Download PDF
CC BY-NC-ND 4.0 · Methods Inf Med 2020; 59(S 02): e90-e99
DOI: 10.1055/s-0040-1713685
Original Article

Health-Enabling Technologies for Telerehabilitation of the Shoulder: A Feasibility and User Acceptance Study

Bianca Steiner
1   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
,
Lena Elgert
2   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, German
,
Birgit Saalfeld
2   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, German
,
Jonas Schwartze
2   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, German
,
Horst Peter Borrmann
3   Deutsche Rentenversicherung Nord Ost West Informationstechnik GmbH, Laatzen, Germany
,
Axel Kobelt-Pönicke
4   Deutsche Rentenversicherung Braunschweig-Hannover, Laatzen, Germany
,
Andreas Figlewicz
5   Deutsche Rentenversicherung Reha-Zentrum Bad Pyrmont, Bad Pyrmont, Germany
,
Detlev Kasprowski
5   Deutsche Rentenversicherung Reha-Zentrum Bad Pyrmont, Bad Pyrmont, Germany
,
Michael Thiel
5   Deutsche Rentenversicherung Reha-Zentrum Bad Pyrmont, Bad Pyrmont, Germany
,
Ralf Kreikebohm
6   Institut für Rechtswissenschaften, Technische Universität Braunschweig, Braunschweig, Germany
,
Reinhold Haux
1   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany
,
Klaus-Hendrik Wolf
2   Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover, German
› Author Affiliations Funding The AGT-Reha project is funded by the Deutsche Rentenversicherung Braunschweig-Hannover, Germany. Open Access funding is provided by the German Research Foundation and the Open Access Publication Funds of the Technische Universität Braunschweig.
› Further Information
   Permissions and Reprints

Abstract

Background After discharge from a rehabilitation center the continuation of therapy is necessary to secure already achieved healing progress and sustain (re-)integration into working life. To this end, home-based exercise programs are frequently prescribed. However, many patients do not perform their exercises as frequently as prescribed or even with incorrect movements. The telerehabilitation system AGT-Reha was developed to support patients with shoulder diseases during their home-based aftercare rehabilitation.

Objectives The presented pilot study AGT-Reha-P2 evaluates the technical feasibility and user acceptance of the home-based telerehabilitation system AGT-Reha.

Methods A nonblinded, nonrandomized exploratory feasibility study was conducted over a 2-year period in patients' homes. Twelve patients completed a 3-month telerehabilitation exercise program with AGT-Reha. Primary outcome measures are the satisfying technical functionality and user acceptance assessed by technical parameters, structured interviews, and a four-dimensional questionnaire. Secondary endpoints are the medical rehabilitation success measured by the active range of motion and the shoulder function (pain and disability) assessed by employing the Neutral-0 Method and the standardized questionnaire “Shoulder Pain and Disability Index” (SPADI), respectively. To prepare an efficacy trial, various standardized questionnaires were included in the study to measure ability to work, capacity to work, and subjective prognosis of work capacity. The participants have been assessed at three measurement points: prebaseline (admission to rehabilitation center), baseline (discharge from rehabilitation center), and posttherapy.

Results Six participants used the first version of AGT-Reha, while six other patients used an improved version. Despite minor technical problems, all participants successfully trained on their own with AGT-Reha at home. On average, participants trained at least once per day during their training period. Five of the 12 participants showed clinically relevant improvements of shoulder function (improved SPADI score > 11). The work-related parameters suggested a positive impact. All participants would recommend the system, ten participants would likely reuse it, and seven participants would have wanted to continue their use after 3 months.

Conclusion The findings show that home-based training with AGT-Reha is feasible and well accepted. Outcomes of SPADI indicate the effectiveness of aftercare with AGT-Reha. A controlled clinical trial to test this hypothesis will be conducted with a larger number of participants.

