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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 57:M294-M301 (2002)
© 2002 The Gerontological Society of America

Performance Tests and Disability

Developing an Objective Index of Mobility-Related Limitation in Older Populations

Tzuo-Yun Lana, David Melzera, Brian D.M. Toma and Jack M. Guralnikb

a Department of Public Health and Primary Care, University of Cambridge, United Kingdom
b Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland

David Melzer, Department of Public Health and Primary Care, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 2SR, UK E-mail: dm214{at}medschl.cam.ac.uk.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
Background. Disability reflects physiological limitations, social and environmental barriers, and "sickness" behavior. Being able to measure these influences separately would greatly assist interpretation of disability comparisons over time or between populations. This study aimed to identify an index of mobility-related limitations composed of physiological measures that are most closely associated with reported mobility disability in elders.

Methods. Data from the Third National Health and Nutrition Examination Survey (NHANES III) were used. Participants aged 60 and older were included in this analysis. Participants included 6596 respondents who were interviewed in their homes, and 5724 (87%) of these attended a further examination. Domains of measurements included body measurements, bone densitometry, physical examination, spirometry, fundus photography, and physical performance measures. Multivariate models were developed on a random half subsample of the data and were validated on the other half. Receiver operating characteristic (ROC) areas and logit rank slopes were used to evaluate sets of measures.

Results. In weighted logistic regression models, six and five measures were significantly associated with difficulty and inability in walking a quarter of a mile, respectively. These mainly included measures of lower extremity and lung function. A relatively minimal loss of sensitivity and specificity occurred from using more economical models, employing a subset of the identified measures.

Conclusions. Subsets of measures associated with reported mobility disability could provide objective indices of mobility-related limitation for comparing populations or long-term population health monitoring.

DISABILITY, the inability to function normally, physically or psychologically, is a fundamental health status measure, often implying need for care. Disability is also important for policy, as it predicts hospital and long-term care utilization (1)(2).

Frameworks of the disablement process (3)(4)(5)(6) generally see disease or injury as initiating physiological impairment, which in turn may lead to functional limitation, disability (activity limitation), and then possibly participation restriction (the inability to fulfill a normal social role). Not only does disability incorporate physiological impairment, it also reflects environmental barriers and attitudes. However, in practice, all components of disability are integrated together in typical survey questionnaires about difficulties with everyday activities.

Increasing life expectancy has triggered interest in monitoring disability trends, partly to detect whether living longer means being disabled for longer. During the 1970s and the early 1980s, rates of disability rose (7)(8). However, more recently, disability prevalences in older populations have declined (9)(10)(11). Possible reasons for the decline include higher rates of education (12)(13), environmental changes (6), greater use of assistive devices (14), and attitude changes (15). Both health risk avoidance and improved diagnostic and therapeutic techniques also will have contributed.

This abundance of possible causes for declining disability rates poses an obvious challenge for future monitoring: is it possible to monitor the separate components of disability? If this could be done, then future disability trends could be quantitatively attributed to its components. In this article we address part of the challenge: the identification of relevant measures of impairments or functional limitations. Tests for a variety of individual impairments are already well established, but identifying the impairments that contribute to a particular disability, and their relative importance, is more challenging.

Fried and colleagues (16) identified four groups of disability in older people, namely mobility, upper extremity, instrumental activities of daily living, and basic activities of daily living. Mobility disability is often an early manifestation of the disablement process and is highly predictive of disability progression (17)(18). In both the screening interview for the Women's Health and Aging Study (19) and the 1984 Supplement on Aging (20), 90% or more of respondents with disability reported problems with mobility. Walking is a basic function and is unlikely to change substantially over time. In addition, mobility difficulty has negative effects on quality of life. Thus, mobility provides a good marker for monitoring disability.

To identify physiological measures associated with mobility disability and to identify a battery of these measures that could be used to track older individuals and populations, we analyzed the third National Health and Nutrition Examination Survey (NHANES III), the largest detailed source of both disability reports and performance tests or biometric measures.


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 References
 
The NHANES III was a cross-sectional study of the United States population, undertaken between 1988 and 1994. Details of the study are described elsewhere (21)(22). NHANES III used complex clustered samples of civilian, noninstitutionalized populations. Included were 6596 persons aged 60 and older who were interviewed in their homes, 5302 who attended the mobile examination center, and 422 who were examined in their own houses because of frailty.

