Classifying and Predicting Long-term Pain and Function in Older Adults

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

6783 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-08-01

Study Completion Date

2020-07-31

Brief Summary

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This project will use novel methods to detect clinically meaningful subgroups of older adults based on long-term trajectories of bothersome pain and function. It will then identify older adults at high risk of experiencing poor long-term pain and function. Anticipated results will provide new insights into long-term patterns of pain and function across the aging process and identify potential predictors of each trajectory.

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Detailed Description

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The long-term goal of this planned line of research is to reduce the burden of pain and maximize function in older adults as they age. This line of research will begin by completing the following Aims: Aim 1) Identify and describe clinically meaningful long-term trajectories of bothersome pain and functional decline in a population-based sample of older adults and Aim 2) Estimate the association between candidate prognostic factors typically available in electronic health records and long-term bothersome pain and function trajectories to inform the development of eventual risk prediction models. This will be a retrospective cohort study using longitudinal data from the population-based National Health and Aging Trends Study (NHATS). This project will use novel methods to identify clinically meaningful subgroups of older adults based on long-term trajectories of bothersome pain and function. Group-based trajectory modeling (GBTM) is a novel method to model dynamic phenomena such as pain and function. Older adults at high risk of experiencing poor long-term pain and function outcomes will be identified by leveraging potential prognostic factors typically available in electronic health records or administrative data. It is anticipated that our results will provide new insights into long-term patterns of pain and function across the aging process and identify potential predictors of each trajectory.