Accepted Jun 8, 2020; Epub ahead of print Jun 16, 2020

J Rehabil Med 2020; 52: jrm00079

Correspondence address: Kazuhiro Miyata, Ami-Machi, Inashiki-gun, Ibaraki, Japan. E-mail: miyatak@ipu.ac.jp

The Balance Evaluation Systems Test (BESTest), a clinical balance measure, categorizes balance into 6 factors. The aim of this study was to determine which short versions of the BESTest, Mini-BESTest, and Brief-BESTest, were most appropriate for assessing balance impairments in older adults with femoral or vertebral fracture. Subjects were older adults (age ≥65 years), who could walk with-out physical assistance. The models’ fitting was evaluated for four BESTest models (BESTest, one-factor Mini-BESTest, four-factor Mini-BESTest, and Brief-BESTest). The four-factor Mini-BESTest model was the only model that had a good fit and reflected the balance ability in older adults with femoral or vertebral fracture. We suggest that the using the four-factor Mini-BESTest model and seeing dynamic balance as composed of four factors may help therapists in making clinical decisions.

Loss of balance control is a major cause of falls and fractures in older adults. In Japan, the incidence rate of hip fractures is 249 per 100,000 person-years and that of vertebral fractures 352 per 100,000 person-years, and most of these are sustained due to falls (1). Thus, in older adults who return to community living after a fracture, fall prevention is important. Many clinical balance assessment tools are typically used to identify balance limitations and the risk of falls. Some of the balance assessment tools commonly used in older adult rehabilitation are the Berg Balance Scale, Timed “Up & Go” test, and Functional Reach Test. However, no gold standard exists for evaluating balance (2), and there is no consensus regarding which assessment tools to use in clinical practice (3, 4).

One of the most recent balance assessment tools is the Balance Evaluation Systems Test (BESTest), the scale developed from a theoretical understanding of 6 postural control systems (5). In a 2015 review, the
BESTest was identified as the only standardized balance measurement tool that evaluates all components of balance consistent with established conceptual models (6). However, its administration is time-consuming (7). Thus, shorter versions have been proposed, such as the Mini-BESTest and the Brief-BESTest (7, 8). The Mini-BESTest was developed to reflect the unidimensional construct “dynamic balance” through psychometric methods. In addition, the Mini-BESTest has recently been recommended as a method of choice for evaluating standing balance in adults (9). The Brief-BESTest includes items to adequately evaluate all postural control systems of balance endorsed by the BESTest (8).

Although some psychometric properties of the 3 BESTests, such as reliability, internal consistency, criterion validity, and construct validity, have been investigated in previous studies (5, 8, 10–12), other properties still need to be clarified for older adults. In particular, in the development of the Mini-BESTest, all items representing the 2 postural control systems were omitted; the remaining items, instead of representing different subsystems, were now collectively thought to reflect the unidimensional construct. However, the test items are arranged in 4 subsystems, and scores may be calculated separately for each subscale (13), which is somewhat contradictory to the concept of a unidimensional construct. Consequently, studies in which the scores for each subsystem are analysed and reported separately are emerging (14–17).

There is currently no unified view on the structure of the 3 BESTests, and it is not clear which BESTest is suitable for evaluating older adults with fractures. In rehabilitation, evaluation and intervention of balance are facilitated by a clear definition of the structure of the balance component associated with various diseases. Therefore, understanding the structure of the 3 BESTests in older adults with fractures may provide better guidance for a more directed clinical decision-making. The objective of this study was to compare the structural validity of the 3 BESTests in older adults with femoral or vertebral fractures and to determine a suitable balance assessment tool.

Subjects

This cross-sectional study analysed 94 older adults with fractures who participated in a rehabilitation programme at 3 hospitals with convalescent rehabilitation wards. The inclusion criteria were: (i) age ≥65 years; (ii) history of femoral or vertebral fracture due to fall; (iii) able to walk without physical assistance from another person (Functional Ambulation Categories; FAC ≥ 3); (iv) independence in basic daily activities; and (v) discharged to home. The exclusion criteria were: (i) vestibular disorder; (ii) cognitive impairment (Hasegawa Dementia Scale-Revised, <21/30) (18); (iii) visual problem in daily living; and (iv) history of neurological disease, such as stroke or Parkinson’s disease. All subjects received a conventional orthopaedic rehabilitation programme, prescribed by a doctor, with physical therapists, and occupational therapists, as required. The therapies were customized and included muscle strength, balance, gait, activities of daily living, and cognitive training. Therapy was carried out 7 days a week, for 1–1.5 h per day on weekdays and 40–60 min on Saturdays, Sundays, and national holidays.

