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a Epidemiology, Demography and Biometry Program, National Institute on Aging, Bethesda, Maryland
b Geriatric Department, National Institute for Research and Care of the Elderly (INRCA), Florence, Italy
c Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, North Carolina
d Department of Preventive Medicine and Community Health, Center on Aging, Galveston, Texas
e Center on Aging, University of Kansas Medical Center, Kansas City, Kansas
f Harvard School of Public Health, Boston, Massachusetts
g Department of Preventive Medicine and Environmental Health, University of Iowa, Iowa City
Jack M. Guralnik, Epidemiology, Demography, and Biometry Program, National Institute on Aging, Gateway Building, Suite 3C-309, 7201 Wisconsin Avenue, Bethesda, MD 20892-9205 E-mail: jg48s{at}nih.gov.
Decision Editor: William B. Ershler, MD
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Abstract |
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 Top Abstract MethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Methods. Tests of balance, time to walk 8 ft, and time to rise from a chair 5 times were administered to 4,588 initially nondisabled persons in the four sites of the EPESE and to 1,946 initially nondisabled persons in the Hispanic EPESE. Follow-up assessment for activity of daily living (ADL) and mobility-related disability occurred from 1 to 6 years later.
Results. In the EPESE, compared with those with the best performance (EPESE summary performance score of 10–12), the relative risks of mobility-related disability for those with scores of 4–6 ranged from 2.9 to 4.9 and the relative risk of disability for those with scores of 7–9 ranged from 1.5 to 2.1, with similar consistent results for ADL disability. The observed rates of incident disability according to performance level in the Hispanic EPESE agreed closely with rates predicted from models developed from the EPESE sites. Receiver operating characteristic curves showed that gait speed alone performed almost as well as the full battery in predicting incident disability.
Conclusions. Performance tests of lower extremity function accurately predict disability across diverse populations. Equations derived from models using both the summary score and the gait speed alone allow for the estimation of risk of disability in community-dwelling populations and provide valuable information for estimating sample size for clinical trials of disability prevention.
INCREASINGLY, standardized tests of physical performance have been applied in research and geriatric assessment settings. Performance tests have been found to be strongly associated with multiple measures of health status and are predictive of important outcomes such as hip fracture, nursing home admission, and death. It has been generally agreed that performance measures supplement, rather than replace, self-report of disability. Along the theoretical pathway from disease to disability proposed by Nagi (1) and the Institute of Medicine (2) and made operational by Verbrugge and Jette (3), most performance measures can be considered functional limitations. These functional limitations are proximal on the pathway to disability, which represents the interaction of performance ability with an individual's environment.
It has also been demonstrated that performance measures can capture a hierarchy of functioning in persons who are not disabled and even in those who are high functioning (4)(5). Furthermore, in nondisabled persons performance measures have been shown to be predictive of the onset of incident disability (6)(7)(8)(9). In research from Project Safety (6) a threshold effect was observed, whereby risk of future disability increased substantially below a certain critical level of performance. In contrast, in the Iowa cohort of the Established Populations for Epidemiologic Research in the Elderly (EPESE), a graded effect was seen, with increasing disability risk at each lower level of performance (9).
One important limitation of the findings from the latter study was that it was restricted to the Iowa EPESE site, a rural, all-white cohort. Data have recently become available that allow for the replication of the study in other EPESE sites and for the comparison of findings both between sites and for different lengths of follow-up. This study evaluates findings across these sets of studies, assesses whether a simple measure of gait speed has the same predictive value for disability as the full performance battery used in the initial EPESE studies (9)(10), presents predictive models of risk of disability and death according to performance level, age, sex, and number of years of follow-up, and examines how well these models predict disability in an external, Hispanic population. It is then demonstrated how estimates of disability outcomes can be used to determine sample size requirements for clinical trials, information that is critical in planning for interventions to prevent disability.
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Methods |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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At follow-up 6, the number of persons not known to have died totaled 9,974 in all sites and 9,130 (91.5%) were interviewed (East Boston 2,345/2,563; Iowa 2,547/2,711; New Haven 1,671/1,819; North Carolina 2,567/2,881). Of these, 1,778 (19.5%) were excluded because they were living in institutions, living at home but needing a proxy respondent because of cognitive or physical impairment, or living outside the area and requiring a telephone interview. Of the remaining 7,352 persons interviewed in person in their homes, 141 (1.9%) refused the performance tests. To select an initially nondisabled cohort, we excluded persons reporting disability in activities of daily living (ADLs) or mobility-related disability (defined below). To further reduce disabled persons being misclassified as nondisabled at baseline, we also excluded those 4% of participants reporting no disability but having scores of 0 to 3 on the summary performance scale (defined below). A large proportion of these persons were unable to walk 8 ft or maintain a simple side-by-side stand, which is incompatible with a report of ability to walk 0.5 mile and climb stairs. This left 4,588 persons who were initially classified as nondisabled (East Boston 1,298; Iowa 1,363; New Haven 700; North Carolina 1,227).
