Skip to main content
PMC home page
Physical Therapy logoLink to Physical Therapy
. 2014 Oct 24;95(3):441–448. doi: 10.2522/ptj.20130571

Considerations in the Efficacy and Effectiveness of Virtual Reality Interventions for Stroke Rehabilitation: Moving the Field Forward

Rachel Proffitt 1,✉, Belinda Lange 2
PMCID: PMC4348718  PMID: 25343960

Abstract

In the past 2 decades, researchers have demonstrated the potential for virtual reality (VR) technologies to provide engaging and motivating environments for stroke rehabilitation interventions. Much of the research has been focused on the exploratory phase, and jumps to intervention efficacy trials and scale-up evaluation have been made with limited understanding of the active ingredients in a VR intervention for stroke. The rapid pace of technology development is an additional challenge for this emerging field, providing a moving target for researchers developing and evaluating potential VR technologies. Recent advances in customized games and cutting-edge technology used for VR are beginning to allow for researchers to understand and control aspects of the intervention related to motivation, engagement, and motor control and learning. This article argues for researchers to take a progressive, step-wise approach through the stages of intervention development using evidence-based principles, take advantage of the data that can be obtained, and utilize measurement tools to design effective VR interventions for stroke rehabilitation that can be assessed through carefully designed efficacy and effectiveness trials. This article is motivated by the recent calls in the field of rehabilitation clinical trials research for carefully structured clinical trials that have progressed through the phases of research.

Nearly 800,000 people experience a new or recurrent stroke every year in the United States, and about 50% report hemiparesis 6 months after their incident.1 Furthermore, at least one quarter of people have lasting deficits that affect their ability to perform daily activities independently.1 Rehabilitation interventions are of utmost importance for this population, as the annual direct and indirect costs of stroke are estimated to be about $65.5 billion.2 Unfortunately, adherence to rehabilitation programs is low. A number of researchers have identified factors such as fatigue, poor health, lack of motivation, and musculoskeletal issues that may prevent people with stroke from initiating or maintaining a structured exercise program.3–5 The field of virtual reality (VR) offers potential benefits to improving motivation and participation in exercise protocols6 and improving outcomes in motor function and participation.7–10

Virtual reality can be defined as “the use of interactive simulations created with computer hardware and software to present users with opportunities to engage in environments that appear and feel similar to real-world objects and events.”11 Using this definition, the scope of technologies and systems that are considered VR is very broad. For the purposes of this article, we will take a more narrow definition of VR and discuss only VR systems for stroke that allow the user to be immersed and interact with the VR environment without assistance (ie, robotic devices). Although many researchers and recent reviews have included robotic devices within the definition of VR, the recent increase in use of commercial video game systems and low-cost customized rehabilitation systems has formed a subfield of VR applications that merit separate consideration. The field of robotics, including robots that have been developed with VR environments, has its own merits and challenges, different methods for measuring performance and providing feedback, and a longer history in stroke rehabilitation and clinical trials.12–15 Furthermore, the design of such VR interventions should recognize the successes and challenges faced by the related field of robotics and VR as well as the components of learning and motivation that are often assumed to be integrated. The field of robotics has had much success in being able to clearly define the active ingredients of the intervention through high-fidelity data capture and recording. Additionally, the US Food and Drug Administration has an accelerated path to clinical translation that many researchers have utilized.13,15 The separate field of VR interventions we have defined can learn much from the success of robotics in defining active ingredients; however, we recognize that the devices and interventions are different and have their own set of challenges.

In order to determine and effectively measure active ingredients in a VR intervention, trial design and the sequence of studies need to be considered. One model for study design includes 4 phases: exploratory phase, intervention development phase, intervention efficacy phase, and scale-up phase. The beginning phase, or exploratory phase, can include an exploration into a gap in clinical practice or an exploration of the benefits and limitations of a piece of technology.16 Following this exploration, researchers should attempt to demonstrate knowledge of how and why an intervention should work in the intervention development phase before moving into feasibility or formal testing.17 Feasibility, safety, and pilot trials should precede larger randomized controlled trials (RCTs) in most cases involving VR interventions for stroke. Not only do pilot and feasibility studies demonstrate that an intervention is safe and feasible for a given population, they also provide important information related to study parameters such as identification of suitable outcome measures and participant adherence rates.18 The move from intervention efficacy trials to scale-up evaluation (or effectiveness trials) can be muddled with challenges when it comes to complex interventions such as VR. These interventions are “built up from a number of components,”16 and often interventions that prove to be efficacious in an intervention efficacy trial do not always “scale up.”19 If the earlier questions in the exploratory phase include those related to the needs of the end user or consumer, the move to a scale-up evaluation of a VR intervention has the potential to be much smoother.

Study design is only one component of determining the active ingredients in a VR intervention. Advances in measurement technologies, VR technologies and the applications of “big data” can provide researchers with the necessary elements for identifying and quantifying the active ingredients of an intervention. In this perspective, we will discuss the history and current status of VR interventions for stroke through the lens of the 4 previously defined research phases. Emerging technologies, “big data,” and learning and motivation will be considered through a discussion of the challenges and potential solutions to advancing the field of VR in stroke rehabilitation forward.

VR Interventions in Stroke Rehabilitation: Exploratory Phase

Virtual reality technologies have been used as stroke interventions with some success over the past several decades. In stroke rehabilitation, as well as other health behavior–related areas such as diet and exercise, providing an intervention that is motivating and engaging is crucial to patient involvement and participation. Naturally, VR technologies were initially heralded as a potentially successful medium for the delivery of engaging and motivating rehabilitation interventions. A SWOT (strengths, weaknesses, opportunities, and threats) analysis of the field of VR and rehabilitation by Rizzo and Kim6 showed that the integration of gaming features into VR applications could enhance motivation. Burke and colleagues20 further explored principles of game design and concluded that both meaningful play and challenge were integral to the use of VR in rehabilitation. Researchers also began to document that the VR applications and interventions needed to have components beyond those related to engagement and enjoyment.21

Much of the early research and development of VR systems for rehabilitation focused on large-scale, laboratory-based systems or complex, custom-made, expensive hardware devices.22 Although the development and initial evaluations of these systems were promising, many of these early prototype systems did not make it into the clinical setting. Early work at the beginning of the 2000s using video-capture systems— such as the GestureTek system (GestureTek, Toronto, Ontario, Canada)—for rehabilitation provided much of the groundwork for the low-cost VR systems in development today.23 The camera-based system captures an image of the user, without the need for cumbersome markers or devices. Although the system was still relatively costly, the camera-based tracking and relative ease of use have allowed greater access to the technology for clinicians.

Initially, researchers used small, exploratory case studies to examine the feasibility of using off-the-shelf games and devices.24,25 Given that the field was still exploring the motivational and engaging aspects of games and VR interventions, this type of research was appropriate. Within months of the release of the Nintendo Wii console (Nintendo, Redmond, Washington) in 2006, news articles began reporting on the topic of “Wii-hab” in hospitals and nursing homes, and research soon followed. The Nintendo Wii was explored as both an alternative and an adjunct to conventional stroke rehabilitation.26,27

The release of the Microsoft Kinect (Microsoft Inc, Redmond, Washington) in 2010 stirred up a new wave of development of games for rehabilitation; however, a few lessons had been learned from the previous 4 years of “Wii-hab” excitement. Researchers and nonrehabilitation programmers have been experimenting with methods of utilizing the internal mechanisms of the Microsoft Kinect sensor for the purposes of accurate tracking, providing specific feedback, and collecting and recording meaningful data.28,29 Lange and colleagues30 have since developed an application for balance using the Kinect camera that has applications for a broad range of populations, including people with stroke. Along with being able to customize gestural input, they have created other features, including a calibration of the Kinect camera to user baseline function, a variety of on-screen user representations, an option for multiple users (using the same Kinect camera and using 2 different cameras in 2 different locations), and recording and quantification of performance.21 Other researchers have developed customized games using the Kinect camera, and findings from initial usability studies are positive.31–36

VR Interventions in Stroke Rehabilitation: Intervention Development and Efficacy

Most of these systems are still in the exploratory phase of research and have not progressed beyond user testing and feasibility and small case studies. Pairing low-cost sensors, such as the Microsoft Kinect sensor, with customized software has allowed researchers to design, describe, and control the active ingredients in VR interventions for stroke rehabilitation. Since 2010, research has circled back around through the phases of research to better understand the mechanisms of impact on people with stroke for both upper limb function and balance and mobility.

Recent feasibility trials using the Nintendo Wii have reached similar conclusions: the most effective types of games, the most effective dose, and the most effective level of clinician interaction need to be further explored.37,38 Saposnik and colleagues39 presented the design and pilot data for a randomized clinical trial using the Nintendo Wii as an intervention for stroke (Effectiveness of Virtual Reality Exercises in STroke Rehabilitation [EVREST]) compared with an equal amount (total time) of recreation therapy (playing cards, Bingo, or Jenga). Their findings were presented in a subsequent article that reported significant unadjusted differences in motor function (as assessed with the Wolf Motor Function Test) between the experimental and control groups in favor of the Wii intervention used in the experimental group.40 It should be noted that this was a pilot clinical trial that was not powered to detect differences. Furthermore, there was little explanation on the part of the authors as to the mechanism for change through playing the Wii games. A similarly designed RCT exploring the efficacy of the PlayStation EyeToy (Sony Computer Entertainment America LLC, San Mateo, California) for people with subacute stroke demonstrated that participants improved in self-care ability but not in Brunnström states for the hand and upper extremity.41 Some of the major limitations mentioned in both studies40,41 included limited evidence for intervention dosing, choice of outcome measures, and comparison groups.

It also is difficult to obtain meaningful data from the game systems beyond time spent playing and game scores, and even with a variety of measures included in the study, it is difficult to determine the level (body structures and functions, activity performance, or activity participation) on which the intervention had an impact. Variables such as amount of movement, intensity of movement, and amount of clinician input are all being explored.41–45 The results from several studies were promising and included benefits for people with stroke, including increased participation in activities of daily living, improvement in upper extremity movement, and improvement in static standing balance.8,46–52

VR Interventions for Stroke Rehabilitation: Moving to Scale-up Evaluation?

As research moves more boldly into the intervention efficacy phase and eventually into the scale-up evaluation phase, choosing appropriate outcome measures and dosing will be crucial. The need for validated outcome measures and specific definitions of intervention dosage was echoed in the extensive Cochrane review by Laver and colleagues53 in 2012. The review identified a number of shortcomings in trials of VR interventions and suggested that research exploring the characteristics of VR (eg, presence, immersion, engagement) will be a crucial component of identifying active ingredients in future studies and will assist researchers in constructing comparison interventions and study design. With the valuable time spent in the early phases of research, researchers will be well positioned to make informed and suitable choices.

Although previous research has provided promising results for the use of VR technologies to improve stroke rehabilitation outcomes, there are still a number of major questions and issues that need to be resolved before larger trials are implemented. A recent review by Fluet and Deutsch54 called for both efficacy trials and comparative effectiveness trials of VR interventions for sensorimotor rehabilitation poststroke. Researchers should be cautious in choosing comparison interventions. Rehabilitation interventions are mostly behavioral in nature and are a complex interaction of many factors, including the inherent influence of the person receiving the intervention; one size may not fit all. A match of dose, duration, and frequency of the intervention active ingredients can help minimize the “challenge of making groups equivalent.”53 This challenge stems from the nature of VR interventions. Virtual environments potentially offer scenarios and interactions that are impossible in the real world. Real-world environments, objects, and scenarios can be modeled in a virtual setting. Feedback and rewards can be structured in the virtual environment and affect the performance of the person with stroke. The various tracking and input devices can help the VR intervention mimic real-world environments; however, the perceptions, movements, and subjective experience of the person in the virtual environment may be slightly altered.55 These devices also offer methods of measuring outcomes that have been previously unavailable or available only in a laboratory setting.

Utilizing Advances in Measurement Technology

Understanding and measuring what the user is doing during an intervention is only one piece of the equation when it comes to demonstrating the efficacy of rehabilitation interventions for motor impairments in stroke. One of the unique and exciting features of using VR technology in the rehabilitation setting is the capability of these systems to record and analyze the user's performance quantitatively. This is a feature that is lacking with many standard treatment tools used within the clinic setting. The Microsoft Kinect offers the potential as both an input device for the control of a VR intervention by the end user and a device to record kinematic data of the end user.

Da Gama and colleagues56 explored the capabilities of the Kinect to detect correct (within specified parameters) movements and provide feedback to the player. Using vectors and angles, computations can be made for a particular movement to help the system determine if it is being performed correctly—something that is typically done by a rehabilitation clinician. They tested this application with clinicians, adults with no disabilities, and older adults in a physical therapy program, and it was able to detect correct movements with high fidelity.56

Along with tracking and movement recognition, researchers have explored other components of the Kinect, such as depth sensing. When compared with laser scanning, the point cloud property (depth) does not contain large systematic errors.57 As an option for measuring joint angles, the Kinect has a 10% error rate, similar to the error rate demonstrated by clinicians trained in goniometer-based range-of-motion testing. The advantage of the Kinect is the ability to obtain those measurements with a similar error rate throughout an entire intervention session.58

Advances in neuroimaging have progressed our understanding about what is occurring at a structural level in the nervous system. A consideration of how a player processes the virtual environment is a crucial piece of understanding how VR interventions affect the nervous system, the “mind-body connection.” Using functional magnetic resonance imaging (fMRI), researchers have shown that the brain views virtual avatars, or representations of the player's own body parts, as embodied “extensions” of the body.59 Furthermore, activations are seen in areas of the brain that are associated with a sense of agency.59 Adamovich and colleagues59 pioneered the use of a virtual avatar in combination with real-time tracking of participant movements and were able to investigate the specific areas of the brain involved with agency through including a temporal delay between the movement and the feedback provided to the participant.

Not only is the virtual avatar embodied, but activation can be seen in areas that are involved in motor preparation, including the anterior intraparietal area and the supplementary motor area, when simply viewing a virtual avatar making a movement.60 The simplicity of this experiment allowed the researchers to see the activation in these areas. They also showed that these areas are activated when the person makes a similar real movement.60

The use of fMRI as a pretest-posttest measure gained momentum in the early and middle parts of the last decade. Results from several studies showed reorganization of the cortex in people with motor impairments (stroke, cerebral palsy). Both increased areas of activation and decreased or focused areas of activation were shown following VR interventions.61 In studies that explored activation relative to the paretic side of the body, results demonstrated reorganization and increased activation in the ipsilesional motor cortices.62,63 The results from the fMRI studies in VR should be interpreted with caution. It is unclear whether the changes in brain structures can be attributed to true recovery of motor function or simply changes in activation in the brain because the person was moving more. Critiques of non-VR motor interventions, such as constraint-induced movement therapy, have alluded to this point.64 If the active ingredients in the VR interventions are poorly defined, the findings from fMRI studies lend little support. However, if trends in the VR field move toward a better understanding of the active ingredients in the interventions, particularly those that can be well controlled by a clinician (ie, customized), the findings from associated fMRI studies offer a glimpse into the structural changes that affect behavior and function.

Accessing “Big Data”

Clinical trials, specifically RCTs, are the gold standard in the medical field, including stroke rehabilitation trials. This type of trial falls under the intervention efficacy phase discussed earlier. It is in this phase that researchers attempt to show that the developed VR intervention or technology has the potential to produce a significant change in outcomes for people with stroke. The cost of an RCT is continuing to rise, with a reported 7.5% annual increase in study costs above and beyond the rate of inflation.65 Studies involving behavioral interventions, such as rehabilitation interventions for stroke, often incur increased costs due to the need for multiple study participant visits, clinician time for assessments and interventions, and large, complex data sets that need to be managed efficiently. Given the advances in the technologies used for stroke VR interventions, data from these systems can be effectively harnessed and utilized to maximize the efficiency of trial design and facilitate a smoother transition to real-world implementation or scale-up phase trials.

Data-driven health care utilizes patient-oriented data to adjust intervention and treatments in an effort to maximize positive health outcomes.66 Electronic medical and health records are only a small piece of data-driven health care. It is through analyzing and interpreting patient data, followed by implementing changes or making treatment decisions, that the power of the data is finally realized. With regard to VR intervention for stroke, large amounts of data collected can be utilized to create highly customized interventions. Virtual reality technologies and systems have the potential to incorporate in vivo assessments of performance. The data can then be used to make minute adjustments to the level of challenge in the game so that the person with stroke remains at that just-right level of challenge. The data also can be analyzed remotely by a therapist who can then make appropriate, informed decisions about patient progress.

The implications for trial design go beyond treatment decisions. Telerehabilitation can provide an efficient vehicle for therapist interactions, and in vivo assessments using the technology and automatic uploading to a database can lessen the burden on bioinformatics and biostatistics personnel. Trials can be done in the home or community for those who have limited transportation, lessening the burden on the study budget. A constant stream of data can help investigators identify outliers or nonresponders more rapidly and adjust the trial design or increase the sample size to ensure sufficient power. Utilizing big data in VR intervention trials for stroke rehabilitation has the potential to shift the structure of clinical trials in rehabilitation and the nature of traditional interventions for stroke rehabilitation. Researchers are encouraged to maximize their approach to using data in trial and intervention design through VR technologies.

Precision Adjustment of Gameplay

Constant monitoring and analysis of data also can be utilized to create VR interventions that are motivating and engaging for the person with stroke. Components of engagement, motivation, and self-efficacy ought to be defined and integrated into the VR intervention tasks. Much of the research by Lewthwaite and Wulf67 is changing the way the field thinks about motor learning and practice. The basic concepts of self-determination theory, autonomy, competence, and relatedness are all vital to creating learning within a game-based or VR structure.68 Both competence and relatedness have unique effects on game performance and task satisfaction, whereas autonomy is a moderator of one of the other concepts.69 Autonomy also is important when considered in conjunction with player typologies. Assessing and integrating player typologies into a VR intervention for stroke can allow for the game tasks to be adjusted to the most precise level of challenge for the player and provide continual, player-specific adjustments according to the individual's typology (eg, wanderer versus conqueror).70 For example, assessment tools, such as questionnaires or cognitive measures, can be integrated into a game, and the next set of game tasks can be structured according to the finding of the assessment tool. A player with an explorer typology might enjoy tasks that involve games taking place at multiple locations on a map, whereas a player who is a conqueror might enjoy games that allow him or her to obtain a certain number of items or prizes. Both players might do the same physical or cognitive task; however, the reward structure will look different. Additionally, feedback can be timed appropriately by considering both player typologies and self-determination theory to deliver a satisfying, motivating, and effective VR intervention for stroke.

Moving Forward

The technologies behind VR interventions and measurement tools have progressed, and we now have the components for moving through the phases of research in a thorough and step-wise fashion. Much of the research with the new technologies in the field is still in the intervention development phase, and we will begin to see intervention efficacy studies followed by larger scale-up evaluations in the next several years. We challenge researchers to be thorough in their early stages and clearly define the active ingredients of their VR interventions. This challenge includes clearly defining parameters such as dosing, frequency of intervention, number of repetitions, intensity, and so on. Managing and utilizing extracted data from the VR technologies are keys to defining these parameters. Clinicians and engineers/programmers can have a better sense of what components of the intervention are most related to patient performance. With the level of customization available through the Kinect sensor, defining the intervention parameters is no longer a time-intensive task.

With advances in neuroimaging and other methods of measurement (eg, motion capture), we can begin to understand how manipulating motivation, engagement, and self-efficacy in VR interventions affects motor performance. Imaging also can help to identify responders and nonresponders for VR interventions as well those who might respond better to virtual environments. As researchers move into the intervention efficacy phase, choosing measurement tools will be a much easier process if the active ingredients of the VR intervention are well defined in the prior phases.71

Conclusion

The field of VR has come a long way from the hype of the Wii to facilitating a return to clearly defined interventions and precision measurement. The advances in technology can be leveraged effectively by researchers to describe and define the “active ingredients” in the VR intervention. Advances in measurement tools, such as neuroimaging, can be used effectively to lend support for the active ingredients. Only then can we move toward well-designed efficacy studies that demonstrate what researchers have attempted to prove for decades: VR interventions are an engaging, motivating, and effective method for delivering interventions in stroke.

Footnotes

Both authors provided concept/idea/study design and writing. Dr Proffitt provided data collection and analysis. Dr Lange provided consultation (including review of the manuscript before submission).

The manuscript was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under award number T32HD064578. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

References

  • 1. Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012;125:e2–e220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Di Carlo A. Human and economic burden of stroke [comment]. Age Ageing. 2009;38:4–5. [DOI] [PubMed] [Google Scholar]
  • 3. Payne A, Greig C, Young A, Mead G. Views of stroke patients on physical fitness training. Age Ageing. 2001;30:429. [DOI] [PubMed] [Google Scholar]
  • 4. Shaughnessy M, Resnick BM, Macko RF. Testing a model of post-stroke exercise behavior. Rehabil Nurs. 2006;31:15–21. [DOI] [PubMed] [Google Scholar]
  • 5. Jurkiewicz MT, Marzolini S, Oh P. Adherence to a home-based exercise program for individuals after stroke. Top Stroke Rehabil. 2011;18:277–284. [DOI] [PubMed] [Google Scholar]
  • 6. Rizzo A., Kim GJ. A SWOT analysis of the field of virtual reality rehabilitation and therapy. Presence: Teleoperators and Virtual Environments. 2005;14:119–146. [Google Scholar]
  • 7. Connelly L, Jia Y, Toro ML, et al. A pneumatic glove and immersive virtual reality environment for hand rehabilitative training after stroke. IEEE Trans Neural Syst Rehabil Eng. 2010;18:551–559. [DOI] [PubMed] [Google Scholar]
  • 8. da Silva Cameirão M, Bermúdez 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;29:287–298. [DOI] [PubMed] [Google Scholar]
  • 9. Cameirão MS, Badia SB, Duarte E, et al. The combined impact of virtual reality neurorehabilitation and its interfaces on upper extremity functional recovery in patients with chronic stroke. Stroke. 2012;43:2720–2728. [DOI] [PubMed] [Google Scholar]
  • 10. Levin MF, Snir O, Liebermann DG, et al. Virtual reality versus conventional treatment of reaching ability in chronic stroke: clinical feasibility study. Neurology and Therapy. 2012;1:1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Weiss PL, Kizony R, Feintuch U, Katz N. Virtual reality in neurorehabilitation. In: Selzer ME, Cohen L, Gage FH, et al., eds. Textbook of Neural Repair and Neurorehabilitation. New York, NY: Cambridge Press; 2006:183. [Google Scholar]
  • 12. Wade E, Winstein CJ. Virtual reality and robotics for stroke rehabilitation: where do we go from here? Top Stroke Rehabil. 2011;18:685–700. [DOI] [PubMed] [Google Scholar]
  • 13. Stein J. Robotics in rehabilitation. Am J Phys Med Rehabil. 2012;91:S199–S203. [DOI] [PubMed] [Google Scholar]
  • 14. Lo AC, Guarino PD, Richards LG, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:1772–1783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Loureir R, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput. 2011;49:1103–1118. [DOI] [PubMed] [Google Scholar]
  • 16. Campbell M, Fitzpatrick R, Haines A, et al. Framework for design and evaluation of complex interventions to improve health. BMJ. 2000;321:694–696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Campbell NC, Murray E, Darbyshire J, et al. Designing and evaluating complex interventions to improve health care. BMJ. 2007;334:455–459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Arain M, Campbell MJ, Cooper CL, Lancaster GA. What is a pilot or feasibility study? A review of current practice and editorial policy. BMC Med Res Methodol. 2010;10:67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Glasgow RE, Lichtenstein E, Marcus AC. Why don't we see more translation of health promotion research to practice? Rethinking the efficacy-to-effectiveness transition. Am J Public Health. 2003;93:1261–1267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Burke JW, McNeill MDJ, Charles DK, et al. Optimising engagement for stroke rehabilitation using serious games. The Visual Computer. 2009;25:1085–1099. [Google Scholar]
  • 21. Lange B, Koenig S, Chang CY, et al. Designing informed game-based rehabilitation tasks leveraging advances in virtual reality. Disabil Rehabil. 2012;34:1863–1870. [DOI] [PubMed] [Google Scholar]
  • 22. Jaffe DL, Brown DA, Pierson-Carey CD, et al. Stepping over obstacles to improve walking in individuals with poststroke hemiplegia. J Rehabil Res Dev. 2004;41:283–292. [DOI] [PubMed] [Google Scholar]
  • 23. Weiss PL, Rand D, Katz N, Kizony R. Video capture virtual reality as a flexible and effective rehabilitation tool. J Neuroeng Rehabil. 2004;1:12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Lange B, Flynn S, Rizzo A. Initial usability assessment of off-the-shelf video game consoles for clinical game-based motor rehabilitation. Phys Ther Rev. 2009;14:355–363. [Google Scholar]
  • 25. Flynn S, Palma P, Bender A. Feasibility of using the Sony PlayStation 2 gaming platform for an individual poststroke: a case report. J Neurol Phys Ther. 2007;31:180–189. [DOI] [PubMed] [Google Scholar]
  • 26. Deutsch JE, Robbins D, Morrison J, Bowlby PG. Wii-based compared to standard of care balance and mobility rehabilitation for two individuals post-stroke. In: Virtual Rehabilitation International Conference, 2009; June 29–July 2, 2009; Haifa, Israel 2009:117–120. [Google Scholar]
  • 27. Joo LY, Yin TS, Xu D, et al. A feasibility study using interactive commercial off-the-shelf computer gaming in upper limb rehabilitation in patients after stroke. J Rehabil Med. 2010;42:437–441. [DOI] [PubMed] [Google Scholar]
  • 28. Suma EA, Lange B, Rizzo A, et al. FAAST: The Flexible Action and Articulated Skeleton Toolkit. In: 2011 IEEE Virtual Reality Conference (VR); March 2011; Singapore 2011:247–248. [Google Scholar]
  • 29. Lauterbach SA, Foreman MH, Engsberg JR. Computer games as therapy for persons with stroke. Games Health J. 2013;2:24–28. [DOI] [PubMed] [Google Scholar]
  • 30. Lange B, Chang CY, Suma E, et al. Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: Engineering in Medicine and Biology Society (EMBC), 2011 Annual International Conference of the IEEE; August 30–September 3, 2011; Boston, Massachusetts 2011:1831–1834. [DOI] [PubMed] [Google Scholar]
  • 31. Pastor I, Hayes HA, Bamberg SJ. A feasibility study of an upper limb rehabilitation system using Kinect and computer games. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE; August 28–September 1, 2012; San Diego, California 2012:1286–1289. [DOI] [PubMed] [Google Scholar]
  • 32. Saini S, Rambli DRA, Sulaiman S, et al. A low-cost game framework for a home-based stroke rehabilitation system. In: 2012 International Conference on Computer & Information Science (ICCIS), Vol. 1; June 12–14, 2012 2012:55–60. [Google Scholar]
  • 33. Brokaw EB, Brewer BR. Development of the Home Arm Movement Stroke Training Environment for Rehabilitation (HAMSTER) and evaluation by clinicians. In: Virtual, Augmented and Mixed Reality. Systems and Applications. Berlin, Germany: Springer; 2013:22–31. [Google Scholar]
  • 34. Shin JH, Ryu H, Jang SH. A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. J Neuroeng Rehabil. 2014;11:32. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Cameirão MS, Badia SB, Oller ED, Verschure PFMJ. Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. J Neuroeng Rehabil. 2010;7:48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Avola D, Spezialetti M, Placidi G. Design of an efficient framework for fast prototyping of customized human-computer interfaces and virtual environments for rehabilitation. Comput Methods Programs Biomed. 2013;110:490–502. [DOI] [PubMed] [Google Scholar]
  • 37. Kim EK, Kang JH, Park JS, Jung BH. Clinical feasibility of interactive commercial Nintendo gaming for chronic stroke rehabilitation. J Phys Ther Sci. 2012;24:901–903. [Google Scholar]
  • 38. Fritz SL, Peters DM, Merlo AM, Donley J. Active video-gaming effects on balance and mobility in individuals with chronic stroke: a randomized controlled trial. Top Stroke Rehabil. 2013;20:218–225. [DOI] [PubMed] [Google Scholar]
  • 39. Saposnik G, Mamdani M, Bayley M, et al. Effectiveness of Virtual Reality Exercises in STroke Rehabilitation (EVREST): rationale, design, and protocol of a pilot randomized clinical trial assessing the Wii gaming system. Int J Stroke. 2010;5:47–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Saposnik G, Teasell R, Mamdani M, et al. Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: a pilot randomized clinical trial and proof of principle. Stroke. 2010;41:1477–1484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Yavuzer G, Senel A, Atay MB, Stam HJ. “Playstation eyetoy games” improve upper extremity-related motor functioning in subacute stroke: a randomized controlled clinical trial. Eur J Phys Rehabil Med. 2008;44:237–244. [PubMed] [Google Scholar]
  • 42. Neil A, Ens S, Pelletier R, et al. Sony PlayStation EyeToy elicits higher levels of movement than the Nintendo Wii: implications for stroke rehabilitation. Eur J Phys Rehabil Med. 2013;49:13–21. [PubMed] [Google Scholar]
  • 43. Levac DE, Miller PA. Integrating virtual reality video games into practice: clinicians' experiences. Physiother Theory Pract. 2013;29:1–9. [DOI] [PubMed] [Google Scholar]
  • 44. Kimberley TJ, Samargia S, Moore LG, et al. Comparison of amounts and types of practice during rehabilitation for traumatic brain injury and stroke. J Rehabil Res Dev. 2010;47:851–862. [DOI] [PubMed] [Google Scholar]
  • 45. Leder RS, Azcarate G, Savage R, et al. Nintendo Wii remote for computer simulated arm and wrist therapy in stroke survivors with upper extremity hemiparesis. In: Proceedings of the Virtual Rehabilitation Conference; August 24–27, 2008; Vancouver, British Columbia, Canada 2008:74. [Google Scholar]
  • 46. Alankus G, Lazar A, May M, Kelleher C. Towards customizable games for stroke rehabilitation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; April 10–15, 2010; Atlanta, Georgia. New York, NY: ACM; 2010:2113–2122. [Google Scholar]
  • 47. Proffitt RM, Alankus G, Kelleher CL, Engsberg JR. Use of computer games as an intervention for stroke. Top Stroke Rehabil. 2011;18:417–427. [DOI] [PubMed] [Google Scholar]
  • 48. Lin J, Kelleher CL, Engsberg JR. Developing home-based virtual reality therapy interventions. Games Health J. 2013;2:34–38. [DOI] [PubMed] [Google Scholar]
  • 49. Gil-Gómez JA, Lloréns R, Alcañiz M, Colomer C. Effectiveness of a Wii balance board-based system (eBaViR) for balance rehabilitation: a pilot randomized clinical trial in patients with acquired brain injury. J Neuroeng Rehabil. 2011;8:30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Harley L, Robertson S, Gandy M, et al. The design of an interactive stroke rehabilitation gaming system. In: Human-Computer Interaction: Users and Applications. Berlin, Germany: Springer; 2011:167–173. [Google Scholar]
  • 51. Kim JH, Jang SH, Kim CS, et al. Use of virtual reality to enhance balance and ambulation in chronic stroke: a double-blind, randomized controlled study. Am J Phys Med Rehabil. 2009;88:693–701. [DOI] [PubMed] [Google Scholar]
  • 52. Kwon JS, Park MJ, Yoon IJ, Park SH. Effects of virtual reality on upper extremity function and activities of daily living performance in acute stroke: a double-blind randomized clinical trial. NeuroRehabilitation. 2012;31:379–385. [DOI] [PubMed] [Google Scholar]
  • 53. Laver K, George S, Thomas S, et al. Virtual reality for stroke rehabilitation. Stroke. 2012;43:e20–e21. [PubMed] [Google Scholar]
  • 54. Fluet GG, Deutsch JE. Virtual reality for sensorimotor rehabilitation post-stroke: the promise and current state of the field. Curr Phys Med Rehabil Rep. 2013;1:9–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Proffitt R, Lange B, Chen C, Winstein C. A comparison of older adults' subjective experience with virtual and real environments during dynamic balance activities. J Aging Phys Act. 2015;23:24–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Da Gama A, Chaves T, Figueiredo L, Teichrieb V. Improving motor rehabilitation process through a natural interaction based system using Kinect sensor. In: Proceedings of the IEEE Symposium on 3D User Interfaces; March 4–5, 2012; Orange County, California. [Google Scholar]
  • 57. Khoshelham K. Accuracy analysis of Kinect depth data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2011;38:133–138. [Google Scholar]
  • 58. Nixon M, Chen Y, Howard AM. Quantitative evaluation of the Microsoft Kinect for use in an upper extremity virtual rehabilitation environment. In: International Conference on Virtual Rehabilitation (ICVR); August 2013; Philadelphia, Pennsylvania. [Google Scholar]
  • 59. Adamovich SV, August K, Merians A, Tunik E. A virtual reality-based system integrated with fMRI to study neural mechanisms of action observation-execution: a proof of concept study. Restor Neurol Neurosci. 2009;27:209–223. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60. Bozzacchi C, Giusti MA, Pitzalis S, et al. Similar cerebral motor plans for real and virtual actions. PLoS One. 2012;7:e47783. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Azpiroz J, Barrios FA, Carrillo M, et al. Game motivated and constraint induced therapy in late stroke with fMRI studies pre and post therapy. In: Proceedings of the IEEE 27th Annual Conference in Engineering in Medicine and Biology; September 1–4, 2006; Shanghai, China. [DOI] [PubMed] [Google Scholar]
  • 62. Jang SH, You SH, Hallett M, et al. Cortical reorganization and associated functional motor recovery after virtual reality in patients with chronic stroke: an experimenter-blind preliminary study. Arch Phys Med Rehabil. 2005;86:2218–2223. [DOI] [PubMed] [Google Scholar]
  • 63. You SH, Jang SH, Kim YH, et al. Cortical reorganization induced by virtual reality therapy in a child with hemiparetic cerebral palsy. Dev Med Child Neurol. 2007;47:628–635. [PubMed] [Google Scholar]
  • 64. Kitago T, Liang J, Huang VS, et al. Improvement after constraint-induced movement therapy: recovery of normal motor control or task-specific compensation. Neurorehabil Neural Repair. 2013;27:99–109. [DOI] [PubMed] [Google Scholar]
  • 65. Shore BJ, Nasreddine AY, Kocher MS. Overcoming the funding challenge: the cost of randomized controlled trials in the next decade. J Bone Joint Surg Am. 2012;94(suppl 1):101–106. [DOI] [PubMed] [Google Scholar]
  • 66. Ryan GW, Bloom EW, Lowsky DJ, et al. Data-driven decision-making tools to improve public resource allocation for care and prevention of HIV/AIDS. Health Aff (Millwood). 2014;33:410–417. [DOI] [PubMed] [Google Scholar]
  • 67. Lewthwaite R, Wulf G. Motor learning through a motivational lens. In: Hodges NJ, Williams AM, eds. Skill Acquisition in Sport: Research, Theory and Practice. New York, NY: Routledge; 2012:173–191. [Google Scholar]
  • 68. Przybylski AK, Rigby CS, Ryan RM. A motivational model of video game engagement. Rev Gen Psychol. 2010;14:154–166. [Google Scholar]
  • 69. Sheldon KM, Filak V. Manipulating autonomy, competence, and relatedness support in a game-learning context: new evidence that all three needs matter. Br J Soc Psychol. 2008;47(pt 2):267–283. [DOI] [PubMed] [Google Scholar]
  • 70. Cowley B, Charles D, Black M, Hickey R. Toward an understanding of flow in video games. ACM Computers in Entertainment, 2008;6(Article 20):1–27. [Google Scholar]
  • 71. Whyte J, Barrett AM. Advancing the evidence base of rehabilitation treatments: a developmental approach. Arch Phys Med Rehabil. 2012;93:S101–S110. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Physical Therapy are provided here courtesy of Oxford University Press

RESOURCES