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Conceptual Limitations of Balance Measures for Community-Dwelling Older Adults

Poonam K. Pardasaney, Mary D. Slavin, Robert C. Wagenaar, Nancy K. Latham, Pengsheng Ni, Alan M. Jette
DOI: 10.2522/ptj.20130028 Published 1 October 2013

Abstract

Background Psychometric limitations of balance measures for community-dwelling elderly may be related to gaps in task and environmental representation.

Objective The purposes of this study were: (1) to conduct item-level content analysis of balance measures for community-dwelling elderly people based on task and environmental factors and (2) to develop profiles of individual measures summarizing their task and environment representation.

Design A systematic content analysis was conducted.

Methods A literature search was conducted to identify balance measures. Item-level content analysis was based on 7 criteria related to task and environment: (1) task role, (2) environmental variation, (3) object interaction, (4) obstacle negotiation, (5) external forces, (6) dual-tasking, and (7) moving people or objects in the environment.

Results Twenty-six measures, containing 167 items, were identified. Task role was fairly evenly distributed, with the majority of items examining gait tasks (32.3%), followed by dynamic body stability (29.9%) and static body stability (25.1%). The majority of items involved no environmental variation (58.1%), followed by variation of support surfaces (20.4%), visual conditions (13.2%), and both support and visual conditions (8.4%). Limited task role variability was seen within measures, with 73.1% of measures examining only one task role. Environmental variation was present in 65.3% of measures, primarily during static body stability tasks. Few measures involved object interaction (23.1%), obstacle negotiation (38.5%), external forces (11.5%), dual-tasking (7.7%), or moving people or objects (0%).

Limitations The classification framework was not externally validated.

Conclusions Existing measures focus on single-task assessment in static environments, underrepresenting postural control demands in daily-life situations involving dynamic changing environments, person-environment interactions, and multitasking. New items better reflecting postural control demands in daily-life situations are needed for more ecologically valid balance assessment. Individual balance measure profiles provided can help identify the most appropriate measure for a given purpose.

Balance, or postural control, depends on interaction of multiple body systems, with postural control demands being influenced by the complexity of the task and the environment in which the task is performed.1–3(pp157–186) Task and environmental conditions influence postural control by affecting associated biomechanical and information-processing demands.1,4 The ability to maintain balance is context-dependent, and different people can become unstable in different task and environmental conditions, depending on the particular body systems impaired.2 To identify balance deficits in people with varying types and degrees of impairment, a balance measure would need to assess balance under task and environmental conditions of varying complexity.

Performance-based balance measures typically assess balance by systematically varying aspects of the task or environment to challenge performance. Balance measures are extensively used to assess balance ability, monitor change, determine fall risk, and classify type of balance dysfunction. A psychometrically strong balance measure would be expected to have adequate reliability, validity, and measurement breadth; minimal floor or ceiling effects for the intended purpose and population; adequate sensitivity and specificity when used for diagnosis; and adequate sensitivity to change and responsiveness when used for assessment of change.5–7 To achieve precision and sensitivity to change uniformly across people of varying balance ability, a balance test would need to include tasks of a wide range of difficulty in order to cover the full spectrum of ability from low to high. With regard to the ability to measure balance and assess change, existing measures have important limitations in community-dwelling elderly people, who typically represent a higher-functioning population compared with institutionalized elderly or clinical populations with balance deficits. Well-recognized psychometric limitations include limited comprehensiveness in assessing the multiple aspects of balance, ceiling effects, and limited sensitivity to change and responsiveness.8–10 These limitations may contribute to frequent use of multiple tests for balance assessment, increasing testing burden.11–13

From a conceptual perspective, psychometric limitations of balance measures may be related to gaps in representation of essential task and environmental components within their items.1 Previous authors have expressed concern that most existing measures assess balance in static, predictable environments, inadequately representing postural control demands in daily life situations that involve complex and dynamically changing environments.1,3(pp257–295) Focus on static environments renders measures susceptible to inaccurately underestimating deficits and reduces their ability to predict people's performance outside of clinical environments.1,3(pp257–295) As a result, individuals who could benefit from balance interventions may not be identified. Furthermore, although balance intervention strategies commonly incorporate dynamic, changing environments and person-environment interaction,3(pp257–295) balance assessment in primarily static environments can limit ability to capture improvement resulting from intervention.

