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Does being physically active prevent future disability in older people? Attenuated effects when taking time-dependent
Stefan H Kreisel1,2, Christian Blahak3, Hansjörg Bäzner4
1Geriatric Psychiatry; Department of Psychiatry and Psychotherapy, Evangelisches Klinikum Bethel, Bielefeld, Germany. stefan.kreisel@evkb.de.
BMC Geriatrics
|December 23, 2017
Summary
Physical activity in older adults does not prevent instrumental disability when time-dependent confounding is accounted for. Marginal structural models reveal no significant protective effect, challenging previous observational findings.
Area of Science:
- Gerontology and Public Health
- Epidemiology and Biostatistics
Background:
- Observational studies suggest physical activity is associated with disability-free survival in older adults.
- However, these studies are susceptible to time-dependent confounding, biasing results.
- Conventional analysis methods like Cox-regression fail to adequately address this bias.
Purpose of the Study:
- To investigate the causal effect of physical activity on preventing instrumental disability in older individuals.
- To address time-dependent confounding using marginal structural models (MSM).
- To compare MSM results with conventional modeling approaches.
Main Methods:
- Analysis of data from 639 elderly individuals in the Leukoaraiosis and Disability (LADIS) study, followed for three years.
- Estimation of physical activity's effect on transitioning to instrumental disability, controlling for confounders.
- Comparison of results from conventional regression models and MSM.
Main Results:
- Conventional Cox-regression indicated a significant risk reduction for disability with physical activity (OR 0.62).
- Marginal structural models attenuated this effect towards the null (OR 1.00).
- No statistically significant protective effect of physical activity on instrumental disability was found using MSM.
Conclusions:
- Physical activity in older adults may not prevent instrumental disability when time-dependent confounding is properly addressed.
- Previous findings might be influenced by unaddressed time-dependent biases.
- Marginal structural models are recommended for analyzing longitudinal observational studies with potential time-dependent confounding.
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