Keywords

musculoskeletal diseases - shoulder - exercise therapy - telerehabilitation - evaluation studies


Publication History

Received: 30 October 2019

Accepted: 14 May 2020

Article published online:
10 August 2020

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Woolf AD, Erwin J, March L. The need to address the burden of musculoskeletal conditions. Best Pract Res Clin Rheumatol 2012; 26 (02) 183-224
    CrossrefPubMedGoogle Scholar
  • 2 Robert Koch-Institut. Gesundheit in Deutschland. Gesundheitsberichterstattung des Bundes. Gemeinsam getragen von RKI und Destatis. Berlin: H.Heenemann GmbH & Co. KG; 2015. [German]
    Google Scholar
  • 3 BARMER GEK. Gesundheitsreport 2014 Niedersachsen. Berlin: BARMER GEK; 2014: 8-75 [German]
    Google Scholar
  • 4 Mitchell C, Adebajo A, Hay E, Carr A. Shoulder pain: diagnosis and management in primary care. BMJ 2005; 331 (7525): 1124-1128
    CrossrefPubMedGoogle Scholar
  • 5 Deutsche Rentenversicherung. Reha-Bericht 2018: Die medizinische und berufliche Rehabilitation der Rentenversicherung im Licht der Statistik. Berlin: Deutsche Rentenversicherung Bund; 2018. [German]
    Google Scholar
  • 6 World Health Organization. WHO Guidelines on Health-Related Rehabilitation (Rehabilitation Guidelines). 2014 Available at: https://www.who.int/disabilities/care/rehabilitation_guidelines_concept.pdf . Accessed October 18, 2019
    PubMedGoogle Scholar
  • 7 Bundesarbeitsgemeinschaft für Rehabilitation. Praxisleitfaden: Strategien zur Sicherung der Nachhaltigkeit von Leistungen zur medizinischen Rehabilitation. Frankfurt/Main: BAR; 2016. [German]
    Google Scholar
  • 8 Haux R, Koch S, Lovell NH, Marschollek M, Nakashima N, Wolf K-H. Health-enabling and ambient assistive technologies: past, present, future. Yearb Med Inform 2016; (Suppl. 01) S76-S91
    PubMedGoogle Scholar
  • 9 Evolv Rehabilitation Technologies. S.L. Virtual Rehab 2019. Available at: https://evolvrehab.com/virtualrehab/ . Accessed September 29, 2019
    PubMedGoogle Scholar
  • 10 Wang Q, Markopoulos P, Yu B, Chen W, Timmermans A. Interactive wearable systems for upper body rehabilitation: a systematic review. J Neuroeng Rehabil 2017; 14 (01) 20
    CrossrefPubMedGoogle Scholar
  • 11 Dimaguila GL, Gray K, Merolli M. Person-generated health data in simulated rehabilitation using Kinect for stroke: literature review. JMIR Rehabil Assist Technol 2018; 5 (01) e11
    CrossrefPubMedGoogle Scholar
  • 12 Bonnechère B, Jansen B, Omelina L, Van Sint Jan S. The use of commercial video games in rehabilitation: a systematic review. Int J Rehabil Res 2016; 39 (04) 277-290
    CrossrefPubMedGoogle Scholar
  • 13 Peretti A, Amenta F, Tayebati SK, Nittari G, Mahdi SS. Telerehabilitation: review of the state-of-the-art and areas of application. JMIR Rehabil Assist Technol 2017; 4 (02) e7
    CrossrefPubMedGoogle Scholar
  • 14 EvoCare Holding. Health is coming home 2019. Available at: https://telemedizin.de/english/home . Accessed October 17, 2019
    PubMedGoogle Scholar
  • 15 Wolf KH, Franz S, Schwartze J. , et al. AGT-Reha: Assistierende Gesundheitstechnologien für das medizinische Tele-Reha-Training. Abstract presented at: 58th Annual Conference of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS); September 01–05, 2013; Lübeck, Germany [German]
    PubMedGoogle Scholar
  • 16 Clark RA, Mentiplay BF, Hough E, Pua YH. Three-dimensional cameras and skeleton pose tracking for physical function assessment: a review of uses, validity, current developments and Kinect alternatives. Gait Posture 2019; 68: 193-200
    CrossrefPubMedGoogle Scholar
