OBJECTIVE: Reducing certain sedentary behaviors (e.g., watching television, using a computer) can be an effective weight loss strategy for youth. Knowledge about whether behaviors cluster together could inform interventions. STUDY DESIGN: Estimates of time spent in 6 sedentary behaviors (watching television, talking on the telephone, using a computer, listening to music, doing homework, reading) were cluster analyzed for a sample of 878 adolescents (52% girls, mean age = 12.7 years, 58% Caucasian). MAIN OUTCOME MEASURES: The clusters were based on the sedentary behaviors listed above and compared on environmental variables (e.g., household rules), psychosocial variables (e.g., self-efficacy, enjoyment), and health behaviors (e.g., physical activity, diet). RESULTS: Four clusters emerged: low sedentary, medium sedentary, selective high sedentary, and high sedentary. Analyses revealed significant cluster differences for gender (p < .002), age (p < .002), body mass index (p < .001), physical activity (p < .01), and fiber intake (p < .01). CONCLUSIONS: Results suggest a limited number of distinct sedentary behavior patterns. Further study is needed to determine how interventions may use cluster membership to target segments of the adolescent population.
OBJECTIVE: Reducing certain sedentary behaviors (e.g., watching television, using a computer) can be an effective weight loss strategy for youth. Knowledge about whether behaviors cluster together could inform interventions. STUDY DESIGN: Estimates of time spent in 6 sedentary behaviors (watching television, talking on the telephone, using a computer, listening to music, doing homework, reading) were cluster analyzed for a sample of 878 adolescents (52% girls, mean age = 12.7 years, 58% Caucasian). MAIN OUTCOME MEASURES: The clusters were based on the sedentary behaviors listed above and compared on environmental variables (e.g., household rules), psychosocial variables (e.g., self-efficacy, enjoyment), and health behaviors (e.g., physical activity, diet). RESULTS: Four clusters emerged: low sedentary, medium sedentary, selective high sedentary, and high sedentary. Analyses revealed significant cluster differences for gender (p < .002), age (p < .002), body mass index (p < .001), physical activity (p < .01), and fiber intake (p < .01). CONCLUSIONS: Results suggest a limited number of distinct sedentary behavior patterns. Further study is needed to determine how interventions may use cluster membership to target segments of the adolescent population.
Authors: Karl E Minges; Neville Owen; Jo Salmon; Ariana Chao; David W Dunstan; Robin Whittemore Journal: Health Psychol Date: 2015-04 Impact factor: 4.267
Authors: Wayne F Velicer; Colleen A Redding; Andrea L Paiva; Leanne M Mauriello; Bryan Blissmer; Karin Oatley; Kathryn S Meier; Steven F Babbin; Heather McGee; James O Prochaska; Caitlin Burditt; Anne C Fernandez Journal: Transl Behav Med Date: 2013-03 Impact factor: 3.046
Authors: Melissa W George; Nevelyn N Trumpeter; Dawn K Wilson; Heather L McDaniel; Bryn Schiele; Ron Prinz; Mark D Weist Journal: Fam Community Health Date: 2014 Jan-Mar