Personalized Mobility Interventions Using Smart Sensor Resources for Lower-Limb Prosthesis Users
Phase: NA
Conditions: Amputation
Sponsor: Shirley Ryan AbilityLab

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PROTOCOL TITLE:  Personalized Mobility Interventions using Smart Sensor Resources for 
Lower-Limb Prosthesis Users
PRINCIPAL INVESTIGATOR:
Arun Jayaraman PT, PhD
Max Nader Center for Rehabilitation Technologies and Outcomes, Center for Bionic Medicine
312-238-6875
a-jayaraman@northwestern.edu
VERSION NUMBER:
1
VERSION DATE:
9/28/18
STUDY SUMMARY:
Investigational Agent(s) 
(Drugs or Devices)
None
IND / IDE / HDE # None
Indicate
Special Population(s)
Children 
 Children who are wards of the state 
 Adults Unable to Consent  
 Cognitively Impaired Adults 
 Neonates of Uncertain Viability 
 Pregnant Women 
 Prisoners (or other detained/paroled individuals) 
 Students/Employees 
Sample Size
125
Funding Source
Department of Defense, Defense Health Program, 
Congressionally Directed Medical Research Programs 
Orthotics and Prosthetics Outcomes Research Program.
Indicate the type of consent 
to be obtained
Written 
Verbal/Waiver of Documentation of Informed Consent
Waiver of HIPAA Authorization 
Waiver/Alteration of Consent Process  
Site
 Lead Site (For A Multiple Site Research Study)
 Data Coordinating Center (DCC)
Research Related 
Radiation Exposure
Yes 
 No 
DSMB / DMC / IDMC
Yes 
No
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OBJECTIVES:
Aim 1: Determine whether a participant’s prosthesis use matches the assigned K-level and/or 
self-reported goals and, if not, determine the reason(s) using an expert panel to evaluate data 
from performance-related measures, participant-reported measures, and smartphone and 
prosthesis sensors (clinical toolbox.)
We will provide participants with a smartphone with the CIMON app and an unlimited 
data plan. We will gather three months of data from the smartphone sensors and from 
mc10 sensors placed on the participant’s primary prosthesis and use machine-learning 
and data mining to establish baseline levels of prosthesis utilization, activity level, 
community mobility, and social interaction. 
After three months, we will administer a set of standard participant-reported 
assessments, and a structured personal interview, and obtain K-level determination from 
medical records and/or prosthetic components. Clinicians and researchers from all three 
sites will form an expert panel to evaluate this comprehensive dataset, or clinical toolbox 
and determine whether each participant is achieving expected levels of community 
activity and meeting desired mobility goals.
Aim 2: Quantify the effects of targeted physical intervention (prosthesis repair/refit, physical 
rehabilitation) or psychological intervention (motivational interviewing) or both on activity levels 
and patient goals. 
Only individuals not achieving expected and desired mobility levels, based on the expert 
panel evaluation, will participate in Aim 2. 
We will analyze data collected in Aim 1 to determine factors limiting prosthesis use, and 
we will provide a targeted intervention for three months—to address physical issues 
(pain, poor prosthesis/socket fit repair, inadequate training, deconditioning), 
psychological issues (e.g., ambivalence, depression, fear of falling), or a combination of 
physical and psychological issues. 
We will collect sensor data during the intervention, and the same toolbox of analyzed 
sensor data, standard assessments and participant-reported measures will be used to 
evaluate activity, psycho-social status, and prosthesis use. Individuals who experience 
an improvement in prosthesis use or goal achievement compared to baseline will be 
monitored for an additional three-month follow-up period with no intervention, and the 
same clinical toolbox of data will be gathered and used to assess maintenance of 
intervention benefits. Clinical toolbox data will be evaluated by the expert panel, after the 
intervention and follow-up periods, as described in Aim 1.
Aim 3: Identify measure(s) that sensitively predict prosthesis use to create a clinically 
deployable toolkit to evaluate and optimize prosthesis use in the community.
We will use data-mining and machine learning techniques to identify which standard 
assessments, participant-reported measures, or sensor data from our clinical toolbox 
most sensitively and accurately predict actual community mobility levels. This subset of 
measures can be deployed as a clinical toolkit to enable more accurate prediction and 
assessment of prosthesis use at home and in the community. 
Although out of the scope of this study, in the event that the smartphone data provides 
the most predictive information, we will develop procedures to deploy CIMON on both 
iOS and Android operating systems for use by military, VA, and civilian clinics. App 
development at the Max Nader Center is currently supported by philanthropy and 
partners at Northwestern University’s Center for Behavior Intervention Technologies.
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BACKGROUND:
Amputation of a limb is one of the most debilitating combat-related injuries. More than 1200 
service men and women have sustained over 1700 amputations in recent and ongoing overseas 
conflicts [1-3]. Furthermore, 1.8 million civilians in the United States live with amputations, with 
an additional 185,000 new amputations occurring every year [4]. The majority of all 
amputations, both in military and civilian populations, occur in the lower limb. Recent statistics 
indicate that 41.8% of amputations due to recent and ongoing conflicts occurred at the 
transtibial (below knee) level, and 34.5% at the transfemoral (above knee) level [2]. Whereas 
service men and women who undergo combat-related amputation are typically young [5], fit, 
and active, nearly 90% of all civilian lower-limb amputations occur in older adults and are due to 
vascular disease, frequently a complication of diabetes. This population is predicted to increase 
[4]. The US Veteran population includes individuals with combat-related amputations from past 
and recent conflicts as well as older individuals, one in four of whom have a diagnosis of 
diabetes and thus a high risk of dysvascular amputation [6, 7].
Loss of the lower limb causes profound disability, limiting mobility, independence, and ability to 
pursue employment or leisure activities. The rehabilitation goal for individuals with amputations 
is to enable reintegration into society, employment, independent living, economic and social 
self-sufficiency—and for Service Members, a return to active duty and redeployment, where 
possible. Individual mobility goals may include performing leisure activities or the ability to 
attend valued social events. Non-use or reduced use of a prosthetic may limit achievement of 
these goals and thus lower quality of life. In addition, reduced or improper use of prosthesis may 
result in overuse injuries of the intact limb, or secondary injuries due to compensatory body 
movements. Especially for older individuals, use of prosthesis may enable independent living 
and community participation and prevent social isolation or further physical decline.
Although a prosthesis is the most effective treatment for limb loss, a substantial number of 
persons with amputations either do not use their prescribed prosthesis or do not use it to the 
extent expected based on their clinically predicted ability level, or Medicare Functional 
Classification Level (MFCL) (K-level), the rating system used by VA, military, and civilian 
organizations to determine eligibility for prosthetic components. Even with excellent treatment 
and exceptional recovery, many otherwise healthy Service Members with lower limb 
amputations experience difficulties in using a prosthesis [8], which limits their ability to 
participate in the community or pursue employment, including redeployment [9, 10]. As of 
December, 2017, only 25% of service members with major lower limb amputations returned to 
active duty, only 8% were redeployed [3], and only 26% were able to perform other high-level 
activities [11]. Expensive, state-of-the-art microprocessor-controlled or powered prostheses are 
often abandoned or not used to their full potential [12]. While documented rates of lower-limb 
prosthesis use vary between 49%-95% [13, 14] with an average of 7.0 to 11.5 hours use per 
day [15, 16], wearing a prosthesis does not correlate with activity. Step-counts for individuals 
with lower limb amputation are considerably lower than the recommended 10,000 steps/day or 
the average of 7,000-13,000 steps per day recorded for able-bodied individuals [17, 18]. Low 
activity levels reduce quality of life, and likely result in secondary health problems or compound 
existing comorbidities over the long term [19].
