Utilizing Gaming Mechanics to Optimize Telerehabilitation Adherence in Persons With Stroke

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), New Jersey Institute of Technology

This trial studies the impact of motivational strategies designed by the gaming industry on adherence to a home tele-rehabilitation program designed to improve hand function in persons with stroke. A growing literature suggests that the extended practice of challenging hand tasks can produce measurable changes in hand function in persons with stroke. Current health care delivery systems do not support this volume of directly supervised rehabilitation, making it necessary for patients to perform a substantial amount of activity at home, unsupervised. Unfortunately, adherence to unsupervised home exercise regimens is quite poor in this population. The investigator's goal is to assess the impact of several well-established game design strategies: 1) Scaffolded increases in game difficulty 2) In-game rewards 3) Quests with enhanced narrative. The investigator's will utilize these enhancements to study their impact on motivation to perform a tele-rehabilitation- based home exercise program, adherence to the program and changes in hand function. The proposed study will utilize a system of novel rehabilitation technologies designed to facilitate home exercise performance. Subjects will perform 3 simulated rehabilitation activities supported by a passive exoskeleton, an infrared camera and software that will allow subjects to exercise at home. The investigator's will investigate: 1) Differences in measures of motivation elicited by motivationally enhanced simulations and un-enhanced control versions.2) The impact of motivational enhancements on actual adherence to a tele-rehabilitation program in persons with stroke and 3) The impact of motivational enhancement on improvements in hand function achieved by these subjects. This proposal will address a critical gap in modern rehabilitation - adherence to autonomous rehabilitation programs. Patient participation in unsupervised rehabilitation is one of the assumptions underpinning our health care system. This said, no data collected to date supports that adherence is acceptable. The technology and methodology in this proposal are an important step towards leveraging extensive research and development done by the computer gaming industry into improved rehabilitation practice.