At follow-up, interviews to assess disability status were performed using proxies and in nursing homes if necessary. Among those not known to have died, follow-up disability information was missing at 1 year in 3.2% and 3.8% of those in Iowa and New Haven, respectively, at 4 years in 4.2% and 6.7% of those in Iowa and North Carolina, respectively, and at 6 years in 5.0% of those in New Haven. After death and loss to follow-up, subsequent information on disability status was obtained at 1 year on 1,342 persons in Iowa and 655 persons in New Haven; at 4 years on 1,121 persons in Iowa and 962 persons in North Carolina; and at 6 years on 455 persons in New Haven.
Chronic Conditions at Baseline
We assessed chronic conditions at baseline and each annual interview through follow-up 6 by asking participants if a physician had told them they had a heart attack, stroke, cancer, hip fracture, or diabetes. Cancer was considered present only if the participants also reported spending one or more nights in the hospital for the cancer. Chronic conditions were considered present if mentioned at baseline or at any follow-up interview.
Measures of Physical Performance
The physical performance battery used in the EPESE studies, previously described in detail (9)(10), evaluated lower extremity function by using tests of gait speed, standing balance, and time to rise from a chair five times. Identical assessments were done in all sites by specially trained interviewers, with a training videotape used to optimize standardization of the procedures across sites. Good to excellent test–retest reliability of these tests has been demonstrated (4)(13)(14)(15). Based on data from over 5,000 persons from the original three EPESE sites (East Boston, Iowa, and New Haven), a five-level categorical score was created for each test, with 0 representing inability to complete the test and 4 representing the highest level of performance (10).
For tests of standing balance, participants attempted to maintain the side-by-side, semitandem, and tandem positions for 10 seconds. Participants were scored 1 if they could hold a side-by side stand for 10 seconds but were unable to hold a semitandem stand for 10 seconds, 2 if they held a semitandem stand for 10 seconds but were unable to hold a full tandem stand for more than 2 seconds, 3 if they held the full tandem stand for 3 to 9 seconds, and 4 if they held the full tandem stand for 10 seconds.
A usual pace, 8-ft walk was timed from a standing start, and participants were scored according to quartiles of performance. Time on the faster of two walks was used to define scores: score of 1: 
5.7 seconds (
0.43 m/s); score of 2: 4.1–5.6 seconds (0.44–0.60 m/s); score of 3: 3.2 to 4.0 seconds (0.61–0.77 m/s); score of 4: 3.1 seconds ( 0.78 m/s).
Participants were asked to fold their arms across their chest and to stand up once from a chair. If successful they were asked to stand up and sit down five times as quickly as possible. Quartiles of performance for the repeat chair stands were used to define scores as follows: score of 1: > 16.7 seconds; score of 2: 16.6–13.7 seconds; score of 3: 13.6–11.2 seconds; score of 4: 11.1 seconds.
A summary performance score was created by summation of the scores for tests of standing balance, gait speed, and rising from a chair 5 times. This scale has been demonstrated to have predictive validity in analyses showing a gradient of risk for mortality, nursing home admission, and incident disability (7)(9)(10). The internal consistency of the summary scale as assessed by Cronbach's alpha was 0.76 (10). The subjects in this report were nondisabled at baseline and had summary performance scores ranging from 4 to 12.
In addition to the summary performance score, gait speed alone was assessed for its relation to incident disability. An 8-ft (2.44 m) walk was used in EPESE because of concern about limited space in participants' homes. However, for the assessment of gait speed over a short distance we believe the 4-m walk is now the distance of choice because it has been demonstrated to be feasible in the home as well as in the clinical setting and its longer distance may improve measurement accuracy. In the Women's Health and Aging Study it was demonstrated that in small houses and apartments in Baltimore a 4-m walk was possible for 90% of subjects, with 9% requiring a 3-m walk and 0.8% not having space for a 3-m walk (16). Because of the advantage of using the 4-m walk for both home and clinic gait speed tests, we converted velocity in the 8-ft walk into estimated velocity for a 4-m walk in all analyses presented here. This conversion was accomplished with data from the University of Kansas Prediction of Elder Performance Study. In that study, 700 assessments were done in which time was measured for both an 8-ft and a 4-m walk. Velocity for both tests was converted to meters per second. The correlation between velocities was 0.97. Piecewise linear regression was used to evaluate whether the relationship between 8-ft walk velocity and 4-m walk velocity was linear. A model with a single break point and change in slope had a significantly better fit than a straight linear model. The break point that maximized the R2, determined iteratively, was at an 8-ft gait speed of 1 m/s (R2 = 0.96). The observed data showed that, on average, the ratio of 4-m walk velocity to 8-ft walk velocity (both expressed in meters per second) was 1.065 for 8-ft walk velocity 1.0 m/s and 1.016 for 8-ft walk velocity >1.0 m/s. The following equations were used to convert 8-ft gait speed to 4-m gait speed:
For 8-ft gait speed 1.0 m/s
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For 8-ft gait speed >1.0 m/s
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When these equations are used, the quartiles used for the categorization of gait speed when a participant is performing a 4-m walk would become 1: < 0.46 m/s; 2: 0.47–0.64 m/s; 3: 0.65–0.82 m/s; 4: 0.83 m/s.
