Feasibility and Cost of Robot-assisted Upper Limb Rehabilitation with Different Levels of Supervision

Upper limb impairments are often a result of neurological or traumatic injuries (e.g., stroke, traumatic brain injuries). These impairments may decrease independence in performing activities of daily living, severely affecting patients' quality of life. Growing evidence shows that increasing upper limb therapy dose for patients could improve functional outcomes and prevent their long-term deterioration. However, due to limited resources (e.g., the number of therapists) and high rehabilitation-related costs, providing a higher therapy dose to patients is challenging, both in the clinic and after discharge, and finding new models of care is therefore critical.

Minimally supervised or unsupervised robot-assisted therapy (i.e., patients training with rehabilitation devices with minimal to no supervision of an external person) holds the promise of allowing an increase in therapy dose with little impact on the additional resources needed. Because of their ability to provide active support and measure a wide set of parameters, actuated (active) rehabilitation robots seem to be the best solution for this therapy setting. However, such devices are often complex to use and have never been extensively tested minimally or unsupervised. Consequently, the data available on their feasibility and cost-effectiveness is limited.

At the Rehabilitation Engineering Laboratory (RELab, ETH Zurich), we developed ReHandyBot, an actuated device for unsupervised upper limb therapy. The device has already been tested with stroke patients in an unsupervised setting, with positive results regarding usability and increase in therapy dose. The main goal of this project is, therefore, to investigate the feasibility and cost of our robot-assisted continuum of care model (i.e., from inpatient to ambulatory). Specifically, we aim to evaluate the feasibility of integrating ReHandyBot into the clinical routine and perform a preliminary cost-benefit analysis of supervised, minimally supervised, and unsupervised robot-assisted therapy. Furthermore, we will also gather data on the overall dose of robot-assisted therapy and the intensity of use of the device in a clinical and ambulatory setting.

The ReHandyBot is a platform for hand rehabilitation with two degrees of freedom, namely forearm pronation/supination and finger flexion/extension. This device allows performing exercises targeting somatosensation and motor functions. Different exercises are implemented using virtual reality and haptic feedback; the interaction forces between subjects and ReHandyBot are measured by force sensors in the handles, allowing the control of a virtual hand and the physical interaction with the virtual environment. Mechanical properties (e.g., size, stiffness) of the objects displayed in the virtual environment are rendered by the motion/force of the instrumented handles. The type and difficulty level of the exercises are patient-specific and are automatically adapted throughout the therapy.

The duration and frequency of the therapy sessions with ReHandyBot depend on the specific patient's needs and are set according to the therapist's instructions.

The main goals of this study are to:

(i) Evaluate the feasibility of integrating ReHandyBot into the routine of rehabilitation clinics and ambulatories.

(ii) Perform a cost-benefit analysis of supervised, minimally supervised, and unsupervised therapy with the device.

The secondary goals are to investigate:

(iii) The intensity of use of ReHandyBot by the facilities. (iv) The increase in therapy dose for the patients using the device minimally or unsupervised compared to usual care.

(v) The quantity and characteristics of patients who can train supervised, minimally, or unsupervised with the device.

(vi) Usability and user experience.

All the patients admitted to the clinic or ambulatory will be screened for participation in this study. Eligible patients will receive robot-assisted therapy sessions with the ReHandyBot in parallel to their conventional therapy program. During therapy with ReHandyBot, the patient sits comfortably on a chair in front of a screen and places the fingers of the impaired hand on the handles of the device.

The eligibility of the patients will be checked during the admission visit, after which, for eligible patients, a trial session with ReHandyBot will be scheduled. During the trial session, the supervising therapist will decide if the patient can really benefit from training with ReHandyBot, and if yes, the therapist will integrate supervised therapy sessions with the device into the therapy schedule of the patient. Then, during the supervised sessions, the therapist will use a checklist to decide if a patient can train minimally supervised (i.e., group sessions) or unsupervised. If this will be the case, the minimally or unsupervised therapy sessions will be added to the therapy schedule of the patient. The therapy sessions with ReHandyBot (e.g., frequency, duration) will be scheduled based on the therapist's instruction and may vary between patients, depending on their specific needs. On the day of the last session with ReHandyBot, patients will fill in questionnaires to rate its usability and user experience.

For each recruited patient, data regarding demographics, medical condition, clinical and cognitive assessments, therapy dose, and device data will be recorded on the CRF. Medical and assessment data will be data routinely collected at the clinic and ambulatory. We will not perform evaluations specifically for this study.

At the end of the study, data regarding costs, intensity of use, and device retention will be recorded.

Eligibility Criteria:
Inclusion Criteria:

* Informed Consent signed by the subject.
* Female and male patients between 18 and 90 years old
* Presence of an upper limb deficit derived from any disease or event that, according to the responsible clinicians, could be improved by therapy with ReHandyBot.

Exclusion Criteria:

* Pathologies that may interfere with the training with ReHandyBot.