Standing Balance as the Fifth Vital Sign in Clinical Setting
Conditions: Accidental Fall, Postural Balance, Clinical Decision-making
Sponsor: Ohio State University

Cover page 
Title: Standing Balance as the Fifth Vital Sign in Clinical Setting 
NCT04139642 
Date: 10/17/2019

 
 
Does use of a quantitative balance measurement device influence clinical decision making? 
PI: Ajit M.W. Chaudhari, PhD 
I. 
Objective  
This 18-month prospective, observational study involving real world data will determine if the use of a standing 
scale that delivers a quantitative measure of standing balance along with weight (balance + weight) in place of a 
scale that only delivers weight (weight only) influences clinical decision making by health care practitioners in 
the ambulatory outpatient setting. Providers will be randomized to receive the balance+weight scale or weight-
only scale for 9 months, then to crossover and receive the other for 9 months. The primary outcome measures 
are (1) providers’ self-reported perceptions of whether the balance measurement influences their clinical 
decision making and (2) the rate of performing falls risk assessments or referring to a specialist for evaluation 
and treatment based on aggregate billing data. The secondary outcome measure is qualitative interviews with 
practitioners regarding their perceptions on the utility and barriers to using the device. 
 
II. 
Background and Rationale 
Falls prevention efforts are poised to reduce the significant impact of this high-risk injury, but a critical 
component for success of a falls prevention program is measurement of risk for falls. While effective 
measurement is available, barriers prevent implementation due to the need for primary care providers to 
undertake assessment within the limited time available in the short patient visit. 
Falls have significant health and financial costs to society, hence identifying fall risk is a National Institute of 
Aging priority. Falls are a significant source of early morbidity and mortality in the aging population [1], 
accounting for more than 50% of Canadian injury-related hospitalizations in seniors over the age of 65 [2]. A fall 
can result in a long hospital stay, perhaps for the rest of an aged and frail person’s life, as well as increased 
dependence, loss of autonomy, cognitive impairment, and depression. Beyond these effects, the financial 
burden of falls also places a major strain on the health care system in the USA and other countries, as an 
injurious fall has been predicted to cost about $35,000 in 2018 dollars and the overall cost of falls to the US 
health care system is predicted to be about $50 billion. Many factors may indirectly lead to increased fall risk, 
from medications to fatigue to orthostatic hypotension, but the proximal cause of a fall is a loss of balance, 
meaning an inability to detect (sensory) or respond to (motor) a change in body posture that occurs due to an 
external or self- imposed perturbation such that gravity pulls the body downward to the floor or a lower level. 
As such, the National Institute of Aging has placed a strategic priority on: 
 
“Measuring ambulation and assessing factors contributing to problems in and/or related to 
ambulation and mobility through development of improved instrumentation for biomechanical 
assessment of ambulation and falls; development of assessments for balance, sway, gait, or postural 
control [emphasis added] to identify stable and unstable patterns of movement during activities of daily 
living; or development of improved quantitative methods of assessing postural perturbations relevant to 
activities of daily living. [emphasis added]” (Omnibus Solicitation of NIH, CDC and FDA for SBIR and STTR 
Applications, PHS 2018-2, p. 21) [3]. 
While assessments of postural control exist, degraded postural control often escapes early identification and 
intervention. The American and British Geriatrics Societies (AGS/BGS) have released a clinical practice guideline 
and algorithm to identify individuals with poor postural control and elevated risk of falls [4], as has the CDC with 
its STEADI program [5], but primary care providers (PCP’s) have been slow to put these guidelines into practice. 
Many PCP’s report that they do not know how to conduct a fall risk assessment or do not have enough 
knowledge about fall prevention [6,7]. Moreover, many PCP’s feel overloaded when the average visitor has 
several health problems to address in a very short encounter (10 minutes or less). As a result, PCP’s may only be 
asking vulnerable patients the important questions that begin the AGS/BGS or STEADI algorithms 25% of the 
time, leaving falls risk unscreened as much as 75% of the time [8]. Moreover, both of these algorithms depend 
on either patient’s subjective recall, which could be unreliable especially in the case of a very gradual decline, or 

 
 
a direct observation of unsteadiness by the PCP or their staff, which may not be reasonable in a very busy clinic 
setting. These problems further underscore the priority of developing methods of assessing postural 
perturbations that do not depend on recall and that minimally impact clinical efficiency. 
Quantitative postural control assessment has been around in research settings and has been commercially 
available for decades, but has not entered widespread clinical practice. Over 50 years ago, Murray et al. 
presented an approach for quantitatively measuring center of pressure [9], and since then many research 
studies have demonstrated the use of this and successively more sophisticated equipment in identifying the 
sensory contributions to postural control [10], predicting falls [11], and quantifying changes in postural control 
over time [12]. This quantitative approach is more sensitive than self-report [12] or clinical balance testing [13] 
and can be done in under a minute [14]. However, the approach has not entered the clinic due in large part to 
perceived barriers that the equipment is too expensive [13], technically too challenging to use in the clinic [13], 
or difficult to interpret. 
The contribution of the proposed project is expected to be an evaluation of whether a quantitative postural 
control assessment that aligns with workflow in the clinical environment influences clinical decision making. This 
contribution will be significant because every patient could receive objective, quantitative postural control 
assessment at every office visit. Repeated measurements of postural control would create a better chance for 
early identification and intervention to manage balance deficits and reduce falls. It would also provide 
information for both providers and patients that a significant change (for better or worse) in balance status has 
occurred, whether due to a change in medication, neurological degeneration, an exercise intervention, or a 
lifestyle change. 
III. 
Procedures 
1. Research Design  
This prospective, observational study uses real world data to understand whether the addition of 
commercially-available quantitative balance measurement informs clinical decision making by physician 
and advanced practice providers. A randomized crossover design will be used: Providers who enroll in 
the study will be randomized to receive either a scale that measures weight only, or a scale that 
measures balance and weight for nine months. After nine months, the scales will be switched so that 
every provider has the other type of scale for nine months. The first primary outcome is self-report from 
providers to determine if they believe the additional balance information influences their clinical 
decision-making within their normal clinical practice. The second primary outcome is aggregate coding 
and billing information for the provider over each 9-month period, to determine if diagnosis, procedure, 
and referral patterns by each provider differ between the two types of scale. The third, secondary 
outcome will be the analysis of focus-group discussions with participants to better understand their 
experiences in using the Balance+Weight device within their normal clinical practice. 
2. Sample 
The desired population includes practitioners who regularly diagnose and treat patients in ambulatory 
outpatient settings. We anticipate recruiting 20 clinical practices in which there are physicians, nurse 
practitioners or physician assistants. In the case where multiple practitioners at a single clinical site 
choose to participate, the clinic site is treated as a single participant with the primary outcome 
measures lumped together for all participating practitioners. 
3. Measurement / Instrumentation  
Immediate feedback from provider on clinical decision-making process. After each patient encounter 
in which the provider chooses to use the balance measurement feature of the Balance+Weight device 
based on their own clinical decision-making process, the provider who made a clinical decision regarding 
balance or falls risk is asked to press a button on a small kiosk that we will provide which logs their 
agreement with the statement, “The balance measurement influenced my clinical decision making with 
this patient.”   

 
 
Data pull of clinic-level diagnosis and referral data. At or just after the crossover between sites has 
occurred, study personnel in conjunction with the IT departments at participating clinics will pull de-
identified aggregated clinic diagnosis and referral data for all participating providers. We will pull this 
data from both the 9 months of participation in the study, as well as the same 9 month-period in the 
previous year before the study began as a historical control. The data will be pulled again upon 
completion of the study for the second 9-month period. The data will include all patients that the 
provider saw. 
Focus group discussions after completion of RCT. Following conclusion of the trial, we will engage all 
participants in focus groups regarding their experience using the Balance+Weight device.  
 
4. Detailed study procedures  
Recruitment of clinics to participate in study. Study personnel will recruit clinics to participate in an 18-
month trial of the quantitative postural control assessment (Balance+Weight) device. Eligible clinics 
include any that self-report a substantial component of their patient population that may be at risk of 
falls, but that are not a referral site for patients previously identified as being at elevated falls risk. In 
clinics where multiple providers see patients, at least one provider must be willing to participate and 
commit to placing the novel device in their clinic, as well as to the other components of participation 
detailed below. Due to the number of ambulatory care clinics within a radius to allow same-day 
servicing of the devices and resource limitations to manufacture units, we will cap recruitment when we 
reach 20 clinics or 60 devices in service, whichever is reached first. Every participating clinic will have the 
opportunity to receive the device that delivers Balance+Weight either immediately or after a 9-month 
waiting period depending on randomization. 
Randomization of clinics. A block randomization scheme by clinic type will be used to assign which 
clinics receive the Balance+Weight device in the first 9 months and which clinics receive the 
Balance+Weight device in the second 9 months. The block randomization scheme is proposed instead of 
simple randomization because of the expected variation between clinics in the types of patients they 
see. Blocks will be determined based on the estimated percent of patients seen who are over 65, 
diabetic, undergoing chemotherapy (all oncology clinics), or are using assistive devices during some of 
their care (e.g. orthopaedic trauma clinics). Clinics for whom 50% or more of their patients are in any of 
these categories will be included in one block, whereas clinics for whom less than 50% of their patients 
are in any of these categories will be included in the other block. While this scheme does not fully 
account for the confounder of multiple risk factors for falls or multiple co-morbities in some clinics, 
equipoise between treatment arms is also achieved through the crossover design whereby every clinic 
serves as its own control as well. 
Training of clinics. At delivery of the Balance+Weight device to the clinic, a one-hour in-service will be 
performed by study personnel to train all providers and medical assistants on its proper use and the 
entry of data into the electronic medical record. This training will include how to operate the device and 
how to use the accompanying documentation with the reported data to inform clinical decision-making. 
This documentation will include threshold values for likelihood of the patient’s balance being Within 
Normal Limits as well as the change in value that would indicate a change in the patient’s balance status 
along with appendices that contain the published peer-reviewed journal articles upon which these 
recommendations are based. Upon request by the clinic, study personnel will repeat the training if 
refreshers are needed by clinic staff. An unlimited number of refresher trainings are permitted for each 
clinic, and the number of trainings requested per clinic will be recorded. 
Incorporation of quantitative postural control assessment device into clinic flow including immediate 
feedback on clinical utility. Each participating clinic will place the study device in a location to allow its 
use instead of their weight scale. In the Balance+Weight arm, the device is capable of either just giving 
weight, or of providing balance data if the patient stands quietly with eyes closed for 30s. In the Weight 
Only arm, the device only gives a weight measurement, just as a standard weight scale would. In both 

 
 
cases, the provider and their staff make their own clinical decisions as to which patients to make 
measurements of and how to use the provided information. The medical assistant may choose to enter 
the appropriate values into the medical record and continue with any other standard procedures in that 
clinic setting as part of the rooming or initial evaluation process. The provider can then examine the 
recorded balance measurement along with their standard examination and determine whether any 
further evaluations in that clinic or referral to a specialist, physical therapy, or other falls prevention is 
appropriate and record that diagnosis or referral into the medical record. 
Immediate feedback from provider on clinical 
decision-making process. After each patient 
encounter in which the provider assessed balance 
using the Balance+Weight device, the provider who 
made a clinical decision regarding balance or falls risk 
is asked to press a button on a small kiosk that we 
will provide which logs their agreement with the 
statement, “The balance measurement influenced 
my clinical decision making with this patient.” These 
kiosks from Happy-Or-Not.com are completely 
wireless and use their own 3G network to 
communicate the results of a single 4-point Likert 
scale question back to a central server that the study 
staff can monitor to collect data and get a real-time 
indication of whether the providers are using the 
Balance+Weight device. 
Crossover of sites after 9 months. At the 9-month point, study personnel will go to every site to 
reprogram each device from Balance+Weight mode to Weight Only mode or vice versa, and to move the 
immediate feedback kiosks to the new Balance+Weight sites.  
Data pull of clinic-level diagnosis and referral data. At or just after the crossover between sites has 
occurred, study personnel in conjunction with the IT departments at participating sites will pull the clinic 
diagnosis and referral data for all participating providers. After Casey et al.[15], our initial plan is to 
count instances of the following Common Procedural Terminology Category II codes: 
 
(Primary) CPT II 3288: Assessment of falls risk 
 
CPT II 0518: Development of a falls care plan 
 
CPT II 1101: Documentation of falls in past year (no falls/ single fall without injury) 
 
CPT II 1100: Documentation of an injurious fall/ multiple falls 
 
Other referral codes as appropriate 
Because CPT codes are updated each calendar year, we will review the new codes each year and revise 
this code list as new codes related to falls assessment are added or subtracted. We will pull this data 
from both the 9 months of participation in the study, as well as the same 9 month period in the calendar 
year before the study began as a historical control. The data will be pulled again upon completion of the 
study for the second 9-month period.  
Focus group discussions after completion of RCT. Following conclusion of the trial, we will engage all 
participants in focus groups regarding their experience using the Balance+Weight device. These 
discussions are intended to complement and enrich our understanding of how the users of the device 
(providers and medical assistants) either benefitted or did not benefit from having it in the clinic. 
Feedback will be used to assess the implementation toolkit and inform the development of the final 
version of the toolkit. Seed questions for these focus groups include: 

 
 
 
In what ways, if any, did you find having a quantitative postural control assessment on every 
patient useful in your clinical decision making? 
 
If you applied the device’s data differently for different patients, how did you use it across these 
different patients? 
 
Did the time it took to make the measurement or interpret the measurement impact the 
efficiency of your clinic (positively or negatively) and how? 
 
In what ways could we better implement and train you to use the device, and use it with the 
electronic medical record? 
5. Internal Validity 
As described above in section D, we use randomization and a crossover design to assure equipoise 
between the two arms of the study. All statistical analyses described below in section F will be 
performed by blinded analysts to mitigate the chance of bias. Given that the study uses real world data 
with commercially available devices in true clinical settings, the external validity of the study results is 
expected to be excellent. 
6. Data Analysis 
Statistical analysis of kiosk data. The kiosk data will be analyzed by tabulating the frequencies of scores 
by kiosk (0-3) and performing a weighted kappa analysis to determine if providers agree upon whether 
the balance measurement influenced their clinical decision making. The proportion of decisions across 
all providers where the balance measurement positively influenced the decision-making process (top 
two options) and its 95% confidence interval will be calculated.  
Statistical analysis of clinical billing data. Counts of each code listed above will be tabulated for each 
provider during the 9 month period in the year before beginning the trial, during the 9 months with the 
Balance+Weight device, and during the 9 months with the Weight Only device; normalized by the total 
patient encounters by that provider over the same time periods to get incidence rates of each code for 
each condition (historical, Balance+Weight, Weight Only). The incidence rate ratios of Balance+Weight 
to the other two conditions will then be calculated, and the 95% confidence intervals of those incidence 
rate ratios will be calculated to see which are significantly different from 1.0, as we have previously 
done with other epidemiological data [16]. Each code is considered separately, because it is possible 
that multiple codes may be used in a single encounter, but as the primary goal of a quantitative postural 
control assessment is to trigger further action by the provider to confirm that the individual has a 
balance impairment, the primary variable of interest is CPT II 3288: Assessment of falls risk. This 
approach would statistically test the hypothesis that the provider is more likely to proceed in any falls 
risk algorithm such as STEADI on to a further assessment of falls risk with quantitative postural control 
information in addition to patient self-report or clinical observation. Using the data reported by Casey et 
al. in their implementation of STEADI [15], where they reported an initial rate of an assessment of falls 
risk of 19% across 16 providers over 3 months leading to 360 screens, we estimate that with 10 
providers in each group over 9 months there could be approximately 3552 patient encounters in each 
group, or 7105 patient encounters over 18 months. With this number of patient encounters, we would 
have sufficient power to detect an incidence rate ratio of 1.1, or a 10% increase in Assessments of falls 
risk. Given the reported rate from Casey et al. that 35% of assessed patients score as high risk, that 10% 
increase could lead to approximately 47 additional high-risk individuals being identified, which we deem 
a clinically significant improvement. 
IV. 
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