Clinical Implementation of Upper Extremity Rehabilitation Robotics

PURPOSE: Minimal research exists pertaining to the use of robot-assisted therapy (RT) in the clinical setting compared with protocols specified in clinical research trials. RT aligns with motor learning theory and can assist people with neurological impairments to regain optimal function of affected extremities with high repetition of movements. The understanding of the brain’s potential to change and reorganize after injury, termed neuroplasticity, has changed rehabilitation practices over the past couple of decades. Unfortunately, clinical barriers may still impede the ability of clinicians to adequately promote neuroplasticity to address functional limitations. Little is known about the implementation of robotics in traditional clinic settings. Therefore, the purpose of this study was to better understand the impact of clinically implemented upper extremity (UE) RT.

METHOD: This retrospective cohort study included 121 patients who received outpatient neurorehabilitation services at the Indiana University Health Neuroscience Center. Participants had a neurological impairment and had received at least one session of RT using the InMotion2 Shoulder–Elbow Robot (Bionik Laboratories, Toronto, Ontario) at the Neurorehabilitation and Robotics Outpatient Clinic at Indiana University Health Neuroscience Center from 2009 to 2016. Of the 121 participants, 38 were female and 83 male; 34 were aged ≤17, 23 were aged 18–35, 19 were aged 36–53, 38 were aged 54–71, and 7 were aged 71–88.

Health records data were accessed in addition to the session and kinematic data from the robotic device. One advantage of this robotic device is the collection of kinematic data throughout the training sessions that can be analyzed (Massie et al., 2016). Session data included number of sessions, number of repetitions during training, and treatment duration. Descriptive statistics were run to quantify the use of robotics clinically and to facilitate a comparison with the published literature.

RESULTS: On average, participants completed about seven total sessions of RT throughout the duration of their program, with 96 (76.19%) completing 1–10 total RT sessions and 24 (19.05%) completing 11–20 total RT sessions. Participants received an average of 0.86 RT sessions per week over their total program duration. Fifty-one participants (40.48%) did not consistently receive RT each week and therefore completed less than one RT session per week when averaged over the total program duration. The vast majority of participants (85.71%) received only up to two RT sessions per week, leaving only six of 121 patients (4.76%) who actually received three or more RT sessions per week.

During each session, participants completed 320–1,600 repetitions, with an average of 681 repetitions per session. The majority (72.22%) completed 320–800 repetitions per session, which translates to 1.0–2.5 training blocks per session (there are 320 repetitions per training block in RT).

CONCLUSION: Clinical implementation in this study occurred at a lower frequency than in previously published clinical research trials. For example, Fasoli et al. (2008) reported 1-hr RT sessions 2×/wk for 8 wk. Other studies reported 15–20 daily sessions of 25–30 min each over 4–8 wk (Palsbo & Hood-Szivek, 2012) and 1 hr 3 days/wk for 3 wk (Fluet et al., 2010). The number of repetitions per session in this study, however, appears to be closer to published research findings. Data from research shows average repetitions per session of 860, compared with 681 in this study. Overall, the difference in the intensity of RT implementation between this study and published research shows that RT was implemented in the clinic at a lower rate.

Occupational therapists using RT in neurorehabilitation should follow suggested protocols in RT research and a consistent RT treatment plan to maximize the effectiveness of RT. Future research should focus on implementation of a structured and consistent RT protocol in clinical settings to establish clinical efficacy. Future research should also expand participant diagnoses to include conditions other than stroke, such as cerebral palsy, multiple sclerosis, spinal cord injury, and traumatic brain injury, to determine the effectiveness of RT across various neurological conditions.

References

Fasoli, S., Fragala-Pinkham, M., Hughes, R., Hogan, N., Krebs, H., & Stein, J. (2008). Upper limb robotic therapy for children with hemiplegia. American Journal of Physical Medicine and Rehabilitation, 87, 929–936. https://doi.org/10.1097/PHM.0b013e31818a6aa4

Fluet, G., Qiu, Q. Y., Kelly, D., Parikh, H., Ramirez, D., Saleh, S, & Adamovich, S. (2010). Interfacing a haptic robotic system with complex virtual environments to treat impaired upper extremity motor function in children with cerebral palsy. Developmental Neurorehabilitation, 13, 335–345. https://doi.org/10.3109/17518423.2010.501362

Massie, C., Du, Y., Conroy, S., Krebs, H., Wittenberg, G., Bever, C., & Whitall, J. (2016). A clinically relevant method of analyzing continuous change in robotic upper extremity chronic stroke rehabilitation. Neurorehabilitation and Neural Repair, 30, 703–712. https://doi.org/10.1177/1545968315620301

Palsbo, S., & Hood-Szivek, P. (2012). Effect of robotic-assisted three-dimensional repetitive motion to improve hand motor function and control in children with handwriting deficits: A nonrandomized Phase 2 device trial. American Journal of Occupational Therapy, 66, 682–690. https://doi.org/10.5014/ajot.2012.004556