Given that psychometric limitations of balance measures may be related to gaps in task and environment representation, a systematic content analysis of existing measures would be valuable in understanding the degree to which essential task and environmental factors are examined. Content analysis of existing measures prior to development of new measures is recommended when several measures relevant to the outcome of interest already exist.14 A systematic analysis would identify task and environmental factors that are well-represented in existing measures, reveal content gaps, and guide new measure development, reducing redundant item development and ensuring that new measures successfully overcome any content gaps. Additionally, given the vast array of balance measures available to clinicians and researchers, developing profiles of measures based on a systematic analysis would allow content comparison across measures, guiding selection of the most appropriate measure for a given purpose. Although a global critique of task and environmental complexity of some balance measures has previously been reported,1 to our knowledge, a systematic item-level content analysis of balance measures for community-dwelling elderly people is lacking.

The first aim of this study was to conduct a systematic item-level content analysis of balance measures for community-dwelling elderly people based on task and environmental factors that influence balance performance and can be systematically varied for balance assessment. The second aim was to develop profiles of balance measures describing the extent to which different aspects of the task and environment are represented within their items.

Method

Search Strategy

An extensive literature search was conducted in consultation with a medical librarian to identify balance measures used in community-dwelling elderly people. Functional mobility measures primarily designed as indicators of fall risk were not included. The electronic databases PubMed (MEDLINE) and CINAHL were searched from their earliest records until September 2012. In PubMed, the following search terms were used: (“Postural Balance” [MeSH] AND “Geriatric Assessment” [MeSH]) AND (“Aged” [MeSH] OR “Aged, 80 and over” [MeSH]) AND ((“residence characteristics” [MeSH Terms] OR (“residence” [All Fields] AND “characteristics” [All Fields]) OR “residence characteristics” [All Fields] OR “community” [All Fields]) AND dwelling [All Fields]). In CINAHL, the following search terms were used: (“balance scales” AND “aged” AND “aged 80 and over” AND “community-dwelling”). Titles and abstracts of search results were reviewed to identify articles reporting development or application of balance measures. When abstracts reporting balance measures were identified, a further search was conducted, as needed, to determine whether the measures were used in community-dwelling elderly people. For relevant abstracts, related citations were reviewed within PubMed to identify further appropriate articles. Relevant article bibliographies, a rehabilitation measures database,15 and rehabilitation textbook3 also were searched to identify balance measures.

Data Extraction

Individual items from each balance measure were extracted. To obtain individual item content, the full text of original articles reporting measure development was obtained. When the original article could not be found, measure content was obtained from secondary sources. When the content of a measure could not be obtained electronically, in the university library, or in rehabilitation textbooks, the measure was not included in the analysis.

Classification Criteria for Item-Level Content Analysis

An extensive literature search was conducted to determine classification criteria for content analysis. Conceptual literature discussing the construct of balance was reviewed, as were studies examining factors that influence postural control demands.1–3(pp157–186,257–295),16–26 In addition to literature directly related to balance, Gentile's taxonomy of tasks,4 a framework for analyzing movement and action based on task and environmental complexity, was reviewed. Gentile's taxonomy, which has been applied to globally examine balance measures,1 describes 4 categories each of task and environmental factors that influence biomechanical and information-processing demands of movement. Gentile's definitions of task role and motion in the environment4 were modified to increase their applicability to postural control and adapted within our classification criteria, as described below. However, Gentile's concept of intertrial variability, which is more relevant to practice and skill acquisition, was not included.

From the above literature review, 7 factors related to task and environment that can influence balance performance and be systematically varied for balance assessment were extracted: (1) task role,3(pp157–186),4 (2) variation of support surface and visual conditions in the environment,1,2,16,21,23–27 (3) interaction with objects,4 (4) obstacle negotiation,1,22,25 (5) external forces,20 (6) dual-tasking,3(pp157–186),17–19,25 and (7) moving people or objects in the environment.1,4 These classification criteria and their operational definitions are provided in the Table and eTable 1, reflecting varying degrees of task and environment complexity.

View this table:
Table.

Classification Criteriaa

Initially, a modified 4-level definition of task role was adapted from Gentile's taxonomy, which dichotomizes tasks into body stability and body transport. A fifth level of task role—transfers and gait—had to be created to accommodate some items that included both transfers and gait. A 4-level definition of environmental variation was operationalized to reflect variation of support surfaces and visual conditions.16 Interaction with objects and obstacle negotiation were defined to reflect different aspects of person-environment interaction that can influence balance performance.1,4,22,25 External forces was included as a criterion, as the ability to respond to external forces is a crucial strategy for postural stability and fall prevention.20,22 Dual-tasking was included as a criterion, as postural instability and fall risk are manifested to a greater extent in dual-task conditions and reduced balance performance under dual-task conditions is highly predictive of falls in older adults.28,29 Based on Gentile's taxonomy, moving people or objects in the environment was included as a criterion, as the presence of such motion, which is outside the individual's control, can increase information-processing and postural control demands.4 Interaction with objects, obstacle negotiation, external forces, dual-tasking, and moving people or objects in the environment were operationalized as being present or absent.

Item-Level Classification

Individual items were coded on each of the 7 classification criteria. Coding was conducted independently by the first author (P.K.P.) and the second author (M.D.S.). Both raters are physical therapists who are familiar with the use of standardized balance measures in the elderly population. Both raters underwent standardized training on application of classification criteria. The 2-hour training session included extensive discussion of operational definitions to ensure consistency in understanding and application and coding of 15 items selected from the balance measures identified. At the end of training, both raters were in full agreement on coding of practice items. The third author (R.C.W.) was available for tie-breaking in the event of any coding discrepancies.

Data Analyses

Data analyses were conducted in Microsoft Excel 2010 (Microsoft Corporation, Redmond, Washington) using pivot tables. To address the first aim of conducting an item-level content analysis, frequency analyses were conducted for each classification criterion. To address the second aim of describing profiles of measures, frequency analyses of classification criteria were summarized by balance measure and are presented as radar plots. All results are reported as percentages.

Results

A total of 217 articles were initially identified from PubMed and CINAHL searches, and their titles and abstracts were examined. A review of relevant full text articles, related citations, article bibliographies, and the rehabilitation measures database and rehabilitation textbook identified 27 balance measures used in community-dwelling elderly people. The content of one measure, the Balance Screen Test,30 could not be obtained and was not included.

Twenty-six balance measures, comprising a total of 167 items, were included in the analysis; a list of these measures and their abbreviations is provided in eTable 2. The number of items within a measure ranged from 1 to 20. Of the 26 measures, 30.8% were single-item measures (Four Square Step Test [FSST], Functional Reach Test [FRT], TURN180, single-leg stance [SLS], tandem stance, tandem walk, Romberg test, step test); 19.2% were multidirectional assessments of leaning, stepping, or reaching tasks (Limits of Stability [LOS], Maximum Step Length [MSL], Rapid Step Test [RST], Multidirectional Reach Test [MDRT], and Lateral Reach Test [LRT]); 19.2% were multi-item measures examining different aspects of balance (Berg Balance Scale [BBS], Mini-Balance Evaluation Systems Test [Mini-BESTest], Fullerton Advanced Balance Scale [FAB], Performance-Oriented Mobility Assessment–Balance Scale [POMA-B], and Frailty and Injuries: Cooperative Studies of Intervention Techniques–4 [FICSIT-4]); 11.5% were focused on assessing sensory systems underlying balance (Dynamic Balance Assessment [DBA], Sensory Organization Test [SOT], and Clinical Test of Sensory Interaction on Balance [CTSIB]); 7.7% were functional mobility-based assessments (Sensory-Oriented Mobility Assessment Instrument [SOMAI] and Functional Obstacle Course [FOC]); 7.7% were gait-based assessments (Functional Gait Assessment [FGA] and Dynamic Gait Index [DGI]); and 3.8% were dual-task balance assessments (Multiple Tasks Test [MTT]).

There were no disagreements between the 2 raters on item coding, and tie-breaking from the third rater was not required. Item-level analysis of task role across the 167 items is presented in Figure 1. Task role was fairly evenly distributed, with the majority of items examining gait tasks, followed by dynamic body stability and static body stability. Although transfers were examined by only 7.2% of items, the operational definition of transfers was restricted to a narrow range of items such as sit-stand and chair-chair transfers. The category of transfers and gait was created to accurately classify 9 items from the Mini-BESTest and MTT that examined both transfers and gait.

Figure 1.
Figure 1.

Distribution of task roles across the 167 items.

The majority of items involved no environmental variation (Fig. 2). Variation of support surface was the most common type of environmental variation, followed by variation of visual conditions and variation of both support surface and visual conditions. Of 48 items incorporating support surface variations, 41.7% involved base of support variations such as narrow base of support; 52% involved compliant, sway-referenced, or inclined surfaces with or without base of support variations; and 6.3% simulated a slippery surface. Of 70 items incorporating any type of environmental variation, 48.6% represented static body stability tasks; 35.7% represented gait tasks, 8.6% represented dynamic body stability tasks, 4.3% represented transfer and gait tasks, and 2.9% represented transfers. A very small proportion of items examined interaction with objects (8.4%), obstacle negotiation (14.4%), external forces (3.0%), and dual-task balance (4.8%). No item incorporated moving people or objects in the environment.

