title: Mobile phone-based pattern recognition and data analysis for patients with type 1 diabetes.
abstract: BACKGROUND
  Persons with type 1 diabetes who use electronic self-help tools, most commonly blood glucose meters, record a large amount of data about their personal condition. Mobile phones are powerful and ubiquitous computers that have a potential for data analysis, and the purpose of this study is to explore how self-gathered data can help users improve their blood glucose management.
  
  
  METHODS
  Thirty patients with insulin-regulated type 1 diabetes were equipped with a mobile phone application for 3-6 months, recording blood glucose, insulin, dietary information, physical activity, and disease symptoms. The data were analyzed in terms of usage of the different modules and which data processing and visualization tools could be constructed to support the use of these data.
  
  
  RESULTS
  Eighteen patients (denoted "adopters") recorded complete data for over 80 consecutive days, up to 247 days. Among those who withdrew or did not use the application extensively, the most common reasons given were outdated or difficult-to-use phone. Data analysis using period finding and scale-space trends was found to yield significant patterns for most adopters. Pattern recognition methods to predict low or high blood glucose were found to be performing poorly.
  
  
  CONCLUSIONS
  Minimally intrusive mobile applications enable users with type 1 diabetes to record data that can provide data-driven feedback to the user, potentially providing relevant insight into their disease.
authors.affiliation: Norwegian Centre for Integrated Care and Telemedicine, University Hospital of North Norway, Tromsø, Norway.
authors.forename: Stein Olav
authors.initials: SO
authors.lastname: Skrøvseth
authors.forename: Eirik
authors.initials: E
authors.lastname: Årsand
authors.forename: Fred
authors.initials: F
authors.lastname: Godtliebsen
authors.forename: Gunnar
authors.initials: G
authors.lastname: Hartvigsen
mesh_terms: D000328:Adult; D001786:Blood Glucose / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring; D040421:Cell Phone; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000188:drug therapy; D005260:Female; D015438:Health Behavior; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage / Q000097:blood*; D007256:Information Systems; D007328:Insulin / Q000008:administration & dosage / Q000097:blood*; D008297:Male; D046709:Pattern Recognition, Physiological; D012648:Self Care / Q000379:methods; D012984:Software; D017216:Telemedicine
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin