Next Article in Journal
Pharmacological Properties of Chemically Characterized Extracts from Mastic Tree: In Vitro and In Silico Assays
Next Article in Special Issue
Comparison of Smoothness, Movement Speed and Trajectory during Reaching Movements in Real and Virtual Spaces Using a Head-Mounted Display
Previous Article in Journal
Repeated Glutathione Sodium Salt Infusion May Counteract Contrast-Associated Acute Kidney Injury Occurrence in ST-Elevation Myocardial Infarction Patients Undergoing Primary PCI: A Randomized Subgroup Analysis of the GSH 2014 Trial
Previous Article in Special Issue
Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Life
Volume 13
Issue 6
10.3390/life13061392
life-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Creation and Evaluation of a Severity Classification of Hyperkyphosis and Hypolordosis for Exercise Therapy

by
David Kaps
1,2,3,†,
Hannah L. Siebers
2,*,†,
Ulrich Betz
4,
Daniel Pfirrmann
3,
Jörg Eschweiler
2,
Frank Hildebrand
2,
Marcel Betsch
5,
Janine Huthwelker
4,
Claudia Wolf
4,
Philipp Drees
6 and
Jürgen Konradi
4
1
Center for Mental Health, Hospital Stuttgart-Bad Cannstatt Hospital, 70374 Stuttgart, Germany
2
Department of Orthopaedics, Trauma and Reconstructive Surgery, Uniklinik RWTH Aachen, 52074 Aachen, Germany
3
Institute of Social Science, Media, and Sports, Johannes Gutenberg-University Mainz, 55128 Mainz, Germany
4
Institute of Physical Therapy, Prevention and Rehabilitation (IPTPR), University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
5
Department of Orthopedics and Trauma Surgery, University Hospital Erlangen of the University Erlangen-Nürnberg, 91054 Erlangen, Germany
6
Department of Orthopedics and Trauma Surgery, University Medical Center of the Johannes Gutenberg University Mainz, 55131 Mainz, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Life 2023, 13(6), 1392; https://doi.org/10.3390/life13061392
Submission received: 19 April 2023 / Revised: 30 May 2023 / Accepted: 6 June 2023 / Published: 14 June 2023
(This article belongs to the Special Issue Physical Therapy and Rehabilitation Support of the Elderly and People with Disabilities)
Browse Figures
Versions Notes

Abstract

:
The rise in the occurrence of musculoskeletal disorders, such as thoracic hyperkyphosis (THK) or lumbar hypolordosis (LHL), is a result of demographic changes. Exercise therapy is an effective approach that can reduce related disabilities and costs. To ensure successful therapy, an individualized exercise program adapted to the severity of the disorder is expedient. Nevertheless, appropriate classification systems are scarce. This project aimed to develop and evaluate a severity classification focused on exercise therapy for patients with THK or LHL. A multilevel severity classification was developed and evaluated by means of an online survey. Reference limits of spinal shape angles were established by data from video rasterstereography of 201 healthy participants. A mean kyphosis angle of 50.03° and an average lordosis angle of 40.72° were calculated as healthy references. The strength of the multilevel classification consisting of the combination of subjective pain and objective spinal shape factors was confirmed by the survey (70% agreement). In particular, the included pain parameters were considered relevant by 78% of the experts. Even though the results of the survey provide important evidence for further analyses and optimization options of the classification system, the current version is still acceptable as therapeutic support.

1. Introduction

Musculoskeletal disorders (MSKDs) are associated with multiple impairments of daily life and different health consequences like falls or mental health limitations [1,2,3,4]. Included, lower back pain is the leading cause of disability worldwide [5,6]. Here, thoracic hyperkyphosis (THK) and lumbar hypolordosis (LHL) or flatback are common causes of lower back pain [7,8]. In addition, THK and LHL can be sequelae or symptoms of other underlying conditions beyond back pain. A significant increase can be seen in the number of people with these two MSKDs [8,9,10,11,12]. This ongoing development is the result of current issues, including demographic change or an inactive everyday life with one-sided strains [8,13,14,15,16,17,18]. However, exact prevalence values are not documented. Population-based estimates for the older population share assume a prevalence of between 20% and 40% for THKs [8]. For LHL, an age-standardized point prevalence of 9.4% can be assumed based on the close association with low back pain and the prevalence of 8.4% of other MSKDs [5,7,12].
Nevertheless, some measures promise to have a lasting positive influence on the course of the disorders [10,11,14,19]. One of them is therapeutic rehabilitation exercise [19,20,21,22,23]. Study results showed that specific exercise programs can reduce subjectively perceived pain [21,22]. Furthermore, the multifactorial induced quality of life (QOL) can be promoted by physical activity [21,24]. Meaningful evidence of exercise therapeutic methods that change the objectively measured curvature angle of the spine is not yet available [14,25,26]. For a successful treatment, it is thus even more important to positively modify the patient-specific accessory symptoms together with negative influencing factors [10,27,28]. Consequently, the therapist’s task is to create an individually tailored rehabilitation plan based on these corresponding characteristics [10,27,28]. However, a challenge here is the increasing demand for rehabilitation measures in combination with the number of trained therapists [18,29,30,31]. Therefore, it is crucial to find a solution that takes into account the individuality of each patient, as well as to counteract the personnel and structural weaknesses in exercise therapy.
Severity classifications are based on the aforementioned aspects [32,33,34,35]. These enable assigning patients to symptom-specific subgroups in an uncomplicated but still diagnostically comprehensive and resource-saving manner [28,33,35,36]. Therapeutic decision-making processes for optimal treatment can thus be facilitated and suitable exercises or entire exercise programs for exercise therapy can be designed [28,33,35]. However appropriate severity classifications established for spinal deformities are mainly aimed at patients with deformities centered in the coronal plane, such as those with scoliosis, and are mostly oriented toward surgical interventions [28,32,37,38]. Apart from accepted severity classifications for patients with THKs or LHLs, there is a fundamental lack of generally confirmed objective reference limits or intervals [8,10,28,39,40]. Only approximate values are present [8,10,11,38,39,40,41]. For thoracic kyphosis (TK) values, below 20° and above 60° are usually considered objectively pathological [8,11,37,38]. The limit values for a measurable physiological lumbar lordosis (LL) are described as 20° to 80° [11,37,38,40,42]. However, all these values are primarily based on radiological measurements, are broadly defined, vary between individual research projects, and mainly differentiate between physiological and pathological curvatures [8,37,38,40,41,42].
Beyond the striking spine shape, the concerned persons also show varied levels of, for example, pain during everyday movements and related lower mobility [8,11,14,43,44]. In addition, the patients have lower values in terms of social life and mental aspects compared with healthy control groups [11,14,44]. Despite these disparities, the existing classifications use the measurable spine shape as the single or central parameter [28,32]. However, such a method is not sufficient, even for exercise therapeutic rehabilitation [10,11,28]. Moreover, given largely invariable objective factors that lead to differences in physiological spine shape [8,10,28,41]. Accordingly, even in healthy persons, variations are common due to age, gender, ethnicity, or body stature [8,10,28,41]. Therefore, a more comprehensive, multilevel concept of severity classification is of considerable interest [28,32,39]. The patient’s individuality, as well as physical and psychological resources, are key factors [28,32,39,41].
The aim of this project was thus to develop and evaluate a severity classification scheme for THK and LHL patients, especially for exercise therapeutic rehabilitation. Using the methodological approach and the conception of a multilevel severity classification based on subjective pain grades and objectively measured spine shape parameters, a further aim was to obtain approval for the developed classification from experts in the context of a survey.