Many issues may limit full use of a prosthesis. Physical issues include phantom limb or residual 
limb pain, discomfort due to misfit of the prosthesis [8]; poor, ineffective, or inappropriate 
prosthetic training or physical rehabilitation; and physical deconditioning. Issues such as 
amputation level and etiology, comorbidities, age, or gender [20]; reinforcing factors (e.g., 
physical and social benefits of prosthesis use); and enabling factors [21] (e.g., ability to don the 
prosthesis, physical ability to walk, and use of other mobility aids [8]) affect device use. Higher 
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amputation levels, bilateral amputations, coronary artery disease, and residence in a nursing 
home all correlate with prosthesis non-use [22, 23], whereas the ability to perform activities such 
as walking up or down ramps, adequate dynamic standing balance, and comfort increase 
prosthesis use [24]. For military personnel and Veterans, other combat-related injuries, such as 
traumatic brain injury or multiple amputations, may affect prosthesis use. The increased energy 
requirements of walking with a prosthesis may also limit usage, particularly for older individuals: 
transfemoral amputees expend up to 60% more energy while walking than able-bodied persons 
[25], and the relative metabolic cost of other activities, such as sit-to-stand transitions, is even 
higher. Psychosocial issues include stigma associated with disability, non-acceptance of the 
amputation, lack of motivation or reluctance to use a prosthesis, depression, and social isolation 
resulting from an inability to resume former activities [26, 27]. Optimizing prosthesis use 
requires a quantitative understanding of multiple interacting physical and psychological pre-
disposing and limiting factors.
Classification systems (such as the Healthcare Common Procedure Coding System, HCPCS, 
[28]) focus solely on the patient’s functional abilities. Conventional methods of assessing 
physical mobility and daily activities of amputees include self-report (e.g., the Prosthesis 
Evaluation Questionnaire (PEQ) [29] or the Orthotics and Prosthetics Users Survey (OPUS) 
[30]) or performance measures (e.g., the six-minute walk test, the Functional Ambulation Profile 
(FAP) [31], and the Timed Up and Go (TUG) test [32]. However, testing within the clinic at best 
provides a snap-shot of potential mobility at a single time point and may be a poor 
representation of real-world prosthesis usage. Participant-reported questionnaires and surveys 
are subject to recall bias and subjective definitions of prosthesis use, and thus have poor 
accuracy and reliability [17]. For Service Members with high levels of physical fitness prior to 
injury, standard assessments are not sufficient to measure their return to previous ability levels 
due to ceiling effects [33]. Thus, not only do physical, social, and psychological factors limit 
prosthesis use [34], but clinicians lack quantitative methods to measure the effects of these 
factors so that they can tailor effective therapeutic intervention. While several studies have 
identified predictors of successful adjustments to amputation and prosthesis use, few studies 
have focused on identifying the prevalence of risk factors for reduced prosthesis use or 
evaluated the effect of interventions [23, 35-37],  and very few have focused on prosthesis use 
in the community. Comprehensive quantification of real-world prosthesis use is a pressing issue 
with implications for effective rehabilitation strategies and prosthesis design.
Medicare Functional Classification Levels (MFCL), or K-levels, were established in 1995 by the 
US Centers for Medicare and Medicaid Services (CMS) to quantify mobility and potential benefit 
from prosthetic devices after lower limb loss. They have since been the designated industry 
standard measure of expected prosthesis use since. K-levels are assigned by physicians, based 
on historical health information including previous activity level, and current medical condition—
including status of the residual limb and other co-morbidities; and the individual’s goals. Use of 
standard assessments reduces subjectivity and increases accuracy [38]. The system is a rating 
from 0 through 4 and it indicates a person’s potential to use a prosthetic device if they had a 
device that worked well for them and they completed rehabilitation to use the device properly. K-
level assessments provide a basis for prescription and insurance coverage of prosthetic 
components: appropriate lower limb prosthesis is covered when the beneficiary will (I) 
reasonably maintain or reach the defined functional state within a reasonable period of time and 
(ii) is motivated to ambulate. Higher K-level designation allows provision of more technologically 
advanced and expensive components to allow users to achieve their ambulatory potential. 
However, K-levels determined by in-clinic assessments are subjective and may not reflect real 
world community mobility, i.e., K-level may represent capability or potential but not actual 
behavior [39]—elderly individuals, stroke survivors, and amputees who demonstrate relatively 
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high levels of ability during in-clinic assessments do not demonstrate expected levels of mobility 
in the community [40, 41]. While some standard assessments are correlated with K-level 
designations [42], and clinical measures may be used to predict prosthesis non-use [43], little 
information exists on how clinical measures and K-level designation correlate with community 
mobility levels.
Prosthesis use can be measured quantitatively using smart phones and advanced sensor 
technologies. Activity monitors, typically accelerometers, are routinely used to assess mobility. 
Various commercial triaxial accelerometers (e.g., Actigraph LLC activity monitors, Fitbit®, or 
Nike + Fuelband) [44-46] have been used to count steps at home and in the community. 
However, these devices use proprietary algorithms trained on data from healthy individuals, 
which can under- or over-predict performance in individuals with amputation who have different 
acceleration and movement profiles, leading to inaccurate step counts. A major limitation of 
these step-monitors is that they simply count steps without taking into consideration where the 
steps were taken—such as inside or outside the home, or participation in social settings, such 
as theatres or houses of worship. These devices also do not discriminate, for example, between 
the use of public transport compared to a disability services van, which may reflect an 
individual’s ability to handle crowds or to move in locations without ramps and other accessibility 
services. The limited knowledge that can be derived from activity monitors is also compromised 
as these devices provide minimal benefit to the user and thus have a high risk of non-
compliance or inconsistent use.
Smartphones, which are now carried by most adults, contain a variety of built-in sensors—
including accelerometers, gyroscopes, physiological sensors, and Global-positioning Systems 
(GPS). Smartphones thus enable multi-modal sensing of physical, social, and psychological 
aspects of behavior, including mobility and physio-psychosocial status. In addition, smartphones 
provide phone and internet service to the user, which are built-in incentives to carry and use the 
device.
Building on prior work on the use and programming of smartphones by the MIT Reality Mining 
group [47-50], our group has built extensive expertise in machine learning and data mining, 
primarily focusing on multi-modal assessment of an individual’s activities using smartphone-
based sensors. We have designed and developed a centralized, robust, and configurable 
custom monitoring app—Configurable Integrated MONitoring Toolkit (CIMON) (Fig. 1), which 
allows download of de-identified phone sensor data to a Health Insurance Portability and 
Accountability Act of 1996 (HIPAA)-compliant, secure server, allowing us to mine the data for 
information about activity level and factors that enhance or limit mobility. CIMON provides 
centralized monitoring in a globally efficient system that minimizes redundancy, providing an 
easy-to-use application programming interface that supports a robust and configurable 
monitoring service. Monitoring requests may be made in several forms: (1) instantaneous 
requests provide an instant value for a desired metric; (2) periodic requests allow applications to 
receive periodic updates on the value of a metric, and (3) event notification requests allow 
notification when a conditional event, such as a fall, occurs. CIMON seamlessly integrates the 
monitoring requests of all applications using the minimum total resources and is lightweight and 
computationally efficient. A detailed description of the architecture and components of the 
efficiency engine handling the integration of sensing requests on a smartphone is available [50, 
51]. CIMON passively collects sensor data, requiring little input from the user other than 
charging the phone and initial labeling of activities.
Smartphone-based and wearable sensor monitoring systems provide non-invasive, quantitative 
measures of activity and behavior outside of the clinic. Data are acquired in real time and thus 
are accurate and unbiased. Furthermore, we can call the participant on the smartphone to 
validate the data. The strength of this approach is the use of multiple sensors on the person and 
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Fig. 1: Left: Tool kit for data collection and analysis (at the 
server). Above: Available smartphone sensors used in the 
CIMON app.
prosthesis to simultaneously obtain relevant data on many aspects of the participant’s 
experience using their prosthesis. By mining data from a variety of sensors and using machine- 
learning techniques to identify relevant features for comparison with measured prosthesis use, 
we will obtain a detailed picture of prosthesis use in the community and a comprehensive 
understanding of factors that adversely affect prosthesis use—at an individual level.
An expert panel will then evaluate all data to quantify prosthesis use. Standard performance 
measures and patient-reported measures predict community mobility of individuals with lower 
limb amputations with varying levels of accuracy. Assessments such as the Amputee Mobility 
Predictor (AMP [42]), Comprehensive high-activity mobility predictor (CHAMP [52]), 2-minute 
walk test, 5-time sit-to-stand, and PEQ may predict an individual’s ability to ambulate with a 
prosthesis [42, 53]. However, none of these measures on their own can predict actual 
prosthesis use in the home and community or achievement of personal mobility and social 
interaction goals. Using smartphone and prosthesis-worn sensors with CIMON, together with 
data mining and machine-learning techniques, we can sensitively and continuously evaluate the 
physical, social, and psychological state of an individual with lower limb amputation. Combining 
information from smartphone sensors with standard assessments and participant feedback will 
generate a unique, rich, multi-layer and multi-factorial clinical toolbox, providing information 
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about daily mobility, 
prosthesis use, and the 
contributing factors that 
enhance or limit 
mobility. To evaluate 
this comprehensive 
dataset and to reach a 
univocal decision about 
each participant’s 
activity level, we will 
utilize an expert panel 
of researchers and 
clinicians from the three 
participating sites: 
Walter Reed National 
Military Medical Center 
(WRNMMC), 
Minneapolis Veterans 
Administration Health Care System (MVAHCS), and The Shirley Ryan AbilityLab (SRALab, 
formerly the Rehabilitation Institute of Chicago), representing Military, Veteran, and civilian 
rehabilitation centers, respectively, who will review clinical toolbox information for each 
participant to determine whether prosthesis use is as expected based on K-level designation 
and stated mobility goals. Participants will be rated as matching their expected or desired 
activity level or not (i.e., a binary Yes or No decision), using a blinded two-step procedure. 
Combining expert opinion, outcomes data, and sensor information is the most accurate, 
consistent, and clinically implementable approach to cohesively combine multi-factorial datasets 
to determine prosthesis use in the community.
Many clinical investigators have developed interventional strategies to promote positive behavior. 
Motivational interviewing (MI) is an evidence-based, participant-centered method to enhance and 
strengthen intrinsic motivation to change behavior. MI has been used successfully in persons with 
physical disabilities—including multiple sclerosis, spinal cord injury, stroke, and arthritis—to 
improve patient motivation and adherence to treatment; in the elderly to increase levels of physical 
activity [54-57]; and has been suggested as an approach to improve prosthetic usage [58]. The 
goal of MI is to guide an individual toward behavior change, by asking questions that allow 
participants to explore their reasons for making a change towards a goal and to explore and 
overcome reluctance or ambivalence toward achieving those goals, taking into account their level 
of motivation. MI methods help establish a collaborative partnership between a coach and the 
participant that fosters respectful evocation of the participant’s perspective and autonomy to make 
decisions regarding their health.
We will provide individualized, tailored counseling sessions using MI techniques, based on 
practice guidelines specific to each study site, focused on motivation and goal setting for 
increased use of the participant’s prosthesis in the home and community. The coach will 
encourage the participant to weigh all aspects of making and committing to behavior change and 
to take a leading role in problem solving. The participant will be asked to set personal goals that 
are relevant, important, and achievable using a semi-structured interview that explores facilitators 
and barriers to prosthesis use. Short-term goals—which could include increasing social 
interactions, managing residual limb pain, and reducing depressive symptoms—will be 
established to support the overarching long-term goal of increasing prosthesis use.
Fig. 3: Experimental Protocol, starting from a smart phone plan to data 
monitoring, prediction models, interventions and re-monitoring

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We propose a clinical trial to identify individuals who do not achieve the level of community 
mobility predicted by K-level and/or are not achieving personal mobility goals, identify the 
barriers to prosthesis use, and evaluate targeted interventions to improve prosthesis use and 
facilitate achievement of participants’ goals (Fig.3).
We will characterize prosthetic device use based on sensor data for stepping activity, physical 
activity levels, and activity types, and GPS locations. We will use machine-learning methods 
and multivariate regression analysis to predict daily mobility with the prosthesis using measures 
of body structure and function, activity limitations, and demographic characteristics. We will use 
this data, together with standard assessments and participant-reported measures, to identify 
what factors or characteristics predict level of prosthesis use. For example, based on 
preliminary data, we expect an average daily stepping activity of 1150 for K2, 1450 for K3, 2550 
for K4. For non-injured people less than 2500 steps/day is considered sedentary, 5000-7499 
daily steps are considered low activity, 7500-9999 as moderately active, and above 10,000 as 
active. GPS data from the smartphone will be analyzed to obtain descriptive statistics of 
community mobility during the 3-month monitoring period. Daily measures of community mobility 
will measure activities similar to those used in previous studies [66, 73] including:
• Time spent stationary vs. Time spent in transit: Movement speed will be estimated from GPS 
data using changes in distances over time. A threshold speed will be defined to determine 
whether the participant is generally stationary (e.g. eating at a restaurant) or in transit (e.g. 
walking or riding in a vehicle). The percentage of time spent in a stationary or transit state will 
be computed.
• Number of locations visited: A distance-based clustering algorithm, K-means clustering [74], 
will be used to identify the number of places participants visit. This algorithm will be applied to 
data in the stationary state.
• Location variance: Variability in GPS location determined from standard deviation of latitude 
and longitude
• Entropy: Variability of time spent at different location clusters.
• Time spent at home: Percentage of time spent at the location cluster representing the 
participant’s home.
• Total distance traveled: Cumulative distance traveled, determined from the distances 
between location data.
Expected Outcomes: Our phone applications will accurately detect and quantify home and 
community mobility in real time. We will be able to verify data by calling the subject or their 
immediate family. We expect that we will collect sufficient data over the course of the project to 
allow data mining and activity prediction at an individual level. We expect that the expert clinical 
panel will make consistent decisions on participants’ ability levels based on all collected data, 
and that this clinical toolbox will provide valuable information on prosthesis usage and reasons 
for lower-than-expected usage. 
STUDY ENDPOINTS:
Following consent, subject will be given a battery of baseline assessments and self-reported 
measures. They will be assigned a cellular device and charger, and a sensor will be placed on 
the most frequently used prosthesis. Participants will carry these devices on their person and 
data will be collected via these devices for a 3-month period (Aim 1). At the end of this period 
participants will return for post-study testing mirroring the original baseline assessments. 
Subjects will return to Shirley Ryan AbilityLab or study site if they run into any issues with use of 
these devices that we cannot troubleshoot via phone call. 
If the participants qualify for the second leg of the study (Aim 2) they will be assigned either 
physical or psychologic therapy or both for an additional 3 months, with a maximum of 18-24 
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Table 1: Performance Measures and Participant-Reported Measures
Performance-based measures:
• 10 meter walk test (self-selected velocity and fast velocity)
• Six-minute walk test
• 5-times sit to stand
• 4-square step test
• Berg balance test
• Dynamic Gait Index
• Functional Gait Assessment
• Comprehensive high-activity mobility predictor (CHAMP) 
Participant-Reported Measures:
• Orthotics Prosthetics User Survey (OPUS)
• Amputee Mobility Predictor (AMP)
• Modified Falls Efficacy Scale (MFES)
• Prosthesis Evaluation Questionnaire (PEQ)
• Prosthetic limb user survey of mobility PLUS-M
• Patent Reported Outcomes Measurement System (PROMIS)
o PROMIS Bank v1.0 – Physical Function for Samples with 
Mobility Aid Users
o PROMIS Bank v1.2 – Mobility
o PROMIS Item Bank v1.0 – Depression
o PROMIS Item Bank v2.0 – Satisfaction with Social Roles 
and Activities 
o PROMIS Item Bank v2.0 – Ability to Participate in Social 
Roles and Activities
• Community Participation Indicators (CPI)
sessions as deemed appropriate. Data will also be recorded via assigned cellular device during 
this period. Outcome measure will again be repeated upon completion of study training. 
Participants will again be dispatched with cellular devices for a period of 3 months and will 
return to the study site to repeat outcome measures and return assigned devices. 
PROCEDURES INVOLVED:
Subjects will be recruited from the amputee registry and the Prosthetics & Orthotics Clinic. 
Those who agree to participate will be asked to come to the Shirley Ryan AbilityLab 
Rehabilitation Technologies & Outcomes Lab.  
A research assistant will conduct a structured interview with each participant to identify 
satisfaction with the prostheses, expectation of use, and goals for community mobility and social 
interaction. A transcript of the interview will be provided to the expert panel for inclusion in their 
review process. The interview format will be based on the PI’s previous study on creating ICF 
model-based tool to measure mobility in individuals with lower limb amputations [70]
We will use a battery of standard assessment and participant-reported measures that assess 
physical function, mobility, social activities, and depression (Table 1). All measures will be 
available online on REDCap (Research Electronic Data Capture), a secure, web-based 
application for building and managing online data capture for research studies. REDCap 
provides a central location for data collection and storage and allows design and management 
of standardized forms for data collection and easy access from all study sites, facilitating review 
by expert panel members.
