Most cohort studies have only a single physical activity (PA) measure and are thus susceptible to reverse causation and measurement error. Few studies have examined the impact of these potential biases on the association between PA and mortality. A total of 133,819 participants from Nurses’ Health Study and Health Professionals Follow-up Study (1986–2014) reported PA through biennial questionnaires. Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for PA and mortality using different analytic approaches comparing single (baseline, simple update = most recent) versus repeated (cumulative average) measures of PA and applying various lag times separating PA measurement and time at risk. Over 3.2 million person-years, we documented 47,273 deaths. The pooled multivariable-adjusted HR (95% CI) of all-cause mortality per 10 MET-hour/week was 0.95 (0.94–0.96) for baseline PA, 0.78 (0.77–0.79) for simple updated PA and 0.87 (0.86–0.88) for cumulative average PA in the range of 0–50 MET-hour/week. Simple updated PA showed the strongest inverse association, suggesting larger impact of reverse causation. Application of 2-year lag substantially reduced the apparent reverse causation (0.85 (0.84–0.86) for simple updated PA and 0.90 (0.89–0.91) for cumulative average PA), and 4–12-year lags had minimal additional effects. In the dose–response analysis, baseline or simple updated PA showed a J or U-shaped association with all-cause mortality while cumulative average PA showed an inverse association across a wide range of PA (0–150 MET-hour/week). Similar findings were observed for different specific mortality causes. In conclusion, PA measured at baseline or with short lag time was prone to bias. Cumulative average PA showed robust evidence that PA is inversely associated with mortality in a dose-response manner.
|Number of pages||11|
|Journal||European Journal of Epidemiology|
|Publication status||Published - 2021 Mar|
Bibliographical noteFunding Information:
This work was supported by the National Institutes of Health (UM1 CA186107, U01 CA167552 and P01 CA87969). NK was supported by grants from the National Research Foundation of Korea (NRF-2018R1C1B6008822; NRF-2018R1A4A1022589).
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