Usability, Usefulness, and Acceptance of a Novel, Portable Rehabilitation System (mRehab) Using Smartphone and 3D Printing Technology: Mixed Methods Study

Sutanuka Bhattacharjya, Lora Anne Cavuoto, Brandon Reilly, Wenyao Xu, Heamchand Subryan, Jeanne Langan, Sutanuka Bhattacharjya, Lora Anne Cavuoto, Brandon Reilly, Wenyao Xu, Heamchand Subryan, Jeanne Langan

Abstract

Background: Smart technology use in rehabilitation is growing and can be used remotely to assist clients in self-monitoring their performance. With written home exercise programs being the commonly prescribed form of rehabilitation after discharge, mobile health technology coupled with task-oriented programs can enhance self-management of upper extremity training. In the current study, a rehabilitation system, namely mRehab, was designed that included a smartphone app and 3D-printed household items such as mug, bowl, key, and doorknob embedded with a smartphone. The app interface allowed the user to select rehabilitation activities and receive feedback on the number of activity repetitions completed, time to complete each activity, and quality of movement.

Objective: This study aimed to assess the usability, perceived usefulness, and acceptance of the mRehab system by individuals with stroke and identify the challenges experienced by them when using the system remotely in a home-based setting.

Methods: A mixed-methods approach was used with 11 individuals with chronic stroke. Following training, individuals with stroke used the mRehab system for 6 weeks at home. Each participant completed surveys and engaged in a semistructured interview. Participants' qualitative reports regarding the usability of mRehab were integrated with their survey reports and quantitative performance data.

Results: Of the 11 participants, 10 rated the mRehab system between the 67.5th and 97.5th percentile on the System Usability Scale, indicating their satisfaction with the usability of the system. Participants also provided high ratings of perceived usefulness (mean 5.8, SD 0.9) and perceived ease of use (mean 5.3, SD 1.5) on a 7-point scale based on the Technology Acceptance Model. Common themes reported by participants showed a positive response to mRehab with some suggestions for improvements. Participants reported an interest in activities they perceived to be adequately challenging. Some participants indicated a need for customizing the feedback to be more interpretable. Overall, most participants indicated that they would like to continue using the mRehab system at home.

Conclusions: Assessing usability in the lived environment over a prolonged duration of time is essential to identify the match between the system and users' needs and preferences. While mRehab was well accepted, further customization is desired for a better fit with the end users.

Trial registration: ClinicalTrials.gov NCT04363944; https://ichgcp.net/clinical-trials-registry/NCT04363944.

Keywords: 3-dimensional printing; rehabilitation; smart technology; stroke; usability.

Conflict of interest statement

Conflicts of Interest: None declared.

©Sutanuka Bhattacharjya, Lora Anne Cavuoto, Brandon Reilly, Wenyao Xu, Heamchand Subryan, Jeanne Langan. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 22.03.2021.

Figures

Figure 1
Figure 1
User transferring bowl with both hands.
Figure 2
Figure 2
User seeing feedback on the smartphone screen inside the mug.
Figure 3
Figure 3
User turning doorknob with a smartphone in the holder and the key with a holder.
Figure 4
Figure 4
App interface: activity selection and feedback pages.
Figure 5
Figure 5
Ordinal scale on the Difficulty Rating Scale (DRS).
Figure 6
Figure 6
Participant using a pill box behind phone when engaging in Turn the Key activity.