Disability Status at Follow-up
Disability status at follow-up, based on self-report or proxy report, used a three-level hierarchical scale (9)(17) that contains a subset of questions from a scale originally developed by Berkman and colleagues (18). This scale, used to exclude those with disability at baseline, was used to classify subjects at follow-up as having no disability, having mobility-related disability (inability to walk 0.5 mile or climb stairs without help), or having disability in ADLs (having mobility-related disability plus the inability to perform one or more of the following activities without help from another person: moving from a bed to a chair, using the toilet, bathing, and walking across a small room). Because the baseline performance tests measured lower extremity function, disability items related to lower extremity function were used. At each follow-up in all sites less than 2% of persons did not fit the hierarchical pattern, reporting disability in ADLs but not in mobility-related disability. These persons were classified as having disability in ADLs.
Hispanic EPESE
Predictive models developed from the original EPESE sites were evaluated with data from the Hispanic EPESE, a population-based study of 3,050 community-dwelling Mexican Americans living in five Southwestern states (19). The baseline assessment, including the same self-report items on disability and the same performance battery, was completed in 1993–1994. A follow-up interview was performed 2 years later, in 1995–1996, at which time mortality and disability data were ascertained (7).
Statistical Analysis
Rates of ADL and mobility disability were calculated according to summary performance score for each site and according to number of follow-up years. Multiple logistic models, adjusting for age, sex, and number of chronic conditions, were used to predict the relative risk of disability for those with summary performance scores of 4–6 and 7–9 compared with the reference group, those with scores of 10–12. Interactions between performance scores and other variables in the models were examined but there was no consistency seen across sites or follow-up times for the few interactions that were significant. Meta-analytic techniques were used to test for homogeneity for the two sites with 1-year follow-up data and the two sites with 4-year follow-up data (20).
To compare the relative predictive ability of gait speed with that of the summary performance score, receiver-operator characteristic (ROC) curves were constructed and tested with AccuROC software (Accumetric Corp., Montreal). Gait speed was categorized as deciles for these analyses. Areas under the curves were calculated, and comparison of these areas was done with the method of Hanley and McNeil (21)(22).
In addition to evaluating relative risk, these data were used to estimate absolute risk of disability. Risk of disability was modeled in multiple logistic equations in which age, sex, and either summary performance score as an ordinal variable or gait speed as a continuous variable were entered. Coefficients from these models were used to construct equations that predict the absolute risk of disability according to these variables. Estimated risks predicted from the models are compared graphically with actual observed risk. For specific levels of performance, we calculated prediction of disability risk by entering into the predictive equation that level of performance and the average age and sex distribution for that level of performance. In the same way, actual rate of disability after 2 years in the Hispanic EPESE cohort was compared with predicted risk for each baseline performance score after 1 and 4 years by use of the EPESE predictive equations.
Additional data are furnished to make it possible to estimate transitions along all steps in the pathway from encountering a community-dwelling population, to estimating the proportion initially disability free, to predicting the rate of survival and incident disability in survivors. The distribution of summary performance scores and categories of gait speed according to age and sex were estimated by being treated as ordinally scaled dependent variables in polychotomous logistic regression models (SAS procedure CATMOD) (23). The estimates calculated from this model for men and women of specific ages are provided in additional tables, which also provide information on the proportion expected to be nondisabled at each performance level. Rather than being adjusted for chronic disease status, which would make results for different age and sex subgroups more similar to each other, the analyses are not adjusted in order to present each age and sex group's performance distribution as you would find it in the community. Multiple logistic models were used to estimate risk of mortality according to age, sex, and performance, and equations are presented that predict absolute risk of mortality according to these variables. Preliminary to these analyses we performed site-specific analyses for mortality and found that the coefficients for age, sex, and performance were similar across sites.
Examples illustrate how these findings can be used to estimate mortality and disability outcomes for 80-year-old men and for a mixed cohort selected for a clinical trial.