Purpose/Specific Aims The overarching aim of this study is to provide a mechanism for patients to engage in progressive motor practice for a meaningful time period. The investigator's aim to improve on the positive outcomes demonstrated in patients in the chronic phase and the pilot work being done on patients in the acute in-patient phase post stroke to determine whether functional recovery can be further improved using a home based system. Aim 1: Evaluate compliance with Home-Telerehabilitation simulated hand/arm gaming activities and two computer game groups, one with motivation enhanced: Home Training Motivation Enhanced (HTme) simulations and one with non-enhanced simulations: Home Training Unenhanced (HTu) versions. Hypothesis: Participants in the HTme group will show significant compliance as compared to the control group (HTu). Aim 2: Evaluate the effectiveness of motivation enhanced HTme home-based virtually simulated hand/arm gaming activities for individuals with stroke as compared to a program unenhanced HTu versions of the same simulations. Hypothesis: Participants completing HTme training will exhibit significantly improved clinical, kinematic and neurophysiological outcomes as compared to the control group (HTu). Aim 3: Evaluate the impact of the motivation enhancements designed into computer games to provide a more enjoyable training experience. Hypothesis: Enjoyment of the games will be a more valid predictor of compliance than personal factors. Background and Significance Studies have shown that sustained hand rehabilitation training is important for continuous improvement and maintenance of function following a stroke. It is unimaginably difficult to pursue education, employment and community participation without being able to independently use one's hands. The primary goal of this study is to test an exciting new technology that can be easily used in the home for long-term hand and upper extremity training. Recovery of hand function post brain injury is particularly recalcitrant to currently available interventions. To date, the best efforts of groups studying traditionally presented as well as technology-based therapeutic interventions for the hemiplegic hand and arm have produced measurable changes in motor function and motor control but fall far short of major reductions in disability. If the amount of therapy is critical to rehabilitation, our current institutional limitations undermine the probabilities for successful outcomes. After discharge from the inpatient stay, access to rehabilitation therapy can be difficult for some patients. This is due in part to inadequate insurance, lack of transportation, and the patient's dependence on their caregiver. Having access to long-term rehabilitation training anywhere and at any time is necessary for sub-acute and chronic patients to continuously improve their functional abilities. Research Design and Methods This study will be a single blind randomized controlled trial. Subjects will be blinded to the purpose of the study. All outcome measures will be performed by a therapist blinded to group assignment. A controlled trial will be utilized to determine the additive effect of presenting rehabilitation activities in a virtual environment as compared to standard upper extremity exercise. The investigators will randomize subjects to treatment and control groups using a computerized random number generator. 3.1. Duration of Study Each subject will perform a pre-study evaluation, train using one of the protocols for three months, perform a post study evaluation as well as one and six month retention evaluations. 3.2 Study Sites Testing and initial training will take place in the Bergen Building of the Rutgers Biomedical and Health Sciences Campus in Newark. Home training will take place in subjects' homes. 3.3 Sample Size Justification The investigators will seek sufficient power to detect a clinically significant difference in the Wolf score changes in these two pre-planned, primary comparisons. To evaluate these effects of training, we will assume a power level of .8 and a significance level of 0.05. With presumed correlation among repeated measures of 0.1 and effect size of 0.3, a sample size of 25 subjects in each of the two groups (HTme and HTu) to observe a significant effect for the first comparison (G*Power, version 3.1.5) is necessary. Although the investigators will screen for patients with homogeneous impairments, by its nature stroke is an extremely variable condition. Due to possible subject attrition, the investigators will use a total of 30 subjects in each of the two groups. 3.4 Subject Recruitment Subjects will be recruited through flyers, stroke support groups, and clinician referrals. The investigators will assume that approximately 15-20% of the population will satisfy our inclusion criteria based on our previous experience with upper extremity rehabilitation in this population. Hence the investigators will approach 300 persons. 3.5 Consent Procedures Example: The study will be explained to the potential subject by the study staff, the consent will be read, and their questions will be answered. If participants wish to enroll, the subject will sign the consent form. The study staff obtaining consent will also sign and date the consent form, and a copy will be given to the subject sought from each prospective subject or the subject's legally authorized representative, in accordance with federal & state law and institutional policy. If the study staff member performing the consent process identifies issues suggesting that the prospective subject may not be capable of participating in the consent process due to dementia, a Folstein Mini Mental Status will be performed. Prospective subjects screening positive for dementia will not be included in the study. 3.5.1 Subject Costs and Compensation There are no costs for the subjects. The subjects will be paid 100$ at each of the retention tests. Study Variables 4.1 Independent Variables or Interventions The two computer game groups, Motivation Enhanced (HTme) and Motivation Non-Enhanced (HTu) will use the NJIT- Home Virtual Rehabilitation System (HoVRS) to play a series of computer games developed to practice movement of the hand and fingers. Subjects will first come into our lab, perform pre-tests as well as a pre-intervention training session. Then a physical therapist and engineer will set up the apparatus in subject's home and will train them on how to use the system and play the games in their home during the first week. The physical therapist and engineer will be in contact with subjects throughout the training and will visit subjects' homes as needed if problems are encountered. Additionally, the system allows the therapist to remotely monitor each day's activity. 4.1.1 Device Description NJIT HoVRS has two sub-systems to deliver home-based training: 1) a patient based platform to provide the training and 2) a server based online data logging and reporting system. In the patient's home, a cross platform virtual reality training application runs video games (developed in the Unity 3D game engine using the language C#) on their home computer. 4.1.11 Hardware The Leap Motion Controller (LMC) a commercially developed infrared tracking device developed for home video game control is used to capture motion of the hand and arm movement without requiring wearable sensors. The device's USB controller reads the sensor data into its own local memory and performs any necessary resolution adjustments. This data is then streamed via USB to the Leap Motion image Application Programming Interface (API). From there, we programmed the system to feed tracking data into virtual reality activities by calling the Leap Motion API. If the patient's arm is weak and cannot support the hand against gravity above the Leap Motion Controller, a commercially available, spring-based arm support, will be provided to the subject (Figure 1). The arm support provides 12 different levels of passive support allowing it to accommodate a wide range of patient sizes and strength levels. It requires a single setting that can be provided during the patient's initial evaluation 4.1.1.2 Software Patients will either use their own home computer or will be provided with a computer if needed. A user-friendly Graphic User Interface (GUI) lists all of the training activities allowing patients to choose which activity they want to begin with using just one mouse click. Currently twelve games have been developed, each one designed to focus on training a specific hand or arm movement such as wrist rotation or finger individuation. All games are downloadable via HoVRS website. 4.2 Dependent Variables: See Outcomes Measures 4.3 Risk of Harm There is less than minimal risk involved. The virtual reality (VR) experiments are non-invasive and pose no obvious risk. Transient fatigue of the hand and arm are possible, but this risk is not greater than that posed by normal daily activities following a stroke. 4.4 Potential for Benefit The benefits of taking part in this study may be: Patient may regain better use of their hand and arm. However, it is possible that patients might receive no direct personal benefit from taking part in this study. Data Handling and Statistical Analysis All efforts will be made to keep subjects' personal information confidential. All subject names will be removed from the data and the data will be tagged using a coded identification (ID) number. Demographic, clinical outcome and survey data will first be recorded on paper. All kinematic and computerized performance data will be collected on computer. These computer files will be identified by the coded subject ID number. All data will be transferred to an Excel spreadsheet with subjects identified by this same ID number. Spreadsheets will be stored on a drive that is password protected. Data will only be accessible to study staff and will be retained for seven years. The link between subject identity and subject ID number will be destroyed when data collection is completed. The primary outcome measures and all secondary outcome measures described above will be subjected to a repeated measured analysis of variance, with between-group factors Therapy Type (HTme, HTu) and within-group factor Test (Before, Post, One Month retention, Six Months Retention). Post-hoc analyses of the Therapy Type by Test interaction effects will focus on the Month 1 versus Month 6 comparison. The investigators will be quantifying training effects by comparing group means as well as by percent change in performance, and by comparing the recovery curves obtained from Tests 1-4. All clinical outcomes used are well established measures of upper extremity functional recovery with published minimum clinically important differences which will be used to evaluate the significance of our findings. 7. Reporting Results 7.1 Individual Results No disease screening data will be collected. Patient's changes on clinical tests will be shared with them during testing sessions. These sessions are conducted by licensed Physical Therapists who have training to help persons with stroke interpret clinical examination findings. 7.2 Aggregate Results Subjects will not be informed of aggregate findings. 7.3 Professional Reporting De-identified, aggregate findings will be published in professional journals and presented at scientific meetings.