  • 17 Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. Gait Posture 2014; 39 (04) 1062-1068
    CrossrefPubMedGoogle Scholar
  • 18 Otte K, Kayser B, Mansow-Model S. , et al. Accuracy and reliability of the Kinect Version 2 for clinical measurement of motor function. PLoS One 2016; 11 (11) e0166532
    CrossrefPubMedGoogle Scholar
  • 19 Mousavi Hondori H, Khademi M. A review on technical and clinical impact of Microsoft Kinect on physical therapy and rehabilitation. J Med Eng 2014; 2014: 846514
    CrossrefPubMedGoogle Scholar
  • 20 Mortazavi F, Nadian-Ghomsheh A. Stability of Kinect for range of motion analysis in static stretching exercises. PLoS One 2018; 13 (07) e0200992
    CrossrefPubMedGoogle Scholar
  • 21 Wang Q, Kurillo G, Ofli F, Bajcsy R. Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect. Paper presented at: International Conference on Healthcare Informatics; October 21–23, 2015; Dallas, TX, USA
    PubMedGoogle Scholar
  • 22 Da Gama A, Chaves T, Figueiredo L, Teichrieb V. Guidance and Movement Correction Based on Therapeutics Movements for Motor Rehabilitation Support Systems. Paper presented at: 14th Symposium on Virtual and Augmented Reality; September 10, 2012; Rio de Janiero, Brazil
    PubMedGoogle Scholar
  • 23 Chung C-E, Chen C-H. The app game interface design for frozen shoulder rehabilitation. In: Soares M, Falcão C, Ahram T. , eds. Advances in Ergonomics Modeling, Usability & Special Populations. Cham: Springer; 2017: 507-516
    Google Scholar
  • 24 Anton D, Berges I, Bermúdez J, Goñi A, Illarramendi A. A telerehabilitation system for the selection, evaluation and remote management of therapies. Sensors (Basel) 2018; 18 (05) 1459
    CrossrefPubMedGoogle Scholar
  • 25 Castro WHM, Jerosch J. Examination and Diagnosis of Musculoskeletal Disorders: History - Physical Examination - Imaging Techniques - Arthroscopy. New York: Thieme; 2011
    Google Scholar
  • 26 Breckenridge JD, McAuley JH. Shoulder Pain and Disability Index (SPADI). J Physiother 2011; 57 (03) 197
    CrossrefPubMedGoogle Scholar
  • 27 Tuomi K, Ilmarinen J, Jahkola A, Katajarinne L, Tulkki A. Work Ability Index. 2nd ed. Helsinki: Institute of Occupational Health; 1998
    Google Scholar
  • 28 Gibson L, Strong J. The reliability and validity of a measure of perceived functional capacity for work in chronic back pain. J Occup Rehabil 1996; 6 (03) 159-175
    CrossrefPubMedGoogle Scholar
  • 29 Mittag O, Raspe H. A brief scale for measuring subjective prognosis of gainful employment: findings of a study of 4279 statutory pension insurees concerning reliability (Guttman scaling) and validity of the scale [in German]. Rehabilitation (Stuttg) 2003; 42 (03) 169-174
    Article in Thieme ConnectPubMedGoogle Scholar
  • 30 Saalfeld B, Pingel I, Wolf KH. Semi-automatically measuring shoulders' range of motion - objective measurements with good reliability and accuracy. Stud Health Technol Inform 2018; 247: 631-635
    PubMedGoogle Scholar
  • 31 Çubukçu B, Yüzgeç U, Zileli R, Zileli A. Reliability and validity analyzes of Kinect V2 based measurement system for shoulder motions. Med Eng Phys 2020; 76: 20-31
    CrossrefPubMedGoogle Scholar
  • 32 Steiner B, Wolf KH. A vision utilizing gamification to enhance patients' with chronic shoulder diseases adherence to rehabilitation. Stud Health Technol Inform 2019; 262: 71-74
    PubMedGoogle Scholar
  • 33 Steiner B, Wolf KH. A Mobile Application Enhancing Adherence to Rehabilitation: A Vision. Abstract presented at: 64th Annual Conference of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS); September 08–11, 2019; Dortmund, Germany
    PubMedGoogle Scholar