BACKGROUND: Developing effective interventions for the 24% to 28% of U.S. adults who are sedentary requires a better understanding of the factors related to sedentary lifestyles as well as the communication channels to reach various subgroups. This study identified key sociodemographic and health communication characteristics of various subgroups with high rates of inactivity using signal detection methodology. METHODS: The sample from the nationally representative Health Information National Trends Survey 2003 (n=6369) was randomly split into two samples. Exploratory analyses (conducted 2004 and 2005) were employed on the first sample to identify various subgroups, and the stability of inactivity rates in those subgroups was examined in the second sample. RESULTS: Eight subgroups with varying levels of inactivity were identified. Three subgroups had inactivity levels of 40% or higher, while the lowest subgroup had a level of less than 15%. The highest inactivity subgroup consisted of individuals with at least some college education who were in fair/poor health and who watched 4 or more hours of television per day. The second-highest inactivity subgroup was composed of those without a college education who tended not to use or attend to many communication channels. The third highest inactive subgroup consisted of those without a college education who read the newspaper and were obese. Levels of inactivity in the second independent sample subgroups were not significantly different from those found in the exploratory sample. CONCLUSIONS: This study identified empirically based, physically inactive subgroups that differed on sociodemographic and health communication characteristics. This information should be useful in creating future evidence-based, targeted, and tailored intervention strategies.
BACKGROUND: Developing effective interventions for the 24% to 28% of U.S. adults who are sedentary requires a better understanding of the factors related to sedentary lifestyles as well as the communication channels to reach various subgroups. This study identified key sociodemographic and health communication characteristics of various subgroups with high rates of inactivity using signal detection methodology. METHODS: The sample from the nationally representative Health Information National Trends Survey 2003 (n=6369) was randomly split into two samples. Exploratory analyses (conducted 2004 and 2005) were employed on the first sample to identify various subgroups, and the stability of inactivity rates in those subgroups was examined in the second sample. RESULTS: Eight subgroups with varying levels of inactivity were identified. Three subgroups had inactivity levels of 40% or higher, while the lowest subgroup had a level of less than 15%. The highest inactivity subgroup consisted of individuals with at least some college education who were in fair/poor health and who watched 4 or more hours of television per day. The second-highest inactivity subgroup was composed of those without a college education who tended not to use or attend to many communication channels. The third highest inactive subgroup consisted of those without a college education who read the newspaper and were obese. Levels of inactivity in the second independent sample subgroups were not significantly different from those found in the exploratory sample. CONCLUSIONS: This study identified empirically based, physically inactive subgroups that differed on sociodemographic and health communication characteristics. This information should be useful in creating future evidence-based, targeted, and tailored intervention strategies.
Authors: T T Fung; F B Hu; J Yu; N F Chu; D Spiegelman; G H Tofler; W C Willett; E B Rimm Journal: Am J Epidemiol Date: 2000-12-15 Impact factor: 4.897
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