Journal of Diabetes Science and Technology

Diabetes devices such as insulin pumps and continuous glucose monitoring (CGM) are associated with improved health and quality of life in adults with type 1 diabetes (T1D). However, uptake remains low. The aim of this study was to develop different “personas” of adults with T1D in relation to readiness to adopt new diabetes technology.

Participants were 1498 T1D Exchange participants who completed surveys on barriers to uptake, technology attitudes, and other psychosocial variables. HbA1c data was available from the T1D Exchange for 30% of the sample. K-means cluster analyses grouped the sample by device barriers and attitudes. The authors assigned descriptive labels based on cluster characteristics. ANOVAs and chi-square tests assessed group differences by demographic and psychosocial variables (eg, diabetes duration, diabetes distress).

Analyses yielded five distinct personas. The d-Embracers (54% of participants) endorsed few barriers to device use and had the highest rates of device use, lowest HbA1c, and were the least distressed. The Free Rangers (23%) had the most negative technology attitudes. The Data Minimalists (10%) used pumps but had lower CGM use and did not want more diabetes information. The Wary Wearers (11%) had lower overall device use, were younger, more distressed, endorsed many barriers, and had higher HbA1c. The High Distress (3%) group members were the youngest, had the shortest diabetes duration, reported the most barriers, and were the most distressed.

These clinically meaningful personas of device readiness can inform tailored interventions targeting barriers and psychosocial needs to increase device uptake.

Many people with type 1 diabetes (T1D) report barriers to using continuous glucose monitoring (CGM). Diabetes care providers may have their own barriers to promoting CGM uptake. The goal of this study was to develop clinician “personas” with regard to readiness to promote CGM uptake.

Diabetes care providers who treat people with T1D (N = 209) completed a survey on perceived patient barriers to device uptake, technology attitudes, and characteristics and barriers specific to their clinical practice. K-means cluster analyses grouped the sample by CGM barriers and attitudes. ANOVAs and chi-square tests assessed group differences on provider and patient characteristics. The authors assigned descriptive names for each persona.

Analyses yielded three clinician personas regarding readiness to promote CGM uptake. Ready clinicians (20% of sample; 24% physicians, 38% certified diabetes educators/CDEs) had positive technology attitudes, had clinic time to work with patients using CGM, and found it easy to keep up with technology advances. In comparison, Cautious clinicians (41% of sample; 17% physicians, 53% CDEs) perceived that their patients had many barriers to adopting CGM and had less time than the Ready group to work with patients using CGM data. Not Yet Ready clinicians (40% of sample; 9% physicians; 79% CDEs) had negative technology attitudes and the least clinic time to work with CGM data. They found it difficult to keep up with technology advances.

Some diabetes clinicians may benefit from tailored interventions and additional time and resources to empower them to help facilitate increased uptake of CGM technology.

Diabetes distress has been linked with suboptimal glycemic control in patients with type 1 diabetes. We evaluated the effect of diabetes distress on self-management behaviors in patients using insulin pumps.

We analyzed the impact of diabetes distress on self-management behaviors using pump downloads from 129 adults treated with continuous subcutaneous insulin infusion (CSII) at a single hospital clinic. Exclusion criteria were CSII treatment <6 months, pregnancy, hemoglobinopathy, and continuous glucose monitoring/sensor use. People were categorized into three groups based on the Diabetes Distress Scale-2 (DDS-2) score: < 2.5, 2.5-3.9, > 4.

Participants had a mean age of 45.2 ± 19.0 years; duration of diabetes 26.6 ± 16.2 years; duration of CSII 6.0 ± 3.5 years; HbA1c 8.0 ± 1.2%; and DDS-2 score 2.7 ± 1.3. Self-monitoring blood glucose (SMBG) frequency and bolus wizard usage was similar between groups. Patients with higher distress had higher HbA1c (7.7 ± 0.9 vs. 8.0 ± 0.9 vs. 8.7 ± 1.8; P = 0.004), lower frequency of set changes (4.7 ± 1.3vs. 4.8 ± 1.9 vs. 3.8 ± 1.1; P = .025), a greater number of appointments booked (5.8 ± 4.4 vs. 8.6 ± 4.8 vs. 8.1 ± 6.9; P = .021), and a greater number of appointments missed (1.9 ± 1.3 vs. 2.5 ± 1.5 vs. 3.8 ± 4.1; P = .004).

Although in some patients, high distress may be caused by reduced self-management, in our highly trained, pump-using patients, high distress was associated with suboptimal biomedical outcomes despite appropriate self-management behaviors. Future work should further explore the relationships between diabetes distress, self-management, and glycemic control.

