title: Effectiveness of IT-based diabetes management interventions: a review of the literature.
abstract: BACKGROUND
  Information technology (IT) is increasingly being used in general practice to manage health care including type 2 diabetes. However, there is conflicting evidence about whether IT improves diabetes outcomes. This review of the literature about IT-based diabetes management interventions explores whether methodological issues such as sample characteristics, outcome measures, and mechanisms causing change in the outcome measures could explain some of the inconsistent findings evident in IT-based diabetes management studies.
  
  
  METHODS
  Databases were searched using terms related to IT and diabetes management. Articles eligible for review evaluated an IT-based diabetes management intervention in general practice and were published between 1999 and 2009 inclusive in English. Studies that did not include outcome measures were excluded.
  
  
  RESULTS
  Four hundred and twenty-five articles were identified, sixteen met the inclusion criteria: eleven GP focussed and five patient focused interventions were evaluated. Nine were RCTs, five non-randomised control trials, and two single-sample before and after designs. Important sample characteristics such as diabetes type, familiarity with IT, and baseline diabetes knowledge were not addressed in any of the studies reviewed. All studies used HbA1c as a primary outcome measure, and nine reported a significant improvement in mean HbA1c over the study period; only two studies reported the HbA1c assay method. Five studies measured diabetes medications and two measured psychological outcomes. Patient lifestyle variables were not included in any of the studies reviewed. IT was the intervention method considered to effect changes in the outcome measures. Only two studies mentioned alternative possible causal mechanisms.
  
  
  CONCLUSIONS
  Several limitations could affect the outcomes of IT-based diabetes management interventions to an unknown degree. These limitations make it difficult to attribute changes solely to such interventions.
authors.affiliation: School of Nursing, Faculty of Health, Medicine, Nursing and Behavioural Sciences, Deakin University, Waterfront Campus, Geelong, 3220 Victoria, Australia. beth.costa@deakin.edu.au
authors.forename: Beth M
authors.initials: BM
authors.lastname: Costa
authors.forename: Kristine J
authors.initials: KJ
authors.lastname: Fitzgerald
authors.forename: Kay M
authors.initials: KM
authors.lastname: Jones
authors.forename: Trisha
authors.initials: T
authors.lastname: Dunning Am
mesh_terms: D001315:Australia / Q000453:epidemiology; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000097:blood / Q000453:epidemiology / Q000628:therapy*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology / Q000628:therapy; D019468:Disease Management; D005260:Female; D006442:Glycated Hemoglobin / Q000032:analysis; D019983:Guideline Adherence; D006801:Humans; D008523:MEDLARS; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000379:methods; D017410:Practice Guidelines as Topic / Q000592:standards; D012107:Research Design; D012648:Self Care; D017216:Telemedicine / Q000379:methods; D016896:Treatment Outcome
chemical_list: D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human