DOCTORAL THESIS
FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN AXIAL
SPONDYLOARTHRITIS AND NON-SPECIFIC LOW BACK PAIN
Universidad de Málaga Vrije Universiteit Brussel
Health Sciences Doctoral Program Rehabilitation Sciences and Physiotherapy Doctoral Program
Faculty of Health Sciences Faculty of Physical Education and Physiotherapy
UNIVERSIDAD DE MÁLAGA
Double PhD presented by:
Manuel Trinidad Fernández
Brussels – Malaga, 2021 Promotors:
Prof. dr. Antonio Ignacio Cuesta Vargas Prof. dr. Manuel González Sánchez
Em Prof. dr. Peter Vaes Prof. dr. David Beckwée
AUTOR: Manuel Trinidad Fernández https://orcid.org/0000-0001-7562-5986
EDITA: Publicaciones y Divulgación Científica. Universidad de Málaga
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Esta Tesis Doctoral está depositada en el Repositorio Institucional de la Universidad de Málaga (RIUMA): riuma.uma.es
Faculty of Health Sciences
Prof. Antonio Ignacio Cuesta Vargas, PhD in Physiotherapy from the Universidad de Málaga and Professor of the Department of Physiotherapy, belonging to the Faculty of Health Sciences.
CERTIFIES that the work presented as an International Doctoral Thesis and Double PhD by Mr Manuel Trinidad Fernández, entitled FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN SPONDYLOARTHRITIS AND LOW BACK PAIN, has been carried out under my supervision* and I consider that it meets the appropriate conditions in terms of content and scientific rigor for be presented for reading.
And for the record, I sign this in Málaga on 30th November, 2020.
Antonio Ignacio Cuesta Vargas
*Supervision as supervisor and promotor, according to the UMA regulations.
*Supervisión como tutor y director, de acuerdo a la normativa de la UMA.
Faculty of Health Sciences
Prof. Manuel González Sánchez, PhD in Physiotherapy from the Universidad de Málaga and Professor of the Department of Physiotherapy, belonging to the Faculty of Health Sciences.
CERTIFIES that the work presented as a Double PhD by Mr Manuel Trinidad Fernández, entitled FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN SPONDYLOARTHRITIS ANO LOW BACK PAIN, has been carried out under my supervision and I consider that it meets the appropriate conditions in terms of content and scientific rigor for be presented for reading.
And for the record, 1 sign this in Málaga on 30th November, 2020.
Manuel González Sánchez
Prof. Peter Vaes, PhD in Physiotherapy from the Vrije Universiteit Brussel and Professor of the Department of Physiotherapy, Human Physiology and Anatomy, belonging to the Faculty of Physical Education and Physiotherapy.
CERTIFIES that the work presented as a Double PhD by Mr Manuel Trinidad Fernández, entitled FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN SPONDYLOARTHRITIS AND LOW BACK PAIN, has been carried out under my supervision and I consider that it meets the appropriate conditions in terms of content and scientific rigor for be presented for reading.
And for the record, 1 sign this in Brussels on 30th November, 2020.
Peter Vaes
Faculty of Physical Education and Physiotherapy
Prof. David Beckwée, PhD in Physiotherapy from the Vrije Universiteit Brussel and Professor of the Department of Physiotherapy, Human Physiology and Anatomy, belonging to the Faculty of Health Sciences.
CERTIFIES that the work presented as a Double PhD by Mr Manuel Trinidad Fernández, entitled FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN SPONDYLOARTHRITIS AND LOW BACK PAIN, has been carried out under my supervision and I consider that it meets the appropriate conditions in terms of content and scientific rigor for be presented for reading.
And for the record, I sign this in Brussels on 30th November, 2020.
David Beckwée
AN□ALUCiA TECH Campus de Exc.elenc.1a lntemac1onal
Declaration of authorship and originality of the thesis submitted to obtain the tille of PhD
-Mr. Manuel Trinidad-Fernández
Student of the Health Sciences Doctorate Program at the Universidad de Málaga and the Rehabilitation Sciences and Physiotherapy Doctoral Program at the Vrije Universiteit Brussel, entitled:
FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN SPONDYLOARTHRITIS ANO LOW BACK PAIN
Carried out under the supervision of Antonio Ignacio Cuesta-Vargas (UMA) and Peter Vaes (VUB) and Manuel González-Sánchez (UMA) and David Beckwée (VUB) as promotors.
1 declare that:
The thesis presented is an original work that does not infringe the rights of intellectual property or industrial property rights or others, in accordance with the current legal order (Real Decreto Legislativo 1/1996, of April 12th, which approves the consolidated text of the Spanish lntellectual Property Law, regulating, clarifying and harmonizing the legal provisions in force on the matter), modified by Ley 2/2019 of March 1 st and following the principies and definitions specified in the regulations concerning breaches of the scientific integrity of the Vrije Universiteit Brussel.
Likewise, 1 assume, before the Universidad de Málaga, the Vrije Universiteit Brussel and before any other instance, the responsibility that could be derived in case of plagiarism of contents in the thesis presented, in accordance with the current legal order.
Málaga, 30th November, 2020.
Manuel Trinidad-Fernández
Universidad de Málaga Vrije Universiteit Brussel
Department of Physiotherapy Department of Physiotherapy, Human Physiology and Anatomy
Thesis submitted in fulfillment of the requirements for the degree of
• Doctor by the Universidad de Málaga within the doctoral programme in Health Sciences (Doctor por la Universidad de Málaga dentro del programa de doctorado en Ciencias de la Salud)
• Doctor in Rehabilitation Sciences and Physiotherapy (Doctor in de Revalidatiewetenschappen en de Kinesitherapie) from the Vrije Universiteit Brussel
by
Manuel Trinidad Fernández
FUNCTIONAL TASK KINEMATIC LIKE BIOMARKERS WITH RGB-D CAMERA IN AXIAL SPONDYLOARTHRITIS AND NON-SPECIFIC LOW BACK PAIN
Promotors: Prof. dr. Antonio Ignacio Cuesta Vargas
Deparment of Physiotherapy, Universidad de Málaga Em. Prof. dr. Peter Vaes
Department of Physiotherapy, Human Physiology and Anatomy, Vrije Universitait Brussel Prof. dr. Manuel González Sánchez
Deparment of Physiotherapy, Universidad de Málaga Prof. dr. David Beckwée
Department of Physiotherapy, Human Physiology and Anatomy, Vrije Universitait Brussel
Members of the jury: Prof. dr. Eva Swinnen (Chair)
Department of Physiotherapy, Human Physiology and Anatomy, Vrije Universitait Brussel
Prof. dr. Noelia Moreno Morales
Deparment of Physiotherapy, Universidad de Málaga Prof. dr. Ulrike Van Daele
Department of Rehabilitation Sciences & Physiotherapy, Universiteit Antwerpen
Prof. dr. Carolina Fernández Lao
Deparment of Physiotherapy, Universidad de Granada
You can't always get what you want But if you try sometimes you just might find You get what you need
Mick Jagger / Keith Richards (1969)
In memoriam of my grandmother Maria Olea García:
(†26/11/2020) Thank you for all your love and I hope you are with me always En memoria de mi abuela Maria Olea García:
(†26/11/2020) Gracias por todo tu amor y espero que estés conmigo siempre
To my mother:
The best mother in the world Thank you for all your dedication and love A mi madre:
La mejor madre del mundo Gracias por toda su dedicación y amor
My eternal gratitude to:
I would start by expressing my gratitude to my promotors. Their time spent on me would not be forgotten, and their help had led to the completion of this work. I am very proud that they have been my guides during this time since I consider them great researchers and role models for me.
