American Diabetes Association 77th Scientific Sessions

June 9-13, 2017; San Diego, CA; Full Report – Digital Health – Draft

Executive Highlights

This document contains our coverage of digital health at ADA 2017, including automatic insulin titration, mobile apps and software, coaching, and related areas. Immediately below, we enclose relevant themes from this category, followed by detailed discussion and commentary. Talk titles highlighted in yellow were among our favorites from ADA 2017; those highlighted in blue are new full report additions from our daily highlights coverage.

Note that there is definitely some subjective overlap with glucose monitoring (e.g., CGM) and closing the loop, and some talks may appear in only one or multiple reports.

Table of Contents 

Themes

Steps Forward in Insulin Dose Titration: Common Sensing Shows the Value of Dose Capture on Pens, New Data from Hygieia and Sanofi, Bigfoot Acquires Timesulin, Partnerships Abound

  • In one of the most important diabetes technology posters in our view, Joslin investigators shared observational data on Common Sensing’s Bluetooth-enabled Gocap pen cap to monitor insulin dosing and timing. The remote dose capture was paired with Dexcom’s G4 CGM data in 31 patients – 16 older individuals (mean age: 74 years) and 15 younger individuals (mean age: 28 years). Participants used the Bluetooth-enabled Gocap + a paired app for one month with Lantus and Apidra insulins (Sanofi funded the study), allowing investigators to collect combined 4-week injection and 14-day CGM data. Gocap identified that 12% of insulin injections were missed/under-bolused over one month, while 20% of injections were extra/over-bolused (relative to what was prescribed). In addition, nearly one in three (29%) basal doses were taken outside the ± 1-hour scheduled time window for taking Lantus. (The range was a remarkable 4%-89%, meaning some users almost always missed the window.) Over the 14-day CGM period, patients spent only 41% of each day in range (70-180 mg/dl), with a striking 107 minutes per day <70 mg/dl (7%) and more than 12 hours per day >180 mg/dl (52%). The three example CGM profile plots, combined with insulin injection data from Gocap, were also FASCINATING. Dr. Irl Hirsch frequently asserts that this dearth of information is the biggest gap in diabetes data – with products like the Gocap moving toward commercialization, providers will soon have the treasure trove of MDI dosing data in their tool belts.
  • Also on the exciting data front, Hygieia showed the results from an RCT of its d-Nav Insulin Guidance Service, while Sanofi presented results on automated basal insulin titration in type 2 diabetes (My Star Dose Coach, LTHome). Hygieia’s poster showed the biggest headline improvement in outcomes – A1c declined a notable 1% in the d-Nav group at six months (baseline: 8.7%) vs. 0.3% in the usual care group (baseline: 8.5%) (p<0.0001). Of note, the d-Nav service supports all types of insulins including basal, pre-mixed, and basal/bolus regimens, and the company continues to build positive data with its titration service. Meanwhile, Sanofi’s 16-week AUTOMATIX study similarly showed that the My Star Dose Coach BGM with built-in automatic Toujeo titration is safe and effective: 34% of Dose Coach users reached their fasting target of 90-130 mg/dl without confirmed+severe hypoglycemia vs. just 15% of routine care users. Both groups also saw a 1% A1c reduction from baseline – obviously “routine” care in a trial undersells how much attention control groups receive (this applies to all insulin titration trials of automatic vs. HCP titration). A separate Sanofi study testing web-based insulin titration software (LTHome) in Canada highlighted the clear value of this field for HCPs and the healthcare system – automated basal insulin titration required fewer HCPs visits and lower costs compared to usual titration.
  • There was also major M&A news on the dose capture/titration front: Bigfoot Biomedical acquired Timesulin, giving it a connected dose capture technology through which it can build an insulin titration service for MDIs (possible 2019 launch). Timesulin co-founder John Sjölund (who also has diabetes) joined the Bigfoot team as of ADA and will lead efforts to bring this service to injectors – what a coup for Bigfoot to acquire this valuable technology and smart leader and what a smart lateral expansion over its ongoing work to improve insulin delivery via a pump+closed-loop algorithms.
    • We also learned in hallway chatter that Novo Nordisk quietly launched a connected pen in Sweden just a few weeks ago – providers can reportedly read the current iteration via NFC, but a pen with Bluetooth connectivity is reportedly coming. This was alluded to in the Glooko partnership announcement in January, and we’re elated to see the insulin giant’s continued commitment to digital health.
    • Companion Medical reps in the exhibit hall indicated that they are essentially ready to launch the Bluetooth-enabled InPen, but are waiting to formalize partnership(s) before coming to market. Assuming it does launch this year, it will be the first connected insulin pen on the US market. How will it set the tone for this field?
  • In the short time that we were in San Diego for the meeting, there were three major BGM-dose titration software partnerships: Ascensia + Voluntis; Amalgam Rx + Hygieia; and Glytec + AgaMatrix. Notably, Glytec and AgaMatrix already have a pilot of their integrated offering underway. These partnerships are proliferating, as illustrated by our crowded insulin dose titration competitive landscape – and they should be. Combining accurate, passively-collected glucose data with clinically-validated dosing algorithms should prove superior to manual dose titration, or at least equivalent in the best of cases. The service model for each of these integrations is still a clear question – presumably, the devices+software could be sold as a bundle (one product for one price), or the meters could simply be one of many devices that integrates glucose data directly into the titration algorithms. The reality is, insulin is a very dangerous drug most patients are not using it optimally, and data can help bring more to goal with less wild-guess dosing and hypoglycemia. This seems like the first wave of titration partnerships for BGMs, and we fully expect CGM companies like Abbott, Dexcom, and Medtronic to quickly move into this area.

The Value of Connected Devices – Enabling Real-World Data Collection and More Insightful Products

  • This ADA brought some notable examples of passive data collection from connected devices driving valuable real-world insights and evidence. We saw brand new, encouraging data from Medtronic/IBM Watson’s Sugar.IQ app – relative to baseline metrics (one month prior), a small group of 81 Sugar.IQ users experienced a solid 37-minute/day improvement in time-in-range, an 11% reduction in sustained hypoglycemia (>120 minutes), and an 8% drop in sustained hyperglycemia (>120 minutes). Notably, within three days of the app delivering a pattern “insight,” 65% of users experienced fewer lows and 55% experienced fewer highs. (It’s unclear when Sugar.IQ will launch fully, but we assume the biggest gating factor is approval of the standalone Guardian Connect mobile CGM, which remains under FDA review and is currently in human factors testing.) Meanwhile, Abbott continued to share fascinating real-world data from a >55,000-user-strong cohort on FreeStyle Libre – increased scanning was linked to higher time in range and decreased time in hyperglycemia. We also loved the FreeStyle Libre country-by-country breakdown, data that is now possible to collect at scale and passively with connected devices! In insulin dose capture, a Common Sensing poster shared very eye-opening results that combined its Bluetooth-enabled pen cap with Dexcom CGM data – boy is this going to make the “invisible” data behind injections visible, driving meaningful dose titration and decision support. Roche and Livongo also presented encouraging data on their connected BGM platforms – A1c changes (-0.9% for Roche, -1.2% for Livongo), higher treatment satisfaction (Roche), cost savings (Livongo: $136 per member per month), and more. Connectivity is a must-have in devices in our view, and the next step will be building excellent systems around products that use the data in meaningful ways – driving more insight for patients/HCPs, collecting real-world evidence for payers, and better understanding real-world product use to inform design improvements.

Remote Diabetes Coaching and Apps Galore in Poster Hall – Virta, Livongo, One Drop, Glooko, mySugr Seek to Augment Face to Face Care

  • Remote coaching and digital health apps had a larger presence at this year’s Scientific Sessions, particularly in the poster hall – Virta Health, Livongo, One Drop, Glooko, mySugr, and others all presented abstracts. See some of the highlights immediately below, which don’t even include all the aforementioned work in insulin titration!
    • Virta shared highly anticipated one-year results from its type 2 diabetes program, combining a low-carb/high-fat diet (to induce nutritional ketosis) and tech-enabled remote care. In an interim analysis of 111 patients with data at one year, A1c dropped a significant 1.3% from a baseline of 7.4% (p<0.0001), and 58% of patients achieved an A1c <6.5% while taking no diabetes medications or metformin only. Wow! Insulin was reduced or halted in 97% of users, and weight was reduced an impressive 14% from baseline, equating to a mean 35 lbs of weight loss (from 255 to 212 lbs; p<0.0001).
    • Livongo presented its first (to our knowledge) ADA oral, showing that its cellular-enabled BGM + remote CDE coaching drove a 1.2% decrease in A1c, a 37% reduction in total cholesterol, and an 8.3% reduction in triglycerides relative to non-Livongo users. These clinical benefits translated to savings of nearly $140 per member per month. The company also had two posters: One randomized crossover trial (out of UMass) demonstrated slight improvements in A1c and treatment satisfaction attributable to Livongo use, and the other showcased a 1% drop in A1c (baseline: 8.5%). Addition of a connected scale also resulted in weight loss.
    • One Drop also had two glycemic outcomes posters at this ADA, adding to its growing  real-world outcomes data (albeit in small user groups and without control groups). A late-breaking abstract showed that use of the in-app One Drop Experts coaching/education resulted in a 0.9% A1c drop in study completers from a high 9.9% baseline. A second poster analyzed app-entered data from the same study, highlighting notably decreased average blood glucose (from 195 to 166 mg/dl), more in-range BGs (from 48% to 64%), and fewer high BGs (from 51% to 33%).
    • Glooko and mySugr presented retrospective analyses of their digital platforms too, which individually now have more than 1 million users each! Both posters showed encouraging, albeit small, improvements in glycemia (mySugr on LBGI, Glooko on hyperglycemia). It’s clear that both companies have built diabetes data apps that resonate with big populations, though the analysis of these massive real-world datasets has not really been done. There is clear upside too from incorporation of decision support (e.g., insulin dosing), remote monitoring programs to prioritize care (Glooko’s Population Tracker, mySugr’s work in Germany), and more seamless connectivity that doesn’t require manual syncing. How much improvement in outcomes can these products drive? As both companies move into population management and more insightful analytics, will these analyses grow in size and robustness?  
  • Data supporting remote coaching + connected devices is expanding, whether real-world results or more familiar clinical trials. We love the potential to scale care and add more real-time monitoring and continuity, though sustained engagement, demonstrated outcomes, and business models will be key questions. Moving ahead, we expect to see more automated, interactive, and insightful decision support systems that leverage AI, machine learning, voice, and multiple data streams (including non-diabetes streams). Ultimately, this field is not about data for its own sake, but providing guidance for patients and HCPs that result in better outcomes.

The DIY Community Going Strong – Autotune, Loop, and Pushing Industry to Move Faster

  • The do-it-yourself (DIY) community is still pushing the envelope of diabetes technology. Patient innovators Dana Lewis and Scott Leibrand shared a late-breaking poster on Autotune, an algorithm that automatically recommends changes in pump settings based on CGM data. Even in an engaged group of users, most found the recommendation helpful and have reportedly changed their pump settings accordingly. We also heard plenty of hallway chatter on Loop/RileyLink, the DIY system that runs hybrid closed loop off an iPhone app. Those we spoke to praised the overnight time-in-range, the algorithm’s customizability (e.g., set point, aggressiveness), the ability to bolus from an iPhone (with TouchID) or Watch, the on-phone user experience (showing exactly what the algorithm is doing), and seeing projected glucose after carbs are entered. At the Diabetes Mine D-Data Exchange, major industry players (Medtronic’s Dr. Fran Kaufman, Insulet’s Dr. Trang Ly,  Bigfoot’s Bryan Mazlish, Tandem’s John Sheridan) showed encouraging openness to engaging with the DIY and broader patient communities. We hope that moving forward, companies can give these innovators a “sandbox’ to play in, allowing lead users to help drive innovation. Dexcom is taking a lead on this with its developer APIs, which will launch later this year and allow third parties to access retrospective data (three-hour-delay), create and manage pre-commercial (prototype) apps, play with simulated (sandbox) data, learn how to become a Dexcom data partner, and even submit an app for commercial approval. Hopefully the time window will shrink, driving an ecosystem of useful real-time decision support. We’ll be fascinated to see how different companies harness the brilliance of this community – there is obviously a fine line to tread here, but one with significant upside in our view.

Detailed Discussion and Commentary

Oral Presentations: Where Is Glucose Monitoring Taking Us?

Sugar.IQ Insights: An Innovative Personalized Machine-Learning Model For Diabetes Management

Huzefa Neemuchwala, PhD, MBA (Head of Innovation, Medtronic Diabetes, Northridge, CA)

Medtronic’s very smart Head of Innovation Dr. Huzefa Neemuchwala shared the first data from the “limited learning launch” phase of the Sugar.IQ app with Watson – now tag-lined, “Intelligent Diabetes Assistant App.” Results came from de-identified CareLink data in 81 users of the Sugar.IQ app using MiniMed 530G/Enlite + MiniMed Connect to send CGM data to the app. Relative to baseline metrics (one month prior), this small group of Sugar.IQ users has experienced a solid 37-minute/day improvement in time-in-range (p=0.04; baseline not shared), an 11% reduction in sustained hypoglycemia (>120 minutes; p<0.001), and an 8% drop in sustained hyperglycemia (>120 minutes; p<0.001). Within three days of the app delivering a pattern “insight,” 65% of users have experienced fewer lows and 55% experienced fewer highs. In total, Sugar.IQ has now been used by 97 people for an average of two weeks each, and engagement has been encouraging in this limited launch: an average of 1.5 unique app sessions per day, 78% of users logging food, and 4.8 logged food items per day – persistence over time, particularly with food and opening the app up, will be THE key question ahead. The very cool Glycemic Assist feature, allowing users to “follow” a particular food item over time (we love this!), has been popular: 1,886 views in these 97 users so far. Users have “followed” their glycemic response to Dunkin Donuts, Panera Bread, corn flakes, ice cream, buttermilk biscuits, etc. – pretty squarely in the junk food (“Diabetes Landmines”) category, but hopefully the app will gradually nudge people away from eating them! We include examples below of the insights Sugar.IQ delivers – so far, this group of users has received 1,119 different contextual, personalized insights ranging from glycemic control and behavior to hyper- and hypoglycemia to rapid rate-of-change to boluses. Users have “liked” a notable 89% of the insights, indicating they are finding useful patterns. Dr. Neemuchwala also shared two Sugar.IQ case studies from patients with longstanding diabetes – the app identified trends (over-correcting highs, eating a high-carb lunch) and nudged them to change their behavior (the latter is a phrase Dr. Neemuchwala emphasized). More details and screenshots below!

