Archive for category REHABILITATION

[BLOG POST] The Brain’s Sensational Juggling Act

(Credit: Shutterstock)

You’re bombarded with sensory information every day — sights, sounds, smells, touches and tastes. A constant barrage that your brain has to manage, deciding which information to trust or which sense to use as a backup when another fails. Understanding how the brain evaluates and juggles all this input could be the key to designing better therapies for patients recovering from stroke, nerve injuries, or other conditions. It could also help engineers build more realistic virtual experiences for everyone from gamers to fighter pilots to medical patients.

Now, some researchers are using virtual reality (VR) and even robots to learn how the brain pulls off this juggling act.

Do You Believe Your Eyes?

At the University of Reading in the U.K., psychologist Peter Scarfe and his team are currently exploring how the brain combines information from touch, vision, and proprioception – our sense of where our body is positioned – to form a clear idea of where objects are in space.

Generally, the brain goes with whichever sense is more reliable at the time. For instance, in a dark room, touch and proprioception trump vision. But when there’s plenty of light, you’re more likely to believe your eyes. Part of what Scarfe’s crew hopes to eventually unravel is how the brain combines information from both senses and whether that combination is more accurate than touch or sight alone. Does the brain trust input from one sense and ignore the other, does it split the difference between the two, or does it do something more complex?

To find out, the team is using a VR headset and a robot called Haptic Master.

While volunteers wear the VR headset, they see four virtual balls – three in a triangle formation and one in the center. They can also reach out and touch four real spheres that appear in the same place as the ones they see in VR: the three in the triangle formation are just plastic and never move, but the fourth is actually a ball bearing at the end of Haptic Master’s robot arm. Researchers use the robot to move this fourth ball between repetitions of the test. Think of the three-ball-triangle as a flat plane in space. The participant has to decide whether the fourth ball is higher or lower than the level of that triangle.

It’s a task that requires the brain to weigh and combine information from multiple senses to decide where the fourth ball is in relation to the other three. Participants get visual cues about the ball’s location through the VR headset, but they also use their haptic sense – the combination of touch and proprioception – to feel where the ball is in space.

The VR setup makes it easier to control the visual input and make sure volunteers aren’t using other cues, like the location of the robot arm or other objects in the room, to make their decisions.

Collectively, volunteers have performed this task hundreds of times. Adams and his colleagues are looking at how accurate the results are when the participant used only their eyes, only their haptic sense, or both senses at once. The team is then comparing those results to several computer models, each predicting how a person would estimate the ball’s position if their brain combined the sensory information in different ways.

So far, the team needs more data to learn which model best describes how the brain combines sensory cues. But they say that their results, and those of others working in the field, could one day help design more accurate haptic feedback, which could make interacting with objects in virtual reality feel more realistic.

On Shaky Footing

Anat Lubetzky, a physical therapy researcher at New York University, is also turning to VR. She uses the burgeoning technology to study how our brains weigh different sensory input to help us when things get shaky — specifically, if people rely on their sense of proprioception or their vision to keep their balance.

Conventional wisdom in sports medicine says that standing on an uneven surface is a good proprioception workout for patients in rehabilitation after an injury. That’s because it forces your somatosensory system, the nerves involved in proprioception, to work harder. So if your balance is suffering because of nerve damage, trying to stabilize yourself while standing on an uneven surface, like a bosu ball, should help.

But Lubetzky’s results tell a different story.

In the lab, Lubetzky’s subjects strap on VR headsets and stand on either a solid floor or an unsteady surface, like a wobble board. She projects some very subtly moving dots onto the VR display and uses a pressure pad on the floor to measure how participants’ bodies sway.

It turns out, when people stand on an unstable surface, they’re more likely to sway in time with the moving dots. But on a stable surface, they seem to pay less attention to the dots.

So rather than working their somatosensory systems harder, it seems people use their vision to look for a fixed reference point to help keep them balanced. In other words, the brain switches from a less reliable sense to a more reliable one, a process called sensory weighting.

Ultimately, Lubetzky hopes her VR setup could help measure how much a patient with somatosensory system damage relies on their vision. This knowledge, in turn, could help measure the severity of the problem so doctors can design a better treatment plan.

As VR gets more realistic and more immersive – partly thanks to experiments like these – it could offer researchers an even more refined tool for picking apart what’s going on in the brain.

Says Lubetzky, “It’s been a pretty amazing revolution.”

Source: The Brain’s Sensational Juggling Act – D-brief


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[ARTICLE] Effect of Robot-Assisted Game Training on Upper Extremity Function in Stroke Patients – Full Text

ObjectiveTo determine the effects of combining robot-assisted game training with conventional upper extremity rehabilitation training (RCT) on motor and daily functions in comparison with conventional upper extremity rehabilitation training (OCT) in stroke patients.

MethodsSubjects were eligible if they were able to perform the robot-assisted game training and were divided randomly into a RCT and an OCT group. The RCT group performed one daily session of 30 minutes of robot-assisted game training with a rehabilitation robot, plus one daily session of 30 minutes of conventional rehabilitation training, 5 days a week for 2 weeks. The OCT group performed two daily sessions of 30 minutes of conventional rehabilitation training. The effects of training were measured by a Manual Function Test (MFT), Manual Muscle Test (MMT), Korean version of the Modified Barthel Index (K-MBI) and a questionnaire about satisfaction with training. These measurements were taken before and after the 2-week training.

ResultsBoth groups contained 25 subjects. After training, both groups showed significant improvements in motor and daily functions measured by MFT, MMT, and K-MBI compared to the baseline. Both groups demonstrated similar training effects, except motor power of wrist flexion. Patients in the RCT group were more satisfied than those in the OCT group.

ConclusionThere were no significant differences in changes in most of the motor and daily functions between the two types of training. However, patients in the RCT group were more satisfied than those in the OCT group. Therefore, RCT could be a useful upper extremity rehabilitation training method.


stroke is a central nervous system disease caused by cerebrovascular problems such as infarction or hemorrhage. Stroke may lead to impairment of various physical functions, including hemiplegia, language disorder, swallowing disorder or cognitive disorder, according to the location and degree of morbidity [1]. Among these, hemiplegia is a common symptom occurring in 85% of stroke patients. In particular, upper extremity paralysis is more frequent and requires longer recovery time than lower extremity paralysis [23]. To maintain the basic functions of ordinary life, the use of the upper extremities is essential; therefore, upper extremity paralysis commonly causes problems in performing the activities of daily living [2].

Robot-assisted rehabilitation treatment has recently been widely investigated as an effective neurorehabilitation approach that may augment the effects of physical therapy and facilitate motor recovery [4]. Robot-assisted rehabilitation treatments have been developed in recent decades to reduce the expenditure of therapists’ effort and time, to reproduce accurate repetitive motions and to interact with force feedback [56]. The most important advantage of using robot-assisted rehabilitation treatment is the ability to deliver high-dosage and high-intensity training [7].

In rehabilitation patients may find such exercises monotonous and boring, and may lose motivation over time [8]. Upper extremity rehabilitation training using video games, such as Nintendo Wii games and the PlayStation EyeToy games, enhanced upper extremity functions and resulted in greater patient satisfaction than conventional rehabilitation treatment [910111213].

The objective of this study was to determine the effects of combining robot-assisted game training with conventional upper extremity rehabilitation training (RCT) on motor and daily functions in comparison to conventional upper extremity rehabilitation training (OCT) in stroke patients. This study was a randomized controlled trial and we evaluated motor power, upper extremity motor function, daily function and satisfaction. […]

Continue —> KoreaMed Synapse

Fig. 1. (A) Neuro-X, an upper extremity rehabilitation robot, consisting of a video monitor, a robot arm and a computer. (B) The patient performing robot-assisted game training with the upper extremity rehabilitation robot.

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[ARTICLE] The Efficacy of a Haptic-enhanced Virtual Reality System for Precision Grasp Acquisition in Stroke Rehabilitation – Full Text PDF

Stroke is a leading cause of long-term disability, and virtual reality (VR)-based stroke rehabilitation is effective in increasing motivation and the functional performance in people with stroke. Although much of the functional reach and grasp capabilities of the upper extremities is regained, the pinch
movement remains impaired following stroke. In this study, we developed a haptic-enhanced VR system to simulate haptic pinch tasks to assist in long-term post-stroke recovery of upper-extremity fine motor function. We recruited 16 adults with stroke to verify the efficacy of this new VR system.
Each patient received 30-min VR training sessions 3 times per week for 8 weeks; all participants attended all 24 training sessions. Outcome measures, Fugl Meyer Assessment (FMA), Test Evaluant les Membres superieurs des Personnes Agees (TEMPA), Wolf Motor Function Test (WMFT), Box and
Block Test (BBT), and Jamar Grip Dynamometer, showed statistically significant progress from pretest to posttest and follow-up, indicating that the proposed system effectively promoted fine motor recovery of function. Additionally, our evidence suggests that this system was also effective under certain challenging conditions such as being in the chronic stroke phase or a co-side of lesion and dominant hand (non- dominant hand impaired). System usability assessment indicated the participants strongly intended to continue using this VR-based system in rehabilitation.

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[VIDEO] Bioness L300 Go Technology Introduction – YouTube

Published on Sep 25, 2017
L300 Go is a functional electrical stimulation (FES) system that satisfies the productivity demands of today’s value-based healthcare system. Key aspects of the L300 experience have been dramatically improved with 3D Motion Detection, multi-channel stimulation, Smart Bluetooth® programming and a home user mobile app that tracks activity to keep patients engaged in the rehabilitation process. All of this in a streamlined design, with a fitting process that is faster and easier than ever before.

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[Abstract+References] Virtual reality software package for implementing motor learning and rehabilitation experiments


Virtual reality games for rehabilitation are attracting increasing growth. In particular, there is a demand for games that allow therapists to identify an individual’s difficulties and customize the control of variables, such as speed, size, distance, as well as visual and auditory feedback. This study presents and describes a virtual reality software package (Bridge Games) to promote rehabilitation of individuals living with disabilities and highlights preliminary researches of its use for implementing motor learning and rehabilitation. First, the study presents seven games in the software package that can be chosen by the rehabilitation team, considering the patient’s needs. All game characteristics are described including name, function presentation, objective and valuable measurements for rehabilitation. Second, preliminary results illustrate some applications of two games, considering 343 people with various disabilities and health status. Based on the results, in the Coincident Timing game, there was a main effect of movement sensor type (in this instance the most functional device was the keyboard when compared with Kinect and touch screen) on average time reached by sample analyzed, F(2, 225) = 4.42, p < 0.05. Similarly, in the Challenge! game, a main effect was found for movement sensor type. However, in this case, touch screen provided better performance than Kinect and Leap Motion, F(2, 709) = 5.90, p < 0.01. Thus, Bridge Games is a possible software game to quantify motor learning. Moreover, the findings suggest that motor skills might be practiced differently depending on the environmental interface in which the game may be used.


