Towards Supporting Data-Driven Practices in Stroke Telerehabilitation Technology

Telerehabilitation technology has the potential to support the work of patients and clinicians by collecting and displaying patients' data to inform, motivate, and support decision-making. However, few studies have investigated data-driven practices in telerehabilitation. In this qualitative study, we conducted interviews and a focus group with the use of data visualization probes to investigate the experience of stroke survivors and healthcare providers with game-based telerehabilitation involving physical and occupational therapy. We find that \hlstudy participants saw potential value in the data to support their work. However, they experienced challenges when interpreting data to arrive at meaningful insights and actionable information. Further, patients' personal relationships with their goals and data stand in contrast with clinicians' more matter-of-fact perspectives. Informed by these results, we discuss implications for telerehabilitation technology design.

[1]  J M Corbin,et al.  The Corbin and Strauss Chronic Illness Trajectory model: an update. , 1998, Scholarly inquiry for nursing practice.

[2]  Bernd Brügge,et al.  KneeHapp: a bandage for rehabilitation of knee injuries , 2015, UbiComp/ISWC Adjunct.

[3]  Pedro Vieira,et al.  Serious games for upper limb rehabilitation: a systematic review , 2018, Disability and rehabilitation. Assistive technology.

[4]  Yunan Chen,et al.  Patient-Generated Health Data: Dimensions, Challenges, and Open Questions , 2020, Found. Trends Hum. Comput. Interact..

[5]  Matthew Chalmers,et al.  Personal tracking as lived informatics , 2014, CHI.

[6]  Francesco Amenta,et al.  Telerehabilitation: Review of the State-of-the-Art and Areas of Application , 2017, JMIR rehabilitation and assistive technologies.

[7]  Yunan Chen,et al.  Senior Care for Aging in Place: Balancing Assistance and Independence , 2017, CSCW.

[8]  E. Steultjens,et al.  Stroke survivors' experiences of rehabilitation: A systematic review of qualitative studies , 2011, Scandinavian journal of occupational therapy.

[9]  Daniel P. Siewiorek,et al.  A technology probe of wearable in-home computer-assisted physical therapy , 2014, CHI.

[10]  Petros Daras,et al.  A novel framework for physical therapy rehabilitation monitoring and assessment in Parkinson disease patients using depth information , 2019, PETRA.

[11]  V. Mathiowetz,et al.  Adult norms for the Box and Block Test of manual dexterity. , 1985, The American journal of occupational therapy : official publication of the American Occupational Therapy Association.

[12]  T Dowswell,et al.  Investigating recovery from stroke: a qualitative study. , 2000, Journal of clinical nursing.

[13]  Andrew D. Miller,et al.  Closing the Gap: Supporting Patients' Transition to Self-Management after Hospitalization , 2016, CHI.

[14]  J. Deutsch,et al.  Use of a Low-Cost, Commercially Available Gaming Console (Wii) for Rehabilitation of an Adolescent With Cerebral Palsy , 2008, Physical Therapy.

[15]  J. Dearani,et al.  Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. , 2013, The Annals of thoracic surgery.

[16]  Gerardo Luis Dimaguila,et al.  Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics , 2020, Journal of medical Internet research.

[17]  D. Reisman,et al.  Observation of amounts of movement practice provided during stroke rehabilitation. , 2009, Archives of physical medicine and rehabilitation.

[18]  Yuta Sugiura,et al.  Rehabilitation support system for patients with carpal tunnel syndrome using smartphone , 2018, OZCHI.

[19]  Linda C. Li,et al.  Oh, I didn't do a good job: How objective data affects physiotherapist-patient conversations for arthritis patients , 2020, PervasiveHealth.

[20]  Bernd Ploderer,et al.  The transition of stroke survivors from hospital to home: understanding work and design opportunities , 2017, OZCHI.

[21]  G. Wulf,et al.  Extrinsic feedback for motor learning after stroke: What is the evidence? , 2006, Disability and rehabilitation.

[22]  Rita Orji,et al.  Persuasive technology for health and wellness: State-of-the-art and emerging trends , 2018, Health Informatics J..

[23]  Sean A. Munson,et al.  Supporting Patient-Provider Collaboration to Identify Individual Triggers using Food and Symptom Journals , 2017, CSCW.

[24]  E M Badley,et al.  Handicap in stroke survivors. , 1999, Disability and rehabilitation.

[25]  A. Strauss,et al.  Managing chronic illness at home: Three lines of work , 1985 .

[26]  Dawn Nafus,et al.  This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice , 2014 .

[27]  Ian Oakley,et al.  User needs in the performance of prescribed home exercise therapy , 2012, CHI Extended Abstracts.

[28]  A. Kimball,et al.  Text-message reminders to improve sunscreen use: a randomized, controlled trial using electronic monitoring. , 2009, Archives of dermatology.

[29]  Jodi Forlizzi,et al.  Designing Personal Informatics Applications and Tools that Facilitate Monitoring of Behaviors , 2009 .

[30]  Sean A. Munson,et al.  Persuasive Performance Feedback: The Effect of Framing on Self-Efficacy , 2013, AMIA.