  • It’s unclear when Sugar.IQ will launch fully, but we assume the biggest gating factor is approval of the standalone Guardian Connect mobile CGM (under FDA review and currently in human factors testing). Guardian Connect will stream CGM data to Sugar.IQ directly via Bluetooth, and should help Medtronic differentiate its standalone mobile CGM offering from rising competition (Abbott, Dexcom). Per Medtronic’s JPM presentation (the last update), a full launch of Sugar.IQ and Guardian Connect were expected in May-October, though today’s Medtronic Diabetes Analyst Day update said that human factors work is ongoing. Medtronic also told us it is working with IBM to finalize algorithm for the Sugar.IQ commercial launch.
    • As a reminder, this app has been fairly delayed. The plan as of last year was to launch Sugar.IQ by the end of 2016, timing that was updated at JPM. Sugar.IQ was demoed and “beta launched” in September at Health 2.0 – presumably “the beta launch” was a previous group, and this data is from the wider release that was alluded to at ATTD.
  • Sugar.IQ insight examples: In a word, wow!  “Planning your day? I see you tend to go low on Saturday between 12 PM and 3 PM.” “I notice that you tend to go low after meals with >20g of protein.” “Great! I noticed that you had only 1 nighttime low(s) in the last month. Whatever you’re doing, seems to be working very well.” “I see that between 6AM and 9AM, your glucose often goes high (300+ mg/dl) after taking an insulin injection.” “After your glucose is high for more than 120 minutes, you then tend to go low.”
  • Looking ahead, Medtronic has also expanded the research on the hypoglycemia prediction feature, which now has >90% accuracy at predicting hypoglycemia within a 2-4 hour window (80%+sensitivity, 67% positive alert rate). This feature is now using 100+ behavioral models based on unsupervised clustering techniques. Last we heard, this will be included in a future version of the app, but not the one at launch.
  • Dr. Neemuchwala provided two case studies of Sugar.IQ noticing a specific glycemic/insulin/food pattern, giving the user an objective insight, and a resulting human behavior change. “Simple judgment-free nudges can lead to sustained behavior improvement.” Both were in people with long-standing diabetes (one with type 2 for 20 years on insulin, another with . One case concerned over-correcting highs, while another concerned eating a high-carb vs. slightly lower-carb lunch. See the slides below!

  • Sugar.IQ uses machine learning to find patterns in diabetes data, and ultimately, hopes to combine many data sources: CGM and insulin, biometrics, meals/logbook, CRM, medical and claims, mood, sleep, location. The focus is on putting all this data in one place, then driving insights and predictions.

Oral Presentations: From Prediction to Transition – Type 1 Diabetes Mellitus Across Stages

Glucose Control as Measured by A1c in Diabeter Clinic Patients Compared with T1D Exchange Patients

Henk Veeze, PhD (Diabeter Clinic, Rotterdam, The Netherlands)

Dr. Henk Veeze presented data showing that Diabeter Clinic type 1 patients achieved lower A1c outcomes compared to the representative T1D Exchange population. The study compared A1c values of patients seen at four Diabeter clinics (n=1,162) in the Netherlands for over one year to published data from T1D Exchange patients (n=13,899), from 2015-2016. According to the findings, Diabeter patients (mean age of 16 years; 47% MDI, 44% pumps, and only 9% sensor-augmented pumps) reached A1c levels 0.85% lower (p<0.0001) vs. T1D Exchange patients (mean age of 14 years; 40% MDI, 43% pumps, and 15% sensor-augmented pumps). In addition, more than twice as many Diabeter patients reached A1c <7.5% compared to the T1D Exchange group: 42% vs. 21%. As background, the now Medtronic-owned Diabeter Clinics deliver standardized, value-based, comprehensive care to pediatric type 1 diabetes patients with the VCare system, which uses meter, pump, and CGM uploads to automatically generate personalized treatment assessments. Therapy advice is then sent via email to patients between clinic visits. Dr. Veeze closed by emphasizing that use of the Diabeter care model with frequent communication may facilitate improved pediatric glucose control. For more on Diabeter and its acquisition by Medtronic, please see our 2015 interview on the acquisition as well as Medtronic’s latest quarterly update. Medtronic has talked generally about expanding this work across the globe, though no material updates have been shared on the number of clinics and pace of expansion.

Questions and Answers

Q: Do you have a plan to make this IT process available for other centers to utilize?

A: A number of countries have questions about this. We have set ourselves up and we need co-investment, which is why Medtronic acquired our clinic. With Medtronic, we’ll go further and we’ll see where we can expand. There are a number of opportunities in different countries and we’re getting ready to get involved.

Q: Do you have any further analysis of outcomes in relation to use of your system? For example, is there an association between the number of uploads or messages with clinical outcomes?

A: The more the patient engagement – uploads, messages, and data – the more tremendously the clinical results improve.

Oral Presentations: Potential Implications of the Affordable Care Act on Diabetes Care

Connected Glucose Meter Plus Coaching Improves Diabetes Clinical Outcomes and Decreases Costs

Jennifer Bollyky, MD (VP Clinical Research & Analytics, Livongo, Mountain View, CA)

Livongo VP of Clinical Research and Analytics Dr. Jennifer Bollyky presented retrospective data showing that Livongo users achieved improved glycemic control and cost savings compared to non-Livongo users. The study compared medical claims and clinical lab outcomes for Livongo users (n=646) with non-Livongo users (n=3,014) 12 months before and 12 months after the launch of the Livongo program. At the end of the period of study, there was a 1.2 percentage point decrease in A1c (p=0.12; we did not catch the baseline), a significant 37% reduction (64 point total decrease) in total cholesterol (p=0.04), and an 8.3% reduction (10 point decrease) in triglycerides (p=0.80) seen in Livongo users compared with non-users. Dr. Bollyky also presented cost data showing that Livongo users experienced significantly slowed increase in the cost of medical claims relative to non-users (5% vs. 13% growth, respectively), resulting in a savings of $136 per Livongo member per month. We’d note that these savings dwarf the per-month DTC price of the Livongo service ($49.99 promotional price until August 10, $65 per month thereafter). We assume Livongo still charges employers and health systems roughly ~$70 per person per month, meaning the ROI is quite good based on these retrospective results. Of course, a prospective study would tease out the real benefits, but given the company’s growth and continued funding, we assume the cost data is quite promising in real-world implementation too.