Stroke, Upper extremity, Hand, Arm, Virtual reality, Telerehabilitation, Gaming, Hemiparesis, Dexterity

The Home Telerehabilitation Motivation Enhanced (HTme) group will use the NJIT-HoVRS system to play a series of three games to train movement of their shoulder, elbow, wrist and fingers. The study team will set up the apparatus in their home at the initial visit and train them to use the system. After this, subjects will practice in their homes with on-line or in-person support as needed (once a week in person for the first month, and then an average of two times per month in person and two times per month on line). Subjects will be instructed to perform three of the simulations assigned to them as much as possible, but at least twenty minutes, daily for twelve weeks. The HTme group will use three simulations that will provide the user with eight to twelve levels of gradually increasing difficulty and complexity. A screen announces each level change and the graphics for each new level change substantially. Scoring opportunities increase at each new level.

The Home Telerehabilitation Motivation Enhanced (HTu) group will use the NJIT-HoVRS system to play a series of three games to train movement of their shoulder, elbow, wrist and fingers. The study team will set up the apparatus in their home at the initial visit and train them to use the system. After this, subjects will practice in their homes with on-line or in-person support as needed (once a week in person for the first month, and then an average of two times per month in person and two times per month on line). Subjects will be instructed to perform three of the simulations assigned to them as much as possible, but at least twenty minutes, daily for twelve weeks. The HTu group will use three simulations. Difficulty will be increased utilizing an adaptive control algorithm that increases difficulty based on performance. Difficulty changes are extremely incremental making them imperceptible for most subjects. Graphics and scoring do not change as difficulty level changes.

The Home Virtual Rehabilitation System (HoVRS) integrates a Leap Motion controller, a passive arm support and a suite of custom designed hand rehabilitation simulations. The Leap Motion provides camera based measurement of finger joint positions, allowing for integrated virtual arm and finger training. If the patient's arm is severely impaired, a forearm orthosis that counter-balances gravity to provide graded support to the arm during activity is issued to the subject. In this study, we utilize 3 task-based simulations that train hand manipulation and arm transport. One simulation trains hand opening integrated with pronation and supination, a second trains wrist movement, by presenting targets that subjects navigate a plane over and around buildings to collect, a third simulation, trains shoulder and elbow disassociation in a horizontal plane integrated with hand opening.

Sum of maximum angular excursions of the paretic metacarpo-phalangeal (MCP), proximal inter-phalangeal(PIP) and distal inter-phalangeal joints (DIP) joints during a hand opening activity

Sum of maximum angular excursions of the paretic metacarpo-phalangeal (MCP), proximal inter-phalangeal(PIP) and distal inter-phalangeal joints (DIP) joints during a hand opening activity

Sum of maximum angular excursions of the paretic metacarpo-phalangeal (MCP), proximal inter-phalangeal(PIP) and distal inter-phalangeal joints (DIP) joints during a hand opening activity

Ability to control hand opening as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control hand opening as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control hand opening as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control wrist flexion and extension as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control wrist flexion and extension as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control wrist flexion and extension as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control shoulder and elbow as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control shoulder and elbow as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Ability to control shoulder and elbow as subject moves a cursor tracking a sine wave. Reported as root mean square error (RMSE) comparing target position and cursor position.

Participant will wear tri-axial accelerometers on both wrists for twenty four hours and upper limb magnitude ratio will be calculated and reported as per Bailey (2015). For each second of this twenty four hour period accelerations across the three axes are combined into a single vector magnitude value. Inactive non-paretic UE is assigned a vector magnitude of -7 when paretic UE is moving alone. Inactive paretic UE is assigned a vector magnitude of 7 when non-paretic UE is moving alone. Paretic wrist vector magnitude will be divided by non-paretic wrist vector magnitude for each second. These calculated values will be transformed using a natural logarithm to prevent skewness of positive, untransformed values. Median of these values for the twenty four hour period will be reported for each individual subject.

Participant will wear tri-axial accelerometers on both wrists for twenty four hours and upper limb magnitude ratio will be calculated and reported as per Bailey (2015). For each second of this twenty four hour period accelerations across the three axes are combined into a single vector magnitude value. Inactive non-paretic UE is assigned a vector magnitude of -7 when paretic UE is moving alone. Inactive paretic UE is assigned a vector magnitude of 7 when non-paretic UE is moving alone. Paretic wrist vector magnitude will be divided by non-paretic wrist vector magnitude for each second. These calculated values will be transformed using a natural logarithm to prevent skewness of positive, untransformed values. Median of these values for the twenty four hour period will be reported for each individual subject.

Participant will wear tri-axial accelerometers on both wrists for twenty four hours and upper limb magnitude ratio will be calculated and reported as per Bailey (2015). For each second of this twenty four hour period accelerations across the three axes are combined into a single vector magnitude value. Inactive non-paretic UE is assigned a vector magnitude of -7 when paretic UE is moving alone. Inactive paretic UE is assigned a vector magnitude of 7 when non-paretic UE is moving alone. Paretic wrist vector magnitude will be divided by non-paretic wrist vector magnitude for each second. These calculated values will be transformed using a natural logarithm to prevent skewness of positive, untransformed values. Median of these values for the twenty four hour period will be reported for each individual subject.

Participant will wear tri-axial accelerometers on both wrists for twenty four hours and upper limb magnitude ratio will be calculated and reported as per Bailey (2015). For each second of this twenty four hour period accelerations across the three axes are combined into a single vector magnitude value. Inactive non-paretic UE is assigned a vector magnitude of -7 when paretic UE is moving alone. Inactive paretic UE is assigned a vector magnitude of 7 when non-paretic UE is moving alone. Paretic wrist vector magnitude will be divided by non-paretic wrist vector magnitude for each second. These calculated values will be transformed using a natural logarithm to prevent skewness of positive, untransformed values. Median of these values for the twenty four hour period will be reported for each individual subject.