I know I would have never come to this moment without the tutelage and training that Prof.
Antonio Cuesta Vargas has assisted me during all of these years. Learning from a physiotherapist with a capital P and a top researcher has made me grow as a person and become the researcher that I am now. I would like to thank him all the knowledge that he has shared with me as well as all his advices and his closeness at many times. Moreover, I am grateful because he gave me the opportunity to work side by side with him and entrusted me with great responsibility. Furthermore, I won’t forget the musical experiences we have lived. I wish I could learn more from your personal and professional values.
My deepest and eternal thankfulness may go to Prof. Manuel González Sánchez since all this work began with him from my Final Degree Project. I have been working with him for more than 6 years and I am sure that I will never come across such a hard-working and good companion as his. In addition to being one of the most beautiful person I know, I must say that our friendship means the world to me. Besides, I know I can always count on him, not only as a research group partner, but also as a friend whom I can have some pints and a great time with. In short, an exceptional figure to look up to. Thank you for training me and being supportive.
This adventure would not have been possible without Prof. Peter Vaes’s support, a great researcher and worker like few others I have ever known. I will always appreciate your knowledge shared and I feel thankful for learning from your work discipline. This work would not have been the same without your help and vision. Thank you for letting me work in your department. Because of you, I had the opportunity to meet fantastic people, have the experience of researching at the Vrije Universiteit Brussel and live in Europe’s main city.
Finally, I would not like to forget Prof. David Beckwée’s help in this thesis. It was a pleasure to meet you and to have worked with you. I will never forget the assistance you gave me during my stay in Brussels, because you were a very important part of making this project. I wish I have learned from you not only your willingness to collaborate but also your dedication. Thank you very much for your contribution to this work and support during my stay.
Acknowledgement:
In this section, I would like to thank all those people who have been in my life. Without them, I would not be the person that I am now. I found their support highly convient when I worked as a clinician and as a researcher. I know this is the end of a chapter in my life and I would like to name all those people who are worth of it, because sometimes my lack of ability to express my feelings has prevented me from doing it.
First, I would like to thank all the help given by my parents, because without their training and dedication, I would have never been able to get to where I am now. Despite different vicissitudes, they will always be there, giving me support. Thanks to my mother for being the most attentive and sacrificed person to me. Thanks to my father for his interest in me. Thanks also to my uncles Fali and Paqui, and my cousin Lucia for loving me so much.
Through my life I have found people of whom I feel very pleased and delighted that they were part of my life.Sometimes, everybody goes through tough times and in such moments the best thing you can do is to have real friends. Undoubtedly, I can ensure that I have them.
Therefore, I want to start by thanking my friends from school, who have been with me for years. Thanks to Nacho Rodríguez and Santi García, for being so important to me when we first met. Adri G Jorge and Juanmi Fajardo because they have always been loyal, funny and trusted. And especially Javi Salgado, he is the person with whom I have the best moments in my life. With no doubt, he is family to me. I wish I was a good person, intelligent, honest and an excellent musician as him.
In university times, you meet people who influence your life in different ways, and I could not stop thanking Juanan Luque and Miguel Ortega for how they have contributed to my life. I cannot help getting excited and having cheerful feelings when I remember all our adventures, trips and dinners with Nachos. I hope these moments were just as wonderful for you as they are for me. I would not like to forget other career colleagues of whom I only have good memories from that period of my life: Paco Arreza, José Cívico, Nacho Lafuente, Elena Marqués, Carlos Garzón or Francis.
I would like to mention now some friends and how lucky I feel because they have marked my life and they unquestionable are part of my daily life. First of all, thank you very much to Marina Triviño, she is such an important person to me, and she has always supported me., even in my hardest times. I would be a different person without their friendship. Thank you for loving me so much and I hope we will always remain friends. On the other hand, I cannot forget Jota
Franco and all the moments that we have shared since we met. Thank you for being a generous, fun and trustworthy friend. Thanks to Patri Jiménez for being one of the happiest and kindest people I know. We have met each other relatively recently but I consider you almost my family. I also want to express my gratitude to other important people, because we have shared good times together: Adri García, Luce Cortés, Oscar Segura, Ale Vidal, Dani Atencia, Ulises Jaimes, Ale Delgado, Meyling Rojas, Angie Restrepo, Betsabé Canillas and Yafea Alyafei. Thank you very much to Emma Gallardo for being an important part of my life during these years although our lives are separated now.
In my PhD student years, because I have the opportunity to be part of the F-14 Clinimetrics research group, I have met wonderful people who treated me kindly. I would like to name those excellent colleagues such as Josan Merchán, Ivan Abolafio, Alvaro Pareja, and especially David Pérez, Cristina Roldán and Adrián Escriche, because we had close contact in the university and in other projects. They are exceptional colleagues, superlative workers and will be future leaders in Spanish research.
I thank all the people who assisted me during my period in Brussels, especially the KIMA Department and all its members who made my time there easier and more enjoyable. I would also like to thank Celia and Alex for their time and friendship. With them, I enjoyed the capital of Europe very much. They were my main support in Brussels.
I would like to thank the people who have allowed this work to go ahead. Thanks to Antonio Fernández and his team for collaborating in the recruitment of patients in Malaga. I am very grateful to Javier González, especially Francisco Moreno, it was a pleasure to collaborate with you. Thank you for all your collaboration in the project, all your time spent on me and I wish you the best. Many thanks to the UZ Brussel Physical Medicine Department team, especially Dr. Erika Joos, Anne Van Everbroeck and Lynn Biscop. It was a pleasure working with you. I only have good words for the support and attention that you put in me. I would also like to thank the Rheumatology Department of the Hospital of Malaga and all the patients from Spain and Belgium who have participated in this work, without them it would be impossible to advance science, which is the most important thing.
Finally, I want to mention important people that they have supported me and trusted me when I finish my Bachelor such as Pilar and Meli.
CONTENTS
CHAPTER I: INTRODUCTION ... 1
1.1. AXIAL SPONDYLOARTHRITIS ... 3
1.1.1. Definition ... 3
1.1.2. Prevalence ... 5
1.1.3. Health costs and work impact ... 6
1.1.4. Diagnosis ... 8
1.1.5. Assessment ... 10
1.2. NON-SPECIFIC LOW BACK PAIN ... 12
1.2.1. Definition ... 13
1.2.2. Prevalence ... 14
1.2.3. Health costs and work impact ... 15
1.2.4. Diagnosis ... 17
1.2.5. Assessment ... 18
1.3. MOTION CAPTURE ... 22
1.3.1. Video-based motion capture systems ... 25
1.3.2. RGB-D camera ... 26
1.4. THE NEED OF MOTION ASSESSMENT ... 31
1.4.1. Spondyloarthritis ... 31
1.4.1. Non-specific low back pain ... 32
1.5. REFERENCES ... 34
CHAPTER II: AIM OF THE THESIS AND RESEARCH QUESTIONS ... 53
2.1. GENERAL OBJECTIVE ... 55
2.2. OBJECTIVES AND RESEARCH QUESTIONS ... 55
CHAPTER III: CLINIMETRIC CHARACTERISTICS OF RGB-D CAMERA IN AXIAL SPONDYLOARTHRITIS ... 59
3.1. BACKGROUND ... 61
3.2. METHODOLOGY ... 62
3.2.1. Design and participants ... 62
3.2.2. Sample size ... 62
3.2.3. Participants ... 63
3.2.4. Ethical approval ... 63
3.2.5. RGB-D camera ... 63
3.2.6. Inertial measurement unit ... 64
3.2.7. Functional tasks and TUG test ... 65
3.2.8. Questionnaires and antropometric information ... 66
3.2.9. Procedure... 67
3.2.10. Data processing ... 67
3.2.11. Data analysis of the RGB-D camera ... 68
3.2.12. Data analysis of the inertial measurement unit ... 70
3.2.13. Outcome variables ... 71
3.2.14. Statistical analysis ... 72
3.3. RESULTS ... 72
3.4. DISCUSSION ... 76
3.4.1. Functional tasks ... 77
3.4.2. Timed up and go test ... 79
3.4.3. Strengths and limitations of the study ... 80
3.5. CONCLUSIONS ... 81
3.6. REFERENCES ... 81
CHAPTER IV: REFERENCE VALUES OF CLUSTERING RGB-D CAMERA OBTAINED ACCORDING TO DISEASE SEVERITY OF AXIAL SPONDYLOARTHRITIS: A PILOT STUDY ... 86
4.1 BACKGROUND ... 88
4.2 METHODOLOGY ... 89
4.2.1 Design ... 89
4.2.2. Inclusion and exclusion criteria ... 89
4.2.3. Setting ... 90
4.2.4. Ethical declarations ... 90
4.2.5. RGB-D camera ... 90
4.2.6. Functional tasks ... 91
4.2.7. Patient-reported outcome measures (PROMs) ... 92
4.2.8. Procedure... 93
4.2.9. Variables ... 94
4.2.10. Data recording ... 94
4.2.11. Data processing ... 95
4.2.12. Statistical analysis ... 96
4.3. RESULTS ... 96
4.4. DISCUSSION ... 102
4.4.1. Bending test ... 103
4.4.2. Sit to Stand test ... 104
4.4.3. Kinematic differences between BASFI and BASDAI ... 105
4.4.4. Limitations ... 105
4.5. CONCLUSIONS ... 106
4.6. REFERENCES ... 107
CHAPTER V: CLINIMETRIC CHARACTERISTICS OF RGB-D CAMERA IN NON- SPECIFIC LOW BACK PAIN ... 114
5.1. BACKGROUND ... 116
5.2. METHODOLOGY ... 117
5.2.1. Design ... 117
5.2.2. Sample size ... 117
5.2.3. Participants and intervention ... 117
5.2.4. Setting ... 118
5.2.5. Ethical considerations ... 119
5.2.6. Motion capture RGB-D camera system and inertial measurement unit . 119 5.2.7. Functional tests ... 121
5.2.8. Questionnaires ... 123 5.2.9. Measurement procedure ... 123 5.2.10. Variables ... 124 5.2.11. Data recording and processing ... 125 5.2.12. Statistical analysis ... 127 5.3. RESULTS ... 128 5.4. DISCUSSION ... 136 5.4.1. Six functional tests ... 137 5.4.2. Timed up and go test ... 139 5.4.3. Strengthnesses and weakness ... 141 5.5. CONCLUSIONS ... 142 5.6. REFERENCES ... 142
CHAPTER VI: CLUSTERING RGB-D CAMERA OBTAINED KINEMATIC OUTCOMES ACCORDING TO DISEASE SEVERITY OF NON-SPECIFIC LOW BACK PAIN ... 149
6.1. BACKGROUND ... 151 6.2. METHODOLOGY ... 152 6.2.1. Design ... 152 6.2.2. Participants and recruitment ... 152 6.2.3. Sample size calculation ... 153 6.2.4. Setting ... 153 6.2.5. Ethical considerations ... 154 6.2.6. Motion capture RGB-D camera ... 154 6.2.7. Functional tests ... 155 6.2.8. Questionnaires ... 156 6.2.9. Intervention ... 157 6.2.10. Data recording and processing ... 157 6.2.11. Primary outcomes ... 158 6.2.12. Data analysis ... 160
6.3. RESULTS ... 160 6.4. DISCUSSION ... 168 6.4.1. Bending trunk test ... 169 6.4.2. Sit to stand test ... 170 6.4.3. Sock test ... 172 6.4.4. Limitations ... 173 6.5. CONCLUSIONS ... 174 6.6. REFERENCES ... 174
CHAPTER VII: GENERAL DISCUSSION ... 181 7.1. CLINIMETRICS ... 183 7.1.1. Highlights of axial spondyloarthritis study... 186 7.1.2. Highlights of non-specific low back pain study ... 187 7.1.3. Comments about large and small range movements ... 188 7.2. PRELIMINARY REFERENCE VALUES IN AXIAL SPONDYLOARTHRITIS ... 189 7.3. CLUSTERING IN NON-SPECIFIC LOW BACK PAIN ... 190 7.4. RECOMMENDATIONS FOR FUTURE RESEARCH ... 192 7.5. REFERENCES ... 193
CHAPTER VIII: CONCLUSIONS OF THE THESIS ... 199
8.1. GENERAL CONCLUSION ... 201 8.2. SPECIFIC CONCLUSIONS ... 201
CHAPTER IX: ACQUIRED SKILLS... 205 CHAPTER X: PROSPECTIVE ... 211 RESUMEN EN ESPAÑOL ... 217
Introducción ... 219 Objetivos ... 221 1. Características clinimétricas de la Cámara RGB-D en espondiloartritis axial . 223 2. Valores de referencia cinemáticos preliminares en espondiloartritis axial ... 227 3. Características clinimétricas de la Cámara RGB-D en dolor lubar no específico ... 229 4. Agrupamiento de la severidad en dolor lumbar no específico según valores cinemáticos ... 233 Discusión general ... 236 Conclusiones ... 237
ABSTRACT IN NEDERLANDS ... 241
Inleiding ... 243 Doelen ... 245 1. Klinimetrische kenmerken van de RGB-D Camera bij axiale spondyloartritis . 247 2. Voorlopige kinematische referentiewaarden bij axiale spondyloartritis ... 251 3. Klinimetrische kenmerken van de RGB-D Camera bij niet-specifieke lage rugpijn ... 253 4. Groepering van de ernstgraad van niet-specifieke lage rugpijn volgens
kinematische waarden ... 257 Algemene discussie ... 260 Conclusies ... 262
GENERAL REFERENCES ... 265 ANNEX I: CERTIFICATES OF STAY ... 293
ANNEX II.1: Validation, reliability, and responsiveness outcomes of kinematic assessment with an RGB-D camera to analyze movement in subacute and chronic low back pain
ANNEX II.2: Differences in movement limitations in different low back pain severity in functional tests using an RGB-D camera
ANNEX II.3: Human motion capture for movement limitation analysis using an RGB-D camera in spondyloarthritis: A validation study
ABBREVIATIONS
AS - Axial spondyloarthritis
ASAS - Assessment of SpondyloArthritis international Society ASAS-HI - ASAS Health Index
AUC - Area under the curve
BASFI - Bath Ankylosing Spondylitis Functional Index BASMI - Bath Ankylosing Spondylitis Metrology Index CNSLBP - Chronic non-specific low back pain
CRP - C-reactive protein
CSI - Central sensitization inventory
ERAP - Endoplasmic reticulum aminopeptidase ES - Electormagnetic sensors
G1 - Group with high disease impact G2 - Group with low disease impact GPE - Global perceived effect IBP - Inflammatory back pain
ICC - Interclass correlation coefficient IMU - Inertial measurement unit IPS - Image processing systems LBP - Low back pain
LTS - Lie-to-sit
MRI - Magnetic resonance imaging
nrAS - Non-radiographic axial spondyloarthritis NSLBP - Non-specific low back pain
OS - Optoelectronic systems PCI - Pain Copying Inventory PCS - Pain Catastrophizing Scale RGB-D - Red green blue and depth RMQ - Roland Morris questionnaire ROC - Receiver operating characteristic SD - Standard deviation
SEM - Standard error of measurement SLBP - Specific low back pain
