Integrating sleep, physical activity, and diet quality to estimate all-cause mortality risk: a combined compositional clustering and survival analysis of the national health and nutrition examination survey 2005-2006 cycle

Borja del Pozo Cruz*, Duncan E. McGregor, Jesús del Pozo Cruz, Matthew P. Buman, Javier Palarea-Albaladejo, Rosa M. Alfonso-Rosa, Sebastien F.M. Chastin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We aimed to compare all-cause mortality risk across clusters of adults ≥50 years of age (n = 1,035) with common lifestyle behaviors patterns, enrolled in the US National Health and Nutrition Examination Survey (2005-2006). Log-ratio coordinates of 24-hour movement pattern and z scores of diet quality were used as input into a model-based clustering analysis. A Cox regression model was fitted to ascertain the all-cause mortality risk associated with each cluster. Participants were clustered into 4 groups: 1) a group characterized by a better physical activity profile and longer sleep duration coupled with an average diet quality (cluster 1); 2) a group with the poorest activity profile and shortest sleep but also the best diet quality (cluster 2); 3) another group featuring lower levels of activity of either intensity and higher levels of sedentary behavior and also a poor diet quality score (cluster 3); and 4) a group with an average diet quality and the best activity profile in the sample (cluster 4). A combination of a poorer diet and activity profile increased the prospective risk of all-cause mortality. Our findings emphasize the importance of considering the combination of diet quality and 24-hour movement patterns when developing interventions to reduce the risk of premature mortality.

Original languageEnglish
Pages (from-to)1057-1064
Number of pages8
JournalAmerican Journal of Epidemiology
Volume189
Issue number10
Early online date14 Apr 2020
DOIs
Publication statusPublished - Oct 2020

Keywords

  • 24-hour lifestyle behaviors; cluster analysis; compositional data analysis; early death

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