2. Materials and Methods

2.1. Collection and Methodical Implementation of Video Rasterstereography Data

Objectively measurable spinal shape indicators were considered as one component of the severity classification. The applied data were obtained from a framework project aimed at establishing a normative reference data pool for spine shapes [45]. The corresponding study was approved by the responsible ethics committee of the Medical Association of Rhineland-Palatinate (837.194.16) and is registered with the World Health Organization (WHO) (INT: DRKS00010834). Participants were recruited consecutively after media announcements according to clearly defined and exhaustive inclusion and exclusion criteria [45,46]. Based on these, participants should be primarily healthy, pain-free, and between the ages of 18–70 years. In addition, paresis and paresthesia, previous pelvic or spinal surgery, use of walking aids, and impaired motor control were deemed exclusion criteria. Each participant’s body mass index (BMI) should be less than 30 kg per meter squared. These features were checked with a standardized interview, as well as the International Physical Activity Questionnaire and appropriate mobility tests, with the Timed “Up & Go”, the Two-Minute Walk Test, the Back Performance Scale, and the Range of Motion Test amongst them [45,46]. Following the precise guidelines, 201 (132 females, 69 males) subjects were recruited and analyzed. All of them gave oral and written informed consent.
The established and validated DIERS formetric III 4D™, DICAM v.3.7.1.7 analyzing system (DIERS International GmbH, Schlangenbad, Germany) [47,48] was used to measure the spine (Figure 1).
This system operates with video rasterstereography (VRS), which is based on the physical principle of triangulation [47,48]. It projects structured light onto the textile-free back of the test person (Figure 1). The inserted camera unit records the reflected light with a frequency of 60 Hz. The related software analyzes the surface topography using up to 150,000 measurement points (depending on body size). With this information about the surface curvature of the back and the anatomical landmarks identified independently at the same time, a 3D model of the spine was constructed. The model included the corresponding 3D position data for each segment of the spine, starting at the spinous process of C7 and ending at the pelvis. Corresponding parameters to describe the spine were automatically calculated and presented [45,46,47,48,49]. To further increase the measurement validity, distinctive anatomical landmarks of the back (e.g., C7, the left and right posterior superior iliac spine (PSIS), and the spine shape approximately at the level of T3 and T12) were additionally marked with infrared reflective stickers for detection by the formetric system. After the data acquisition, all measurements were visually inspected by two experienced researchers. The calculations of all TK and LL values were performed by considering fixed surface tangents (Figure 2). The TK was calculated from the angle of the tangents of ICT (inflexion point cervical-thoracic) and ITL (inflexion point thoracic-lumbar). The angle of the tangents of ITL and ILS (inflexion point lumbar-sacral) gives the degree of LL. Twelve frames were recorded for each stance measurement. Thus, twelve values were available per subject for each spine parameter [46].
The associated twelve individual values of each participant were averaged to obtain a mean parameter for each subject. Statistical figures calculated from these curvature angles were applied to establish the reference intervals. The approach utilized for this purpose by using means and their standard deviations (SDs) or percentiles is a common method [39,40,41]. Accordingly, individuals with TK or LL between the mean and ±1 SD were assigned as normative healthy references [40,41]. All values between ±1 and ±2 SDs indicated an increased sagittal curvature. Corresponding angles above or below ±2 SDs were classified as THK or LHL.

2.2. Evaluation of the Severity Classification

Due to various advantages, an online questionnaire was selected as the central measuring instrument for evaluating the severity classification [50]. It was developed using the open-source survey software “LimeSurvey” by LimeSurvey GmbH [51]. Expert therapists were selected as addressees. Initially, the decisive inclusion criteria were their qualifications and the field of activity. At the beginning of the survey, each participant was explicitly informed that their data would be treated anonymously and that the survey was voluntary.
Since the evaluation of the severity classification was a sub-project to evaluate a rehabilitation product, the complete questionnaire contained 35 questions. Of these, only nine were related to the current research topic and 12 to socio-demographic content. The assessment was performed using unipolar, discretely graded verbal rating scales with positive item polarity. Additional free text fields permitted personal additions.
After a pretest, the survey started on 12 December 2019 and ended on 2 February 2020.

2.3. Data Processing and Statistical Analysis

Before all statistical steps, the corresponding data of the VRS measurements (TK and LL) and the survey were anonymized. All following statistical procedures were performed using IBM® SPSS Statistics software (version 23, v. 25, IBM Cooperation, Chicago, IL, USA). The presence of a normal distribution was determined based on the Shapiro–Wilk test with a level of significance of α = 0.05.
Indicators and graphs of descriptive and exploratory statistics were used when working with the VRS data and the survey findings. Location and dispersion, as well as frequency and distribution of the data, could thus be examined.
In the case of the VRS data, these characteristics were used to establish the reference limits and finally elaborate the severity classification via the outlined methodology.
When analyzing the survey results, initially, individual participants outside the inclusion criteria were ruled out. Only partially completed questionnaires without research-relevant information were excluded from all further analyses. In addition, the participants involved were divided into three expert groups (“moderately experienced experts”, “experienced experts”, or “very experienced experts”) and two age groups (≤37 years and ≥38 years) for supplementary investigations. The two age groups were recommended due to the bimodal distribution of ages. Appropriate group comparisons were examined using descriptive methods. The severity-specific answers were coded numerically. Unanswered questions were marked as missing. The highest degree of agreement always received a value of “1”, while the strongest rejection always received a value of “4”. The non-contextual statement “I cannot judge that” was coded as the number “5”. In evaluations focusing on expert groups or age groups, the respective non-context-based answer options were removed for better comparability.
To precisely determine the mean participant rating, the more meaningful arithmetic mean ( x − ) was recommended instead of the median given the limited number of response categories of the rating scales. For these calculations, the 5-coded non-context response option was previously defined as a missing value. Therefore, means between one and four were possible. The interpretation of these values was consequently determined by the coding used. Irrespective of this, all figures were rounded. The qualitative results of the free text fields were examined individually and compiled in a separate file (Table A8).

3. Results

3.1. Evaluation of the Video Rasterstereographic Reference Data

Within the scope of the statistical tests, two average TK angles and one average LL angle could be identified as outliers via boxplots. The concerning subjects and their mean values were removed from the data set in order not to falsify subsequent analyses. The histograms and the Shapiro–Wilk test (TK: p = 0.747, LL: p = 0.659) confirmed a normal distribution of the measured values. The statistical analysis included 199 kyphosis angles and 200 lordosis angles. The average kyphosis angle was 50.03° (standard error: 0.58°). The 95% confidence interval of the mean value ranged from 48.89° to 51.17°. For the lordosis angle, the mean value was 40.72° (standard error: 0.63°). The lower limit of the 95% confidence interval of the mean value was 39.47° and the upper limit was 41.98°. Table A1 lists all associated statistical parameters for the two focused spinal angles of the included subjects.

