Chandra Y. Osborn

Background: In 2017, mobile app support for managing diabetes was available to 64% of the global population of adults with diabetes. One Drop’s digital therapeutics solution includes an evidence-based mobile app with global reach, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Among people with type 1 diabetes and an estimated hemoglobin A1c level≥7.5%, using One Drop for 3 months has been associated with an improved estimated hemoglobin A1c level of 22.2 mg/dL (–0.80%). However, the added value of integrated activity trackers is unknown. Objective: We conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new-to-market activity tracker against One Drop only on the 3-month hemoglobin A1c level of adults with type 1 diabetes. Methods: Social media advertisements and online newsletters were used to recruit adults (≥18 years old) diagnosed (≥1 year) with T1D, naïve to One Drop’s full solution and the activity tracker, with a laboratory hemoglobin A1c level≥7%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline hemoglobin A1c, were used to evaluate 3-month hemoglobin A1c differences in intent-to-treat (ITT) and per protocol (PP) analyses. Results: The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73% female, 88% White, diagnosed for a mean of 20 (SD 11) years, and had a mean hemoglobin A1c level of 8.4% (SD 1.2%); 11% of the participants did not complete follow up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.60%, 95% CI 7.8-8.2) than that of the participants in the One Drop only condition (mean 8.4%, SD 0.62%, 95% CI 8.2-8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.59%, 95% CI 7.7-8.1) than that of participants in the One Drop only condition (mean 8.2%, SD 0.58%, 95% CI 8.0-8.4). Conclusions: Participants exposed to One Drop and the activity tracker for the 3-month study period had a significantly lower 3-month hemoglobin A1c level compared to that of participants exposed to One Drop only during the same timeframe. One Drop and a tracker may work better together than alone in helping people with type 1 diabetes. Trial Registration: ClinicalTrials.gov NCT03459573; https://clinicaltrials.gov/ct2/show/NCT03459573.

Background Mobile- and Internet-delivered (collectively, digital) interventions are widely used by persons with diabetes (PWD) to assist with self-management and improve/maintain glycemic control (hemoglobin A1c [A1c]). However, evidence concerning the acceptance and benefits of such interventions among disadvantaged/vulnerable PWD is still quite limited. Purpose of Review We reviewed studies published from 2011–April 2019 evaluating the impact of diabetes self-management interventions delivered via mobile device and/or Internet on glycemic control of disadvantaged/vulnerable adults with type 2 diabetes (T2D). Included studies reported ≥ 50% of the sample having a low socioeconomic status and/or being a racial/ethnic minority, or living in a rural setting or low-/middle-income country (LMIC). We identified 21 studies evaluating a digital intervention among disadvantaged/vulnerable PWD. Recent Findings Although many digital interventions found within-group A1c improvements (16 of 21 studies), only seven of the seventeen studies with a control group found between-group differences in A1c. Three studies found reductions in emergency room (ER) visits and hospitalizations. We synthesize this information, and provide recommendations for increasing access, and improving the design and usability of such interventions. We also discuss the role of human support in digital delivery, issues related to study design, reporting, economic value, and available research in LMICs. Summary There is evidence suggesting that digital interventions can improve diabetes control, healthcare utilization, and healthcare costs. More research is needed to substantiate these early findings, and many issues remain in order to optimize the impact of digital interventions on the health outcomes of disadvantaged/vulnerable persons with diabetes.

