzo Denaro <sup>1</sup>**

**Abstract:** Chronic low back pain (CLBP) is the most common cause of disability worldwide, affecting about 12% to 30% of the adult population. Psychological factors play an important role in the experience of pain, and may be predictive of pain persistence, disability, and long-term sick leave. The aim of this meta-analysis was to identify and to describe the most common psychological approaches used to treat patients who suffer from CLBP. A systematic search was performed on PubMed/MEDLINE and Cochrane Central. Overall, 16 studies with a total of 1058 patients were included in the analysis. Our results suggest that cognitive behavioral therapy (CBT) and mindfulnessbased stress reduction (MBSR) interventions are both associated with an improvement in terms of pain intensity and quality of life when singularly compared to usual care. Disability also improved in both groups when compared to usual care. Significant differences in fear-avoidance beliefs were noted in the CBT group compared to usual care. Therefore, psychological factors are related to and influence CLBP. It is crucial to develop curative approaches that take these variables into account. Our findings suggest that CBT and MBSR modify pain-related outcomes and that they could be implemented in clinical practice.

**Keywords:** low back pain; cognitive behavioral therapy; mindfulness-based stress reduction; depression; disability; fear-avoidance beliefs

## **1. Introduction**

Chronic low back pain (CLBP) is the most common cause of disability worldwide [1,2], affecting about 12% to 30% of the adult population [3,4]. It is estimated that 50% to 80% of adults feel at least one episode of back pain during their lifetime [5]. Therefore, managing CLBP becomes crucial for both individuals and health care systems [1]. Chronic pain has a multidimensional nature and in addition to nociceptive and physiological aspects, it also includes aspects relating to the emotional and cognitive sphere [6]. Low back pain pathogenesis can also be diverse, including organic, non-specific etiology, and psychological causes [7,8]. Psychological factors play an important role in the experience of pain [9,10], as patients with CLBP who experience anxiety tend to exacerbate the painful sensation and increase illness behavior [11], catastrophizing pain [12–14]. These factors can make the pain experience, as well as the mechanical and physiological processes, last longer [15,16], causing physical and psychosocial disability [9]. In this regard, it has been shown that patients with CLBP suffering from depression experience higher levels of pain, functional disability, and lower levels of health-related quality of life (QoL) [16]. So, all psychological variables may be predictive of pain persistence, disability, long-term sick leave [11,15], significantly influencing the quality of life perceived by patients. Therefore, it is crucial to assess and address the psychological sphere as much as the other aspects, designing a

**Citation:** Petrucci, G.; Papalia, G.F.; Russo, F.; Vadalà, G.; Piredda, M.; De Marinis, M.G.; Papalia, R.; Denaro, V. Psychological Approaches for the Integrative Care of Chronic Low Back Pain: A Systematic Review and Metanalysis. *IJERPH* **2022**, *19*, 60. https://doi.org/10.3390/ijerph 19010060

Academic Editor: Paul B. Tchounwou

Received: 20 November 2021 Accepted: 14 December 2021 Published: 22 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

holistic and integrative framework to treat patients affected by LBP [7,17]. The American Pain Society (APS) published specific evidence-based guidelines for an interdisciplinary treatment and rehabilitation (defined as an integrated intervention with rehabilitation plus a psychological and/or social/occupational component) as a treatment option for patients with chronic LBP [18]. With the advancements in health psychology, several approaches were implemented in the care of patients with chronic pain. To our knowledge, there exist different systematic reviews in literature [16,19–22] that analyze psychological approaches to treating patients who suffer from CLBP. These studies do not evaluate which approach is most used. Moreover, a comparison between different types of psychological approaches, in order to evaluate the effectiveness in terms of improvement of clinical outcomes, has not been performed. The objectives of this systematic review and meta-analysis are (1) to identify and to describe the most common psychological approaches used to treat patients who suffer from Chronic LBP, and (2) to study the effectiveness of these approaches in terms of reduction of pain, disability, fear-avoidance behaviors, anxiety, depression, and of increase in quality of life of patients with Chronic LBP.

## **2. Materials and Methods**

This systematic review was performed in agreement with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [23]. The protocol was previously registered on PROSPERO (registration number CRD42021255687). This review included only randomized clinical trials (RCTs) that assessed the effectiveness of the most common psychological approaches on quality of life (QoL), pain, disability and fear-avoidance behaviors in adult patients suffering from chronic low back pain (CLBP).

## *2.1. Inclusion Criteria*

We included RCTs published in the last 25 years that included adult patients with CLBP; compared psychological interventions with either comparator (usual care such as health education, physical exercise, information package and waiting list); and assessed reduction of pain, disability, fear-avoidance behaviors, anxiety, depression, and increase in quality of life. Studies were excluded if they were not RCTs, if they analyzed acute or sub-acute low back pain and if they included back-surgery patients.

## *2.2. Search Methods*

We performed a systematic literature search on the following databases: PubMed/ MEDLINE, Scopus and Cochrane Central. No language restrictions were set. The search strategy was checked by three reviewers (G.P., G.F.P. and F.R.). We developed a specific question defining the intervention, the population, and outcomes to analyze (according with PICO method).

PICO methods. Definition of elements.

The search string included the following keywords (both Mesh and free-terms in PubMed/MEDLINE): Low back pain OR "Low back pain \*" OR lumbago OR "lower back pain" OR "lower back pain \*" OR "Low Back Ache" OR "Low Back Ache \*" OR "Low Backache" OR "Low Backache \*"; cognitive behavioral therapy OR "behavioral treatment" OR "behavior treatment" OR "behavior therapy" OR "cognitive behavior treatment" OR "cognitive treatment" OR "cognitive therapy", Mindfulness OR Meditation OR "mindfulness meditation", "operant behavioral therapy", hypnotism OR hypnoanalysis OR hypnotherapy, "acceptance and commitment therapy". The reference lists of the included RCTs were examined to choose additional studies for inclusion. After removing duplicates, two reviewers (G.P. and G.F.P.) independently analyzed the abstracts. Conflicts of opinion were solved discussing with a third reviewer (F.R.). In the end, the full texts were read and checked by two reviewers (G.P. and G.F.P.), choosing the studies to include in the review and meta-analysis.

## *2.3. Data Collection, Analysis, and Outcomes*

Two authors (G.P. and G.F.P.) independently extracted the following data from the studies selected: authors, year of publication, country, sample size, patients' age and sex, intervention (s) in the experimental and in the control group, follow-up period, outcomes analyzed, tools used and conclusions.

## *2.4. Risk of Bias Assessment*

Two independent reviewers (G.P. and G.F.P.) evaluated the risk of bias of the included RCTs using the Cochrane risk-of-bias tool [24]. Possible differences in the assessment were checked by a third reviewer (F.R.). Each item was classified with a low, unclear, or high risk of bias. Thus, the studies present low risk of bias in case of six or seven domains at low risk of bias, unclear risk of bias in presence of four or five domains at low risk of bias, and high risk of bias if fewer than four domains were reported at low risk of bias.

## *2.5. Statistical Analysis*

A meta-analysis was produced by Review Manager (RevMan) software Version 5.4.1. Pain, disability, quality of life, depression and fear-avoidance beliefs were assessed between CBT, MBRS and control groups as continuous outcomes. In presence of different scores, the relative outcome was presented as standard mean difference (SMD) with 95% confidence intervals, while we adopted mean difference (MD) for the outcomes assessed by the same score. Instead, days without pain was calculated as a dichotomous outcome using odds ratio (OR) with 95% confidence intervals. The evaluation of the samples' weight for this outcome was assessed by the mean value of days without pain per number of patients as events and the number of patients per number of weeks of follow-up as total. The I2 test was adopted to check the heterogeneity of studies included. In case of low heterogeneity (I<sup>2</sup> < 55%), a fixed-effect model was used, otherwise, we adopted a random-effect model. The statistical significance of the results was set at *p* < 0.05.

## **3. Results**

## *3.1. Results of the Literature Search*

The literature search yielded 3277 articles. After removal of duplicates, the reading of titles and abstracts led to 48 eligible papers. All 48 full-texts were read. Afterwards, 32 studies were eliminated for these reasons: patients who suffered from acute pain (*n* = 8), patients who suffered from sub-acute low back pain (*n* = 7), not reporting selected outcomes (*n* = 5), back surgery patients (*n* = 5), inpatients (*n* = 4), pediatric patients (*n* = 2), and hypochondriacal patients (*n* = 1). At the end of selection, 16 RCTs were included in the systematic review and meta-analysis (Figure 1).

**Figure 1.** Preferred reporting items for systematic review and meta-analysis (PRISMA 2020).

## *3.2. Demographic Data*

The total sample consisted of 2038 adults with CLBP reviewed—1058 were in the intervention group and 980 were in the control group. Most studies were published in the USA (*n* = 8; 50%), two studies were published in UK (12.5%) and Germany (12.5%), one study was published in Italy (6.25%), in The Netherlands (6.25%), in Pakistan (6.25%) and in Sweden (6.25%). The age of the patients ranged from 40.7 to 78 years in the experimental groups, and from 40.5 to 75.6 in the control groups. The percentage of women in the studies ranged from 13% to 80% in the intervention groups and from 6% to 87% in the control groups. In Table 1 the main characteristics of included studies and samples are reported.

**Table 1.** Main characteristics of the included studies and samples.

## *3.3. Type of Interventions*

The psychological approaches most used are the cognitive behavioral therapy (CBT) and mindfulness-based stress reduction (MBSR). CBT was evaluated in eleven studies, while the remaining three studies [25–27] examined MBSR. Two studies evaluated both CBT and MBSR [9,28] versus usual care (Table 2). The mean follow-up was 7.8 months and ranged from 3 weeks to 15 months.

## *3.4. Clinical Outcome Data*

The outcomes were analyzed by different tools. Disability was assessed using the Roland and Morris Disability Questionnaire (RMDQ) in 11 studies [9,13,26,27,29–34], the Oswestry Disability Index (ODI) in one study [35], the pain-related disability (PRD) in one study [36], the Dusseldorf disability scale in one study [37], and the PROMIS—physical function in one study [28]. Intensity of pain was assessed using the Numeric Rating Scale (NRS) in six studies [27–31,37], the Visual Analogue Scale (VAS) in four studies [12,31,35,37], the Brief Pain Inventory (BPI) in two studies [34,35], and the McGill pain Questionnaire Short Form and SF-36 pain scale in two studies [25,26]. Quality of life was assessed using the Short-Form Health Survey (SF-36) in five studies [25–27,29,34], the EQ-5D in two studies [32,34], and the Short Form Health Survey (SF-12) in one study [9]. Fearavoidance behaviors were assessed using the Fear-Avoidance Belief Questionnaire (FABQ) in two studies [36,38], and the Tampa Scale of Kinesiophobia (TSK) in two studies [29,34]. Psychological disorders were assessed using the Generalized Anxiety Disorder 2-item (GAD-2) in three studies [8,33,37], the Personal Health Questionnaire Depression Scale (PHQ-8) in one study [9], the Hospital Anxiety and Depression Scale (HADS) in one study [34], the Beck's Depression Inventory (BDI) in two studies [13,31] and the PROMIS depression in one study [28].

## *3.5. Methodological Evaluation*

After the application of the Cochrane risk-of-bias tool, nine studies (56%) were at moderate risk of bias, four studies (25%) were at low risk of bias and three studies were determined to be at high risk of bias (Table 3).

**Table 2.** Clinical results of the included studies.

**Table**

**2.**

*Cont*.

**Table 2.**

*Cont*.

**Table 3.** Cochrane risk-of-bias tool for randomized controlled trials.

L: low; U: unclear; H: high.

## *3.6. Effect of Intervention*

The meta-analysis analyzed the effectiveness of CBT and MBSR in terms of pain, disability, quality of life, depression and Fear-Avoidance Beliefs compared to controls.

## 3.6.1. Pain

Pain showed a significant decrease both in CBT and MBSR group compared with the control group, respectively SMD −0.73, 95% CI −1.20 to −0.26, *p* = 0.002 for CBT (Figure 2) and SMD −0.30, 95% CI −0.47 to −0.13, *p* = 0.0005 for MBSR (Figure 3). No significant pain reduction was demonstrated (MD −0.05, 95% CI −0.50 to 0.39, *p* = 0.81) when comparing MBSR and CBT (Figure 4).

#### *IJERPH* **2022**, *19*, 60

**Figure 2.** Pain: CBT versus control.

**Figure 3.** Pain: MBSR versus control.

**Figure 4.** Pain: MBSR versus CBT.

## 3.6.2. Disability

Disability scores demonstrated significant improvements after CBT in comparison with controls (SMD −0.88, 95% CI −1.50 to −0.26, *p* = 0.005) (Figure 5). Instead, the reduction of disability after MBSR was not statistically significant compared to controls (MD −0.71, 95% CI −1.53 to −0.11, *p* = 0.09) (Figure 6).

**Figure 5.** Disability: CBT versus control.

**Figure 6.** Disability: MBSR versus control.

## 3.6.3. Quality of Life

Quality of life showed significant improvement in CBT and MBSR group compared to controls, respectively SMD 0.69, 95% CI 0.00 to 1.37, *p* = 0.05 for CBT (Figure 7) and MD 2.84, 95% CI 0.31 to 5.37, *p* = 0.03 for MBSR (Figure 8). Moreover, comparing the two intervention groups, a significant difference in quality of life was shown in favor of MBSR (MD 2.54, 95% CI 0.84 to 4.24, *p* = 0.003) (Figure 9).

**Figure 7.** Quality of Life: CBT versus control.

**Figure 8.** Quality of Life: MBSR versus control.

**Figure 9.** Quality of Life: MBSR versus CBT.

## 3.6.4. Depression

Depression scales did not show significant differences between the groups. More precisely, depression did not report statistical improvements between CBT and controls (SMD −0.26, 95% CI −0.72 to 0.19, *p* = 0.26) (Figure 10), MBRS and controls (SMD −1.55, 95% CI −4.53 to 1.43, *p* = 0.31) (Figure 11), and also MBRS and CBT (SMD 0.00, 95% CI −0.27 to 0.27, *p* = 1.00) (Figure 12).

#### *IJERPH* **2022**, *19*, 60

**Figure 10.** Depression: CBT versus control.

**Figure 11.** Depression: MBSR versus control.

**Figure 12.** Depression: MBSR versus CBT.

## 3.6.5. Fear-Avoidance Beliefs

The meta-analysis demonstrated lower fear-avoidance beliefs in patients who underwent CBT compared to control group (SMD −2.17, 95% CI −4.22 to −012, *p* = 0.04) (Figure 13).

**Figure 13.** Fear-Avoidance beliefs: CBT versus control.

3.6.6. Days without Pain

Finally, the number of days without LBP increased in CBT group compared to controls, but without statistical significance (OR 1.38, 95% CI 0.73 to 2.61, *p* = 0.32) (Figure 14).

**Figure 14.** Days without pain: CBT versus control.

## **4. Discussion**

The link between psychological factors and CLBP has been widely demonstrated in several studies. The aims of this Systematic Review and Meta-Analysis were (1) to identify and describe the most frequently used psychological approaches to treat patients affected by CLBP, and (2) to study the effectiveness of these approaches in terms of reduction of pain, disability, fear-avoidance behaviors, anxiety, depression, and of increase in quality of life. According to the literature [16,19,21], the most common psychological approaches used to treat CLBP are cognitive behavioral therapy and mindfulness-based intervention. CBT demonstrated its effectiveness for different chronic pain conditions [7]. This approach helps patients with maladaptive emotions, behaviors, and cognitions through a goal-oriented and systematic process. CBT was initially used to treat disorders like insomnia, anxiety, and depression, and was later implemented to manage chronic pain [39]. The CBT intervention consists in several sessions guided by a skilled therapist, with different frequency and duration. In these sessions, activities like pain education, relaxation training, managing of automatic thoughts, stress reduction, problem solving and sleep education [39] are performed. MBSR is also becoming increasingly popular and available in the United States [9]. With this treatment, patients are educated about the psychophysiology of stress and are provided opportunities to apply MBSR skills to specific situations [40]. This approach has several contemporary interpretations, based on formal and informal systematic meditation training, patient education, yoga exercises, and individual and group dialogue [41].

The findings of this systematic review and meta-analysis suggest that CBT and MBSR interventions were both associated with an improvement in terms of pain intensity and quality of life when singularly compared to usual care. Disability also improved in both groups when compared to usual care, although it was only statistically significant in patients treated with CBT, which may be due to the paucity of studies that analyzed MBSR intervention. Significant differences in fear-avoidance beliefs were noted in the CBT group compared to usual care. However, no studies analyzed this outcome for the MBSR approach. No meaningful results were noted for depression in both MBSR and CBT groups. Moreover, only two RCT compared CBT to MBSR, showing no significant improvements in pain intensity and depression along with a better quality of life for the MBSR intervention [9,28].

