A Balanced Reach Training Platform to Address Balance Disorders in Older and Neurologically Disabled Veterans
A Balanced Reach Training Platform to Address Balance Disorders in Older and Neurologically Disabled Veterans
Falls are by far the leading cause of accidental injury and death in older adults. The
 Veteran population is more severely affected by falls since it is significantly older than
 the overall population (45% over 65 years of age vs. 13%); and Veterans would benefit
 substantially more from an accurate diagnosis and treatment of fall propensity. Despite its
 importance, much is still unknown about the manner in which balance control is compromised by
 age and disease. Therapeutic interventions for people who are at risk of falling have proven
 to be of limited utility. Engineering methods are well suited to study and evaluate balance;
 but have to date been applied to overly simplified scenarios that lack the complexity to
 probe the musculoskeletal and neurophysiological bases for balance and falls.

 The long term objective of this research, which began with a VA Rehabilitation Research &
 Development (RR&D) Career Development Award (CDA-2), is to develop improved directives and
 protocols for the diagnosis and treatment of balance-related posture and movement
 coordination problems. This proposal significantly advances engineering methods to address
 existing gaps in the diagnosis and treatment of balance impairments through the development
 of a Balanced Reach Training Protocol (BRTP). The BRTP continuously challenges subjects to
 perform reaching tasks at the limits of their balance for an extended period of time, and
 increases these limits as subjects demonstrate improved performance. The goal of this tool is
 to quantitatively assess and improve at-risk individuals' ability to maintain balance when
 disturbed by volitional movements of the body and its parts-an important class of balance
 disturbances integral to many activities of daily living that can precipitate falls. The BRTP
 focuses on performance at and just beyond the limits of balance, unlike most such tests and
 training protocols that do not challenge subjects in this way. The BRTP's most immediate and
 salient metric is the limiting boundary of standing reach; and we hypothesize that expanding
 this boundary, as the BRTP is designed to do, will improve balance and make individuals more
 resistant to falls (in the context of expected balance disturbances).

 Confirmation of this hypothesis could provide a new perspective on existing training
 protocols' modest success rates, and direction for the design of new protocols with the
 potential to significantly improve these rates. [Though the BRTP is a training platform, we
 also believe that the performance metrics and analytical results produced by it can form the
 basis for new diagnostic measures that more reliably and precisely quantify and explain
 balance performance problems; and track changes in them over time.] Such diagnostic and
 treatment protocols would be particularly beneficial to the VA Health Care System, as it
 would lead to improvements in: patient throughput, quality of care, and treatment costs.
 Though this proposal targets the aging Veteran population, the BRTP is a general tool that
 can aid in the diagnosis and treatment of balance disorders arising from conditions other
 than aging. These include obesity, diabetes (which often leads to lower extremity muscle
 degeneration and peripheral neuropathy), sarcopenia, vestibular disorders, and neurological
 disorders such as stroke. Veterans whose balance has been compromised by Traumatic Brain
 Injury (TBI) (whether combat-related or not) may also benefit from the BRTP.
Overview: This study comprises three Specific Aims (SAs): SA-1) Development of the Balanced
 Reach Training Protocol (BRTP). SA-2) Assessment of the BRTP's training efficacy. SA-3)
 Assessment of the BRTP's time course of motor learning.

