Comparison of 3 Different Analytic Approaches for Determining Risk-Related Active and Sedentary Behavioral Patterns in Adolescents

被引:26
作者
Beets, Michael W. [1 ]
Foley, John T. [2 ]
机构
[1] Univ S Carolina, Dept Exercise Sci, Columbia, SC 29208 USA
[2] SUNY Coll Cortland, Dept Phys Educ, Cortland, NY USA
关键词
methods; television; physical activity; adolescents; behavior patterns; BODY-MASS INDEX; PHYSICAL-ACTIVITY; CHILDRENS TELEVISION; CLUSTER-ANALYSIS; OBESITY; OVERWEIGHT; INTERVENTIONS; DEPRESSION; YOUTH;
D O I
10.1123/jpah.7.3.381
中图分类号
R1 [预防医学、卫生学];
学科分类号
100235 [预防医学];
摘要
Background: Much of the research conducted to date implies overweight youth exhibit uniform active and sedentary behavioral patterns. This approach negates the possibility that multiple co-occurring, and seemingly contrasting, behaviors may manifest within the same individual. We present a substantive dialogue on alternative analytical approaches to identifying risk-related active/sedentary behavioral patterns associated with overweight in adolescents. Methods: Comparisons were made among latent profile analysis (LPA), cluster analysis (CA), and multinomial logistic regression (MLR). A cross sectional sample of youth (N = 6603; 12-18 yrs) completed a questionnaire assessing: physical activity (PA); competing activities (COMP); and sedentary activities (SED). Demographics associated with PA (age, sex, BMI) were used as covariates/predictors. Results: Comparisons among methods revealed that LPA and CA detected subgroupings of behavioral patterns associated with overweight, each unique in regards to behaviors and demographic characteristics, whereas MLR results followed established associations of low PA and high SED without subgroup separation. Conclusions: Use of LPA and CA provides a rich understanding of behavioral patterns and the related demographic characteristics. Decisions guiding the selection of analytical techniques are discussed.
引用
收藏
页码:381 / 392
页数:12
相关论文
共 38 条
[1]
Television Watching and Risk of Obesity in American Adolescents [J].
Atherson, Martin J. ;
Metcalf, James .
AMERICAN JOURNAL OF HEALTH EDUCATION, 2005, 36 (01) :2-7
[2]
Mediating variable framework in physical activity interventions - How are we doing? How might we do better? [J].
Baranowski, T ;
Anderson, C ;
Carmack, C .
AMERICAN JOURNAL OF PREVENTIVE MEDICINE, 1998, 15 (04) :266-297
[3]
Calinski T., 1974, Communications in Statistics-theory and Methods, V3, P1, DOI [10.1080/03610927408827101, DOI 10.1080/03610927408827101]
[4]
Physical activity levels in 10-to 11-year-olds: clustering of psychosocial correlates [J].
Cardon, G ;
Philippaerts, R ;
Lefevre, J ;
Matton, L ;
Wijndaele, K ;
Balduck, AL ;
Bourdeaudhuij, ID .
PUBLIC HEALTH NUTRITION, 2005, 8 (07) :896-903
[5]
Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal, population-based study [J].
Chen, LS ;
Eaton, WW ;
Gallo, JJ ;
Nestadt, G .
JOURNAL OF AFFECTIVE DISORDERS, 2000, 59 (01) :1-11
[6]
The development of depression in children and adolescents [J].
Cicchetti, D ;
Toth, SL .
AMERICAN PSYCHOLOGIST, 1998, 53 (02) :221-241
[7]
Adolescent physical activity and inactivity vary by ethnicity: The National Longitudinal Study of Adolescent Health [J].
Gordon-Larsen, P ;
McMurray, RG ;
Popkin, BM .
JOURNAL OF PEDIATRICS, 1999, 135 (03) :301-306
[8]
Reducing obesity via a school-based interdisciplinary intervention among youth - Planet health [J].
Gortmaker, SL ;
Peterson, K ;
Wiecha, J ;
Sobol, AM ;
Dixit, S ;
Fox, MK ;
Laird, N .
ARCHIVES OF PEDIATRICS & ADOLESCENT MEDICINE, 1999, 153 (04) :409-418
[9]
Cluster analysis in family psychology research [J].
Henry, DB ;
Tolan, PH ;
Gorman-Smith, D .
JOURNAL OF FAMILY PSYCHOLOGY, 2005, 19 (01) :121-132
[10]
A Multivariate associative finite growth mixture modeling approach examining adolescent alcohol and marijuana use [J].
Hix-Small, H ;
Duncan, TE ;
Duncan, SC ;
Okut, H .
JOURNAL OF PSYCHOPATHOLOGY AND BEHAVIORAL ASSESSMENT, 2004, 26 (04) :255-270