Another meta-analysis [20] studied several psychosocial interventions to treat patients affected by CLBP. This study demonstrated an improvement in pain, QoL and work-related disability in the intervention group towards the waiting list group.

Our results agree with those by Gotink et al. [22], who studied MBSR applied to chronic illness. Their review shows the large use of this treatment with patients affected by cancer, cardiovascular diseases, mental disorders, and non-specific chronic pain. Indeed, an improvement in depressive symptoms and physical health and a decrease in pain burden, intensity and disability are reported in patients affected by non-specific chronic pain.

Regarding the implementation of CBT, Richmond et al. [19] shows its effectiveness for non-specific low back pain, with improvement of pain, functional disability, and quality of life. In addition, Morley et al. [42] show the improvement of pain and functional disability in patients with chronic pain.

Psychological factors in people affected by LBP are associated with increased risk of developing disability [43]. For instance, the symptom of depression and the catastrophizing of pain predict poor low back pain-related outcomes [44,45]. Therefore, cognitive and emotional factors have a crucial impact on pain perception and, in line with the literature [46], it is fundamental to identify and take care of psychological factors, through a multidisciplinary approach in patients with chronic pain. Indeed this approach should be considered because each aspect requires specific interventions [47].

This review has several limitations. Firstly, there is some difference in heterogeneity between studies regarding CBT and MBSR. In particular, the studies involving CBT presented high heterogeneity due to the greater number of studies included, the different types of CBT performed, the different duration of interventions, and the tools used. Instead, the studies investigating MBSR used the same tools for the analyzed outcomes, resulting therefore in lower heterogeneity. Additionally, demographic characteristics of the participants were different in the included studies, with various gender and age distribution. However, this did not influence the statistical analysis of the studies. There are differences regarding the quality of the studies; indeed, the major number of the studies included were of moderate quality (*n* = 9), whereas four studies had a low risk of bias, and three studies had a high risk of bias. We also decided to include in the meta-analysis the studies with a high risk of bias, which is another limitation regarding the number of studies included. The participants of most studies, except for three studies [25–27], were not blind to treatment allocation, given the nature of the intervention. Another limitation of this study is the heterogeneity of the types of treatment in the control groups. Indeed, we found different types of treatments in the control groups such as physiotherapy, physical exercises, educational programs and drug treatments. In only two studies we found the waiting list, and we suppose it may be the most adequate control group for the reduction of bias, hence the need to develop RCTs with waiting lists as a control group for an appropriate analysis of the effectiveness of psychological interventions. To reduce the heterogeneity in the analysis of outcomes as pain, it would be appropriate to develop RCTs that use the same tools.

#### **5. Conclusions**

In the present study we analyzed the most used and effective psychological approaches to treat patients affected by chronic LBP. CBT and MBSR have proven their significant effectiveness to improve pain intensity and quality of life compared to controls. These approaches also demonstrated their efficacy in reducing disability and fear-avoidance, but without significant results. The importance of treating psychological aspects is widely proven, but the paucity and heterogeneity of the studies included cannot make us confident to affirm which is the most effective treatment. Further studies are needed to compare CBT and MBSR.

**Author Contributions:** Conceptualization, G.P., G.F.P., F.R. and G.V.; methodology, G.P., G.F.P., F.R. and G.V.; formal analysis, G.P., G.F.P. and F.R.; data curation, G.P., G.F.P. and F.R.; writing—original draft preparation, G.P., G.F.P., F.R., G.V., M.P. and M.G.D.M.; writing—review and editing, G.P., G.F.P., F.R., G.V., M.P., M.G.D.M., R.P. and V.D.; supervision, F.R., G.V., M.P., M.G.D.M., R.P. and V.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Research Grant (BRiC-2018 ID3) of the Italian Workers' Compensation Authority (INAIL).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Article* **Thirty Minutes Identified as the Threshold for Development of Pain in Low Back and Feet Regions, and Predictors of Intensity of Pain during 1-h Laboratory-Based Standing in Office Workers**

**Beatriz Rodríguez-Romero 1,\*, Michelle D. Smith 2, Sonia Pértega-Díaz 3, Alejandro Quintela-del-Rio <sup>4</sup> and Venerina Johnston <sup>2</sup>**

**Abstract:** This study with 40 office workers investigated (a) the effect of time spent standing on low-

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +34-981-167-000 (ext. 5841)

back and lower limb pain during a 1-h laboratory-based task; (b) the standing time after which a significant increase in pain is likely; and (c) the individual, physical and psychosocial factors that predict pain. The primary outcome was bodily location of pain and pain intensity on a 100-mm Visual Analogue Scale recorded at baseline and every 15 min. Physical measures included trunk and hip motor control and endurance. Self-report history of pain, physical activity, psychosocial job characteristics, pain catastrophizing and general health status were collected. Univariate analysis and regression models were included. The prevalence of low-back pain increased from 15% to 40% after 30 min while feet pain increased to 25% from 0 at baseline. The intensity of low-back and lower limb pain also increased over time. A thirty-minute interval was identified as the threshold for the development and increase in low-back and feet pain. Modifiable factors were associated with low-back pain intensity—lower hip abductor muscle endurance and poorer physical health, and with feet symptoms—greater body mass index and less core stability.

**Keywords:** low-back pain; standing position; musculoskeletal pain; lower extremity

## **1. Introduction**

Office workers are known to adopt sedentary behaviors at work [1]. Although the available evidence has not confirmed a consistent causal relationship between occupational sitting and musculoskeletal pain [2–4], a growing body of evidence suggests that prolonged sitting is a major concern for the development of several chronic diseases, such as cardiovascular disease, diabetes type 2 and premature mortality [5]. Thus, it is no surprise that there is heightened interest in workplace initiatives to reduce the amount of sitting time for office workers [6,7] with alternatives such as sit-stand workstations or breaking up seated-work with standing-work. However, some concerns have been expressed that substituting sitting with standing may expose workers to new hazards and/or other health consequences. Two systematic reviews with meta-analysis in laboratory [8] and occupational [9] settings suggested that prolonged standing is associated with the occurrence of low-back and lower extremity symptoms, although the conclusions are inconclusive for the association with lower extremity symptoms. Similarly, causality between occupational standing and LBP has not been resolved, and not all people who are exposed to prolonged standing will develop LBP [2,10].

**Citation:** Rodríguez-Romero, B.; Smith, M.D.; Pértega-Díaz, S.; Quintela-del-Rio, A.; Johnston, V. Thirty Minutes Identified as the Threshold for Development of Pain in Low Back and Feet Regions, and Predictors of Intensity of Pain during 1-h Laboratory-Based Standing in Office Workers. *IJERPH* **2022**, *19*, 2221. https://doi.org/10.3390/ ijerph19042221

Academic Editors: Sergio Iavicoli, Vincenzo Denaro, Fabrizio Russo and Gianluca Vadalà

Received: 29 December 2021 Accepted: 13 February 2022 Published: 16 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Experimental laboratory studies which simulate occupational standing have used an induced pain paradigm to identify factors which could be associated with developing low-back and/or lower extremity pain [8]. Some factors suggested to predispose a person to the development of LBP during prolonged standing are: (i) fatigue of the trunk and hip muscles [11–13]; (ii) movement control dysfunction [13–16]; and (iii) postural stiffness through increased levels of coactivation of hip and trunk muscles [14,17]. Specific factors associated with an increase in LBP during prolonged standing are unknown. Discomfort experienced in the feet and lower limbs during standing is often attributed to reductions in venous return and muscular fatigue [18]. The flowmetry, leg circumference, skin temperature, force through feet, and lower limb and trunk muscle activity have been reported as the main outcomes to study the possible mechanisms for lower limb symptoms; although the underlying mechanisms require further investigation [8,9,18].

Evidence for determining thresholds of excessive standing has not been elucidated. In a 2-year prospective study, Andersen et al. [19] demonstrated that standing at work for 30 min or more every hour elevated the odds for LBP by a factor of 2.1, and for pain in the hip, knee or foot by a factor of 1.7. A meta-analysis [9] suggested a statistically significant association between 2 or 4 h/workday of occupational standing and the occurrence of low back/lower extremity symptoms, although the authors highlighted that conclusions on the dose–response association cannot be drawn.

The aims of this laboratory study in office workers were to determine: (i) the effect of time spent standing on pain status during a 1-h laboratory-based standing task; (ii) the point after which significant increases in pain are likely; and (iii) the individual (e.g., age, sex, history of LBP, self-rated health), physical (e.g., deficits in motor control, muscle endurance) and psychosocial (e.g., job demands) factors that are associated with higher levels of low-back and lower limb pain after a 1-h standing task. Given the findings of previous studies, it was hypothesized that there would be a significant effect of time spent standing on the prevalence and intensity of low-back and lower limb pain, with a common threshold time when significant increases occur. We expect that specific individual (health status, BMI), physical (deficits in muscle endurance) and psychosocial (e.g., low job control) factors would be associated with higher scores of low-back and lower limb pain after a one-hour standing task.

## **2. Materials and Methods**

#### *2.1. Participants*

A convenience sample of forty office workers, aged ≥ 18 years, who performed mostly sedentary work for ≥30 h per week were recruited. Participants were excluded if they: (i) were pregnant or less than six months postpartum, (ii) had any major trauma or surgery to the spine or lower limb in the last 12 months or (iii) had a diagnosis of neurological or systemic pathology. The recruitment process and sample size are described elsewhere [13].

The University of Queensland Human Research Ethics Committee B approved this study (Approval Number: #2017000666) and all participants provided informed consent prior to study participation. This study was registered in the Protocol Registration and Results System (PRS) (NCT03678623).

## *2.2. Study Procedure*

Participants completed self-reported measures, undertook a physical examination conducted by a trained physiotherapist, and then participated in a 1-h standing task. These self-report measures were administered via an online survey completed the day prior or the same day as the laboratory testing session.

#### *2.3. Measurements*

Self-reported measures included (i) demographics; (ii) history of LBP (7-day prevalence); (iii) location of any bodily pain assessed with the Nordic Musculoskeletal Questionnaire [20] and pain assessed with a 100-mm Visual Analogue Scale (VAS) anchored with

"no pain" at 0 and "worst pain imaginable" at 100 mm for each body location [21]; (iv) total and occupational physical activity assessed with the International Physical Activity Questionnaire (IPAQ) (MET-min/week) [22] and the Occupational Sitting and Physical Activity Questionnaire (OSPAQ) (minutes) [23], respectively; (v) psychosocial job characteristics evaluated through the Job Content Questionnaire (JCQ) which includes four domains (job control, psychological job demands, social support and physical demands) (4-point Likert) [24]; (vi) propensity for pain catastrophizing assessed with the Pain Catastrophizing Scale (PCS-total) (scores ranged from 0 to 52 with greater scores indicate a greater degree of catastrophizing) [25]; and (vii) general health status evaluated with the SF-12 through the Physical (PCS) and Mental (MCS) Component Summary scores [26].

At the start of the laboratory session, participants were given 15 min of seated rest while the testing protocol was explained. Physical testing was then undertaken in the following order: (i) height and weight; (ii) three motor control impairment tests: the active hip abduction test (AHAbd) (ranging from 0–5 as rated by participants and 0–3 by the examiner, with lower scores indicating better motor control for both ratings) [27], and the active straight leg raise test (ASLR) (ranging from 0–10 as the summed score of participant and examiner ratings, with lower scores indicating better motor control) [28]; (iii) endurance tests of the following trunk and hip muscles (measured as seconds able to holding a static position): abdominal endurance [29], supine bridge [30], isometric hip abduction [31] and Biering–Sorensen test [29]. The specific methodology on how each of these tests was applied is explained elsewhere [13].

The standing paradigm consisted of participants standing for an hour while performing their usual computer-based work. Participants stood within a rectangular floor space (122 × 61 cm) with their body fist-width away from the edge of a height-adjustable workstation. The workstation was standardized to each participant so that the desk height was 5–6 cm below the lateral epicondyle, the computer monitor was at arm's length from the body, and the top of the computer monitor was at eye level. Participants were allowed to shift their weight as often as desired but were asked to keep both feet on the ground the majority of the time. The participant was not allowed to lean on the workstation with their arms, legs or trunk [13].

The primary outcome was pain status (yes/no) and severity of pain (VAS, 0–100 mm) in the low back and lower extremity (Figure 1). The workers self-reported the location of their pain on the body map and indicated pain intensity on the VAS at baseline, every 15 min during, and at the end of the standing test. The investigator verbally asked the participants to rate their pain. Participants were not given access to their previous scores.

## *2.4. Statistical Analysis*

Descriptive statistics were computed for pain ratings over the 1-h task (0, 15, 30, 45 and 60 min). At each timepoint, prevalence of pain for each location was computed, and VAS scores were summarized as mean ± standard deviation and median.

For hip-thigh and knee-calf regions, the number of subjects who developed pain was small, and only descriptive analyses were performed. Therefore, only low-back and ankle-feet regions were analyzed in more detail. For those locations, an increment of ≥10 mm in VAS pain at any time between start and end of the test was considered to classify participants as Pain Developers (PD) or Non-Pain Developers (NPD) [9,32], and both groups were analyzed independently.

Since repeated pain evaluations were obtained for each subject at different timepoints, appropriate tests for paired data were employed in bivariate analysis. McNemar's test for paired data was used to analyze the significance of changes in the prevalence of pain for each location during the 1-h task. In the same way, Wilcoxon's signed-rank test was employed to compare pain scores between different timepoints.

**Figure 1.** Body Pain Diagram and Visual Analogue Scale used every 15 min for reporting location and intensity of pain during the standing task.

Association of the different individual, physical and psychosocial factors recorded with pain ratings during the 1-h task was explored. First, the maximum change in VAS score from baseline and during the task was considered as the outcome using univariate and multivariate analyses. Spearman's correlations were used to determine the strength of the relationship between each of the individual, physical and psychosocial factors included and the increase in low-back and ankle-feet pain during the task. Then, a stepwise multivariate linear regression model was adjusted, including as covariates those with the highest associated correlations in the univariate analysis, both for the whole group of workers and for the PD group. Finally, in order to compare the consistency of the results, univariate and multivariate linear mixed-effects random-slope repeated measures models were also adjusted [33]. This type of model assumes that time effects (changes in pain rating over time) are random among individuals, considering the correlation among repeated measures in the same subject. Regression coefficients were estimated for the interaction between each of the covariates and time, allowing the rate of change to vary for different baseline characteristics.

Statistical analyses were performed using software SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and R version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria), with a bilateral significance level set at *p* < 0.05.

## **3. Results**

Forty office workers (22 females; mean age: 37.4 ± 6.6 years; BMI: 26.3 ± 5.7 and 58% considered within healthy weight range) were included in the study. All participants completed the laboratory testing (physical testing and 1-h standing task) with no adverse events reported.

## *3.1. Standing-Time Effect on Pain Status: Any Reported Pain*

The number of workers who reported pain in the low back and lower limb, throughout the task, increased over time (Table 1). At the beginning of the standing task (0 min), 15% of the participants reported some degree of LBP, increasing up to 30% at 15 min (*p* = 0.070) and reaching 40% (*p* = 0.006) and 42.5% (*p* = 0.003) at 30 and 45 min, respectively (Figure 2a). None of the participants had ankle-feet pain at the beginning of the task. Prevalence of

ankle-feet pain was 10% after 15 min, increasing to 25% at 30 min (*p* = 0.031) and reaching 35.0% both at 45 min (*p* = 0.006) and 60 min (*p* = 0.002) (Figure 2b). Low-back and ankle-feet pain prevalence did not significantly increase after 30 min. While there was an increase in the number of participants who reported lower limb pain between baseline and 60 min (2.5% to 15% for the hip-thigh region and 5% to 27.5% for the knee-calf), this did not reach statistical significance.

**Table 1.** Prevalence of any low-back and lower extremity pain at 0, 15, 30, 45 and 60 min throughout the 1-h standing task.

## *3.2. Standing-Time Effect on Pain Status: Intensity of Pain*

Of the 40 participants, 14 office workers were considered low-back pain developers and 9 were ankle-feet-pain developers (abbreviated as ankle-feet-PD onwards). For the hip-thigh and knee-calf regions, the number of participants who reported having a change of ≥10 on the VAS was small (3 and 6, respectively) preventing analysis.

The raw VAS score and VAS score increased over time for the total sample, PD and NPD groups, both for low-back and ankle-feet regions, are shown in Table 2 and Supplementary Material Figure S1 There was a significant standing-time effect, with individuals identified as PD showing increased levels of pain over time and the NPD group remaining at a very low level. The low-back-PD group averaged a mean VAS score of 30.8 ± 20.5 mm and the ankle-feet-PD group averaged a mean VAS score of 22.6 ± 9.7 mm at the end of standing. In addition, these results show that after 30 min of standing, significant differences in pain scores appear from the baseline.

