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Gait Speed and Survival in Older Adults

The size of the data markers is proportional to the square root of the number of participants. The Q statistic for heterogeneity is Pooled using random effects and shared frailty models. Gait Speed and Survival in Older Adults. Physical performance measures, such as gait speed, might help account for variability, allowing clinicians to make more individualized estimates.

Participants were a mean SD age of Gait speed was associated with survival in all studies pooled hazard ratio per 0.

Survival increased across the full range of gait speeds, with significant increments per 0. Predicted survival based on age, sex, and gait speed was as accurate as predicted based on age, sex, use of mobility aids, and self-reported function or as age, sex, chronic conditions, smoking history, blood pressure, body mass index, and hospitalization. Remaining years of life vary widely in older adults, and physicians should consider life expectancy when assessing goals of care and treatment plans.

We used individual participant data from 9 cohort studies, baseline data for which were collected between and Table 1. Analyses performed herein were conducted in and All studies required written informed consent and institutional review board approval. All studies recruited community-dwelling older adults. Although some sought representative samples, 8 , 15 , 20 , 23 others focused on healthier participants, 16 , 17 single sex, 19 , 22 or older adults from primary care practices.

Individual study goals, recruitment methods, and target populations have been published. Gait speed was calculated for each participant using distance in meters and time in seconds. All studies used instructions to walk at usual pace and from a standing start. The walk distance varied from 8 ft to 6 m. For 8 ft, we converted to 4-m gait speed by formula.

For 15 feet 4. Where available, data on fast gait speed walk as fast as comfortably able 25 and the Short Physical Performance Battery were obtained. Time from gait speed baseline to death was calculated in days. Measures of self-reported functional status were not collected in all studies and varied in content and form. We created a dichotomous variable reflecting dependence in basic activities of daily living ADLs based on report of being unable or needing help from another person to perform any basic activity, including eating, toileting, hygiene, transfer, bathing, and dressing.

For individuals independent in ADLs, we created a dichotomous variable reflecting difficulty in instrumental ADLs based on report of difficulty or dependence with shopping, meal preparation, or heavy housework due to a health or physical problem.

Participants were then classified into 1 of 3 groups; dependent in ADLs, difficulty with instrumental ADLs, or independent. Physical activity data were collected in 6 studies, but time frames and items varied widely. Covariates were identical for height, weight, BMI, and systolic blood pressure.

Hospitalization within the prior year was determined largely by self-report, and chronic conditions were by self-report of physician diagnosis, with heart disease encompassing angina, coronary artery disease, heart attack, and heart failure. Descriptive statistics summarized participant characteristics, follow-up period, and median survival from baseline.

A study-wide a priori P value of. Kaplan-Meier product-limit survival curves graphically summarize lifetimes for each gait speed category. Cox proportional hazards regression models were used to assess associations between gait speed and survival, adjusting for age at baseline, for which hazard ratios HRs correspond to a 0.

The analyses were repeated adjusting for height, sex, race, BMI, smoking history systolic blood pressure, diseases, prior hospitalization, and self-reported heath. Proportionality of hazards was verified by examining Schoenfeld residual plots. To examine the influence of early deaths, we repeated analyses excluding deaths within 1 year of gait speed measurement and moved up the 0 time for survival assessment results were similar; eTable 1.

To obtain simple and clinically usable estimates of survival probability based on sex, age, and gait speed, we fit logistic regression models separately for each sex with dichotomized 5- and year survival as the response variable and age, gait speed, and their interaction as continuous predictors.

To obtain estimates of median survival further life expectancy , we fit Weibull accelerated failure—time models separately for each with time to death as the response variable, and age, gait speed, and their interaction as continuous predictors. To compare ability to predict survival among candidate variables and to determine whether gait speed improves predictive accuracy beyond other clinical measures, we fit logistic regression models with dichotomized 5-year or year survival as the response variable and various combinations of predictors as independent variables with both linear and squared terms for BMI.

The area under the receiver operating characteristic ROC curve or C statistic was used as a measure predictive of accuracy for mortality.

All study-specific statistical analyses were performed using SAS version 9. Age-adjusted HRs were pooled from all studies using standard meta-analytic statistical methodology. Heterogeneity of HRs across studies was assessed using the Q and I 2 statistics. We further used the standard random effects meta-analytic model to combine sex-specific regression coefficients for age, gait speed, and their interaction from logistic regression models for 5- and year survival and used the overall estimates to construct clinically usable survival probability nomograms; combine sex-specific regression coefficients for age, gait speed, and their interaction from accelerated failure time models for time to death and used the overall estimates to construct clinically usable life-expectancy nomograms; and combine areas under ROC curves obtained from 9 studies.

