A Personalized Self-Management Rehabilitation System for Stroke Survivors: A Quantitative Gait Analysis Using a Smart Insole

Background In the United Kingdom, stroke is the single largest cause of adult disability and results in a cost to the economy of £8.9 billion per annum. Service needs are currently not being met; therefore, initiatives that focus on patient-centered care that promote long-term self-management for chronic conditions should be at the forefront of service redesign. The use of innovative technologies and the ability to apply these effectively to promote behavior change are paramount in meeting the current challenges. Objective Our objective was to gain a deeper insight into the impact of innovative technologies in support of home-based, self-managed rehabilitation for stroke survivors. An intervention of daily walks can assist with improving lower limb motor function, and this can be measured by using technology. This paper focuses on assessing the usage of self-management technologies on poststroke survivors while undergoing rehabilitation at home. Methods A realist evaluation of a personalized self-management rehabilitation system was undertaken in the homes of stroke survivors (N=5) over a period of approximately two months. Context, mechanisms, and outcomes were developed and explored using theories relating to motor recovery. Participants were encouraged to self-manage their daily walking activity; this was achieved through goal setting and motivational feedback. Gait data were collected and analyzed to produce metrics such as speed, heel strikes, and symmetry. This was achieved using a “smart insole” to facilitate measurement of walking activities in a free-living, nonrestrictive environment. Results Initial findings indicated that 4 out of 5 participants performed better during the second half of the evaluation. Performance increase was evident through improved heel strikes on participants’ affected limb. Additionally, increase in performance in relation to speed was also evident for all 5 participants. A common strategy emerged across all but one participant as symmetry performance was sacrificed in favor of improved heel strikes. This paper evaluates compliance and intensity of use. Conclusion Our findings suggested that 4 out of the 5 participants improved their ability to heel strike on their affected limb. All participants showed improvements in their speed of gait measured in steps per minute with an average increase of 9.8% during the rehabilitation program. Performance in relation to symmetry showed an 8.5% average decline across participants, although 1 participant improved by 4%. Context, mechanism, and outcomes indicated that dual motor learning and compensatory strategies were deployed by the participants.

[1]  I. Solopova,et al.  Assisted leg displacements and progressive loading by a tilt table combined with FES promote gait recovery in acute stroke. , 2011, NeuroRehabilitation.

[2]  Shyamal Patel,et al.  A review of wearable sensors and systems with application in rehabilitation , 2012, Journal of NeuroEngineering and Rehabilitation.

[3]  Janet H. Carr,et al.  Enhancing Physical Activity and Brain Reorganization after Stroke , 2011, Neurology research international.

[4]  Janet H Carr,et al.  Balancing the Centre of Body Mass During Standing Up , 1992 .

[5]  Jacob O. Wobbrock,et al.  Self-Conscious or Self-Confident? A Diary Study Conceptualizing the Social Accessibility of Assistive Technology , 2016, ACM Trans. Access. Comput..

[6]  Antonio J Salazar,et al.  Low-Cost Wearable Data Acquisition for Stroke Rehabilitation: A Proof-of-Concept Study on Accelerometry for Functional Task Assessment , 2014, Topics in stroke rehabilitation.

[7]  L. Bradley,et al.  Electromyographic biofeedback for gait training after stroke , 1998, Clinical rehabilitation.

[8]  Huiru Zheng,et al.  Developing and testing a telerehabilitation system for people following stroke: issues of usability , 2010 .

[9]  Huiru Zheng,et al.  A Personalized Self-Management Rehabilitation System with an Intelligent Shoe for Stroke Survivors: A Realist Evaluation , 2016, JMIR rehabilitation and assistive technologies.

[10]  Roberta B. Shepherd,et al.  Adaptive Motor Behaviour in Response to Perturbations of Balance , 1992 .

[11]  C. Hui-Chan,et al.  Does the use of TENS increase the effectiveness of exercise for improving walking after stroke? A randomized controlled clinical trial , 2009, Clinical rehabilitation.