Figure 2.
Figure 2.

Distribution of environmental variations across the 167 items.

Distributions of the different content areas across measures are summarized in Figures 3, 4, and 5. In terms of task role, 73.1% of the tests examined only one type of task; these tests included the 18 measures containing 8 items or less, and the 10-item FGA (Fig. 3). The remaining 26.9% of tests examined different task roles to varying extents, with the Mini-BESTest being the only measure to examine all 5 task roles. Environmental variation was present in 65.3% of tests to varying extents (Fig. 4). Of the 17 tests incorporating environmental variation, 47.1% varied environmental conditions during static body stability tasks (BBS, POMA-B, SOT, CTSIB, FICSIT-4, SLS, tandem stance, and Romberg test), 23.5% during gait tasks (FOC, FGA, DGI, and tandem walk), and 29.4% during more than one type of task role (SOMAI, Mini-BESTest, DBA, FAB, and MTT). Of the 16 tests incorporating support surface variations, 50% consisted uniquely of base-of-support variations (BBS, FGA, POMA-B, FICSIT-4, SLS, tandem stance, tandem walk, and Romberg test); 43.7% consisted of compliant, sway-referenced, or inclined surfaces with or without base-of-support variations (SOMAI, Mini-BESTest, DBA, FOC, FAB, SOT, and CTSIB); and 6.3% simulated a slippery surface (MTT). A minority of measures incorporated interaction with objects (23.1%), obstacle negotiation (38.5%), external forces (11.5%), and dual-task balance (7.7%) (Fig. 5).

Figure 3.
Figure 3.

Distribution of task roles across the 26 measures. See eTable 2 for balance measures and abbreviations.

Figure 4.
Figure 4.

Distribution of environmental variations across the 26 measures. See eTable 2 for balance measures and abbreviations.

Figure 5.
Figure 5.

Distribution of object interaction, obstacle negotiation, external forces, and dual tasks across the 26 measures. See eTable 2 for balance measures and abbreviations.

Profiles of individual balance measures are presented as radar plots in the Appendix, summarizing the different content areas represented within each measure. For instance, the Mini-BESTest radar plot shows incorporation of all but one content area including static body stability (35.7%), dynamic body stability (21.4%), transfers (7.1%), gait (28.6%), transfers and gait (7.1%), variation of support surfaces (21.4%), variation of visual conditions (7.1%), variation of support surface and visual conditions (14.3%), obstacle negotiation (7.1%), external forces (21.4%), and dual-tasking (7.1%). In contrast, the DGI radar plot shows incorporation of only 3 content areas including gait (100%), variation of visual conditions (25%), and obstacle negotiation (37.5%). The CTSIB and SOT radar plots show incorporation of all 3 types of environmental variations within each measure, including variation of support surfaces (16.7%), visual conditions (33.3%), and support surface and visual conditions (33.3%); however, these environmental variations occur in the context of static body stability tasks (100%) only. Other radar plots can be similarly interpreted to determine the extent to which different content areas are encompassed within each measure. The category of moving people or objects is not included in these radar plots, as no measure incorporated moving people or objects in the environment.

Discussion

To our knowledge, this is the first study to report an extensive, systematic content analysis of balance measures for community-dwelling elderly people based on task and environmental factors. Our study raises awareness of the breadth of task and environment factors that influence postural control demands and are important to incorporate in balance assessments. A striking observation of the study was the plethora of balance measures available for community-dwelling elderly people, with considerable overlap in content noted across several measures. Important content gaps were observed across most measures, with limited comprehensiveness in content areas represented and limited incorporation of environmental variations. Most measures focused on single-task assessment in static environments, underrepresenting postural control demands in daily-life situations, which frequently involve changing environments, person-environment interactions, unexpected external forces, and multitasking. No measure incorporated environments with moving people or objects, an important limitation for assessment of community-dwelling elderly people. These content gaps may contribute to the ceiling effects and reduced sensitivity to change of balance measures in the community-dwelling elderly population. The content gaps also provide important insights into areas that should be incorporated in new items for more comprehensive and ecologically valid balance assessment.