An increase of 0. We used Comprehensive Meta Analysis version 2. Although most studies included men and women, 2 were sex specific.

The studies had a wide age range, including persons older than 85 years. Similarly, there was a wide range of gait speeds, from less than 0. Study follow-up time ranged from 6. Mortality rates appear to be related to length of follow-up Table 1. To assess consistency across studies, risk of death was estimated per 0. Age-adjusted HRs by study ranged from 0.

There were consistent associations across studies, although given the large sample sizes, Q statistics were often statistically significant details available in eFigure 1A-M. Because physical activity measures were not sufficiently consistent across studies, effects could not be pooled. Pooled HRs for all subgroups except functional status were consistently in the range of 0. The overall HR for survival per each 0. Further adjustment for sex, BMI, smoking status, systolic blood pressure, diseases, prior hospitalization, and self-reported health did not change the results overall HR, 0.

Using data from all studies, we created for each sex, 5- and year survival tables Table 2 , data derived from pooled Kaplan-Meier estimates evaluated at 5 and 10 years, presented in 3 age groups and graphs eFigure 3 and eFigure 4 predicted survival based on pooled logistic regression coefficients, data presented with age as a continuous variable. Gait speed was associated with differences in the probability of survival at all ages in both sexes, but was especially informative after age 75 years.

In men, the probability of 5-year survival at age 85 ranged from 0. In women, the probability of 5-year survival remained greater than 0. Stratification by sex-specific median height failed to show systematic differences in survival rates between short and tall participants, so results presented are not stratified by height.

Confidence intervals were often wide. We also used our analyses to estimate median years of remaining life based on sex, age, and gait speed. Figure 2 , predicted survival data are based on an accelerated failure time model with Weibull distribution, with age as a continuous variable, and eTable 3 , data are derived from pooled Kaplan-Meier estimates evaluated at 5 and 10 years in 3 age groups.

In the pooled sample, median survival in years for the age groups 65 through 74 years was Predicted years of remaining life for each sex and age increased as gait speed increased, with a gait speed of about 0. Gait speeds of 1. In this older adult population, the relationship of gait speed with remaining years of life was consistent across age groups, but the absolute number of expected remaining years of life was larger at younger ages. For year-old men, life expectancy ranged from 7 to 23 years and for women, from 10 to 30 years.

To compare the 5-year survival predictive ability between demographics and gait speed vs other combinations of variables, we used areas under the ROC curve C statistics in logistic regression models for individual studies and pooled across studies Table 3.

Gait speed added substantially 37 to age and sex in 7 of the 9 studies and in the pooled analysis. C statistics for age, sex, and gait speed were greater than those for age, sex, and chronic diseases in 4 of 9 studies, approximately equivalent in 5 studies and inferior in no studies.

C statistics for age, sex, and gait speed were approximately equivalent to those for age, sex, chronic diseases, BMI, systolic blood pressure, and prior hospitalization in all 9 studies and in the pooled analysis. There were 4 studies that had sufficiently consistent data on functional status to create 3 categories: dependent in ADLs, difficulty with instrumental ADLs, and independent.

For these studies, gait speed, age, and sex yielded a C statistic 0. For year survival, 6 studies had sufficient follow-up time to perform many of the analyses Table 3. Gait speed added predictive ability to age and sex in 4 of 6 studies and in the pooled analysis. C statistics for age, sex, and gait speed were not significantly different from C statistics with all the other factors for any study nor for the pooled analysis. Three studies had sufficiently consistent data on functional status at baseline to allow pooling.

Gait speed, age, and sex yielded a C statistic 0. In addition, we used C statistics to assess the ability of usual gait speed to predict survival compared with other physical performance measures, such as fast gait speed and the Short Physical Performance Battery SPPB , a brief measure that includes walk speed, chair rise ability, and balance. We assessed usual vs fast gait speed in the single study with both measures Invecciare in Chianti 18 study: usual, 0.

Gait speed, age, and sex may offer the clinician tools for assessing expected survival to contribute to tailoring goals of care in older adults.

The accuracy of predictions based on these 3 factors appears to be approximately similar to more complex models involving multiple other health-related factors, or for age, sex, use of mobility aids, and functional status.

Gait speed might help refine survival estimates in clinical practice or research because it is simple and informative. Why would gait speed predict survival? Walking requires energy, movement control, and support and places demands on multiple organ systems, including the heart, lungs, circulatory, nervous, and musculoskeletal systems. Slowing gait may reflect both damaged systems and a high-energy cost of walking.

