Effect of automated bio-behavioral feedback on the control of type 1 diabetes

Mobile Momentary Assessment and Biobehavioral Feedback for Adolescents with Type 1 Diabetes: Feasibility and Engagement Patterns

BACKGROUND

Integration of momentary contextual and psychosocial factors within self-management feedback may provide more specific, engaging, and personalized targets for problem solving.

METHODS

Forty-four youth ages 13-19 with type 1 diabetes (T1D) were provided a Bluetooth meter and completed the 30-day protocol. Participants were randomized to "app + meter" or "meter-only" groups. App + meter participants completed mealtime and bedtime assessment each day. Assessments focused on psychosocial and contextual information relevant for self-management. Graphical feedback integrated self-monitored blood glucose (SMBG), insulin, and Bluetooth-transmitted blood glucose data with the psychosocial and contextual data. App + meter participants completed an interview to identify data patterns.

RESULTS

The median number of momentary assessments per participant was 80.0 (range 32-120) with 2.60 per day. By 2 weeks participants had an average of 40.77 (SD 12.23) assessments. Dose-response analyses indicated that the number of app assessments submitted were significantly related to higher mean daily SMBG (r = -0.44, P < 0.05) and to lower% missed mealtime SMBG (r = -0.47, P < 0.01). Number of feedback viewing sessions was also significantly related to a lower% missed mealtime SMBG (r = -0.44, P < 0.05). Controlling for baseline variables, mixed-effects analyses did not indicate group × time differences in mean daily SMBG. Engagement analyses resulted in three trajectory groups distinguished by assessment frequencies and rates of decline. Engagement group membership was significantly related to gender, mean daily SMBG, and HbA1c values.

CONCLUSIONS

Momentary assessment combined with device data provided a feasible means to provide novel personalized biobehavioral feedback for adolescents with T1D. A 2-week protocol provided sufficient data for self-management problem identification. In addition to feedback, more intensive intervention may need to be integrated for those patients with the lowest self-management at baseline.

The effectiveness, reproducibility, and durability of tailored mobile coaching on diabetes management in policyholders: A randomized, controlled, open-label study

This randomized, controlled, open-label study conducted in Kangbuk Samsung Hospital evaluated the effectiveness, reproducibility, and durability of tailored mobile coaching (TMC) on diabetes management. The participants included 148 Korean adult policyholders with type 2 diabetes divided into the Intervention-Maintenance (I-M) group (n = 74) and Control-Intervention (C-I) group (n = 74). Intervention was the addition of TMC to typical diabetes care. In the 6-month phase 1, the I-M group received TMC, and the C-I group received their usual diabetes care. During the second 6-month phase 2, the C-I group received TMC, and the I-M group received only regular information messages. After the 6-month phase 1, a significant decrease (0.6%) in HbA1c levels compared with baseline values was observed in only the I-M group (from 8.1 ± 1.4% to 7.5 ± 1.1%, P < 0.001 based on a paired t-test). At the end of phase 2, HbA1c levels in the C-I group decreased by 0.6% compared with the value at 6 months (from 7.9 ± 1.5 to 7.3 ± 1.0, P < 0.001 based on a paired t-test). In the I-M group, no changes were observed. Both groups showed significant improvements in frequency of blood-glucose testing and exercise. In conclusion, addition of TMC to conventional treatment for diabetes improved glycemic control, and this effect was maintained without individualized message feedback.

Automated insulin pump suspension for hypoglycaemia mitigation: development, implementation and implications

In type 1 diabetes (T1D), insulin replacement therapy should ideally replicate endogenous insulin secretion, but achieving this goal requires frequent adjustments to insulin delivery based on glucose levels and trends, carbohydrate intake and physical activity. An overriding concern for people taking insulin is hypoglycaemia, which remains the most feared consequence of insulin therapy and limits therapy intensification options. Although fully automated systems that achieve consistent euglycaemia in T1D remain an elusive goal, improvements in continuous glucose monitoring (CGM) sensors and control algorithms have enabled semi-automated systems that lower the risk of hypoglycaemia, especially nocturnal hypoglycaemia. The present review focuses on an important advance in insulin delivery systems: the use of CGM data to stop insulin delivery in the presence of hypoglycaemia. Although conceptually simple, this strategy represents a critical step in the journey toward a fully closed-loop artificial pancreas; the next steps in this journey are also discussed.

Hemoglobin A1c and Self-Monitored Average Glucose: Validation of the Dynamical Tracking eA1c Algorithm in Type 1 Diabetes

BACKGROUND

Previously we have introduced the eA1c-a new approach to real-time tracking of average glycemia and estimation of HbA1c from infrequent self-monitoring (SMBG) data, which was developed and tested in type 2 diabetes. We now test eA1c in type 1 diabetes and assess its relationship to the hemoglobin glycation index (HGI)-an established predictor of complications and treatment effect.

