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Research Reports |
LE Dibble, PT, PhD, ATC, is Associate Professor (Clinical), Department of Physical Therapy, University of Utah, 520 Wakara Way, Salt Lake City, UT 84108 (USA)
J Christensen, PT, DPT, is Physical Therapist–Sports Medicine Resident, Howard Head Sports Medicine Center, Vail, Colo
DJ Ballard, PT, DPT, is Instructor (Clinical), Division of Physical Therapy, University of Utah
KB Foreman, PT, PhD, is Associate Professor, Division of Physical Therapy and Division of Plastic Surgery, University of Utah
Address all correspondence to Dr Dibble at: lee.dibble{at}hsc.utah.edu
Submitted March 14, 2007;
Accepted November 15, 2007
 | Abstract |
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Participants: Seventy people with PD (mean age=73.91 years) participated in this study.
Method: Clinical balance tests were conducted during the initial examinations of people with PD. Validity indices were calculated for individual tests and compared with validity indices calculated for a combination of multiple tests.
Results: Thirty-six participants reported a fall history. Analysis of individual tests revealed broad variations in validity indices, whereas the collective interpretation of multiple tests improved sensitivity and negative likelihood ratios.
Discussion and Conclusion: Collective interpretation of clinical balance tests resulted in fewer false-negative results and more substantial adjustments to the posttest probability of being a "faller" than the interpretation of one test alone. These results should be confirmed in a prospective examination of fall risk in PD.
| Introduction |
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 TopAbstract Introduction MethodResultsDiscussionConclusionReferences |
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The heterogeneity of motor deficits associated with PD is relevant to an examination of fall risk in people with PD. People with PD, who are at risk for falls, can demonstrate problems with broad areas of movement control, including, but not limited to, sensory integration, functioning with a narrow base of support, controlling their center-of-mass movement within their base of support, and coordination of anticipatory postural control tasks.9,10 Additional factors that can contribute to fall risk in people with PD include comorbid conditions (eg, peripheral neuropathy) and medication side effects (eg, postural hypotension and dyskinesias).
Although some degree of postural instability is present in many people with PD, not all people with PD demonstrate losses of balance or falls during daily activities. A variety of clinical balance tests have been used to attempt to provide valid diagnostic tools for people who may be at risk for falls. Four such clinical balance tests are the Berg Balance Scale (BBS), the Dynamic Gait Index (DGI), the Functional Reach Test (FRT), and the 2.44-m (8-ft) Up and Go Test.11–15 These tests appear to be reliable and valid for examining fall risk in geriatric populations, but limited validity studies have been performed with populations with PD.
Previous researchers have reported that a history of 2 or more falls in the preceding year is the best predictor of future falls in people with PD.5 However, such data do not help clinicians who are attempting to diagnosis fall risk in a person who has PD but who has not yet fallen or has not yet experienced recurrent falls. Researchers and clinicians concerned with falls in people with PD have commented on the inaccuracy of individual clinical balance tests in identifying people who have PD and who may be at risk for falls and have called for alternative means of diagnosing fall risk.16–20 Considered together, these studies consistently concluded that reliance on the results of one clinical balance test to diagnose fall risk in people with PD provides a limited perspective of each person's risk that is incompatible with the heterogeneous nature of fall risk in people with PD.
For this reason, the overall objectives of this study were to describe and compare the diagnostic accuracies associated with individual clinical balance tests and the collective interpretation of multiple tests for a sample of people with PD. To accomplish these overall objectives, we systematically examined the following questions:
Finally, we used our statistical results to propose a data-driven clinical balance examination algorithm for people with PD.
| Method |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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Over the 2-year period during which data were collected, approximately 350 participants with varied neurologic and musculoskeletal diagnoses were examined at the facility. Seventy people with idiopathic PD (54 men and 16 women), with a mean age of 73.91 years (SD=6.45 years), and with a median modified Hoehn and Yahr staging level of 2.5 (range=1–4) met the inclusion and exclusion criteria and were the participants included in this analysis. Thirty-six people (51%) reported a history of at least 2 falls in the previous year (fall group), whereas the remaining 34 people reported fewer than 2 falls in the previous year (nonfall group) (Tab. 1). There were no significant differences between the fall and nonfall groups in any of the demographic variables (P>.05).