3.2. Development of Severity Classification

For the severity classification, distinctive subjective and objective parameters were chosen, the VRS data (TK and LL) were analyzed, and the final score was generated.
Thus, the kyphosis or lordosis angle with the described measurement technique was selected as the first parameter for the multilevel severity classification (Figure 2).
To the objective spinal angles, a second parameter was added. Subjective pain perception in combination with pain-related impairments was found to be a suitable variable.
The Chronic Pain Grade (CPG) questionnaire was used as the measurement instrument in this regard [34]. It provides a tool that has been tested on patients similar to the target group, includes the desired pain parameters, defines established limit values, and offers adequate quality at the same time [52,53,54,55].
For the focused severity classification, the pain graduation of CPG and the maximum video rasterstereographic spinal angles were combined in one scheme. The extent of the disease is therefore graded using a scoring system. Both the previously defined reference intervals of the VRS data evaluation and the graduation of CPG according to Korff et al. [34] were assigned numbers for this purpose. When added up, these result in a sum between one and six. The obtained value ultimately corresponds to one of five severities, which can be taken from the classification system via the total score (Figure 3). Since initial objective proof for one of the two pathologies has to be available first, individuals with an asymptomatic TK or LL were not included in the classification system. The respective values of the interval between the mean and ±1 SD are therefore not part of the graduation.

3.3. Sample Description of the Online Survey

Of the 108 questionnaire returns in total, 27 female and 19 male experts commented on the severity-relevant topics, as well as two further participants without specifying their gender. Their average age was 38 (SD ± 13.47) years, whereas two individuals (4%) did not provide any details. Thus, 24 participants (50%) were classified in the younger age group (≤37) and 22 (46%) in the older group (≥38). The most represented occupational groups were physio/exercise and sports therapists (63%, n = 30). With 28 votes (58%), the majority of the interviewees reported that they had already been working for more than six years in exercise therapy. Based on these and other socio-demographic data, 15 persons each (31%) could be categorized as moderately experienced and experienced experts and 14 (29%) as very experienced experts. The remaining four participants (4%) could not be assigned to any expert group due to partially missing information.

3.4. Evaluation of the Severity Classification

The relevance of the spine shape as part of a classification system for patients with THK or LHL to choose the appropriate exercise therapeutic measure received lower ratings than the pain parameters (Table 1). The mean evaluation of the curvature angle ( x − : 1.98, SD: 0.80, n = 40) and the pain parameters ( x − : 1.35, SD: 0.70, n = 40) demonstrate this.
Results regarding the use of a severity classification that does not apply only one parameter to develop exercise therapeutic rehabilitation programs for patients with THK or LHL ( x − : 2.00, SD: 0.98, n = 32), the differentiation of five severity degrees in each case (THKs: x − : 1.96, SD: 0.98, n = 25 and LHLs: x − : 1.92, SD: 0.95, n = 25), and the developed severity classification ( x − : 1.72, SD: 0.92, n = 29) are shown in Table 2.
In an additional answer-linked follow-up question to an alternative severity number for patients with THK, one participant recommended one degree of severity, another recommended more than five severities, and yet another person could not assess it. Seven participants (47%) advised a two-to-four-level concept. On the other hand, five respondents (33%) favored a completely different type of classification. In the scheme for LHLs, the feedback was almost identical. Only a single vote less was cast, meaning that the number of one degree of severity was not supported. The answers to the multiple-choice follow-up question on the developed severity classification according to alternative or supplementary severity components are shown in Table 3.
To the classification components previously covered, five experts added “ADL” (activities of daily living), impairment of respiration and dynamics of the internal organs, spinal mobility, compliance, and the ability to perform active posture correction as potential factors in an optional free text field.
Among the age groups, it was particularly evident that older survey participants were strikingly more likely to agree completely with both a multidimensional severity classification in general for determining the intensity of exertion and the designed classification. Categorized by expert status, it was noticeable that the very experienced experts most often agreed completely with the developed severity classification. Further detailed group characteristics can be found in Appendix B Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7.
In turn, Table A8 documents the comments of the cross-topic free text field of the last survey page. These largely focus on the individuality of each patient and the problem of capturing this uniqueness in a general scheme.