References

    1. Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jiménez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P, American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation. 2017 Mar 07;135(10):e146–e603. doi: 10.1161/CIR.0000000000000485.
    1. Doman CA, Waddell KJ, Bailey RR, Moore JL, Lang CE. Changes in Upper-Extremity Functional Capacity and Daily Performance During Outpatient Occupational Therapy for People With Stroke. Am J Occup Ther. 2016;70(3):7003290040p1–7003290040p11. doi: 10.5014/ajot.2016.020891.
    1. Argent R, Daly A, Caulfield B. Patient Involvement With Home-Based Exercise Programs: Can Connected Health Interventions Influence Adherence? JMIR Mhealth Uhealth. 2018 Mar 01;6(3):e47. doi: 10.2196/mhealth.8518.
    1. Novak I. Effective home programme intervention for adults: a systematic review. Clin Rehabil. 2011 Dec;25(12):1066–85. doi: 10.1177/0269215511410727.
    1. Mobile Fact Sheet. Pew Research Center. 2019. Jun 12, [2021-03-01].
    1. mHealth Economics 2017 – Current Status and Future Trends in Mobile Health. Research2Guidance. 2017. Nov, [2021-03-01].
    1. DeRuyter F, Jones M. Mobile Healthcare and mHealth Apps for People with Disabilities. In: Miesenberger K, Kouroupetroglou G, editors. Computers Helping People with Special Needs. ICCHP 2018. Lecture Notes in Computer Science, vol 10897. Cham, Switzerland: Springer Publishing Company; 2018.
    1. DeRuyter F, Jones ML, Morris JT. Mobile Health Apps and Needs of People with Disabilities: A National Survey. Journal on Technology & Persons with Disabilities. 2018;6:161.
    1. Hornbæk K. Current practice in measuring usability: Challenges to usability studies and research. International Journal of Human-Computer Studies. 2006 Feb;64(2):79–102. doi: 10.1016/j.ijhcs.2005.06.002. doi: 10.1016/j.ijhcs.2005.06.002.
    1. Zapata BC, Fernández-Alemán JL, Idri A, Toval A. Empirical studies on usability of mHealth apps: a systematic literature review. J Med Syst. 2015 Feb;39(2):1. doi: 10.1007/s10916-014-0182-2.
    1. Dorsey ER, Yvonne Chan YF, McConnell MV, Shaw SY, Trister AD, Friend SH. The Use of Smartphones for Health Research. Acad Med. 2017 Feb;92(2):157–160. doi: 10.1097/ACM.0000000000001205.
    1. Mayberry LS, Mulvaney SA, Johnson KB, Osborn CY. The MEssaging for Diabetes Intervention Reduced Barriers to Medication Adherence Among Low-Income, Diverse Adults With Type 2. J Diabetes Sci Technol. 2017 Jan;11(1):92–99. doi: 10.1177/1932296816668374.
    1. mHealth Economics - How mHealth App Publishers Are Monetizing Their Apps. Research2Guidance. 2018. Mar, [2021-03-01].
    1. Ribeiro N, Moreira L, Barros A, Almeida AM, Santos-Silva F. Guidelines for a cancer prevention smartphone application: A mixed-methods study. Int J Med Inform. 2016 Oct;94:134–42. doi: 10.1016/j.ijmedinf.2016.07.007.
    1. Smith SA, Claridy MD, Whitehead MS, Sheats JQ, Yoo W, Alema-Mensah EA, Ansa BE, Coughlin SS. Lifestyle Modification Experiences of African American Breast Cancer Survivors: A Needs Assessment. JMIR Cancer. 2015;1(2) doi: 10.2196/cancer.4892.
    1. Benefits of User-Centered Design. US General Services Administration. 2021. [2021-03-01]. .
    1. Bhattacharjya S, Stafford MC, Cavuoto LA, Yang Z, Song C, Subryan H, Xu W, Langan J. Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement. J Neuroeng Rehabil. 2019 Oct 29;16(1):127. doi: 10.1186/s12984-019-0592-y.
