Article

Effect of Automated Bio-Behavioral Feedback on the Control of Type 1 Diabetes

Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, Virginia, USA.
Diabetes care (Impact Factor: 7.74). 02/2011; 34(2):302-7. DOI: 10.2337/dc10-1366
Source: PubMed

ABSTRACT To test the effect of an automated system providing real-time estimates of HbA(1c), glucose variability, and risk for hypoglycemia.
For 1 year, 120 adults with type 1 diabetes (69 female/51 male, age = 39.1 [14.3] years, duration of diabetes 20.3 [12.9] years, HbA(1c) = 8.0 [1.5]), performed self-monitoring of blood glucose (SMBG) and received feedback at three increasingly complex levels, each continuing for 3 months: level 1--routine SMBG; level 2--adding estimated HbA(1c), hypoglycemia risk, and glucose variability; and level 3--adding estimates of symptoms potentially related to hypoglycemia. The subjects were randomized to feedback sequences of either levels 1-2-3 or levels 2-3-1. HbA(1c), symptomatic hypoglycemia, and blood glucose awareness were evaluated at baseline and at the end of each level.
For all subjects, HbA(1c) was reduced from 8.0 to 7.6 from baseline to the end of study (P = 0.001). This effect was confined to subjects with baseline HbA(1c) >8.0 (from 9.3 to 8.5, P < 0.001). Incidence of symptomatic moderate/severe hypoglycemia was reduced from 5.72 to 3.74 episodes/person/month (P = 0.019), more prominently for subjects with a history of severe hypoglycemia (from 7.20 to 4.00 episodes, P = 0.008) and for those who were hypoglycemia unaware (from 6.44 to 3.71 episodes, P = 0.045). The subjects' ratings of the feedback were positive, with up to 89% approval of the provided features.
Feedback of SMBG data and summary SMBG-based measures resulted in improvement in average glycemic control and reduction in moderate/severe hypoglycemia. These effects were most prominent in subjects who were at highest risk at the baseline.

2 Bookmarks
 · 
105 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Abstract Background: Laboratory hemoglobin A1c (HbA1c) assays are typically done only every few months. However, self-monitored blood glucose (SMBG) readings offer the possibility for real-time estimation of HbA1c. We present a new dynamical method tracking changes in average glycemia to provide real-time estimation of A1c (eA1c). Materials and Methods: A new two-step algorithm was constructed that includes: (1) tracking fasting glycemia to compute base eA1c updated with every fasting SMBG data point and (2) calibration of the base eA1c trace with monthly seven-point SMBG profiles to capture the principal components of blood glucose variability and produce eA1c. A training data set (n=379 subjects) was used to estimate model parameters. The model was then fixed and applied to an independent test data set (n=375 subjects). Accuracy was evaluated in the test data set by computing mean absolute deviation (MAD) and mean absolute relative deviation (MARD) of eA1c from reference HbA1c, as well as eA1c-HbA1c correlation. Results: MAD was 0.50, MARD was 6.7%, and correlation between eA1c and reference HbA1c was r=0.76. Using an HbA1c error grid plot, 77.5% of all eA1c fell within 10% from reference HbA1c, and 97.9% fell within 20% from reference. Conclusions: A dynamical estimation model was developed that achieved accurate tracking of average glycemia over time. The model is capable of working with infrequent SMBG data typical for type 2 diabetes, thereby providing a new tool for HbA1c estimation at the patient level. The computational demands of the procedure are low; thus it is readily implementable into home SMBG meters. Real-time HbA1c estimation could increase patients' motivation to improve diabetes control.
    Diabetes Technology &amp Therapeutics 12/2013; 16(5). DOI:10.1089/dia.2013.0224 · 2.29 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Aims We explored people's reasons for, and experiences of, using bolus advisors to determine insulin doses; and, their likes/dislikes of this technology. Subjects and methods 42 people with type 1 diabetes who had received instruction in use of bolus advisors during a structured education course were interviewed post-course and 6 months later. Data were analysed thematically. Results Participants who considered themselves to have poor mathematical skills highlighted a gratitude for, and heavy reliance on, advisors. Others liked and chose to use advisors because they saved time and effort calculating doses and/or had a data storage facility. Follow-up interviews highlighted that, by virtue of no longer calculating their doses, participants could become deskilled and increasingly dependent on advisors. Some forgot what their mealtime ratios were; others reported a misperception that, because they were pre-programmed during courses, these parameters never needed changing. Use of data storage facilities could hinder effective review of blood glucose data and some participants reported an adverse impact on glycaemic control. Discussion While participants liked and perceived benefits to using advisors, there may be unintended consequences to giving people access to this technology. To promote effective use, on-going input and education from trained health professionals may be necessary.
    Diabetes Research and Clinical Practice 12/2014; 106:443-450. DOI:10.1016/j.diabres.2014.09.011 · 2.54 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: 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. 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. 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 < .01) and fasting plasma glucose (p < .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. The on-device Pattern tool identified meaningful blood glucose patterns, highlighting potential opportunities for improving glycemic control in patients who self-adjust their insulin.
    Journal of diabetes science and technology 01/2013; 7(4):970-8.

Full-text (5 Sources)

Download
63 Downloads
Available from
May 19, 2014