  • Dr. Bollyky opened with the rationale of Livongo, emphasizing that today’s acute care management approach to diabetes is ineffective. With this, she introduced Livongo’s focus on: a cellular-enabled, two-way messaging BGM device that measures blood glucose; free unlimited blood glucose test strips; and access to CDEs for real-time support and goal-setting.

Questions and Answers

Q: You’re working with specific employers now. What’s the cost of service and plans for expansion to other populations?

A: We just launched a DTC campaign, which is $50 a month (promotional price). It includes the cost of test strips so you can test as much as you want.

Q: What does the retention of people look like?

A: We have a turnover rate of about 1%, meaning that someone may not be eligible due to employer change. This usually happens because the person is no longer eligible for the benefits for some reason. We also have something called “last users,” who are not checking their blood glucose. But, employers introduce different incentives to check. We try to make it as fun as possible and give external reasons to check.

Oral Presentations: Thinking and Working Outside the Box – Prevention and Intervention Approaches

Effects of a Gamified Mobile Application to Support a Lifestyle Change Program in Adults: A Controlled Pilot

Sam Oddsson, MD (Sahlgrenska University Hospital, Sweden)

Dr. Sam Oddsson presented findings from his study demonstrating the capacity of a mobile health engagement platform to significantly enhance an in-person lifestyle modification program aimed at achieving weight loss. In a transatlantic collaboration, overweight participants (n=153; mean BMI = 36 kg/m2) were randomly assigned to receive either in-person coaching only or coaching along with a mobile health app providing encouragement and motivation in the form of social networks, commitment contracts, and awarded “health points.” The intervention group lost significantly more weight over 16 weeks than the control group and were three times more likely to achieve a pre-specified 5% weight loss goal. It wasn’t clear what the absolute weight reduction was in either group, though 3x greater efficacy over in-person alone does show the value-add of apps. The app drove adherence to the weight loss regimen by: targeting user emotion and reasoning, with attendance increasing in the intervention group and decreasing in the control group over the course of the study. We wonder what happened in months following the trial – many interventions are effective at helping people lose weight, but very few have lasting effects that help to keep the pounds off or maintain healthy habits. Dr. Oddsson’s results echo what many have said at this ADA: Advances in technology have the potential to be extremely beneficial, but it is their positioning and interface with the real world – effectively established through extensive human factors research and ongoing engagement– that grant them true potency.

Questions and Answers

Q. How often did participants have interactions? Any insights for if this is in-person time is necessary?

A. Participants attended weekly meetings over 16 weeks. We believe the mobile app is an extension and augments the work of the lifestyle coaches. We received great feedback on the in- person visits from the participants and believe these sessions to be motivating.

Q. What exactly did the mobile app do?

A. The app motivates the individual, trying to target the emotional systems in the brain via different methods. On the surface, it looks like a game (emphasizing nutrition, physical activity, stress reduction) harnesses social networks (involving goal setting, support), and includes commitment contracts. We are conscious of the effects of health literacy, so we aimed to keep it text-light, colorful, and visual. There is no form of coaching through the application.  We did internal analysis prior to the study to determine what can be harnessed.

The Effect of the Patient-Centered, Smartphone-Based, Diabetes Care System in Patients with Uncontrolled Type 2 Diabetes: A Randomized Controlled Trial for 24 Weeks

Eun Ky Kim, MD (Seoul National University, South Korea)

Dr. Eun Ky Kim presented her findings from a 24-week randomized controlled trial (n=172; CT.gov) demonstrating the positive clinical impact of the patient-centered, smartphone-based Diabetes Care System (PSDCS) on those with inadequately-controlled type 2 diabetes. Developed in 2013, PSDCS features individualized diabetes management algorithm, automatic integration of daily glucose levels (via Bluetooth meter) and physical activity tracking (step counter device), guidance for basal insulin dosage, and various interactive components, including a social network system. Results were promising, with PSDCS utilization facilitating significant reductions in A1c compared to the use of a paper logbook (-0.4% vs. -0.1%), as well nearly twice as many patients reaching A1c goal <7.0% (41% vs. 21%). In addition, the proportion of PSDCS patients that achieved A1c <7.0% without any hypoglycemic events was nearly twice that of the control group: 31% vs. 17%. The PSDCS group also demonstrated higher frequencies of blood glucose testing (makes sense, given passive upload from the meter instead of paper logging) and significantly greater weight loss. Diabetes self-care activities such as medication, exercise, and foot care were found to be comparable between the two groups; however, the PSDCS group did express greater improvements in quality of life at the study’s end. There were no differences in incidences of hyperglycemia or hypoglycemia – presumably with CGM this would have emerged, but we’re not sure.. These results build upon the 12-week pilot data presented at ADA 2015. PSDCS has shown to be rather efficacious, and we wonder how it might scale up and be sold commercially.

Posters

Remote Care Promotes Low Carbohydrate Diet Adherence and Glycemic Control Allowing Medication Reduction in Type 2 Diabetes (76-LB)

S Hallberg, A McKenzie, N Bhanpuri, B Volk, T Hazbun, J McCarter, S Phinney, and J Volek

This poster shared highly anticipated one-year results on Virta Health’s type 2 diabetes program, combining a low-carb/high-fat diet (to induce nutritional ketosis) and tech-enabled remote care. In this interim analysis of 111 patients with data at one year (mean age: 54 years; mean BMI: 40 kg/m2), A1c dropped a significant 1.3% from a baseline of 7.4% (p<0.0001), and 58% of patients achieved an A1c <6.5% while taking no diabetes medications or metformin only. Insulin was reduced or halted in 97% of users, and weight was reduced an impressive 14% from baseline, equating to a mean 35 lbs of weight loss (from 255 to 212 lbs; p<0.0001). Overall, 84% of patients lost a clinically significant >5% of body weight at one year. Cardiovascular risk markers also improved as expected, including triglycerides (176 to 132 mg/dl; p=.002), HDL-C (from 46 to 53 mg/dl; p<0.001), and hsCRP (7.5 to 5 mg/dl; p<.0001). LDL-P – a CVD-relevant measure of the number of LDL particles – did not change significantly (1269 to 1218 mmol/l; p=0.16), countering concerns that a high-fat diet is dangerous for CVD risk. There were also no significant adverse events attributed to the intervention. Impressively, these results are an acceleration from the 10-week data published in JMIR Diabetes in tandem with the company’s March debut – in 262 patients at the time, A1c had declined by 1%, weight loss was 7%, 56% were in remission (A1c <6.5%), and 87% using insulin had eliminated use or decreased their dose. Overall, these sustained results are a definite confidence boost for Virta, as some criticized the initial results as “too short-term” or “unsustainable.” We look forward to seeing full two-year results and watching the company’s progress towards its bold goal of treating 100 million people with type 2 diabetes by 2025. 

  • The poster notes an “82%” study retention rate at one year (“130 of 158 subjects” remain actively enrolled), though the non-randomized study originally enrolled 262 patients, per the 10-week publication – we’re not sure what explains the discrepancy between 158 vs. 262 (e.g., Was this dropout, but not categorized as such?). Whatever the denominator, the retention is better than most would expect for an ultra-low-carb approach (typically <30 grams per day), countering a key criticism of Virta’s approach. Certainly, frequent remote contact with a care team also helps significantly.
  • For a much deeper dive on Virta’s program and debut, see our coverage from March.

