title: Evaluation of a blood glucose monitoring system with automatic high- and low-pattern recognition software in insulin-using patients: pattern detection and patient-reported insights.
abstract: BACKGROUND
  This study aimed to evaluate the performance of a glucose pattern recognition tool incorporated in a blood glucose monitoring system (BGMS) and its association with clinical measures, and to assess user perception and understanding of the pattern messages they receive.
  
  
  METHODS
  Participants had type 1 or type 2 diabetes mellitus and were self-adjusting insulin doses for ≥1 year. During a 4-week home testing period, participants performed ≥6 daily self-tests, adjusted their insulin regimen based on BGMS results, and recorded pattern messages in the logbook. Participants reflected on usability of the pattern tool in a questionnaire.
  
  
  RESULTS
  Study participants (n = 101) received a mean ± standard deviation of 4.5 ± 1.9 pattern messages per week (3.6 ± 1.8 high glucose patterns and 0.9 ± 1.3 low glucose patterns). Most received ≥1 high (96.5%) and/or ≥1 low (46.0%) pattern message per week. The average number of high- and low-pattern messages per week was associated with higher and lower, respectively, baseline hemoglobin A1c (p &lt; .01) and fasting plasma glucose (p &lt; .05). Participants found high- and low-pattern messages clear and easy to understand (84.2% and 83.2%, respectively) and considered the frequency of low (82.0%) and high (63.4%) pattern messages about right. Overall, 71.3% of participants indicated they preferred to use a meter with pattern messages.
  
  
  CONCLUSIONS
  The on-device Pattern tool identified meaningful blood glucose patterns, highlighting potential opportunities for improving glycemic control in patients who self-adjust their insulin.
authors.affiliation: LifeScan Scotland Ltd, Inverness, UK. mgrady@its.jnj.com
authors.forename: Mike
authors.initials: M
authors.lastname: Grady
authors.forename: Denise
authors.initials: D
authors.lastname: Campbell
authors.forename: Kirsty
authors.initials: K
authors.lastname: MacLeod
authors.forename: Aparna
authors.initials: A
authors.lastname: Srinivasan
mesh_terms: D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000379:methods; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000188:drug therapy; D003924:Diabetes Mellitus, Type 2 / Q000097:blood* / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D007328:Insulin / Q000627:therapeutic use; D010363:Pattern Recognition, Automated / Q000379:methods*; D057566:Self Report; D012984:Software; D016896:Treatment Outcome
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin
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