title: Stages of change for reducing dietary fat to 30% of energy or less.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Cancer Prevention Research Center, University of Rhode Island, Kingston 02881.
authors.forename: G W
authors.initials: GW
authors.lastname: Greene
authors.forename: S R
authors.initials: SR
authors.lastname: Rossi
authors.forename: G R
authors.initials: GR
authors.lastname: Reed
authors.forename: C
authors.initials: C
authors.lastname: Willey
authors.forename: J O
authors.initials: JO
authors.lastname: Prochaska
mesh_terms: D000328:Adult; D000465:Algorithms; D018752:Diet, Fat-Restricted; D004041:Dietary Fats / Q000008:administration & dosage*; D002149:Energy Intake; D005247:Feeding Behavior; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires
chemical_list: D004041:Dietary Fats