Standard questionnaires covering disease status and self-reported physical functioning were administered at home. Measurements used in our analyses are listed in Table 1 . Body mass index (BMI) was calculated as body weight adjusted by stature (weight in kg/height2 in m2). Body cell mass (BCM) was calculated using an equation (23) that comprises two measures of body composition: bioelectrical impedance analysis reactance and resistance.


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  		Table 1. Weighted Numbers (%) of Full Sample, by Demographic Characteristics and Physical Measures and Difficulty or Inability to Walk a Quarter of a Mile

 
Test measurements were categorized, to avoid imposing linear assumptions in models. Data marked "blank" or "cannot grade" were re-coded "missing." Univariate and forward multiple logistic regression analyses of self-reported difficulty in walking a quarter of a mile were performed. "Difficulty model" contrasted those reporting difficulty or inability to walk a quarter of a mile with the remainder, and "inability model" contrasted respondents unable to walk a quarter of a mile with those who could. These analyses used the Public Use Data File (24).

To adjust for the complex sampling, all statistical procedures were weighted to reflect the U.S. community-living population aged 60 and older. Descriptive analysis and logistic regression models were carried out with SPSS-PC version 9 (SPSS Inc., Chicago, IL). Variables that entered into the final logistic regression models were further rerun by WesVar, employing replicate or resampling methods to calculate the correct confidence intervals for odds ratios from complex samples (25).

Prognostic indices developed on datasets are usually overoptimistic and overinclusive (due to multiple testing), so the original sample was randomly divided into a training sample (subsample 1) to estimate model parameters and a validation sample (subsample 2).

To select a small number of key measures from those identified by regression modeling, receiver operating characteristic (ROC) curves and logit rank plots (26) of each set of variables in the two models were plotted. The standard errors for both the areas under the ROC curve and the logit rank slope (26) were calculated using AccuROC (Accumetric Corporation, Montreal, Canada) and S-PLUS (MathSoft, Inc., Cambridge, MA). The predicted probability, calculated as elogit(p)/(1 + elogit(p)), where logit(p) is the prognostic index obtained from the regression model, for each sample person was produced also.

In the validation analysis, ROC curves for the two subsamples were plotted, and the difference between the areas under the curves was tested by the independent Z test. In addition, the logit rank slopes were calculated for the subsamples. These two methods assessed the discriminating validity of the model. To assess the calibration validity of a model developed from subsample 1 (i.e., whether the predicted probabilities obtained for subsample 2, using the models developed from subsample 1, agree with the observed probabilities for subsample 2), we applied the prognostic index formula obtained for this model to subsample 2 in order to create a new prognostic index (predictive value) variable. This new index alone (without an intercept term) was then regressed against the disability or inability outcome in a logistic model. The coefficient estimated from this logistic model was then compared to 1 to assess the calibration of the model. If there is no statistical difference between the calibration coefficient and 1, then calibration of the prognostic index developed from subsample 1 is not needed. A similar strategy was adopted by Van Houwelingen and Thorogood (27) for survival data.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 References
 
Table 1 shows the descriptive details of the sample: 56.7% were women, and 14.9% of both genders were aged 80 and older. Overall, 28.7% of the weighted sample reported difficulty walking a quarter of a mile, and 10.7% reported that they were unable to walk a quarter of a mile.

In univariate cross-tabulations, all the included measures were significantly associated with difficulty or inability in walking a quarter of a mile (p < .001). In summary, women reported more difficulty and inability in walking a quarter of a mile than men. Difficulty or inability rates increased with older age; impairment in shoulder and hip and knee range of motion; longer time to unlock a lock; and longer time to complete five stands from sitting in a chair. On the other hand, inverse gradient relationships were seen for bone density, peak expiratory flow, forced vital capacity (FVC), and gait speed.

Table 2 presents the results of the multivariate analyses between two models and covariates. Multivariate analysis on the difficulty model identified six significant factors in the model obtained from subsample 1 (at p < .001). These factors were gait speed, time to complete five chair stands, forced expiratory flow (FEF) at 75% of FVC, peak expiratory flow, hip and knee range of motion, and BMI.