STS - Sit to stand
TAMPA - Tampa Kinesiophobia Scale TUG - Timed up and go
VAS - Visual analogue scale
LIST OF TABLES
Table 1. Advantages (green) and disadvangates (red) of the different human motion capture systems clasificaed by cost, usability, clinimetry and others ... 24 Table 2. Anthropometric and clinical data in women and men ... 73 Table 3. Results of the outcome variables of each test. Means and standard deviation between brackets ... 74 Table 4. Criterion validity and reliability obtained from the RGB-D camera and IMU in functional tasks ... 75 Table 5. Criterion validity and reliability obtained from the RGB-D camera and IMU in Timed up and go test... 75 Table 6. Anthropometric characteristics and questionnaires outcomes of each cluster according to the BASFI and BASDAI ... 97 Table 7. Kinematic outcomes of clusters according to BASFI in Bending test ... 99 Table 8. Kinematic outcomes of clusters according to BASFI in Sit to stand test .. 100 Table 9. Kinematic outcomes of clusters according to BASDAI in Bending test .... 101 Table 10. Kinematic outcomes of clusters according to BASDAI in Sit to stand test ... 102 Table 11. Anthropometric and questionnaire data of the sample (baseline and 1 month later) ... 129 Table 12. Mean (standard deviation) of repetitions and the outcomes (time, displacement, velocity, and acceleration) from the kinematic tools during the functional tests (baseline and 1 month later) ... 131 Table 13. Internal validity, reliability and responsiveness outcomes from the variables extracted from the RGB-D camera in the functional tests ... 132 Table 14. Internal validity, reliability and responsiveness outcomes from the variables extracted from the RGB-D camera in the Timed up and go test ... 133
Table 15. Anthropometric characteristics and questionnaires outcomes of each cluster according to the RMQ ... 161 Table 16. Anthropometric characteristics and questionnaires outcomes of each cluster according to the RMQ ... 162 Table 17. Kinematic outcomes of Bending trunk test in NSLBP group and control group ... 163 Table 18. Kinematic outcomes of Sit to stand test in NSLBP group and control group ... 164 Table 19. Kinematic outcomes of Sock test in NSLBP group and control group .... 165 Table 20. Kinematic outcomes of Bending trunk test in severity subgroups according to RMQ ... 166 Table 21. Kinematic outcomes of Sit to stand test in severity subgroups according to RMQ ... 167 Table 22. Kinematic outcomes of Sock test in severity subgroups according to RMQ ... 168
LIST OF FIGURES
Figure 1. Conceptualization of the natural history of the AS illustrated as a highway.
Adapted from Garg et al. 2014, originally from van Vollenhoven, 2011... 4 Figure 2. Management and part of the assessment of patients with NSLBP.
Information extracted from these scientific works: Airaksinen et al., 2006; Almeida et al., 2018; Downie et al., 2013; Maher et al., 2017; Papi et al., 2018; Vlaeyen et al., 2018 ... 22 Figure 3. Types of RGB-D camera: a) passive camera, b) active camera, c) structured light-based sensors, d) time-of-flight sensors. Active cameras must project its own light, as opposed to passive cameras. Secondly, structured light-based sensors cameras analyse the pattern looking for disparities in the image using an infrared emitter and time-of-flight sensors camaras measure the depth by returning continuous pulse of light ... 28 Figure 4. 3D coordinates references system in both instruments. A) 3D coordinate references and detected joints with the RGB-D camera. B) Inertial measurement unit placement and reference system ... 64 Figure 5. Functional tasks based on the BASFI items and Timed up and go test. A) Climbing stairs, b) Bending, c) Reaching, d) Putting socks, e) Getting up from the floor, f) Reclining and declining from a chair, g) Timed up and go test ... 65 Figure 6. Employed software libraries for the getting and analysing the data ... 69 Figure 7. Experimental setup and coordinates reference system: RGB-D camera and patient placement ... 91 Figure 8. Performance of functional tasks: a) Bending and b) Sit to stand ... 91 Figure 9. Experimental setup for (a) the functional tests and (b) the timed up-and-go test ... 120 Figure 10. Joints information collected by the camera and 3D reference system of the camera and the inertial measurement unit ... 121
Figure 11. Displacement pattern graphs of functional tests and demonstration of intervals ... 123 Figure 12. Bland–Altman plots for displacement, velocity, and acceleration in the bending test comparing the RGB-D camera and IMU. The lines represent the mean of the differences and limits of agreement ... 134 Figure 13. Bland–Altman plots for displacement, velocity and acceleration in the STS test comparing the RGB-D camera and IMU. The lines represent the mean of the differences and limits of agreement... 135 Figure 14. Bland–Altman plots for displacement, velocity and acceleration in the sock test comparing the RGB-D camera and IMU. The lines represent the mean of the differences and limits of agreement... 136 Figure 15. Participant and RGB-D camera placement. Orientation and coordinates reference system ... 155 Figure 16. Displacement pattern graphs of functional tests and demonstration of intervals ... 156
CHAPTER I:
INTRODUCTION
1.1. AXIAL SPONDYLOARTHRITIS
1.1.1. Definition
Spondyloarthritis includes an umbrella term of chronic inflammatory diseases with common clinical and genetic features (Garg et al., 2014). The term has had a lot of changes over the last decades due to the advances in the assessment and classification of different subtypes (Sieper et al., 2009 a). Spondyloarthritis can affect mainly toe spine or other parts of the body (Dougados et al., 1991). Axial spondyloarthritis (AS) affects predominantly the axial skeleton and includes several inflammatory spine diseases as ankylosing spondylitis, non-radiographic axial spondyloarthritis (nrAS), spondyloarthritis associated with inflammatory bowel disease, and undifferentiated spondyloarthritis (Sieper & Poddubnyy, 2017; Wang &
Ward, 2018). Although this classification aims to organise spondyloarthritis patients, it is possible that some features from the other type appear in both conditions. For example, arthritis and enthesitis are found in 30-50% of axial spondyloarthritis (Rudwaleit et al., 2011; Sieper & Poddubnyy, 2017).
The inflammation of AS appears between the cartilage and bone in the spine and sacroiliac joints (Sieper & Poddubnyy, 2017). Due to mechanical stress, inflammation encourages the formation of new bone tissue as a consequence of previous damage (Jacques et al., 2014; Lories, 2011). The nrAS can be seen as an early phase of AS but the structural damage might or might not develop. In addition, it may not be visible on X-ray since this is detected in only 10% of nrAS over 2 years (Boonen et al., 2015;
Sieper & Poddubnyy, 2017). Figure 1 attempts to represent the natural history of the AS. AS and nrAS have a similar activity and function burden but they may differ in age,
symptom duration and gender distribution (Annelies Boonen et al., 2015; López- Medina et al., 2019).