Closed-loop insulin delivery has the potential to improve day-to-day glucose control in type 1 diabetes pregnancy. However, the psychosocial impact of day-and-night usage of automated closed-loop systems during pregnancy is unknown. Our aim was to explore women’s experiences and relationships between technology experience and levels of trust in closed-loop therapy.

We recruited 16 pregnant women with type 1 diabetes to a randomized crossover trial of sensor-augmented pump therapy compared to automated closed-loop therapy. We conducted semistructured qualitative interviews at baseline and follow-up. Findings from follow-up interviews are reported here.

Women described benefits and burdens of closed-loop systems during pregnancy. Feelings of improved glucose control, excitement and peace of mind were counterbalanced by concerns about technical glitches, CGM inaccuracy, and the burden of maintenance requirements. Women expressed varied but mostly high levels of trust in closed-loop therapy.

Women displayed complex psychosocial responses to day-and-night closed-loop therapy in pregnancy. Clinicians should consider closed-loop therapy not just in terms of its potential impact on biomedical outcomes but also in terms of its impact on users’ lives.

Medical device technology is evolving at a rapid pace, with increasing patient expectations to use modern technologies for diabetes management. With the significant expansion of the use of wireless technology and complex, securely connected digital platforms in medical devices, end user needs and behaviors have become essential areas of focus.

This article provides a detailed description of the user-centered design approach implemented in developing the Omnipod DASH™ Insulin Management System (Insulet Corp., Billerica, MA) Bluetooth^®-enabled locked-down Android device handheld controller (Personal Diabetes Manager, PDM). Key methodologies used in the PDM design are described, including how the science of user experience (UX) was integrated into new agile product development. UX methods employed included heuristic evaluations of insulin pumps, iterative formative usability testing, information architecture studies, in-home ethnographic visits, participatory design activities, and interviews.

Over 343 users participated in UX research and testing. Key design choices informed by UX research included updating the layout of critical data on the PDM home page, providing access to requested contextual information while a bolus is in progress, and creating an easy-to-understand visual of a 24-hour basal program. Task completion rates for comprehending information on the PDM home page were 87% or greater. The System Usability Scale result for the design prior to limited market release was 84.4 ± 13.4 (out of 100; n = 37).

The UX process described in this article can serve as a blueprint for medical device manufacturers seeking to enhance product development. Adopting UX research methodologies will help ensure that new diabetes devices are safe, easy-to-use, and meet the needs of users.

The goal of this uncontrolled pilot study was to assess the feasibility of a self-care management mobile app, called Sugar Sleuth, which incorporates the FreeStyle Libre™ glucose sensor, to help clinicians and people with type 1 diabetes (PWD) identify and mitigate self-care behaviors that contribute to glucose variability.

PWDs with a baseline A1c between 7.5 and 9.0% used the mobile app for 14 weeks. The app prompted the PWD to enter the suspected cause of detected glycemic excursions, and to record food and insulin information. PWDs met with clinicians to collaboratively review data, identify challenges, and devise a specific self-care plan. Outcome measures included a single glycemic outcome score (SGOS) and attitude rating scales to better understand how participant attitudes could affect glycemic outcome.

Thirty enrolled PWDs had a mean age of 55 ± 2.6 years, and a mean diabetes duration of 32 ± 2.9 years. A significant average reduction in A1c of 0.5 ± 0.07% (P < .01) and in mean daily carbohydrate intake of 43 ± 21 grams (P = .05) was found. No statistically significant change in glycemic metrics, body weight, or total daily insulin dose was found. A significant negative association occurred between SGOS and “hypoglycemia tolerance” (P = .04), and a positive correlation occurred that approached significance with “motivation to change behavior” (P = .06).

These findings suggest that this mobile app system, in conjunction with CGM, provides a useful platform for helping clinicians and adults with T1D improve self-management skills to improve glycemic control.

Many patients with diabetes on insulin therapy develop lipohypertrophies (LHTs). So far, LHTs are diagnosed by conventional methods (CM; visual inspection, palpation and/or ultrasound). In everyday life, it would be advantageous to have a quick, simple and inexpensive alternative, for example, diagnosing them by obtaining infrared (IR) images.

We obtained IR images from 43 subjects (21 patients with type 1 diabetes, conventional subcutaneous insulin therapy and known LHTs, 8 patients with CSII and LHTs, 7 patients without LHTs, and 7 healthy people), all from one specialized diabetes practice. The IR images were taken under standardized conditions with a high-resolution infrared camera (VarioCam® HDx Jenoptic, IR pixel 640 × 480, thermal resolution 0.003K) and compared with LHT diagnoses with CM.

In 14 of the 29 (48%) patients, CM diagnosed LHTs were “cold spots” in the IR images. The temperature difference to “healthy” skin (without LHTs) was up to 6°C. Of the 14 patients, 11 also showed such spots, without findings with CM. Four patients did not show clearly identifiable cold spots as LHT and 2 patients showed no changes in the IR images. The remaining 9 patients did not show clearly identifiable cold spots as LHT, but the diagnosis with CM was also ambiguous.