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  		Table 2. Odds Ratio and 95% CI of Entered Variables in Difficulty and Inability Models From Subsample 1 (n = 2769)

 
In the inability model, p < .01 was chosen as the significance threshold, due to the relatively smaller number of cases. Five factors—gait speed, time to complete five chair stands, FEF at 75% of FVC, peak expiratory flow, and shoulder range of motion—were identified as statistically significant. Significant odds ratios associated with the missing data categories for several variables were found.

Validation of the Models
To validate the models (in terms of discrimination), the prognostic index formulae from the regression models on subsample 1 were used on subsample 2. Fig. 1 shows the resulting ROC curves. For both the difficulty and inability models in Table 2 , differences in areas under the two ROC curves were not statistically significant, suggesting that the models are robust. The logit rank slopes (with standard errors) for two subsamples were 0.8098 (0.0364) and 0.7195 (0.0340) for the difficulty model and 0.9704 (0.0506) and 0.9219 (0.0486) for the inability model. The differences in the logit rank slopes between the two subsamples were not significant, which again suggests reasonable robustness of the models.



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  		Figure 1. Comparison of ROC curves between two subsamples in difficulty (A) and inability (B) models. Subsample 1: n = 2769, difficulty cases = 956, inability cases = 351, ROC area in difficulty model = 0.7988 (95% CI: 0.7812–0.8163) in inability model = 0.8509 (95% CI: 0.8286–0.8732); Subsample 2: n = 2762, difficulty cases = 945, inability cases = 355, ROC area in difficulty model = 0.7799 (95% CI: 0.7618–0.7981) in inability model = 0.8417 (95% CI: 0.8201–0.8633). Z statistic and two-tailed p values were used to compare the difference in areas under two ROC curves derived from two independent samples: subsample 1 and subsample 2. ROC = receiver operating characteristic; CI = confidence interval.

 
The calibration coefficients (with standard errors) obtained from subsample 2 were 0.88 (0.052) and 0.96 (0.043) for both models, respectively. These coefficients were reasonably close to 1 to suggest that the models were well calibrated, although there was some evidence of overestimation of coefficients for the difficulty model. Formal statistical tests of the null hypotheses that the calibration coefficients were 1 were not rejected at the 1% level. However, the null hypothesis was rejected at the 5% level for the difficulty model, suggesting that these regression coefficients could be shrunk using a multiplicative factor of around 0.88 to calibrate the predicted probabilities.

Identifying Optimal Combinations of Measures
In developing mobility function indices for populations, economy in the extent of required testing is important. To estimate how much additional information is gained by each additional test, we calculated the ROC areas and the logit rank slopes for different combinations of measures.

Given the large sample sizes, null hypotheses for ROC curve differences are easily rejected, and clinical comparison of the areas would be more appropriate. However, we chose the logit rank slopes to compare possible combinations of tests, from gait speed only through to the full model. The relative percentage loss in the logit rank slope was compared with the full model when different tests or combinations of tests are added to gait speed only.

From Table 3 , we observe that the area under the ROC curve and the logit rank slope increases with more variables in both models. If the cutoff for the lost percentage of the logit rank slope (relative to the full model) is set at 10%, for example, the sets of gait speed, time to complete five chair stands, and peak expiratory flow [No. 12 in Table 3 (Difficulty Model) and No. 8 in Table 3 (Inability Model)] in the two models perform adequately. Thus, gait speed and five chair stands together with peak expiratory flow could be used as a brief functional index for mobility-related limitations.


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  		Table 3. Area Under ROC Curves and Calculation of Logit Rank Scores in Different Sets in Difficulty and Inability Models

 
Table 4 presents sensitivity and specificity data, using the reported prevalence of difficulty or inability to walk a quarter of a mile as "case" cutoffs. The abbreviated index of gait speed, peak expiratory flow, and chair stands has very similar sensitivities and specificities to the full model.