Smartphone and mc10 sensors 
will provide accelerometer, 
gyroscope, barometer, 
temperature, and GPS data, 
which we will analyze using 
machine-learning algorithms 
previously created to compute 
prosthesis use, functional 
status, mobility, activity levels, 
community mobility, social 
interactions, and emotional 
status i.e. depression and 
satisfaction with life[59, 71]. 
Subjects will carry a 
smartphone, e.g., Samsung 
Galaxy S4 running Android OS 
4.4.4 with the associated 
Hyperion 3500 mAh extended 
battery. These phones are 
equipped with up to 2.75 GHz 
CPUs and a sampling rate of 
over 100Hz. We have used 
these devices at the SRALab 
for multi-week clinical 
assessments of activity levels 
and movements in over 50 
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individuals with impaired mobility and for pilot data collection for this proposal. The extended 
internal battery life provided consistent cellular network connectivity and continuous 
accelerometer recordings at a 100 Hz variable sampling rate. Intensive continuous sensing on 
mobile devices imposes energy constraints: continuous data collection can deplete the battery 
within a few hours, which makes comprehensive context sensing infeasible. Therefore, we will 
use an adaptive sensing approach that adjusts sensing types, rates, and other parameters to 
ensure satisfactory battery life while providing complete and accurate context information. We 
will place mc10 sensors on participants’ primary prostheses to obtain accurate data on device 
use during all measured activities. 
We will ask patients to carry their phones in their pocket or on a dedicated belt, pocket, or 
handbag to maximize our ability to detect activities; we will automate analysis to handle 
recordings from phones carried in nonstandard locations. In a study including able-bodied 
subjects wearing a phone on a belt, or in a pocket, hand, or bag/purse) while performing varied 
activities (sitting, standing, sit/stand transitions, and walking) we were able to predict how the 
phone was being carried with 91.6% accuracy [72]. Orientation-independent features have been 
used to improve classification accuracy when the phone is worn in different ways. This method 
estimates the direction of gravity using the accelerometer signal and, in turn, the orientation of 
the phone, providing estimation of the vertical and horizontal components of the user’s motion, 
which can be used to calculate orientation independent features. We will improve accuracy by 
instructing participants to calibrate the phone by performing a fixed set of activities in the lab 
and at home (e.g., walking, sit-to-stand transitions). Labeling an activity involves removing the 
phone from its pouch, selecting an activity label from a dropdown menu on the user interface, 
replacing the phone in the pouch, and performing the activity.  We will incorporate each 
subject’s preliminary labeled data into our classifiers. False positives and negatives will be used 
to validate the classification and improve the algorithms.
Subjects activities of daily living will not be restricted in any way. After 3 months of data 
collection participants will be asked to return to the Shirley Ryan AbilityLab or study site to 
repeat a comprehensive set of performance-related measures, participant-reported measures 
(See Table 1), and structured interviews to get a snapshot of current prosthesis use, assess 
patient satisfaction with their level of mobility, and determine their goals for mobility and social 
activities. A complete medical history, radiological reports, physical assessments together with 
socio-economic information such as living in a house or apartment, suburb or city, employed or 
unemployed, level of family support, will be collected. We anticipate that this initial testing will 
require 2-4 hours. All information will be collected, together with analyzed sensor data, into a 
clinical toolbox.   
If prosthesis use is as expected, and the participant’s mobility goals are being achieved, they 
will leave the study. If prosthesis use is lower than predicted, or if patient goals are not being 
achieved, we will mine the data to identify reasons for reduced use: physical (prosthesis fit, 
pain, conditioning level, device training), psychological (lack of motivation, depression, etc.), or 
a combination of both. These participants will continue to Aim 2 for an additional 3 months.
Interventions will be provided during a three-month period (between T_0 and T_1, Fig. 7), while 
smartphone and sensor monitoring continues. After three months, we will repeat all standard 
assessments and participant-reported measures to create a clinical toolbox to determine the 
effects of the intervention (via expert panel as described for Aim 1). Subjects who improve 
prosthesis use as a result of the intervention will be monitored and assessed three months after 
the end of the intervention (at T_2) in the same way, to assess maintenance of mobility or goal 
attainment. 
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Clinicians will provide the intervention using existing clinical care models at each site to 
minimize disruption, comply with local regulations and processes, and ensure that the 
intervention is clinically implementable in these and similar settings. Clinicians may deliver 
interventions in person or remotely, via satellite sites, outpatient centers, or through 
telemedicine, to minimize cost and disruption and enhance convenience for participants. 
— At the VA site, participants will be assessed by the MVAHCS regional amputation center 
(RAC) clinicians, either at in-person or via a telehealth clinic session. A physiatrist, a 
physical therapist, and a prosthetist will be present at the assessment. The RAC 
clinicians will co-treat and provide follow-up care for any physical interventions. For a 
psychosocial intervention, a VA psychologist will be consulted per standard procedures.
— At the DoD site, participants will be assessed by Advanced Rehabilitation Center 
clinicians, including a physiatrist, a physical therapist, and a prosthetist. The clinicians 
will co-treat and provide follow-up care for physical interventions. For psychosocial 
interventions, participants will be referred to a DoD psychologist.
— At SRALab, clinician interventions will follow standard practice guidelines based on 
Center for Medicare/Medicaid Services recommendations, which provide a maximum of 
18-24 physical rehabilitation sessions over 3 months. Counseling sessions will occur 
either in person or by telephone with a minimum of 13 sessions over 3 months. 
Participants will also meet with a physiatrist and prosthetist as needed. Interventions will 
be provided according to the needs of the individual; a typical timeline is described 
below. No cost will incur to participants for these interventions. 
Physical Intervention: Individuals identified as having physical issues (poor prosthesis fit, 
function, comfort or physical conditioning) will receive prosthetic care and physical therapy to 
address these issues. This includes adjusting socket fit, component fit and repair and in some 
instances replacement as per clinician recommendation and reimbursement situations. 
Rehabilitation strategies include gait and prosthesis use retraining, balance fall-prevention, and 
range-of-motion training, strengthening, pain management (phantom-limb pain, low back pain, 
other musculoskeletal pain), and activities of daily living training, based on participant-reported 
mobility goals.  
Individuals whose prosthesis use is reduced due to psychological issues will be provided with 
MI and other related psychological interventions. This intervention will begin with an individual 
face-to-face counseling session lasting approximately 30-45 minutes, the goals of which are to 
(1) establish a relationship between the coach (a person trained in motivational interviewing) 
and the participant; (2) perform a systematic assessment of factors that may influence wear of 
the participant’s prosthesis; and (3) establish an individual action plan based on the results of 
the systematic assessment. The participant will be asked to set personal goals that he/she finds 
relevant, important, and achievable using a semi-structured interview that explores facilitators 
and barriers to wearing their prosthesis. Short-term goals will be established to support an 
overarching long-term goal, which could include increasing social interactions, managing 
residual limb pain, depression, etc. Together, the participant and coach negotiate the strategies 
and an action plan to help the participant achieve his/her self-identified goals to increase 
prosthesis wear time. The participant and coach determine a strategy to record the participant’s 
progress. Follow-up individual intervention contacts will occur by telephone or Skype, lasting 
approximately 10 minutes and will include an assessment of achievement of short-term goals, 
assessment of long-term goals and revision of short and/or long-term goals as needed. The 
coach can make referrals as necessary to other members of the team to give individualized 
guidance as needed. Typically, at SRALab, these sessions will occur at the start of the 
motivational interviewing intervention in person, and then weekly by telephone for 6 sessions, 
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once every two weeks for 3 sessions, and then monthly for 3 sessions for a total of 13 sessions 
over 3 months; however, timelines and frequency of sessions may vary depending on the 
individual participant’s needs. 
Combined Intervention: Individuals who are identified as having both physical and psychological 
issues will be provided with both interventions simultaneously.