4. Discussion

While research and treatment of scoliotic spinal deformities are well advanced, there is conspicuously less accepted knowledge about THKs and LHLs [8,10,32,40,56]. However, current trends, living conditions, behavioral patterns, or even social processes require effective and economic measures to be able to help affected persons appropriately [8,15,16,17,18,29,30,31]. In order to support not only the patients but also the practitioners, and to close research gaps, it was possible to develop and evaluate an appropriate severity classification. Based on a normative reference VRS dataset of the spine with the resulting objective curvature angles, combined with subjective pain parameters, a suitable scheme for the exercise therapeutic rehabilitation of patients with THK or LHL was established. This scheme takes into account both the individual symptoms and various complaints, as well as an adequate application of physical and psychological resources.
As research has already shown and the survey was able to confirm, the composition of the fitting variables was a central challenge. On the one hand, objective easy-to-measure parameters are of interest, while on the other hand, comparable angles of spine shape differ fundamentally in terms of impairment or treatment [10,28]. Accordingly, patients with an objective deformity can remain asymptomatic, while other patients with similar spinal column findings show clear adverse health consequences [10,28]. Particularly in adult patients, further disease characteristics besides the objective curvature are decisive due to the unique physiology of the spine [10,11,28]. Measuring a complex set of objective parameters would provide the desired comprehensive health status. However, this would be time-consuming and therefore less effective. Ames et al. [11] also supported a more individualized view of the patient, not least in order to incorporate disease-specific measures and personal preferences. Long-term adherence and finally a successful therapy are important aspects that consequently benefit from this [57,58,59]. The focus was hence on a combination of two parameters. On the one side, the non-invasively measured maximum curvature angles as the objective parameters. On the other side, the pain parameter is included in the assessment of the individual disability. Pain perception is an appropriate component for several reasons: the relationship between pain and spinal deformities has been well studied, pain can comprehensively describe an individual’s health status, and pain perception represents a personal state of mind that cannot be determined by one or more additional objective parameters [10,11,28,32,44,60,61]. For the pain parameter, pain-related impairments were included in conjunction with subjectively perceived pain since pain should not be measured solely based on its intensity [61]. The CPG questionnaire chosen for this purpose also enables recording pain persistence as a further strength [34]. Combined with the spinal angle, it is consequently possible to assess the broad scope of the deformity in a precise and comprehensive, yet time- and cost-saving, manner [10,11,32,61]. Simultaneously, patient-friendly handling is ensured [10,32].
Moreover, concerning the documented limited or unclear effectiveness of exercise therapy methods exclusively based on the spine shape, the integration of pain components presents a supplementary advantage [14,25,26]. Consequently, if the severity classification were based purely on the measurable curvature, the severity of a patient would change little or not at all over time. A rehabilitation program oriented toward the severities would therefore not vary either. The one-sidedness thus caused and the therapy-impairing lack of motivation encouraging low adherence would be disadvantageous consequences [57,58,59]. However, with the augmented pain variables, a diversified, severity-based treatment is possible, counteracting such negative effects.
The majority of the participating experts in the online survey also considered a severity classification consisting of several components to be relevant for the rehabilitative treatment of THKs and LHLs. The specifically designed scheme was also considered to be effective. Even those respondents who were even more critical in their initial separate assessment of the curvature angle were largely convinced by the combination with the pain parameters of the designed multilevel severity classification. The significance of the components utilized was additionally confirmed by the answers to the multiple-choice follow-up question on the developed severity classification according to alternative or supplementary severity elements (Table 3).
As the survey results indicate, the selected variables received the most support, but age and BMI were also considered significant. In addition to age and BMI, in the literature, gender; mental abilities; the occurrence of neurological symptoms; osteoporosis; and physical functions, such as spinal mobility, were explicitly highlighted [10,15,28,32,62,63]. To influence self-confidence, treatment motivation, and further psychological issues positively, aspects that do not follow a purely deficit-oriented methodology should be used, which involves reflecting the patient’s strengths, including the performance state to be recorded [58,59]. However, the inclusion of all possible variables without exception would be too extensive and would result in an extremely complex and elaborate classification. Nevertheless, it is also still unclear which criteria should be applied to effectively complete the variables used [10,32,62]. The lack of a distinct scientific consensus on the classification of patients with THK or LHL to design a generally accepted severity classification contributes significantly to this problem. Additionally, in the last free text field, the survey participants discussed the complexity of generalizing individual symptoms in a classification system (Table A8). Regardless of the current ambiguities and challenges, it will be crucial, especially for exercise therapy, to regularly determine the severity to assess the current condition and to adjust the exercises (Table A8).
Apart from the essential parameters, no binding scheme could be defined based on the survey results regarding the exact number of severities. Furthermore, the findings demonstrated the optimizable state of research concerning multilevel concepts compared with the more widespread allocations with explicitly fewer gradations. This was primarily demonstrated by the relatively high number of non-judgeable answers. According to the statements on an alternative severity number; however, it can be concluded that classification into only physiological and pathological TKs or LLs is inadequate. Naresh-Babu et al. [28] arrived at a similar conclusion. They considered several clinical–radiological subgroups in spinal deformities as being crucial [28]. On the other hand, more than five severities were also rejected by the survey participants. A differentiation of physiological and two to five pathological spinal column characteristics can thus be seen as the most practical. The defined premise for the presence of one of the two deformities and hence for grading by the classification system initially via an objectively pathological diagnosis is, however, unambiguous [10,11,32]. The physiological interval marked in this context between the mean and ±1 SD is also documented [40,41].
The significance of the results should also be judged regarding the sufficiently reliable and valid VRS technology used [47,48]. The general applicability was shown [47,48]. However, there are also results that show deviations that still need to be considered, especially for the measurement of LL [64]. X-rays in conjunction with the modified Cobb technique remain the gold standard in the diagnostic imaging of sagittal spinal deformities [13,65]. Although the innovative VRS method is thus not generally established, it offers further advantages in addition to the abovementioned limitations and strengths [47,48,66,67]. Due to the three-dimensional, non-invasive, high-resolution, and radiation- and contact-free approach, which can be carried out quickly, the procedure will be a common part of diagnostics in the future [48,66]. This is especially true for the follow-up examinations that are common for many patients with spinal deformities [66,67]. The resulting widespread usage will require corresponding classifications, such as the elaborated severity classification. Simultaneously, the existing scheme can be easily adapted for radiological-based treatments utilizing data from an appropriate reference population. Major facilitation with this process and further topics involve the acceptable correlation of the VRS angle with the angle according to Cobb [66]. Thus, fundamental transfers and comparisons are a priori possible. The decisive strengths of this research can be seen in the initial individual consideration of the patients through the combination of objective and subjective parameters, the advanced VRS technology, and the methodically structured online questionnaire.
However, the limitations of this study should be kept in mind since the number of respondents could have produced representativeness issues. Although the precisely defined target group of experts and the totality of the responses permitted an adequate interpretation of the results, the absolute number of data also had an impact on the further subdivision into age and expert groups. Thus, it was possible to identify divergent viewpoints for certain subgroups on some questions (Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7), but it was not possible to identify clear opinion tendencies that were constant across all questions due to this state of affairs. It is also important to be aware of the partially unclear reasons that led to the selection of the non-context response category. Less a limitation than a future field of research arose from the values of symptom-free reference individuals used for severity classification. Even if it is normal to select healthy reference individuals, the reference intervals of the severity classification based on them should be assessed. Furthermore, concerning the reference individuals, the ratio between male (n = 69) and female (n = 132) subjects should be noted. As was previously shown, gender has an influence on the spinal anatomy [15,28]. Thus, the unequal distribution may have affected the statistical parameters evaluated. Together with the complementary variables just mentioned, as well as the further optimization and adaptation possibilities considered, practical studies on patients will be required. Thus, the applicability can be tested, and a need-based and long-term successful rehabilitation can be ensured not only for people with THK or LHL but also for various other MSKDs. In addition, the next crucial step will be to connect the different severities with appropriate rehabilitative exercise recommendations [28]. The supplementary concept designed for this purpose and the results, which were equally recorded with the survey, were found to be a suitable basis for continuing research projects. Independently of these findings, a general issue needs to be faced. Accordingly, it is advisable to conduct more multidisciplinary research on spinal deformities in the sagittal plane, aside from scoliosis, which has been the focus of the scientific literature. If the corresponding knowledge gaps are closed, the insights gained can additionally be used to optimize the current severity classification into an accepted classification system.

5. Conclusions

During the course of this research, it was possible to establish reference limits for TK and LL using the latest measurement technology via VRS data. With these, an individual-related scheme could be developed for patients with THK or LHL, which makes it possible to determine the severity of the respective disease for exercise therapeutic rehabilitation by means of objective spine shape angles and subjective pain parameters. The subsequent online evaluation demonstrated that the classification system has the potential to save time and costs, relieve therapists in exercise therapy, counteract personnel shortages, and guarantee rehabilitation patients a therapy that can be implemented flexibly.
However, a holistically sophisticated system, which includes all morphological body structures, as well as any personal circumstances and needs of the patients, is not feasible with the current scientific state of the art. This severity classification and its future optimizations will, nevertheless, be able to provide a basic therapy-supporting guideline for the decision-making processes of therapists and physicians on the way to the optimal treatment.

Author Contributions

Conceptualization, D.K., H.L.S. and J.K.; methodology, D.P. and J.K.; software, J.H.; investigation, D.K. and C.W.; resources, U.B. and F.H.; data curation, J.H. and D.K.; writing—original draft preparation, D.K. and H.L.S.; writing—review and editing, J.K. and C.W.; visualization, D.K.; supervision, J.K. and D.P.; project administration, J.E. and J.K.; funding acquisition, M.B. and P.D. All authors have read and agreed to the published version of the manuscript.

Funding

Parts of the comprehensive project were funded by the German Federal Ministry of Education and Research (BMBF 13GW0257). Meanwhile, the project was managed by DIERS International GmbH. The research work behind the study presented here, however, was not financially supported.

Institutional Review Board Statement

The VRS study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical Association of Rhineland-Palatinate (837.194.16 (10513) on the date of approval 20 July 2016).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The datasets generated and/or analyzed during the current study are not publicly available due these are part of other studies and contain personal values but are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Table A1. Statistical parameters of TK (ICT-ITL) and LL (ITL-ILS) of the included healthy subjects.
Table A1. Statistical parameters of TK (ICT-ITL) and LL (ITL-ILS) of the included healthy subjects.
Statistical ParameterSpinal Angle
Thoracic KyphosisLumbar Lordosis
Number of included subjects199200
Mean value50.03 (0.58)40.72 (0.63)
95% confidence interval of the mean valueLower limit48.8939.47
Upper limit51.1741.98
5% trimmed mean50.0740.71
Median50.5640.89
Variance66.9480.48
Standard deviation8.188.97
Minimum29.0918.06
Maximum69.2561.28
Range40.1643.22
Interquartile range11.1312.68
Skewness−0.04 (0.17)0.01 (0.17)
Kurtosis−0.31 (0.34)−0.35 (0.34)
TK: thoracic kyphosis. ICT: inflexion point cervical-thoracic. ITL: inflexion point thoracic-lumbar. LL: lumbar lordosis. ILS: inflexion point lumbar-sacral. The standard errors are given in brackets.

