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Related Concept Videos

Applications of Life Tables01:22

Applications of Life Tables

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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Life Tables01:22

Life Tables

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A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
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Lifestyle Factors and Health01:20

Lifestyle Factors and Health

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Lifestyle factors play a critical role in maintaining overall health and preventing chronic diseases. Key elements, such as regular physical activity, a nutritious diet, and abstinence from smoking, can significantly enhance physical, mental, and emotional well-being while reducing the risk of several life-threatening conditions.
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Related Experiment Video

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Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software
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Life-Space Mobility Change Predicts 6-Month Mortality.

Richard E Kennedy1,2, Patricia Sawyer1,2, Courtney P Williams1

  • 1Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama.

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|February 3, 2017
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Changes in life-space mobility predict mortality risk in older adults. A decline in life-space over six months significantly increases the odds of dying in the subsequent six months.

Keywords:
comorbiditylife spacemobilitymortalityrisk assessment

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Area of Science:

  • Gerontology
  • Public Health
  • Epidemiology

Background:

  • Life-space mobility is crucial for older adults' independence and well-being.
  • Understanding predictors of mortality in community-dwelling older adults is essential for public health interventions.

Purpose of the Study:

  • To investigate if changes in life-space mobility over a 6-month period can predict mortality in the subsequent 6 months among community-dwelling older adults.

Main Methods:

  • A prospective cohort study involving 1,000 Medicare beneficiaries.
  • Life-space mobility was assessed using the UAB Life-Space Assessment every 6 months for 8.5 years.
  • Generalized linear mixed models analyzed the association between life-space change and mortality.

Main Results:

  • A 10-point decrease in life-space score over 6 months was associated with a 72% increase in the odds of mortality in the following 6 months.
  • Both baseline life-space score and 6-month change in life-space predicted subsequent mortality.
  • These associations remained significant after controlling for age, sex, race, residence, and comorbidity.

Conclusions:

  • Life-space mobility, both at baseline and its change over 6 months, is a significant predictor of short-term mortality in older adults.
  • Life-space assessment can be a valuable tool for clinicians to identify older adults at high risk of imminent death.