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For the purposes of this study, demographic as well as numerical clinical balance test data were extracted and recorded for each individual. Each participant's disease severity was determined using Hoehn and Yahr staging criteria.22 In 5 of the cases, the participants could not estimate their PD duration or the PD duration was not recorded in the medical records. In those cases, a value of 1 was assigned to acknowledge that the participants were diagnosed with PD. Participants were divided into groups based on fall history. Participants placed in the fall group were those who reported 2 or more falls within the previous year, and participants placed in the nonfall group were those with fewer than 2 self-reported falls in the previous year. A fall was classified as an unexpected contact of any part of the body with the ground.23 This definition of a fall was consistent with previous studies of prediction of falls in people with PD and people without PD.5,23,24
Data Analysis
Data analysis was performed with SPSS for Macintosh (version 11.0)* and Confidence Interval Analysis (version 2.0).
Demographic and disease-related variables were summarized with descriptive statistics. Between-group comparisons were performed to determine whether people in the fall group and people in the nonfall group performed differently on the clinical balance tests of interest (BBS, DGI, FRT, and Up and Go Test). Comparisons of demographic and balance test performance differences between groups were accomplished with separate nonparametric tests for 2 independent groups (Mann-Whitney U tests) with a level of significance set at P<.05.25
With each participant's self-reported fall history as the gold standard for the measurement of fall risk, sensitivity, specificity, and LRs (positive and negative)26,27 were calculated. In order to explore these validity indexes, we first had to interpret the test results in a dichotomous fashion in relation to an established cutoff score for fall risk. Because we had concerns that adjusting cutoff scores and performing collective interpretation of tests were overly complicated, for the purposes of this research, we chose to use cutoff scores that are currently accepted in the literature.13,14,18,23,28–30 Participants were determined to be positive for fall risk if they reached the threshold value for a specific clinical balance test (FRT value of 
25.4 cm, BBS score of 46/56, DGI of 19/24, and Up and Go Test value of 
8.5 s), whereas they were considered to be negative for fall risk if they did not reach the threshold value (FRT value of 25.4 cm, BBS score of 46/56, DGI of 19/24, and Up and Go Test value of <8.5 s).13,14,18,23,28–30
Sensitivity in this context was defined as how often a clinical balance test detected fall risk for a participant in the fall group.26,27,31 Sensitivity values close to 1.0 indicated that the majority of "true fallers" were identified.26,27,31 Specificity was defined as how often a clinical balance test result was negative for a participant in the nonfall group. Specificity values close to 1.0 indicated that the majority of "true nonfallers" were identified.26,27,31 Although sensitivity and specificity are the validity indices most recognizable to clinicians, what they actually tell clinicians is how likely a clinical balance test result is to be positive or negative given that a person with PD is at high risk for falls or is not at high risk for falls. There is a paradox of relying on sensitivity and specificity because knowledge of a person's degree of fall risk would eliminate the need for a diagnostic clinical balance test in the first place.
To overcome the limitations of sensitivity and specificity, positive and negative LRs were calculated. The advantage of LRs is that they allow a clinician to quantitatively estimate the posttest probability of an individual participant being in the fall group.26,27,31 The positive LR was calculated as sensitivity/(1 – specificity) and used to answer the following question: How much more likely is a positive clinical balance test result to be found in a person in the fall group than in a person in the nonfall group? A larger positive LR amplified the probability of a person being in the fall group, given a positive clinical balance test result.26,27,31 The negative LR was determined as (1 – sensitivity)/specificity and used to answer the following question: How much more likely is a negative clinical balance test result to be found in a person in the nonfall group than in a person in the fall group? A smaller negative LR reduced the probability of a person being in the fall group, given a negative clinical balance test result.26,27,31
In addition to the analysis of each individual test, we calculated these same statistical measures (sensitivity, specificity, positive LR, and negative LR) for 3 differential groups of tests:
Because sensitivity and a negative LR are both used to "rule out" a person from the fall group (minimizing false-negative results), they were grouped together in the presentation of our results. Because specificity and a positive LR are both used to "rule in" a person in the fall group (minimizing false-positive results), they were grouped together in the presentation of our results.