Qualitative data related to subjects experience during the testing and training periods will be collected using a structured interview.

Inclusion Criteria: unilateral stroke score of 22 or greater on the Montreal Cognitive Assesment Score of 1 or better on extinction and inattention portion of NIH Stroke Scale Fugl-Meyer (FM) between 36-58/66 ( Score of 1 or better on language portion of NIHSS intact cutaneous sensation (ability to detect <4.17 Newton stimulation using Semmes-Weinstein nylon filaments) Exclusion Criteria: Orthopedic issues that would limit the ability to perform regular upper extremity activity

Immediately following completion of our proposed study we will submit a de-identified data set our protocol and links to published papers based on the data set to the Centralized Open Access Rehabilitation Data Base for Stroke (SCOAR).

We will make our data available immediately after study completion. Data will remain available indefinitely.

McAuley E, Duncan T, Tammen VV. Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis. Res Q Exerc Sport. 1989 Mar;60(1):48-58. doi: 10.1080/02701367.1989.10607413.

Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004 Aug;39(4 Pt 1):1005-26. doi: 10.1111/j.1475-6773.2004.00269.x.

Winstein CJ, Wolf SL, Dromerick AW, Lane CJ, Nelsen MA, Lewthwaite R, Cen SY, Azen SP; Interdisciplinary Comprehensive Arm Rehabilitation Evaluation (ICARE) Investigative Team. Effect of a Task-Oriented Rehabilitation Program on Upper Extremity Recovery Following Motor Stroke: The ICARE Randomized Clinical Trial. JAMA. 2016 Feb 9;315(6):571-81. doi: 10.1001/jama.2016.0276.

Kwakkel G. Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil. 2006 Jul 15-30;28(13-14):823-30. doi: 10.1080/09638280500534861.

Yozbatiran N, Der-Yeghiaian L, Cramer SC. A standardized approach to performing the action research arm test. Neurorehabil Neural Repair. 2008 Jan-Feb;22(1):78-90. doi: 10.1177/1545968307305353. Epub 2007 Aug 17.

Fluet GG, Patel J, Qiu Q, Yarossi M, Massood S, Adamovich SV, Tunik E, Merians AS. Motor skill changes and neurophysiologic adaptation to recovery-oriented virtual rehabilitation of hand function in a person with subacute stroke: a case study. Disabil Rehabil. 2017 Jul;39(15):1524-1531. doi: 10.1080/09638288.2016.1226421. Epub 2016 Sep 27.

Mathiowetz V, Volland G, Kashman N, Weber K. Adult norms for the Box and Block Test of manual dexterity. Am J Occup Ther. 1985 Jun;39(6):386-91. doi: 10.5014/ajot.39.6.386.

Oxford Grice K, Vogel KA, Le V, Mitchell A, Muniz S, Vollmer MA. Adult norms for a commercially available Nine Hole Peg Test for finger dexterity. Am J Occup Ther. 2003 Sep-Oct;57(5):570-3. doi: 10.5014/ajot.57.5.570.

Jurkiewicz MT, Marzolini S, Oh P. Adherence to a home-based exercise program for individuals after stroke. Top Stroke Rehabil. 2011 May-Jun;18(3):277-84. doi: 10.1310/tsr1803-277.

Miller KJ, Adair BS, Pearce AJ, Said CM, Ozanne E, Morris MM. Effectiveness and feasibility of virtual reality and gaming system use at home by older adults for enabling physical activity to improve health-related domains: a systematic review. Age Ageing. 2014 Mar;43(2):188-95. doi: 10.1093/ageing/aft194. Epub 2013 Dec 17.

Duncan PW, Wallace D, Lai SM, Johnson D, Embretson S, Laster LJ. The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change. Stroke. 1999 Oct;30(10):2131-40. doi: 10.1161/01.str.30.10.2131.