    1. Langan J, Bhattacharjya S, Subryan H, Xu W, Chen B, Li Z, Cavuoto L. In-Home Rehabilitation Using a Smartphone App Coupled With 3D Printed Functional Objects: Single-Subject Design Study. JMIR Mhealth Uhealth. 2020 Jul 22;8(7):e19582. doi: 10.2196/19582.
    1. Wolf SL, Catlin PA, Ellis M, Archer AL, Morgan B, Piacentino A. Assessing Wolf motor function test as outcome measure for research in patients after stroke. Stroke. 2001 Jul;32(7):1635–9. doi: 10.1161/01.str.32.7.1635.
    1. Wolf SL, Catlin PA, Ellis M, Link A, Morgan B, Piacento A. Wolf Motor Function Test (WMFT) Measurement Instrument Database for the Social Sciences (MIDSS) 2021. [2021-03-01]. .
    1. Ekstrand E, Lexell J, Brogårdh C. Grip strength is a representative measure of muscle weakness in the upper extremity after stroke. Top Stroke Rehabil. 2016 Dec;23(6):400–405. doi: 10.1080/10749357.2016.1168591.
    1. Resnick B, Jenkins LS. Testing the reliability and validity of the Self-Efficacy for Exercise scale. Nurs Res. 2000;49(3):154–9. doi: 10.1097/00006199-200005000-00007.
    1. Alharbi S, Drew S. Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. IJACSA. 2014;5(1) doi: 10.14569/ijacsa.2014.050120.
    1. Jeffrey DA. Testing the technology acceptance model 3 (TAM 3) with the inclusion of change fatigue and overload, in the context of faculty from Seventh-day Adventist universities: A revised model. Dissertations. 2015. [2021-03-01]. .
    1. Park SY. An Analysis of the Technology Acceptance Model in Understanding University Students' Behavioral Intention to Use e-Learning. Journal of Educational Technology & Society. 2009;12(3):150–162.
    1. Meldrum D, Glennon A, Herdman S, Murray D, McConn-Walsh R. Virtual reality rehabilitation of balance: assessment of the usability of the Nintendo Wii(®) Fit Plus. Disabil Rehabil Assist Technol. 2012 May;7(3):205–10. doi: 10.3109/17483107.2011.616922.
    1. Vanbellingen T, Filius SJ, Nyffeler T, van Wegen EEH. Usability of Videogame-Based Dexterity Training in the Early Rehabilitation Phase of Stroke Patients: A Pilot Study. Front Neurol. 2017;8:654. doi: 10.3389/fneur.2017.00654. doi: 10.3389/fneur.2017.00654.
    1. Sauro J. Measuring usability with the system usability scale (SUS) MeasuringU. 2011. Feb 2, [2021-02-12].
    1. Bangor A, Kortum PT, Miller JT. An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction. 2008 Jul 30;24(6):574–594. doi: 10.1080/10447310802205776. doi: 10.1080/10447310802205776.
    1. Lewis JR, Sauro J. The Factor Structure of the System Usability Scale. In: Kurosu M, editor. Human Centered Design. HCD 2009. Lecture Notes in Computer Science, vol 5619. Berlin, Germany: Springer Publishing Company; 2009.
    1. Nehrujee A, Vasanthan L, Lepcha A, Balasubramanian S. A Smartphone-based gaming system for vestibular rehabilitation: A usability study. J Vestib Res. 2019;29(2-3):147–160. doi: 10.3233/VES-190660.
    1. Pedroli E, Greci L, Colombo D, Serino S, Cipresso P, Arlati S, Mondellini M, Boilini L, Giussani V, Goulene K, Agostoni M, Sacco M, Stramba-Badiale M, Riva G, Gaggioli A. Characteristics, Usability, and Users Experience of a System Combining Cognitive and Physical Therapy in a Virtual Environment: Positive Bike. Sensors (Basel) 2018 Jul 19;18(7) doi: 10.3390/s18072343.
    1. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989 Sep;13(3):319. doi: 10.2307/249008.
    1. Davis FD, Bagozzi RP, Warshaw PR. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science. 1989 Aug;35(8):982–1003. doi: 10.1287/mnsc.35.8.982. doi: 10.1287/mnsc.35.8.982.
