Stages of change for reducing dietary fat to 30% of energy or less

J Am Diet Assoc. 1994 Oct;94(10):1105-10; quiz 1111-2. doi: 10.1016/0002-8223(94)91127-4.


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.

Design: 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.

Subjects: 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.

Statistics: 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).

Applications: 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.

Publication types

  • Clinical Trial
  • Randomized Controlled Trial
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Algorithms*
  • Diet, Fat-Restricted*
  • Dietary Fats / administration & dosage*
  • Energy Intake*
  • Feeding Behavior*
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Sensitivity and Specificity
  • Surveys and Questionnaires


  • Dietary Fats