Gunma University Ethical Review Board for Medical Research Involving Human Subjects (No. 15–73) and the ethics committees of Public Nanokaichi Hospital (20160208), Hidaka Rehabilitation Hospital (No. 151101), and Hidaka Hospital (No. 112) approved this study. The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.

Data collection procedures

Data were collected on age, type of fracture, time since admission, use of walking aid to transfer in the hospital from the subject’s medical records and the rehabilitation centre’s database at discharge. The BESTest was performed at discharge as indices. Based on the subject’s performance on the BESTest, the therapist provided a rating according to specific scoring criteria of the Mini-BESTest and Brief-BESTest.

Instrumentations

Table I shows the outline and items of the 3 BESTests.

BESTest. This test consists of 36 items; each item is scored on a 4-level ordinal scale from 0 to 3. The score for the total, as well as that for each section, is expressed as a percentage of the total points. The BESTest items are categorized into 6 postural control systems: biomechanical constraints, stability limits and verticality, anticipatory postural adjustments, postural response, sensory orientation, and stability in gait (5).

Mini-BESTest. This test consists of 14 items, each scored on a 3-level ordinal scale from 0 to 2, and the maximum score was 28 points. The Mini-BESTest items derived from the 4 of the 6 subsystems of the BESTest were: anticipatory postural adjustments, postural responses, sensory orientation, and stability in gait (7).

Brief-BESTest. This test consists of 8 items. The scoring -method for each item was the same as that in the BESTest, and the maximum score was 24 points. The Brief-BESTest items also corresponded to the 6 subsystems of the BESTest (8).

Data analysis

Structural validity is defined as “the degree to which the scores of a measurement instrument are an adequate reflection of the dimensionality of the construct to be measured” and is regarded as 1 aspect of construct validity (19). This is usually assessed through confirmatory factor analysis (CFA) (20). CFA assesses the degree to which responses on a p × 1 vector of observable random variables (3 BESTest items) can be used to assign a value to 1 or more unobserved variables (balance subsections). For this purpose, a specific mathematical model is identified and fitted to the subject’s data. Features of CFA include the ability to compare several models, relatively low dependence on data, and the need for a clear hypothesis about factor structure (21).

The structures of BESTest, Mini-BESTest, and Brief-BESTest were modelled from each original and related literature. Model 1 was designed based on Horak et al.’s (5) BESTest, which includes 6 factors with 36 items. Model 2 was the original Mini-BESTest shown by Franchignoni et al. (7), consisting of one factor with 14 items. As demonstrated by the authors, this model has been confirmed to have one-factor (unidimensional) structure in neurological diseases causing balance impairment. Model 3, which is another model of Mini-BESTest, consisting of 4 factors with 14 items, and the properties of these 4 factors are clearly different in several studies (14–17). The final model (model 4) is the Brief-BESTest presented by Padgett et al. (8), consisting of one factor with 8 items.

This study examined the structural validity of these 4 models (termed below as: models 1, 2, 3, and 4) through CFA with robust maximum likelihood estimation methods using structural equation modelling. In addition, the models’ goodness-of-fit was assessed using the following indexes: comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR). Goodness-of-fit indices were examined in light of the following standards used in past literature: CFI and TLI values should be > 0.9 for acceptable fit and > 0.95 for good fit (22, 23), RMSEA value should be < 0.10 for acceptable fit and < 0.06 for good fit (23), and SRMR values should be < 0.10 for acceptable fit and < 0.08 for good fit (21, 23). The CFA was conducted using Mplus 8.3 statistical software (Muthen & Muthen, Los Angeles, CA, USA).

Rasch analysis (partial credit model) was used to examine unidimensionality, internal construct validity, and item-difficulty hierarchy of the best-fitting model. If the best-fitting model was multidimensional, Rasch analysis was performed separately. The unidimensionality of the best-fitting model is evaluated using principal component analysis (PCA) of the standardized residuals. The subsequent criteria were variance explained by the measured construct (called “Rasch factor”) greater than 40% and the eigenvalue of the first residual factor ≤ 2 (24). Internal construct validity was assessed by determining how well the empirical data fit the Rasch model. Fit statistics were calculated as infit, which is the most sensitive for rating items located close to the ability of a person, and outfit, which is more influenced by the rating of off-target items. Fit statistics are routinely reported as means of squared residuals (MNSQ). According to Linacre (24), a MNSQ of 0.5–1.5 could be considered a reasonable range for infit and outfit measures in clinical observations. Rasch modelling provides estimates of the level of difficulty achieved for each item (item difficulty measure), and this parameter is expressed on a common interval scale in logit units. The sequencing of difficulty thresholds and ceiling/floor effects were evaluated by the person-item maps, using a standardized logit unit to place item difficulty and person ability in the same continuum. Rasch analysis was performed using WINSTEPS software 4.2.0 (Winsteps Rasch measurement computer program, Beaverton, OR, USA).

Complete sets of data were obtained from all 94 subjects, and Table II shows their assessment outcomes. The item “stance on firm surface, eyes open” was excluded from the subsequent analysis because the overall score of all subjects was perfect.

Structural validity

Table III summarizes the goodness-of-fit of each model. Fig. 1 shows the CFA solution for model 3 (four-factor model) of the Mini-BESTest. Model 3 shows significantly better goodness-of-fit (CFI = 0.952, TLI = 0.937, RMSEA = 0.060, and SRMR = 0.062) than other models. The CFI and SRMR values reached the good-fitting index values, and the TLI and RMSEA values have also reached the accept-fitting index values. The factor loadings of each item were significant (p < 0.001) and higher than 0.4 for model 3. The item “timed get up & go test with dual task” had the lowest factor loading.

Rasch analysis

In 4 separate Rasch analyses for the proposed factors of model 3, the PCA of standardized residuals showed that the variance attributable to Rasch factor was good (56.9–72.9%), and the eigenvalues of the unexplained variance in the first construct were 2.0, 1.8, 0.1, and 1.5 for anticipatory postural adjustments, postural response, sensory orientation, and stability in gait, respectively, confirming the factor’s unidimensionality.

Table IV shows item difficulty measures and fit information of each factor. One item of model 3 had low outfit values, including the item “sit to stand” (outfit MNSQ=0.12). The difficulty level of the item range from –3.91 to 4.04, –2.33 to 2.39, –1.21 to 1.21, and –3.74 to 4.71 logit for anticipatory postural adjustments, postural response, sensory orientation, and stability in gait, respectively. Items “stand on one leg” (4.04 ± 0.29 logits), “compensatory stepping correction, lateral” (2.39 ± 0.26), “stance on foam surface, eyes closed” (1.21 ± 0.43), and “timed get up & go test with dual task” (4.75 ± 0.44) were the most difficult items. Conversely, items “sit to stand” (–3.91 ± 0.59), “compensatory stepping correction, forward” (–2.33 ± 0.30), “incline, eyes closed” (–1.21 ± 0.43), and “change in gait speed” (–3.74 ± 0.31) were the easiest items.

Person ability ranged from –5.19 to 8.46, –6.51 to 5.67, –3.45 to 4.38, and –8.79 to 6.26 logit for anticipatory postural adjustments, postural response, sensory orientation, and stability in gait, respectively (Fig. 2). The sensory orientation factor was the only factor for which a majority of the subjects (69.1%) had a maximum raw score, indicating a ceiling effect

This study compared the structural validity of the 2 different short versions of the BESTest (Mini-BESTest and Brief-BESTest) with the original BESTest in older adults with femoral or vertebral fractures. The four-factor Mini-BESTest model was the only model that had a good fit and reflected the balance ability in older adults with fractures. To the best of our knowledge, this study is the first to investigate the BESTest and its short versions to identify the best assessment tool that can be recommended for assessing the balance of older adults with femoral or vertebral fractures.

Results from the CFA and Rasch PCA showed the structural validity of the four-factor Mini-BESTest model in older adults with femoral or vertebral fractures. Therefore, this study clarified that the four-factor Mini-BESTest model was structurally more stable than the one-factor model. Although the Mini-BESTest is widely used to assess balance in older adults (11, 12, 16), the structural validity of the assessment tool has not been investigated in this population. The Mini-BESTest was developed by Franchignoni et al. (7) as a one-factor balance assessment tool to evaluate “dynamic balance.” Previous studies have also confirmed that the Mini-BESTest is a unidimensional scale for neurological disorders such as stroke (25, 26). How-ever, the unidimensional structure was reported not suitable for people with mild to moderate Parkinson’s disease (27). Therefore, there is a possibility that a different factor structure is exhibited depending on the populations.

In general, balance is a composite ability that involves rapid, automatic, anticipatory, reactive integration and sensory strategies based on information derived from several systems (28). In older adults, several aspects are related to balance that decrease with ageing (29, 30), which is especially true for those who had a history of falls (16). Because the concept of balance includes many components, a balance assessment tool should be multidimensional. These results suggest that the four-factor Mini-BESTest model can evaluate multiple aspects of dynamic balance in older adults with femoral or vertebral fractures. This study suggested that using the four-factor Mini-BESTest model and considering dynamic balance as composed of 4 factors may help therapists in making clinical decisions, specifically intervening in factors that indicated decline in function.