In addition, decreasing mobility may induce a vicious cycle of reduced physical activity and deconditioning that has a direct effect on health and survival. The association between gait speed and survival is known.

Similarly, mortality prediction models have been developed. Only a few models assess overall predictive capacity using C statistics; the reported values are in the range found in the present study published area under the curve range, 0. The strengths of this study are the very large sample of individual participant data from multiple diverse populations of community-dwelling elders who were followed up for many years and use of consistent measures of performance and outcome.

We provide survival estimates for a broad range of gait speeds and calculate absolute rates and median years of survival. This study has the limitations of observational research; it cannot establish causal relationships and is vulnerable to various forms of healthy volunteer bias. The participating study cohorts, while large and diverse, do not represent the universe of possible data.

Self-selected gait speed: A critical clinical outcome

Survival estimates help individualize goals of care for geriatric patients, but life tables fail to account for the great variability in survival. Physical performance measures, such as gait speed, might help account for variability, allowing clinicians to make more individualized estimates. Pooled analysis of 9 cohort studies collected between and , using individual data from 34 community-dwelling older adults aged 65 years or older with baseline gait speed data, followed up for 6 to 21 years. Participants were a mean SD age of There were 17 deaths; the overall 5-year survival rate was Gait speed was associated with survival in all studies pooled hazard ratio per 0. Survival increased across the full range of gait speeds, with significant increments per 0.

Gait speed and survival in older adults.

Metrics details. Among community-dwelling older adults, mean values for gait speed vary substantially depending not only on the population studied, but also on the methodology used. Despite the large number of studies published in developed countries, there are few population-based studies in developing countries with socioeconomic inequality and different health conditions, and this is the first study with a representative sample of population. Usual gait speed s to walk 3 meters was stratified by sex and height into quartiles. Multiple regression analysis was performed to investigate the independent effect of each factor associated with a slower usual gait speed.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Studenski and S. Perera and K.

Daniel K.

Gait Speed in the Emergency Department: Improving Assessment Among Older Adults

Tanya M. Wildes; Make time for gait speed: vital to staging the aging. Blood ; 4 : — In this issue of Blood , Liu and colleagues demonstrate that gait speed is a valuable predictor of outcomes in older adults with hematologic malignancies, independent of age, performance status, comorbidities, aggressiveness of malignancy, and treatment type. The authors showed that gait speed predicted unplanned hospitalizations, emergency department visits, and survival, with each 0. Slower gait speed results from convergence of aging-associated vulnerabilities on physiologic systems involved in walking.

The size of the data markers is proportional to the square root of the number of participants. The Q statistic for heterogeneity is Pooled using random effects and shared frailty models. Gait Speed and Survival in Older Adults.

There is a significant shortage of studies on the syndrome of frailty in Brazilian older adults, principally referring to components in isolation. Given that gerontological nursing is at an early stage regarding this issue, it is understood that the identification of the prevalence must be the key point of the research on the matter. Senescence involves a wide range of physiological and psychosocial changes which accompany the natural process of aging in humans 1. The action of personal and environmental factors over the course of an individual's life, in conjunction with the interaction caused by the person's genetic inheritance whether this is protective or detrimental can, as the years go by, impede the more traditional delimitation of concepts such as natural aging senescence and aging with frailty, which are usually separated - principally in very elderly individuals - by a tenuous line. Although there is not a consensus regarding its definition, aging with frailty is understood as a clinical state of vulnerability to stressing factors, which results in a decline in the physiological reserves, with a subsequent reduction in the efficiency of homeostasis 2 - 3. Two large prospective populational studies, the Women's Health and Aging Studies WHAS 1 and the Cardiovascular Health Study CHS 4 , operationalized and validated the phenotypic criteria for the syndrome of frailty and pre-frailty most used at the present time, through evaluating the following components: unintentional weight loss, reduction in the strength of hand grip, reduction in physical activity, self-reported fatigue and reduction in gait speed. According to these criteria, an older adult is considered frail if he or she meets three or more of the five characteristics of the syndrome of frailty; the pre-frail phenotype encompasses those individuals who meet only one or two of the above-mentioned components; the older adults with negative or normal responses to the five elements are considered non-frail 2.


To evaluate the relationship between gait speed and survival. data from 9 selected cohorts, gait speed was associated with survival in older adults. for both sexes (Figure 2; a PDF of enlarged graphs is available at katcompany.org​com).


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  1. Rex J.

    A PDF of enlarged graphs is available. Table 1. Characteristics of Participants in the 9 Cohort Studies. View LargeDownload.

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