Purpose: Adults tend to increase their television (TV) viewing time as they age, but little is known about attributes associated with change in TV viewing over time. This study examined individual, social, and environmental correlates of change in TV viewing time for 4 yr.
Methods: Adult participants (n = 897) from a longitudinal epidemiological study in Adelaide, Australia, reported TV viewing time at baseline (2003–2004) and at follow- up (2007–2008). Generalized linear modeling was used to examine correlates of change in TV viewing time.
Results: The mean TV viewing time increased from 112 to 116 min·d−1 from baseline to follow-up. Adjusted for TV viewing time at baseline, having a tertiary education was associated with a 13% lower TV time at follow-up (P = 0.007). Each additional hour of occupational and transport physical activity at baseline was associated with a 2% and 7% lower TV viewing at follow-up (P = 0.031 and P = 0.023, respectively). For men, an additional hour of domestic physical activity was associated with a 7% higher TV viewing time at follow-up (P = 0.006). A significant neighborhood walkability × working status interaction (P = 0.035) indicated that, for those who were not working, living in a highly walkable neighborhood was associated with a 23% lower TV viewing time at follow-up (P = 0.003).
Conclusions: Adults with lower educational attainment, adults with lower occupational and transport physical activity, men with higher domestic physical activity, and nonworking adults living in lowly walkable neighborhoods were at higher risk of increase in TV viewing time. Interventions should target multiple variables at the individual, social, and environmental levels to address age-related increases in TV viewing time.
1Department of Family and Preventive Medicine, University of California San Diego, La Jolla, CA; 2Graduate School of Public Health, San Diego State University, San Diego, CA; 3Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, AUSTRALIA; 4School of Population Health, University of Queensland, Brisbane, Queensland, AUSTRALIA; 5Institute of Human Performance, University of Hong Kong, Hong Kong, CHINA; 6Research Foundation Flanders (FWO), Brussels, BELGIUM; 7Ghent University, Department of Movement and Sports Sciences, Ghent, BELGIUM; and 8MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, UNITED KINGDOM
Address for correspondence: Ding Ding, M.P.H., 3900 5th Ave., Suite 310, San Diego, CA 92103; E-mail: email@example.com.
Submitted for publication November 2011.
Accepted for publication January 2012.