[12]  Jack Parker,et al.  The Effectiveness of Lower-Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: A Systematic Review , 2016, Journal of medical Internet research.

[13]  J. Krakauer Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[14]  Huiru Zheng,et al.  Developing a telemonitoring system for stroke rehabilitation , 2007 .

[15]  M. Grasso,et al.  Rehabilitation of Walking With Electromyographic Biofeedback in Foot‐Drop After Stroke , 1994, Stroke.

[16]  Huiru Zheng,et al.  SMART project: Application of emerging information and communication technology to home-based rehabilitation for stroke patients , 2006 .

[17]  Huiru Zheng,et al.  Web-based monitoring system for home-based rehabilitation with stroke patients , 2005, 18th IEEE Symposium on Computer-Based Medical Systems (CBMS'05).

[18]  J. Kleim,et al.  Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. , 2008, Journal of speech, language, and hearing research : JSLHR.

[19]  Adam Fletcher,et al.  Realist randomised controlled trials: a new approach to evaluating complex public health interventions. , 2012, Social science & medicine.

[20]  Jack Parker,et al.  A review of the evidence underpinning the use of visual and auditory feedback for computer technology in post-stroke upper-limb rehabilitation , 2011, Disability and rehabilitation. Assistive technology.

[21]  A. Shumway-cook,et al.  Postural sway biofeedback: its effect on reestablishing stance stability in hemiplegic patients. , 1988, Archives of physical medicine and rehabilitation.

[22]  C. Mathers,et al.  Preventing stroke: saving lives around the world , 2007, The Lancet Neurology.

[23]  Julie Luker,et al.  Stroke Survivors' Experiences of Physical Rehabilitation: A Systematic Review of Qualitative Studies. , 2015, Archives of physical medicine and rehabilitation.

[24]  Huiru Zheng,et al.  The SMART project: an ICT decision platform for home-based stroke rehabilitation system , 2006 .

[25]  Richard Wootton,et al.  Adoption of telemedicine: from pilot stage to routine delivery , 2012, BMC Medical Informatics and Decision Making.

[26]  K. J. Miller,et al.  Recovery of standing balance and functional mobility after stroke. , 2003, Archives of physical medicine and rehabilitation.

[27]  C. Wolfe,et al.  Cost of stroke in the United Kingdom. , 2008, Age and ageing.

[28]  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.

[29]  R. Willmann,et al.  Home Stroke Rehabilitation for the Upper Limbs , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Huosheng Hu,et al.  The SMART project: A user led approach to developing applications for domiciliary stroke rehabilitation , 2006 .

[31]  M. Hennerici,et al.  Pathophysiology of Stroke Rehabilitation: The Natural Course of Clinical Recovery, Use-Dependent Plasticity and Rehabilitative Outcome , 2006, Cerebrovascular Diseases.

[32]  Huosheng Hu,et al.  Inertial sensors for motion detection of human upper limbs , 2007 .

[33]  S. Michie,et al.  The behaviour change wheel: A new method for characterising and designing behaviour change interventions , 2011, Implementation science : IS.

[34]  A. Geurts,et al.  Motor recovery after stroke: a systematic review of the literature. , 2002, Archives of physical medicine and rehabilitation.

[35]  J R Jenner,et al.  Changing patterns of postural hip muscle activity during recovery from stroke , 2000, Clinical rehabilitation.

[36]  A. Mirelman,et al.  Effects of Training With a Robot-Virtual Reality System Compared With a Robot Alone on the Gait of Individuals After Stroke , 2009, Stroke.

[37]  Hideyuki Saitou,et al.  Locomotion improvement using a hybrid assistive limb in recovery phase stroke patients: a randomized controlled pilot study. , 2014, Archives of physical medicine and rehabilitation.

[38]  Jane Shiels,et al.  A feasibility study to investigate the clinical application of functional electrical stimulation (FES), for dropped foot, during the sub-acute phase of stroke – A randomized controlled trial , 2013, Physiotherapy theory and practice.