Figure 1. Conceptualization of the natural history of the AS illustrated as a highway. Adapted from Garg et al. 2014, originally from van Vollenhoven, 2011.
The ankylosing spondylitis or radiographic axial spondyloarthritis is an AS where patients developed structural damage in the spine or the sacroiliac joints and the damage is visible on radiographs (Garg et al., 2014; Sieper & Poddubnyy, 2017). The main difference with nrAS is that there is no structural damage to the spine or sacroiliac joints and that it is not possible to see radiographic changes (Garg et al., 2014; Sieper
& Poddubnyy, 2017) so this term is unclear and confusing to rheumatologists (Deodhar et al., 2014). The symptomatology of both conditions is characterized by inflammation, lumbar pain, morning stiffness and limited mobility of the pelvis and spine (Ward et al., 2016).
The AS usually starts from the age of 30 and the presence of HLA-B27 five years
2006). HLA-B27 is a gene of the human major histocompatibility complex and has been related with spondyloarthritis (Laval et al., 2001). This gene was detected in 65- 93% of AS patients and appears in most of assessment criteria because it increases the probability to success the criteria for AS (Jaakkola et al., 2006; M. Rudwaleit et al., 2009). In addition, other genetic loci have been related to AS: the endoplasmic reticulum aminopeptidase (ERAP) and the interleukin-23 receptor (International Genetics of Ankylosing Spondylitis Consortium (IGAS) et al., 2013).
1.1.2. Prevalence
There are many studies that measure the prevalence and incidence of this pathology in the world, but the difficult classification and broad terminology make the results segmented.
A comprehensive systematic review was conducted in 2016 about the prevalence of the spondyloarthritis and various subtypes of the disease (Stolwijk et al., 2016). The prevalence was bigger in northern arctic communities (1.61%) and North America (1.35%), as opposed to Europe (0.54%) (Stolwijk et al., 2016). The highest European prevalence was recorded in Germany (1.73%) (Akkoc & Khan, 2005). The prevalence of ankylosing spondylitis did not follow the same tendency because Europe is in the first place (0.19%) and North America in the second one (0.07%) (Stolwijk et al., 2016).
The prevalence of the AS has been less analysed than other terms related to the pathology. The prevalence was 0.36% in French employees and 0.70% in medical records from United States (Costantino et al., 2015; Strand et al., 2013). There are
some differences between men and women, for example, women usually have less structural damage in the spine and sacroiliac joints (Boonen et al., 2015; Deodhar et al., 2016) but the burden of AS appears in both genders and, whether ankylosing spondylitis or nrAS, is similar (Wright et al., 2020). Therefore, many more cases have been recorded in men have been historically recorded than in women due to a number of implicit biases in the diagnosis based on radiographic evidence (Wright et al., 2020).
Regarding the incidence of the spondyloarhtitis, it was 0.05% and 0.06% in Finland and Spain, respectively (Muñoz-Fernández et al., 2010; Savolainen et al., 2003). A low incidence was registered in Japan (0.00048%) (Hukuda et al., 2001). The incidence of ankylosing spondylitis varied from 0.003 to 0.02% in different studies (Stolwijk et al., 2012).
1.1.3. Health costs and work impact
Spondyloarthritis increases the healthcare costs, the productivity losses (Harvard et al., 2016), and there is a cost increase if the symptomathology is worse (Harvard et al., 2016; Kobelt et al., 2008). The 5-years average direct and indirect cost per patient in the United States was US$1781 and US$4999 per patient/year, respectively (Ward, 2002). The cost per patient is different among the European countries. The annual total cost of ankylosing spondylitis per patient was €5000 in France (Harvard et al., 2016), €5402 in Spain (Merino et al., 2020), and direct costs were €2837 in Netherlands and 1790€ in Belgium (A. Boonen et al., 2003).
The two most important features studied about the costs of the disease were the delay of the diagnosis and the expensive cost of biological treatments. A delayed
diagnosis is related to higher direct and indirect costs and increases the work disability (Yi et al., 2020). For this reason, the importance of an early diagnosis has been analysed. Patients from Italy received an average of 4 specialist health services in 3 years before being diagnosed with AS, costing €140.90 per patient or more than €5 million for the Italian health system (Mennini et al., 2018). Treatment costs were more than US$3000 in 67.4% of patients with 10 years of delay in the diagnosis against the 25.6% of patient with less than 5 years of delay (Grigg et al., 2011).
Additionally, the biological treatment increases the total costs (Harvard et al., 2016; White et al., 2019). For example, the annual cost in New Zealand per patient was NZ$15677 with NZ$12189 direct cost of which NZ$10701 was used to biologic treatments (White et al., 2019). Patients who received a biologic treatment had mean 3-year total costs of €38206, almost 10 times more than traditional treatment (Harvard et al., 2016).
Finally, up to 25% of patients report that their AS affects their productivity at work and it is estimated that the absenteeism from work ranges between 14 and 20 days (Ariza-Ariza et al., 2009; Rafia et al., 2012), with greater impact in patients from rural areas (Hollick et al., 2020). The most important determinants of indirect costs were days of absence from work and loss of related productivity (Rafia et al., 2012; Ward, 2002). In addition, 90% of patients with a 10 years of delayed diagnosis had problems with employability (Grigg et al., 2011). Besides the diagnosis delay, an older age at onset and some physical impairments affect the work life in AS patients (Cakar et al., 2009).
1.1.4. Diagnosis
Succesful diagnosis of AS are very important because they are associated with physical function and psychological features (Yi et al., 2020). Due to the lack of gold standards in AS (Sieper & Poddubnyy, 2017), various classification criteria have been created for spondyloarthritis:
• The Modified New York criteria for ankylosing spondylitis were the first most important classification criteria (van der Linden et al., 1984). The disease was confirmed if the patient has sacroiliitis grade 2 bilaterally or 3-4 unilaterally, and one of these clinical criteria: low back pain and stiffness for more than 3 months, limitation of motion of lumbar spine in the sagittal and frontal planes and limitation of chest expansion relative to normal values correlated for age and sex (van der Linden et al., 1984).
• The Amor criteria for spondyloarthritis were an important classification based on a final score (Amor et al., 1990). A diagnosis of spondyloarthritis was considered if the sum of points scored is 6 or more. The criterion was based on lumbar pain during the night, morning stiffness, oligoarthritis, buttock pain, dactylitis, enthesitis, iritis, non-gonococcal urethritis or cervicitis, acute diarrhea, presence or history of psoriasis, balanitis or inflammatory bowel disease, sacroiliitis grade 2 or more, presence of HLA-B27, familial history of ankylosing spondylitis and good response to nonsteroidal anti-inflammatory drugs (Amor et al., 1990).
• The European Spondyloarthropathy Study Group criteria simplified the criterion (Dougados et al., 1991). The presence of inflammatory spinal pain or
the diagnosis. In addition, one or more of these following variables must appear: positive family history, psoariasis, inflammatory bowel disease, urethritis, cervicitis, buttock pain alternating gluteal areas, enthesopathy or sacroiliitis (Dougados et al., 1991).