**Figure 2.** Prevalence and time-based changes of any reported pain (VAS scores > 0) for each area: (**a**) for low back; (**b**) for ankle-feet. *p*: McNemar test.

**Table 2.** VAS scores (0–100) and change in VAS scores from baseline at the low-back and anklefeet regions at 0, 15, 30, 45 and 60 min of the 1 h standing task for all study participants, PD and NPD groups.

\* *p*-values from two-sided Wilcoxon's signed-rank test comparing VAS score at each moment relative to baseline VAS pain. **#** Positive scores indicate an increase in VAS; PD: pain developers; NPD: non-pain developers.

## *3.3. Association of Individual, Physical and Psychosocial Factors with Pain Ratings over Time*

Table 3 shows the Spearman's rho correlation coefficients between the maximum increment of VAS scores for the low back and ankle-feet during the task and different individual, physical and psychosocial factors in the whole sample. Correlations for PD and NPD groups are shown in Supplementary Material Table S1.

**Table 3.** Spearman correlation coefficients between the maximum increment at VAS scores at lowback and ankle-feet regions throughout of the standing task and variables for the entire sample (*n* = 40).

\* *p* ≤ 0.05; BMI, Body Mass Index; MET, Metabolic Equivalent of Task (computed as the sum of walking, moderate-intensity, and vigorous-intensity physical activity); OSPAQ, Occupational Sitting and Physical Activity Questionnaire; JCQ, Job Content Questionnaire; ASLR, Active Straight Leg Raise; AHAbd, Active Hip Abduction; s, seconds.

Considering the whole sample, the maximum increase in VAS scores at the low back throughout of the 1-h standing task was significantly correlated with (i) history of LBP measured as the severity of LBP in the last 7 days (Rho = 0.54, *p* = 0.000); (ii) SF-12 (physical component summary) (Rho = −0.35, *p* = 0.029); (iii) ASLR, total participantscore (Rho =0.35, *p* = 0.029); (iv) AHAbd left side, participant-score (Rho = 0.33, *p* = 0.04); (v) isometric hip abduction (right leg) (Rho = −0.47, *p* = 0.002); (vi) isometric hip abduction (left leg) (Rho = −0.48, *p* = 0.002). In the feet area, the maximum increase in VAS scores was significantly correlated with (i) BMI (Rho = 0.38, *p* = 0.016); (ii) SF-12 (mental component summary) (Rho = 0.39, *p* = 0.014); (iii) Supine Bridge (Rho = −0.33, *p* = 0.04); and (iv) Biering–Sorensen test (Rho = −0.32, *p* = 0.048).

## *3.4. Regression Analysis-Predicting the Magnitude of Low-Back Pain throughout of 1-h Standing Task*

A multivariate linear regression model was adjusted with the maximum increment in VAS scores throughout of standing as the dependent variable and the magnitude of the highest and significant correlated variables (determined using Spearman's correlation) as covariates (Table 4). Lower hip abductor muscle endurance (B = −0.23, *p* = 0.007) and lower physical health (PCS-SF-12) (B = −0.86, *p* = 0.008) predicted higher level of LBP throughout of the 1-h standing task for the entire sample. These two variables explained 41% of the variability in the increment of the pain (R2 = 0.41).

**Table 4.** Multivariate lineal and mixed regression models for the prediction of low-back pain throughout of the 1-h standing task.

SE: Standard error; CI: Confidence Interval.

The mixed regression model reinforced these results, with the interaction time × minimum isometric hip abduction and time×PCS-SF-12 being significant predictors of the highest level of LBP during the task. These results indicate that workers with lower hip abductor muscle endurance and lower physical health experience a higher rate of increase of LBP during the task.

## **4. Discussion**

This study showed significant time-based changes for the reported prevalence and severity of pain in the low back and ankle-feet regions of the body, with 30 min identified as the threshold for observing these differences. The regression models demonstrated that less hip abductor muscle endurance and less physical health (as measured with the SF-12) predicted a greater increase of LBP at the end of a 1-h standing task. The correlation analysis suggested that the maximum increase in VAS score in the ankle-feet area was associated with higher BMI, less back and hip muscle endurance (Supine Bridge and Biering–Sorensen test) and mental health (SF-12).

There was a significant effect of standing at a workstation for 1-h on the presence and intensity of pain in the low-back and ankle-feet regions. This has consistently been reported for the low back [9,11,14,17,34], but has been less commonly investigated in the ankle-feet region [9,18,35]. The percentage of office workers who developed LBP at the end of the 1-h standing task (42.5%) was similar to the average of 44% reported in the systematic review of Coenen et al. [8]; but lower than other studies which reported rates of 81% [34], 71% [11] and 65% [17]. The VAS level of pain reported by low-back PD in this study was within the range of what has been reported previously (19 mm [34] to 32 mm [11]), despite most previous work using populations of students without a history of LBP and using a longer standing task. The average increase in pain scores from baseline in our study was 27.9 ± 18.1 mm which is lower than clinical low back populations but higher than the absolute cut-off value of 15 points for the minimal important change (MIC) on the VAS for LBP patients [36].

Lower limb pain was commonly reported during the standing task by our participants, with 15% reporting hip-thigh pain, 27.5% reporting pain in the knee-calf region and 35% developing ankle-feet pain at the end of standing. It should be noted that no participants had ankle-feet pain at the beginning of the standing task. The increase in ankle-feet-PD pain was 22.6 ± 21 mm. Although this level of pain could be interpreted as low, it is higher than the MIC (9.3 mm) reported for the clinical interpretation of results in patients with foot or ankle pathologies [37]. This pain intensity for the ankle-feet region is similar to that reported by Antle and Cote [18] after 34 min of standing (3.5 out of 10) and Smith et al. [35] after 2 h of standing (1.8 out of 10).

Our results demonstrate that 30 min of standing affects the prevalence and severity of pain at the low-back and ankle-feet regions in office workers. After 30 min, the PD group reported pain scores which exceeded the MIC [36,37]. It has been demonstrated that in PD low back usually begins to develop within 30–60 min of standing [11,14,17,35]. Prolonged periods of occupational standing (greater than 30 min each hour), was one of the strongest predictors of LPB in employees [19] and low-back symptoms were reached after 71 min with this reduced to 42 min in those considered PD, after pooled data from laboratory studies [8]. Dose–response associations for lower extremity symptoms are more heterogeneous in the literature [8], with one study showing lower limb discomfort after 34 min of standing [18]. While a significant interaction of time on pain development during standing has been illustrated [11,14,17], most studies did not determine the point of time when the significant increase in pain occurred. Consequently, based on our results, we recommend that office workers restrict their maximum standing time to no more than 30 min which is less than the suggested time of 40 min [8].

Few studies have considered the predictors of pain intensity during standing. Multivariate regression modelling demonstrated that lower hip abductor muscle endurance and lower physical health were independently associated with a higher level of LBP, explaining 41% of the variability in pain in our study. In turn, mixed regression modelling demonstrated a significant interaction for time×isometric hip abduction and time×SF-12 (physical component summary), reinforcing the importance of these two factors as predictors of the highest-level LBP during the 1-h standing task. Hip abductor muscle endurance has previously suggested to be associated with LBP development during standing. Viggiani and Callaghan [12] determined that hip abductor fatigability (measured using isometric hip abduction) differentiated those PD from NPD, with PD having lower hip abductor endurance. Marshall et al. [11] showed that low-back PD had lower gluteus medius endurance (measured with a side-bridge test), there was no association between the side bridge test and pain levels in linear regression analysis. Finally, Hwang et al. [38] identified that hip abductor muscle strength (measured in the similar way as this study) was the variable that most contributed to VAS scores in workers with LBP who performed occupational standing. While causality between LBP and hip abductor muscle function cannot be confirmed from existing research, the negative slope in our analysis indicates that low-back VAS scores decrease as endurance of the hip abductors muscles increases. Together, data suggest that hip abduction muscle weakness and fatiguability may be important to consider in preventing or managing standing-induced LBP.

To our knowledge, this is the first study to investigate self-perceived physical health, measured through Physical Component Summary (SF-12), as predictor of higher pain intensity during a prolonged standing task. In the calculation of the PCS summary score, the highest weights were given to four domains: physical functioning, role physical, bodily pain, and general health. Bodily Pain was the domain with the lowest scores for our sample (mean: 49.6, IQR: 46.9–57.5, data not shown previously) and for the LBP-PD group (47.4 ± 10.5). This is consistent with previous reports stating that the presence of pain (in the low back and/or other sites) may be associated with the incidence and prevalence of LBP [39,40].