Bluetooth Pen Cap Identifies Gaps in Adherence to Insulin Dosing and Timing: A Critical Step in Safe Diabetes Management (1043-P)

M Munshi, C Slyne, T Macneil, and E Toschi

In a first-of-its-kind observational study (to our knowledge), Joslin investigators evaluated use of Common Sensing’s Bluetooth-enabled Gocap paired with Dexcom’s G4 CGM data in 31 patients – 16 older individuals (mean age: 74 years) and 15 younger individuals (mean age: 28 years). Patients used the Bluetooth-enabled Gocap + paired app for four weeks with Lantus and Apidra insulins (Sanofi funded the study), allowing investigators to view combined injection and 14-day CGM data. Gocap identified that 12% of insulin injections were missed/under-bolused over one month, while 20% of injections were extra/over-bolused (relative to what was prescribed). In addition, nearly one in three (29%) basal doses were taken outside the ± 1-hour scheduled time window for taking Lantus. (The range was a remarkable 4%-89%, meaning some users almost always missed the window.) Over the 14-day CGM period, patients spent only 41% of each day in range (70-180 mg/dl), with a striking 107 minutes per day <70 mg/dl (7%) and more than 12 hours per day >180 mg/dl (52%). The three example CGM profile plots, combined with insulin injection data from Gocap, were also FASCINATING (see below) – in our view, combining these technologies is going to change the game for safer and more effective insulin prescribing/titration. This was also a pretty broad diabetes population, as the groups had high A1cs (8.3% in the younger cohort, 9.1% in the older cohort), 58% of the older group had mild cognitive dysfunction, 18%-25% of the cohorts were hypoglycemia unaware, and 23%-36% had elevated diabetes distress. Though this was not an outcomes study, we can already see the enormous potential here of making invisible data on injections finally visible to HCPs. We certainly expect the field of injection dose capture + CGM to explode in the coming years.

  • Per our last coverage of Common Sensing in October, the company was actively pursuing partnering, distribution, and study opportunities before launch (no timing), with the goal of patients not having to pay anything. Gocap and the mobile app were registered with the FDA as 510(k)-exempt (it does not have a bolus calculator).

Reduced Health Care Costs with Automated Basal Insulin Titration in Patients with Type 2 Diabetes (1322-P)

J Sieber, H Bajaj, T Kottman, K Venn, R Aronson, and F Flacke

According to this 12-week Sanofi-sponsored study, type 2 patients using a web-based basal insulin glargine titration tool (LTHome; n=72) had significantly reduced HCP utilization and cost vs. a group on provider-initiated “enhanced usual therapy” (EUT; n=67). Outside of three scheduled clinic visits at weeks 4, 8, and 12, the usual care group had more contacts with their providers than did the group using the LTHome tool to titrate their basal insulin (1.22 vs. 0.18), and were significantly more likely to reach out to their providers within seven days before the week 4 and week 8 visits. Remarkably, total number of visits for all patients was 7x greater (!) with usual care vs. LTHome (78 vs. 11; p<0.001). This lower utilization with LTHome led to major cost savings (calculated using the Ontario Schedule of Benefits fee codes) using the web-based titration tool: for endocrinologists, mean total cost with LTHome was ~$159 vs. $199 with usual care (p<0.001), and for general practitioners, mean cost with LTHome was ~$141 vs. ~$176 with EUT (p<0.001). Meanwhile, average baseline A1c of 8.8% dropped ~1% in both groups (p=0.66), meaning automatic, software-driven titration was not inferior to HCP-driven titration. Incidence of documented hypoglycemia was also not statistically different (37% in LTHome group vs. 31% in EUT group; p=0.40). We see this as yet another major vote of confidence in software-driven automatic basal (and hopefully basal-bolus!) titration algorithms – automatic titration drives similar glycemic improvements vs. enhanced usual therapy. Moreover, the cost savings on a population level with automatic software-driven titration could be immense. Many insulin titration products are starting to make their way through development and clearance (see our competitive landscape), and we continue to see strong potential for better use of insulin, lower costs, and less HCP hassle.

Device-Supported vs. Routine Titration of Insulin Glargine 300 U/mL (Gla-300) in T2DM: Efficacy and Safety (131-LB)

S Edelman, S Bain, C Hasslacher, G Charpentier, G Vespasiani, F Flacke, H Goyeau, M Woloschak, and M Davies

Sanofi presented encouraging results from the AUTOMATIX study comparing its myStar DoseCoach BGM (integrated insulin glargine dose titration algorithm and BGM) to investigator-recommended Toujeo titration regimens. In a randomized, multicenter treat-to-target trial, patients with type 2 diabetes (n=151) were randomized 1:1 to device-supported or routine titrations. After 16 weeks, a higher proportion of patients achieved the mean fasting glucose target of 90-130 mg/dl without severe hypoglycemia with the DoseCoach BGM: 46% vs. 37% (not significant). We’d emphasize that in these sorts of studies – comparing best-case scenario HCP titration vs. patient-driven automatic systems – showing non-inferiority is a huge achievement and positive sign. Between study arms, comparable numbers of patients experienced hypoglycemia and adverse events. In addition, 34% of patients using DoseCoach reached mean fast glucose of 90-130 mg/dl without confirmed blood glucose reading ≤70 mg/dl, while this percentage was just 15% in the investigator-recommended titration group (superiority not determined). Similarly, fasting glucose dropped five additional mg/dl and A1c fell an additional 0.15% in the DoseCoach group, with slightly fewer events of hypoglycemia (both during the day and at night). In line with many other studies at this ADA, we see this one as another vote of confidence in automatic basal insulin titration – it works as well as best-case scenario HCP-driven titration (and often better!), but is much more efficient for the healthcare system and for patients. We’d love to see how myStar DoseCoach compares to other insulin titration products that are starting to emerge on the market (see our insulin dose titration competitive landscape) – is an integrated BGM with titration preferred to an app? How will Sanofi choose to commercialize the DoseCoach BGM vs. DoseCoach app in the US – will both be offered?

Automated Frequent Insulin Dosage Titrations: Essential for Successful Insulin Management in Type 2 Diabetes (1016-P)

I Hodish, M Johnson, E Bashan, D Kruger, A Bhargava, and R Bergenstal

This well-conducted six-month, randomized controlled study enrolled 181 patients with type 2 diabetes, comparing use of Hygieia’s d-Nav Insulin Guidance Service (BGM with built-in insulin titration + remote care) to enhanced standard care from a diabetes specialist team. A1c declined a notable 1% in the d-Nav group at six months (baseline: 8.7%) vs. 0.3% in the usual care group (baseline: 8.5%) (p<0.0001). In addition, A1c improved by at least 0.3% in 81% of d-Nav users vs. 45% of the control group. We’d add that this finding is even more impressive considering that the control group was probably managed as well as possible by Dr. Rich Bergenstal’s team at IDC, Ms. Davida Kruger’s team at Henry Ford, and Dr. Anuj Bhargava’s team at Iowa Diabetes and Endocrinology Center. Indeed, both groups were contacted seven times throughout the six-month study and at the same frequency, meaning the device’s titration was what really drove the difference. In the d-Nav group, automatic titrations were made every seven days (control group comparison not provided) and the frequency of hypoglycemia <60mg/dl was just 0.6 per month. Nice! Total daily insulin increased by a pretty significant 73% (though in 11% of users it decreased), no surprise considering the high baseline A1c. On the safety front, severe hypoglycemia occurred in two patients in the d-Nav group and none in the control group, a non-significant difference. The results complement Hygieia’s positive outcomes in the UK (2%+ A1c reductions from a higher 9.4% baseline, sustained for three years), and we look forward to seeing what comes of the ongoing demonstration project in Michigan with BCBS. We see extremely high potential here and have heard very good things about Hygieia, particularly because there is a support team in addition to the device alone. 

Impact of a Diabetes Mobile App with In-App Coaching on Glycemic Control (63-LB)

S Kumar, H Moseson, J Uppal, C Osburn, M Heyman, and J Juusola

Evidation Health presented results of a third-party evaluation of the One Drop mobile app and Experts in-app education and coaching service. A total of 146 people with type 2 diabetes were recruited (A1c >7.5%), of which 127 completed the study. Among study completers, A1c declined by 0.9% (collected with an at-home, mail-in kit) over three months from a high mean baseline of 9.9%. Further analysis showed slightly larger reductions among “active users,” those who used the app for at least one additional day and messaged a coach at least once after starting the program: 1.0% improvement in 93 patients from a high baseline of 9.7%. On a final subgroup note, A1c improved 1.3% in “active” patients with a starting A1c ≥9.0% (n=53; baseline: 10.9%). While there was not a control group here, the results are encouraging and add to One Drop’s prior data showing A1c and glycemic improvements in patients at a high baseline (see our previous report). The small startup is lacking a large RCT, but the steady stream of real-world data in tough patients is good to see. We wonder how the A1cs turned out for those 46 participants who were not active, and what factors make them less likely to engage. As a reminder, One Drop Experts also launched worldwide last month as a standalone direct-to-consumer, in-app coaching service for $11-$13/month.

  • The One Drop Mobile app assists with tracking self-care activities (a mix of manual and automatic entry), goal setting, and delivers data-driven insights, tips, and advice. There’s also a community support feature, where users can comment on others’ data.
  • One Drop’s Experts On Track is a CDE-led nine-week course with on-call messaging support. One Drop shared in a press release in April that it received “ADA Recognition” for diabetes self-management education and support.

Digital Behavioral Counseling for Diabetes Risk Reduction in a Workforce (85-LB)

CM Castro Sweet, MG Wilson, MD Edge, EN Madero, M McGuire, M Pilsmaker, D Carpenter, and S Kirschner

This poster compared 634 employees enrolled in Omada Health’s diabetes prevention program (those with annual biometric data) vs. a propensity-matched comparison group (n=1,268) of non-participating employees at Iron Mountain. In longitudinal analyses, the workforce on average was gaining 3.5 lbs annually before program inception (see 2013-2014 in chart below). Nearly one in three Omada participants (31%) lost >5% of their initial body weight over one year (2014-2015), with 22% dropping a full BMI category. At the same time, the control group experienced a slight increase in weight gain. However, the absolute weight reduction in the Omada group was actually very small – 2 lbs (~1% body weight loss) vs. ~1.3 lbs of weight gain in the control group. Given that 31% of Omada users lost >5% of their weight, we assume there was a pretty wide dispersion of responders vs. non-responders. Engagement was pretty encouraging, as 83% of participants completed 9 or more Program lessons, meeting CDC standards. We’re glad to see the longer-term data – especially relative to the natural weight-gain course these employees were on – though the weight loss outcomes are lower than those Omada has previously published.

  • Employees at Iron Mountain, Inc., a global storage and information company, were invited to receive the program if they met eligibility requirements, including having a BMI of at least 24 kg/m2 and being deemed at risk for diabetes.

Use of the Accu-Chek Connect System is Associated with Increased Treatment Satisfaction and Improved Glycemic Control in Individuals with Insulin-Treated Diabetes (105-LB)

P Mora, A Buskirk, M Lyden, C Parkin, L Borsa, and B Petersen

In a six-month, prospective, multi-center study (n=84), Mora et al. found use of the Accu-Chek Connect system significantly reduced A1c levels, boosted patient satisfaction, and reduced diabetes-related distress in insulin users. The Accu-Chek Connect system includes a Bluetooth-enabled BGM (Aviva Connect), smartphone app (including a bolus advisor), and online web portal, allowing for automatic transmission of patient data to clinician and patient platforms. At three months, use of the integrated system resulted in a mean A1c drop of 1.1% (baseline: 8.8%; p<0.001), which was sustained out to six months (-0.9% vs. baseline; p<0.0001). Satisfaction and distress were measured using the DTSQ and DDS (diabetes distress scale) respectively: The DTSQ showed high baseline treatment satisfaction (~30 out of a possible 36), and satisfaction significantly improved nevertheless (see below). Importantly, mean diabetes distress scores dropped significantly by 0.3 points (p<0.0001), with a notable reduction in regimen-related distress from “moderate distress” to “not distressed.” Daily SMBG frequency increased non-significantly from 2.4 times/day at baseline to 2.6 times/day at six months. These results strongly point towards the psychosocial and clinical benefits of connected meters, a major win.( There was no control group, however, so some of the benefit may be due to a study effect.) We be fascinated to know how much of the benefits could be attributed to the connected meter and use of the data vs. the bolus calculator function in the Accu-Chek Connect app. We’d also be interested to see future studies in patients with lower baseline patient satisfaction who may be less likely to engage with the system regularly. Overall, it’s terrific to see a prospective, longer-term study showing the benefit of a connected meter, which is obviously not a given!

The One Drop Mobile App with In-App Coaching Improves Blood Glucose and Self-Care (885-P)

CY Osborn, M Heyman, B Huddleston, J Van Ginkel, D Rodbard, and J Dachis

A second One Drop retrospective outcomes posters at this ADA suggested that the One Drop Mobile app with in-app coaching (One Drop Experts) had favorable effects on app-collected glycemic and self-care metrics in people with type 2 diabetes (n=146; baseline A1c =9.9%) – this was an internal One Drop analysis of the late breaker from Evidation Health discussed above. The three-month outcomes looked at two separate study periods – one in January 2017 (n=148) and another in May 2017 (n=146). The app showed a consistent benefit in these users: in-app-entered average blood glucose dropped from ~195 mg/dl in the first week to ~166 mg/dl in the twelfth week (p<0.001), glycemic variability (measured by SD) dropped from ~41 to ~34 mg/dl (week 1 vs. 12; p<0.001), and percentage of in-range blood glucose improved from ~48% to ~64% (week 1 vs. 12; p<0.001). Participants also picked up healthier habits by the end of the 12 weeks, exercising more (adding ~25 minutes per day, an ~25% improvement; p<0.001) and reducing carbohydrate load ~13-18 grams/meal fewer (p<0.001). A surprise from the study was that individuals who tracked less actually had a greater improvement in average blood glucose than those who tracked more. This strikes us as counterintuitive for digital health, though it’s possible that those inclined to track more had lower mean glucose levels at baseline, and so had a shorter runway for improvement. It’s also possible that those who tracked less benefited more from other aspects of the app or coaching program. Similarly, the study population’s starting A1c was quite high (9.9%), which may explain some of the more impressive glycemic improvements.

