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ISSN 1561-3011 (Electronic)

2022, Number 1

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Rev Cubana Invest Bioméd 2022; 41 (1)

Acceptability of the use of devices with user interfaces in neurological motor rehabilitation

Coromina HJC, Pérez PM, García ML, Soto LA, Álvarez GMÁ

Language: Spanish
References: 24
Page: 1-15
PDF size: 636.31 Kb.


ABSTRACT

Introduction: The advance of new technologies has contributed to raising the interaction options of people with products from the use of user interfaces. The use of devices with user interfaces, designed as supports aimed at neurological rehabilitation, can enhance and diversify this process in intra-hospital and extra-hospital contexts.
Objective: Identify the criteria that determine the acceptability of these devices in the motor rehabilitation of patients with neurological diseases.
Methods: The sample consisted of 31 patients from the Institute of Neurology and Neurosurgery who needed motor rehabilitation and a control group (N = 62) with healthy individuals. A questionnaire with 32 items was created and it explored the acceptability criteria of user interfaces. The final version of the questionnaire was obtained from the evaluation according to expert criteria and the calculation of internal consistency using Cronbach's alpha. To the values obtained during the application of the questionnaire, there was performed a factor analysis using the normalized varimax as the rotation method. The criterion for factor extraction was the Scree eigenvalue method.
Results: The acceptability of the use of devices with user interfaces is conditioned by three fundamental categories: security, expectations and ease of assimilation.
Conclusions: The acceptability of these devices depends on guaranteeing safety in their use, meeting the expectations of an autonomous rehabilitation and making evident and viable their ease of assimilation. Having these criteria, results in obtaining ergonomic requirements for the design of these devices.


REFERENCES

  1. Herrera R. Interfaces para humanos: más allá de los teclados y ratones. Ingeniare. Revista Chilena de Ingeniería. 2015;23(2):162-3.

  2. Pitale A, Bhumgara A. Human Computer Interaction Strategies—Designing the User Interface. In: IEEE, editor. 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT); Tirunelveli, India. 2019. pp. 752-8.

  3. Ruiz A, Arciniegas JL, Giraldo WJ. Caracterización de marcos de desarrollo de la interfaz de usuario para sistemas interactivos basados en distribución de contenido de video. Ingeniare. Revista Chilena de Ingeniería. 2018;26(2):339-53.

  4. Alonso-Valerdi LM, Ramirez- Mendoza RA. Motor imagery based brain–computer interfaces: An emerging technology to rehabilitate motor deficits. Neuropsychologia. 2015;79(part B):354-63.

  5. Gelber DA. Neurologic Examination in Rehabilitation. Continuum Lifelong Learning Neurol. 2011;17(3):449-61.

  6. Renaud K, Van Biljon J. Predicting technology acceptance and adoption by the elderly: a qualitative study. In: Enlighten, editor. Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists; Wilderness, South Africa: Universidad de Glasgow; 2008 octubre. pp. 210-9.

  7. Li Q, Luximon Y. Understanding Older Adults’ Post-adoption Usage Behavior and Perceptions of Mobile Technology. International Journal of Design. 2018;12(3):93-110.

  8. Bevan N, Carter J, Earthy J, Geis T, Harker S. New ISO standards for usability, usability reports and usability measures. Human Computer Interaction. Theory, design, Development and practice; 2016: Springer, Cham.

  9. Zahabi M, Kaber DB, Swangnetr M. Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Hum Factors. 2015 Aug;57(5):805-34.

  10. Dhillon G, Oliveira T, Susarapu S, Caldeira M. Deciding between information security and usability: Developing value based objectives. Computers in Human Behavior. 2016;61:656-66.

  11. Harte R, Quinlan LR, Glynn L, Rodríguez-Molinero A, Baker PMA, Scharf T, et al. Human-centered design study: enhancing the usability of a mobile phone app in an integrated falls risk detection system for use by older adult users. 2017: 5(5); e71. JMIR mHealth and uHealth. 2017 May;5(5):e71.

  12. Kalakoski V, Henelius A, Oikarinen E, Ukkonen A, Puolamäki K. Cognitive ergonomics for data analysis. Experimental study of cognitive limitations in a data-based judgement task. Behaviour and Information Technology. 2019;38(10):1038-47.

  13. Diego-Mas J, Garzon-Leal D, Poveda-Bautista R, Maerzal-Alcaide J. User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. Applied Ergonomics. 2019;78:197-209.

  14. Pérez M, García-Morales L, Coromina-Hernández JC, Álvarez-González MA, Balmaseda-Serrano R, Manzanero AL. Memoria visual en la tercera edad. Regularidades para el diseño de interfaces. Ingeniería Industrial. 2020;41(3):1-11.

  15. Mayer S, Gad P, Wolf K, Wozniak PW, Henze N. Understanding the ergonomic constraints in designing for touch surfaces. Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services; 2017; Viena, Austria.

  16. Aiken LR. Content Validity and Reliability of Single Items or Questionnaires. Educational and Psychological Measurement. 1980;40(4):955–9.

  17. Penfield RD, Giacobbi PRJ. Applying a score confidence interval to Aiken’s item content-relevance index. Measurement in Physical Education and Exercise Science. 2004;8(4):213–25.

  18. Wilson E. Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association. 1927;22(158):209–12.

  19. Soto C, Segovia J. Intervalos de confianza asimétricos para el índice la validez de contenido: Un programa Visual Basic para la V de Aiken. Annals of Psychology. 2009;25(1):169-71.

  20. Ramírez Fernández C, Morán AL, García Canseco E, Montalvo Gómez JR. Evaluation Results of an Ontology-based Design Model of Virtual Environments for Upper Limb Motor Rehabilitation of Stroke Patients. Methods Inf Med. 2017 Mar;56(2):145-55.

  21. Steiner B, Elgert L, Saalfeld B, Schwartze J, Borrmann HP, Kobelt-Ponicke A, et al. Health-enabling technologies for telerehabilitation of the shoulder: a feasibility and user acceptance study. Methods Inf Med. 2020;Aug 10:artículo en impresión.

  22. Lennon S, McKenna S, Jones F. Self-management programmes for people post stroke: a systematic review. Clin Rehabil. 2013;27(10):867–78.

  23. Stephenson A, Pedlow K, McDonough Z, Holmes D, Charles D, Barbabella F, et al. Evaluation of the acceptability and usability of the MAGIC-GLASS virtual reality solution as part of the care pathway in people with acute, sub-acute and chronic stroke: a study protocol. Physical Therapy Reviews. 2020;25(2):118-27.

  24. Vaezipour A, Whelan BM, Walk K, Theodoros D. Acceptance of rehabilitation technology in adults with moderate to severe traumatic brain injury, their caregivers, and healthcare professionals: a systematic review. J Head Trauma Rehabil. 2019 Jul/Aug;34(4):e67-e82.




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