BACKGROUND In 2017, mobile app support for managing diabetes was available to 64% of the global population of adults with diabetes. One Drop’s digital therapeutics solution includes an evidence-based mobile app with global reach, a Bluetooth-connected glucometer, and in-app coaching from Certified Diabetes Educators. Among people with type 1 diabetes and an estimated hemoglobin A1c level≥7.5%, using One Drop for 3 months has been associated with an improved estimated hemoglobin A1c level of 22.2 mg/dL (–0.80%). However, the added value of integrated activity trackers is unknown. OBJECTIVE We conducted a pragmatic, remotely administered randomized controlled trial to evaluate One Drop with a new-to-market activity tracker against One Drop only on the 3-month hemoglobin A1c level of adults with type 1 diabetes. METHODS Social media advertisements and online newsletters were used to recruit adults (≥18 years old) diagnosed (≥1 year) with T1D, naïve to One Drop’s full solution and the activity tracker, with a laboratory hemoglobin A1c level≥7%. Participants (N=99) were randomized to receive One Drop and the activity tracker or One Drop only at the start of the study. The One Drop only group received the activity tracker at the end of the study. Multiple imputation, performed separately by group, was used to correct for missing data. Analysis of covariance models, controlling for baseline hemoglobin A1c, were used to evaluate 3-month hemoglobin A1c differences in intent-to-treat (ITT) and per protocol (PP) analyses. RESULTS The enrolled sample (N=95) had a mean age of 41 (SD 11) years, was 73% female, 88% White, diagnosed for a mean of 20 (SD 11) years, and had a mean hemoglobin A1c level of 8.4% (SD 1.2%); 11% of the participants did not complete follow up. Analysis of covariance assumptions were met for the ITT and PP models. In ITT analysis, participants in the One Drop and activity tracker condition had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.60%, 95% CI 7.8-8.2) than that of the participants in the One Drop only condition (mean 8.4%, SD 0.62%, 95% CI 8.2-8.5). In PP analysis, participants in the One Drop and activity tracker condition also had a significantly lower 3-month hemoglobin A1c level (mean 7.9%, SD 0.59%, 95% CI 7.7-8.1) than that of participants in the One Drop only condition (mean 8.2%, SD 0.58%, 95% CI 8.0-8.4). CONCLUSIONS Participants exposed to One Drop and the activity tracker for the 3-month study period had a significantly lower 3-month hemoglobin A1c level compared to that of participants exposed to One Drop only during the same timeframe. One Drop and a tracker may work better together than alone in helping people with type 1 diabetes. CLINICALTRIAL ClinicalTrials.gov NCT03459573; https://clinicaltrials.gov/ct2/show/NCT03459573.

Background: A growing number of people use apps to track diabetes data and identify retrospective patterns that can inform management behavior. In September 2018, One Drop launched Automated Decision Support, which predicts blood glucose 8 hours in the future for non-insulin-dependent users with type 2 diabetes (T2D). Predictions help inform diabetes decision-making prospectively, but only if users find them useful. Objective: To understand how predictive insights facilitate behavior change in people with T2D, One Drop collected feedback from users and trained a machine learning model to predict when forecasts would be perceived as useful. This model was used to select timing and content of future forecasts and accompanying support messages. We then measured changes in feedback to assess forecast utility. Methodology: An initial sample of 23,876 forecasts were sent via in-app notifications to 4679 users with T2D. Forecasts consisted of BG trend, duration, and level (“rising, but not too high, over the next 3 hours”) and, when appropriate, a support message relevant to forecast and user history. Forecast delivery was random, triggered with 50% probability when information was logged, no more than once/day/user. Forecasts could be rated “Useful” or “Not Useful.” A machine learning model, trained on the initial sample, predicted the probability of each type of feedback. A second sample of 28838 forecasts were sent to 5506 users, with probability of usefulness determined by the model. Results: In the 1st sample, 42.8% of forecasts received feedback from 69.6% of users; 87.1% was “Useful.” In the 2nd sample, 63.7% of forecasts received feedback from 67.1% of users; 92.4% was “Useful.” Discussion: A new machine learning model tailored forecast delivery, reducing the “Not Useful” rate by 41.1% (from 12.9% of feedback to 7.6%). Correlations between useful forecasts and behavior change will be explored in future work. Disclosure: D.R. Goldner: Employee; Self; One Drop. Stock/Shareholder; Self; One Drop. M. Heyman: Consultant; Self; Lexicon Pharmaceuticals, Inc., Lilly Diabetes, Tandem Diabetes Care. Employee; Self; One Drop. A. Hirsch: Employee; Self; Informed Data Systems Inc. Research Support; Self; Fitbit, Inc., MannKind Corporation. Stock/Shareholder; Self; Informed Data Systems Inc. C.Y. Osborn: Employee; Self; One Drop. O. Lubling: Employee; Self; One Drop. B. Huddleston: Employee; Self; One Drop. R.L. Vlaha: Employee; Self; One Drop. K. Prenger: Employee; Self; One Drop. R. Murthy: Employee; Self; One Drop. J. Starke: Employee; Self; One Drop. J. Briceno: Employee; Self; One Drop. D. Ledvina: Employee; Self; One Drop. J. Dachis: Stock/Shareholder; Self; Informed Data Systems Inc.