Appendix B

Table A2. Cross-tabulation to the evaluation of pain components as a training plan element by age groups.
Table A2. Cross-tabulation to the evaluation of pain components as a training plan element by age groups.
Age GroupEvaluation of Pain Components for Choosing Appropriate Exercise Therapeutic
Very RelevantPredominantly RelevantLess Relevant
Participants ≤ 37 years5% (1)-5% (1)
Participants ≥ 38 years28% (5)11% (2)-
Total15% (6)5% (2)3% (1)
Figures in brackets correspond to the number of participant votes.
Table A3. Cross-tabulation to the evaluation of a multidimensional severity classification in general for the determination of the exercise intensity by age groups.
Table A3. Cross-tabulation to the evaluation of a multidimensional severity classification in general for the determination of the exercise intensity by age groups.
Age GroupEvaluation of a Multidimensional Severity Classification in General
I AgreeI Predominantly AgreeI Agree
Less		I Do Not
Agree at All		Total
Participants ≤ 37 years27% (4)27% (4)40% (6)7% (1)100% (15)
Participants ≥ 38 years50% (8)44% (7)-6% (1)100% (16)
Total39% (12)35% (11)19% (6)6% (2)100% (31)
Figures in brackets correspond to the number of participant votes.
Table A4. Cross-tabulation to the evaluation of the developed severity classification by age groups.
Table A4. Cross-tabulation to the evaluation of the developed severity classification by age groups.
Age GroupEvaluation of the Developed Severity Classification
I AgreeI Predominantly AgreeI Agree
Less		I Do Not
Agree at All		Total
Participants ≤ 37 years43% (6)57% (8)--100% (14)
Participants ≥ 38 years60% (9)7% (1)20% (3)13% (2)100% (15)
Total52% (15)31% (9)10% (3)7% (2)100% (29)
Figures in brackets correspond to the number of participant votes.
Table A5. Cross-tabulation to the evaluation of pain components as a training plan element by expert groups.
Table A5. Cross-tabulation to the evaluation of pain components as a training plan element by expert groups.
Expert GroupCross-Tabulation to the Evaluation of Pain Components as a Training Plan Element by Expert Groups
Very
Relevant		Predominantly RelevantLess
Relevant		Not Relevant at AllTotal
Moderately experienced experts83% (10)17% (2)--100% (12)
Experienced experts83% (10)8.5% (1)8.5% (1)-100% (12)
Very experienced experts64% (9)22% (3)7% (1)7% (1)100% (14)
Total76% (29)16% (6)5% (2)3% (1)100% (38)
Figures in brackets correspond to the number of participant votes.
Table A6. Cross-tabulation to the evaluation of a multidimensional severity classification in general for the determination of the exercise intensity by expert groups.
Table A6. Cross-tabulation to the evaluation of a multidimensional severity classification in general for the determination of the exercise intensity by expert groups.
Expert GroupEvaluation of a Multidimensional Severity Classification in General
I AgreeI Predominantly AgreeI Agree
Less		I Do Not
Agree at All		Total
Moderately experienced experts11% (1)33% (3)33% (3)23% (2)100% (9)
Experienced experts60% (6)40% (4)--100% (10)
Very experienced experts33% (4)33% (4)25% (3)9% (1)100% (12)
Total35% (11)35% (11)20% (6)10% (3)100% (31)
Figures in brackets correspond to the number of participant votes.
Table A7. Cross-tabulation to the evaluation of the developed severity classification by expert groups.
Table A7. Cross-tabulation to the evaluation of the developed severity classification by expert groups.
Expert GroupEvaluation of the Developed Severity Classification
I AgreeI Predominantly AgreeI Agree
Less		I Do Not
Agree at All		Total
Moderately experienced experts43% (3)57% (4)--100% (7)
Experienced experts33% (3)33% (3)22% (2)11% (1)100% (9)
Very experienced experts67% (8)17% (2)8% (1)8% (1)100% (12)
Total50% (14)32% (9)11% (3)7% (2)100% (28)
Figures in brackets correspond to the number of participant votes.

Appendix C

Table A8. Translated literal supplementary comments of the participants from the last free-text field, sorted by topics.
Table A8. Translated literal supplementary comments of the participants from the last free-text field, sorted by topics.
IDComment
17Technical support in exercise therapy is highly desirable. However, clinical reasoning, the central therapeutic approach of physiotherapy, will not be technically feasible in the foreseeable future. Therefore, depending on the clinical situation, physiotherapists should continue to choose exercises and decide on exercise levels.
72I consider the therapy for these patients to be very individual. A generally applicable evaluation is therefore difficult to formulate. But if possible, I have tried to mark something that applies to many of my patients.
107The different patient profiles for flat backs and hyperkyphosis do not really enable me to make an overall assessment. However, I have nevertheless given statements as they would probably apply to most of my patients.
56I believe that therapy is extremely individualized and that no patient can be placed in a grid. The condition of a patient is influenced by so many factors and every day anew. So I consider this assistant as a possible addition, but I don’t see any benefit or relief for my work. What you include in this assistant is what I do with every patient every day.
58I found it very interesting to participate in the survey. Of course, I am in favor of a very individual approach to the patients.
109In my opinion, a rehabilitation protocol should be discussed individually with the patient, especially to determine the intensity. Prior to this, it is necessary to inform a patient about pain per se and to adapt the rehabilitation protocol to his daily stress.
98I believe that the individual symptoms of each patient and the complexity of the spine complicate general statements. My work with patients is based primarily on my own experience. Since most of the documented treatment recommendations tend to apply more to scoliosis, I was not sure about some questions, which is why I could not judge them.
ID: Number for anonymous identification of the participants.