• The Assessment of SpondyloArthritis International Society (ASAS) criteria for classification of axial spondyloarthritis are the most used criteria currently (Rudwaleit et al., 2009). It includes the most of cases of nrAS with more success than the other criteria. It is used in patients with more than 3 months back pain and an age at onset less than 45 years. The diagnosis is positive if there is a sacroilits on imaging plus 1 or more AS features, or HLA-B27 positive plus 2 or more AS features (Rudwaleit et al., 2009). The sacroiliitis on imaging was defined as active inflammation on magnetic resonance imaging (MRI) highly suggestive of sacroiliitis or the same radiographic sacroiliitis according to the Modified New York criteria (Rudwaleit et al., 2009). The AS features in criteria are: inflammatory back pain, arthritis, enthesitis, uveitis, dactylitis, psoriasis, Crohn’s, good response to nonsteroidal anti-inflammatory drugs, family history for AS or elevated c-reactive protein (CRP) (Rudwaleit et al., 2009). The ASAS criteria have improved the classification of AS in health costs and physichian visits (Abdelrahman & Mortada, 2018). These criteria propose a core set of assessment index and it recommends the use of spinal mobility tests and a visual analogue scale (VAS) in order to registier the pain during the last week and night, the global disease activity and stiffness during the morning (van der Heijde et al., 1999).
The inflammatory back pain (IBP) is a term used also in the AS assessment and describes a specific kind of back pain (Sieper et al., 2009 b). The most important classification of IBP was developed by the ASAS experts for more than 3-months back pain and it is fulfilled with the presence of at least four out of five parameters: age at onset less than 40 years, insidious onset, improvement with exercise, no improvement with rest and pain at night (Sieper et al., 2009 b). Previous criteria, such as Calin criteria or Berlin criteria, have similar parameters than the LBP according to the ASAS experts (Calin et al., 1977; Rudwaleit et al., 2006).
1.1.4.1. Medical imaging
Imaging is decisive for the correct and early diagnosis (Sieper & Poddubnyy, 2017). Usually, radiography of sacroiliac joints is recommended as the first imaging method but has limitations in early AS patients because of structural damage (Poddubnyy et al., 2011; van der Linden et al., 1984). The next step after a confusing radiographic result is the MRI where a presence of bone marrow oedema in the subchondral bone is the definition of an active sacroiilitis on MRI (Lambert et al., 2016;
Mandl et al., 2015). The use of imaging methods of the sacroiliac joints together with the spine improves the diagnosis of AS more than only the sacroiliac joints, therefore the imaging of the spine is a good complement although its degeneration appears later in time (Sieper & Poddubnyy, 2017; Weber et al., 2015).
1.1.5. Assessment
The structural damage and inflammation produce spinal mobility impairment (P.
Machado et al., 2010). Mobility impairments appear first in lumbar spine and then thoracic and cervical spine (Ramiro et al., 2015). Several validated movement measures are based on the lumbar and cervical spine (Sieper et al., 2009 a). The ASAS core set includes some spinal mobility measures based on visual analysis and joint goniometry required in the AS assessment such as cervical rotation, lateral spinal flexion, modified Schober test, occiput to wall distance and chest expansion (van der Heijde et al., 1999). Despite its wide use, caution must be exercised when using these classic mobility measures because they do not show good criterion-concurrent validity, so objective tools are needed to improve the visual analysis of clinicians (Castro et al., 2015). The Bath Ankylosing Spondylitis Metrology Index (BASMI) is used to assess the spinal mobility in 5 items scored from 0 to 10 (Jenkinson et al., 1994). The items measure cervical rotation, tragus to wall distance, lumbar flexion, lumbar side flexion and intermalleolar distance (Jenkinson et al., 1994). Like other mobility measures, it has good reliability in expert clinicians because of their experience (Madsen et al., 2008).
Objective tools have been examined in AS patients in several studies, for example, the inertial measurement units are acceptable devices to evaluate the spinal mobility (ICC = 0.90-0.97, r = 0.40-0.91) (Aranda Valera et al., 2018; Aranda-Valera et al., 2020; Gardiner et al., 2020) and a video-based motion capture system with optical markers had good results assessing movement in a validation study (ICC = 0.90-0.99, r =0.68-0.92) (Garrido-Castro et al., 2012).
1.1.5.2. Patient-reported outcome measures
Clinicians use assessment tools as patient report outcomes measures or questionnaires (Gabel et al., 2009). These intruments are used in the daily clinical practice and in the clinical research in AS patients (Zochling, 2011). The most important questionnaires in AS are:
• The Bath Ankylosing Spondylitis Functional Index (BASFI) assesses the functional capacity and daily activities with 10 items that give a score from 0 to 10 (Calin et al., 1994). BASFI has questions about putting on socks, pick up a pen from the floor, take something from a high shelf, getting up from a chair, getting up from the floor, standing, walking up the stairs, neck mobility and activity performances (Calin et al., 1994). The higher scores indicate more limitation of the patient (Calin et al., 1994).
• The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) is a questionnaire consisting of 6 items relating to typical symptoms of ankylosing spondylitis and each item gives a score from 0 to 10 (Garrett et al., 1994).
There are questions about fatigue, pain, discomfort and morning stiffness (Garrett et al., 1994). Like the previous questionnaire, the higher scores indicate more limitation of the patient (Garrett et al., 1994).
• The ASAS Health Index (ASAS-HI) is a unidimensional scale providing different levels of functioning and contains 17 dichotomous items addressing different categories as pain, sleep or mobility (Kiltz et al., 2015). A lower score in ASAS-HI indicates a better health status (Kiltz et al., 2015).
1.2. NON-SPECIFIC LOW BACK PAIN
1.2.1. Definition
Low back pain (LBP) is defined as pain and discomfort, localised below the costal margin and above the inferior gluteal folds, with or without leg pain (van Tulder et al., 2006). LBP is very common in populations worldwide and appears in all age groups (Dunn et al., 2013; Hartvigsen et al., 2018). To provide the reader with a comprehensive overview of the main classification models for LBP, different classification models will be briefly discussed in the next paragraphs.
According to the etiology, Waddell et al. created a classification based on three categories to classifiy LBP: specific spine pathology, nerve root pain and non-specific low back pain (Waddell et al., 1986). The previous terms have been updated through the years and at present, a two-categories classification is used: specific and non- specific LBP (van Tulder et al., 2006). If the LBP is attributed to a recognisable reason such as infection, tumour, osteoporosis, inflammatory process, radicular syndrome or cauda equina syndrome, it is called specific low back pain (SLBP) (van Tulder et al., 2006; Waddell et al., 1986). Non-specific low back pain (NSLBP) is defined as low back pain but no specific cause can be identified. NSLBP represents a heterogeneous group of conditions and these patients may respond differently to the clinical interventions (Vining et al., 2019). About 90% of LBP patients can be classified into this type of LBP (B. W. Koes et al., 2006; Royal Dutch Society for Physical Therapy, 2013; Waddell et al., 1986).