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Results |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Table 4 demonstrates how the data provided here can be used to characterize a population and predict outcomes over time. With data from Appendix A, the distribution of the summary performance score in one-thousand 80-year-old men is shown in column 2. Further, with data from Appendix A on the proportion of nondisabled persons, column 3 shows the actual number expected to be nondisabled. The probability of death at 1 year is estimated when age, sex, and summary performance score are inserted into the first equation listed in Appendix C. The number of men surviving at each level of performance score can then be calculated. The probability of mobility disability is then estimated when age, sex, and summary performance score are inserted into the first equation in Table 3 . Applying this probability (Table 4 , column 6) to the number surviving for each level of performance gives an estimate of the number predicted to be disabled by level of performance (column 7). Summing across all levels of performance, for one-thousand 80-year-old men, 705 are expected to be nondisabled at baseline, 675 to survive for 1 year, and 137 to develop mobility disability. Calculations for 4-year outcomes are done in a similar way by use of the appropriate equations. The Appendix illustrates how rates of death and disability incidence can be estimated for a population encountered in the community. These rates are critical for making power calculations for clinical trials of disability prevention.
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Discussion |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Our findings may be directly compared with the results in a recent report by Ostir and colleagues on the 2-year follow-up of the Hispanic EPESE, for which the performance battery was performed in the same way (7). The relative risk of mobility-related disability was 4.8 in their low-performance group and 2.4 in their middle-performance group, compared with the high-performance group. Relative risks for ADL disability were 6.2 and 2.0, respectively. These relative risks are very similar to the estimates shown in Table 1 and Table 2 for three EPESE sites with different lengths of follow-up.
It is essential to examine whether the models developed to test the relationship between performance and incident disability not only indicate an elevated relative risk but also accurately predict the actual rate of disability. We first found that predictions made from the models worked very well in predicting the observed outcomes in the cohorts used to develop the models (Fig. 3), indicating that the models fit the data from which they were derived quite well. The true test of a model's predictive ability, however, is to test it by using an external data set. This was done with the Hispanic EPESE. Although we could not make direct predictions for the 2-year Hispanic EPESE follow-up period, it was possible to compare these observations with 1- and 4-year predictions. Considering the differences in the ethnicity of the cohorts and the regions in which the studies were done, it was remarkable how well the models predicted the range of the observed rates of disability in the Hispanic EPESE at 2 years (Fig. 4). This evidence supports the validity and the robustness of these simple measures of performance and supports their use in developing disability risk estimates for diverse populations. Analyses within the North Carolina cohort also revealed no clear evidence for differences in the relationship of performance with disability between whites and African Americans (data not shown).
This research also demonstrated that assessing gait speed alone is nearly as good as performing the full battery of performance tests in the prediction of incident disability. This is a valuable finding to support the routine measurement of gait speed in older persons in the clinical setting, in which it may be quite useful to have an objective measure of lower extremity functioning, but in which there may not be time to perform a full performance battery. Gait speed assessment may also be an efficient tool as the first step in screening a large number of older persons to identify and recruit into clinical trials persons with a specific level of functioning. The equations presented in this report allow for estimation of disability and mortality risk in persons who are initially nondisabled, as determined from the few self-report questions used here, and for whom a gait speed has been measured.
The question of whether to abandon the full performance battery and use gait speed alone in all clinical and research settings is a difficult one. Certainly the assessment of rising from a chair and balance give information that may be of value in understanding the pathway from disease to disability (3). Specific diseases and impairments may affect specific aspects of lower extremity function, which may then determine the characteristics of an individual's disability. Furthermore, measuring a specific construct with multiple measures increases reliability (25), so some measurement accuracy may be gained by using the full battery. For this reason, the full battery is likely to be a better instrument in which to assess change over time. In the Iowa EPESE cohort, the summary performance battery had a mean decline of 1.6 points over 4 years, with 67% of the sample declining, 17% improving, and 16% remaining unchanged (26). Validity of the change scores was supported by the findings that, with adjustment for baseline performance, the summary performance score declined more in those 80 years old, having less education, and having coronary heart disease and chronic lung disease. Furthermore, declines in summary performance score were sensitive to the presence of depression at baseline, even after adjustment for baseline performance, demographic characteristics, behavioral risk factors, and chronic disease status.
Ultimately, knowledge gained from observational epidemiologic studies must be translated into strategies to prevent or reduce disease and disability. The real value of the findings on performance measures presented here is that they provide a strategy to target disability prevention interventions to high-risk groups that can be identified with a simple screening battery or even just a test of walking speed. The reference tables here provide the information needed to estimate the distribution of performance levels that can be anticipated to be present in the community and the probability that persons of a specific age, sex, and performance level will develop disability. This information allows for the rational development of estimates of disability risk that will put the calculation of sample sizes needed for these studies on a much more empirical footing. It should be noted that these estimates are for persons living in the community who are in their usual state of health and are not appropriate for estimating risk for persons who have acute illnesses or are just being discharged from the hospital.
The largest benefit for the effort expended in disability prevention programs may come from intervening not on those at the best level of functioning but on persons with lower levels of functioning. Thus, screening a nondisabled population and recruiting persons with summary performance scores ranging from 4 to 9 would yield a population with a high risk of disability. The higher rate of events in this population would allow for a reduced sample size compared with intervening on very high functioning individuals. Furthermore, nondisabled persons who are objectively demonstrated to have reduced performance may be on a progressively downward trajectory, so their functional abilities and quality of life could be substantially improved with a timely and well-focused intervention. This approach can support future efforts to reduce disability at both an individual and a public health level.
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Acknowledgments |
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Received March 22, 1999
Accepted August 18, 1999
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Appendix |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Appendix B |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Appendix C |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Appendix D |
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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Table 1A shows the expected distribution of participants according to the sample drawn from the local census (column 2). The distribution of summary performance score at each age is estimated from Appendix A, and column 3 shows the total number of persons expected to have a summary score of 4 to 9 by age group. For each specific age, sex, and summary score level, the proportion of nondisabled persons is estimated from data in Appendix A, the proportion of surviving persons is estimated from equations in Appendix C, and the proportion of persons becoming disabled in 1 year is estimated from equations in Table 3 . This is done in the same way that the estimates were made for a specific age and sex in Table 4 . Numbers shown in the table aggregate estimates across summary scores of 4 to 9 for each age/sex group.
Overall, of 168 men in the cohort who are predicted to have a summary performance score of 4–9 and survive 1 year, 28 (16.7%) are expected to have mobility disability at one year. Of 242 surviving women with an initial performance score of 4–9, 44 (18.0%) are expected to have mobility disability at 1 year. The intervention is hypothesized to reduce disability by half from the 17% expected. For an alpha of .05 and a power of 50%, this magnitude of effect could be demonstrated in a study with over 120 persons in both the intervention and the control groups (27). Thus this demonstration project, which is limited to 100 persons, is substantially underpowered to show a significant effect of exercise, even with a proposed halving of disability rates. To reach an acceptable power as a formal clinical trial this demonstration project would require an increased sample size.
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TopAbstractMethodsResultsDiscussionAppendixAppendix BAppendix CAppendix DReferences |
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M. A. Espeland, T. M. Gill, J. Guralnik, M. E. Miller, R. Fielding, A. B. Newman, M. Pahor, and for the Lifestyle Interventions and Independence f Designing Clinical Trials of Interventions for Mobility Disability: Results From the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) Trial J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2007; 62(11): 1237 - 1243. [Abstract] [Full Text] [PDF]
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 M. L Puthoff and D. H Nielsen Relationships Among Impairments in Lower-Extremity Strength and Power, Functional Limitations, and Disability in Older Adults Physical Therapy, October 1, 2007; 87(10): 1334 - 1347. [Abstract] [Full Text] [PDF]
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C. A Clay, S. Perera, J. M Wagner, M. E Miller, J. B Nelson, and S. L Greenspan Physical Function in Men With Prostate Cancer on Androgen Deprivation Therapy Physical Therapy, October 1, 2007; 87(10): 1325 - 1333. [Abstract] [Full Text] [PDF]
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 A. Russo, M. Cesari, G. Onder, V. Zamboni, C. Barillaro, M. Pahor, R. Bernabei, and F. Landi Depression and Physical Function: Results From the Aging and Longevity Study in the Sirente Geographic Area (ilSIRENTE Study) J Geriatr Psychiatry Neurol, September 1, 2007; 20(3): 131 - 137. [Abstract] [PDF]
