Clinical Trial Details
— Status: Completed
Administrative data
NCT number |
NCT02824991 |
Other study ID # |
CH-Sp-01 |
Secondary ID |
|
Status |
Completed |
Phase |
N/A
|
First received |
|
Last updated |
|
Start date |
January 2016 |
Est. completion date |
April 2016 |
Study information
Verified date |
June 2016 |
Source |
University of Malaga |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Interventional
|
Clinical Trial Summary
Background: One treatment of Muscle trigger points (MTrPs) is deep dry needling (DN) with the
purpose of obtaining local twitch responses (LTRs), which are highly effective in releasing
MTrPs. In DN procedures the mechanisms associated with LTRs and the LTR intensities required
for optimal clinical results are poorly understood, especially in relation to range of motion
after intervention. Therefore, evaluating the intensity of LTRs is relevant for implementing
DN in different clinical settings, such as in patients with muscular or neurological
dysfunctions. One way to assess muscle contractions and LTRs is through ultrasound video
analysis.
The aim of this report was to evaluate the reliability of semi-automatic LTR intensities
tracking during dry needling by assessing ultrasound signals with an image processing method,
in addition to evaluating the relationship between LTR intensities and muscle elasticity.
Method: Young males without signs or symptoms of musculoskeletal pain were included. Primary
outcome measure was the LTR intensities determined by percentage change of muscle thickness.
The secondary outcome measures were the muscle elasticity of medial gastrocnemius and
hamstring, previous and posterior DN in both lower limbs for each subject. Three LTRs,
detected by a physical therapist and ultrasound assessment, were induced by inserting a
filament needle into muscle latent trigger points in medial gastrocnemius. The intensities of
LTRs were measured by assessing ultrasound images using an optical flow method and through
comparisons with manual detection.
Description:
The aim of this report was to evaluate the reliability of semi-automatically tracking local
twitch responses (LTR) intensities during dry needling by assessing ultrasound signals with
an optical flow method designed to estimate movement of ROIs, in addition to evaluating the
relationship between LTR intensities and muscle elasticity.
Experimental protocol and measurements For each subject, the ROM of the medial gastrocnemius
(MG) and hamstring (HA) was determined in both lower limbs using weight bearing and the
active knee extension test, respectively, using a universal goniometer. For the knee
extension test, 180° were subtracted from the obtained range, and the values were presented
as negative. Both tests present excellent intra- and inter-rater reliability.
All participants were placed in a prone position with the ankle locked at 90º. Latent MTrPs
were identified in both MG using according the presence of a palpable taut band and
hypersensitive spot in the palpable taut band as diagnostic criteria. The ultrasound
transducer was positioned over the MG muscle using a fixing device that was composed of a
thermoplastic polymer, with enough space for the dry needling (DN) technique to be performed.
The deep DN was administered by a physical therapist with three years of experience in the
technique. The ultrasound video (SonoSite Titan; Sonosite, Bothell, WA, USA) was recorded at
60 frames per second captured through an external capture device from Epiphan Systems Inc.
(Ottawa, Ontario, Canada). According to the perception of the physical therapist and the
ultrasound assessment, the filament needle was inserted into the MTrP to achieve three LTRs.
Posterior to DN application, the ROMs of the MG and HA were measured.
The recorded ultrasound video was examined by the Camtasia software (Tech-Smith Corp, Okemos,
MI, USA) to select three LTRs for latter analysis. Each LTR was tracked by the Lucas-Kanade
optical flow (LKOF) algorithm with affine optic flow extension [12]. All analyses were
performed using the Matlab® 2014 software (Mathworks Inc., Natick, MA, USA). For tracking
muscle thickness, two ROIs (size 250 x 70 pixels) positioned on the superficial and deep
aponeurosis were manually selected.
Muscle thickness was normalized as a percentage of recruitment using the following equation:
(thickness contracted-thickness rest/thickness rest)*100. For manual tracking of LTRs, the
recorded ultrasound video was analyzed in the Matlab® 2014 software. Peak muscle thickness
was determined by selecting muscle borders using hyperechoic aponeurosis lines.