Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative exploration

Objectives This study explores the opinions of orthopaedic healthcare professionals regarding the opportunities and challenges of using wearable technology in rehabilitation. It continues to assess the perceived impact of an exemplar exercise biofeedback system that incorporates wearable sensing, involving the clinician in the user-centred design process, a valuable step in ensuring ease of implementation, sustained engagement and clinical relevance. Design This is a qualitative study consisting of one-to-one semi-structured interviews, including a demonstration of a prototype wearable exercise biofeedback system. Interviews were audio-recorded and transcribed, with thematic analysis conducted of all transcripts. Setting The study was conducted in the orthopaedic department of an acute private hospital. Participants Ten clinicians from a multidisciplinary team of healthcare professionals involved in the orthopaedic rehabilitation pathway participated in the study. Results Participants reported that there is currently a challenge in gathering timely and objective data for the monitoring of patients in orthopaedic rehabilitation. While there are challenges in ensuring reliability and engagement of biofeedback systems, clinicians perceive significant value in the use of wearable biofeedback systems such as the exemplar demonstrated for use following total knee replacement. Conclusions Clinicians see an opportunity for wearable technology to continuously track data in real-time, and feel that feedback provided to users regarding exercise technique and adherence can further support the patient at home, although there are clear design and implementation challenges relating to ensuring technical accuracy and tailoring rehabilitation to the individual. There was perceived value in the prototype system demonstrated to participants which supports the ongoing development of such exercise biofeedback platforms.

[1]  M Friedrich,et al.  The effect of brochure use versus therapist teaching on patients performing therapeutic exercise and on changes in impairment status. , 1996, Physical therapy.

[2]  H. Boeije.  A Purposeful Approach to the Constant Comparative Method in the Analysis of Qualitative Interviews , 2002 .

[3]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[4]  Sandra Bassett,et al.  The assessment of patient adherence to physiotherapy rehabilitation , 2003 .

[5]  J. Fereday,et al.  Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development , 2006 .

[6]  Jan Brutovsky,et al.  Low-cost motivated rehabilitation system for post-operation exercises , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Ian Robinson,et al.  Benefits of and barriers to involving users in medical device technology development and evaluation , 2007, International Journal of Technology Assessment in Health Care.

[8]  Diana Hodgins,et al.  Inertial sensor-based knee flexion/extension angle estimation. , 2009, Journal of biomechanics.

[9]  Richard J. Holden,et al.  The Technology Acceptance Model: Its past and its future in health care , 2010, J. Biomed. Informatics.

[10]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.

[11]  Kirsten Jack,et al.  Barriers to treatment adherence in physiotherapy outpatient clinics: A systematic review , 2010, Manual therapy.

[12]  A. Mcgregor,et al.  Body-Worn Sensor Design: What Do Patients and Clinicians Want? , 2011, Annals of Biomedical Engineering.

[13]  Oonagh M. Giggins,et al.  Biofeedback in rehabilitation , 2013, Journal of NeuroEngineering and Rehabilitation.

[14]  Thomas Ralph,et al.  Older adults’ experiences regarding discharge from hospital following orthopaedic intervention: A metasynthesis , 2012, Disability and rehabilitation.

[15]  Sriram Subramanian,et al.  Talking about tactile experiences , 2013, CHI.

[16]  J. Ritchie,et al.  Qualitative Research Practice: A Guide for Social Science Students and Researchers , 2013 .

[17]  S. Donnelly,et al.  Review Series – Connected Health What is Connected Health and why will it change your practice? , 2013 .

[18]  Lynne Baillie,et al.  A novel knee rehabilitation system for the home , 2014, CHI.

[19]  Sionnadh McLean,et al.  Recommendations for exercise adherence measures in musculoskeletal settings: a systematic review and consensus meeting (protocol) , 2014, Systematic Reviews.

[20]  Ali Idri,et al.  Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review , 2015, Journal of Medical Systems.

[21]  Brian Caulfield,et al.  Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study , 2014, Journal of NeuroEngineering and Rehabilitation.

[22]  Chris Lonsdale,et al.  Measurement tools for adherence to non-pharmacologic self-management treatment for chronic musculoskeletal conditions: a systematic review. , 2015, Archives of physical medicine and rehabilitation.

[23]  Cynthia S Crowson,et al.  Prevalence of Total Hip and Knee Replacement in the United States. , 2015, The Journal of bone and joint surgery. American volume.

[24]  Kun-Hui Chen,et al.  Wearable Sensor-Based Rehabilitation Exercise Assessment for Knee Osteoarthritis , 2015, Sensors.

[25]  Rachelle Buchbinder,et al.  Interventions to improve adherence to exercise for chronic musculoskeletal pain in adults. , 2015, Australian family physician.

[26]  Ali Idri,et al.  Compliance of Blood Donation Apps with Mobile OS Usability Guidelines , 2015, Journal of Medical Systems.

[27]  Anton Umek,et al.  Suitability of Smartphone Inertial Sensors for Real-Time Biofeedback Applications , 2016, Sensors.

[28]  Enrica Papi,et al.  Wearable technologies in osteoarthritis: a qualitative study of clinicians’ preferences , 2016, BMJ Open.

[29]  Diane J. Cook,et al.  Designing Wearable Sensor-Based Analytics for Quantitative Mobility Assessment , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[30]  L. Piwek,et al.  The Rise of Consumer Health Wearables: Promises and Barriers , 2016, PLoS medicine.

[31]  Mariko Carey,et al.  Interventions to aid patient adherence to physiotherapist prescribed self-management strategies: a systematic review. , 2016, Physiotherapy.

[32]  Lucy Yardley,et al.  Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop , 2017, Journal of medical Internet research.

[33]  S. Bryan,et al.  The importance of informational, clinical and personal support in patient experience with total knee replacement: a qualitative investigation , 2017, BMC Musculoskeletal Disorders.

[34]  Darragh F Whelan,et al.  Technology in Strength and Conditioning: Assessing Bodyweight Squat Technique With Wearable Sensors. , 2017, Journal of strength and conditioning research.

[35]  K. Radon,et al.  Prevalence of musculoskeletal disorders among school teachers from urban and rural areas in Chuquisaca, Bolivia: a cross-sectional study , 2017, BMC Musculoskeletal Disorders.

[36]  James G. Phillips,et al.  Video Games and Virtual Reality as Persuasive Technologies for Health Care: An Overview , 2017, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[37]  M. Tahar Kechadi,et al.  Automatic classification of knee rehabilitation exercises using a single inertial sensor: A case study , 2018, 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[38]  Brian Caulfield,et al.  Patient Involvement With Home-Based Exercise Programs: Can Connected Health Interventions Influence Adherence? , 2018, JMIR mHealth and uHealth.

[39]  William Johnston,et al.  Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review , 2018, Sports Medicine.

[40]  M. Chignell,et al.  Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice. , 2018, Physiotherapy Canada. Physiotherapie Canada.

[41]  John P. Sullivan,et al.  A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations , 2018, Front. Physiol..