Factors associated with ankle-feet pain have not previously been investigated in relation to occupational standing in office workers. While regression analysis was not undertaken for the maximum VAS for ankle-feet pain due to small proportion of participants with this pain, ankle-feet region VAS was moderately correlated with greater BMI, lower trunk/hip muscle function (lower supine bridge and Biering–Sorensen test ability) and mental health. There is a strong association between high BMI and non-specific foot pain in the general population [41] and in people with plantar heel pain [42]. Mechanical loading with increased body mass is a possible mechanism for this relationship, as well as metabolic and psychological factors [41]. Trunk/hip stability is thought to be important for the production, transfer and control of force throughout the entire kinetic chain [43]. Gluteus maximus and medius muscles weakness has been identified in people with ankle injuries [44,45], and it has been hypothesized that deficits in core stability may increase risk of lower extremity injury [46,47]. Altered loading throughout the lower kinetic due to hip/trunk muscle deficits may contribute to ankle-feet pain during prolonged standing at a standing workstation. The association between better mental health and ankle-feet pain could be due to a spurious finding and a consequence of the small sample size.

There are limitations to this study that must be considered. First, the study has a relatively small sample size (*n* = 40), with only a small proportion of subjects classified as low-back or ankle-feet PD. Although previous studies have had similar sample sizes [12,14], this sample size may have led to two important issues: (i) failing to detect a real effect on pain development for any of the variables studied, and (ii) finding effects that seem supported by the data but are spurious. The use of alternative statistical methods would have been preferred [48] but were difficult due to the small sample. The results of this study should be confirmed in future studies involving more individuals and more analysis techniques. Second, this study did not include outcomes to identify potential vascular mechanisms that may be associated with the development of musculoskeletal ankle-feet symptoms [8,18]. We recommend that future research include such measures.

This study has shown that there is an impact of 1 h-standing exposure on several aspects of pain status in office workers, determined the significant dose–response relationship for standing, and clarified the factors associated with the intensity of low-back and ankle-feet pain. Based on the findings of our study, practitioners and clinicians should advise office workers to avoid standing for more than 30 min in light of the dose–response relationship for standing and pain. Due to the relationship between hip abductor muscle endurance, physical health status and intensity of LBP, future research is needed to determine if improving these factors decreases LBP intensity during standing. Similarly, further investigation is needed to understand the relationship between BMI, trunk/hip muscle function and mental health on ankle-feet pain during standing.

## **5. Conclusions**

This study in office workers demonstrated that the prevalence and intensity of lowback and ankle-feet pain increased during a 1-h laboratory-based standing task, with 30 min identified as the threshold for the development/provocation of pain. Lower hip abductor muscle endurance and physical health predicted the low-back pain intensity. In the ankle-feet area, results suggest that the increase in pain scores was correlated with greater BMI and lower trunk/hip muscle function.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/ijerph19042221/s1, Figure S1: Pain development during 1 h-standing

for PD and NPD groups: (a) at low-back; (b) at ankle-feet areas; Table S1: Spearman correlation coefficients between the maximum increment at VAS scores at low-back and ankle-feet regions throughout of the standing task and measured variables for PD groups.

**Author Contributions:** Conceptualization, V.J., B.R.-R. and M.D.S.; methodology, V.J., B.R.-R., M.D.S. and S.P.-D.; formal analysis, S.P.-D. and A.Q.-d.-R.; investigation, B.R.-R., V.J. and M.D.S.; resources, V.J. and B.R.-R.; writing—original draft preparation, B.R.-R. and S.P.-D.; writing—review and editing, V.J., M.D.S., B.R.-R. and S.P.-D.; visualization, B.R.-R., S.P.-D., M.D.S., A.Q.-d.-R. and V.J.; supervision, V.J.; project administration, V.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of The University of Queensland Human Research Ethics Committee B (Approval Number: #2017000666; 5 March 2018).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** https://zenodo.org/record/5947650#.YfwuJviCFhF.

**Acknowledgments:** The authors thank all the participants who took part in the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

## *Article* **The Effect of Core Stabilization Exercise with the Abdominal Drawing-in Maneuver Technique on Stature Change during Prolonged Sitting in Sedentary Workers with Chronic Low Back Pain**

**Pongsatorn Saiklang 1, Rungthip Puntumetakul 2,3,\* and Thiwaphon Chatprem <sup>2</sup>**

**Abstract:** To enhance stature recovery, lumbar spine stabilization by stimulating the deep trunk muscle activation for compensation forces originating from the upper body was introduced. The abdominal drawing-in maneuver (ADIM) technique has been found mainly to activate deep trunk muscles. The purpose of the current study was to determine whether 5 weeks of training of deep trunk muscles using the ADIM technique could improve stature recovery, delay trunk muscle fatigue, and decrease pain intensity during prolonged sitting. Thirty participants with chronic low back pain (CLBP) conducted a core stabilization exercise (CSE) with the ADIM technique for 5 weeks. Participants were required to sit for 41 min before and after the exercise intervention. Stature change was measured using a seated stadiometer with a resolution of ±0.006 mm. During sitting, the stature change, pain intensity, and trunk muscle fatigue were recorded. A comparison between measurements at baseline and after 5 weeks of training demonstrated: (i) stature recovery and pain intensity significantly improved throughout the 41 min sitting condition; (ii) the bilaterally trunk muscle showed significantly decreased fatigue. The CSE with the ADIM technique was shown to provide a protective effect on detrimental reductions in stature change and trunk muscle fatigue during prolonged sitting in young participants under controlled conditions in a laboratory. This information may help to prevent the risk of LBP from prolonged sitting activities in real life situations.

**Keywords:** spinal load; core stability; ergonomics

## **1. Introduction**

Sedentary behavior is characterized by an energy expenditure less than or equal to 1.5 metabolic equivalents (METs) while in a sitting or reclining position when awake [1]. Today, increasing numbers of individuals spend extended periods in a seated position at work as well as during leisure time [2,3]. Recently, sedentary workers in Thailand reported recurring low back pain, with 63% showing that their low back pain was aggravated by sitting during working hours [4]. Chronic low back pain (CLBP) has a global prevalence of 11–23% among people with low back pain [5,6]. The socioeconomic burden of CLBP stems from a prolonged loss of function, which consequently results in decreased work productivity and increased medical costs [5,7].

Deep trunk muscle fatigue may arise from continuous contraction of the trunk muscles during prolonged sitting [8–10]. This fatigue reduces muscular support to the spine and increases stress on ligaments and intervertebral discs [9,10]; consequently, it reduces intervertebral disc height [11,12]. Reductions in disc height could increase compressive

**Citation:** Saiklang, P.; Puntumetakul, R.; Chatprem, T. The Effect of Core Stabilization Exercise with the Abdominal Drawing-in Maneuver Technique on Stature Change during Prolonged Sitting in Sedentary Workers with Chronic Low Back Pain. *IJERPH* **2022**, *19*, 1904. https:// doi.org/10.3390/ijerph19031904

Academic Editors: Sergio Iavicoli, Vincenzo Denaro, Gianluca Vadalà and Fabrizio Russo

Received: 23 December 2021 Accepted: 5 February 2022 Published: 8 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

stress on sensitive spinal structures [13,14] and may stimulate nociceptor activity, leading to pain [14]. Stature change measurement is a method used to reflect alterations in spinal length, and the reduction of spinal length is known as spinal shrinkage or stature loss [15]. Prolonged sitting postures could lead to stature reduction and ultimately to low back pain [9,16,17].

Trunk muscles play an essential role in contributing to spinal stability [18]. There are two types of trunk muscle systems: superficial and deep [19,20]. The internal oblique (IO), transversus abdominis (TrA), and lumbar multifidus (LM) muscles represent a deep muscle system that compensates for forces on the upper body of the spine and increases lumbar stability [18,21]. Previous studies reported changes in the muscle recruitment pattern and timing of muscle onset in people with low back pain [22,23]. Increased superficial trunk muscle activation occurs to compensate for deep trunk muscle dysfunction [24,25], in which the neural control subsystem attempts to maintain spinal stability [18,26]. Increased activation of the superficial trunk muscle can compress the spinal structure and lead to delayed stature recovery [25,27].

