Prevalence of and intention to change dietary and physical activity health risk behaviours
ABSTRACT Poor nutrition and insufficient physical activity contribute to high rates of obesity. Prevalence, intention to change and co-occurrence of four health risk behaviours (inadequate fruit and vegetables, excessive dietary fat, excessive sugary beverages and inadequate physical activity in comparison to public health recommendations) were investigated in an Australian population of working adults. Participants (n=105) completed sociodemographic and stage of change questionnaires. A subsample (n=40) were assessed twice to estimate test-retest repeatability. In the full sample, 73% were female, mean age was 33.8 years and mean BMI was 23.8 kg/m(2). Eighty-seven percent of participants consumed inadequate fruit and vegetables, 43% had excessive dietary fat, 42% had excessive sugary beverages and 29% had inadequate physical activity. The proportions intending to change each behaviour were 57%, 25%, 18% and 24%, respectively. Two-thirds exhibited multiple risk behaviours and 38% intended to change multiple risk behaviours. Fruit and vegetables and dietary fat were the most commonly paired risk behaviours (39%) and the pair most intended to change (19%). Occurrence of multiple risk behaviours was associated with being male (OR 3.10, 95% CI 1.06-9.03) or overweight/obese (OR 2.66, 95% CI 1.02-6.93). Targeting two risk behaviours, particularly fruit and vegetables and dietary fat, may be appropriate when designing health promotion programs in working populations.
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ABSTRACT: This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.Biometrics 04/1977; 33(1):159-74. DOI:10.2307/2529310 · 1.52 Impact Factor
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ABSTRACT: To develop an algorithm that defines a person's stage of change for fat intake < or = 30% of energy. The Stages of Change Model describes when and how people change problem behaviors; change is defined as a dynamic variable with five discrete stages. A stage of change algorithm for determining dietary fat intake < or = 30% of energy was developed using one sample and was validated using a second sample. Sample 1 was a random sample of 614 adults who responded to mailed questionnaires. Sample 2 was a convenience sample of 130 faculty, staff, and graduate students. Subjects in sample 1 were initially classified in a stage of change using an algorithm based on their behavior related to avoiding high-fat foods. Dietary markers were selected for a Behavioral algorithm using logistic regression analyses. Sensitivity, specificity, and predictive value of the Behavioral algorithm were determined, then compared between samples using the Z test. The following dietary markers predicted intake < or = 30% of fat (chi 2 = 131; P < .0001): low-fat cheese, breads without added fat, chicken without skin, low-calorie salad dressing, and vegetables for snacks. The specificity of the Behavioral algorithm was validated; the algorithm classified subjects consuming > 30% of energy from fat with 93% specificity in sample 1 and 87% in sample 2 (Z = 1.36; P > .05). Predictive value was also validated; 64% and 58% of subjects meeting the behavioral criteria had fat intakes < or = 30% of energy (Z = 1.1; P > .05). The algorithm was not sensitive, however; most subjects with fat intakes < or = 30% of energy from fat failed to meet the behavioral criteria. The sensitivity differed between samples 1 and 2 (44% and 27%, respectively; Z = 3.84; P < .0001). The Behavioral algorithm determines stage of change for fat reduction to < or = 30% of energy in populations with high fat intakes. The algorithm could be used in dietary counseling to tailor interventions to a patient's stage of change.Journal of the American Dietetic Association 10/1994; 94(10):1105-10; quiz 1111-2. DOI:10.1016/0002-8223(94)91127-4 · 3.92 Impact Factor
Article: Dietary assessment resource manual.Journal of Nutrition 12/1994; 124(11 Suppl):2245S-2317S. · 4.23 Impact Factor