  1. Anderson F, Annett M, Bischof WF (2010) Lean on Wii: physical rehabilitation with virtual reality Wii peripherals. Stud Health Technol Inform 154:229–234. doi:10.3233/978-1-60750-561-7-229Google Scholar
  2. Anderson KR, Woodbury ML, Phillips K, Gauthier LV (2015) Virtual reality video games to promote movement recovery in stroke rehabilitation: a guide for clinicians. Arch Phys Med Rehabil 96(5):973–976. doi:10.1016/j.apmr.2014.09.008CrossRefGoogle Scholar
  3. Antunes TPC, de Oliveira ASB, Crocetta TB, Antao J, Barbosa RTD, Guarnieri R, Massetti T, Monteiro CBD, de Abreu LCs (2017) Computer classes and games in virtual reality environment to reduce loneliness among students of an elderly reference center Study protocol for a randomised cross-over design. Medicine 96(10):e5954. doi:10.1016/j.apmr.2014.09.008 CrossRefGoogle Scholar
  4. Bieryla KA (2016) Xbox Kinect training to improve clinical measures of balance in older adults: a pilot study. Aging Clin Exp Res 28:451–457. doi:10.1007/s40520-015-0452-yCrossRefGoogle Scholar
  5. Bonnechere B, Jansen B, Omelina L, Sholukha V, Jan SV (2017) Patients’ follow-up using biomechanical analysis of rehabilitation exercises. Int J Serious Games 4(1):3–13. doi:10.17083/ijsg.v4i1.121CrossRefGoogle Scholar
  6. Crabtree DA, Antrim LR (1988) Guidelines for measuring reaction-time. Percept Mot Skills 66(2):363–370CrossRefGoogle Scholar
  7. Crocetta TB, Andrade A (2015) Retrasos en la medición del tiempo con el uso de computadoras en la investigación del Tiempo de Reacción: Una revisión sistemática. Revista de Psicología del Deporte 24:341–349Google Scholar
  8. Crocetta TB, Oliveira SRD, Liz CMD, Andrade A (2015) Virtual and augmented reality technologies in human performance: a review. Fisioterapia em Movimento 28(4):823–835. doi:10.1590/0103-5150.028.004.ar01CrossRefGoogle Scholar
  9. Da Gama A, Chaves T, Figueiredo L, Teichrieb V (2012) Guidance and movement correction based on therapeutics movements for motor rehabilitation support systems. 14th symposium on virtual and augmented reality (SVR), 2012. IEEE, pp 191–200Google Scholar
  10. da Silva TD, de Monteiro CBM, Corrêa AGD, Alonso AC, Greve JMDA (2015) Realidade Virtual na Paralisia Cerebral – Definição Tipos e Possibilidades de Intervenção. In: da Monteiro CBM, de Abreu LC, Valenti VE (eds) Paralisia Cerebral – Teoria e Prática. São Paulo, Plêiade, p 484Google Scholar
  11. Herrero D, Crocetta T, Massetti T, de Moraes I, Trevizan I, Guarnieri R (2015) Total reaction time performance of individuals with autism after a virtual reality task. IJN an open access journal 2(2376–0281):1000189. doi:10.4172/2376-0281.1000189Google Scholar
  12. Hocine N, Gouaich A, Cerri SA, Mottet D, Froger J, Laffont I (2015) Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes. User Model User-Adap Inter 25(1):65–98. doi:10.1007/s11257-015-9154-6CrossRefGoogle Scholar
  13. Hondori HM, Khademi M (2014) A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. J Med Eng 2014:846514Google Scholar
  14. Hondori HM, Khademi M, Dodakian L, McKenzie A, Lopes CV, Cramer SC (2016) Choice of human–computer interaction mode in stroke rehabilitation. Neurorehabilitation Neural Repair 30(3):258–265. doi:10.1177/1545968315593805CrossRefGoogle Scholar
  15. Iwasaki Y, Kinoshita M, Ikeda K, Takamiya K, Shiojima T (1990) Cognitive impairment in amyotrophic lateral sclerosis and its relation to motor disabilities. Acta Neurol Scand 81(2):141–143CrossRefGoogle Scholar
  16. Juanes JA, Gomez JJ, Peguero PD, Ruisoto P (2016) Digital environment for movement control in surgical skill training. J Med Syst 40(6):133. doi:10.1007/s10916-016-0495-4CrossRefGoogle Scholar
  17. Kim R, Nauhaus G, Glazek K, Young D, Lin S (2013) Development of coincidence-anticipation timing in a catching task. Percept Mot Skills 117(1):319–338. doi:10.2466/10.23.PMS.117x17z9CrossRefGoogle Scholar
  18. Levac D, Espy D, Fox E, Pradhan S, Deutsch JE (2015) “Kinect-ing” with clinicians: a knowledge translation resource to support decision making about video game use in rehabilitation. Phys Ther 95(3):426–440. doi:10.2522/ptj.20130618CrossRefGoogle Scholar
  19. Lohse K, Shirzad N, Verster A, Hodges N, Van der Loos HFM (2013) Video games and rehabilitation: using design principles to enhance engagement in physical therapy. J Neurol Phys Ther 37(4):166–175. doi:10.1097/npt.0000000000000017CrossRefGoogle Scholar
  20. Lv ZH, Penades V, Blasco S, Chirivella J, Gagliardo P (2016) Evaluation of Kinect2 based balance measurement. Neurocomputing 208:290–298. doi:10.1016/j.neucom.2015.12.128CrossRefGoogle Scholar
  21. Malheiros SR, da Silva TD, Favero FM, de Abreu LC, Fregni F, Ribeiro DC, de Mello Monteiro CB (2016) Computer task performance by subjects with Duchenne muscular dystrophy. Neuropsychiatr Dis Treat 12:41–48. doi:10.2147/NDT.S87735Google Scholar
  22. Microsoft (2015) Timer class—.NET framework 4.6 and 4.5. In: Library, N. F. C., (ed) Microsoft Corporation. Accessed sept 2015
  23. Monteiro CBM, Massetti T, da Silva TD, van der Kamp J, de Abreu LC, Leone C, Savelsbergh GJ (2014) Transfer of motor learning from virtual to natural environments in individuals with cerebral palsy. Res Dev Disabil 35(10):2430–2437. doi:10.1016/j.ridd.2014.06.006CrossRefGoogle Scholar
  24. Monteiro CBD, da Silva TD, de Abreu LC, Fregni F, de Araujo LV, Ferreira F, Leone C (2017) Short-term motor learning through nonimmersive virtual reality task in individuals with down syndrome. BMC Neurol 17:71. doi:10.1186/s12883-017-0852-zCrossRefGoogle Scholar
  25. Nooijen CFJ, de Groot JF, Stam HJ, van den Berg-Emons RJG, Bussmann HBJ, Fit Future C (2015) Validation of an activity monitor for children who are partly or completely wheelchair-dependent. J Neuroeng Rehabil 12:11. doi:10.1186/s12984-015-0004-xCrossRefGoogle Scholar
  26. Pastor I, Hayes HA, Bamberg SJM, IEEE (2012) A feasibility study of an upper limb rehabilitation system using kinect and computer games. 34th annual international conference of the IEEE engineering-in-medicine-and-biology-society (EMBS). San Diego, pp 1286–1289Google Scholar
  27. Patrizia M, Claudio M, Leonardo G, Alessandro P, IEEE (2009) A robotic toy for children with special needs: from requirements to design 11th IEEE international conference on rehabilitation robotics. Kyoto, p 1070Google Scholar
  28. Rodrigues PC, Vasconcelos O, Barreiros J, Barbosa R, Trifilio F (2009) Functional asymmetry in a simple coincidence-anticipation task: effects of handedness. Eur J Sport Sci 9(2):115–123. doi:10.1080/17461390802603903CrossRefGoogle Scholar
  29. Stanmore E, Stubbs B, Vancampfort D, de Bruin ED, Firth J (2017) The effect of active video games on cognitive functioning in clinical and non-clinical populations: a meta-analysis of randomized controlled trials. Neurosci Biobehav Rev 78:34–43. doi:10.1016/j.neubiorev.2017.04.011CrossRefGoogle Scholar
  30. Thomson K, Pollock A, Bugge C, Brady M (2014) Commercial gaming devices for stroke upper limb rehabilitation: a systematic review. Int J Stroke 9(4):479–488. doi:10.1111/ijs.12263CrossRefGoogle Scholar

Source: Virtual reality software package for implementing motor learning and rehabilitation experiments | SpringerLink

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[ARTICLE] SITAR: a system for independent task-oriented assessment and rehabilitation

Over recent years, task-oriented training has emerged as a dominant approach in neurorehabilitation. This article presents a novel, sensor-based system for independent task-oriented assessment and rehabilitation (SITAR) of the upper limb.

The SITAR is an ecosystem of interactive devices including a touch and force–sensitive tabletop and a set of intelligent objects enabling functional interaction. In contrast to most existing sensor-based systems, SITAR provides natural training of visuomotor coordination through collocated visual and haptic workspaces alongside multimodal feedback, facilitating learning and its transfer to real tasks. We illustrate the possibilities offered by the SITAR for sensorimotor assessment and therapy through pilot assessment and usability studies.

The pilot data from the assessment study demonstrates how the system can be used to assess different aspects of upper limb reaching, pick-and-place and sensory tactile resolution tasks. The pilot usability study indicates that patients are able to train arm-reaching movements independently using the SITAR with minimal involvement of the therapist and that they were motivated to pursue the SITAR-based therapy.

SITAR is a versatile, non-robotic tool that can be used to implement a range of therapeutic exercises and assessments for different types of patients, which is particularly well-suited for task-oriented training.

The increasing demand for intense, task-specific neurorehabilitation following neurological conditions such as stroke and spinal cord injury has stimulated extensive research into rehabilitation technology over the last two decades.1,2 In particular, robotic devices have been developed to deliver a high dose of engaging repetitive therapy in a controlled manner, decrease the therapist’s workload and facilitate learning. Current evidence from clinical interventions using these rehabilitation robots generally show results comparable to intensity-matched, conventional, one-to-one training with a therapist.35 Assuming the correct movements are being trained, the primary factor driving this recovery appears to be the intensity of voluntary practice during robotic therapy rather than any other factor such as physical assistance required.6,7 Moreover, most existing robotic devices to train the upper limb (UL) tend to be bulky and expensive, raising further questions on the use of complex, motorised systems for neurorehabilitation.

Recently, simpler, non-actuated devices, equipped with sensors to measure patients’ movement or interaction, have been designed to provide performance feedback, motivation and coaching during training.812 Research in haptics13,14 and human motor control15,16 has shown how visual, auditory and haptic feedback can be used to induce learning of a skill in a virtual or real dynamic environment. For example, simple force sensors (or even electromyography) can be used to infer motion control17and provide feedback on the required and actual performances, which can allow subjects to learn a desired task. Therefore, an appropriate therapy regime using passive devices that provide essential and engaging feedback can enhance learning of improved arm and hand use.

Such passive sensor-based systems can be used for both impairment-based training (e.g. gripAble18) and task-oriented training (ToT) (e.g. AutoCITE8,9, ReJoyce11). ToT views the patient as an active problem-solver, focusing rehabilitation on the acquisition of skills for performance of meaningful and relevant tasks rather than on isolated remediation of impairments.19,20 ToT has proven to be beneficial for participants and is currently considered as a dominant and effective approach for training.20,21

Sensor-based systems are ideal for delivering task-oriented therapy in an automated and engaging fashion. For instance, the AutoCITE system is a workstation containing various instrumented devices for training some of the tasks used in constraint-induced movement therapy.8 The ReJoyce uses a passive manipulandum with a composite instrumented object having various functionally shaped components to allow sensing and training of gross and fine hand functions.11 Timmermans et al.22reported how stroke survivors can carry out ToT by using objects on a tabletop with inertial measurement units (IMU) to record their movement. However, this system does not include force sensors, critical in assessing motor function.

In all these systems, subjects perform tasks such as reach or object manipulation at the tabletop level, while receiving visual feedback from a monitor placed in front of them. This dislocation of the visual and haptic workspaces may affect the transfer of skills learned in this virtual environment to real-world tasks. Furthermore, there is little work on using these systems for the quantitative task-oriented assessment of functional tasks. One exception to this is the ReJoyce arm and hand function test (RAHFT)23 to quantitatively assess arm and hand function. However, the RAHFT primarily focuses on range-of-movement in different arm and hand functions and does not assess the movement quality, which is essential for skilled action.2428

To address these limitations, this article introduces a novel, sensor-based System for Independent Task-Oriented Assessment and Rehabilitation (SITAR). The SITAR consists of an ecosystem of different modular devices capable of interacting with each other to provide an engaging interface with appropriate real-world context for both training and assessment of UL. The current realisation of the SITAR is an interactive tabletop with visual display as well as touch and force sensing capabilities and a set of intelligent objects. This system provides direct interaction with collocation of visual and haptic workspaces and a rich multisensory feedback through a mixed reality environment for neurorehabilitation.

The primary aim of this study is to present the SITAR concept, the current realisation of the system, together with preliminary data demonstrating the SITAR’s capabilities for UL assessment and training. The following section introduces the SITAR concept, providing the motivation and rationale for its design and specifications. Subsequently, we describe the current realisation of the SITAR, its different components and their capabilities. Finally, preliminary data from two pilot clinical studies are presented, which demonstrate the SITAR’s functionalities for ToT and assessment of the UL. […]

Continue —> SITAR: a system for independent task-oriented assessment and rehabilitation Journal of Rehabilitation and Assistive Technologies Engineering – Asif Hussain, Sivakumar Balasubramanian, Nick Roach, Julius Klein, Nathanael Jarrassé, Michael Mace, Ann David, Sarah Guy, Etienne Burdet, 2017

Figure 1. The SITAR concept with (a) the interactive table-top alongside some examples of intelligent objects developed including (b) iJar to train bimanual control, (c) iPen for drawing, and (d) iBox for manipulation and pick-and-place.

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[Abstract+References] Neuroplastic Changes Induced by Cognitive Rehabilitation in Traumatic Brain Injury: A Review 

Background. Cognitive deficits are among the most disabling consequences of traumatic brain injury (TBI), leading to long-term outcomes and interfering with the individual’s recovery. One of the most effective ways to reduce the impact of cognitive disturbance in everyday life is cognitive rehabilitation, which is based on the principles of brain neuroplasticity and restoration. Although there are many studies in the literature focusing on the effectiveness of cognitive interventions in reducing cognitive deficits following TBI, only a few of them focus on neural modifications induced by cognitive treatment. The use of neuroimaging or neurophysiological measures to evaluate brain changes induced by cognitive rehabilitation may have relevant clinical implications, since they could add individualized elements to cognitive assessment. Nevertheless, there are no review studies in the literature investigating neuroplastic changes induced by cognitive training in TBI individuals.

Objective. Due to lack of data, the goal of this article is to review what is currently known on the cerebral modifications following rehabilitation programs in chronic TBI.

Methods. Studies investigating both the functional and structural neural modifications induced by cognitive training in TBI subjects were identified from the results of database searches. Forty-five published articles were initially selected. Of these, 34 were excluded because they did not meet the inclusion criteria.

Results. Eleven studies were found that focused solely on the functional and neurophysiological changes induced by cognitive rehabilitation.

Conclusions. Outcomes showed that cerebral activation may be significantly modified by cognitive rehabilitation, in spite of the severity of the injury.

1. Laatsch LLittle DThulborn K. Changes in fMRI following cognitive rehabilitation in severe traumatic brain injury: a case study. Rehabil Psychol. 2004;49:262267Crossref
2. Voelbel GTGenova HMChiaravalotti NDHoptman MJ. Diffusion tensor imaging of traumatic brain injury review: implications for neurorehabilitation. NeuroRehabilitation. 2012;31:281293Medline
3. Kou ZIraji A. Imaging brain plasticity after trauma. Neural Regen Res. 2014;9:693700CrossrefMedline
4. Whyte JPolansky MFleming MCoslett HBCavallucci C. Sustained arousal and attention after traumatic brain injury. Neuropsychologia. 1995;33:797813CrossrefMedline
5. McAvinue LO’Keeffe FMcMackin DRobertson IH. Impaired sustained attention and error awareness in traumatic brain injury: implications for insight. Neuropsychol Rehabil. 2005;15:569587CrossrefMedline
6. Ziino CPonsford J. Selective attention deficits and subjective fatigue following traumatic brain injury. Neuropsychology. 2006;20:383390CrossrefMedline
7. Vakil E. The effect of moderate to severe traumatic brain injury (TBI) on different aspects of memory: a selective review. J Clin Exp Neuropsychol. 2005;27:9771021CrossrefMedline
8. Kennedy MRCoelho CTurkstra L, et al. Intervention for executive functions after traumatic brain injury: a systematic review, meta-analysis and clinical recommendations. Neuropsychol Rehabil. 2008;18:257299CrossrefMedline
9. Chen AJWD’Esposito MTraumatic brain injury: from bench to bedside to society. Neuron. 2010;66:1114CrossrefMedline
10. Tomaszczyk JCGreen NLFrasca D, et al. Negative neuroplasticity in chronic traumatic brain injury and implications for neurorehabilitation. Neuropsychol Rev. 2014;24:409427Medline
11. Chiaravalloti NDDobryakova EWylie GRDeLuca J. Examining the efficacy of the modified story memory technique (mSMT) in persons with TBI using functional magnetic resonance imaging (fMRI): the TBI-MEM trial. J Head Trauma Rehabil. 2015;30:261269CrossrefMedline
12. Cicerone KDDahlberg CKalmar K, et al. Evidence-based cognitive rehabilitation: recommendations for clinical practice. Arch Phys Med Rehabil. 2000;81:15961615CrossrefMedline
13. Laatsch LKThulborn KRKrisky CMShobat DMSweeney JA. Investigating the neurobiological basis of cognitive rehabilitation therapy with fMRI. Brain Inj. 2004;18:957974CrossrefMedline
14. Lemmens RJaspers TRobberecht WThijs VN. Modifying expression of EphA4 and its downstream targets improves functional recovery after stroke. Hum Mol Genet. 2013;22:22142220CrossrefMedline
15. Faralli ABigoni MMauro ARossi FCarulli D. Noninvasive strategies to promote functional recovery after stroke. Neural Plast. 2013;2013:854597CrossrefMedline
16. Lorber BHowe MLBenowitz LIIrwin N. Mst3b, an Ste20-like kinase, regulates axon regeneration in mature CNS and PNS pathways. Nat Neurosci. 2009;12:14071414CrossrefMedline
17. Benowitz LICarmichael ST. Promoting axonal rewiring to improve outcome after stroke. Neurobiol Dis. 2010;37:259266CrossrefMedline
18. Chen HEpstein JStern E. Neural plasticity after acquired brain injury: evidence from functional neuroimaging. PM R. 2010;2(12 suppl 2):S306S312CrossrefMedline
19. Sacco KGabbatore IGeda E, et al. Rehabilitation of communicative abilities in patients with a history of TBI: behavioral improvements and cerebral changes in resting-state activity. Front Behav Neurosci. 2016;10:48CrossrefMedline
20. Cernich ANKurtz SMMordecai KLRyan PB. Cognitive rehabilitation in traumatic brain injury. Curr Treat Options Neurol. 2010;12:412423CrossrefMedline
21. Cicerone KDLangenbahn DMBraden C, et al. Evidence-based cognitive rehabilitation: updated review of the literature from 2003 through 2008. Arch Phys Med Rehabil. 2011;92:519530CrossrefMedline
22. Amen DGWu JCTaylor DWilleumier K. Reversing brain damage in former NFL players: implications for traumatic brain injury and substance abuse rehabilitation. J Psychoactive Drugs. 2011;43:15CrossrefMedline
23. Harch PGAndrews SRFogarty EF, et al. A phase I study of low-pressure hyperbaric oxygen therapy for blast-induced post-concussion syndrome and post-traumatic stress disorder. J Neurotrauma. 2012;29:168185CrossrefMedline
24. Irimia AVan Horn JD. Functional neuroimaging of traumatic brain injury: advances and clinical utility. Neuropsychiatr Dis Treat. 2015;11:23552365CrossrefMedline
25. Folmer RLBillings CJDiedesch-Rouse ACGallun FJLew HL. Electrophysiological assessments of cognition and sensory processing in TBI: applications for diagnosis, prognosis and rehabilitation. Int J Psychophysiol. 2011;82:415CrossrefMedline
26. Dockree PMRobertson IH. Electrophysiological markers of cognitive deficits in traumatic brain injury: a review. Int J Psychophysiol. 2011;82:5360CrossrefMedline
27. Johnstone JThatcher RW. Quantitative EEG analysis and rehabilitation issues in mild traumatic brain injury. J Insur Med. 1991;23:228232Medline
28. Stathopoulou SLubar JF. EEG changes in traumatic brain injured patients after cognitive rehabilitation. J Neurother. 2004;8:2151Crossref
29. Carter BGButt W. Are somatosensory evoked potentials the best predictor of outcome after severe brain injury? A systematic review. Intensive Care Med. 2005;31:765775CrossrefMedline
30. Strangman GEO’Neil-Pirozzi TMSupelana CGoldstein RKatz DIGlenn MB. Regional brain morphometry predicts memory rehabilitation outcome after traumatic brain injury. Front Hum Neurosci. 2010;4:182CrossrefMedline
31. Strangman GEO’Neil-Pirozzi TMSupelana CGoldstein RKatz DIGlenn MB. Fractional anisotropy helps predicts memory rehabilitation outcome after traumatic brain injury. NeuroRehabilitation. 2012;31:295310Medline
32. Strangman GEO’Neil-Pirozzi TMGoldstein R, et al. Prediction of memory rehabilitation outcomes in traumatic brain injury by using functional magnetic resonance imaging. Arch Phys Med Rehabil. 2008;89:974981CrossrefMedline
33. Chantsoulis MMirski ARasmus AKropotov JDPachalska M. Neuropsychological rehabilitation for traumatic brain injury patients. Ann Agric Environ Med. 2015;22:368379CrossrefMedline
34. Krawczyk DCde la Plata CMSchauer GF, et al. Evaluating the effectiveness of reasoning training in military and civilian chronic traumatic brain injury patients: study protocol. Trials. 2013;14:1CrossrefMedline
35. Arnemann KLChen AJNovakovic-Agopian TGratton CNomura EMD’Esposito M. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology. 2015;84:15681574CrossrefMedline
36. Becker FReinvang I. Event-related potentials indicate bi-hemispherical changes in speech sound processing during aphasia rehabilitation. J Rehabil Med. 2007;39:658661CrossrefMedline
37. Chen AJNovakovic-Agopian TNycum TJ, et al. Training of goal-directed attention regulation enhances control over neural processing for individuals with brain injury. Brain. 2011;134(pt 5):15411554CrossrefMedline
38. Halko MADatta APlow EBScaturro JBikson MMerabet LB. Neuroplastic changes following rehabilitative training correlate with regional electrical field induced with tDCS. Neuroimage. 2011;57:885891CrossrefMedline
39. Laatsch LThomas JSychra JLin QBlend M. Impact of cognitive rehabilitation therapy on neuropsychological impairments as measured by brain perfusion SPECT: a longitudinal study. Brain Inj. 1997;11:851864CrossrefMedline
40. Castellanos NPPaúl NOrdóñez VE, et al. Reorganization of functional connectivity as a correlate of cognitive recovery in acquired brain injury. Brain. 2010;133(pt 8):23652381CrossrefMedline
41. Munivenkatappa ARajeswaran JIndira Devi BBennet NUpadhyay N. EEG neurofeedback therapy: can it attenuate brain changes in TBI? NeuroRehabilitation. 2014;35:481484Medline
42. Sacco KCauda FD’Agata F, et al. A combined robotic and cognitive training for locomotor rehabilitation: evidences of cerebral functional reorganization in two chronic traumatic brain injured patients. Front Hum Neurosci. 2011;5:146CrossrefMedline
43. Lima FPLima MOLeon D, et al. fMRI of the sensorimotor cortex in patients with traumatic brain injury after intensive rehabilitation. Neurol Sci. 2011;32:633639CrossrefMedline
44. Garnett MRCadoux-Hudson TAStyles P. How useful is magnetic resonance imaging in predicting severity and outcome in traumatic brain injury? Curr Opin Neurol. 2001;14:753757CrossrefMedline
45. Giaquinto S. Evoked potentials in rehabilitation. A review. Funct Neurol. 2004;19:219225Medline
46. Muñoz-Cespedes JMRios-Lago MPaul NMaestu F. Functional neuroimaging studies of cognitive recovery after acquired brain damage in adults. Neuropsychol Rev. 2005;15:169183CrossrefMedline
47. Strangman GO’Neil-Pirozzi TMBurke D, et al. Functional neuroimaging and cognitive rehabilitation for people with traumatic brain injury. Am J Phys Med Rehabil. 2005;84:6275CrossrefMedline
48. Garcia ANShah MADixon CEWagner AKKline AE. Biologic and plastic effects of experimental traumatic brain injury treatment paradigms and their relevance to clinical rehabilitation. PM R. 2011;3(6 suppl 1):S18S27CrossrefMedline
49. Marcano-Cedeño AChausa PGarcía ACáceres CTormos JMGómez EJ. Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients. Artif Intell Med. 2013;58:9199CrossrefMedline
50. Palacios EMSala-Llonch RJunque C, et al. Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury. JAMA Neurol. 2013;70:845851.CrossrefMedline
51. Hibino SMase MShirataki T, et al. Oxyhemoglobin changes during cognitive rehabilitation after traumatic brain injury using near infrared spectroscopy. Neurol Medico Chir (Tokyo). 2013;53:299303CrossrefMedline
52. Jiang Q. Magnetic resonance imaging and cell-based neurorestorative therapy after brain injury. Neural Regen Res. 2016;11:714CrossrefMedline
53. Reid LBBoyd RNCunnington RRose SE. Interpreting intervention induced neuroplasticity with fMRI: the case for multimodal imaging strategies. Neural Plast. 2016;2016:2643491CrossrefMedline
54. Douglas DBIv MDouglas PK, et al. Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging. 2015;24:241251CrossrefMedline
55. Ham TESharp DJ. How can investigation of network function inform rehabilitation after traumatic brain injury? Curr Opin Neurol. 2012;25:662669CrossrefMedline
56. Lerner AMogensen MAKim PEShiroishi MSHwang DHLaw MClinical applications of diffusion tensor imaging. World Neurosurg. 2014;82:96109CrossrefMedline
57. Shenton MEHamoda HMSchneiderman JS, et al. A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 2012;6:137192CrossrefMedline
58. Strauss SHulkower MGulko E, et al. Current clinical applications and future potential of diffusion tensor imaging in traumatic brain injury. Top Magn Reson Imaging. 2015;24:353362CrossrefMedline
59. Sherer MStouter JHart T, et al. Computed tomography findings and early cognitive outcome after traumatic brain injury. Brain Inj. 2006;20:9971005CrossrefMedline
60. Sidaros AEngberg AWSidaros K, et al. Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study. Brain. 2008;131(pt 2):559572.CrossrefMedline
61. Caglio MLatini-Corazzini LD’Agata F, et al. Virtual navigation for memory rehabilitation in a traumatic brain injured patient. Neurocase. 2012;18:123131CrossrefMedline
62. Laatsch LKrisky C. Changes in fMRI activation following rehabilitation of reading and visual processing deficits in subjects with traumatic brain injury. Brain Inj. 2006;20:13671375CrossrefMedline
63. Kim YHYoo WKKo MHPark CHKim STNa DL. Plasticity of the attentional network after brain injury and cognitive rehabilitation. Neurorehabil Neural Repair. 2009;23:468477Link
64. Sacco KGaletto VDimitri D, et al. Concomitant use of transcranial direct current stimulation and computer-assisted training for the rehabilitation of attention in traumatic brain injured patients: behavioral and neuroimaging results. Front Behav Neurosci. 2016;10:57CrossrefMedline
65. Musiek FEBaran JAShinn J. Assessment and remediation of an auditory processing disorder associated with head trauma. J Am Acad Audiol. 2004;15:117132CrossrefMedline
66. Pachalska MŁukowicz MKropotov JDHerman-Sucharska ITalar J. Evaluation of differentiated neurotherapy programs for a patient after severe TBI and long term coma using event-related potentials. Med Sci Monit. 2011;17:CS120CS128CrossrefMedline
67. Dundon NMDockree SPBuckley V, et al. Impaired auditory selective attention ameliorated by cognitive training with graded exposure to noise in patients with traumatic brain injury. Neuropsychologia. 2015;75:7487CrossrefMedline
68. Nebel KWiese HStude Pde Greiff ADiener HCKeidel M. On the neural basis of focused and divided attention. Brain Res Cogn Brain Res. 2005;25:760776CrossrefMedline
69. Snyder SMHall JR. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol. 2006;23:440455CrossrefMedline
70. Barcelò FSanz MMolina VRubia FJ. The Wisconsin Card Sorting Test and the assessment of frontal function: a validation study with event-related potentials. Neuropsychologia. 1997;35:399408CrossrefMedline
71. Barcelò FRubia FJ. Non-frontal P3b-like activity evoked by the Wisconsin card sorting test. Neuroreport. 1998;9:747751CrossrefMedline
72. Squire LRStark CEClark RE. The medial temporal lobe. Annu Rev Neurosci. 2004;27:279306CrossrefMedline
73. Coelho CALiles BZDuffy RJ. Impairments of discourse abilities and executive functions in traumatically brain-injured adults. Brain Inj. 1995;9:471477CrossrefMedline
74. Gabbatore ISacco KAngeleri RZettin MBara BGBosco FM. Cognitive pragmatic treatment: a rehabilitative program for traumatic brain injury individuals. J Head Trauma Rehabil. 2015;30:E14E28CrossrefMedline
75. Duncan CCBarry RJConnolly JF, et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin Neurophysiol. 2009;120:18831908CrossrefMedline
76. Rasmussen IAXu JAntonsen IK, et al. Simple dual tasking recruits prefrontal cortices in chronic severe traumatic brain injury patients, but not in controls. J Neurotrauma. 2008;25:10571070CrossrefMedline
77. Mahncke HWConnor BBAppelman J, et al. Memory enhancement in healthy older adults using a brain plasticity-based training program: a randomized, controlled study. Proc Natl Acad Sci U S A. 2006;103:1252312528CrossrefMedline
78. Kim JWhyte JPatel S, et al. A perfusion fMRI study of the neural correlates of sustained-attention and working-memory deficits in chronic traumatic brain injury. Neurorehabil Neural Repair. 2012;26:870880.Link
79. Bryer EJMedaglia JDRostami SHillary FG. Neural recruitment after mild traumatic brain injury is task dependent: a meta-analysis. J Int Neuropsychol Soc. 2013;19:751762CrossrefMedline
80. Kleim JAJones TASchallert T. Motor enrichment and the induction of plasticity before or after brain injury. Neurochem Res. 2003;28:17571769CrossrefMedline
81. Friston KJPrice CJ. Dynamic representations and generative models of brain function. Brain Res Bull. 2001;54:275285CrossrefMedline
82. Christodoulou CDeLuca JRicker J, et al. Functional magnetic resonance imaging of working memory impairment after traumatic brain injury. J Neurol Neurosurg Psychiatry. 2001;71:161168CrossrefMedline
83. Sanchez-Carrion RFernandez-Espejo DJunque C, et al. A longitudinal fMRI study of working memory in severe TBI patients with diffuse axonal injury. Neuroimage. 2008;43:421429CrossrefMedline
84. Sánchez-Carrión RGómez PVJunqué C, et al. Frontal hypoactivation on functional magnetic resonance imaging in working memory after severe diffuse traumatic brain injury. J Neurotrauma. 2008;25:479494CrossrefMedline
85. McAllister AKKatz LCLo DC. Neurotrophins and synaptic plasticity. Annu Rev Neurosci. 1999;22:295318CrossrefMedline
86. McAllister TWSparling MBFlashman LASaykin AJ. Neuroimaging findings in mild traumatic brain injury. J Clin Exp Neuropsychol. 2001;23:775791CrossrefMedline
87. Scheibel RSNewsome MRTroyanskaya M, et al. Effects of severity of traumatic brain injury and brain reserve on cognitive-control related brain activation. J Neurotrauma. 2009;26:14471461CrossrefMedline
88. Turner GRLevine B. Augmented neural activity during executive control processing following diffuse axonal injury. Neurology. 2008;71:812818CrossrefMedline
89. Turner GRMcIntosh ARLevine B. Prefrontal compensatory engagement in TBI is due to altered functional engagement of existing networks and not functional reorganization. Front Syst Neurosci. 2011;5:9CrossrefMedline
90. Zhou YMilham MPLui YW, et al. Default-mode network disruption in mild traumatic brain injury. Radiology. 2012;265:882892CrossrefMedline
91. Sharp DJScott GLeech R. Network dysfunction after traumatic brain injury. Nat Rev Neurol. 2014;10:156166CrossrefMedline
92. Pandit ASExpert PLambiotte R, et al. Traumatic brain injury impairs small-world topology. Neurology. 2013;80:18261833CrossrefMedline
93. Fork MBartels CEbert ADGrubich CSynowitz HWallesch CW. Neuropsychological sequelae of diffuse traumatic brain injury. Brain Inj. 2005;19:101108CrossrefMedline
94. Wallesch CWCurio NKutz SJost SBartels CSynowitz H. Outcome after mild-to-moderate blunt head injury: effects of focal lesions and diffuse axonal injury. Brain Inj. 2001;15:401412CrossrefMedline

Source: Neuroplastic Changes Induced by Cognitive Rehabilitation in Traumatic Brain Injury: A ReviewNeurorehabilitation and Neural Repair – Valentina Galetto, Katiuscia Sacco, 2017

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[WEB SITE] OCD: Cognitive behavioral therapy improves brain connectivity

MRI scans show that people diagnosed with OCD who have undergone CBT have intensified connectivity between key brain networks.

Researchers have used brain scans to measure changes in the cerebral activity of people with obsessive-compulsive disorder after undergoing a type of cognitive behavioral therapy. They found that the connectivity of key brain networks is improved, suggesting new targets for therapy.

Obsessive-compulsive disorder (OCD) is a condition marked by inescapable, intrusive thoughts that cause anxiety (hence “obsessive”), and repetitive, ritualistic behaviors aimed at reducing that feeling (hence “compulsive”).

OCD can be a debilitating condition and can severely impair daily functioning. The National Institutes of Mental Health estimate that, in the United States, the yearly prevalence of OCD amounts to 1 percent of the total adult population. Around half of these cases are deemed “severe.”

Treatments for OCD include the administration of selective serotonin reuptake inhibitors and cognitive behavioral therapy (CBT), a type of therapy that aims to improve damaging mind associations.

Researchers from the University of California, Los Angeles – who were led by Dr. Jamie Feusner – have conducted a study aiming to find out whether and how CBT might change levels of activity and network connectivity in the brains of people diagnosed with OCD.

They explain that although the efficacy of CBT in treating OCD has been previously explored, this is likely the first study to use functional MRI (fMRI) to monitor what actually happens in the brains of people with OCD after exposure to this kind of therapy.

The researchers’ findings were recently published in the journal Translational Psychiatry.

Changes in key brain regions following CBT

The team specifically targeted the effects of exposure and response prevention (ERP)-based CBT, which entails exposure to triggering stimuli and encouraging the individual to wilfully resist responding to those stimuli in the way that they normally would.

For the study, 43 people with OCD and 24 people without it were recruited. The results for the two groups were later compared, at which point the 24 individuals without OCD were taken as the control group.

All the participants diagnosed with OCD received intensive ERP-based CBT on an individual basis in 90-minute sessions on 5 days per week, for a total of 4 weeks.

Participants from both groups underwent fMRI. Those diagnosed with OCD, who had received CBT, were scanned both before the treatment period and after the 4 weeks of treatment. Participants from the control group, who did not undergo CBT, also had fMRI scans after 4 weeks.

When the scans of participants with OCD were compared, the results from before exposure to CBT and after it were found to be largely contrasting.

The researchers noticed that the brains of people with OCD exhibited a significant increase in connectivity between eight different brain networks, including the cerebellum, the caudate nucleus and putamen, and the dorsolateral and ventrolateral prefrontal cortices.

 The cerebellum is involved with processing information and determining voluntary movements, while the caudate nucleus and putamen are key in learning processes and controlling involuntary impulses.

The dorsolateral and ventrolateral prefrontal cortices are involved with planning action and movement, as well as regulating certain cognitive processes.

Dr. Feusner and team point out that an increased level of connectivity between these cerebral regions suggests that the brains of the people who underwent CBT were “learning” new non-compulsive behaviors and activating different thought patterns.

He suggests that these changes may be novel ways of coping with the cognitive and behavioral idiosyncrasies of OCD.

The changes appeared to compensate for, rather than correct, underlying brain dysfunction. The findings open the door for future research, new treatment targets, and new approaches.”

Dr. Jamie Feusner

First study author Dr. Teena Moody adds that being able to show that there are quantifiable positive changes in the brain following CBT may give people diagnosed with OCD more confidence in following suitable treatments.

“The results could give hope and encouragement to OCD patients,” says Dr. Moody, “showing them that CBT results in measurable changes in the brain that correlate with reduced symptoms.”

Source: OCD: Cognitive behavioral therapy improves brain connectivity

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[ARTICLE] User Acceptance of Computerized Cognitive Behavioral Therapy for Depression: Systematic Review – Full Text


Background: Computerized cognitive behavioral therapy (cCBT) has been proven to be effective in depression care. Moreover, cCBT packages are becoming increasingly popular. A central aspect concerning the take-up and success of any treatment is its user acceptance.

Objective: The aim of this study was to update and expand on earlier work on user acceptance of cCBT for depression.

Methods: This paper systematically reviewed quantitative and qualitative studies regarding the user acceptance of cCBT for depression. The initial search was conducted in January 2016 and involved the following databases: Web of Science, PubMed, the Cochrane Library, and PsycINFO. Studies were retained if they described the explicit examination of the user acceptance, experiences, or satisfaction related to a cCBT intervention, if they reported depression as a primary outcome, and if they were published in German or English from July 2007 onward.

Results: A total of 1736 studies were identified, of which 29 studies were eligible for review. User acceptance was operationalized and analyzed very heterogeneously. Eight studies reported a very high level of acceptance, 17 indicated a high level of acceptance, and one study showed a moderate level of acceptance. Two qualitative studies considered the positive and negative aspects concerning the user acceptance of cCBT. However, a substantial proportion of reviewed studies revealed several methodical shortcomings.

Conclusions: In general, people experience cCBT for depression as predominantly positive, which supports the potential role of these innovative treatments. However, methodological challenges do exist in terms of defining user acceptance, clear operationalization of concepts, and measurement.



Depressive disorders are among the most common and serious mental illnesses [1]. Globally, 350 million people of all ages are estimated to suffer from depression. If depressive disorders are detected at an early stage, they are highly treatable in the majority of cases [2]. There are known effective psychological treatments, for example, cognitive behavioral therapy (CBT) [3]. However, individuals suffering from depression often find themselves confronted with barriers to receiving appropriate care such as social stigma associated with mental disorders, long waiting times, or the logistical difficulties of appearing in person for treatment [4,5]. For these reasons, computerized programs present an innovative approach to improving access to psychological treatments for depression. There is evidence that computerized cognitive behavioral therapy (cCBT) is effective in the treatment of various mental disorders, including depression [610]. There are a number of advantages that are associated with cCBT such as anonymity, wide availability, or location-independent and around-the-clock access [9,11]. Well-known cCBT programs such as Beating The Blues and MoodGYM have been shown to provide a promising option for the treatment of mental health problems [9,12,13]. A prerequisite for cCBT programs to be effective is its user acceptance, as the implementation of an innovative intervention such as cCBT can be affected negatively because of individuals being unwilling to use it. For example, the absence of a contact person and the resulting anonymity can have a negative impact on the user’s motivation to start or keep up with a cCBT program. Therefore, it is of utmost importance to consider user acceptance when developing and implementing a cCBT program for the treatment of depression.

The concept of user acceptance arose as a key term in the scientific discourse. Definitions of the term differ widely depending on the intended use [14]. One of the most popular approaches is the technology acceptance model (TAM) developed by Davis [15]. TAM illustrates user acceptance determined by two factors: perceived usefulness and perceived ease of use. According to Davis [15], both have a significant impact on a person’s attitude toward using a new technology. Kollmann [16] and Rogers [17] went one step further and combined different phases in their acceptance models. Therefore, the user passes through phases from getting to know a new technology, to forming an attitude toward it, to a decision whether to use or not to the confirmation of the decision. On this basis, user acceptance can be defined as the willingness of individuals to employ information technology for the tasks it is designed to support, the realization, and approval of the decision to employ. All of these models have one thing in common: user acceptance is considered to be a process beginning with an attitude toward the innovation and developing into satisfaction with the innovation; it is not an instantaneous act. Accordingly, we have conceived acceptance as the act of accepting, experiencing, and being satisfied.

Since the emergence of the first cCBT programs, there have been a number of reviews addressing the user acceptance of cCBT; however, they have utilized different approaches. In their reviews, Titov [18], Andrews et al [8], and Vallury et al [19] focused broadly on effectiveness and user acceptance of cCBT for several mental disorders, including depression and anxiety disorders. Waller and Gilbody [20] reviewed quantitative and qualitative studies examining adverse consequences, accessibility, and acceptability of cCBT programs for treating anxiety and depression. However, Kaltenthaler et al [21] provide the only review with a very comprehensive and focused insight into the user acceptance of cCBT for depression, including research up to June 2007. They systematically reviewed sources of information on acceptability to patients of cCBT for depression. As a result, they documented several studies reporting positive expectancies and high satisfaction in routine care cCBT services for those completing the treatment and argued that studies should reveal more detailed information on patient recruitment methods, dropout rates, and reasons for dropping out. Furthermore, they drafted well-designed surveys and qualitative studies included alongside trials to determine levels of patient acceptability as implications for further research.

On this basis, we provide a systematic overview on user acceptance of cCBT for depression over the last 10 years and widen the perspective to include the notion that the process of user acceptance spans a number of phases, including accepting, experiencing, and being satisfied with cCBT. We intend to answer the following research questions: (1) which measures were used to examine the user acceptance of cCBT for depression? and (2) to what degree do users accept cCBT for depression? […]

Continue —> JMIR-User Acceptance of Computerized Cognitive Behavioral Therapy for Depression: Systematic Review | Rost | Journal of Medical Internet Research

Figure 2. Recommended examination of user acceptance.

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[Abstract] Robot-assisted mirroring exercise as a physical therapy for hemiparesis rehabilitation


The paper suggests a therapeutic device for hemiparesis that combines robot-assisted rehabilitation and mirror therapy. The robot, which consists of a motor, a position sensor, and a torque sensor, is provided not only to the paralyzed wrist, but also to the unaffected wrist to induce a symmetric movement between the joints. As a user rotates his healthy wrist to the direction of either flexion or extension, the motor on the damaged side rotates and reflects the motion of the normal side to the symmetric angular position. To verify performance of the device, five stroke patients joined a clinical experiment to practice a 10-minute mirroring exercise. Subjects on Brunnstrom stage 3 had shown relatively high repulsive torques due to severe spasticity toward their neutral wrist positions with a maximum magnitude of 0.300kgfm, which was reduced to 0.161kgfm after the exercise. Subjects on stage 5 practiced active bilateral exercises using both wrists with a small repulsive torque of 0.052kgfm only at the extreme extensional angle. The range of motion of affected wrist increased as a result of decrease in spasticity. The therapeutic device not only guided a voluntary exercise to loose spasticity and increase ROM of affected wrist, but also helped distinguish patients with different Brunnstrom stages according to the size of repulsive torque and phase difference between the torque and the wrist position.

Source: Robot-assisted mirroring exercise as a physical therapy for hemiparesis rehabilitation – IEEE Conference Publication

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