It is important to note that in the context of determining fall risk, we believe that misclassifying a participant as not being at risk for falls when that participant is at high risk for falls (a false-negative result) carries more significant consequences than misclassifying a participant as being at high risk for falls when that participant is not at high risk for falls (a false-positive result). For this reason, our goals for the interpretation of validity indexes were aimed at minimizing false-negative results, thus increasing sensitivity and reducing the negative LR.
| Results |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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The highest level of specificity (95% CI) of any individual clinical balance test was 0.85 (0.68–0.94), for the DGI, whereas the largest positive LR value (95% CI) for any individual balance test was calculated for the DGI: 4.26 (1.67–11.18) (Tab. 3). In the context of a positive clinical balance test result, such specificity and positive LR values indicate a relatively small number of false-positive results and clinical significance through amplification of the pretest probability of being in the fall group.
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The combination of 3 or more positive tests resulted in the highest levels of specificity (95% CI) and positive LR (95% CI): 0.80 (0.57–0.85) and 2.48 (1.35–4.57), respectively; however, the levels of sensitivity and negative LR were relatively low. In the context of 3 or more positive clinical balance tests, such specificity and positive LR values indicate a moderate number of false-positive results. However, such positive LR values support clinical significance by helping to "rule in" fall risk through amplification of the pretest probability of being in the fall group.
The combination of 2 or more positive tests resulted in validity indexes between the extremes seen with the other 2 combinations (sensitivity [95% CI]: 0.82 [0.67–0.92]; negative LR [95% CI]: 0.34 [0.16–0.76]; specificity [95% CI]: 0.51 [0.36–0.67]; and positive LR [95% CI]: 1.70 [1.17–2.47]) (Tab. 4).
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| Discussion |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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In response to these experiences, we sought ways to improve diagnostic accuracy through the reduction of false-negative decisions. Prioritization of sensitivity and negative LR values allowed us to correctly diagnose as many "true fallers" as possible while accepting an increased tendency to label some "nonfallers" as "fallers" (false-positive results). This article reports our analysis of potential diagnostic solutions.
On the basis of our findings, we conclude that the performance and collective interpretation of multiple clinical balance tests allow clinicians the ability to more accurately identify people with PD at true risk of falling than does individual test interpretation. In our opinion, multiple tests more accurately sample the various components of postural instability and fall risk in people with PD.
Testing the Difference Between People With a History of Falls and People Without a History of Falls
The first research question analyzed pertained to the validity of each individual clinical balance test as a measure of fall risk. We hypothesized that individual clinical balance tests could accurately distinguish people with PD and a history of falls from those without a history of falls. In agreement with other studies, all of the clinical balance tests examined in the present study could discriminate people with PD and a history of falls from those without a history of falls.16,18,32 These findings strengthen the evidence that all of these measures have a basic level of content validity in that they have the ability to detect a difference in balance test performance in people with PD when one exists.
In contrast to previous studies of the Timed "Up & Go" Test,16,33,34 2 factors question the diagnostic utility of the Up and Go Test in our sample. First, the overlap of the 95% CIs of the fall and nonfall groups on the Up and Go Test suggests that this test was less useful than the other tests at distinguishing people in the fall group from those in the nonfall group (Tab. 2). In addition, the 95% CIs of the LRs for the Up and Go Test encompassed 1, indicating that the Up and Go Test results contributed little ability to modify the posttest probability of falling.
Are Multiple Tests More Clinically Useful Than an Individual Test?
Once we felt confident that the clinical balance tests were valid, the next diagnostic decision analyzed was the relative benefit of individual test interpretation versus collective interpretation of multiple tests. This analysis was done with a priority placed on ruling out a diagnosis of fall risk (maximizing sensitivity and minimizing the negative LR). The results of the present study indicated that the collective interpretation of multiple clinical balance tests (when more than 1 or 2 tests were positive) reduced the false-negative rate to a greater degree than did the interpretation of any individual test alone. To our knowledge, although this type of analysis was previously suggested,16,18,19 such a study had not been previously reported.
Despite the improved sensitivity of collective interpretation, the results of the present study in isolation are of limited clinical utility. As stated in the Method section, sensitivity and specificity alone are limited in their diagnostic abilities because they require a clinician to know the fall diagnosis (falls versus no falls). The more clinically useful tools derived from our data are the LRs.