At the end of three months’ patient will return to the Shirley Ryan AbilityLab or study site to 
repeat a comprehensive set of performance-related measures, participant-reported measures 
(See Table 1), and structured interviews to get a snapshot of current prosthesis use, assess 
patient satisfaction with their level of mobility, and determine their goals for mobility and social 
activities. A complete medical history, including physical findings and radiological reports 
together with socio-economic information will be repeated. We anticipate that this initial testing 
will require 2-4 hours. 
Participants that do not show improvement at the end of this period will terminate participation in 
this study and will be referred to other services as necessary. These participants will return all 
study equipment assigned to them at this point, including but not limited to cellular device, 
charger, and any sensors adhered to their prosthesis and in doing so complete participation of 
the study at this time.
Participants which show an improvement in activity relating to assigned K-levels or self-set 
goals will be asked to stay enrolled for an additional 3-month period between visits T1 and T2 
(Fig. 8). During this time cellular and sensor data collection will continue, no interventions will be 
provided. Participants may be contacted during this period via phone to perform wellness 
checks of labeling activities. 
At the end of this period participants will return to the Shirley Ryan AbilityLab (or study site) for 
visit T2 (Fig. 8) and repeat all outcome and patient-reported measures (Table 1). This visit will 
signal the end of patient participation, all equipment assigned to participants will be returned to 
study site. 
We will use the data collected to identify measure(s) that sensitively predict prosthesis use to 
create a clinically deployable toolkit to evaluate and optimize prosthesis use in the community. 
(Aim 3)
We will use data-mining and machine learning techniques to determine which standard 
assessments and participant-reported measures predict prosthesis use and community mobility, 
which measures are sensitive to changes in activity, and which measures correlate most 
strongly with K-level designation. 
Outcome measure scores, including performance-based and self-reported measures, and 
participant characteristics will be used as input features for machine learning models. These 
models will attempt to classify K-level (e.g. using random forests, support vector machines, or 
Naïve Bayes models) and estimate community mobility (e.g. using regularized regression such 
as LASSO - Least Absolute Shrinkage and Selection Operator, or Elastic Net models). 
Classification models will be evaluated for their accuracy, sensitivity, and specificity in predicting 
the K-level, as compared to the ground truth determined by the expert panel. Community 
mobility estimates will be evaluated using the root mean squared error (RMSE) and the variance 
explained (adjusted R2), as compared to the ground truth determined by analysis of the GPS 
data. We will consider both population models (which use all participants’ data to make 
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predictions about each individual) and personal models (which use a single participant’s data to 
make predictions for that individual). Models will be trained and cross-validated using the leave-
one-observation-out method. The classification and estimation models with the lowest errors will 
be used to determine the relative importance of the different input features. This will allow us to 
assess the outcome measures and participant characteristics that contribute most to K-level and 
community mobility.
We will use this information to create a clinical toolkit to predict function in the community. This 
information could also be used to predict which patients are likely to benefit from a specific 
intervention. We anticipate that different measures may be of value in different populations (VA 
vs. Military vs. Civilian) depending amputation etiology and level, participant age, or goals.
We will also interview clinicians on the expert panels at each site to determine whether the 
smartphone/sensor data provided valuable information. If so, future work could focus on 
development of a clinically implementable smartphone app. While outside the scope of this 
study, we have access to philanthropic funding and collaborators who have indicated that they 
would develop and maintain a clinically useful app: results from this study would emphasize the 
clinical need and benefits of this app. We would develop the app for both Android and iOS 
operating systems and it would be provided free of charge to military and civilian institutions.
Outcome Measures:
1.
10 Meter Walk Test (10MWT): Assesses subject walking speed in meters per second for 
10 meters. Subjects will repeat each measure 3 times at their normal self-selected 
walking speed and 3 times at a fast speed while still able to maintain safety. 
2.
Six-minute Walk Test (6MWT): The 6MWT is performed as an objective evaluation of 
functional exercise capacity. The 6MWT is easy to administer, well tolerated, and 
typically reflective of activities of daily living. The test measures the distance that the 
patient can walk on a flat, hard surface, indoors, in a period of 6 minutes. The walk test 
is patient self-paced and assesses the level of functional capacity. Patients are allowed 
to stop and rest during the test, however, the timer does not stop. If the patient is unable 
to complete the time, the time stopped is noted and reason for stopping prematurely is 
recorded. This test will be administered while wearing a mask to measure oxygen 
consumption.
3.
5-times Sit to Stand Test (5XSST): The 5 Time Sit to Stand Test is a measure of 
functional lower limb strength during transitional movement. The individual sits in a 
standard height chair (43-45 cm) and is instructed to stand up and sit down 5 times as 
quickly as possible. The individual is not allowed to use upper extremities to push off and 
is instructed to cross arms over chest if able to do so. The test is scored by measuring 
the time it takes to complete the 5 stands. If the patient is unable to complete all 5 
stands or requires assist greater than contact guard, the test is scored as a fail. 
4.
4-Square Step Test: Test of dynamic balance that clinically assesses the person’s ability 
to step over objects forward, sideways and backwards. The test takes less than 5 
minutes to administer and tests activity of daily living and motor skills.
5.
Berg Balance Scale: this is a standardized 14-item objective measure of postural 
stability and static balance; it is an established fall predictor in adults.
6.
Dynamic Gait Index (DGI): Assesses individual’s ability to modify balance while walking 
in the presence of external demands.
7.
“The FGA is a 10-item test for assessing postural stability during various walking tasks. It 
includes 7 of the 8 items from the original Dynamic Gait Index, and 3 new items, 
including “gait with narrow base of support,” “ambulating backwards,” and “gait with eyes 
closed.” The FGA demonstrates excellent concurrent validity with the Berg Balance 
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Scale for individuals with stroke. The maximum score is 30 points; minimal detectable 
change for chronic stroke is 4.2 points.”
8.
Comprehensive high-activity mobility predictor (CHAMP): Is a performance based 
assessment instrument to quantify high-level mobility in service members with traumatic 
lower limb loss.
Patient-Reported Measures: 
1.
Orthotics Prosthetics User Survey (OPUS): The OPUS is a self-report questionnaire 
which is designed to evaluate the outcome of orthotic and prosthetic services. We will 
administer three of the five domains: lower limb functional measure, health-related 
quality of life and satisfaction with device. 
2.
Amputee Mobility Predictor (AMP): A tool used to predict the ambulatory potential of 
lower limb amputees, and measure function post-rehabilitation. It was developed to 
provide a more objective approach to rating amputees under the various K 
Classifications. The test can be performed with or without the prosthesis. The test 
involves activities of transfers, balance and walking. 
3.
Modified Falls Efficacy Scale (mFES): The mFES is self-report questionnaire consisting 
of 14 items which is designed to measure fear of falling in the elderly. It assesses an 
individual’s perception of balance during activities of daily living by asking “how confident 
are you that you can do the following activities without falling.”
4.
Prosthesis Evaluation Questionnaire (PEQ): PEQ was developed specifically to provide 
functional outcome measures in prosthetics that are more tuned to prosthesis-related 
changes in quality of life. It is a self-report questionnaire containing 54 questions 
organized into nine functional domain scales. Each of the scales may be used 
individually to measure only a specific domain of interest. The PEQ has been used in a 
wide variety of studies and is validated.
5.
Prosthetic limb user survey of mobility (PLUS-M): The PLUS-M™ is a self-report 
instrument for measuring mobility of adults with lower limb amputation. It has been 
rigorously developed using modern psychometric methodology and is intended for use in 
clinical practice and research. PLUS-M™ instruments are based on a set of 44 
calibrated questions called an item bank. PLUS-M™ instruments measure prosthetic 
users’ mobility (i.e., the ability to move intentionally and independently from one place to 
another). PLUS-M™ questions assess respondents’ perceived ability to carry out actions 
that require use of both lower limbs, ranging from household ambulation to outdoor 
recreational activities. The described activities relate to two primary forms of movement, 
locomotion (i.e., movement in a continuous, repeatable pattern) and/or postural 
transitions (i.e., movement from one position to another or one type of activity to 
another).
6.
Patient Reported Outcomes Measurement System (PROMIS): is a set of person-
centered measures that evaluates and monitors physical, mental, and social health in 
adults and children. It can be used with the general population and with individuals living 
with chronic conditions. The following PROMIS assessments will be assessed:
a.