In 4 separate Rasch analyses for the 4 proposed factors of the Mini-BESTest, the fit was good except for the item “sit to stand,” and each factor was unidimensional. Rasch analysis also revealed the difficulty of each factor task. This was in agreement with a previous finding that items “stand on one leg,” “compensatory stepping correction, lateral,” and “timed get up & go test with dual task” were difficult tasks in people with subacute stroke (26). Therefore, the difficulty of the Mini-BESTest may not be dependent on the disease. In this study, the difficulty level of the problem was clarified within the four-factor Mini-BESTest model. This identifies the next challenge to achieve among factors and will help therapists to set goals for intervention. Our results showed only that the sensory orientation factor had a ceiling effect. The sensory orientation factor is composed of static standing tasks, such as items “stance on foam surface, eyes closed” and “incline, eyes closed”, and evaluates any increase in body sway during stance associated with altering visual or surface somatosensory information for control of standing balance (5, 7). Neurological disorders, such as stroke, may reduce sensory orientation ability; that is, standing postural control depends on ones’ vision, which is affected by somatosensory impairment, muscle weakness, and asymmetric muscle tone (31). Ageing also decreases somatosensory and increases postural sway in older adults (32, 33). However, the sensory orientation factor was unable to distinguish the decline in somatosensory in older adults, suggesting that it was very easy to perform. In addition, since the study subjects were those who could walk without assistance, sensory orientation ability may be essential for walking.

This study confirmed that the structural validity of the original model of the BESTest and Brief-BESTest in older adults with femoral or vertebral fractures was not sufficient. In recent years, studies have reported the structural validity of the BESTest and Brief-BESTest in neurological diseases and proposed different structures from the original model (34, 35). Godi et al. compared 3 Brief-BESTest models, and found that the model with the item “hip/trunk lateral strength” removed showed better fit than the original model (35). Since it is important to understand the structure of the balance component of the subject, further research on various diseases is needed. In this study, the one-factor model of Mini-BESTest was more stable in structure than the BESTest and Brief-BESTest. This may involve the number of scoring levels on the assessment tool. In fact, the number of scoring levels is different between Mini-BESTest, BESTest, and Brief-BESTest, with Mini-BESTest having 3 levels and BESTest and Brief-BESTest having 4 levels. Previous studies have indicated that the BESTest and Brief-BESTest scoring stages are redundant (7, 36). This redundancy also affects the structure, suggesting that the Mini-BESTest showed better fit than the BESTest and Brief-BESTest.

The findings of this study have the following implications for clinical rehabilitation practice. The structure of the balance component in older adults suggests that it is effective to evaluate the four-factor structure of Mini-BESTest, including anticipatory postural adjustments, postural response, sensory orientation, and stability in gait. This clarifies the problematic balance component and facilitates selection of intervention. Furthermore, since the difficulty level of each factor is shown, the difficulty level of practice can easily be adjusted. Thus, providing effective interventions in these balance abilities may lead to the prevention of secondary fractures.

Study limitations

This study has a number of limitations. First, the sample size was small. It is desirable to exceed 100 samples in structural equation modelling (37); therefore, large-scale investigations are warranted. Secondly, subjects with femoral or vertebral fractures were analysed together. Femoral and vertebral fractures are osteoporosis-related fractures, and no previous studies have shown differences in the balance ability of patients. Therefore, they were analysed together, but separate analysis is more appropriate if a sufficient sample size can be obtained. Thirdly, our subjects were inpatients at a rehabilitation hospital, had no cognitive function decline, and were able to walk independently. It is unclear whether the results of the structural validity of the Mini-BESTest obtained herein can be applied to community-based older adults with fractures and cognitive function decline and need physical assistance to walk. Fourthly, the item “stance on firm surface, eyes open” was excluded from the analysis because all subjects had a perfect score. Thus, the BESTest and Mini-BESTest containing the item “stance on firm surface, eyes open” were analysed, with the exception of the item “stance on firm surface, eyes open.” Therefore, generalization of the results may be limited. Further studies involving large samples, participants with different walking abilities, and community-based older adults with femoral or vertebral fractures are needed.

Conclusion

The structural validities of the 3 BESTest models were compared in older adults with femoral or vertebral fractures. This study showed that the four-factor Mini-BESTest model adequately reflects balance ability and has a good fit. Based on these results, we suggest that using the four-factor Mini-BESTest model and seeing dynamic balance as composed of 4 factors, namely, anticipatory postural adjustments, postural responses, sensory orientation, and stability in gait, may help therapists in making clinical decisions.

This work was supported by the Sasakawa Scientific Research Grant from The Japan Science Society and JSPS KAKENHI Grant Number 18K17724.

The authors have no conflicts of interest to declare.