To emphasize the clinical utility of our calculated LRs, a clinical case example is provided. Clinical case 1 illustrates the utility of the negative LR in answering the following question: How much more likely is a negative result on multiple clinical balance tests to be found in a person who has PD but no fall history than in a person who has PD and a fall history?
In clinical case 1, a 65-year-old man with PD, mild hypometria, unilateral resting tremor, and no prior history of falling attends a clinic. Using the incidence of falling in our sample as the pretest probability of falling, a 51% pretest probability of falling is assumed. During the physical examination, the man is able to reach only 20 cm on the FRT but does not reach the fall risk threshold for any of the other clinical balance tests. Because the man has only 1 positive test (and 3 negative clinical balance tests), a negative LR of 0.09 is applied (Tab. 4). By using an LR nomograph27,31,35 (Fig. 1), a clinician can calculate that the man has an 8% posttest probability of falling. On the basis of severity and symptoms,36,37 the clinician may determine that the man is not currently at high risk and that intensive one-on-one physical therapy may be deferred. The man can be counseled on alternative therapeutic options, such as community- or home-based exercises designed to reduce fall risk or improve postural stability.38–40 It is critical that serial examinations over time be scheduled and performed because it is likely that postural instability and fall risk will worsen over time as the severity of the PD progresses (path A in Fig. 2).
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The following clinical case illustrates the utility of this clinical decision-making algorithm. In clinical case 2, a 72-year-old woman with PD, bradykinesia, dementia, and a history of 2 falls in the previous year is referred to a physical therapist for examination and treatment. During the examination, the woman scores 15 of 24 on the DGI. Because the woman has a positive DGI result, she can be classified as having a significant fall risk, and physical therapy intervention may be justified (path B in Fig. 2). If the woman had a negative score on the DGI, then the physical therapist could perform the BBS (because it possesses the second largest positive LR and contains the FRT). If the woman had a positive score on the BBS or the FRT, then she would be classified as having a significant fall risk, and physical therapy would be justified (path C in Fig. 2). Given her report of previous falls, if the woman had negative scores on both the BBS and the FRT, then the physical therapist should consider other potential causes for the previous falls (path D in Fig. 2).
Limitations and Directions for Future Research
Although our findings appear to have clinical relevance, they should be interpreted cautiously. From a research design standpoint, our sample was relatively small and was derived from only one outpatient facility. For this reason, people with mild PD and people with severe PD were not represented in large numbers. In addition, although the research plan and data gathering were prospective, we relied on each participant's self-reported fall history as the diagnostic gold standard for fall risk. A similar method should be applied to a sample of people with PD, with prospective observation of fall history as the diagnostic gold standard. Finally, we used the cutoff scores most commonly referenced in the literature, as opposed to altering the cutoff scores, as has been suggested in other research.16,18 The rationales for the recalculation of cutoff scores and the collective interpretation of multiple tests are similar, that is, minimizing false-negative results. In the present study, we chose to constrain our analysis and examine only the effect of collective interpretation. Future research should compare these methods directly.
Although the accurate identification of fall risk is important so that people at risk and their caregivers are aware of the risk for injury, we have made the assumption that accurate identification of fall risk will lead to effective treatment. However, there are relatively few studies documenting improved postural stability, reduced fall risk, or reduced fall occurrence as a result of physical therapy intervention. Additional research should be performed to add to the few studies that have directly examined the effects of rehabilitation interventions on fall risk and fall occurrence.39–42
| Conclusion |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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| Footnotes |
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Dr Christensen participated in the study in partial fulfillment of the requirements for his Doctor of Physical Therapy degree from the University of Utah.
A platform presentation of the research was given at the Annual Meeting of the Utah Chapter of the American Physical Therapy Association, September 2006.
Funding for this study was provided, in part, by grants from the American Parkinson Disease Association (National and Utah Chapter).
* SPSS Inc, 233 S Wacker Dr, Chicago, IL 60606. 
University of Southampton, Mail Point 820, Southampton General Hospital, Southampton, United Kingdom SO16 6YD.
| References |
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TopAbstractIntroductionMethodResultsDiscussionConclusionReferences |
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