STAGES OF CHANGE FOR REDUCING DIETARY-FAT TO 30-PERCENT OF ENERGY OR LESS

Objective To develop an algorithm that defines a person's stage of change for fat intake less than or equal to 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 less than or equal to 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 less than or equal to 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 less than or equal to 30% of energy (Z=1.1; P>.05). The algorithm was not sensitive, however; most subjects with fat intakes less than or equal to 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 less than or equal to 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.