Integrating a gait analysis test in hospital rehabilitation: A service design approach

English version

Autoři: Javier Marín ^aff001; Teresa Blanco ^aff003; José J. Marín ^aff001; Alejandro Moreno ^aff001; Elena Martitegui ^aff006; Juan C. Aragüés ^aff006
Působiště autorů: IDERGO (Research and Development in Ergonomics) Research Group, I3A (Aragon Institute of Engineering Research), University of Zaragoza, Zaragoza, Spain ^aff001; Department of Design and Manufacturing Engineering, University of Zaragoza, Zaragoza, Spain ^aff002; HOWLab (Human Openware Research Lab) Research Group, I3A, University of Zaragoza, Zaragoza, Spain ^aff003; GeoSpatiumLab, S.L. Zaragoza, Spain ^aff004; Department of Health and Sports Sciences, University of Zaragoza, Zaragoza, Spain ^aff005; Rehabilitation and Physical Medicine Service, HUMS (Miguel Servet University Hospital), Zaragoza, Spain ^aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0224409

Souhrn

Background

Gait analysis with motion capture (MoCap) during rehabilitation can provide objective information to facilitate treatment decision making. However, designing a test to be integrated into healthcare services requires considering multiple design factors. The difficulty of integrating a ‘micro-service’ (gait test) within a ‘macro-service’ (healthcare service) has received little attention in the gait analysis literature. It is a challenge that goes beyond the gait analysis case study because service design methods commonly focus on the entire service design (macro-level).

Objective

This study aims to extract design considerations and generate guidelines to integrate MoCap technology for gait analysis in the hospital rehabilitation setting. Specifically, the aim is to design a gait test to assess the response of the applied treatments through pre- and post-measurement sessions.

Methods

We focused on patients with spasticity who received botulinum toxin treatment. A qualitative research design was used to investigate the integration of a gait analysis system based on inertial measurement units in a rehabilitation service at a reference hospital. The methodological approach was based on contrasted methodologies from the service design field, which materialise through observation techniques (during system use), semi-structured interviews, and workshops with healthcare professionals (13 patients, 10 ‘proxies’, and 6 doctors).

Results

The analysis resulted in six themes: (1) patients’ understanding, (2) guiding the gait tests, (3) which professionals guide the gait tests, (4) gait test reports, (5) requesting gait tests (doctors and test guide communication), and the (6) conceptual design of the service with the gait test.

Conclusions

The extracted design considerations and guidelines increase the applicability and usefulness of the gait analysis technology, improving the link between technologists and healthcare professionals. The proposed methodological approach can also be useful for service design teams that deal with the integration of one service into another.

Klíčová slova:

Medical doctors – Physicians – Qualitative studies – Decision making – Gait analysis – Walking – Botulinum toxin – Gait rehabilitation

Zdroje

1. Cimolin V, Galli M. Summary measures for clinical gait analysis: a literature review. Gait Posture 2014;39(4):1005–1010. doi: 10.1016/j.gaitpost.2014.02.001 24613461

2. Baker R. Gait analysis methods in rehabilitation. Journal of NeuroEngineering and Rehabilitation 2006;3(1):1.

3. Chambers HG, Sutherland DH. A practical guide to gait analysis. JAAOS-Journal of the American Academy of Orthopaedic Surgeons 2002;10(3):222–231.

4. Zhou H, Hu H. Human motion tracking for rehabilitation—A survey. Biomedical Signal Processing and Control 2008;3(1):1–18.

5. Simon SR. Quantification of human motion: gait analysis—benefits and limitations to its application to clinical problems. J Biomech 2004;37(12):1869–1880. doi: 10.1016/j.jbiomech.2004.02.047 15519595

6. Cook RE, Schneider I, Hazlewood ME, Hillman SJ, Robb JE. Gait analysis alters decision-making in cerebral palsy. Journal of pediatric orthopaedics 2003;23(3):292–295. 12724589

7. Duncan G, Currie G, Evans A, Gartnavel WG. Gait analysis: a step in the right direction. Clin Rehabil 1992;6(2):111–116.

8. Guzik A, Drużbicki M, Przysada G, Brzozowska-Magoń A, Wolan-Nieroda A, Kwolek A. An assessment of the relationship between the items of the observational Wisconsin Gait Scale and the 3-dimensional spatiotemporal and kinematic parameters in post-stroke gait. Gait Posture 2018;62:75–79. doi: 10.1016/j.gaitpost.2018.03.009 29529516

9. Teunis Cloete and Cornie Scheffer. Benchmarking of a full-body inertial motion capture system for clinical gait analysis. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: IEEE; 2008.

10. Mooney R, Corley G, Godfrey A, Quinlan LR, ÓLaighin G. Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review. Sensors 2015;16(1):18.

11. Cooper G, Sheret I, McMillian L, Siliverdis K, Sha N, Hodgins D, et al. Inertial sensor-based knee flexion/extension angle estimation. J Biomech 2009;42(16):2678–2685. doi: 10.1016/j.jbiomech.2009.08.004 19782986

12. El-Gohary M, McNames J. Human joint angle estimation with inertial sensors and validation with a robot arm. IEEE Transactions on Biomedical Engineering 2015;62(7):1759–1767. doi: 10.1109/TBME.2015.2403368 25700438

13. Thewlis D, Bishop C, Daniell N, Paul G. Next generation low-cost motion capture systems can provide comparable spatial accuracy to high-end systems. Journal of applied biomechanics 2013;29(1):112–117. 22813783

14. Ståle A. Skogstad, Nymoen Kristianand Høvin ME. Comparing inertial and optical mocap technologies for synthesis control. Proc. of Int. Sound and Music Computing Conference; 2011.

15. Marin J, Blanco T, Marin JJ. Octopus: A Design Methodology for Motion Capture Wearables. Sensors 2017;17(8):1875.

16. Perera C, Zaslavsky A, Christen P, Georgakopoulos D. Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials 2014;16(1):414–454.

17. Benedetti MG, Beghi E, De Tanti A, Cappozzo A, Basaglia N, Cutti AG, et al. SIAMOC position paper on gait analysis in clinical practice: General requirements, methods and appropriateness. Results of an Italian consensus conference. Gait Posture 2017;58:252–260. doi: 10.1016/j.gaitpost.2017.08.003 28825997

18. Mulder T, Nienhuis B, Pauwels J. Clinical gait analysis in a rehabilitation context: some controversial issues. Clin Rehabil 1998;12(2):99–106. doi: 10.1177/026921559801200202 9619651

19. Hausdorff JM. Gait variability: methods, modeling and meaning. Journal of neuroengineering and rehabilitation 2005;2(1):19.

20. Boudarham J, Roche N, Pradon D, Bonnyaud C, Bensmail D, Zory R. Variations in kinematics during clinical gait analysis in stroke patients. PloS one 2013;8(6):e66421. doi: 10.1371/journal.pone.0066421 23799100

21. Ferber R, Osis ST, Hicks JL, Delp SL. Gait biomechanics in the era of data science. J Biomech 2016;49(16):3759–3761. doi: 10.1016/j.jbiomech.2016.10.033 27814971

22. Prakash C, Kumar R, Mittal N. Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif Intell Rev 2018;49(1):1–40.

23. Tobias J. Uebbing. User experience in smart environments: design and prototyping. PhD. Thesis.University of Twente; 2016.

24. Andersen RM, Davidson PL, Baumeister SE. Improving access to care. Changing the US health care system: Key issues in health services policy and management 2013:33–69.

25. Jones DA, Vetter NJ. A survey of those who care for the elderly at home: their problems and their needs. Soc Sci Med 1984.

26. Blanco Teresa. Metodologías de diseño como plataforma para la x-disciplinaridad en proyectos tecnológicos: surfing disciplines. Universitat Politècnica de València; 2016.

27. R. Tassi. Dervice Design Tools Communication Methods Supporting Design Processes. Ph.D. ThesisPolitecnico di Milano, Milano, Italy; 2008.

28. Han N, Han SH, Chu H, Kim J, Rhew KY, Yoon J, et al. Service design oriented multidisciplinary collaborative team care service model development for resolving drug related problems. PloS one 2018;13(9):e0201705. doi: 10.1371/journal.pone.0201705 30265678

29. McMurray J, McNeil H, Lafortune C, Black S, Prorok J, Stolee P. Measuring Patients' Experience of Rehabilitation Services Across the Care Continuum. Part I: A Systematic Review of the Literature. Arch Phys Med Rehabil 2016;97(1):104–120. doi: 10.1016/j.apmr.2015.08.407 26299752

30. Maguire M. Methods to support human-centred design. International journal of human-computer studies 2001;55(4):587–634.

31. Kujala S. User involvement: a review of the benefits and challenges. Behaviour & information technology 2003;22(1):1–16.

32. Vargo SL, Lusch RF. Service-dominant logic 2025. International Journal of Research in Marketing 2017;34(1):46–67.

33. Wetter-Edman K, Vink J, Blomkvist J. Staging aesthetic disruption through design methods for service innovation. Des Stud 2018;55:5–26.

34. VanderKaay S, Moll SE, Gewurtz RE, Jindal P, Loyola-Sanchez A, Packham TL, et al. Qualitative research in rehabilitation science: opportunities, challenges, and future directions. Disabil Rehabil 2018;40(6):705–713. doi: 10.1080/09638288.2016.1261414 27973927

35. Graczyk EL, Gill A, Tyler DJ, Resnik LJ. The benefits of sensation on the experience of a hand: A qualitative case series. PloS one 2019;14(1):e0211469. doi: 10.1371/journal.pone.0211469 30703163

36. Pohl P, Carlsson G, Käll LB, Nilsson M, Blomstrand C. Experiences from a multimodal rhythm and music-based rehabilitation program in late phase of stroke recovery–A qualitative study. PloS one 2018;13(9):e0204215. doi: 10.1371/journal.pone.0204215 30226862

37. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. International journal for quality in health care 2007;19(6):349–357. doi: 10.1093/intqhc/mzm042 17872937

38. Guba EG, Lincoln YS. Paradigmatic controversies, contradictions, and emerging confluences. 2005.

39. Ponterotto JG. Qualitative Research Training in Counseling Psychology: A Survey of Directors of Training. Teaching of Psychology 2005.

40. Blanco T, Berbegal A, Blasco R, Casas R. Xassess: crossdisciplinary framework in user-centred design of assistive products. J Eng Des 2016;27(9):636–664.

41. Marín Zurdo J., Boné Pina M., Ros Mar R. and Martínez Gamarra M. Move‐Human Sensors: Sistema portátil de captura de movimiento humano basado en sensores inerciales, para el análisis de Lesiones Musculoesqueléticas y utilizable en entornos reales. Proceedings of the Sixth International Conference on Occupational Risk Prevention; 2008.

42. X-io technologies. Next Generation IMU (NGIMU). 2019 [Internet]; available from: http://x-io.co.uk/ngimu/.

43. X-io technologies. Related scientific publications. 2019 [Internet]; available from: https://x-io.co.uk/publications/.

44. Thibaut A, Chatelle C, Ziegler E, Bruno M, Laureys S, Gosseries O. Spasticity after stroke: physiology, assessment and treatment. Brain injury 2013;27(10):1093–1105. doi: 10.3109/02699052.2013.804202 23885710

45. Rizzo M, Hadjimichael O, Preiningerova J, Vollmer T. Prevalence and treatment of spasticity reported by multiple sclerosis patients. Multiple Sclerosis Journal 2004;10(5):589–595. doi: 10.1191/1352458504ms1085oa 15471378

46. Maynard FM, Karunas RS, Waring WP,3rd. Epidemiology of spasticity following traumatic spinal cord injury. Arch Phys Med Rehabil 1990 Jul;71(8):566–569. 2369291

47. Olvey EL, Armstrong EP, Grizzle AJ. Contemporary pharmacologic treatments for spasticity of the upper limb after stroke: a systematic review. Clin Ther 2010;32(14):2282–2303. doi: 10.1016/j.clinthera.2011.01.005 21353101

48. Baker JA, Pereira G. The efficacy of Botulinum Toxin A for spasticity and pain in adults: a systematic review and meta-analysis using the Grades of Recommendation, Assessment, Development and Evaluation approach. Clin Rehabil 2013;27(12):1084–1096. doi: 10.1177/0269215513491274 23864518

49. Nieuwenhuys A, Papageorgiou E, Pataky T, De Laet T, Molenaers G, Desloovere K. Literature review and comparison of two statistical methods to evaluate the effect of botulinum toxin treatment on gait in children with cerebral palsy. PloS one 2016;11(3):e0152697. doi: 10.1371/journal.pone.0152697 27030973

50. Glaser BG, Strauss AL. Grounded theory: Strategies for qualitative research. Chicago, lL: Aldine Publishing Company 1967.

51. Jonsdottir J, Recalcati M, Rabuffetti M, Casiraghi A, Boccardi S, Ferrarin M. Functional resources to increase gait speed in people with stroke: strategies adopted compared to healthy controls. Gait Posture 2009;29(3):355–359. doi: 10.1016/j.gaitpost.2009.01.008 19211250

52. Dickstein R. Rehabilitation of gait speed after stroke: a critical review of intervention approaches. Neurorehabil Neural Repair 2008;22(6):649–660. doi: 10.1177/15459683080220060201 18971380

53. Patton MQ. Qualitative research and evaluation methods. Thousand Oaks. Cal.: Sage Publications 2002.

54. Bitner MJ, Ostrom AL, Morgan FN. Service blueprinting: a practical technique for service innovation. Calif Manage Rev 2008;50(3):66–94.

55. Marxreiter F, Gaßner H, Borozdina O, Barth J, Kohl Z, Schlachetzki JC, et al. Sensor-based gait analysis of individualized improvement during apomorphine titration in Parkinson’s disease. J Neurol 2018;265(11):2656–2665. doi: 10.1007/s00415-018-9012-7 30196324

56. McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveys adequate? Quality of life research 1995;4(4):293–307. 7550178

57. Cella David, Bullinger Monika, Scott Charles, Barofsky Ivan and Clinical Significance Consensus Meeting Group. Group vs individual approaches to understanding the clinical significance of differences or changes in quality of life. Mayo Clinic Proceedings: Elsevier; 2002.

58. Martin S, Armstrong E, Thomson E, Vargiu E, Solà M, Dauwalder S, et al. A qualitative study adopting a user-centered approach to design and validate a brain computer interface for cognitive rehabilitation for people with brain injury. Assistive Technology 2018;30(5):233–241. doi: 10.1080/10400435.2017.1317675 28708963

59. Killington M, Fyfe D, Patching A, Habib P, McNamara A, Kay R, et al. Rehabilitation environments: Service users’ perspective. Health Expectations 2019;22(3):396–404. doi: 10.1111/hex.12859 30632258

60. Medina-Mirapeix F, Del Baño-Aledo ME, Oliveira-Sousa SL, Escolar-Reina P, Collins SM. How the rehabilitation environment influences patient perception of service quality: a qualitative study. Arch Phys Med Rehabil 2013;94(6):1112–1117. doi: 10.1016/j.apmr.2012.11.007 23154133

61. Varshney U, Chang CK. Smart Health and Well-Being. Computer 2016;49(11):11–13.

62. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation 2012;9(1):21.