Rimmer JH, Wang E, Smith D. Barriers associated with exercise and community access for individuals with stroke. J Rehabil Res Dev. 2008;45(2):315-22. doi: 10.1682/jrrd.2007.02.0042.

Standen PJ, Threapleton K, Connell L, Richardson A, Brown DJ, Battersby S, Sutton CJ, Platts F. Patients' use of a home-based virtual reality system to provide rehabilitation of the upper limb following stroke. Phys Ther. 2015 Mar;95(3):350-9. doi: 10.2522/ptj.20130564. Epub 2014 Sep 11.

Nijenhuis SM, Prange GB, Amirabdollahian F, Sale P, Infarinato F, Nasr N, Mountain G, Hermens HJ, Stienen AH, Buurke JH, Rietman JS. Feasibility study into self-administered training at home using an arm and hand device with motivational gaming environment in chronic stroke. J Neuroeng Rehabil. 2015 Oct 9;12:89. doi: 10.1186/s12984-015-0080-y.

Wittmann F, Held JP, Lambercy O, Starkey ML, Curt A, Hover R, Gassert R, Luft AR, Gonzenbach RR. Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system. J Neuroeng Rehabil. 2016 Aug 11;13(1):75. doi: 10.1186/s12984-016-0182-1.

Patel J, Qiu Q, Yarossi M, Merians A, Massood S, Tunik E, Adamovich S, Fluet G. Exploring the impact of visual and movement based priming on a motor intervention in the acute phase post-stroke in persons with severe hemiparesis of the upper extremity. Disabil Rehabil. 2017 Jul;39(15):1515-1523. doi: 10.1080/09638288.2016.1226419. Epub 2016 Sep 16.

Timmermans AA, Seelen HA, Willmann RD, Kingma H. Technology-assisted training of arm-hand skills in stroke: concepts on reacquisition of motor control and therapist guidelines for rehabilitation technology design. J Neuroeng Rehabil. 2009 Jan 20;6:1. doi: 10.1186/1743-0003-6-1.

Adamovich SV, Fluet GG, Mathai A, Qiu Q, Lewis J, Merians AS. Design of a complex virtual reality simulation to train finger motion for persons with hemiparesis: a proof of concept study. J Neuroeng Rehabil. 2009 Jul 17;6:28. doi: 10.1186/1743-0003-6-28.

Fluet GG, Merians AS, Qiu Q, Lafond I, Saleh S, Ruano V, Delmonico AR, Adamovich SV. Robots integrated with virtual reality simulations for customized motor training in a person with upper extremity hemiparesis: a case study. J Neurol Phys Ther. 2012 Jun;36(2):79-86. doi: 10.1097/NPT.0b013e3182566f3f.

Fluet GG, Merians AS, Qiu Q, Davidow A, Adamovich SV. Comparing integrated training of the hand and arm with isolated training of the same effectors in persons with stroke using haptically rendered virtual environments, a randomized clinical trial. J Neuroeng Rehabil. 2014 Aug 23;11:126. doi: 10.1186/1743-0003-11-126.

Merians AS, Fluet GG, Qiu Q, Saleh S, Lafond I, Davidow A, Adamovich SV. Robotically facilitated virtual rehabilitation of arm transport integrated with finger movement in persons with hemiparesis. J Neuroeng Rehabil. 2011 May 16;8:27. doi: 10.1186/1743-0003-8-27.

Lang CE, Macdonald JR, Reisman DS, Boyd L, Jacobson Kimberley T, Schindler-Ivens SM, Hornby TG, Ross SA, Scheets PL. Observation of amounts of movement practice provided during stroke rehabilitation. Arch Phys Med Rehabil. 2009 Oct;90(10):1692-8. doi: 10.1016/j.apmr.2009.04.005.

Lohse KR, Lang CE, Boyd LA. Is more better? Using metadata to explore dose-response relationships in stroke rehabilitation. Stroke. 2014 Jul;45(7):2053-8. doi: 10.1161/STROKEAHA.114.004695. Epub 2014 May 27.