Previous research reported that superficial trunk muscle activity can be reduced by activating the deep trunk muscles using the abdominal drawing-in maneuver (ADIM) technique [15,28,29]. The ADIM technique is known to elicit preferential recruitment of the transversus abdominis muscle with minimal activation of the superficial trunk muscles. For this technique, participants are instructed to 'gently draw in their lower abdomen toward the spine' [13]. Saiklang et al. (2020) investigated the change in stature recovery in patients with CLBP following the immediate effect of the ADIM technique for 1 min repeated three times throughout a 41 min prolonged sitting period. The results demonstrated that the ADIM technique significantly improved stature recovery and increased TrA and IO muscle activities and TrA and IO/RA ratios compared with upright sitting without exercise [15].

To date, no investigation has reported the effect of the long-term impact of the core stabilization exercise (CSE) with the ADIM technique program focusing on deep trunk muscle on stature recovery during prolonged sitting. The CSE with ADIM technique aims to improve neuromuscular control skills, relearn normal function, and enhance endurance of the deep muscles around the lumbar spine, such as the TrA and LM muscles [28,30].

The aim of the current study was to investigate differences in stature change, pain, and trunk muscle fatigue during prolonged sitting in seated sedentary workers with CLBP between baseline and the first day after a 5-week CSE with the ADIM technique. We hypothesized that the 5-week CSE with the ADIM technique can improve deep trunk muscle endurance, reduce pain, and delay stature reduction during prolonged sitting.

## **2. Materials and Methods**

## *2.1. Design and Setting*

The study used a within-subject repeated-measures design. It was conducted at the research center of the Back, Neck, Other Joint Pain and Human Performance (BNO-JPH) laboratory, Khon Kaen University, Thailand. Ethics approval for this research was granted before the study by the Human Research Ethics Committee (HE612220) of Khon Kaen University. The study was registered at clinicaltrials.in.th (registration number: TCTR20180823004).

## *2.2. Participants*

Thirty participants, aged 20–39 years, were recruited via posters on bulletin boards at Khon Kaen University. Fifteen males and fifteen females were recruited to reduce the influence of gender. Inclusion criteria for the participants were established as follows: CLBP lasting more than three months, mild to moderate levels of pain on the numerical rating scale (NRS; ≤7 score) [31,32], low levels of activity limitation on the Roland Morris disability questionnaire (RMDQ; ≤12 score) [33], and reported sitting for at least two hours on any working day [9]. Participants were excluded if they had previous vertebral surgery, had been identified with a medical condition that affected spinal soft tissues, or were pregnant [32,34].

## *2.3. Sample Size Determination*

The sample size was calculated after preliminary data collection from 12 participants (six male and six female) who performed the CSE with ADIM for 5 weeks. The mean difference of the stature changes before and after the exercise intervention was set at 3 mm. A significance level of 0.05 (Zα (0.05) = 1.96) and a power of 90% (Zβ (0.1) = 1.28) were used in the calculation. After an additional 15% correction for dropouts, the number of subjects was 21. Thus, the current study required at least 30 participants (15 males and 15 females for balanced gender) to achieve sufficient statistical power for the analyses.

#### *2.4. Outcome Measurements*

## 2.4.1. Stature Change Response

Stature change response was measured using a seated stadiometer device (certified Thai petty-patent No. 5607; Figure 1) [15,34]. The Digimatic Indicator identifies variations in stature change with a resolution of ±0.006 mm and was used to measure stature change. The device displays real-time data and repeatedly records data up to 5 Hz (ID-C 150, 1050 Digimatic Indicator, Manual No. 3061, Series No. 543, Mitutoyo, Kawasaki, Japan).

**Figure 1.** A seated stadiometer device: (**A**) participant position, (**B**) feedback chart. Source: [15].

The position of the participants was controlled during the measurements as follows: (i) The Digimatic Indicator settings allowed the distal end to rest directly on the highest apex of the skull to ensure its consistent positioning throughout participant repositioning (Figure 1) [15,34].

(ii) Head positioning with the eyes kept level was maintained by coaching the participants to concentrate on a visual cue, a letter on an alphabet chart, placed at eye level on the opposite end (Figure 1B) [15,34].

(iii) The wooden seat platform and footrest were adjustable so that the participants' ankle, knee, and hip joints were positioned at 90◦ throughout the measurements. Heels touched the back of the footrest [35]. The head and sacral supports were adjustable to accommodate the participants' spinal posture [35]. A pillow was placed on the participants' lap to support their forearms positioned at 90◦ to their upper arms (Figure 1A).

(iv) The spinal alignment was controlled by sensors placed on the spinous processes of the following vertebrae: cervical spine 4, thoracic spine 4, thoracic spine 12, and lumbar spine 3. These sensors were connected to a light diode feedback located opposite the seated participant (Figure 1B) [12,15]. These sensors were used as a measure for control to ensure that the participants maintained the same posture throughout the experiment period. The process of stature change measurement was performed by Researcher P.S.

## 2.4.2. Trunk Muscle Fatigue

Eight pairs of Ag-AgCl disposable surface electromyography (sEMG) electrodes (EL 503) with electrical contact surface areas of 1 cm<sup>2</sup> and a center-to-center spacing of 2.5 cm were attached parallel to each muscle on both sides: to the rectus abdominis muscle (RA) [36], the internal oblique and transversus abdominis (IO and TrA) [37], the iliocostalis lumborum pars thoracis (ICLT) [38], and the lumbar multifidus (LM) [30] after skin abrasion and cleaning with alcohol. Electromyography (EMG) data were recorded at 2000 Hz using the Wireless Bipolar Cometa Mini Wave Plus 16-channel EMG system (Cometa, Bareggio, Italy), an online band-pass filter (10–500 Hz), and a 60 Hz notch filter (power line in Thailand). The raw EMG signal was first visually checked for electrocardiac artifacts. The raw EMG signal was processed with fast Fourier transformation to determine the median frequency (MDF) value (Hz). The decrease in the MDF of the EMG signal was taken as an indirect measure of muscle fatigue [6]. Trunk muscle fatigue was collected by Researcher P.S.

## 2.4.3. Pain Rating Scale

Pain intensity was assessed using an 11-point numerical rating scale (0–10 NRS). Subjective measures of pain were obtained from the NRS, employed to assess pain on a scale ranging from 0 (no pain) to 10 (worst possible pain) [31,39]. This outcome measurement was evaluated by Researcher T.C.

## 2.4.4. Functional Disability

The Roland Morris disability questionnaire (RMDQ) Thai version was used to assess functional disability due to low back pain [40]. This questionnaire includes 24 items [41], which were rated by Researcher T.C.

## *2.5. Procedure*

The flowchart of the current study is presented in Figure 2. Thirty-three participants were recruited from the advertisements. After the screening process, 30 participants were included in the study. Three participants were excluded due to experiencing low back pain >7 based on the NRS. Thirty participants meeting the inclusion criteria were asked to visit the research laboratory.

**Figure 2.** Flowchart of the study.

On the first day, the participants were familiarized with the stature change measurement. These involved participants practiced stepping in and out of the stadiometer until a standard deviation (SD) of <0.5 mm was achieved over ten repeated stature measurements [12,42]. Then, they were asked about their disability score (RMDQ) and pain intensity at rest (NRS).

On the second day, all participants arrived within an hour of waking, between 8 and 10 a.m. [42,43] to avoid stature loss before the test trial. They were requested to sleep for at least 8 h each night before the days of the experiment [44]. They were asked to undertake normal activities of daily living, refrain from vigorous physical activities, and refrain from alcohol consumption for 24 h before the experimental sessions [45]. After the application of surface electrodes, the participants were asked to maintain the Fowler's position (lying) for 20 min to eliminate any abnormal spinal loading that may have been present before arrival [34,45]. Then, they were asked to sit for 41 min, and the outcome measurements were collected, as shown in Figure 3.

**Figure 3.** Overview of the prolonged sitting condition. The upright sitting periods (back zone) are presented below the axis. Arrows illustrate times of outcome measurement: stature change, pain intensity, and muscle fatigue. Participants sit in the seated stadiometer and perform the upright sitting three times (at 12–13, 25–26, and 38–39 min) throughout the prolonged sitting time of 41 min. Stature change measurements are collected at Tsit (10–12 min), T1 (13–15 min), T2 (26–28 min), T3 (39–41 min). Pain intensity measurements are collected at T1 (13–15 min), T2 (26–28 min), and T3 (39–41 min). Muscle fatigue measurements are collected at 0–10, 15–25, and 28–38 min.

Next, the participants were asked to practice CSE using the ADIM technique with the researcher R.P. When they could perform this correctly, they were required to exercise CSE with ADIM for 5 weeks. After 5 weeks of training, all participants were asked to stop their exercises completely. The outcomes were re-measured on the first day after the 5-week CSE with the ADIM technique.