HLRP: Health Literacy Research and Practice | Many people with type 2 diabetes (T2D) do not take medications as prescribed, resulting in suboptimal glycemic control and a greater risk of diabetes complications. Taking medications regularly requires adequate health literacy and numeracy skills, but associations between health literacy and numeracy skills and medication adherence have been mixed. We used validated, reliable, and widely

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

Emerging Technologies, Medical Devices, Apps, Sensors, and Informatics to Help People with Diabetes

Non-traditional indicators of socioeconomic status (SES; e.g., home ownership) may be just as or even more predictive of health outcomes as traditional indicators of SES (e.g., income). This study tested whether financial strain (i.e., difficulty paying monthly bills) predicted medication non-adherence and worse self-rated health. Research assistants administered surveys to 1,527 patients with acute coronary syndromes or acute decom-pensated heart failure. In adjusted models, having a higher income was associated with being more adherent (p < .001), but was non-significant when adjusted for financial strain. Education, income, less financial strain, and being employed were each associated with better self-rated health (p < .001). Financial strain was associated with less adherence (β =-.17, p < .001) and worse self-rated health (β = -.23, p < .001), and mediated the effect of income on adherence (coeff = .078 [BCa 95% CI: .051 to .108]). Future research should further explore the nuanced link between SES and health behaviors and outcomes.

IN BRIEF Nonadherence to diabetes medications is a primary contributor to health complications and avoidable hospitalizations. This article discusses the evidence for taking a person-centered (as opposed to illness-centered) approach to promoting medication adherence among diabetes patients, provides suggestions for ways in which diabetes clinicians can best promote medication adherence, and argues for needed changes in how health care systems support providers in their efforts at adherence promotion.

Background: Mobile health (mHealth) interventions are improving the medication adherence of adults with type 2 diabetes mellitus (T2DM), but few studies examine how users experience these interventions. Therefore, we used a mixed-methods approach to understand how T2DM users experience a text messaging and interactive voice response (IVR)-delivered medication adherence intervention called MEssaging for Diabetes (MED). Methods: Adults with T2DM used MED as part of a 3-month pilot study. MED sends daily tailored text messages addressing adherence barriers, daily assessment text messages asking about adherence, and weekly tailored IVR calls providing adherence feedback, encouragement, and questions to facilitate problem solving. Sixty participants completed feedback interviews. We used a mixed-methods approach to understand their experience, examining associations between participants' characteristics and their feedback. Results: Participants who completed feedback interviews were on average 50.0 ± 10.1 years old; 65% female, 62% non-white; 15% had less than a high school education, 70% had annual incomes less than $20K; and average hemoglobin A1c was 8.0% ± 1.9%. Participants rated each intervention element favorably; common reasons for MED's helpfulness included receiving novel information about diabetes medications, emotional support, and reminders to take medication. People who were younger and more recently diagnosed with T2DM had more favorable experiences using MED. In general, users valued text messages more than IVR calls. Conclusions: Consideration of the user experience is critical for developing engaging mHealth interventions. User feedback reveals what mHealth elements have the most value and why, which users to target, and how to optimize an intervention's utility and appeal.