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  		Table 4. Suitable Cutoff Points for the Set of GS, PEF, and FS in Difficulty and Inability Models When Predicted Prevalence Equals Actual Prevalence

 

   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
Reported disability reflects physiological impairments plus environmental and attitude factors. In this study, we aimed to identify an objective indicator of mobility-related impairment and limitation relevant to monitoring difficulty or inability in walking a quarter of a mile, in the older American population. An index of such measures could be used to quantitatively monitor the contribution of changing physiological status in any disability trends, as opposed to changes in other factors related to disability.

This analysis has shown that all of the measures in NHANES III that were examined were univariately associated with reported difficulty in walking a quarter of a mile. However, in multivariate logistic regression models on half of the sample, only six factors were significantly associated with difficulty in walking. These included measures of lower limb performance, lung function, and BMI. For inability to walk a quarter of a mile, the relevant tests were similar, although shoulder range of motion test was also a significant factor. Overall, measures of lower limb performance and lung function emerged for both models.

In assessing these results, we need to remember the shortcomings of the analysis. First, NHANES III includes community-living older people only. Inevitably, a limited (although large) set of tests was available for analysis, and some measures that might be candidates for a mobility-related limitation index, such as muscle strength (28), were not available. More fundamentally, a regression model of tests against a gold standard identification of mobility-related limitation may have been methodologically better than our models against reported disability, but no such gold standard exists. Our approach assumes that the objective physical impairment tests associated with mobility disability can be used together to identify or monitor the underlying characteristics of mobility-related limitation.

Together with the limitations, the advantages of this analysis also need to be considered. Data from a large, nationally representative study have been used, which provide a large and diverse set of measurements. The complex nature of the sampling has been dealt with, and established methods have been used to identify the tests that are independently associated with mobility difficulty or inability.

The two selected lower extremity physical measures, gait speed and time to complete five chair stands, have been shown to have a graded relationship with mobility-related or activity of daily living disability in the nondisabled population in longitudinal studies (29)(30). Furthermore, gait speed predicts future nursing home admission, morbidity, mortality (31), and mobility disability (32). However, in our analysis, gait speed alone is not as sensitive or specific as a full model of tests for mobility difficulty or inability.

In items related to lung function, maximum oxygen uptake (Peak .VO2) is the standard measure of the functional limit of the cardiorespiratory system (33)(34). Peak .VO2 is also associated with self-reported disability (35) and physical performance (36), possibly because cardiovascular, pulmonary, and metabolic function provide the energy necessary for muscular contraction. In our analysis, peak expiratory flow and FEF at 75% FVC emerged as the two important measures of lung function from those available.

Compared with lung function, body measurements focus on a different physiological mechanism. Higher BMI values have been previously shown to be associated with higher risk of disability (37), and its presence in the model predicting difficulty walking a quarter of a mile is not surprising.

Gait speed and time to do five chair stands are two well established performance-based measures of mobility function in population surveys, because of their relatively high and stable intrarater (test-retest) reliability. Reported correlations of test-retest reliability for gait speed and chair stands are 0.90 and 0.82 (38). Jette and colleagues (39) reported that intraclass correlation coefficients (ICCs) for 8-foot walk and repetitive chair stands were 0.79 and 0.67, respectively. However, it should be remembered that extraneous factors including chair heights (40) could affect test results.

In large-scale population surveys, undertaking a large battery of tests in thousands of study subjects is expensive. In this analysis, we have shown how abbreviated testing combinations can perform satisfactorily, in comparison with the full models. Our illustration of a battery consisting of gait speed measurement, timing of five chair stands, and a peak flow measurement would involve relatively inexpensive equipment and is shown to perform almost as well as the full models, for both inability and difficulty in walking a quarter of a mile.

In summary, a limited set of lower extremity, lung function, and other measures are together associated with reported mobility disability. Employing these objective measurements (or a more economical subset) in long-term monitoring of the health of older populations could provide a measure of mobility-related physiological limitations, independent of changes in attitudes, environment, or other factors influencing reporting of disability. Similarly, these measures could be used for comparing older populations who may have differing attitudes to reporting disability.

Received July 31, 2001

Accepted November 1, 2001


   References
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 Abstract
 Methods
 Results
 Discussion
 References
 

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J. E. Morley
The Need for a Men's Health Initiative
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J. E. Morley
Editorial: Hot Topics in Geriatrics
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