References

  1. Banzer, W. (Ed.) Körperliche Aktivität und Gesundheit: Präventive und therapeutische Ansätze der Bewegungs-und Sportmedizin; Mit 60 Abbildungen; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
  2. Roux, C.H.; Guillemin, F.; Boini, S.; Longuetaud, F.; Arnault, N.; Hercberg, S.; Briançon, S. Impact of musculoskeletal disorders on quality of life: An inception cohort study. Ann. Rheum. Dis. 2005, 64, 606–611. [Google Scholar] [CrossRef] [PubMed]
  3. Palazzo, C.; Ravaud, J.-F.; Papelard, A.; Ravaud, P.; Poiraudeau, S. The Burden of Musculoskeletal Conditions. PLoS ONE 2014, 9, e90633. [Google Scholar] [CrossRef] [PubMed]
  4. Sebbag, E.; Felten, R.; Sagez, F.; Sibilia, J.; Devilliers, H.; Arnaud, L. The world-wide burden of musculoskeletal diseases: A systematic analysis of the World Health Organization Burden of Diseases Database. Ann. Rheum. Dis. 2019, 78, 844–848. [Google Scholar] [CrossRef] [PubMed]
  5. Hoy, D.; March, L.; Brooks, P.; Blyth, F.; Woolf, A.; Bain, C.; Williams, G.; Smith, E.; Vos, T.; Barendregt, J.; et al. The global burden of low back pain: Estimates from the Global Burden of Disease 2010 study. Ann. Rheum. Dis. 2014, 73, 968–974. [Google Scholar] [CrossRef] [PubMed]
  6. James, S.L.; Abate, D.; Abate, K.H.; Abay, S.M.; Abbafati, C.; Abbasi, N.; Abbastabar, H.; Abd-Allah, F.; Abdela, J.; Abdelalim, A.; et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018, 392, 1789–1858. [Google Scholar] [CrossRef]
  7. Chun, S.-W.; Lim, C.-Y.; Kim, K.; Hwang, J.; Chung, S.G. The relationships between low back pain and lumbar lordosis: A systematic review and meta-analysis. Spine J. 2017, 17, 1180–1191. [Google Scholar] [CrossRef]
  8. Kado, D.M.; Prenovost, K.; Crandall, C. Narrative Review: Hyperkyphosis in Older Persons. Ann. Intern. Med. 2007, 147, 330–338. [Google Scholar] [CrossRef]
  9. Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM). Bundesinstitut für Arzneimittel und Medizinprodukte, ICD-10-GM Version 2019, Systematisches Verzeichnis, Internationale Statistische Klassifikation der Krankheiten und Verwandter Gesundheitsprobleme. 10. Revision. 1975. Available online: https://www.dimdi.de/static/de/klassifikationen/icd/icd-10-gm/kode-suche/htmlgm2019/ (accessed on 21 November 2021).
  10. Diebo, B.G.; Shah, N.V.; Boachie-Adjei, O.; Zhu, F.; Rothenfluh, D.A.; Paulino, C.B.; Schwab, F.J.; Lafage, V. Adult spinal deformity. Lancet 2019, 394, 160–172. [Google Scholar] [CrossRef]
  11. Ames, C.P.; Scheer, J.K.; Lafage, V.; Smith, J.S.; Bess, S.; Berven, S.H.; Mundis, G.M.; Sethi, R.K.; Deinlein, D.A.; Coe, J.D.; et al. Adult Spinal Deformity: Epidemiology, Health Impact, Evaluation, and Management. Spine Deform 2016, 4, 310–322. [Google Scholar] [CrossRef]
  12. Smith, E.; Hoy, D.; Cross, M.; Vos, T.; Naghavi, M.; Buchbinder, R.; Woolf, A.D.; March, L. The global burden of other musculoskeletal disorders: Estimates from the Global Burden of Disease 2010 study. Ann. Rheum. Dis. 2014, 73, 1462–1469. [Google Scholar] [CrossRef]
  13. Been, E.; Kalichman, L. Lumbar lordosis. Spine J. 2014, 14, 87–97. [Google Scholar] [CrossRef]
  14. Katzman, W.B.; Wanek, L.; Shepherd, J.A.; Sellmeyer, D.E. Age-related hyperkyphosis: Its causes, consequences, and management. J. Orthop. Sport. Phys. Ther. 2010, 40, 352–360. [Google Scholar] [CrossRef]
  15. Arshad, R.; Pan, F.; Reitmaier, S.; Schmidt, H. Effect of age and sex on lumbar lordosis and the range of motion. A systematic review and meta-analysis. J. Biomech. 2019, 82, 1–19. [Google Scholar] [CrossRef]
  16. Sohn, S.; Chung, C.K.; Kim, Y.J.; Han, I.; Kang, S.M.; Yoon, J.W.; Kim, H. Sagittal spinal alignment in asymptomatic patients over 30 years old in the Korean population. Acta Neurochir. 2017, 159, 1119–1128. [Google Scholar] [CrossRef] [PubMed]
  17. Roghani, T.; Zavieh, M.K.; Manshadi, F.D.; King, N.; Katzman, W. Age-related hyperkyphosis: Update of its potential causes and clinical impacts-narrative review. Aging Clin. Exp. Res. 2017, 29, 567–577. [Google Scholar] [CrossRef] [PubMed]
  18. Christensen, K.; Doblhammer, G.; Rau, R.; Vaupel, J.W. Ageing populations: The challenges ahead. Lancet 2009, 374, 1196–1208. [Google Scholar] [CrossRef]
  19. Weiss, H.-R. Spinal deformities rehabilitation—State of the art review. Scoliosis 2010, 5, 28. [Google Scholar] [CrossRef]
  20. Kristensen, J.; Franklyn-Miller, A. Resistance training in musculoskeletal rehabilitation: A systematic review. Br. J. Sport. Med. 2012, 46, 719–726. [Google Scholar] [CrossRef]
  21. Anwer, S.; Alghadir, A.; Abu Shaphe, M.; Anwar, D. Effects of Exercise on Spinal Deformities and Quality of Life in Patients with Adolescent Idiopathic Scoliosis. BioMed Res. Int. 2015, 2015, 123848. [Google Scholar] [CrossRef] [PubMed]
  22. Geneen, L.J.; Moore, R.A.; Clarke, C.; Martin, D.; Colvin, L.A.; Smith, B.H. Physical activity and exercise for chronic pain in adults: An overview of Cochrane Reviews. Cochrane Database Syst. Rev. 2017, 1, CD011279. [Google Scholar] [PubMed]
  23. Hwangbo, P.-N.; Hwangbo, G.; Park, J.; Lee, S. The Effect of Thoracic Joint Mobilization and Self-stretching Exercise on Pulmonary Functions of Patients with Chronic Neck Pain. J. Phys. Ther. Sci. 2014, 26, 1783–1786. [Google Scholar] [CrossRef]
  24. Naderi, A.; Rezvani, M.H.; Shaabani, F.; Bagheri, S. Effect of Kyphosis Exercises on Physical Function, Postural Control and Quality of Life in Elderly Men With Hyperkyphosis. Iran. J. Ageing 2019, 13, 464–479. [Google Scholar] [CrossRef]
  25. Bansal, S.; Katzman, W.B.; Giangregorio, L.M. Exercise for improving age-related hyperkyphotic posture: A systematic review. Arch. Phys. Med. Rehabil. 2014, 95, 129–140. [Google Scholar] [CrossRef]
  26. González-Gálvez, N.; Gea-García, G.M.; Marcos-Pardo, P.J. Effects of exercise programs on kyphosis and lordosis angle: A systematic review and metaanalysis. PLoS ONE 2019, 14, e0216180. [Google Scholar] [CrossRef] [PubMed]
  27. Booth, J.; Moseley, G.L.; Schiltenwolf, M.; Cashin, A.; Davies, M.; Hübscher, M. Exercise for chronic musculoskeletal pain: A biopsychosocial approach. Musculoskelet. Care 2017, 15, 413–421. [Google Scholar] [CrossRef] [PubMed]