    1. Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science. 2000 Feb;46(2):186–204. doi: 10.1287/mnsc.46.2.186.11926. doi: 10.1287/mnsc.46.2.186.11926.
    1. Brown W, Yen P, Rojas M, Schnall R. Assessment of the Health IT Usability Evaluation Model (Health-ITUEM) for evaluating mobile health (mHealth) technology. J Biomed Inform. 2013 Dec;46(6):1080–7. doi: 10.1016/j.jbi.2013.08.001.
    1. Georgsson M, Staggers N. Patients' Perceptions and Experiences of a mHealth Diabetes Self-management System. Comput Inform Nurs. 2017 Mar;35(3):122–130. doi: 10.1097/CIN.0000000000000296.
    1. Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform. 2017 May;101:75–84. doi: 10.1016/j.ijmedinf.2017.02.002.
    1. Ma Q, Liu L. The Technology Acceptance Model: A Meta-Analysis of Empirical Findings. Journal of Organizational and End User Computing (JOEUC) 2004 Jan;16(1):59–72. doi: 10.4018/joeuc.2004010104.
    1. Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev. 2007 Dec;64(6):650–72. doi: 10.1177/1077558707305942.
    1. Sauro J. 5 ways to interpret a SUS score. MeasuringU. 2018. Sep 19, [2021-03-01].
    1. Edwards DF, Hahn M, Baum C, Dromerick AW. The impact of mild stroke on meaningful activity and life satisfaction. J Stroke Cerebrovasc Dis. 2006;15(4):151–7. doi: 10.1016/j.jstrokecerebrovasdis.2006.04.001.
    1. Carayon P, Kianfar S, Li Y, Xie A, Alyousef B, Wooldridge A. A systematic review of mixed methods research on human factors and ergonomics in health care. Appl Ergon. 2015 Nov;51:291–321. doi: 10.1016/j.apergo.2015.06.001.
    1. Kim D, Chang H. Key functional characteristics in designing and operating health information websites for user satisfaction: an application of the extended technology acceptance model. Int J Med Inform. 2007 Dec;76(11-12):790–800. doi: 10.1016/j.ijmedinf.2006.09.001.
    1. Cameron JI, Tsoi C, Marsella A. Optimizing stroke systems of care by enhancing transitions across care environments. Stroke. 2008 Sep;39(9):2637–43. doi: 10.1161/STROKEAHA.107.501064.
    1. Guadagnoli MA, Lee TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav. 2004 Jun;36(2):212–24. doi: 10.3200/JMBR.36.2.212-224.
    1. Woodbury ML, Anderson K, Finetto C, Fortune A, Dellenbach B, Grattan E, Hutchison S. Matching Task Difficulty to Patient Ability During Task Practice Improves Upper Extremity Motor Skill After Stroke: A Proof-of-Concept Study. Arch Phys Med Rehabil. 2016 Nov;97(11):1863–1871. doi: 10.1016/j.apmr.2016.03.022.
    1. Harrison RA, Field TS. Post stroke pain: identification, assessment, and therapy. Cerebrovasc Dis. 2015;39(3-4):190–201. doi: 10.1159/000375397.
    1. Klit H, Finnerup NB, Andersen G, Jensen TS. Central poststroke pain: a population-based study. Pain. 2011 Apr;152(4):818–824. doi: 10.1016/j.pain.2010.12.030.
    1. Damush TM, Plue L, Bakas T, Schmid A, Williams LS. Barriers and facilitators to exercise among stroke survivors. Rehabil Nurs. 2007;32(6):253–60, 262. doi: 10.1002/j.2048-7940.2007.tb00183.x.
    1. Widar M, Ek A, Ahlström G. Coping with long-term pain after a stroke. J Pain Symptom Manage. 2004 Mar;27(3):215–25. doi: 10.1016/j.jpainsymman.2003.07.006.
    1. O'Reilly M, Caulfield B, Ward T, Johnston W, Doherty C. Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review. Sports Med. 2018 May;48(5):1221–1246. doi: 10.1007/s40279-018-0878-4.

Source: PubMed

3
Suscribir