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F. Landi, A. Russo, M. Cesari, M. Pahor, R. Bernabei, and G. Onder HDL-cholesterol and physical performance: results from the ageing and longevity study in the sirente geographic area (ilSIRENTE Study) Age Ageing, September 1, 2007; 36(5): 514 - 520. [Abstract] [Full Text] [PDF]
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J. S. Brach, S. A. Studenski, S. Perera, J. M. VanSwearingen, and A. B. Newman Gait Variability and the Risk of Incident Mobility Disability in Community-Dwelling Older Adults J. Gerontol. A Biol. Sci. Med. Sci., September 1, 2007; 62(9): 983 - 988. [Abstract] [Full Text] [PDF]
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C. Rosano, H. J. Aizenstein, S. Studenski, and A. B. Newman A Regions-of-Interest Volumetric Analysis of Mobility Limitations in Community-Dwelling Older Adults J. Gerontol. A Biol. Sci. Med. Sci., September 1, 2007; 62(9): 1048 - 1055. [Abstract] [Full Text] [PDF]
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G. V. Ostir, Y.-F. Kuo, I. M. Berges, K. S. Markides, and K. J. Ottenbacher Measures of Lower Body Function and Risk of Mortality over 7 Years of Follow-up Am. J. Epidemiol., September 1, 2007; 166(5): 599 - 605. [Abstract] [Full Text] [PDF]
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E. C. Hadley Testing Interventions to Preserve Walking Ability: Progress Against Disability, One Step at a Time J. Gerontol. A Biol. Sci. Med. Sci., August 1, 2007; 62(8): 834 - 836. [Full Text] [PDF]
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H. H. Atkinson, C. Rosano, E. M. Simonsick, J. D. Williamson, C. Davis, W. T. Ambrosius, S. R. Rapp, M. Cesari, A. B. Newman, T. B. Harris, et al. Cognitive Function, Gait Speed Decline, and Comorbidities: The Health, Aging and Body Composition Study J. Gerontol. A Biol. Sci. Med. Sci., August 1, 2007; 62(8): 844 - 850. [Abstract] [Full Text] [PDF]
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S. Stenholm, P. Sainio, T. Rantanen, S. Koskinen, A. Jula, M. Heliovaara, and A. Aromaa High Body Mass Index and Physical Impairments as Predictors of Walking Limitation 22 Years Later in Adult Finns J. Gerontol. A Biol. Sci. Med. Sci., August 1, 2007; 62(8): 859 - 865. [Abstract] [Full Text] [PDF]
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S. R Piva, E. A Goodnite, K. Azuma, J. D Woollard, B. H Goodpaster, M. C. Wasko, and G K. Fitzgerald Neuromuscular Electrical Stimulation and Volitional Exercise for Individuals With Rheumatoid Arthritis: A Multiple-Patient Case Report Physical Therapy, August 1, 2007; 87(8): 1064 - 1077. [Abstract] [Full Text] [PDF]
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D. Kuh and the New Dynamics of Ageing (NDA) Preparatory Netwo A Life Course Approach to Healthy Aging, Frailty, and Capability J. Gerontol. A Biol. Sci. Med. Sci., July 1, 2007; 62(7): 717 - 721. [Full Text] [PDF]
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 K. V. Patel, T. B. Harris, M. Faulhaber, S. B. Angleman, S. Connelly, D. C. Bauer, L. H. Kuller, A. B. Newman, J. M. Guralnik, and for the Health, Aging, and Body Composition Study Racial variation in the relationship of anemia with mortality and mobility disability among older adults Blood, June 1, 2007; 109(11): 4663 - 4670. [Abstract] [Full Text] [PDF]
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C. O. Weiss, L. P. Fried, and K. Bandeen-Roche Exploring the Hierarchy of Mobility Performance in High-Functioning Older Women J. Gerontol. A Biol. Sci. Med. Sci., February 1, 2007; 62(2): 167 - 173. [Abstract] [Full Text] [PDF]
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T. M. Frayling, S. Rafiq, A. Murray, A. J. Hurst, M. N. Weedon, W. Henley, S. Bandinelli, A.-M. Corsi, L. Ferrucci, J. M. Guralnik, et al. An Interleukin-18 Polymorphism Is Associated With Reduced Serum Concentrations and Better Physical Functioning in Older People J. Gerontol. A Biol. Sci. Med. Sci., January 1, 2007; 62(1): 73 - 78. [Abstract] [Full Text] [PDF]
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L. Ferrucci and E. M. Simonsick A Little Exercise J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2006; 61(11): 1154 - 1156. [Full Text] [PDF]
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The LIFE Study Investigators* [*See Appendix for L Effects of a Physical Activity Intervention on Measures of Physical Performance: Results of the Lifestyle Interventions and Independence for Elders Pilot (LIFE-P) Study J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2006; 61(11): 1157 - 1165. [Abstract] [Full Text] [PDF]
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K. Avlund, T. Rantanen, and M. Schroll Tiredness and Subsequent Disability in Older Adults: The Role of Walking Limitations J. Gerontol. A Biol. Sci. Med. Sci., November 1, 2006; 61(11): 1201 - 1205. [Abstract] [Full Text] [PDF]