[31]  W. McIlroy,et al.  Effectiveness of Virtual Reality Using Wii Gaming Technology in Stroke Rehabilitation: A Pilot Randomized Clinical Trial and Proof of Principle , 2010, Stroke.

[32]  Sylvain Bouchigny,et al.  Ergotact: Including Force-based Activities into Post-stroke Rehabilitation , 2019, CHI Extended Abstracts.

[33]  Carmen Egido,et al.  Video conferencing as a technology to support group work: a review of its failures , 1988, CSCW '88.

[34]  Timothy W. Bickmore,et al.  A Virtual Self-care Coach for Individuals with Spinal Cord Injury , 2016, ASSETS.

[35]  João Guerreiro,et al.  Investigating the Opportunities for Technologies to Enhance QoL with Stroke Survivors and their Families , 2020, CHI.

[36]  Jesper Kjeldskov,et al.  Understanding Individual Differences for Tailored Smoking Cessation Apps , 2015, CHI.

[37]  Yuta Sugiura,et al.  An Application for Wrist Rehabilitation Using Smartphones , 2019, MobileHCI.

[38]  Madhu C. Reddy,et al.  Sharing Patient-Generated Data in Clinical Practices: An Interview Study , 2016, AMIA.

[39]  Robert Teasell,et al.  The experience of living with stroke: a qualitative meta-synthesis. , 2008, Journal of rehabilitation medicine.

[40]  S AckermanMark,et al.  Understanding Individual and Collaborative Problem-Solving with Patient-Generated Data , 2017 .

[41]  Michael Lawo,et al.  Clinical Rehabilitation Experience Utilizing Serious Games , 2018, Advanced Studies Mobile Research Center Bremen.

[42]  Kathleen Gray,et al.  Person-Generated Health Data in Simulated Rehabilitation Using Kinect for Stroke: Literature Review , 2018, JMIR rehabilitation and assistive technologies.

[43]  Francesca N. Delling,et al.  Heart Disease and Stroke Statistics—2018 Update: A Report From the American Heart Association , 2018, Circulation.

[44]  S. Raj,et al.  Clinical Data in Context: Towards Sensemaking Tools for Interpreting Personal Health Data , 2019 .

[45]  Gina Neff,et al.  Self-Tracking , 2016 .

[46]  Predrag V. Klasnja,et al.  Supporting Coping with Parkinson's Disease Through Self Tracking , 2019, CHI.

[47]  Paolo Bonato,et al.  Towards the Design of a Ring Sensor-based mHealth System to Achieve Optimal Motor Function in Stroke Survivors , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[48]  E A Bullock,et al.  Post-stroke rehabilitation. , 1975, Royal Society of Health journal.

[49]  Yunan Chen,et al.  Using Data to Approach the Unknown , 2021, Proc. ACM Hum. Comput. Interact..

[50]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[51]  Cathy C. Y. Chou,et al.  A Standardized Approach to the Fugl-Meyer Assessment and Its Implications for Clinical Trials , 2013, Neurorehabilitation and neural repair.

[52]  M. Kafri,et al.  Nintendo Wii Sports and Wii Fit Game Analysis, Validation, and Application to Stroke Rehabilitation , 2011, Topics in stroke rehabilitation.

[53]  S. Kaufman Illness, biography, and the interpretation of self following a stroke , 1988 .

[54]  Huiru Zheng,et al.  Stroke patients’ utilisation of extrinsic feedback from computer-based technology in the home: a multiple case study realistic evaluation , 2014, BMC Medical Informatics and Decision Making.

[55]  Christopher Wee Keong Kuah,et al.  A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke. , 2010, Journal of rehabilitation medicine.

[56]  J. S. Skouen,et al.  Striving for a life worth living: stroke survivors' experiences of home rehabilitation. , 2015, Scandinavian journal of caring sciences.

[57]  Suranga Nanayakkara,et al.  ArmSleeve: A Patient Monitoring System to Support Occupational Therapists in Stroke Rehabilitation , 2016, Conference on Designing Interactive Systems.

[58]  Pan Wang,et al.  Designing Two-player Competitive Games for the Rehabilitation of Upper-Limb Motor Function after Stroke , 2017, CHI Extended Abstracts.

[59]  Matthew Delbridge,et al.  Embodied Imagination: An Approach to Stroke Recovery Combining Participatory Performance and Interactive Technology , 2019, CHI.

[60]  Oliver Korn,et al.  Strategies for Playful Design when Gamifying Rehabilitation: A Study on User Experience , 2017, PETRA.

[61]  Bernd Brügge,et al.  A smart textile sleeve for rehabilitation of knee injuries , 2017, UbiComp/ISWC Adjunct.

[62]  Sean A. Munson,et al.  Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data , 2015, CSCW.

[63]  P. Burnard A method of analysing interview transcripts in qualitative research. , 1991, Nurse education today.

[64]  William B. Lober,et al.  A patient-centered system in a provider-centered world: challenges of incorporating post-discharge wound data into practice , 2016, J. Am. Medical Informatics Assoc..

[65]  William G. Griswold,et al.  Usability and Feasibility of PmEB: A Mobile Phone Application for Monitoring Real Time Caloric Balance , 2006, 2006 Pervasive Health Conference and Workshops.

[66]  T. Hafsteinsdóttir,et al.  Being a stroke patient: a review of the literature. , 1997, Journal of advanced nursing.

[67]  Noel E. O'Connor,et al.  A Demonstration of the PATHway System for Technology-enabled Exercise-based Cardiac Rehabilitation , 2016, MMHealth@ACM Multimedia.

[68]  James Deaton,et al.  Development of a smart insole tracking system for physical therapy and athletics , 2014, PETRA.

[69]  Nigel Shadbolt,et al.  Common Barriers to the Use of Patient-Generated Data Across Clinical Settings , 2018, CHI.

[70]  David Geerts,et al.  Video games in therapy: a therapist's perspective , 2013, Int. J. Arts Technol..

[71]  John W Krakauer,et al.  Agreed definitions and a shared vision for new standards in stroke recovery research: The Stroke Recovery and Rehabilitation Roundtable taskforce , 2017, International journal of stroke : official journal of the International Stroke Society.

[72]  Sean A. Munson,et al.  From "nobody cares" to "way to go!": A Design Framework for Social Sharing in Personal Informatics , 2015, CSCW.

[73]  Nervo Verdezoto,et al.  Understanding challenges and opportunities of preventive blood pressure self-monitoring at home , 2013, ECCE.

[74]  Anna Vallgårda,et al.  Careful Devices , 2019, HTTF.

[75]  P. Celnik,et al.  Stroke Rehabilitation. , 2015, Physical medicine and rehabilitation clinics of North America.

[76]  Xing-Dong Yang,et al.  Physio@Home: Exploring Visual Guidance and Feedback Techniques for Physiotherapy Exercises , 2015, CHI.

[77]  Mona Bendz,et al.  The first year of rehabilitation after a stroke - from two perspectives. , 2003, Scandinavian journal of caring sciences.

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

[79]  Geraldine Fitzpatrick,et al.  Understanding the Mundane Nature of Self-care: Ethnographic Accounts of People Living with Parkinson's , 2018, CHI.

[80]  Elise van den Hoven,et al.  Exploring in-hospital rehabilitation exercises for stroke patients: informing interaction design , 2017, OZCHI.

[81]  Ross Zafonte,et al.  Efficacy of Home-Based Telerehabilitation vs In-Clinic Therapy for Adults After Stroke: A Randomized Clinical Trial. , 2019, JAMA neurology.

[82]  G. Becker,et al.  Managing an uncertain illness trajectory in old age: patients' and physicians' views of stroke. , 1995, Medical anthropology quarterly.

[83]  Steven C Cramer,et al.  Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program. , 2016, Journal of rehabilitation research and development.

[84]  Jodi Forlizzi,et al.  A stage-based model of personal informatics systems , 2010, CHI.

[85]  S. Wood-Dauphinée,et al.  Activity, participation, and quality of life 6 months poststroke. , 2002, Archives of physical medicine and rehabilitation.

[86]  Gunnar Ellingsen,et al.  A Review of 25 Years of CSCW Research in Healthcare: Contributions, Challenges and Future Agendas , 2012, Computer Supported Cooperative Work (CSCW).

[87]  Stefan Wagner,et al.  Challenges in applying standard telemedicine solutions in the home of type 2 diabetics , 2014, PervasiveHealth.

[88]  Helena M. Mentis,et al.  Crafting a View of Self-Tracking Data in the Clinical Visit , 2017, CHI.

[89]  C. Winstein Knowledge of results and motor learning--implications for physical therapy. , 1991, Physical therapy.

[90]  J. Eng,et al.  Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association , 2016, Stroke.

[91]  Yu Chen,et al.  Home-based technologies for stroke rehabilitation: A systematic review , 2019, Int. J. Medical Informatics.

[92]  Noor Azah Abd Aziz,et al.  Stroke survivors' and informal caregivers' experiences of primary care and community healthcare services – A systematic review and meta-ethnography , 2018, PloS one.

[93]  N. Hoffart Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory , 2000 .

[94]  P. Lehoux,et al.  A systematic review of clinical outcomes, clinical process, healthcare utilization and costs associated with telerehabilitation , 2009, Disability and rehabilitation.

[95]  Jeong-Whun Kim,et al.  Prescribing 10,000 Steps Like Aspirin: Designing a Novel Interface for Data-Driven Medical Consultations , 2017, CHI.

[96]  Sean A. Munson,et al.  Taming data complexity in lifelogs: exploring visual cuts of personal informatics data , 2014, Conference on Designing Interactive Systems.

[97]  S. Lou,et al.  Stroke patients’ and informal carers’ experiences with life after stroke: an overview of qualitative systematic reviews , 2017, Disability and rehabilitation.

[98]  Mark S. Ackerman,et al.  Toward Health Information Technology that Supports Overweight/Obese Women in Addressing Emotion- and Stress-Related Eating , 2018, CHI.