Clinical trial NCT05819047: Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback
Official title: Assessment of Upper Limb Motor Performance Using an Interface With Haptic Feedback
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Healthy
Keywords: Haptic technology
Musculoskeletal pain
Virtual environment
Lead sponsor: Universidad Nacional de Entre Rios
Sponsor class: OTHER
First posted: 2023-04-19
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Maximum age (years): 60
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Healthy volunteers

Brief summary: The purpose of the study is to test an evaluation environment based on a device that has the ability to provide the user with tactile (haptic) sensations. This environment will be used to investigate how the arm movements of a healthy person are performed, and then - at a later stage - to find out whether it is possible to measure changes during musculoskeletal pain. Electrical signals produced by the brain (called electroencephalogram or EEG) will be recorded by means of electrodes on the surface of the scalp (non-invasive). In addition, the angle of the elbow joint during movement will be measured, with the intention of using objective measures to aid future evidence-based clinical decision making.

It is expected that the developed environment can be used -in the near future-, to evaluate the progression of pathologies associated with muscle pain, or to quantify the effectiveness of rehabilitation therapies.

Detailed description: Experimental setup Volunteers will be received at the Center of Rehabilitation Engineering and Neuromuscular and Sensory Research (CIRINS). They will be informed, in simple terms, of the purpose of the project and the measurements that will be done during the session. The Informed Consent Form will be read to each volunteer and, if they decide to participate, they will be asked to sign a copy that will be filed in the Laboratory together with their contact information, which will be stored confidentially.

At the beginning of the session, the volunteer will be asked to sit comfortably. The EEG electrodes will then be attached using a 16-electrode cap in the standard 10-20 location, together with the exoskeleton in the position determined by the investigator. The position of the camera that will be used to follow the movements of the upper limb will also be adjusted to ensure that the movements will be within the camera's field of view.

Subsequently, the subject will be placed in its initial position: seated, with approximately 45° of shoulder abduction, 10° of shoulder flexion and 90° of elbow flexion.

Once the subject is in the proper position, they will be asked, using the implemented test interface, to perform the virtual nine-hole peg test (NHPT) in Cue Based and Self Paced variants. The volunteer will be asked to complete these tests with both hands, in order to assess whether there are differences in performance associated with the side with which the test is performed, and whether using the system is sensitive enough to detect these changes. The researcher conducting the experiment should record which test was performed with the dominant hand and which was not.

To evaluate whether the results obtained are satisfactory, they will be compared with those reported for healthy individuals.

Evaluation tests Cue-based nine-hole peg test: this test works with time or event dependent cues, dividing the test into several repetitions of the same task. Each trial starts at the same predetermined position, and consists of placing as quickly as possible a random peg in a random hole. This randomness is given in the order of the first nine attempts without repeating the peg. Then, in case of additional attempts, the order of the first nine attempts is repeated. In case of failure to complete an attempt within a certain time or placing the peg in the wrong hole, it is given as failed. The signals presented to the user are visual. The pegs change color from white (inactive) to red, which marks the peg that should move (but without giving the signal to move yet), when the peg changes to green, the movement should begin. Likewise, the hole into which the peg should be inserted is marked with the addition of a translucent light blue object. Inactive holes have no color or object inside. At the end of a repetition, the position of the pegs is reset. The starting point of each repetition of the test is marked with a translucent sphere, which is the position that the robot cursor must take before the next attempt. This object sees its position reflected along with the other elements of the virtual environment when changing hands.

Self-paced nine-hole peg test : this test gives the user the freedom to place the desired peg in the desired hole at the desired speed. This test is the most similar to the original NHPT, and ends when all nine pegs are placed in the nine holes, giving as a result the total duration of the test.

Eligibility criteria: Inclusion Criteria:

* No history of neurological disease, chronic pain, or musculoskeletal disorders.
* Willingness and ability to fully understand the content and scope of the experiment and to comply with the instructions.
* Signature of the informed consent document.

Exclusion Criteria:

* Pregnancy.
* History of chronic pain or neuromuscular disorders.
* History of addictive behavior, defined as abuse of alcohol, cannabis, opioids or other drugs.
* History of heat sensitivity disorders.
* Presence of fever, tuberculosis, malignant tumors, infectious processes, acute inflammatory processes.
* Implantation of pacemakers or metallic prostheses.
* Use of analgesics within 24 hours prior to participation in the experiment.
* Lack of cooperation.
* Trauma of the segment to be evaluated in the last 4 weeks.
* Surgical history of the upper quadrant.
* Metabolic diseases.
* Ingestion of pain medication in the last 24 hs.

Primary outcomes:
measure: Movement smoothness
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
Secondary outcomes:
measure: Log jerk transport
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: Log jerk return
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: SPARC return
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: Path length ratio transport
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: Path length ratio return
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: Velocity max. return
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
measure: Jerk peg approach
timeFrame: Average of 36 trials during a single experimental session with a duration of 30 minutes
Other outcomes:
Locations:
city: Oro Verde
country: Argentina
facility: Facultad de Ingeniería, Universidad Nacional de Entre Ríos
state: Entre Ríos
Study officials:
affiliation: Universidad Nacional de Entre Rios
name: Rosa M Weisz, MSc. in Biomed Eng
role: PRINCIPAL_INVESTIGATOR
References:
citation: Mista CA, Monterde S, Ingles M, Salvat I, Graven-Nielsen T. Reorganized Force Control in Elbow Pain Patients During Isometric Wrist Extension. Clin J Pain. 2018 Aug;34(8):732-738. doi: 10.1097/AJP.0000000000000596.
citation: Hodges PW, Tucker K. Moving differently in pain: a new theory to explain the adaptation to pain. Pain. 2011 Mar;152(3 Suppl):S90-S98. doi: 10.1016/j.pain.2010.10.020. Epub 2010 Nov 18. No abstract available.
citation: Karos K, Meulders A, Gatzounis R, Seelen HAM, Geers RPG, Vlaeyen JWS. Fear of pain changes movement: Motor behaviour following the acquisition of pain-related fear. Eur J Pain. 2017 Sep;21(8):1432-1442. doi: 10.1002/ejp.1044. Epub 2017 Apr 25.
citation: Tsay A, Allen TJ, Proske U, Giummarra MJ. Sensing the body in chronic pain: a review of psychophysical studies implicating altered body representation. Neurosci Biobehav Rev. 2015 May;52:221-32. doi: 10.1016/j.neubiorev.2015.03.004. Epub 2015 Mar 14.
citation: Rigsby B, Reed KB. Accuracy of Dynamic Force Compensation Varies With Direction and Speed. IEEE Trans Haptics. 2019 Oct-Dec;12(4):658-664. doi: 10.1109/TOH.2019.2912375. Epub 2019 Apr 23.
citation: Kanzler CM, Schwarz A, Held JPO, Luft AR, Gassert R, Lambercy O. Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals. J Neuroeng Rehabil. 2020 Sep 25;17(1):128. doi: 10.1186/s12984-020-00748-5.
citation: Kanzler CM, Rinderknecht MD, Schwarz A, Lamers I, Gagnon C, Held JPO, Feys P, Luft AR, Gassert R, Lambercy O. A data-driven framework for selecting and validating digital health metrics: use-case in neurological sensorimotor impairments. NPJ Digit Med. 2020 May 29;3:80. doi: 10.1038/s41746-020-0286-7. eCollection 2020.
citation: Schwarz A, Kanzler CM, Lambercy O, Luft AR, Veerbeek JM. Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke. Stroke. 2019 Mar;50(3):718-727. doi: 10.1161/STROKEAHA.118.023531.
citation: Wright DJ, Holmes PS, Smith D. Using the movement-related cortical potential to study motor skill learning. J Mot Behav. 2011;43(3):193-201. doi: 10.1080/00222895.2011.557751.
citation: Feys P, Lamers I, Francis G, Benedict R, Phillips G, LaRocca N, Hudson LD, Rudick R; Multiple Sclerosis Outcome Assessments Consortium. The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler. 2017 Apr;23(5):711-720. doi: 10.1177/1352458517690824. Epub 2017 Feb 16.
citation: Spuler M, Niethammer C. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci. 2015 Mar 26;9:155. doi: 10.3389/fnhum.2015.00155. eCollection 2015.
citation: J. D. Guzmán, E. F. Fonseca, C. F. Rengifo, D. E. Guzmán, J. Londoño and E. Muñoz, "Implementación de la prueba de funcionalidad motriz de miembro superior nine-hole peg test en un entorno virtual 3D", Iberdiscap, 2017
citation: Ahmed T, Thopalli K, Rikakis T, Turaga P, Kelliher A, Huang JB, Wolf SL. Automated Movement Assessment in Stroke Rehabilitation. Front Neurol. 2021 Aug 19;12:720650. doi: 10.3389/fneur.2021.720650. eCollection 2021.
citation: Mista CA, Laugero SJ, Adur JF, Andersen OK, Biurrun Manresa JA. A new experimental model of muscle pain in humans based on short-wave diathermy. Eur J Pain. 2019 Oct;23(9):1733-1742. doi: 10.1002/ejp.1449. Epub 2019 Jul 24.
citation: Gatti R, Atum Y, Schiaffino L, Jochumsen M, Biurrun Manresa J. Decoding kinetic features of hand motor preparation from single-trial EEG using convolutional neural networks. Eur J Neurosci. 2021 Jan;53(2):556-570. doi: 10.1111/ejn.14936. Epub 2020 Aug 25.
citation: W. Wei, "Virtual reality enhanced robotic systems for disability rehabilitation," in Virtual and Augmented Reality: Concepts, Methodologies, Tools, and Applications, 2018
citation: S. Mahamad, S. M. Taib, and M. N. Ibrahim, "Analyzing speed accuracy trade-off in control movement mechanism with error enforcement," in Applied Mechanics and Materials, 2011.