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A. Ortega-Alonso, N. L. Pedersen, U. M. Kujala, S. Sipila, T. Tormakangas, J. Kaprio, M. Koskenvuo, and T. Rantanen A twin study on the heritability of walking ability among older women. J. Gerontol. A Biol. Sci. Med. Sci., October 1, 2006; 61(10): 1082 - 1085. [Abstract] [Full Text] [PDF]
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 G. Onder, R. Liperoti, A. Russo, M. Soldato, E. Capoluongo, S. Volpato, M. Cesari, F. Ameglio, R. Bernabei, and F. Landi Body mass index, free insulin-like growth factor I, and physical function among older adults: results from the ilSIRENTE study Am J Physiol Endocrinol Metab, October 1, 2006; 291(4): E829 - E834. [Abstract] [Full Text] [PDF]
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 C. W. McIntyre, N. M. Selby, M. Sigrist, L. E. Pearce, T. H. Mercer, and P. F. Naish Patients receiving maintenance dialysis have more severe functionally significant skeletal muscle wasting than patients with dialysis-independent chronic kidney disease Nephrol. Dial. Transplant., August 1, 2006; 21(8): 2210 - 2216. [Abstract] [Full Text] [PDF]
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D. Kuh, R. Hardy, S. Butterworth, L. Okell, M. Richards, M. Wadsworth, C. Cooper, and A. A. Sayer Developmental Origins of Midlife Physical Performance: Evidence from a British Birth Cohort Am. J. Epidemiol., July 15, 2006; 164(2): 110 - 121. [Abstract] [Full Text] [PDF]
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 A Russo, G Onder, M Cesari, V Zamboni, C Barillaro, E Capoluongo, M Pahor, R Bernabei, and F Landi Lifetime occupation and physical function: a prospective cohort study on persons aged 80 years and older living in a community. Occup. Environ. Med., July 1, 2006; 63(7): 438 - 442. [Abstract] [Full Text] [PDF]
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T. M. Manini, S. B. Cook, T. VanArnam, M. Marko, and L. Ploutz-Snyder Evaluating task modification as an objective measure of functional limitation: repeatability and comparability. J. Gerontol. A Biol. Sci. Med. Sci., July 1, 2006; 61(7): 718 - 725. [Abstract] [Full Text] [PDF]
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T.-M. Asikainen, J. H Suni, M. E Pasanen, P. Oja, M. B Rinne, S. I Miilunpalo, C.-H. A Nygard, and I. M Vuori Effect of Brisk Walking in 1 or 2 Daily Bouts and Moderate Resistance Training on Lower-Extremity Muscle Strength, Balance, and Walking Performance in Women Who Recently Went Through Menopause: A Randomized, Controlled Trial Physical Therapy, July 1, 2006; 86(7): 912 - 923. [Abstract] [Full Text] [PDF]
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S. Ayis, R. Gooberman-Hill, A. Bowling, and S. Ebrahim Predicting catastrophic decline in mobility among older people Age Ageing, July 1, 2006; 35(4): 382 - 387. [Abstract] [Full Text] [PDF]
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B. J. Edwards, C. B. Langman, K. Martinez, M. Johnson, M. L. Mille, and M. W. Rogers Women with wrist fractures are at increased risk for future fractures because of both skeletal and non-skeletal risk factors Age Ageing, July 1, 2006; 35(4): 438 - 441. [Full Text] [PDF]
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H.-K. Kuo, J. F. Bean, C.-J. Yen, and S. G. Leveille Linking C-Reactive Protein to Late-Life Disability in the National Health and Nutrition Examination Survey (NHANES) 1999-2002. J. Gerontol. A Biol. Sci. Med. Sci., April 1, 2006; 61(4): 380 - 387. [Abstract] [Full Text] [PDF]
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 J. Giri, M. M. McDermott, P. Greenland, J. M. Guralnik, M. H. Criqui, K. Liu, L. Ferrucci, D. Green, J. R. Schneider, and L. Tian Statin Use and Functional Decline in Patients With and Without Peripheral Arterial Disease J. Am. Coll. Cardiol., March 7, 2006; 47(5): 998 - 1004. [Abstract] [Full Text] [PDF]
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 M E Busse, C M Wiles, and R W M van Deursen Community walking activity in neurological disorders with leg weakness. J. Neurol. Neurosurg. Psychiatry, March 1, 2006; 77(3): 359 - 362. [Abstract] [Full Text] [PDF]
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A. K. Coppin, L. Ferrucci, F. Lauretani, C. Phillips, M. Chang, S. Bandinelli, and J. M. Guralnik Low Socioeconomic Status and Disability in Old Age: Evidence From the InChianti Study for the Mediating Role of Physiological Impairments J. Gerontol. A Biol. Sci. Med. Sci., January 1, 2006; 61(1): 86 - 91. [Abstract] [Full Text] [PDF]
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D. Melzer, E. Gardener, and J. M. Guralnik Mobility disability in the middle-aged: cross-sectional associations in the English Longitudinal Study of Ageing Age Ageing, November 1, 2005; 34(6): 594 - 602. [Abstract] [Full Text] [PDF]
