Clinical Trial Details
— Status: Not yet recruiting
Administrative data
NCT number |
NCT04946747 |
Other study ID # |
19-013 |
Secondary ID |
|
Status |
Not yet recruiting |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
June 2021 |
Est. completion date |
April 10, 2022 |
Study information
Verified date |
June 2021 |
Source |
University of Missouri, Kansas City |
Contact |
Kim Dyer, MSN |
Phone |
816.404.1380 |
Email |
kim.dyer[@]tmcmed.org |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
Concussions are consequences of inopportune interactions between an impact force and the head
that causes the head (and brain) to move too rapidly. This project involves two parts.
1. The outcome of head-impact depends upon the force and the biomechanical properties of
the head-and-neck. Modern microelectrical mechanical systems (MEMS) head-impact sensors
only measure the physical parameters of external forces. The researchers have developed
a next-generation smart MEMS sensor fortified with artificial intelligence (AI) that can
help define a personalized concussive threshold.
The researchers sensor machine-learns the biomechanical properties of the participant's
head-and-neck and accurately determines the likelihood for concussive injuries. The
researchers first goal is to field-test the sensor in soccer players.
2. Researchers hypothesize that an increase in neck stiffness should reduce concussive
risks. The researchers have developed a training protocol that involves a conditioned
response (CR) to increase neck stiffness during a head-impact event and thereby decrease
concussion risk. The Researchers have also developed technology to monitor neck
stiffness.
The smart sensor is fully integrated into the training protocol and monitors the neck
stiffness to validate the effectiveness of the training. The second goal is to optimize and
finalize our training protocol and conduct a field-test in soccer players.
Description:
The hypothesis is that MEMS sensors fortified with artificial intelligence and machine
learning can incorporate individualized human factors into consideration and help to define
concussive threshold for diagnosis that is personalized as in precision medicine. In
laboratory tests, the prototype smart sensor the researchers built can machine-learn the
biomechanical properties of the head-and-neck of the user without being programmed with that
information. It then measures the magnitude of the impact against a personalized threshold of
the user, in real time. These capabilities allow the smart sensor to accurately determine the
potential for concussive injuries for a given individual.
The first aim is (a) to optimize and finalize the sensor into a wearable, field-test-ready
prototype and (b) to conduct a field-test in soccer players in order to examine the accuracy
of the sensors in setting personalized concussive thresholds.
The hypothesis is that the dynamic increase in neck stiffness should reduce concussive risks
significantly. The smart sensor is fully integrated into the training in order to monitor the
increase in neck stiffness and validate the effectiveness of the acquired CR in reducing
concussion risks.
The second aim therefore is (a) to optimize and finalize our training protocol and (b) to
conduct a field-test in soccer players.
The long-term objectives are to develop methods and technology for rapid and reliable
concussion risk assessment in the field as well as for the prevention and mitigation of
concussive injuries.
PROCEDURES
Part 1 of study:
Study information will be given to parents and soccer players (study participants) by Dr.
Moncure and Dr. Huang at the Local soccer academy during normal hours. This will be a
one-time session for approximately 1 hour, depending on questions.
The study information provided by investigators will also be recorded on video in order to
assure that all parents and soccer players receive the same information even if they are not
present at the meeting.
All players will be given the opportunity to participate based on inclusion and exclusion
criteria.
Informed Consent and Assent will be reviewed with participants and parents by Dr. Moncure at
the Local soccer academy after parents and soccer players have been fully informed of the
study and their participation requirements. All participants and their parents must attend
the informational session or watch the study informational video prior to participation. All
study related questions will be answered by the Dr. Moncure or Dr. Huang. Completing the ICF
and Assent will occur after the study informational session and will take approximately 30
minutes.
A Training session will be held to show all Local soccer academy trainers how the sensors
work and how the sensors are affixed to the players. Later, the trainers will see to it that
the soccer players will properly wear the sensors every time during practice. This training
session will take approximately 30 minutes.
Once a participant has been enrolled, the participant can begin wearing the sensor during
training or playing soccer at Local soccer academy. Sensors will be worn over a period of at
least 4 weeks when the players are at the Local soccer academy. The participant has completed
Part 1 at the end of 4 weeks. The purpose of Part 1 is to collect head movement data from the
soccer players before concussion avoidance training.
Part 2 of study:
Randomization of participants will occur. Participants will be randomized into 2 groups --
Trained group and Control group.
Trainers will be shown how to conduct the Virtual Reality (VR) training session. Trainers
will be shown proper placement of the head-impact sensor system to measure participant
response to training.
After Part 1 of the study has been completed, the participants will be randomized and divided
into the Trained group and the Control group. Considerations will be given to obtain the best
possible age-match and gender match in the Trained group and the control group. Both groups
will be shown how to use the Virtual Reality goggles. During the one to two week VR training
period, both groups will be using the VR goggles. The Trained group will have the conditioned
stimulus (CS, images of opposing players approaching) and the unconditioned stimulus (US, a
voice cue to stiffen the neck by the coach) always being delivered with a consistent timing
relationship (e.g. a 250 msec delay between the CS and the US), causing the conditioned
response (neck stiffening) to emerge. The Control group will also receive the same CS and the
same US, but the CS and the US will bear no consistent timing relationship, therefore never
causing any CR to emerge. Both groups will also wear our smart head-impact sensor system to
measure their response to training. One purpose of this arrangement is to provide an avenue
for a double-blind analysis. A second purpose will be the provision for an age-and
gender-matched control group for the study.
CR training will be carried out over a period of approximately 10 days with daily 30-minute
sessions (or roughly over a period of one to two weeks). The training involves wearing the VR
goggles and our smart head-impact sensor system (head sensor and body sensor), at the local
soccer academy in the training area. The participant will receive virtual visual stimuli of
usual soccer play as CS. The participant will also receive a mildly unpleasant auditory
stimuli (such as those from a coach) as US. The training will be done in the local soccer
academy training room, with only those randomized participants and their trainers present.
The Control group will also participate in VR training but will not know that they are in the
Control group. All data will be kept within software that comes with the sensors and VR
goggles.
Once the CR training has been completed, all participants will again wear our smart sensors
whenever they are playing soccer at the local soccer academy for another 4 weeks.
Informed Consent and Assent forms will be completed in person, original copies will be
collected and stay with the Investigators, a copy of both will be given to the participants.
All other data will be collected electronically. Sensors send data directly to researcher
(Chi-Ming Huang).