  28. Naresh-Babu, J.; Viswanadha, A.-K.; Ito, M.; Park, J.-B. What Should an Ideal Adult Spinal Deformity Classification System Consist of?: Review of the Factors Affecting Outcomes of Adult Spinal Deformity Management. Asian Spine J. 2019, 13, 694–703. [Google Scholar] [CrossRef] [PubMed]
  29. World Health Organization. Rehabilitation 2030: A Call for Action: The Need to Scale Up Rehabilitation; World Health Organization: Geneva, Switzerland, 2017. [Google Scholar]
  30. World Health Organization. World Report on Disability; WHO Press: Geneva, Switzerland, 2011. [Google Scholar]
  31. Zimbelman, J.L.; Juraschek, S.P.; Zhang, X.; Lin, V.W.-H. Physical Therapy Workforce in the United States: Forecasting Nationwide Shortages. PM R J. Inj. Funct. Rehabil. 2010, 2, 1021–1029. [Google Scholar] [CrossRef]
  32. Dagdia, L.; Kokabu, T.; Ito, M. Classification of Adult Spinal Deformity: Review of Current Concepts and Future Directions. Spine Surg. Relat. Res. 2019, 3, 17–26. [Google Scholar] [CrossRef]
  33. Garbuz, D.S.; Masri, B.A.; Esdaile, J.; Duncan, C.P. Classification Systems in Orthopaedics. J. Am. Acad. Orthop. Surg. 2002, 10, 290–297. [Google Scholar] [CrossRef]
  34. von Korff, M.; Ormel, J.; Keefe, F.J.; Dworkin, S.F. Grading the severity of chronic pain. Pain 1992, 50, 133–149. [Google Scholar] [CrossRef]
  35. Rose, S.J. Physical Therapy Diagnosis: Role and Function. Phys. Ther. 1989, 69, 535–537. [Google Scholar] [CrossRef] [PubMed]
  36. Geffré, A.; Friedrichs, K.; Harr, K.; Concordet, D.; Trumel, C.; Braun, J.-P. Reference values: A review. Vet. Clin. Pathol. 2009, 38, 288–298. [Google Scholar] [CrossRef] [PubMed]
  37. Schwab, F.; Patel, A.; Ungar, B.; Farcy, J.-P.; Lafage, V. Adult Spinal Deformity—Postoperative Standing Imbalance: How Much Can You Tolerate? An Overview of Key Parameters in Assessing Alignment and Planning Corrective Surgery. Spine 2010, 35, 2224–2231. [Google Scholar] [CrossRef] [PubMed]
  38. Adler, D.; Almansour, H.; Akbar, M. Was ist eigentlich eine adulte spinale Deformität?: Entwicklung, Klassifikation und Indikation zur operativen Therapie. Orthopäde 2018, 47, 276–287. [Google Scholar] [CrossRef]
  39. Ohlendorf, D.; Fisch, V.; Doerry, C.; Schamberger, S.; Oremek, G.; Ackermann, H.; Schulze, J. Standard reference values of the upper body posture in healthy young female adults in Germany: An observational study. BMJ Open 2018, 8, e022236. [Google Scholar] [CrossRef]
  40. Porto, A.B.; Okazaki, V.H.A. Thoracic Kyphosis and Lumbar Lordosis Assessment by Radiography and Photogrammetry: A Review of Normative Values and Reliability. J. Manip. Physiol. Ther. 2018, 41, 712–723. [Google Scholar] [CrossRef]
  41. Noshchenko, A.; Hoffecker, L.; Cain, C.M.J.; Patel, V.V.; Burger, E.L. Spinopelvic Parameters in Asymptomatic Subjects Without Spine Disease and Deformity. Clin. Spine Surg. 2017, 30, 392–403. [Google Scholar] [CrossRef]
  42. Celestre, P.C.; Dimar, J.R.; Glassman, S.D. Spinopelvic Parameters: Lumbar Lordosis, Pelvic Incidence, Pelvic Tilt, and Sacral Slope: What Does a Spine Surgeon Need to Know to Plan a Lumbar Deformity Correction? Neurosurg. Clin. N. Am. 2018, 29, 323–329. [Google Scholar] [CrossRef]
  43. Katzman, W.B.; Vittinghoff, E.; Ensrud, K.; Black, D.M.; Kado, D.M. Increasing Kyphosis Predicts Worsening Mobility in Older Community-Dwelling Women: A Prospective Cohort Study. J. Am. Geriatr. Soc. 2011, 59, 96–100. [Google Scholar] [CrossRef]
  44. Petcharaporn, M.; Pawelek, J.; Bastrom, T.; Lonner, B.; Newton, P. The Relationship Between Thoracic Hyperkyphosis and the Scoliosis Research Society Outcomes Instrument. Spine 2007, 32, 2226–2231. [Google Scholar] [CrossRef]
  45. Dindorf, C.; Konradi, J.; Wolf, C.; Taetz, B.; Bleser, G.; Huthwelker, J.; Drees, P.; Fröhlich, M.; Betz, U. General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of sex differences in spinal posture during stance and gait. Comput. Methods Biomech. Biomed. 2020, 24, 299–307. [Google Scholar] [CrossRef] [PubMed]
  46. Betz, U.; Konradi, J.; Huthwelker, J.; Wolf, C.; Schmidtmann, I.; Drees, P. First results of spinal segment-related motion analysis in human gait using rasterstereography. Gait Posture 2017, 57 (Suppl. S1), 345–346. [Google Scholar] [CrossRef]
  47. Schröder, J.; Reer, R.; Braumann, K.M. Video raster stereography back shape reconstruction: A reliability study for sagittal, frontal, and transversal plane parameters. Eur. Spine J. 2015, 24, 262–269. [Google Scholar] [CrossRef] [PubMed]
  48. Drerup, B.; Hierholzer, E. Back shape measurement using video rasterstereography and three-dimensional reconstruction of spinal shape. Clin. Biomech. 1994, 9, 28–36. [Google Scholar] [CrossRef] [PubMed]
  49. Drerup, B.; Hierholzer, E. Objective determination of anatomical landmarks on the body surface: Measurement of the vertebra prominens from surface curvature. J. Biomech. 1985, 18, 467–471, 473–474. [Google Scholar] [CrossRef] [PubMed]
  50. Evans, J.R.; Mathur, A. The value of online surveys: A look back and a look ahead. Internet Res. 2018, 28, 854–887. [Google Scholar] [CrossRef]
  51. LimeSurvey GmbH. Available online: https://www.limesurvey.org/de/ (accessed on 3 June 2023).
  52. Longo, U.G.; Loppini, M.; Denaro, L.; Maffulli, N.; Denaro, V. Rating scales for low back pain. Br. Med. Bull. 2010, 94, 81–144. [Google Scholar] [CrossRef]
  53. Burckhardt, C.S.; Jones, K.D. Adult measures of pain: The McGill Pain Questionnaire (MPQ), Rheumatoid Arthritis Pain Scale (RAPS), Short-Form McGill Pain Questionnaire (SF-MPQ), Verbal Descriptive Scale (VDS), Visual Analog Scale (VAS), and West Haven-Yale Multidisciplinary Pain Inventory (WHYMPI). Arthritis Care Res. 2003, 49, S96–S104. [Google Scholar]
  54. Goldsmith, E.S.; Murdoch, M.; Taylor, B.; Greer, N.; MacDonald, R.; McKenzie, L.G.; Rosebush, C. Rapid Evidence Review: Measures for Patients with Chronic Musculoskeletal Pain. In VA Evidence Synthesis Program Evidence Briefs; Department of Veterans Affairs (US): Washington, DC, USA, 2011. [Google Scholar]
  55. International Centre for Allied Health Evidence. iCAHE Outcomes Calculator Musculoskelletal Version User Manual: Monitoring Patient Status over Time Using Common Pain and Musculoskeletal Outcome Measures; International Centre for Allied Health Evidence: Adelaide, SA, Australia, 2013. [Google Scholar]