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C. Peel, P. S. Baker, D. L Roth, C. J Brown, E. V Bodner, and R. M Allman Assessing Mobility in Older Adults: The UAB Study of Aging Life-Space Assessment Physical Therapy, October 1, 2005; 85(10): 1008 - 1019. [Abstract] [Full Text] [PDF]
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S. E. Hardy, J. A. Dubin, T. R. Holford, and T. M. Gill Transitions between States of Disability and Independence among Older Persons Am. J. Epidemiol., March 15, 2005; 161(6): 575 - 584. [Abstract] [Full Text] [PDF]
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C. Blake and Y. M. O'Meara Subjective and objective physical limitations in high-functioning renal dialysis patients Nephrol. Dial. Transplant., December 1, 2004; 19(12): 3124 - 3129. [Abstract] [Full Text] [PDF]
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T. M. Gill, H. G. Allore, T. R. Holford, and Z. Guo Hospitalization, Restricted Activity, and the Development of Disability Among Older Persons JAMA, November 3, 2004; 292(17): 2115 - 2124. [Abstract] [Full Text] [PDF]
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 L. M. Verbrugge and P. Sevak Disability Symptoms and the Price of Self-Sufficiency J Aging Health, November 1, 2004; 16(5): 688 - 722. [Abstract] [PDF]
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S. E. Hardy and T. M. Gill Recovery From Disability Among Community-Dwelling Older Persons JAMA, April 7, 2004; 291(13): 1596 - 1602. [Abstract] [Full Text] [PDF]
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T. M. Gill and B. F. Kurland Prognostic Effect of Prior Disability Episodes among Nondisabled Community-living Older Persons Am. J. Epidemiol., December 1, 2003; 158(11): 1090 - 1096. [Abstract] [Full Text] [PDF]
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 F. Lauretani, C. R. Russo, S. Bandinelli, B. Bartali, C. Cavazzini, A. Di Iorio, A. M. Corsi, T. Rantanen, J. M. Guralnik, and L. Ferrucci Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia J Appl Physiol, November 1, 2003; 95(5): 1851 - 1860. [Abstract] [Full Text] [PDF]
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 M. M. McDermott, P. Greenland, D. Green, J. M. Guralnik, M. H. Criqui, K. Liu, C. Chan, W. H. Pearce, L. Taylor, P. M Ridker, et al. D-Dimer, Inflammatory Markers, and Lower Extremity Functioning in Patients With and Without Peripheral Arterial Disease Circulation, July 1, 2003; 107(25): 3191 - 3198. [Abstract] [Full Text] [PDF]
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M. M. McDermott, J. M. Guralnik, P. Greenland, W. H. Pearce, M. H. Criqui, K. Liu, L. Taylor, C. Chan, L. Sharma, J. R. Schneider, et al. Statin Use and Leg Functioning in Patients With and Without Lower-Extremity Peripheral Arterial Disease Circulation, February 11, 2003; 107(5): 757 - 761. [Abstract] [Full Text] [PDF]
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J. S Brach and J. M VanSwearingen Physical Impairment and Disability: Relationship to Performance of Activities of Daily Living in Community-Dwelling Older Men Physical Therapy, August 1, 2002; 82(8): 752 - 761. [Abstract] [Full Text] [PDF]
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A. Shumway-Cook, A. E Patla, A. Stewart, L. Ferrucci, M. A Ciol, and J. M Guralnik Environmental Demands Associated With Community Mobility in Older Adults With and Without Mobility Disabilities Physical Therapy, July 1, 2002; 82(7): 670 - 681. [Abstract] [Full Text] [PDF]
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G. Onder, B. W.J.H. Penninx, P. Lapuerta, L. P. Fried, G. V. Ostir, J. M. Guralnik, and M. Pahor Change in Physical Performance Over Time in Older Women: The Women's Health and Aging Study J. Gerontol. A Biol. Sci. Med. Sci., May 1, 2002; 57(5): M289 - 293. [Abstract] [Full Text]
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D. R. Thomas The Critical Link Between Health-Related Quality of Life and Age-Related Changes in Physical Activity and Nutrition J. Gerontol. A Biol. Sci. Med. Sci., October 1, 2001; 56(10): M599 - 602. [Full Text] [PDF]
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K. S. Markides, S. A. Black, G. V. Ostir, R. J. Angel, J. M. Guralnik, and M. Lichtenstein Lower Body Function and Mortality in Mexican American Elderly People J. Gerontol. A Biol. Sci. Med. Sci., April 1, 2001; 56(4): 243M - 247. [Abstract] [Full Text]
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W.T. Longstreth Jr., C. Bernick, A. Fitzpatrick, M. Cushman, L. Knepper, J. Lima, and C.D. Furberg Frequency and predictors of stroke death in 5,888 participants in the Cardiovascular Health Study Neurology, February 13, 2001; 56(3): 368 - 375. [Abstract] [Full Text] [PDF]
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