 Methods Related to Specific Aim 1: Development of the BRTP SA-1.1: Develop of the BRTP's
 Assessment Module. The investigators will develop a BRTP such that each foot's position and
 its associated vertical ground reaction force (used to detect stepping), as measured by the
 investigators' extant measurement systems, are continuously streamed to a Real Time Computing
 Workstation and read by the experimental control program, enabling it to monitor these
 quantities on an ongoing basis. The investigators will next incorporate an adaptive staircase
 algorithm into the real time experimental control program, that will establish target disk
 position corresponding to the subject's Limit of Balance (LoB), based on stepping. The
 investigators will establish LoB corresponding to seven target disk motion directions, each
 corresponding to a particular direction of Center of Mass (CoM) Motion . Target motion will
 be programmed to move unpredictably within a small circular region with center r and
 centerline coinciding with one of the specified directional lines. The subject then tracks
 the target using their dominant hand index finger. The adaptive staircase algorithm
 in-rementally increases r, causing the target to move away from the subject until the subject
 (wearing a harness to guard against falling) must step to avoid a loss of balance, at which
 point r is reduced by some increment and the process is repeated. During this task the
 subject will be instructed to step rather than curtail their tracking movements when they
 believe they are at risk of falling. The degree to which r is increased and decreased depends
 upon the subject's prior stepping responses and is determined by the adaptive staircase
 algorithm. As this process continues the algorithm develops a maximum likelihood estimate of
 the value of r that invokes stepping. When this estimate falls within the desired confidence
 interval (here 95%) the process terminates; and LoB is computed from the associated force
 plate and body segment position measurements. Disk displacement along the seven directions
 will be presented to the subject in random order.

 The seven values of r determined by the adaptive staircase algorithm will then be reduced by
 5% and an ellipse fitted to them. The disk will be programmed to move unpredictably for 90
 sec according to the same sum-of-sines function used previously, within a band centered on
 this ellipse; and subjects will track it. This trajectory will keep subjects at or near their
 LoB (and at the limits of their balance capability) throughout the tracking task.
 Establishing LoB in this way eliminates the subjectivity inherent in extant clinical measures
 of LoB, and also accounts for subjects' aforementioned uncertainty in correctly perceiving
 their State of Balance and Limit of Balance. The investigators believe that this approach
 also more accurately simulates the real-life conditions under which people fall, and that LoB
 will be a better measure of individuals' actual balance capabilities. Furthermore, it makes
 available a number of other psychophysical measures and functions with which to better
 understand and describe the role the perceptual and sensory processes play in determining
 out-of-balance conditions.

 The Assessment Module will record the following data to characterize performance in the
 tracking task

 1. The values of r, , and LoB corresponding to each (Figs. 7A and 1);

 2. The motion (position vs. time) of the target disk;

 3. The motion of each of 15 body segments (Table 1), and the tip of the tracking finger
 (measured by the investigators' Vicon Motion Capture System);

 4. Ground reaction forces, moments, and CoP for each foot and for both feet combined (dual
 Bertec force plates); Item 1 is collected during the adaptive staircase procedure. Items
 2, 3, and 4 are collected during both the adaptive staircase procedure and the
 subsequent tracking task (as well as the training bouts, see SA-2.1). All data will be
 saved for offline post-test processing and analyses.

 SA-1.2: Develop the BRTP's Training Module. A single training session begins by administering
 the Assessment Module to establish the subject's LoB and corresponding overall target
 excursion amplitude, and computing the unpredictable target disk trajectory based upon it.
 The Training Module then presents an operator-specified number of training bouts; a single
 one of which consists of presenting the trajectory for the subject to track for four minutes
 without stepping, followed by a three minute sitting rest period. Performance data is
 recorded throughout the tracking period. Training bout and rest period durations can be
 varied (with the goal that a training session deliver 60 minutes of actual training) to
 optimize training efficacy and accommodate individual subject needs and capabilities.
 Subjects will be harnessed while performing the tracking task to guard against falling. As
 training progresses and the subject's performance improves, the BRTP will present
 increasingly difficult target trajectories based upon the increased LoBs and corresponding
 overall target excursion amplitudes as measured by the Assessment Module at the beginning of
 each training session.

 Methods Related to Specific Aim 2: Assessment of the BRTP's training efficacy SA-2.1: Conduct
 the BRTP and MCET training protocols. All subject testing is performed in this Specific Aim.
 The investigators will screen 90 older men and women with high fall risk, enrolling 80 of
 these, and completing training for 68, assuming an overall 25% attrition rate.

 Subjects will be randomly assigned to either the BRTP or MCET training group. Each group will
 receive 60 minutes of training three times per week for six weeks. BRTP training is as
 described previously (see SA-1.2). The VA Maryland team developed an MCET protocol for older
 individuals with balance and mobility disability. The program is individualized to each
 participant's capacity based on their balance profile and endurance level. Across the six
 weeks, training advances from five "fundamental" exercises that are essential for
 instrumental activities of daily living function to 13 exercises, as participants meet
 metrics for exercise safety and movement quality. Standardized progressions for each exercise
 advance according to the level of hand contact support required, dose (duration, sets and
 repetitions), intensity (movement amplitude and cadence), and multi-segmental motor challenge
 or complexity. The exercise cadence is adjusted up to provide greater challenge to aerobic
 fitness and is adjusted down to facilitate greater resistive training benefit (i.e. holding a
 squat longer) or to accommodate the participant's fatigue level. MCET training will be
 performed in a group setting.

 All subjects will be assessed with the clinical balance measures (see below) and the BRTP
 Assessment Module at the same time points before, during, and after training (see SA-2.2).

 Outcome Measures: The following "clinical measures" of balance, reach, and fall risk will be
 used to asses training effects induced by the BRTP and MCET.

 1. Multi-Directional Reach Test

 2. Falls Efficacy Scale, which assesses FoF

 3. Physiological Profile Assessment (PPA)

 4. Tinetti Balance Test (Balance Section)

 5. Mini Balance Evaluation Systems Test (Mini-BESTest)

 6. DASH (Disabilities of the Arm, Shoulder, and Head) Test

 7. Falls experienced by each subject, as well as the conditions in which they fell, during
 and throughout the first year following training.

 These measures will be obtained on a separate day from the BRTP Assessment and Training
 procedures so as not to unduly fatigue the test subjects. The investigators will also
 employ the following BRTP-based measures of balance and reach.

 8. RMSE

 9. RMSD-Primary Outcome Measure

 10. Average SoB

 11. LoB RMSD will be the investigators' primary outcome measure. SA-2.2: Assess the training
 efficacy of BRTP training in comparison to MCET. Prior to training subjects will undergo
 double baseline tests 48 hours apart using the clinical measures and the BRTP-related
 measures. This assessment will be repeated midway through training (three weeks after
 training begins), immediately post training, and six-weeks after the last training
 session (retention). Although the investigators include a number of clinical and
 BRTP-related performance measures in the assessment of training efficacy, RMSD will be
 the investigators' primary outcome measure. The primary purpose of this study is to
 assess the efficacy of the BRTP in improving balance. It is possible that while both the
 BRTP and MCET improve balance the magnitude of the difference in improvement between
 groups may be small. As a consequence of the relatively modest number of subjects the
 investigators will be testing (a result of the demands of the testing and the limited
 funds available to support the testing) the investigators may show no between-group
 difference. In order to be able to demonstrate that each intervention is efficacious,
 the investigators will test subjects twice at baseline and compare the change during
 this non-intervention period separately to the change engendered by each active
 intervention. The investigators' balance-related clinical measures have been shown to
 correlate with fall risk and fear of falling (FoF). Clinically meaningful improvements
 in the training group's performance based on the clinical measures will indicate that
 the BRTP improves balance function and reduces fall risk.

 The investigators will also request that subjects report any falls that they experience
 during the training period, and the conditions under which they fell. Once subjects have
 completed training the investigators will contact them monthly via e-mail, social media, or
 using stamped return-address post cards to obtain fall information; and follow by telephone
 to learn of the conditions under which they fell.

 Statistical Analysis and Expected Results: The investigators will start with Exploratory Data
 Analysis designed to find extreme values, which will be checked to make sure there are no
 transcription errors. The investigators will have data at five time points, baseline 1 (B1)
 baseline 2 (B2), mid-training (MT), post-training (PT), and retention six-weeks after the
 last training session (R). The investigators will use repeated measures ANOVA (SAS proc
 mixed) in which the dependent variable will be the within person change from one time period
 to the next, i.e. B2-B1, MT-B2, PT-MT, R-PT. The model will be adjusted for group (BRTP,
 MCET); time period (baseline [B1 to B2], early training [B2 to MT], late training [MT-PT],
 overall training (B2-PT], retention [PT to R]); and a time period x group interaction. If the
 investigators find a significant period x group effect the investigators will use appropriate
 post-hoc comparisons to determine the time points that show significant differences. The
 investigators will use linear contrasts 1) to demonstrate the efficacy of the BRTP and MCET
 by comparing the mean change across both groups during the baseline to the mean change within
 each of the two-groups during training; 2) to compare the efficacy of the BRTP to that of the
 MCET by comparing the within group changes during training; and 3) to compare the ability of
 the two interventions to engender a long-lasting improvement in balance by comparing the
 percentage change during retention. The investigators will use AICC, a modification of
 Akaike's information criteria, to determine the covariance structure [e.g. unstructured,
 compound symmetry and first order auto-regressive (AR(1)] that best accounts for the serial
 autocorrelation of repeated observations obtained from the same subject.

 Prior to accepting results from the analyses, the investigators will assure that data conform
 to the assumptions of the analyses (e.g. normal distribution of residuals), and that no datum
 has excessive influence or leverage (e.g. Cook's D). The investigators will use multiple
 imputation to impute follow-up data for subjects missing values. Each primary hypotheses will
 be tested independently of the others. The investigators will deem a two-tailed test p<0.05
 as indicating statistical significance. The investigators expect the BRTP to be more
 effective in improving functional balance than MCET, and that this improvement will last six
 weeks after training. The investigators further expect the improvement to be clinically
 significant.

 To compare fall rates between the BRTP and MCET training groups the investigators will use
 Poisson regression in which the investigators will use the General Estimating Equations of
 Liang and Zeiger to account for the serial autocorrelation of repeated measures from the same
 subject.

 Methods Related to Specific Aim 3: Assessment of the BRTP's time course of motor learning.

 SA-3.1: Assess the BRTP's time course of motor learning. In addition to comparing the overall
 training efficacy and retention of the BRTP and MCET, the analyses of SA-2.2 will also
 provide a coarse profile of each training regimen's time course of overall motor learning.
 These analyses will be based on the previously specified clinical measures of balance and the
 BRTP-based measures, obtained at five time points before, during, and after training. More
 frequent time points could not be specified because the manual assessments necessary to
 obtain the clinical measures for this many subjects are too time consuming to permit this.

 Newell, et.al. have noted that for a dynamic system such as the human balance system,
 training-induced changes in performance over time (i.e., motor learning) are an aggregation
 of the performance changes of the elements making it up. These elemental changes proceed over
 shorter periods of time than the measurement intervals specified above. A deeper
 investigation of performance in the BRTP over a finer time scale would not only provide a
 more detailed motor learning profile, it would allow us to qualitatively and quantitatively
 characterize some of the more important elemental performance changes that drive the overall
 learning profile-particularly those changes associated with the higher level predictive and
 motor planning processes. The clinical measures are not appropriate for this analysis, even
 if they could be measured more frequently, because they do not directly measure performance
 in the BRTP. The BRTP-based measures do, however, and will be recorded with the necessary
 frequency. Data from which SoB, RMSE, and RMSD are computed will be automatically recorded
 continuously throughout each training bout.

 This analysis involves the development and interpretation of Input-Output Polynomial models
 and empirical transfer functions describing tracking and balance performance. The polynomial
 model that the investigators will employ is an AutoRegressive, Moving Average, eXogenous
 input (ARMAX) model, which takes the form A(q)y(ti)=B(q)u(ti-nk)+C(q)e(ti)1 For a given
 system the time series y(ti) is the system's response, recorded at discrete time instants ti;
 u(ti-nk) is the input to the system; and e represents the residuals unaccounted for by the
 other terms in the model. A, B, and C are polynomial functions of the time shift operator q;
 and n is the number of discrete time steps of length k sec, representing the system's
 response delay. For the tracking system y is tracking error (disk position-finger position)
 and u is target disk position. For the balance system y is SoB and u is tracking finger
 position; since SoB is controlled to support the tracking task. The models are constructed by
 determining the optimal number of terms in each of the A, B, and C polynomial functions
 (usually 1-4), and the values of these terms' constant coefficients that result in the best
 fit to the data, in the sense of minimizing the sum of squared deviations. This process will
 be carried out using MatLab's Systems Identification Toolbox.

 Polynomial models separate a system's underlying response dynamics [given by A(q) y*(ti) =
 B(q)u(ti-nk)] from the unexplained deviations from this response [C(q)e(ti)], permitting us
 to more accurately characterize the system's input-output relationship and the nature of the
 deviations. Response delays are also estimated. The structure of the tracking error model's
 polynomial functions A(q) and B(q) (number and magnitudes of their constant coefficients)
 provide both a qualitative and quantitative description of the manner in which the higher
 level predictive and motor planning processes utilize tracking error and disk motion
 information to control tracking finger performance and minimize errors. Whereas a single
 coefficient would imply that the higher level processes employ a low order representation of
 the error process or disk motion, containing only spatial (position) information; two or more
 coefficients would imply a richer representation that also incorporates velocity and
 acceleration information. Greater magnitude coefficients indicate that their respective terms
 are utilized to a greater degree in performing the control task. For the SoB model,
 interpretation of A(q) is more challenging because this function also describes the mechanics
 of body and body segment movement. Interpretation of B(q), however, proceeds similarly to
 that for tracking error; though here the investigators note that the higher level processes
 have greater knowledge of the tracking finger's motion than that of the target disk because
 these processes control the former. This could result in more or greater magnitude B(q)
 coefficients, as well as reduced response delays relative to the finger tracking model.

 Discrete Fourier Transforms (DFTs) will be computed for disk motion and the resulting
 responses of the tracking finger and whole body CoM. From these, empirical transfer functions
 relating the motion of the target to these responses will be computed by dividing the DFT of
 the response by the DFT of the disk motion, frequency by frequency. Magnitude gains and phase
 lags obtained from the empirical transfer functions will be used to construct Bode gain and
 phase plots to assess the frequency response and bandwidth of the tracking and CoM responses.
 Bode plots provide a measure of how well the system is able to respond its input. Superior
 performance is indicated by greater bandwidth and gain, and lower phase lags. The use of
 these methods to analyze balance is described in.

 Polynomial models and transfer functions will be constructed for each subject participating
 in the BRTP. Because motor learning and resulting changes in performance proceed continuously
 over the course of each training bout, polynomial models and transfer functions will be
 computed for a 30 sec window (containing 3,000 data points) that advances across each bout in
 15 sec intervals. The investigators will also compute RMSE, average SoB, and RMSD for each
 window. A four minute training bout will thus provide 15 "snapshots" of performance across
 the bout. Window size and advancement interval will be adjusted if necessary to best capture
 the profile of performance changes over time. These models, functions, and performance
 measures will also be constructed for the two baseline assessments and the retention
 assessment performed using the BRTP Assessment Module (see Specific Aim-1.1 in Methods).
 These models, functions, and performance measures will provide a detailed portrayal of the
 manner in which motor learning (changes in performance in the BRTP) evolves over the course
 of a training bout, a multi-bout training session, the entire multi-session training regimen,
 and the retention period.

 As training proceeds the investigators expect tracking error (per the polynomial model) and
 RMSE to decrease, and LoB (measured at the beginning of each training session), SoB (per the
 polynomial model), average SoB, and RMSD to increase. The investigators further expect the
 number and magnitude of each polynomial model's A(q) and B(q) coefficients to increase. This
 indicates that the brain is developing richer and more elaborate mental models with which to
 control both tracking and balance performance in the balanced reach task. The investigators
 expect the Bode plots to reveal greater bandwidth and gain, and lower phase lags; indicating
 that the tracking and balance systems are better able to both process their inputs and
 respond to them. In particular, the investigators expect improved high frequency performance,
 which deteriorates with aging. These analyses and assessments will be carried out on a
 per-subject basis, in order to ensure that inter-subject averaging does not obscure the
 performance attributes the investigators seek to identify. If the investigators encounter
 sufficiently similar performance across a group of subjects, however, the investigators will
 construct group-average models, functions, and performance measures to describe it. A
 particular goal of these assessments is to determine whether and when subjects "plateau"
 during the course of the individual training bouts, individual training sessions, or the
 overall training regimen. This information will be used to determine the optimal durations of
 each of these training components (training bout duration, number of training bouts per
 training session, total number of training sessions).

 SA-3.2: Assess correlations between accepted clinical measures of balance and BRTP-based
 measures. Implementation of the BRTP necessarily involves the use of performance measures
 specific to the balanced reaching task, in order to tailor it to individual subjects'
 capabilities (LoB) and measure ensuing performance (LoB, SoB, and RMSD). The goal of this
 sub-Aim is to assess the degree of correlation between changes in these BRTP-measures and
 changes in the clinical measures that the investigators are using to assess the BRTP's effect
 on balance performance.

 Statistical Analysis and Expected Results: The ability to compare performance in the BRTP to
 that in the clinical measures of balance is limited due to the different scales of
 measurement on which the data from the BRTP and clinical measures are obtained. BRTP measures
 (RMSD, average SoB, and LoB,) are obtained on a ratio scale. Some clinical measures are also
 obtained on a ratio scale, but others are obtained on an ordinal scale. The investigators
 will use Pearson's to assess correlations between the BRTP measures and those clinical
 measures obtained on a ratio scale; and Spearman's to assess correlations between the BRTP
 measures and those clinical measures obtained on an ordinal scale. The investigators expect
 the BRTP measures to correlate with the clinical measures, but to vary in a "smoother" and
 more continuous manner, and exhibit greater range and sensitivity. The investigators expect
 these correlations to be statistically significant, providing evidence that the BRTP-based
 balance measures have clinical validity. Since the Multifunctional Reach test, Physiological
 Profile Assessment, Tinetti Balance tests and Falls Efficacy Scale have also been shown to
 correlate with fall risk, statistically significant correlations of BRTP measures with these
 will provide evidence that they are indicative of fall risk. Finally, a statistically
 significant correlation of LoB with the Falls Efficacy Scale will provide evidence that LoB
 can serve as a quantitative measure of FoF.
Inclusion Criteria:

 -  High Fall Risk: Fallen twice or more in the past year

 -  Adequate language and neurocognitive function to participate in testing and training

 -  Able to give adequate informed consent

 -  Ability to perform BRTP: Able to perform the balanced reach task without assistive
 devices

 -  Ability to perform MCET: Able to rise from a chair unaided and walk 10 meters without
 human assistance

 -  Vision adequate to see a 1 inch diameter black disk against a white background at 3
 feet

 Exclusion Criteria:

 Clinical history of:

 -  Unstable angina

 -  Recent myocardial infarction (< 3 month) or hemodynamically significant congestive
 heart failure (NYHA II) or valvular dysfunction

 -  Peripheral arterial occlusive disease with claudication

 -  Hip fractures

 -  other lower extremity large bone fractures occurring within the past year, other
 serious musculoskeletal injuries

 -  upper or lower body orthopedic issues

 -  and/or major chronic pain that would prevent the subject from performing the
 balanced reach task

 -  Pulmonary failure

 -  Body Mass Index (BMI) >40

 -  Active vertigo

 -  Symptomatic orthostatic hypotension

 -  Poorly controlled hypertension (>190/105) on at least two separate occasions

 -  Poorly controlled type 1 or 2 diabetes (HbA1c >10)

 -  Recent hospitalization for severe disease or surgery (<3 month)

 -  Excessive daily alcohol consumption (>3 oz. liquor; >12 oz. wine; or >36 oz. beer) or
 illicit drug abuse

 -  Untreated major clinical depression or dementia

 -  Neurological disease such as stroke, TBI, Parkinson's disease, etc.

 -  Vestibular disorders of sufficient severity to prevent the subject from performing the
 balanced reach task

 -  Any other condition (e.g., extreme frailty) that would preclude safe completion of the
 BRTP or MCET