title: Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms.
abstract: BACKGROUND
  Treating multiple health behavior risks on a population basis is one of the most promising approaches to enhancing health and reducing health care costs. Previous research demonstrated the efficacy of expert system interventions for three behaviors in a population of parents. The interventions provide individualized feedback that guides participants through the stages of change for each of their risk behaviors. This study extended that research to a more representative population of patients from primary care practice and to targeting of four rather than three behaviors.
  
  
  METHODS
  Stage-based expert systems were applied to reduce smoking, improve diet, decrease sun exposure, and prevent relapse from regular mammography. A randomized clinical controlled trial recruited 69.2% of primary care patients (N = 5407) at home via telephone. Three intervention contacts were delivered for each risk factor at 0, 6, and 12 months. The primary outcome measures were the percentages of at-risk patients at baseline who progressed to the action or maintenance stages at 24-month follow-up for each of the risk behaviors.
  
  
  RESULTS
  Significant treatment effects were found for each of the four behaviors, with 25.4% of intervention patients in action or maintenance for smoking, 28.8% for diet, and 23.4% for sun exposure. The treatment group had less relapse from regular mammography than the control group (6% vs. 10%).
  
  
  CONCLUSIONS
  Proactive, home-based, and stage-matched expert systems can produce relatively high population impacts on multiple behavior risks for cancer and other chronic diseases.
authors.affiliation: Cancer Prevention Research Center, University of Rhode Island, Kingston, RI 02881, USA. jop@uri.edu
authors.forename: James O
authors.initials: JO
authors.lastname: Prochaska
authors.forename: Wayne F
authors.initials: WF
authors.lastname: Velicer
authors.forename: Colleen
authors.initials: C
authors.lastname: Redding
authors.forename: Joseph S
authors.initials: JS
authors.lastname: Rossi
authors.forename: Michael
authors.initials: M
authors.lastname: Goldstein
authors.forename: Judith
authors.initials: J
authors.lastname: DePue
authors.forename: Geoffrey W
authors.initials: GW
authors.lastname: Greene
authors.forename: Susan R
authors.initials: SR
authors.lastname: Rossi
authors.forename: Xiaowu
authors.initials: X
authors.lastname: Sun
authors.forename: Joseph L
authors.initials: JL
authors.lastname: Fava
authors.forename: Robert
authors.initials: R
authors.lastname: Laforge
authors.forename: William
authors.initials: W
authors.lastname: Rakowski
authors.forename: Brett A
authors.initials: BA
authors.lastname: Plummer
mesh_terms: D000328:Adult; D005103:Expert Systems; D005247:Feeding Behavior; D005260:Female; D006293:Health Promotion / Q000379:methods*; D006801:Humans; D008297:Male; D008327:Mammography; D008875:Middle Aged; D015999:Multivariate Analysis; D009369:Neoplasms / Q000517:prevention & control*; D011320:Primary Health Care; D012044:Regression Analysis; D016037:Single-Blind Method; D012878:Skin Neoplasms / Q000517:prevention & control; D016540:Smoking Cessation; D013689:Telephone; D013813:Therapy, Computer-Assisted