METHODS

Reanalysis of previously published 12-month data from 120 patients with type 1 diabetes, age 39.15 (14.35) years, 51/69 males/females, baseline HbA1c = 7.99% (1.48), duration of diabetes 20.28 (12.92) years, number SMBG/day = 4.69 (1.84). Surrogate fasting BG and 7-point daily profiles were derived from these unstructured SMBG data and the previously reported eA1c method was applied without any changes. Following the literature, we calculated HGI = HbA1c - (0.009 × Fasting BG + 6.8).

RESULTS

The correlation of eA1c with reference HbA1c was r = .75, and its deviation from reference was MARD = 7.98%; 95% of all eA1c values fell within ±20% from reference. The HGI was well approximated by a linear combination of the eA1c calibration factors: HGI = 0.007552*θ1 + 0.007645*θ2 - 3.154 (P < .0001); 73% of low versus moderate-high HGIs were correctly classified by the same factors as well.

CONCLUSIONS

The eA1c procedure developed in type 2 diabetes to track in real-time changes in average glycemia and present the results in HbA1c-equivalent units has shown similar performance in type 1 diabetes. The eA1c calibration factors are highly predictive of the HGI, thereby explaining partially the biological variation causing discrepancies between HbA1c and its linear estimates from SMBG data.

Adjustment of Open-Loop Settings to Improve Closed-Loop Results in Type 1 Diabetes: A Multicenter Randomized Trial

CONTEXT

Closed-loop control (CLC) relies on an individual's open-loop insulin pump settings to initialize the system. Optimizing open-loop settings before using CLC usually requires significant time and effort.

OBJECTIVE

The objective was to investigate the effects of a one-time algorithmic adjustment of basal rate and insulin to carbohydrate ratio open-loop settings on the performance of CLC.

DESIGN

This study reports a multicenter, outpatient, randomized, crossover clinical trial.

PATIENTS

Thirty-seven adults with type 1 diabetes were enrolled at three clinical sites.

INTERVENTIONS

Each subject's insulin pump settings were subject to a one-time algorithmic adjustment based on 1 week of open-loop (i.e., home care) data collection. Subjects then underwent two 27-hour periods of CLC in random order with either unchanged (control) or algorithmic adjusted basal rate and carbohydrate ratio settings (adjusted) used to initialize the zone-model predictive control artificial pancreas controller. Subject's followed their usual meal-plan and had an unannounced exercise session.

MAIN OUTCOMES AND MEASURES

Time in the glucose range was 80-140 mg/dL, compared between both arms.

RESULTS

Thirty-two subjects completed the protocol. Median time in CLC was 25.3 hours. The median time in the 80-140 mg/dl range was similar in both groups (39.7% control, 44.2% adjusted). Subjects in both arms of CLC showed minimal time spent less than 70 mg/dl (median 1.34% and 1.37%, respectively). There were no significant differences more than 140 mg/dL.

CONCLUSIONS

A one-time algorithmic adjustment of open-loop settings did not alter glucose control in a relatively short duration outpatient closed-loop study. The CLC system proved very robust and adaptable, with minimal (<2%) time spent in the hypoglycemic range in either arm.

Interventions That Restore Awareness of Hypoglycemia in Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis

OBJECTIVE

Impaired awareness of hypoglycemia (IAH) increases the risk of severe hypoglycemia (SH) sixfold and affects 30% of adults with type 1 diabetes (T1D). This systematic review and meta-analysis looks at the educational, technological, and pharmacological interventions aimed at restoring hypoglycemia awareness (HA) in adults with T1D.

RESEARCH DESIGN AND METHODS

We searched The Cochrane Library, MEDLINE, Embase, Science Citation Index Expanded, Social Sciences Citation Index, PsycINFO, and CINAHL from inception until 1 October 2014. Included studies described HA status at baseline. Outcome measures were SH rates, change in HA, counterregulatory hormone responses, and glycemic control.

RESULTS

Forty-three studies (18 randomized controlled trials, 25 before-and-after studies) met the inclusion criteria, comprising 27 educational, 11 technological, and 5 pharmacological interventions. Educational interventions included structured diabetes education on flexible insulin therapy, including psychotherapeutic and behavioral techniques. These were able to reduce SH and improve glycemic control, with greater benefit from the latter two techniques in improving IAH. Technological interventions (insulin pump therapy, continuous glucose monitoring, and sensor-augmented pump) reduced SH, improved glycemic control, and restored awareness when used in combination with structured education and frequent contact. Pharmacological studies included four insulin studies and one noninsulin study, but with low background SH prevalence rates.