## *2.6. Prolonged Sitting Condition*

An overview of the prolonged sitting condition, with time points and their outcome measurements, is shown in Figure 3. Participants sat in the seated stadiometer, according to conditions described in 2.4.1, with the Digimatic Indicator in contact with the skull apex, marked by a waterproof pen. During the measurements, the participants remained in the same posture without speaking. To reduce errors in the spinal change measurements due to involuntary movements and slight differences in the breathing phase, all measurements were taken at the end of the expiration phase of the breathing cycle [35,46]. Each measurement set, consisting of 75 data points sampled over 15 s, was considered at time 0 and at the end of a 2 min interval, which reduced the effect of variations in the stature change assessment due to both breathing patterns and uncontrolled movements [12,34,35].

A baseline stature measurement set was recorded (T0). During the test trials, the participants remained in a freestyle sitting position, which did not require a straight back, without a backrest for 10 min. Then, the stature change (Tsit) in the participants was measured to be used as a normalized value. Next, the participant was asked to sit upright for 1 min. The stature change and pain intensity were measured at the end of each session (T1 [at 13–15 min], T2 [at 26–28 min], and T3 [at 39–41 min]). The raw sEMG signal was processed using the triangle-Bartlett method of fast Fourier transformation to determine the median frequency (MDF) value. The sEMG data were retrieved every 10 min block of sEMG data from the 41 min sitting period (at 0–10, 15–25, and 28–38 min) for analysis, Figure 3. The total time for each test trial was 41 min. Participants were not allowed to stand during the test trials.

## *2.7. Core Stabilization Exercise (CSE) with ADIM Technique*

The exercise program was supervised by a physical therapist with 30 years of experience (RP). This exercise program was modified from Puntumetakul et al. [28]. The details of the CSE each week are appended (Table A1). Researcher R.P. trained this exercise to all participants face-to-face in a 20 min session. The participants were re-assessed with researcher R.P. twice a week for 5 weeks at the laboratory to determine whether they could successfully perform the previous exercise. As the CSE with the ADIM technique was a milestone exercise, if the participants failed to perform the previous exercise accurately, they were retrained in the previous exercise until they succeeded. The participants were required to perform a daily set of home exercises of the same level, position, and frequency as those demonstrated during the exercise session with the physical therapist. The participants were asked to record in their logbook a daily home exercise program, including position, duration, and frequency of the exercise, as well as a record of their drug and alternative treatment throughout the study period and any adverse effects of the exercise. During the exercise, one of the researchers contacted the participants by telephone every week to motivate them to continue their daily home exercises. After 5 weeks of training, all participants were asked to stop their exercises completely.

## *2.8. Data Analysis*

The mean and standard deviation (SD) were used to assess participants' demographics and data of stature change at each time of measurement and were calculated from the reference point of Tsit. Differences in stature within a condition were assessed using a one-way repeated measure ANOVA for time effect (T1, T2, and T3) with the Bonferroni post-hoc analysis (significant at *p* < 0.017; 0.05/3).

The differences in trunk muscle fatigue and pain intensity within the condition for non-normally distributed data were analyzed using the Friedman test, and post hoc tests were conducted using Wilcoxon signed-rank tests. A significance level was set at *p* < 0.05 for trunk muscle fatigue and pain intensity.

Data comparisons before and after the first day of the 5-week CSE regarding pain at rest and functional disability were analyzed using the paired *t*-test (*p* < 0.05). During the prolonged sitting condition, the data comparison before and after the first day of 5 weeks of the exercise on stature change was analyzed using the paired *t*-test (*p* < 0.05). Further, the pain intensity and trunk muscle fatigue were analyzed using Wilcoxon signed-rank tests (*p* < 0.05).

All analyses were performed using SPSS version 19.0 software (SPSS Inc., Chicago, IL, USA). The Shapiro–Wilk test was performed to check the data distribution.

## **3. Results**

## *3.1. Participant Characteristics*

All participants achieved the preferred level of repeatability for the stature change measurements (SD ≤ 0.5 mm). The participants reported that they did not use any drug for reducing their low back pain and had no adverse repercussions of the exercise throughout the 5 weeks of the training. The demographic data and clinical characteristics are presented in Table 1.

**Table 1.** Demographic characteristics of participants.

## *3.2. Pain Intensity at Rest and Functional Disability*

The results showed a significant reduction in pain intensity (mean difference: 2.14 ± 1.50 (95% CI: 1.57 to 2.69) at *p* < 0.001) and functional disability (mean difference: 2.33 ± 1.81 (95% CI: 1.66 to 3.01) at *p* < 0.001) between baseline and the 5-week CSE with the ADIM, as shown in Figures 4 and 5, respectively.

**Figure 4.** Mean and standard deviation (SD) of pain intensity from baseline to 5 weeks of CSE (\*\* *p* < 0.001).

**Figure 5.** Mean and standard deviation (SD) of functional disability from baseline to 5 weeks of CSE (\*\* *p* < 0.001).

#### *3.3. Stature Changes during Sitting*

The stature changes during sitting before and after CSE are shown in Table 2. The result of stature change after sitting for 10 min (Tsit) showed no significant differences between baseline and after the 5-week CSE with the ADIM technique (*p* = 0.458). This result indicates that Tsit between baseline and after the 5-week CSE with the ADIM technique was comparable and could be used as a reference point for the stature changes calculation at T1, T2, and T3.

**Table 2.** The stature change during sitting before and after CSE with ADIM.

Note: Data presented as Mean ± standard deviation (SD), Tsit = after sitting for 10 min, T1 = 13th–15th min, T2 = 26th–28th min, T3 = 39th–41st min, f = significant difference from T1, g = significant difference from T2, h = significant difference from T3 (\* significant difference at *p*-value < 0.008, \*\* significant difference at *p*-value < 0.001).

At baseline, the results of the current study illustrated that the baseline showed a significant reduction in stature due to time (T1, T2, and T3) (*p* < 0.017). In the same pattern, the results illustrated that after the 5-week CSE with the ADIM technique, there was a significant reduction in stature due to time.

Comparing baseline and the 5-week CSE with the ADIM technique, the stature changes at T1, T2, and T3 were significantly improved in the 5-week CSE with the ADIM technique (Table 2).

## *3.4. Pain Intensity during Sitting*

At baseline, the pain intensity of T2 and T3 were significantly increased compared to other time of measurements (Tsit and T1). After the 5-week CSE with the ADIM technique, the pain intensity of T2 (*p* < 0.05) and T3 (*p* < 0.001) was significantly increased from Tsit. At T3, pain intensity was significantly increased from T1 and T2 (*p* < 0.05).

Comparing baseline and the 5-week CSE with the ADIM technique, the pain intensity at T1, T2, and T3 was significantly decreased in the 5-week CSE with the ADIM technique, as shown in Table 3.

**Table 3.** Comparisons pain intensity during sitting before and after received CSE with ADIM.

Note: Data presented as Median (interquartile range), *p*-value from the Friedman test, \*\* significant difference at *p*-value < 0.001, \* significant difference at *p*-value < 0.05 by the Wilcoxon signed-rank test), Tsit = after sitting for 10 min, T1 = 13th–15th min, T2 = 26th–28th min, T3 = 39th–41st min, e = significant difference from Tsit, f = significant difference from T1, g = significant difference from T2, h = significant difference from T3.

## *3.5. Trunk Muscle Fatigue during the Experiment*

At baseline, the MDF in the sitting condition is shown in Table 4. The Friedman test revealed a significant difference in the MDF values in the muscles and both sides at each time of measurement during prolonged sitting. The Wilcoxon signed-rank tests showed a significant difference between the measurement times. For both sides of the TrA and IO muscles, the MDF value at the 15th–25th min was significantly decreased compared to that of the 0–10th min. A further reduction in the MDF value was observed at the 28th–38th min.

**Table 4.** Comparison muscle fatigue between before and after performed CSM with ADIM exercise during prolong sitting.

**Table 4.** *Cont.*

Note: Data presented as Median (interquartile range). Significant difference at \* *p*-value < 0.05, \*\* *p*-value < 0.001 by Wilcoxon signed-rank test. a = significant difference from 0–10th, b = significant difference from 15th–25th, c = significant difference from 28th–38th.

After the 5-week CSE with the ADIM technique, the Friedman test did not reveal a significant difference in the MDF value in trunk muscles and both sides at all times of measurement. Compared with baseline values, the 5-week CSE with the ADIM technique showed a significant improvement in the MDF values (both sides of TrA and IO, the MDF value of 15th–25th and 28th–38th min).

### **4. Discussion**