PROMIS Bank v1.0 – Physical Function for Samples with Mobility Aid Users
b.
PROMIS Bank v1.2 – Mobility
c.
PROMIS Item Bank v1.0 – Depression
d.
PROMIS Item Bank v2.0 – Satisfaction with Social Roles and Activities 
e.
PROMIS Item Bank v2.0 – Ability to Participate in Social Roles and Activities
7.
Community Participation Indicators (CPI): A self-reported measure that uses a scale of 
0-7 days per week to assess how often a subject participates in community activities.
A research staff will be present during all testing of subjects and the experiment will be promptly 
stopped should any signs and symptoms of discomfort be observed. Subjects will be allowed to 
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take rest breaks as needed during the testing and may voluntarily withdraw from the study at any 
time.  Finally, any adverse reactions that result from participation in this study will be documented 
by the principal investigator on the subject’s data file and reported in writing to Northwestern’s 
Office for Protection of Human Subjects.
Video recording and/or pictures of each participant using each prosthetic device may be taken 
during the training and testing sessions. These items may be used to help troubleshoot potential 
issues for fit or functionality of the device. They may also be used for presentations and training 
of other research personnel. Each Subject may choose to limit if/how these items may be used, 
as indicated during their Consent process.
DATA AND SPECIMEN BANKING
De-identified data will be stored indefinitely. Electronic data will be stored on secure, password 
protected computers and paper data will be stored in locked cabinets accessible only by 
authorized research personnel at the Shirley Ryan AbilityLab.
If participants give written consent to be contacted for future studies, this information will be kept 
separate from de-identified data files, in locked cabinets accessible only by authorized research 
personnel. All other information will be destroyed in accordance with HIPAA and IRB compliant 
guidelines.
SHARING RESULTS WITH PARTICIPANTS
Results of data collection will not be shared unless specifically requested by a participant. 
STUDY TIMELINES
Subjects will participate in Aim 1 of this study for 3 months.
If participants qualify for Aim 2 they will participate for an additional 6 months as details above. 
INCLUSION AND EXCLUSION CRITERIA
Participants will be contacted via the Amputee Registry and Orthotics & Prosthetics Clinics at 
the Shirley Ryan AbilityLab. Subjects will also be recruited at Walter Reed National Military 
Medical Center (WRNMMC), Minneapolis Veterans Administration Health Care System 
(MVAHCS). 
We expect all participants to be involved with the study for a minimum of three months, while 
initial sensor data are collected (Aim 1). Individuals who require an intervention will be involved 
with the study for approximately nine months. The interventions (Aim 2) will follow clinical 
protocols at each study site to increase compliance, minimize disruption, and ensure that the 
results will be rapidly deployable. Clinical work flows may differ across sites but will be 
consistent within each site. Licensed psychologists will administer MI at each site, with oversight 
from trained MI experts. As MI protocols are dependent on individual need, no standard protocol 
is possible within or between sites; clinicians will document the number of sessions and patient-
identified goals and strategies to enhance mobility and attain goals and we will summarize this 
information to guide MI deployment.
Inclusion Criteria
• Unilateral or bilateral lower limb amputation at transtibial or transfemoral level
• Ability to wear and use a prosthesis
• Designated K-level 2 – 4, or equivalent 
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• Prescription of definitive prosthesis 
• VA participants must receive care through the VA or related remote-site clinic  
• Age: 18-89 years—to reflect the age range of eligible participants at DoD and VA study 
sites, including young, combat-injured Service Members and healthy older Veterans, aged 
70-80. 
Exclusion Criteria
• Co-morbidities that limit prosthesis use, independently of prosthetic function, training, or 
motivation, such as stroke, obesity, severe traumatic brain injury, and neuralgia, which 
require interventions outside the scope of this study. Suitability to participate in the study 
will be determined by the individual’s physician.
• Unable or unwilling to learn to use CIMON or to allow transmission of study data
• Unable or unwilling to provide informed consent
• Residing in an area with poor cell phone coverage
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PARTICIPANT POPULATION(S)
Accrual 
Number:
Category/Group:
(Adults/Children 
Special/Vulnerable 
Populations)
Consented:
Maximum Number to be 
Consented or 
Reviewed/Collected/Screened
Enrolled:
Number to Complete 
the Study or Needed 
to Address the 
Research Question
Adult -Lower Limb 
Amputee
80
50 
Local
Walter Reed 
National Military 
Medical Center 
(WRNMMC)- Adult -
Lower Limb 
Amputee
35
25
Study-wide
Minneapolis 
Veterans 
Administration 
Health Care System 
(MVAHCS) - Adult -
Lower Limb 
Amputee
35
25
Total:
150
150
100
RECRUITMENT METHODS
Participants will be recruited from the Shirley Ryan AbilityLab amputee registry and Orthotics 
and Prosthetics (O&P) Clinic based on clinician recommendations. 
Flyers containing study and contact information will be displayed in the O&P Clinic to encourage 
recruitment. Study information will also be posted on the hospital website as required by Shirley 
Ryan AbilityLab guidelines. 
COMPENSATION FOR PARTICIPATION IN RESEARCH ACTIVITIES
Participants will be compensated by a prepaid debit card i.e. Clincard for participating in the 
study. A payment of $50 will be made per each scheduled assessment (testing) visit, for the 
duration of their participation to offset time and travel. 
Participants will also be given a cellular device equipped with an unlimited data plan for the 
duration of their participation in the study. Participants are required to return all study materials 
upon completion of study.
Participants will not incur any charges for physiatrist or prosthetics & orthotics appointments, or 
physical therapy and counseling sessions i.e. interventions provided in the course of this study. 
Participants will be compensated $20 for time and travel if they are required to travel to the 
Shirley Ryan AbilityLab for these sessions. 
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Participants will not be given results of research study unless a formal request for such results is 
made. 
WITHDRAWAL OF PARTICIPANTS
Subjects will be withdrawn from the study only in the event of a medical event or complication 
(i.e. hospitalization) that may alter the inclusion/exclusion criteria or which limits the patient from 
safely completing the remainder of the study, or at the discretion of the PI. Subjects can 
voluntarily discontinue the study at any time. Participants are required to return all study 
materials including provided cellular device at this time. 
Any participant who voluntarily withdraws will be requested to notify the Principal Investigator, 
Dr. Arun Jayaraman, in writing or call at 312-238-6875, if assistance is needed in this process. 
Information collected prior to the study discontinuation by a participant may still be used by the 
research team. The researchers reserve the right to discontinue study participation for any 
individual or for the study as a whole at their discretion.
RISKS TO PARTICIPANTS
The risks associated with study participation which include risk of breach of confidentiality, 
soreness during physical interventions (Aim 2), and behavioral assessments are no greater than 
minimal risk as defined in 45 CFR 46.102(i): “Minimal risk means that the probability and 
magnitude of harm or discomfort anticipated in the research are not greater in and of 
themselves than those ordinarily encountered in daily life or during the performance of routine 
physical or psychological examinations or tests.”
1.
Data security: One potential risk related to study participation is breach of data security 
with data collected from assigned cellular devices, which could potentially result in a loss 
of privacy of sensitive medical information.  A number of steps have been taken and will 
be taken to maintain this risk as extremely small. In all instances, data will be kept in files 
that are stripped of subject identifiers, containing only subject study-ID numbers. A 
separate file that links subject study-ID numbers with subject identifying data will be 
generated and password-protected, and the password for this file will only be entrusted to 
core study personnel.  In all cases, data and information will be kept on computers for 
which usage is password-protected and which are housed in a locked secure room.  
HIPAA compliance will remain a top priority at all times.  
2.
Behavioral assessments: It is possible that some respondents may experience stress or 
discomfort as a result of being asked about psychological symptoms or traumatic life 
experiences, but we believe such stress or discomfort presents no more than minimal risk. 
The absence of any reported reactions, in combination with a very low attrition rate in prior 
studies, suggests that our questions are not generating distress.  In fact, we feel that the 
risk of distress as a result of our measures is quite low.  Of course, there may still be 
residual distress as a result of any traumatic event patients may have experienced, but 
we are confident that the distress will not be TRIGGERED by our questions. In addition, 
our study consent and behavioral assessments will remind respondents that participation 
is completely voluntary, they can choose to terminate participation at any time without 
penalty, and that receipt of medical care is not contingent on the completion of the study. 