There is a classic and well-known ‘time-based’ classification according to the duration of the pain: acute low back pain is an episode of low back pain persisting less than 6 weeks, subacute low back pain is an episode of low back pain between 6 and
12 weeks and chronic low back pain: an episode of low back pain persisting 12 weeks or more (van Tulder et al., 2006). Although this “time-based” classification is outdated and does not fit with the irregular course of the LBP in recurrent cases (Hancock et al., 2015; G. C. Machado et al., 2017; Stanton et al., 2008), recent clinical guidelines still mention it (Almeida et al., 2018). The Royal Dutch Society for Physical Therapy propused a classification in two groups according to the course of the pain: a normal course means the activity level and degree of participation of the patient gradually increases over time and then the pain usually diminishes, and an abnormal course when the pain does not change and there is no clear increase in activity level and reduction in the participation restrictions after 3 weeks. The recurrence rate of the LBP has to be taken into account because after the acute LBP one-third of patients will experience a recurrent episode, and approximately half of those will seek clinical treatment (G. C. Machado et al., 2017). It is very important to limit the transition of acute to chronic LBP (Gatchel et al., 2018; Qaseem et al., 2017).
1.2.2. Prevalence
LBP is the most common disability in many countries in large part due to a high prevalence (Vlaeyen et al., 2018; Vos et al., 2016). This pathology mostly has a benign character and it is managed as a trivial problem compared to other severe conditions, but it is an important cause of work absence and high cost in health systems (Buchbinder et al., 2013).
The lifetime prevalence of NSLBP is approximately at 60% to 70% in developed countries and the one-year prevalence is estimated between 15- 45% (Duthey, 2013).
When analysing data from different countries, one third of adults from the United States have consulted a physician at least for 1 day during the last 3 months due to LBP (Deyo et al., 2006). The incidence of LBP in Belgium was 5.14% and higher between 50 and 55 years old (Fatoye et al., 2019).
On the other hand, the chronic NSLBP (CNSLBP) affects the functional disability directly and influences the work capacity and the quality of life (Vlaeyen et al., 2018).
In addition, chronic pain is an important source of human suffering and one of the most costly pathologies in modern industrialized societies (Vlaeyen et al., 2018). CNSLBP has a high prevalence that increases linearly in the working age as all the LBP in age groups from 30 until the 60 years (Meucci et al., 2015). In Spain, the prevalence of CNSLBP was 22% and was more common in women and older subjects with low educational level, less social support, more work stress and more active limitations (Dueñas et al., 2020).
1.2.3. Health costs and work impact
LBP is an urgent global public health problem because it has a financial impact on the health care systems in every country (Anema et al., 2009; Maniadakis & Gray, 2000), although the approach is different according to the economy level of the country (Hartvigsen et al., 2018). The economic costs of LBP are high in numerous countries and the indirect costs such as production losses are higher than direct healthcare costs (Vlaeyen et al., 2018; Vos et al., 2016). Spinal condition is the third medical condition in health national costs in United States (Dieleman et al., 2016) where indirect costs were between US$ 18 and 28 billion (Dagenais et al., 2008). United Kingdom and Australia also report high costs of LBP in their health economy: £1.6 and
AUS$ 1 billion as direct costs, and £10.7 and AUS$ 8 billion as indirect ones, respectively (Maniadakis & Gray, 2000; Walker et al., 2003).
The costs of LBP for society in Europe are extremely high (Duthey, 2013;
Shmagel et al., 2016; van Dongen et al., 2016). The annual total costs of Netherlands were €8.5 billion (A Boonen et al., 2005) and the absenteeism from work due to LBP costed 1260€ per worker/year in Spain (González Viejo & Condón Huerta, 2000).
There were approximated datas about the costs of LBP in Belgium: €36 million in imaging, €128 million in physiotherapy, €73 million in rehabilitation and €21 million in surgery (Nielens et al., 2006). Furthermore, the total direct medical cost of chronic LBP is between €81 and €167 million (Nielens et al., 2006).
The impact of LBP at work is a health problem worldwide and the health policies try to moderate the physical work but they are very few and different between poor and rich regions (Bevan et al., 2009; Fabunmi et al., 2005; Lucchini & London, 2014).
LBP is one of the reasons for loss of work days in many industrialised countries (Hashemi et al., 1998). The most obvious cause of absenteesim is heavy physical work although it can be influenced by job satisfaction, economic situation and other psychosocial factors (Serranheira et al., 2020). It is also influenced by to age, where LBP forces people to retire prematurely if they have comorbidities or a lower economic level (Schofield et al., 2011).
How LBP can influence work absenteesim is different even in nearby and developed countries, for example, the return to work was faster in the Netherlands than in Germany over 2 years (Anema et al., 2009). In Spain, there was an association between absenteeism and chronic low back pain (OR = 1.22) and people who reported
et al., 2014). The 72% of acute LBP patients due to a work accident were absent from work and of this total percentage 8.2% were absent more than three months in Belgium (Nielens et al., 2006).
1.2.4. Diagnosis
Screening LBP patients at early stages is a challenge (Vlaeyen et al., 2018).
Physicians and reseraches need to focus the examination on disability, pain and function using validated and realible tools to perform good assessments in LBP (Airaksinen et al., 2006).
Most of the clinical guidelines recommend history taking, physical examination, neurologic examination such as Straight Leg Raise test and assessment of strength, reflexes, and sensation (Oliveira et al., 2018). The next step in the diagnosis in LBP is to differentiate between spinal pathology or visceral diseases (Vlaeyen et al., 2018).
The first tool that clinicians can use are the red flags (Vlaeyen et al., 2018). The red flags are potential risk factors of serious biological pathology that they can be hidden by LBP (Bigos, 1994). The use of red flags in LBP is still mentioned in current reference documents (Hartvigsen et al., 2018; Vlaeyen et al., 2018). The traditional red flags are unexplained weight loss, bladder or bowel dysfunction, drug use, fever, recent infection, previous history of cancer or suspiction of a spine pathology. Despite the high use of these risk factors, only some red flags have low accuracy (Downie et al., 2013).
1.2.4.1. Medical imaging
Regarding the medical imaging as radiography or MRI, it is costly, time- consuming, an exposure to raditation and does not improve clinical outcomes (Chou et al., 2009; Maher et al., 2017), although it is widely accepted that medical imaging should be used to exclude serious pathology (Bart W. Koes et al., 2010). Some observed imaging findings can negatively influence the treatment and concern the patient (Brinjikji et al., 2015; Webster et al., 2014). Other medical imaging technique such as ultrasound can classify LBP patients and healthy people measuring thickness, stiffness and pennation angle of erector spinae, transversus abdominis, thoracolumbar fascia and multifidus (Cheung et al., 2020; Cuesta-Vargas & González- Sánchez, 2014).
1.2.5. Assessment
1.2.5.1. Patient-reported outcome measures
Although the good prognosis, about 10-15% of patients develop chronic pain and complaints (Vlaeyen et al., 2018). In order to evaluate these patients, it is necessary to assess the psychological and social factors of patients in order to classify the risk level and prevent chronic pain and disability (Vlaeyen et al., 2018). The patient report outcomes measures or questionnaires give precise and important information in a specific part of the clinical intervention and they evaluate the subjective dimension of the pathology (Gabel et al., 2009). These screening tools assess psychological and social factors possibly related to behavior when confronted with LBP and usually there are questions about pain, distress, beliefs, functioning, return to work and disability
(Gatchel et al., 2007; Linton et al., 2018). Due to the frecuency of use or the psychometric characteristics, the more important questionnares to assess LBP are:
• Roland Morris Questionnaire (RMQ): It is used to determine the degree of physical disability derived from LBP in 24 questions. The physical disability is assessed according to the performance of daily activities and the level of dependency (Roland & Morris, 1983).