  56. Zhang, Y.; Wumaier, M.; He, D.; Xiao, B.; Zhang, J. The 100 Top-Cited Articles on Spinal Deformity: A Bibliometric Analysis. Spine 2020, 45, 275–283. [Google Scholar] [CrossRef]
  57. Hayden, J.A.; van Tulder, M.W.; Tomlinson, G. Systematic Review: Strategies for Using Exercise Therapy To Improve Outcomes in Chronic Low Back Pain. Ann. Intern. Med. 2005, 142, 776–785. [Google Scholar] [CrossRef]
  58. Slade, S.C.; Patel, S.; Underwood, M.; Keating, J.L. What are patient beliefs and perceptions about exercise for nonspecific chronic low back pain? A systematic review of qualitative studies. Clin. J. Pain 2014, 30, 995–1005. [Google Scholar] [CrossRef] [PubMed]
  59. Jack, K.; McLean, S.M.; Moffett, J.K.; Gardiner, E. Barriers to treatment adherence in physiotherapy outpatient clinics: A systematic review. Man. Ther. 2010, 15, 220–228. [Google Scholar] [CrossRef] [PubMed]
  60. Scoliosis Research Society. Scoliosis Research Society—Dedicated to the Optimal Care of Patients with Spinal Deformity, Patient Outcome Questionnaires; n.d. Available online: https://www.srs.org/professionals/online-education-and-resources/patient-outcome-questionnaires (accessed on 27 November 2021).
  61. Booker, S.Q.; Herr, K.A. Assessment and Measurement of Pain in Adults in Later Life. Clin Geriatr Med 2016, 32, 677–692. [Google Scholar] [CrossRef] [PubMed]
  62. Kotwicki, T.; Negrini, S.; Grivas, T.B.; Rigo, M.; Maruyama, T.; Durmala, J.; Zaina, F. Methodology of evaluation of morphology of the spine and the trunk in idiopathic scoliosis and other spinal deformities—6th SOSORT consensus paper. Scoliosis 2009, 4, 26. [Google Scholar] [CrossRef] [PubMed]
  63. Youssef, J.A.; Orndorff, D.O.; Patty, C.A.; Scott, M.A.; Price, H.L.; Hamlin, L.F.; Williams, T.L.; Uribe, J.S.; Deviren, V. Current status of adult spinal deformity. Glob. Spine J. 2013, 3, 51–62. [Google Scholar] [CrossRef]
  64. Bassani, T.; Stucovitz, E.; Galbusera, F.; Brayda-Bruno, M. Is rasterstereography a valid noninvasive method for the screening of juvenile and adolescent idiopathic scoliosis? Eur. Spine J. 2019, 28, 526–535. [Google Scholar] [CrossRef]
  65. Vrtovec, T.; Pernus, F.; Likar, B. A review of methods for quantitative evaluation of spinal curvature. Eur. Spine J. 2009, 18, 593–607. [Google Scholar] [CrossRef]
  66. Knott, P.; Sturm, P.; Lonner, B.; Cahill, P.; Betsch, M.; McCarthy, R.; Kelly, M.; Lenke, L.; Betz, R. Multicenter Comparison of 3D Spinal Measurements Using Surface Topography With Those From Conventional Radiography. Spine Deform 2016, 4, 98–103. [Google Scholar] [CrossRef]
  67. Cohen, L.; Kobayashi, S.; Simic, M.; Dennis, S.; Refshauge, K.; Pappas, E. Non-radiographic methods of measuring global sagittal balance: A systematic review. Scoliosis Spinal Disord. 2017, 12, 30. [Google Scholar] [CrossRef]
Life 13 01392 g001
Figure 1. Setup of the measuring method “DIERS formetric” for the surface measurement of the video rasterstereography (based on pictures from the DIERS Company).
Figure 1. Setup of the measuring method “DIERS formetric” for the surface measurement of the video rasterstereography (based on pictures from the DIERS Company).
Life 13 01392 g001
Life 13 01392 g002
Figure 2. Selected angular measurements for the calculation of thoracic kyphosis and lumbar lordosis (based on pictures from the DIERS Company).
Figure 2. Selected angular measurements for the calculation of thoracic kyphosis and lumbar lordosis (based on pictures from the DIERS Company).
Life 13 01392 g002
Life 13 01392 g003
Figure 3. Classification system to determine the severity of thoracic hyperkyphosis or lumbar hypolordosis.
Figure 3. Classification system to determine the severity of thoracic hyperkyphosis or lumbar hypolordosis.
Life 13 01392 g003
Table 1. Rating of the components of the severity classification.
Table 1. Rating of the components of the severity classification.
Severity ComponentRating
Very RelevantPredominantly RelevantLess RelevantNot Relevant at AllNot Assessable
Angle of curvature28% (13)32% (15)26% (12)-15% (7)
Pain components63% (30)15% (7)4% (2)2% (1)17% (8)
Figures in brackets correspond to the number of participant votes.
Table 2. Rating of the severity classification and its related aspects.
Table 2. Rating of the severity classification and its related aspects.
Evaluation TopicRating
I AgreeI Predominantly AgreeI Agree LessI Do Not Agree at AllNot Assessable
Multidimensional severity classification for exercise therapy35% (12)32% (11)18% (6)9% (3)6% (2)
Severity number THK29% (10)24% (8)15% (5)6% (2)26% (9)
Severity number LHL29% (10)26% (9)12% (4)6% (2)26% (9)
Developed severity classification44% (15)26% (9)9% (3)6% (2)15% (5)
Figures in brackets correspond to the number of participant votes.
Table 3. Participant feedback on additional severity classification components.
Table 3. Participant feedback on additional severity classification components.
ComponentParticipant Comments
NumberProportion of Cases
Angle of curvature750%
Pain perception964%
Pain-related impairments1393%
Age643%
Sex321%
Pelvic tilt321%
Body mass index643%
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kaps, D.; Siebers, H.L.; Betz, U.; Pfirrmann, D.; Eschweiler, J.; Hildebrand, F.; Betsch, M.; Huthwelker, J.; Wolf, C.; Drees, P.; et al. Creation and Evaluation of a Severity Classification of Hyperkyphosis and Hypolordosis for Exercise Therapy. Life 2023, 13, 1392. https://doi.org/10.3390/life13061392

AMA Style

Kaps D, Siebers HL, Betz U, Pfirrmann D, Eschweiler J, Hildebrand F, Betsch M, Huthwelker J, Wolf C, Drees P, et al. Creation and Evaluation of a Severity Classification of Hyperkyphosis and Hypolordosis for Exercise Therapy. Life. 2023; 13(6):1392. https://doi.org/10.3390/life13061392

Chicago/Turabian Style

Kaps, David, Hannah L. Siebers, Ulrich Betz, Daniel Pfirrmann, Jörg Eschweiler, Frank Hildebrand, Marcel Betsch, Janine Huthwelker, Claudia Wolf, Philipp Drees, and et al. 2023. "Creation and Evaluation of a Severity Classification of Hyperkyphosis and Hypolordosis for Exercise Therapy" Life 13, no. 6: 1392. https://doi.org/10.3390/life13061392

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop