Modified bathroom scale and balance assessment: a comparison with clinical tests
© Duchêne et al. 2016
Received: 29 September 2015
Accepted: 31 March 2016
Published: 18 April 2016
Frailty and detection of fall risk are major issues in preventive gerontology. A simple tool frequently used in daily life, a bathroom scale (balance quality tester: BQT), was modified to obtain information on the balance of 84 outpatients consulting at a geriatric clinic. The results computed from the BQT were compared to the values of three geriatric tests that are widely used either to detect a fall risk or frailty (timed get up and go: TUG; 10 m walking speed: WS; walking time: WT; one-leg stand: OS). The BQT calculates four parameters that are then scored and weighted, thus creating an overall indicator of balance quality. Raw data, partial scores and the global score were compared with the results of the three geriatric tests. The WT values had the highest correlation with BQT raw data (r = 0.55), while TUG (r = 0.53) and WS (r = 0.56) had the highest correlation with BQT partial scores. ROC curves for OS cut-off values (4 and 5 s) were produced, with the best results obtained for a 5 s cut-off, both with the partial scores combined using Fisher’s combination (specificity 85 %: <0.11, sensitivity 85 %: >0.48), and with the empirical score (specificity 85 %: <7, sensitivity 85 %: >8). A BQT empirical score of less than seven can detect fall risk in a community dwelling population.
KeywordsModified bathroom scale Fall risk Frailty Modelling Prevention
Balance is essential in order to perform typical activities of daily living. However, the ability of people to maintain their balance decreases with age and/or neurological or musculoskeletal disorders. Older adults with poor balance have an increased risk of falls and adverse health outcomes (Fried et al. 2001), and also reduced physical activity levels, leading to an increased risk of frailty (Fried et al. 2005) and a decline towards dependence (Tinetti and Williams 1997; Gill et al. 1995). Despite this gradual decline, it is possible to slow down or even prevent the decline for community-dwelling elderly if an appropriate intervention program is put in place (Gill et al. 2002). In order to be effective, the intervention program needs to start as early as possible, which means there is a need for tools that are able to detect as early as possible any decrease in balance quality. Thus, an adapted prevention or rehabilitation program could be put in place, with progress followed over time. Accordingly, it is essential to detect balance impairment as early as possible, and then to monitor balance quality under ecologically valid conditions. Any evaluation tool needs to be easy to use (non professional users), socially acceptable (community dwelling elderly) and relevant with respect to well-established clinical tests.
Balance quality is typically measured under controlled conditions such as a research laboratory using force plates, which provide measures based on the centre of pressure (CoP) displacement. Such measures are considered to be the gold standard measure of balance, with these tests providing comparable results with those of clinical balance tests (Haas and Burden 2000; Berg et al. 1992). However, force plates are expensive and cannot be considered for home use, or even routine clinical practice.
A large number of clinical tests are available to measure balance in older adults. For detailed reviews and comparisons, see the comprehensive review of Langley and Mackintosh (2007), that identified 17 different clinical tests to measure balance, and the comparison of Mancini and Horak (2010) between different clinical and objective tests. Among all these tests, several are widely used in clinical practice to assess balance, namely: the Berg balance test (Berg and Norman 1996; Berg et al. 1992), which focuses specifically on balance impairment; the Performance Oriented Mobility Assessment (POMA) test, which includes both balance and gait walking tasks (Tinetti 1986); and the timed up and go (TUG) test (Mathias et al. 1986), which has been described as a good predictor of the falls in community-dwelling older adults (Shumway-Cook et al. 2000).
An initial attempt to compare the results of the BQT and the three clinical tests outlined above was performed with two groups of older adults, the first of which was living in nursing homes, while the second was a group living in the community (Vermeulen et al. 2012). Although these results suggested that the BQT was a useful tool for measuring balance in older adults, the added value of the BQT in clinical practice remained to be demonstrated. In addition, this work made use of an empirical score first defined in (Duchêne and Hewson 2011), which, although able to discriminate between the two groups in the study, has not been optimized with respect to a reference objective.
The use of tests with cut-offs offers the possibility of assessing the sensitivity and specificity of the score of the BQT with respect to an objective of risk of falls.
Balance quality tester
Stand in front of the BQT, whereby an infrared detector detects the presence of the person
Wait for the scale to display “0.0”
Step onto the scale
Wait for the scale to display body weight
Step off the scale.
Scoring of the four parameters in the BQT score
Coefficient of variation (%)
Rising rate (kg s−1)
Stabilogram surface (cm2)
Trajectory velocity (cm s−1)
Eighty-four participants (27 men, aged 81.5 ± 7.5 years; 57 women, aged 84.0 ± 5.5 years) were recruited among patients coming for a geriatric examination at the Toulouse University Hospital (CHU Toulouse, France) from June 2009 to July 2011. The criteria for inclusion were an age over 65 years, and to be capable to step onto the BQT. People with severe handicaps, acute pathologies or current treatment for physical injury were excluded from the experiment. All participants volunteered and signed an informed consent. The protocol was approved by a Regional Ethical Committee (CCPPRB ref: 2007-A00320-53, date: 2007-05-24).
All participants had a geriatric evaluation, which included the TUG, WS, and OS, with the latter taken as clinical reference test for BQT validation in respect to fall risk. A range of other tests were also performed as part of the geriatric evaluation, such as the GDS, MMS, Tinetti, and Stop Walking when talking, but these tests are not presented in the present paper.
Each participant followed the protocol described above when using the BQT. Subjects performed three repetitions of the BQT test, with the score calculated from the mean of each variable, estimated from all validated repetitions achieved within the same session (tests were validated by the clinician when all steps of the protocol were respected).
In addition to the population of older adults, 20 control subjects (10 men and 10 women) recruited within the university were also tested (aged 28.8 ± 9.4 year). The control subjects were tested using the BQT as well as for the OS, with this test stopped if subjects reached 15 s of single leg stance. The number of control subjects was not matched to the older subjects tested, as the aim of the control group testing was to validate the maximal score of the BQT.
In previous work, the overall BQT balance score was computed in an empirical manner, by adding up the partial scores produced by each of the variables extracted from the BQT raw data. The native variables (NV) were all given the same weighting in the overall balance score, with this original score refered to as the empirical score (ES). Although acceptable results were found, such an arbitrary score might not have been the best representation of balance. In the present study both the partial scores (PS) and the NV were weighted in order to optimize the correlation with each of the clinical tests (TUG, WS and OS). Regression models were constructed for each of the three clinical tests and each of the three types of data (NV, PS, and ES). Models were only for computed for the clinical tests that had a normal distribution.
As indicated above, prior to multiple regression analysis, the Gaussian nature of the observed variables was tested using the Kolmogorov–Smirnov statistical test (Dudewicz and Mishra 1988). Correlations were then computed between the normally distributed clinical tests and the various outputs obtained from the linear modelling process. Regression coefficients were computed using the “regress” function available in MATLAB® (Mathworks Inc, Natick, MA, USA). The correlation coefficient was obtained from the R2 statistic (square of the correlation coefficient R) produced by the same MATLAB® function.
In the second step of the data analysis, an estimation of the sensitivity and specificity of ES, or an optimized combination of PS, was computed in relation to fall risk. In this case, performance in the OS was taken as fall-risk, with subjects classified with respect to the 5-s cut-off value. receiver-operator characteristic (ROC) curves were used to express sensitivity (vertical axis) and specificity (horizontal axis) of the variables to classify subjects as at risk, or not at risk, of falling (Zweig and Campbell 1993). In addition the area S under the ROC curve, which can be taken as a global index of the accuracy of the classification, was used to compare different conditions in terms of classification accuracy (Hanley and McNeil 1982). The optimized combination of PS was obtained with respect to the best linear classification, with Fisher’s linear discriminant function used (Fisher 1936).
Normality of the data
Linear regression results
BQT native variables
BQT partial scores
Classification was conducted in respect to fall risk classified using the OS test with a 5-s cut-off (Vellas et al. 1997). Fisher’s coefficients were obtained after normalization, with clinical tests centred and divided by their standard deviation. Better results were obtained for PS than for NV, with discrimination ratios of λ = 33 % and λ = 22.5 % for PS and NV, respectively.
The weightings obtained for the individual variables were 0.61 for the trajectory velocity, 0.57 for the coefficient of variation, 0.42 for the surface area, and 0.36 for the rise rate. Classification using ES, which had the same weighting for all PS, produced a discrimination ration of λ = 25.5 %.
ROC curve limits for 85 % sensitivity and specificity
Fisher’s combination of partial scores
Specificity 85 %
Sensitivity 85 %
None of the control subjects obtained an ES of <15 out of a maximum score of 16, with all subjects able to stand on one leg for at least 15 s. For those subjects for whom the score was 15, (9 out of 20 subjects), the variable that reduced the score was the trajectory velocity in 78 % of cases (7 out of 9 subjects).
In the present study results were presented in two ways. Firstly, in respect to how the BQT scores compare to standard clinical tests, and secondly, the accuracy with which the BQT can classify fall risk, in reference to one-leg stand time.
Slower walking speed as a single variable has been shown to predict subsequent adverse events (Montero-Odasso et al. 2005), with this variable forming one of the five indices of frailty proposed by Fried et al. (2001). Several studies have suggested that walking speed at preferred velocity might be the best single factor in predicting frailty (Theou et al. 2011; Mitnitski et al. 2001). Similar results were observed in the present study, with a high correlation found between the TUG and WS (r = 0.75), and the TUG and WT (r = 0.80). The higher correlation might be due to the non-linear nature of the transformation from time to velocity. Although the BQT does not measure the same physical capacity as the WS test, significant correlations between the results of both tests were observed. The best correlation was obtained between WS and the optimal combination of PS (r = 0.55). The fact that the raw values of the variables do not produce the best performance can be explained by the non-linear transformation from true values towards the corresponding PS. It should be noted, however, that the ES, which is a simple addition of all PS, had a correlation that did not differ significantly from that obtained for the optimal combination (r = 0.54).
For the classification performance, only partial and ES were considered. The OS test was not considered for correlation analyses, due to the highly non-normal nature of the data produced by this test. Such a finding was expected, as the nature of this and any test where the time for which an action can be performed tends to produce a skewed distribution. Despite this lack of normality, the OS has been shown to accurately classify people at risk of falling (Vellas et al. 1997). In addition, the cut-off for the OS test is independent of the anthropometric characteristics of the subject, unlike the WS test where different cut-offs are used depending on the height and gender of the people tested (Fried et al. 2001). Classification results were accurate, with values exceeding 80 % in all cases. Similar performances were observed for both the optimal combination of PS and ES, although, as expected, the Fisher combination produced the best results. Following on from this finding, it was possible to propose a three-level decision making process from the two thresholds defined from sensitivity and specificity, as shown in Table 3. Subjects could be classified as “definitely at risk”, “definitely not at risk”, and “further examination needed”). The overall conclusion is that the BQT provides information on frailty and risk of falls, however some issues modulating this general conclusion need to be addressed.
The subjects included in the present study were patients coming to a consultation for geriatric problems, and accordingly would be expected to have a higher probability of frailty and greater fall risk. The absence of “healthy” older subjects without an appreciable fall risk could have had an effect on the performance achieved. Despite this limitation, the distribution of the values for the clinical tests and the balance scores were normally distributed, as shown for instance in Fig. 2 for the ES. The inclusion of a group of older control subjects may have modified the distribution of the variables, thereby potentially increasing the classification performance. Such a classification with additional control subjects was not possible due to the homogeneous population studied. Nevertheless, the hypothesis that the score was at a maximum for control subjects was verified with a younger subject group. None of these subjects were unable to stand on one leg for the required 15 s, or obtained an ES of <15.
In the present study, measurements for TUG and WS were obtained according to validated protocols by a clinician with a manual chronometer, with clinicians retaining only the integer part of the measurement in seconds. This methodology created an uncertainty between consecutive values in time, as well as an irregular distribution of the possible values on the range of WS. Such a method could be potentially critical when making a decision on the basis of a single cut-off value, for instance after a one-leg stand test. This method could be improved in two ways, either by providing an automated measurement with more precision, or by adding an uncertainty zone. The second option will be discussed in more detail below in relation to the classification results. In respect to an improved measurement system, the OS test could be performed on the BQT, with stand time calculated based on mediolateral displacement of the CoP. An automated device could also be used to measure walking speed, a prototype of which the present authors have already developed (Jaber et al. 2014).
Empirical score versus optimized combinations
Refine the weight of PS by using a greater number of subjects, and then testing the performance of PS on additional subjects,
Changing the thresholds used to attribute scores to each of the four NV used to create the PS, in order to optimize the performance of the ES. The results produced by the control population show that at the very least, the thresholds for trajectory velocity should be reassessed, something that is currently under way.
In addition to these improvements, it could be interesting to explore other ways to characterize the different phases of the weighing process, especially the stabilogram. Thus far only the most widely used variables from stabilogram analysis have been used. However, other approaches could be considered, such as taking into account the possible non-linear nature of the signal. Finally, the use of a median value for multiple tests rather than the mean would reduce the influence of any outliers.
Clinical reference tests
The three reference tests used (WS, TUG, OS) were chosen as they are recognized for their pertinence in respect to frailty and fall risk (Fried et al. 2001; Shumway-Cook et al. 2000; van Kan et al. 2009; Vellas et al. 1997). In addition, all three provide objective results from instrumented tests. The choice of these tests is obviously an issue for discussion, as none of the tests measure the same underlying physical process as the BQT, with all three producing indirect measures of the final aim of identifying fall risk. At this point in time, there is no “Gold Standard” that could be taken as a reference for fall risk, something that would need an extensive longitudinal experiment. Such an objective is worthy of further investigation.
Fall risk and decision making
ES < 7: fall risk
ES > 8: no risk of fall
7 ≤ ES ≤ 8: further tests are needed.
Given that ES is computed automatically, without requiring any specific learning, the BQT is well suited for monitoring community-dwelling older adults who are at the pre-frail stage, or who have recently returned home after a fall requiring hospitalization.
Balance quality measured by a modified bathroom scale is correlated with standard clinical tests, which are frequently used to assess frailty or fall risk. The BQT device is very easy to use, user friendly, and fits well in the usual environment of older adults, with no difference detected when compared to a typical bathroom scale. The BQT can, therefore, be used as part of a set of tests for frailty detection, or as a stand-alone tool for balance quality assessment and ecological balance monitoring. Further investigations can be envisaged in order to refine some of the steps used to build the balance score, in particular the thresholds used to attribute scores to each of the four variables. Despite these plans, the BQT in its current form is well suited to measure balance quality and as a screening tool for fall risk.
JD co-designed the study, performed the data analysis, and drafted the manuscript. DH co-designed the study, provided input for data analysis, and finalised the manuscript. PR co-designed the study with particular emphasis on the clinical tests to be used. He also and oversaw the data collection and edited the manuscript. All authors read and approved the final manuscript.
The authors would like to thank J. Y. Hogrel, V. Michel-Pélégrino, P. Hourseau and M. Mordefroy for their contribution to the protocol definition, set-up and monitoring.
The authors declare that they have no competing interests.
This work was partly supported by the French National Agency for Research (ANR), the Champagne-Ardenne Region, European FEDER funding and CNSA (French National Fund for Autonomy).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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