• Oswestry Disability Index: 10 questions where the first questions ask about the intensity of pain and the other questions are about disability such as: basic activities of daily life affected by pain such as personal care, walking, sleep, lifting, sexual activity and social life (Fairbank & Pynsent, 2000).
• Örebro Musculoskeletal Pain Screening Questionnaire: It allows to clinicians to evaluate psychosocial flags as a complement to the examination and can help to predict long-term disability. This questionnaire has 25 items about pain, psychological factors and work (Linton & Boersma, 2003).
• STarTBack tool: It is a simple prognostic questionnaire that helps clinicians to identify biopsychosocial risk factors in disability due to LBP. There are 9 items that evaluate pain, fear, anxiety and depression. The score can stratify patients according to the low, medium and high risk (Hill et al., 2008).
• Spine Functional Index: It allows to assess the whole-spine functionaliy of the patient with 25 questions about daily activity tasks (Gabel et al., 2013).
1.2.5.2. Movement control
The back movement variability due to errors in the motor control system is more present in LBP patients and it is not beneficial for them (Ippersiel et al., 2018; Salvioli et al., 2019) so movement and mobility control is evaluated by clinicians (Haj et al., 2019; Hartvigsen et al., 2018; Koch & Hänsel, 2018). Kinematic outcomes of the back movement can support in the clinical assessment of patients with LBP symptoms independent of etiology (Vaisy et al., 2015). In addition, the lumbo-sacral movement pattern is different between subgroups of LBP patients (Marich et al., 2017; Scholtes et al., 2009). According to a systematic review about assessment, the reliability of most of the physical examination procedures used in NSLBP found either a lack of reliability or conflicting evidence about their reliability (May et al., 2006). Clinical testing should be part of the assessment of physical functioning and tests based on movement are recommended in clinical practice (Denteneer et al., 2018; Salvioli et al., 2019).
Functional tests should be considered in the assessment of these patients because they complete a multimodal assessment since subjective findings and psychosocial questionnaires are insufficient (Papi et al., 2018). Kinematic analysis of human motion can demonstrate the quality of movement changes in LBP during repetitive functional tests (Cuesta-Vargas et al., 2010; Dideriksen et al., 2014). The assessments of movement using kinematics are valid, reliable measures and can be used clinically to diagnose consequences of NSLBP (Hidalgo et al., 2012).
1.2.5.3. Management
Besides the functional impairment, LBP is influenced by the range of biophysical, phychological and social dimensions and these features affect the quality of life, social
model of LBP tries to explain how biological and non-biological factors contribute to the development of chronic LBP. These non-biological factors can be negative beliefs, negative expectations about pain, emotional responses, pain behaviours and the relationship perceived between pain and work (Vlaeyen et al., 2018). These factors are called the yellow flags (Nicholas et al., 2011). Besides non-biological factors, comorbidities and the altered pain-processing mechanisms create complex interactions in the patients and decrease the response to a range of treatments of LBP (Hartvigsen et al., 2013, 2018). Therefore, the management plan in these patients has to be individualized taking into account the personally relevant goals and how they experience pain and suffering (O’Sullivan et al., 2018)
Considering this information, there are two approaches to manage patients with NSLBP: the traditional stepped approach and the risk stratification approach (Figure 2) (Almeida et al., 2018). The stepped approach classifies patients depending on the duration of the symptoms: acute, sub-acute and chronic (Almeida et al., 2018). The risk stratification approach in NSLBP patients uses the score of the questionnaires ir order to classify patients in low risk, medium risk and high risk (Almeida et al., 2018).
The support in the low risk group is less-intensive, as opposed to medium and high risk where the support should be more complex and intensive (Almeida et al., 2018).
This new approach has been used in the UK and Belgian health system guidelines (National Institute for Health and Care Excellence, 2016; Van Wambeke et al., 2017) using the Örebro Musculoskeletal Pain Screening Questionnaire (Linton & Boersma, 2003) and the STarTBack tool as assessment tools (Hill et al., 2008).
Figure 2. Management and part of the assessment of patients with NSLBP. Information extracted from these scientific works: Airaksinen et al., 2006; Almeida et al., 2018; Downie et al., 2013; Maher et al., 2017; Papi et al., 2018; Vlaeyen et al., 2018.
1.3. MOTION CAPTURE
As mentioned previously, both NSLBP and AS impact in the mobility of the patient. This not only affects functioning and quality of life, but the assessment of movement is also crucial for clinical management. However, the existing measurement tools have weaknesses such as clinical measures based on joint assessment that may be overcome by using human motion capture.
Human motion capture is the process of recording human movement and kinematics data (van der Kruk & Reijne, 2018). Motion capture is used in medical applications, sports, entertainment, robotics and ergonomics (Xsens Technologies B.V., 2020). The evaluation of the human movement is a cornerstone in the assessment and development of treatments in many pathologies (Saber-Sheikh et al.,
2010). Motion capture systems were created to study human movement in biomechanical labs in the 80s and as a cheaper and faster method than animation in the entertainment. Some important characteristics that have helped the development of motion capture is the promptness with which the results are obtained, being almost in real time, in addition to the large amount of data that is generated with great accuracy (Ribeiro, 2016).
Table 1 resumes the motion capture systems reported in the literature. The accuracy of these systems usually is inverse to the positioning system working area, being a limiting factor in the selection of a measurement (van der Kruk & Reijne, 2018).
It is possible to divide these systems in two groups: the non video-based motion capture and the video-based motion capture.
Table 1. Advantages (green) and disadvangates (red) of the different human motion capture systems clasificaed by cost, usability, clinimetry and others.
Image Processing Systems (IPS)
Example product: Microsoft Kinect and RGB- D or depth cameras
Optoelectronic Systems (OS)
Example product: Vicon Motion Capture Systems
Video-based human motion capture
Cost - Low-cost - Expensive
Usability - Easy-to-use
- Uncomplicated preparation - Markerless tracking - No need for calibration
- Complete information from the individual
- Requieres self-development:
codes, algorithms, test validation…
- Outperformed in sports - Line-of-sight necessary
- Complete information from the individual - Setup difficulty with previous calibration with the subject
- Use of markers
- Bound to restricted area - Line-of-sight necessary
Clinimetry - Good accuracy and reliability (Depending on site and task specific)
- High accuracy and reliability
Other - Light quality interferes measurements
- Light quality interferes measurements
Human motion capture without video system Electromagnetic Sensors (ES)
Example product: Polhemus Fastrak
Inertial Measurement Unit (IMU)
Example product: Shimmer3
Cost - Expensive - Low-cost
Usability - Large volumes
- No line-of-sight necessary - Human interaction
- Information partially from the individual
- Low sample frequency
- Minimally invasive - Large volumes
- Optimal for functional tests
- Information partially from the individual - Human interaction
- No possibility to measure position stand-alone
Clinimetry - High accuracy and reliability - Less accurate than OS
- Good accuracy and reliability (Depending on site and task specific) Other - Sensitive to ferromagnetic
disturbances
- Noise progressive to distance from the receptor
- Dependent on fusion filter
Red, Green, Blue and Depth (RGB-D). Information extracted from these scientific works: Clark et al., 2019; Cuesta-Vargas et al., 2010; Moreno et al., 2017; van der Kruk & Reijne, 2018; Zhou et al., 2018