Miller KK, Porter RE, DeBaun-Sprague E, Van Puymbroeck M, Schmid AA. Exercise after Stroke: Patient Adherence and Beliefs after Discharge from Rehabilitation. Top Stroke Rehabil. 2017 Mar;24(2):142-148. doi: 10.1080/10749357.2016.1200292. Epub 2016 Jun 23.

Simpson LA, Eng JJ, Tawashy AE. Exercise perceptions among people with stroke: Barriers and facilitators to participation. Int J Ther Rehabil. 2011 Sep 6;18(9):520-530. doi: 10.12968/ijtr.2011.18.9.520.

Rand D, Givon N, Weingarden H, Nota A, Zeilig G. Eliciting upper extremity purposeful movements using video games: a comparison with traditional therapy for stroke rehabilitation. Neurorehabil Neural Repair. 2014 Oct;28(8):733-9. doi: 10.1177/1545968314521008. Epub 2014 Feb 10.

Peters DM, McPherson AK, Fletcher B, McClenaghan BA, Fritz SL. Counting repetitions: an observational study of video game play in people with chronic poststroke hemiparesis. J Neurol Phys Ther. 2013 Sep;37(3):105-11. doi: 10.1097/NPT.0b013e31829ee9bc.

da Silva Cameirao M, Bermudez I Badia S, Duarte E, Verschure PF. Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: a randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming system. Restor Neurol Neurosci. 2011;29(5):287-98. doi: 10.3233/RNN-2011-0599.

Shirzad N, Van der Loos HF. Adaptation of task difficulty in rehabilitation exercises based on the user's motor performance and physiological responses. IEEE Int Conf Rehabil Robot. 2013 Jun;2013:6650429. doi: 10.1109/ICORR.2013.6650429.

Laver KE, Lange B, George S, Deutsch JE, Saposnik G, Crotty M. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev. 2017 Nov 20;11(11):CD008349. doi: 10.1002/14651858.CD008349.pub4.

Zondervan DK, Friedman N, Chang E, Zhao X, Augsburger R, Reinkensmeyer DJ, Cramer SC. Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program. J Rehabil Res Dev. 2016;53(4):457-72. doi: 10.1682/JRRD.2015.04.0057.

Lum PS, Mulroy S, Amdur RL, Requejo P, Prilutsky BI, Dromerick AW. Gains in upper extremity function after stroke via recovery or compensation: Potential differential effects on amount of real-world limb use. Top Stroke Rehabil. 2009 Jul-Aug;16(4):237-53. doi: 10.1310/tsr1604-237.

Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF. Getting neurorehabilitation right: what can be learned from animal models? Neurorehabil Neural Repair. 2012 Oct;26(8):923-31. doi: 10.1177/1545968312440745. Epub 2012 Mar 30.

Folstein MF, Robins LN, Helzer JE. The Mini-Mental State Examination. Arch Gen Psychiatry. 1983 Jul;40(7):812. doi: 10.1001/archpsyc.1983.01790060110016. No abstract available.

Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-management behaviors? Health Serv Res. 2007 Aug;42(4):1443-63. doi: 10.1111/j.1475-6773.2006.00669.x.

Bollen JC, Dean SG, Siegert RJ, Howe TE, Goodwin VA. A systematic review of measures of self-reported adherence to unsupervised home-based rehabilitation exercise programmes, and their psychometric properties. BMJ Open. 2014 Jun 27;4(6):e005044. doi: 10.1136/bmjopen-2014-005044.

Rohafza M, Fluet GG, Qiu Q, Adamovich S. Correlation of reaching and grasping kinematics and clinical measures of upper extremity function in persons with stroke related hemiplegia. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3610-3. doi: 10.1109/EMBC.2014.6944404.

Merians AS, Poizner H, Boian R, Burdea G, Adamovich S. Sensorimotor training in a virtual reality environment: does it improve functional recovery poststroke? Neurorehabil Neural Repair. 2006 Jun;20(2):252-67. doi: 10.1177/1545968306286914.

Puthenveettil S, Fluet G, Qiu Q, Adamovich S. Classification of hand preshaping in persons with stroke using Linear Discriminant Analysis. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4563-6. doi: 10.1109/EMBC.2012.6346982.