Sensory-Motor Mechanisms Underlying Fall Risk in Transtibial Amputees
Conditions: Amputation, Elderly
Sponsor: VA Office of Research and Development

 
 
 
 
 
 
 
 
Sensory-Motor Mechanisms Underlying Fall Risk in Transtibial Amputees 
 
NCT03670004 
 
November 1, 2021 
 
 
 
 

 
Study Protocol 
 
Prior to any involvement in the study, a study investigator read the VA-approved 
consent forms in full to the potential subject. Having been read the consent forms and 
provided with an explanation of the experimental tasks, the potential subject was 
provided an opportunity to ask questions. Following this, if they agreed to participate, 
they signed a consent form. Each prosthesis user subject was assessed using their 
clinically prescribed prosthesis (i.e., socket, suspension system, pylon, and foot). 
 
Following demographic information data collection, retro-reflective markers were 
attached to the subject with hypoallergenic adhesive tape to anatomical landmarks as 
specified in the modified Helen Hayes full-body marker set with additional markers placed 
on the right and left acromion processes, lateral epicondyle of the humerus, and between 
the styloid process of the radius and ulna. Position of the markers on the prosthetic limb 
approximated matched those on the contralateral side. Additionally, surface electrodes 
for measurement of electromyography (EMG), or muscle activity, were placed and 
secured over the following muscles: tibialis anterior of the sound limb (or both limbs in 
case of control subjects), medial gastrocnemius of the sound limb (or both limbs in case 
of control subjects), rectus femoris of both limbs, medial hamstring of both limbs, and 
gluteus medius of both limbs.  
 
Subjects were requested to perform three biomechanical tasks (randomized prior to their 
visit) at the JBVAMC Motion Analysis Research Laboratory: 
 
1. Quiet standing with comfortable stance width and eyes open, 
2. Quiet standing with comfortable stance width and blindfolded, and 
3. Walking at two self-selected speeds (normal and fast) across a level walkway. 
 
Walking Data Collection—During the walking trials, subjects walked at a comfortable, 
normal, self-selected speed down the center of a 10 meter level walkway. The subjects 
were requested to walk back and forth along this walkway as many times as necessary 
to produce 10 clean foot strikes (i.e., each foot within the boundaries of at least one force 
plate). After walking at their normal speed, subjects were then requested to perform the 
same walking task while at their self-selected “fast” speed, defined as “a speed as if you 
were hurrying for a bus.” 
 
Quiet Standing Data Collection—During standing trials, subjects stood quietly with a 
comfortable stance width, as determined during an initial practice trial, with each foot on 
one adjacent force plate. This allowed recording of the plantar center of pressure (CoP) 
under both limbs by each force plate. The stance position was marked by tracing the feet 
on the floor. The subject was asked to replicate this stance position for all subsequent 
trials. During the eyes open condition, subjects were instructed to focus on a visual target 
mounted at eye level on the wall 5 meters in front. During the eyes closed standing 
condition, a blindfold was worn by the subjects to remove all visual cues. For each task, 

data were recorded for 30 seconds. Three trials of both the eyes open and closed 
conditions will be performed. 
 
Self-selected walking speeds and stance width were tested to better reflect real-life 
conditions and facilitate the subjects’ comfort during testing. Subjects were provided with 
seated rest between the three tasks to successfully carry out all walking and standing 
trials. 
 
Prospective fall data collection—Prospective data on falls were collected monthly over 
a 12 month period following their second visit using a validated, rigorous ‘falls calendar’ 
approach. Subjects were provided with twelve (prepaid postage) postcards that consist 
of a falls calendar. Subjects were instructed on how to use the postcards following 
established guidelines and post them to the primary investigator directly at the JBVAMC 
Motion Analysis Research Laboratory every month following the data collection session. 
This postcard was mailed with no return address to ensure confidentiality. The identity of 
each subject was determined by their unique subject code, which was written on the 
postcards before distribution and known only to the research team. Following receipt of a 
postcard that reported one or more falls during the month data collection period, the 
subject was contacted via telephone or email and asked standardized questions 
regarding the location and activity engaged in preceding the fall, what they felt caused 
the fall, and if injuries were sustained. Subjects were contacted via post, telephone or 
email if a postcard is not returned within 7 days after each month period and/or to query 
unclear entries.  
 
Data Analysis—Kinematic data were filtered with a low-pass Butterworth filter with a 6Hz 
cut-off frequency and smoothed in post-processing software. Raw EMG signals were 
filtered and processed using custom Matlab software. EMG data were normalized to gait 
cycle time (initial contact to ipsilateral initial contact). To quantify differences in muscle 
activity during the gait cycle, the integrated EMG (iEMG) for each muscle was calculated 
as the integral of the EMG profile over time of four regions: 1) braking phase (0-50% of 
stance), 2) propulsive phase (50-100% of stance), 3) swing phase, and 4) over the entire 
gait cycle. The iEMG of each subject was normalized by their average iEMG magnitude 
over the entire gait cycle. Plantar CoP of each limb in the coronal plane (elliptical area) 
were derived. 
 
Statistical Analysis Plan 
 
The main effect of amputation and limb side on kinematic variables will be assessed using 
a linear mixed model analysis for each walking speed separately. The main effect of 
amputation and limb side on standing balance variables will be assessed using a linear 
mixed model analysis for each vision condition separately. The main effect of amputation 
and walking speed on EMG variables will be assessed using a linear mixed model 
analysis for each muscle group separately. The total number of falls for each group will 
be calculated for each group.