The results of this small (pilot) study do not clearly support the value of IR images for the diagnosis of LHTs, but they do not refute this approach. Diagnosis of LHT might be hampered due to the existence of different types of LHTs. Usage of IR images can apparently detect LHTs before they can be diagnosed with CM. Further targeted investigations are required to make statements about the usability of this method.

Intensive monitoring of blood glucose levels is crucial in diabetes management. This article presents a new device, the TensorTip Combo Glucometer (CoG), developed by Cnoga Medical Ltd, which enables to predict capillary tissue glucose concentration noninvasively.

Noninvasive glucose readings usually provide irregular or disordered mathematical manifold over the measurement space. To establish a transfer function, which correctly correlates the noninvasive raw data and the actual invasive glucose level, we suggest a mathematical concept that employs a personal calibration procedure to associate glucose pattern and multiple optical signals derived from tissue response to light emission in the range of visible to IR. The traversed light is detected by a color image sensor to predict the tissue glucose concentration at the fingertip. This article presents the mathematical concept underlying the technology and the requirements for device operation.

The device was clinically evaluated and compared to standard invasive blood glucose monitoring devices in few medical centers and by home users. Based on consensus error grid analysis, more than 98% of the measurements of each study were in zones A (more than 81%) and B (more than 11%). Postmarketing evaluations showed high correlations comparing the CoG to other invasive reference devices.

The CoG device employs a unique mathematical approach to predict glucose concentrations based on multiple optical signals. The first clinical results indicate that the device may show appropriate agreement with reference methods to be used for pain-free glucose assessment in daily routine.

Frequent blood glucose readings are the most cumbersome aspect of diabetes treatment for many patients. The noninvasive TensorTip Combo Glucometer (CoG) component employs dedicated mathematical algorithms to analyze the collected signal and to predict tissue glucose at the fingertip. This study presents the performance of the CoG (the invasive and the noninvasive components) during a standardized meal experiment.

Each of the 36 participants (18 females and males each, age: 49 ± 18 years, 14 healthy subjects, 6 type 1 and 16 type 2 patients) received a device for conducting calibration at home. Thereafter, they ingested a standardized meal. Blood glucose was assessed from capillary blood samples by means of the (non)invasive device, YSI Stat 2300 plus, Contour Next at time points –30, 0, 15, 30, 45, 60, 75, 90, 120, 150, and 180 minutes. Statistical analysis was performed by consensus error grid (CEG) and calculation of mean absolute relative difference (MARD) in comparison to YSI.

For the noninvasive (NI) CoG technology, 100% of the data pairs were found in CEG zones A (96.6%) and B (3.4%); 100% were seen in zone A for the invasive component and Contour Next. MARD was calculated to be 4.2% for Contour Next, 9.2% for the invasive component, and 14.4% for the NI component.

After appropriate individual calibration of the NI technology, both the NI and the invasive CoG components reliably tracked tissue and blood glucose values, respectively. This may enable patients with diabetes to monitor their glucose levels frequently, reliably, and most of all pain-free.

Few studies have evaluated continuous glucose monitoring (CGM) in older patients with type 2 diabetes mellitus (T2DM) not using injectable therapy. CGM is useful for investigating hypoglycemia and glycemic variability, which is associated with complications in T2DM.

A CGM substudy of Individualized treatMent aPproach for oldER patIents in a randomized trial in type 2 diabetes Mellitus (IMPERIUM)) was conducted. Patients were vulnerable (moderately ill and/or frail) older (≥65 years) individuals with suboptimally controlled T2DM. Strategy A comprised glucose-dependent therapies (n = 26) with a nonsulfonylurea oral antihyperglycemic medication (OAM) and a glucagon-like peptide-1 receptor agonist as the first injectable. Strategy B comprised non-glucose-dependent therapies (n = 21) with sulfonylurea as the preferred OAM and insulin glargine as the first injectable. Primary endpoints were duration and percentage of time spent with blood glucose (BG) ≤70 mg/dL over 24 hours at week 24.

Duration and percentage of time spent with hypoglycemia at ≤70 mg/dL were similar for Strategy A and Strategy B; glycemic control improved similarly in both arms (LSM change in HbA1c at week 24; A = −1.2%, B = −1.4%). Duration and percentage time spent with euglycemia and hyperglycemia were also similar in both arms. However, Strategy A was associated with lower within-day (21.1 ± 1.2 vs 25.1 ± 1.4, P = .046) and between-day (5.4 ± 1.0 vs 9.1 ± 1.3, P = .038) BG variability (coefficient of variance [LSM ± SE]) at week 24.

This CGM substudy in older patients with T2DM showed lower within- and between-day BG variability with glucose-dependent therapies but similar HbA1c reductions and hypoglycemia duration with glucose-independent strategies.

Hypoglycemia and hypoglycemia unawareness are common in long-standing type 1 diabetes (T1D). This pilot study examined the real-world use of a smartphone application (app), which receives meter readings and logs hypoglycemic symptoms, causes, and treatments to reduce hypoglycemia.

Adults with T1D and recent hypoglycemia synchronized their glucose meter to their smartphone and used the Joslin HypoMap™ app powered by Glooko to track hypoglycemic events. At baseline, and after 6 and 12 weeks of using the app, a blinded continuous glucose monitor (CGM; Dexcom G4) was used for 2 weeks and surveys administered.

Participants (n = 22) at baseline had mean (SD) age 43 (14) years, duration of diabetes 26 (13) years, A1c 8.0% (0.87) and 21/22 had reduced hypoglycemia awareness per Clarke Hypoglycemia Unawareness survey scores; 13 (59%) were “CGM completers” (CGM data available at baseline and follow-up). Most noncompletion related to time required/difficulties using the mobile app. After 6 weeks, 8/13 completers (62% of CGM completers, 36% of total participants) had reduced daytime minutes with glucose <54 mg/dL (mean ↓331 minutes) and 10/13 (77% of CGM completers; 45% of total participants) had reduced time ≤ 70 mg/dL (mean ↓449 minutes). This was not sustained at 12 weeks, at which time half of the completers had less time (“improved”). Five participants reported improved hypoglycemia awareness; 9 stated the app helped them better recognize hypoglycemia.

Use of this phone app has the potential to help reduce daytime hypoglycemia in a subset of T1D adults with reduce hypoglycemia awareness; larger studies are needed.

Diabetes disproportionately affects the US Latino population, due to socioeconomic pressures, genetics, reduced access to care and cultural practices. While efforts to improve self-care through interventions incorporating family are highly rated by Latinos, family can be both supportive and obstructive. To develop effective interventions, this role needs clarification.

We conducted group interviews in Spanish and English with 24 participants with diabetes from a mobile health diabetes self-care intervention. We imported transcripts into Dedoose, a qualitative computer analysis program and analyzed them with a modified grounded theory technique. Utilizing an iterative process, we reexamined transcripts with new codes derived in each round of analysis until saturation was reached. We employed techniques to improve trustworthiness (co-coding, member checking). Broad categorical themes arose from the initial codes and were developed into a conceptual model of barriers to and strategies for diabetes management.

Family and family responsibilities emerged as both a supportive and obstructive force for diabetes self-care. While the desire to care for family motivated patients, food at family gatherings and pressure from managing multiple family responsibilities contributed to poor diet choices. Yet, some patients believed their diabetes caused their immediate family to make healthier choices.

Among these predominantly Latino patients, family and family responsibilities were key motivators as well as obstacles to self-care, particularly regarding nutrition. Finding the ideal design for social support mHealth-based interventions will require careful study and creation of culturally based programs to match the needs of specific populations, and may require educating family members to provide effective social support.

The ability of patients to improve glycemic control depends partly on their ability to interpret and act on blood glucose results. We investigated whether switching people with diabetes to blood glucose meters (BGMs) featuring a color range indicator (CRI) could improve glycemic control compared to remaining on their current BGM without color.

163 adults with type 1 (T1D) or type 2 diabetes (T2D) and a hemoglobin A1c (A1c) of 7.5-11% were randomized to: One Touch Verio™ (Verio), OneTouch Verio Flex™ (Flex), or controls remaining on their current BGM. Diabetes nurses had standard conversations about diabetes management with all subjects at baseline. No changes in medication, insulin dosing, or SMBG frequency were recommended.

After 12 weeks, subjects who switched to Verio or Flex meters with CRI (n = 108) had a mean change in A1c 0.36% lower than controls (n = 55) (P = .017). A1c reductions were greatest in T1D subjects (n = 45), with a decrease of 0.50% (P = .004). T1D subjects using Verio meters (n = 25) contributed a 0.59% reduction compared to controls (P < .008), whereas T1D subjects using Flex meters (n = 20) had a clinical meaningful reduction in A1c of 0.40% without reaching statistical significance (P > .05). Verio and Flex users reported taking more action and easier understanding of diabetes management compared to previous BGMs.

This study demonstrated that switching patients to BGMs featuring a CRI resulted in improvements in glycemic control compared to subjects using currently marketed BGMs that do not use a CRI.

Registration: Clinicaltrials.gov NCT02929654 https://clinicaltrials.gov/ct2/show/NCT02929654