Finally, and given the number of assessments this study entails, there may be associated 
discomfort of fatigue while completing these assessments. However, the study-team will 
remain sentinel to this associated discomfort.
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P1 vs P0 by n
Alpha=0.050 Power=0.80 2-Sided Exact Test
P0
P1
0.02
0.04
0.06
0.08
0.10
0.06
0.10
0.14
0.18
0.22
0.26
0.30
n
2
5
1
Fig. 9: Power curves for testing 
 vs. 
  

 with 80% power and two-sided 
  
alpha=0.05 based on a one-sample exact test for 
proportions. 
3.
Soreness or mild discomfort from physical interventions (Aim 2): Participants may 
experience discomfort from donning and doffing prosthesis or from physical therapy. 
These side effects are no more than that which would be experienced during traditional 
rehabilitation training. 
Given the attendant safeguards we have put in place these risks are minimal and justifiable by 
the information that will be gained to aid the amputee population. Nonetheless, we will remain 
sentinel for any indication of discomfort on the part of our participants.
POTENTIAL BENEFITS TO PARTICIPANTS
There may be no direct benefit to participants in this study. Participants with suboptimal 
prosthesis use based on assigned K-levels compared to data collected will be provided physical 
and motivational interventions for the following 3-month period (Aim 2.) Interventions will be 
aimed at improving physical and mental fitness to promote prosthesis use. There is potential for 
these interventions to improve overall quality of life in these participants. 
DATA MANAGEMENT AND CONFIDENTIALITY
Statistical analysis will be performed by Masha Kocherginsky, PhD, Associate Professor of 
Preventative Medicine (Biostatistics) at Northwestern University.
Aim 1: Patients will be enrolled at 
 (Fig. 8). We will use smartphone sensors to obtain 
	 
 
summary measures of mobility and social activity during the three-month evaluation period. 
Standard assessment and participant-reported measures will be assessed at an in-person 
evaluation during the baseline evaluation visit (
). These data will be reviewed by the expert 
	
panel in a 2-stage process (as described in Research Strategy, Aim 1), which will determine 
whether the subject’s prosthesis use does or does not match their expected or desired goals. 
The primary endpoint in Aim 1 will be the binary outcome of matching these goals (Yes or No). 
We expect 80% of the patient population across 
the three sites will not initially match goals, so to 
obtain n=100 participants for Aim 2 we will need to 
evaluate n=150 subjects. Accounting for a 20% 
attrition rate and assuming the true proportion of 
the patient population that does not match their 
goals is 80%, we can estimate this proportion with 
precision of 
 (half of the 95% confidence 
 
interval). 
In order to identify factors that are related to 
whether a participant matches their prosthesis use 
goals, we will first conduct univariate analysis for 
each outcome variable, comparing it between the 
n=25 patients who match vs. the n=100 subjects 
who do not match using t-test or the Wilcoxon 
Rank-Sum tests for continuous variables, and chi-
squared or Fisher’s exact tests for the categorical 
variables (e.g. ability to go to social locations). 
Next, we will use exploratory factor analysis to 
group the variables found to be significant in 
univariate analysis (using a liberal p-value cut-off 
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of 0.15) into several factors. These factors will then be used as predictors in a logistic 
regression model with the binary match (Yes or No) outcome to whether these factors, 
representing and interpretable as latent variables, are associated with the outcome. As a 
secondary analysis identifying predictors of any particular component of under-use of the 
prosthesis (e.g. low mobility levels, poor functional or patient-reported outcomes) will be used in 
a similar manner. Depending on the outcome being considered, factor analysis may be 
repeated using different subsets of measures so as to not overlap with the outcome being 
considered. 
Aim 2: Participants who do not match their prosthesis use goals will be eligible for an 
intervention in Aim 2. The type of intervention will be recommended by the expert panel 
following comprehensive case review; patients will not be randomized to the different 
interventions. Smartphone-based monitoring will continue for 3 months, and the performance-
based and participant-reported outcome measures will be repeated at an in-person evaluation 
during the post-intervention evaluation visit (
). The expert panel will review these data and 
	
again determine whether the subject matches their expected prosthesis use (Yes or No). The 
primary endpoint in this Aim will be the proportion of subjects who achieve “Yes” status following 
the intervention. We base our power calculation on a one-sample exact test of the proportion of 
subjects who improve, i.e., match their goals following an intervention. We expect very few 
subjects will improve without an intervention. With sample sizes of n=25 or 50 at individual sites 
and n=100 overall, we will have 80% power with alpha=0.05 (two-sided) to detect a difference 
between the assumed (in the absence of preliminary data) proportion 
=5% under the null 

hypothesis 
 vs. 
=20.3%, 15.3%  and 12%, respectively, under the alternative hypothesis. 


Fig. 9 presents detectable differences for the three sample sizes for 
 ranging from 2% to 10%. 

Although there is ample power to detect small differences in the proportions of subjects who 
improve in the overall sample, the proposed sample size also provides adequate power to 
detect meaningful differences within the different populations enrolled at each site (e.g. with 
n=25). Even proportions as low as 12% (in the overall sample) are clinically meaningful. 
Changes in individual outcome measures from baseline to post-intervention will examined using 
paired t-test or signed rank tests for continuous measures and McNemar test for categorical 
variables. Linear regression models will be used to identify predictors of the outcome measure 
at 
, adjusting for the baseline level. Standard regression model diagnostics will be applied, 
	
and outcome variables may be transformed to satisfy the normality assumption. In addition, 
factor analysis as described in Aim 1 may be used as a data reduction technique. Because 
these analyses are exploratory, tests will not be adjusted for multiple comparisons. Logistic 
regression models will be used to look at binary outcome measures in a similar fashion. 
Linear mixed models with repeated measures will be used to look at outcome measures across 
all 3 time points in a subset of patients who match their goals at T1. Such models will also allow 
us to appropriately model the within-subject correlation between repeated measurements (e.g. 
using an autoregressive variance-covariance structure). In addition, linear mixed models provide 
a better mechanism for handling missing data by using all available data, rather than performing 
a complete case analysis, if the data are assumed to be missing at random.
Aim 3: We will use similar approaches to those outlined in Aims 1 and 2, together with machine-
learning algorithms, as described in the method for Aim 3.
Military Benefit and Impact:
Advances in prosthetic technology, such as powered lower limb devices and intuitive control 
systems, may provide superior function for Service Members and Veterans; however, achieving 
the potential benefits from these expensive technologies requires consistent, daily use of the 
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device. Despite state-of-the-art medical and rehabilitation services provided by the United 
States military—through Dept. of Defense (DoD) amputation care programs at Advanced 
Rehabilitation Centers and in the Veterans Administration (VA) Amputation system of Care, leg 
amputation may impose significant challenges to maintaining previous activity levels and 
achieving leisure, employment, and social goals. Few Service Members with major lower limb 
amputations return to active duty and redeployment [3]. For older civilians and Veterans, 
efficient use of a prosthesis may enable independent living and prevent social isolation or 
further physical decline. Current prosthetic assessments are based on in-clinic evaluations that 
do not correlate with prosthesis use in the community; many individuals do not attain expected 
activity levels or achieve personal mobility goals. Smartphone-based and wearable sensors 
provide a non-invasive, real-time monitoring system to assess prosthesis use in the home and 
community. Correlating actual prosthesis use and activity levels with in-clinic outcome and 
participant-reported measures will allow us to identify a toolkit of measures that sensitively and 
accurately predict prosthesis use and the barriers to optimal use. Although outside the scope of 
this study, this approach could be used to evaluate use of different prostheses for different 
activities in order to optimize DoD prescription practices to reduce costs and maximize benefits 
for Service Members and Veterans. A robust clinical toolkit that can be used to identify limiting 
factors in prosthesis use of Service members, Veterans, or civilians and allow effective relevant 
interventions would improve prosthesis use and quality of life, reducing the burden of 
amputation on the individual, their family and caregivers. 
Outcome measures and session data will be collected by trained research personnel in order to 
ensure quality control. We will conduct an interim analysis after collecting data from 20 
participants to determine if we have achieved adequate power to complete the aims of this 
study and re-estimate our sample size accordingly, within the limitations of the budget and 
timeline of the award. 
Data will be collected and kept confidential in compliance with HIPAA requirements. Subjects 
will be assigned confidential codes for data collection. Code assignment will be kept separate 
from data information; subject code will be kept in a secure room under lock and key. No 
personal information will be stored with their data. Any data, photos or video will be stored for as 
long as it is deemed scientifically valuable and will be stored on a secure, password protected 
computer at Shirley Ryan AbilityLab. In the event this study will be published, data may be 
released to report the outcomes of this study. No released data will include identifiable 
information
Data will be uploaded automatically to a data server/database that is used for data processing, 
correlation, analysis, and visualization (Fig. 3). All data collected will be de-identified, encrypted, 
and transmitted to a HIPAA-compliant server at the Shirley Ryan AbilityLab for cloud-side 
analysis and interpretation. Because the GPS and movement data are Personal Health 
Information, we will use a HIPAA-compliant server to store de-identified sensor data and activity 
labels transmitted via Wi-Fi and LTE to the Shirley Ryan AbilityLab. We will run our server-side 
analysis code on an Ubuntu Linux virtual server on the Shirley Ryan AbilityLab VMware ESX 4.1 
computer virtualization infrastructure. The system typically runs ~170 virtual machines and 
relies on two data centers holding 200 terabytes of patient information with periodic backups. 
When an activity is detected by the phone, the recent accelerometer and GPS data signals, as 
well as phone-based classification information, are sent to a separate virtual server. All data will 
be de-identified and coded to safeguard protected health information in compliance with Shirley 
Ryan AbilityLab HIPAA and federal privacy regulations, and data security will be monitored 
continuously and regulated by the Shirley Ryan AbilityLab IT security department. We will not 
save or transmit data with protected health information, IP addresses, or device identifiers on 
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the CIMON app or server. We will save date and GPS information relative to preassigned 
values. VA and Military staff will manage data from Veterans and Service Members, 
respectively, and maintain their respective cross-walk tables encoding participants’ identities. 
We have used this approach successfully in previous studies to acquire human subject data via 
smartphone sensors and ensure data security.
PROVISIONS TO PROTECT THE PRIVACY INTERESTS OF PARTICIPANTS
Every possible precaution will be taken to protect the privacy interests of subjects. To begin with 
participation in this is completely voluntary. Trained research personnel will explain the purpose 
of the study and intended use of subject’s personal health information and precautions taken to 
keep the study information and data confidential. Subjects will be notified if any new information 
concerning the safety of the study becomes available which may affect their decision to remain 
in the study. 
All personal information (names, addresses, email or phone numbers, etc.) gathered for this 
study that can identify participants will be kept secure to protect their privacy and will never be 
shared at any time with any person or entity. Data collected during the study and shared with 
others will reference participants only by an alphanumeric code. The “master list” linking 
personal information to the alphanumeric code will not be shared, and will be kept by the study 
PI in a secure location. All personal information linking participants to their data will be 
destroyed after 7 years following the completion of the study, unless the participant agrees to 
future contact.
COMPENSATION FOR RESEARCH-RELATED INJURY
This is a minimal risk study. 
ECONOMIC BURDEN TO PARTICIPANTS
There is no anticipated economic burden to participants. A smartphone with an unlimited data 
plan will be provided to participants. Participants will be reimbursed for time and expenses 
incurred during travel to the Shirley Ryan AbilityLab. 
CONSENT PROCESS
Before recruitment and enrollment onto this study, the patient will be given a full explanation of 
the study and will be given the opportunity to review the consent form. Each consent form must 
include all the relevant elements as approved by the Northwestern University Institutional 
Review Board. Once this essential information has been provided to the patient and the 
investigator is assured that the patient understands the implications of participating in the study, 
the patient will be asked to give consent to participate in the study by signing an IRB-approved 
consent form. Subjects will be consented with a new consent form if changes are made to the 
protocol. Prior to a patient’s participation in the trial, the written informed consent form must be 
signed and personally dated by the patient and by the person who conducted the informed 
consent discussion. The consent process will take place at the Shirley Ryan AbilityLab on Floor 
11 in the Rehabilitation Technologies & Outcomes Lab1401. All study sites will be required to 
obtain written consent prior to study participation. 
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QUALIFICATIONS TO CONDUCT RESEARCH AND RESOURCES AVAILABLE
This study will enroll a total of 150 participants study-wide for Aim 1, with a 20% attrition rate 
and an anticipated 80% enrolled exhibiting suboptimal prosthesis use and qualifying for Aim 2. 
Of those qualifying for Aim 2, 50 participants will be recruited from the Shirley Ryan AbilityLab, 
and 25 each from the Walter Reed National Military Medical Center (WRNMMC) and 
Minneapolis Veterans Administration Health Care System (MVAHCS). This sampling strategy 
ensures broad applicability to Service Members, Veterans, and civilians. We will attempt to 
recruit approximately equal numbers of individuals with transfemoral- and transtibial-level 
amputations, across sites; however, equal stratification will depend on availability of 
participants.
The Shirley Ryan Ability Lab houses the Center for Bionic Medicine (CBM) which is an 
internationally recognized research center focused on the design, control, and evaluation of 
multi-function prostheses for amputee patients. The CBM is a large, fully equipped facility with a 
research team of more than 35 staff members including physicians, prosthetists, therapists, 
neuroscientists, engineers, software developers, graduate students, post-doctoral researchers, 
and administrative staff that facilitates integration of teaching, research, and clinical knowledge. 
The PI of this study, Arun Jayaraman, PT, PhD, is Director of the Max Nader Center for 
Rehabilitation Technologies and Outcomes (RT & O Lab), within CBM. The RT&O Lab has 
laboratory space of 1000 sq. ft. for clinical evaluation of patients and assessment of ambulation 
and certified orthotist-prosthetist function. This includes ramps, stairs, parallel bars, and other 
clinical evaluation equipment. This area is covered by an overhead support system such that a 
patient can move throughout the room and over obstacles while being safely harnessed in case 
of a stumble or fall. Adequate dedicated office space is available for private meetings with 
potential subjects, performing physical evaluations, explaining the study protocol and obtaining 
study consent, performing data analysis, and writing manuscripts.
The Shirley Ryan Ability Lab is a 242 bed capacity hospital, admitting a wide range of patients 
for comprehensive rehabilitation. It is the first of its kind in integrating research into the clinical 
setting by building the research labs into the clinical spaces. A subset of clinical employees from 
various backgrounds are hired and trained to divide time between clinical and research work. 
All research personnel are required to complete IRB-compliant training and additional study 
specific training in compliance with standard clinical training methods to ensure that all persons 
assisting with the research are adequately informed about the protocol, the research 
procedures, and their duties and functions. Subject safety will be paramount at all times; clinical 
oversight will be in place at all times. 
STUDY-WIDE RECRUITMENT METHODS
Participants at the Shirley Ryan AbilityLab will be recruited via phone call using the amputee 
research registry or in person through clinician recommendation from the prosthetics and 
orthotics clinic. Participants can also reach out to study coordinators with information provided 
via flyers hung outside of the prosthetics and orthotics clinic or through the Shirley Ryan 
AbilityLab website in accordance with hospital guidelines. 
Recruitment at other study site will use local registries or clinician recommendations. 
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All required approvals (initial, continuing review and modifications) have been obtained at each 
site (including approval by the site’s IRB of record).
All modifications have been communicated to sites, and approved (including approval by the 
site’s IRB of record) before the modification is implemented.
All engaged participating sites will safeguard data, including secure transmission of data, as 
required by local information security policies.
All local site investigators conduct the study in accordance with applicable federal regulations 
and local laws.
All non-compliance with the study protocol or applicable requirements will reported in 
accordance with local policy.
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IRB #: STU00208186 Approved by NU IRB for use on or after 10/17/2018

STU00208186: Personalized Mobility Interventions using Smart Sensor Resources for Lower-
Limb Prosthesis Users
Version #:  1.1
Version Date:  9/28/18
Page 28 of 28
HRP-593 / v092818
IRB #: STU00208186 Approved by NU IRB for use on or after 10/17/2018