The detailed and comprehensive profiles of balance measures have significant practical applications, as they summarize the content areas examined by each measure within a simple, easily interpretable format. These profiles depict the strengths and limitations in content coverage of individual measures and serve as a valuable guide to clinicians and researchers seeking to identify the most appropriate measure for a given purpose. An important strength of the balance measure profiles is their development based on systematic item-level analysis using standardized, comprehensive criteria.

The identified content gaps across measures may partly result from lack of application of a conceptual framework clearly outlining essential task and environment components during measure development. A strong conceptual model has been described as a key attribute in developing sound health outcome measures.31 Although balance assessment in physical therapy has historically been based on strong conceptual frameworks of postural control systems, a systems approach—albeit necessary—may be insufficient for developing psychometrically strong balance measures. A task and environment approach is additionally needed to develop improved and ecologically valid assessments that are adequately challenging for the community-dwelling elderly population.

The importance of incorporating task and environmental conditions of varying complexity in balance assessments due to their varying influence on postural control demands has previously been emphasized.1,3(pp257–295) Evaluating patients using a broad range of activities based on a framework of task and environment complexity also has been suggested so functional assessments are more representative of people's performance in daily-life situations.4,32 From a broader perspective, the widely adopted International Classification of Functioning, Disability and Health33 has highlighted the important interaction among the person, task, and environment in determining an individual's function and disability levels. Application of a comprehensive conceptual framework of task and environment would be a crucial step toward developing improved balance measures and achieving standardization across measures. Although a framework of postural control systems underlies the recently developed BESTest,21,34 given its diagnostic purpose of identifying disordered postural control systems, the framework does not comprehensively outline task and environment factors that can be systematically varied for balance assessment. The classification criteria compiled for this study can serve as a preliminary framework when developing new measures, to ensure that items along the entire spectrum of task and environment complexity are represented.

A single measure encompassing all content areas was not identified, with nearly three fourths of measures examining only one type of task role. The Mini-BESTest was the most comprehensive measure, followed by the FAB. Nevertheless, the Mini-BESTest has demonstrated a ceiling effect trend even in inpatients with neurological disorders,34 and the FAB has been found to have very few items to assess community-dwelling elderly people with above-average balance ability.35 Methodologically, the ceiling effect trend in these measures may be related to the difficulty in selecting a reasonably small number of items that can discriminate across a wide range of balance abilities. Conceptually, 2 important reasons may explain the ceiling effect trend. First, environmental variation, though present in these tests, primarily occurs in the context of less challenging, static body stability tasks. Second, neither measure assesses balance in environments involving moving people or objects, which are more representative of dynamic, real-world environments such as pedestrian crossings or crowded supermarkets.1,4 Compared with stationary environments, postural control demands increase considerably in moving environments due to the changing amount and nature of sensory information, and the need to predict and respond to changing paths of people and objects in a timely manner.1,4 Previous authors have expressed concern that until balance measures can incorporate dynamic, moving environments, their ability to measure balance and predict performance outside of clinical environments is likely to remain limited.1

To truly develop more challenging items, environmental variation across the spectrum of task roles, from static body stability to gait, is recommended. Additionally, incorporating object interactions, obstacle negotiations, and dual-tasking across varied environmental conditions is recommended to replicate complex, real-world environments. Although the majority of support surface variations comprised base-of-support changes, balance assessment on compliant surfaces also is encouraged to challenge sensory inputs in addition to biomechanical constraints. Response to external forces and dual-tasking are other essential areas that were minimally examined by measures. The ability to respond to unexpected external forces using reactive strategies has long been acknowledged as a critical aspect of postural control,22 as a significant proportion of falls in elderly people are related to inadequate responses to external disturbances.20 Given the evidence that information-processing demands and postural instability increase with secondary task performance, balance assessment under dual-task conditions also is critical to isolate deficits not apparent in single-task paradigms. Dual-task assessment is particularly important in older adults because attentional capacity has been found to decline with aging25 and impaired performance of one or both tasks has been noted in older adults in dual-task paradigms where attention is divided.36,37 Although the MTT was specifically developed as a measure of balance under multiple task conditions,38 very limited application of the measure was found during the literature search.

The lack of incorporation of moving people and objects in balance assessments may be related to the challenge in incorporating such motion in a standardized, practical, and reproducible manner. Virtual reality systems may offer promising mechanisms to overcome this challenge and systematically assess balance in dynamic, moving environments.39 Virtual reality has been described as an immersive and interactive system that provides users with the illusion of entering and exploring a virtual world that can be responsive to actions of the user.40 Virtual reality environments have been suggested as ideal, ecologically valid environments to understand postural control strategies.39 Although effectiveness of virtual reality systems in delivering balance interventions has been examined,41–43 their application as balance assessment tools remains limited. Applying virtual reality environments to balance assessments may be a potential mechanism to reduce ceiling effects of balance measures.

When advocating the importance of comprehensive balance assessment, it is equally important to underscore the practical challenges of administering a lengthy, comprehensive measure in the traditional, fixed-form format. Traditional fixed-form measures require administration of all items in the measure to every person.44 A comprehensive fixed-form balance measure will be associated with increased testing burden on both administrators and elderly people due to the large number of items needed to cover the full spectrum of balance ability and components. Two recently developed comprehensive fixed-form balance measures have limited practicality due to their testing burden, with the BESTest containing 36 items and the Unified Balance Scale containing 27 items.21,45 Contemporary measurement methods of item response theory and computerized adaptive testing offer promising approaches to overcome methodological challenges associated with fixed-form testing and develop precise and efficient balance measures.44 Item response theory and computerized adaptive testing methods require development of a comprehensive item pool measuring the construct of interest46–49; relevant and tailored item subsets from the item pool then are administered to individuals based on their ability level. Qualitative review and classification of collective items from existing measures is the recommended first step toward developing comprehensive item pools.14

In the future, it is conceivable that a comprehensive item response theory–calibrated balance item pool could be developed for the community-dwelling elderly population, which can be used to administer tailored balance assessments. Our analyses, by providing qualitative understanding of items contained in existing measures, represent an important first step toward building an improved, expanded item pool that fills existing content gaps. Recent unpublished research by our group has demonstrated the superior validity of a preliminary computerized adaptive balance measure over 3 traditional, fixed-form measures in discriminating between community-dwelling elderly fallers and nonfallers. Continued investigation of item response theory and computerized adaptive testing methods for balance assessment is encouraged.

Limitations

Our study had some limitations. First, although we conducted an extensive literature search in consultation with a medical librarian to identify balance measures for community-dwelling elderly people, it is possible that some measures were not identified. Second, although the classification criteria for our content analysis were based on an extensive literature review that included examination of existing postural control frameworks, our classification framework was not externally validated. Validation of the classification framework by external experts in the field of balance assessment as well as clinician focus groups would strengthen its validity for future application. Nevertheless, our framework is timely in raising awareness of task and environment influences, given the proliferation of balance measures that fail to adequately represent the spectrum of task and environment components important for balance assessment. Finally, although we recognize that our classification framework is not diagnostic of disordered postural control systems, developing a single framework of postural control systems, task, and environment is challenging. The classification framework used in our study can be integrated with postural control system frameworks to develop items of varying complexity targeted to specific systems.

Conclusions

The majority of balance measures used in the community-dwelling elderly population are deficient in incorporating important task and environmental variations, showing limited variability in types of task roles examined. These content gaps may contribute to the ceiling effects and reduced sensitivity to change of balance measures in this population. New measures should include items of greater task and environmental complexity to better replicate postural control demands in real-world environments. The most critical and underrepresented task and environment components recommended for inclusion in new measures include support surface and visual variations, obstacle negotiation, external forces, dual-tasking, and moving people or objects in the environment.

Footnotes

  • Dr Pardasaney, Dr Slavin, Dr Wagenaar, and Dr Latham provided concept/idea/research design. Dr Pardasaney, Dr Slavin, Dr Wagenaar, Dr Latham, and Dr Jette provided writing. Dr Pardasaney and Dr Slavin provided data collection. Dr Pardasaney, Dr Slavin, Dr Ni, and Dr Jette provided data analysis. Dr Pardasaney provided project management. Dr Slavin and Dr Wagenaar provided consultation (including review of manuscript before submission).

  • Received February 4, 2013.
  • Accepted May 20, 2013.

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Conceptual Limitations of Balance Measures for Community-Dwelling Older Adults
Poonam K. Pardasaney, Mary D. Slavin, Robert C. Wagenaar, Nancy K. Latham, Pengsheng Ni, Alan M. Jette
Physical Therapy Oct 2013, 93 (10) 1351-1368; DOI: 10.2522/ptj.20130028

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Conceptual Limitations of Balance Measures for Community-Dwelling Older Adults
Poonam K. Pardasaney, Mary D. Slavin, Robert C. Wagenaar, Nancy K. Latham, Pengsheng Ni, Alan M. Jette
Physical Therapy Oct 2013, 93 (10) 1351-1368; DOI: 10.2522/ptj.20130028
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