CONCLUSIONS

This review provides evidence for the effectiveness of a stepped-care approach in the management of patients with IAH, initially with structured diabetes education in flexible insulin therapy, which may incorporate psychotherapeutic and behavioral therapies, progressing to diabetes technology, incorporating sensors and insulin pumps, in those with persisting need.

Technology to Reduce Hypoglycemia

Hypoglycemia is a major barrier toward achieving glycemic targets and is associated with significant morbidity (both psychological and physical) and mortality. This article reviews technological strategies, from simple to more advanced technologies, which may help prevent or mitigate exposure to hypoglycemia. More efficient insulin delivery systems, bolus advisor calculators, data downloads providing information on glucose trends, continuous glucose monitoring with alarms warning of hypoglycemia, predictive algorithms, and finally closed loop insulin delivery systems are reviewed. The building blocks to correct use and interpretation of this range of available technology require patient education and appropriate patient selection.

Evaluation of a Methodology for Estimating HbA1c Value by a New Glucose Meter

BACKGROUND

Accuracy/robustness of HbA1c estimation (eA1c) with an algorithm built into the MyStar Extra blood glucose (BG) meter has been demonstrated by in silico testing. We evaluated the performance and use of eA1c in a clinical setting.

METHODS

Subjects took the BG meter home for 4 months to obtain eA1c in this open-label, single-center study. Laboratory HbA1c values were obtained approximately every 2 weeks and the corresponding eA1c documented. Subjects completed a questionnaire at study end (NCT01885546).

RESULTS

There were 133 enrolled subjects (mean [SD] age 60.0 [15.0] years, 69 males, 104 with diabetes, HbA1c 7.0% [1.4]). A total of 1008 pairs of eA1c and laboratory HbA1c values were available. In subjects with diabetes, 97.5% of the eA1c results fell within ± 20% of the laboratory HbA1c, 95.0% within ± 18%, and 90.7% within ± 15%. When results were limited to the reportable HbA1c range of ≥ 6 to ≤ 10%, 99.3% of eA1c values fell within ± 20% of the laboratory HbA1c, 98.5% within ± 18%, and 96.2% within ± 15% Most subjects agreed/strongly agreed that the eA1c section in the user guide and flash cards was easy to follow (72%), they would use the system to track their eA1c (70%), they found the eA1c tool helpful (79%), and the tool may motivate them to manage their diabetes better (83%).

CONCLUSIONS

Accuracy of the eA1c feature in this clinical setting was similar to the performance in silico. The majority of subjects found this tool helpful and agreed it may motivate to manage their diabetes better.

Perceptions and experiences of using automated bolus advisors amongst people with type 1 diabetes: a longitudinal qualitative investigation

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.

Accuracy and robustness of dynamical tracking of average glycemia (A1c) to provide real-time estimation of hemoglobin A1c using routine self-monitored blood glucose data

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.

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

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.

Fear of hypoglycaemia in patients with type 1 diabetes: do patients and diabetologists feel the same way?

AIM

This study described and compared the perception of hypoglycaemia in both patients with type 1 diabetes and diabetologists.

METHODS

This was an observational cross-sectional study undertaken in France in 2011. Data for what hypoglycaemia represents and practices related to it were collected using a questionnaire completed by patients with type 1 diabetes (all>12 years of age) and their diabetologists. Agreement between patients and physicians was evaluated by the intraclass correlation coefficient (ICC) and Gwet's coefficient (GC).

RESULTS

A total of 485 patients were enrolled by 118 diabetologists. Half the patients thought that hypoglycaemia was always symptomatic. According to both patients and diabetologists, hypoglycaemia impaired quality of life, caused anxiety and was disturbing, especially at night. Clinical symptoms of hypoglycaemia (sweating, shakiness, anxiety) were linked to patient's age and diabetes duration. Regarding hypoglycaemia frequency, agreement was good for severe hypoglycaemia (GC: 0.61 and 0.72 for diurnal and nocturnal hypoglycaemia, respectively) and poor for mild hypoglycaemia (ICC: 0.44 and 0.40, respectively). Diabetologists correctly evaluated the impact of hypoglycaemia on quality of life, but overestimated the hypoglycaemia-induced burden and anxiety. Counteractive behaviours were frequent: 23% of patients decreased their insulin dose, 20% increased their sugar intake and 12% ate extra snacks. Diabetologists were generally aware of these measures, but not of how often patients used them.

CONCLUSION

Diabetologists and patients do not share enough information about hypoglycaemia. Fear of hypoglycaemia and counteractive behaviours should be looked for by diabetologists. Systematic advice and specially adapted education should also be provided to increase patients' awareness of hypoglycaemia.