  • As of May, One Drop Mobile has over 200,000 users worldwide, a ~25% increase since the value reported in March. We’ll be interested to see how One Drop’s recent rollout of new programs at lower prices impacts uptake, given their appeal to less-frequent testers at a better price. One Drop Experts On Track delivers evidence-based content and behavioral tools with the help of an on-demand Certified Diabetes Educator (currently offered at $11-$13/month) – this means that anyone with a smartphone can have access to a CDE for just $130 year, which is potentially disruptive assuming it can scale. Experts On Track exemplifies the potential of mHealth as a less expensive, on-demand healthcare delivery/education/support service. Given the rapidly growing field of diabetes coaching, we’re thrilled to see efforts aimed at critically evaluating efficacy.

High Risk Population Using mobile Logging Application SHows Significant Reduction in LGBI (952-P)

M Hompesch, K Kalcher, and F Debong

A retrospective mySugr poster demonstrated that real-world use of the mySugr logbook results in a significant decrease in and severity of hypoglycemia. To zero in on the “high risk” population, researchers extracted data from 4,000 engaged (logging ≥5 days/week for ≥6 months) patients with type 1 diabetes. After over half were eliminated due to incomplete or inconsistent data sets, the quartile with the highest LBGI (low blood glucose index) at baseline (n=457) was analyzed. After four to six months of use, a reduction of 17% (from 1.07 to 0.88; p<0.001) was observed. (As a rule of thumb, an LBGI of <1 means minimal hypoglycemia risk.) This change was accompanied by a statistically significant increase in blood glucose (141 to 148 mg/dl), though the clinical implication of this increase is outweighed by the diminished hypoglycemia. There were no significant changes in measures of glycemic variability, nor high blood glucose index (HBGI), and sub-analyses showed no impact of location (Germany, France, Great Britain, or US) or sex. The obvious criticism of this analysis is that those examined already had more-than-acceptable LGBIs at inclusion – hypoglycemia risk isn’t even considered “moderate” until LGBI hits 2.5. This isn’t mySugr’s strongest data (at ATTD it showed larger improvements), but we’re glad to see it publishing data in these fairly large retrospective analyses – this is not common enough in digital health and is so key for getting payer/clinical buy-in. How can the app appeal to more people by adding more value with lower interaction? We assume more outcomes are coming from this collaboration with Prosciento, especially once the logbook is coupled with mySugr’s bolus calculator (launched in EU); mySugr coaching; and presumably better analytics, device integration, and decision support.

  • At ATTD, mySugr and Prosciento presented a retrospective analysis suggesting a 1.3% estimated A1c reduction over six months in 440 randomly selected high-risk Logbook app users (baseline estimated A1c: 9.0%). To be in the retrospective analysis, patients had to have a mean baseline blood glucose of ≥183 mg/dl (estimated A1c >8%) and high engagement on the mySugr Logbook app (logging ≥5 days/week for ≥6 months). Mean blood glucose fell 18%, from 211 mg/dl at baseline to 173 mg/dl, an impressive drop. Both high blood glucose index (HBGI) and low blood glucose index (LBGI) improved too (see the poster here). It’s great to see the company digging into their one million-plus strong user base.

Lifestyle Coaching Plus Connected Glucose Meter with CDE Support Improves Blood Glucose and Weight Loss for People with Type 2 Diabetes (957-P)

J Bollyky, D Bravata, J Yang, and J Schneider

In this poster, Livongo teamed up with Restore Health to investigate the incremental effect of supplementing its service with intensive lifestyle coaching in overweight patients with type 2 diabetes and elevated A1C levels. Study participants were randomized to receive one of four possible interventions over the course of 12 weeks: Livongo only, meaning a cellular BGM and CDE coaching (n=75); Livongo + a connected weight scale (n=115); Livongo, the connected scale, and “lightweight” coaching (n=73); and Livongo, the connected scale, and “intensive” coaching (n=67). Coaching consisted of an on-boarding call and daily text messages and activities aimed at improving factors associated with insulin resistance. Lightweight and intensive coaching differed in the length of the on-boarding call and the degree of lesson, meal rating, text, and activity personalization. The investigators found that the Livongo program, alone, significantly boosted blood glucose control (mean estimated A1c decreased from 8.5% to 7.5%; p=0.01). Adding coaching into the equation, mean weight loss and mean blood glucose improved more among those in the intensive group as compared to those in the lightweight and scale only groups (-6.4, -4.1, -1.1 pounds, respectively; and  -19.4, -11.3, -2.9 mg/dl respectively). Interestingly, even when participants did not reach their target A1c, many still achieved weight loss and improved blood glucose control. Intensive coaching also boasted greater engagement, as those in the intensive group had on average 36 more coaching conversations than those in the lightweight group (44 vs. just 8). While the clinical data alone makes Livongo + intensive coaching seem like a no-brainer, it should be noted that the 12-week program costs were a whopping 5.5 times higher for those receiving intensive versus lightweight coaching. This begs for a deeper analysis from a health economics perspective – how much does the intensive coaching save in healthcare costs vs. lightweight coaching? We’d also be interested in sub-analyses sorting out whether some patients are more likely to be responders in one group or another. Plus, we weren’t clear on how intensive coaching alone compares to Livongo alone. To mitigate overall costs, future work should also focus on investigating which aspects of the intensive coaching are integral to the major improvements indicated in this study.

Symposium: Harnessing the Power of Digital Connectivity in Sickness and in Health

New Research – Perspectives from NIH

Judith Fradkin, MD (Director, Division of Diabetes, Endocrinology, and Metabolism, NIH, Bethesda, MD)

Dr. Judith Fradkin offered her take on a range of NIDDK diabetes research priorities – EHR renovation, telemedicine, mHealth, and artificial pancreas – concluding that advances in all of these technologies will converge to improve the lives of people with diabetes. Overall, she sounded very positive and pleased with the progress within these fields ripe for innovation and progress, calling EHR and telemedicine “home runs,” though she was hesitant to call mHealth as anything more than “promising.”

  • Dr. Fradkin reviewed a recent UCLA study (Rushakoff et al., Annals of Internal Medicine, 2017), where researchers sought to leverage the Epic EHR to improve diabetes inpatient glucose management. Currently, in-patients with diabetes are cared for by people who are typically not well versed in diabetes care – if hyperglycemia occurs on the oncology or transplant floors, there are few people around who have specific diabetes expertise. For the purposes of the pilot, researchers finagled the EHR to require electronic order sets for glucose management with insulin – it asked for multiple point-of-care glucose readings (with the frequency determined by insulin and feeding statuses). Using this data, a central hub automatically detected and generated a daily report of patients in the hospital with uncontrolled blood glucose. This allowed for efficient, population-level monitoring and smarter management. In just the second year of the program, researchers found a 39% reduction in blood glucose readings >180 mg/dl, a 36% reduction in blood glucose readings <70 mg/dl, and a decrease in the number of patients included in the report – and the time for a single trained nurse to review the report and make insulin recommendations was just 20-40 minutes per day. Wow – every hospital should institute a system like this. Inpatient glycemic control is, ironically, notoriously poor, and using connected sensors + smart algorithms will obviously help immensely. We see this as an important next frontier for the diabetes technology field. Complaints about the EHR are commonplace at medical conferences, but when manipulated, they do have strong potential.
    • Dr. Fradkin hopes that future EHRs will directly incorporate Ambulatory Glucose Profiles, along with analytic tools for pattern recognition, and data from connected insulin delivery devices to monitor adherence. Stanford University has run pilots integrating Apple HealthKit, Epic EHR, and Dexcom G4 CGM to better incorporate glucose data with the physician’s workflow. The experiment showed feasibility – improved communication and population monitoring  – but some experts, such as UCSF’s Dr. Aaron Neinstein, don’t believe the EHR is the right place to store (diabetes) data. He is more of the mind that the EHR is a “semi-good” tool for meeting legal interest, financial, and billing purposes, but shouldn’t necessarily be mistaken for or merged with a data collection platform. We kind of agree with Dr. Neinstein – let’s use separate diabetes data platforms built for that purpose, rather than jerry-rig EHRs to sort of fit diabetes data needs. However, the reality is that EHRs are now a mainstay in clinical practice, and many systems have to fit other technologies within those constraints.
  • Within telemedicine, Dr. Fradkin focused on the numerous success stories of early detection and treatment of retinopathy. Only 60% of people with diabetes in the US get annual exams, largely due to geographic barriers. Taking pictures and transmitting them for remote analysis has addressed these barriers and improved efficiency in essentially every case of implementation. In the Indian Health Service-Joslin Vision Network Teleophthalmology Program, encompassing 97 healthcare facilities in 25 states, 100% of sites increased diabetic retinopathy identification, and the advent of improved camera technology has reduced the “ungradable” rate from 35% to 3%, meaning 4,000 fewer patients needed subsequent clinic-based eye exams due to poor image quality. Similar programs have been implemented widely in many countries (e.g. Singapore, India, China), but they are limited in the US. Where they have been piloted in the states, they have been very effective: In Los Angeles, the screening rate increased from 41% to 57%, there was an 89% reduced wait time at the clinic, and 69% of those who sent in pictures didn’t require a referral. In rural North Carolina, screening rate rose from 25% to 40%, and 80% of those screened didn’t have diabetic retinopathy. Overall, the data is clear: these programs tremendously reduce burden, both on patients and their healthcare systems, while lowering the barriers to quality healthcare.
  • Dr. Fradkin expressed concern that only a small percentage of the >1,100 health and wellness apps focused on diabetes have been rigorously studied.  “There are more meta-analyses of trials of smartphone apps for diabetes than actual trials,” she pointed out, “and there are very low rates of inclusion of behavioral change techniques. There are also few apps that interface with measurement devices to offer personalized feedback based on patient data.” Her complaint is not uncommon – at last November’s DTM 2016, at least three distinguished speakers called for improved quality control and regulation of mHealth apps, and Dr. Joyce Lee previously called apps a “Wild, Wild West” in JAMA. That was back in 2015, but now, we’re seeing a strong emphasis on outcomes data generation from at least a few apps and their paired connected devices, including mySugr, Accu-Chek Connect, One Drop, Livongo, and the slew of now FDA-cleared insulin dose titration apps. We are serious optimists about this field, mainly because now medical device companies understand the potential and are doing the proper work to design apps with clinical input, get regulatory approval, and generate outcomes data.    
  • Dr. Fradkin touted “steady progress” in the development of artificial pancreas systems, and reminded attendees of the four large-scale, NIDDK-funded trials starting in 2017-2018. Notably, when describing the benefits of these systems, she never said “A1c”– she instead focused on time in range and hypoglycemia in her verbal remarks. (A1c was listed on the slide, however, since it IS an endpoint in two of the trials.) It’s so great to have someone at the top of the field recognize the importance of outcomes beyond A1c! Kudos to Dr. Fradkin and NIH for significantly advancing the automated insulin delivery field over the past decade…it would be nowhere without NIH’s funding and leadership.

Symposium: Digital Data – Clinical Liability and Patient Safety

Guidance From The Food and Drug Administration

Courtney Lias, PhD (FDA, Silver Spring, MD)

In an encore of previous presentations (NIH Workshop, AADE), the FDA’s Dr. Courtney Lias shared her long-term hopes for an ecosystem of compatible and interchangeable diabetes devices (pumps, CGMs, apps) that patients can swap in and out. She reiterated her goal of device communication standardization, where automated insulin delivery components can exchange data seamlessly without separate approvals for every conceivable system combination (i.e. sensor A + algorithm A vs. sensor A + algorithm B). We continue to love this vision and look forward to tangible steps that make it a reality. While much of this vision we have heard from Dr. Lias before, we are glad to see her continuing to share it publicly – it reinforces the agency’s commitment to making it happen. We also noticed that Dr. Lias included in her message today that type 2s can benefit from this ecosystem too, suggesting that FDA is aware of the importance of enabling this population (i.e., not just technology for type 1s). Indeed, there is tons that type 2s – especially those on insulin – would gain from the CGM, pump, and algorithm developments in type 1, and we hope with greater innovation and population expansion costs will come down. We were thrilled to hear Dr. Lias’ broader message of prioritizing patient choice. Below, we include a few of the standout quotes that highlighted her patient- and innovation-friendly  presentation:

  • “What we need to work toward is communication that is SO CLEARLY worked out that FDA won’t even have to worry about [next-gen devices] being interoperable with existing platforms. They just will be. We need more standardization.”
  • “Diabetes devices do not meet the expectations of consumer devices. For example, I just bought a Sleep Number bed, which is yet another example of a connected device. It lets me optimize my sleep and – unsurprisingly – it comes with an app. And guess what? If I wanted to, I could even set up an Apple Watch with a linked app. And for ALL of this, I would NOT have to look at a manual. I’m just able to use it. That’s where we need to get in diabetes.”
  • “You may say that the downside of these connected, consumer devices being used incorrectly is not as high as the risk of an insulin pump being used incorrectly. That’s true. So security is important. But, I do my banking online. So as a consumer, I have the expectation that these things are possible.”
  • “For patients with diabetes, the numbers can make all the difference. But if you can’t get to data in a format you need or when you need it, then it’s no use at all … Ultimately, our current solutions don’t help patients as much as they could.”

What Does the e-Patient Expect?

Amy Tenderich (Diabetes Mine, San Francisco, CA)

Ms. Amy Tenderich presented findings from the Diabetes Mine Patient Voices Research project (n=500) indicating a strong desire amongst the diabetes patient community for interoperability of devices; improved provider education on the realities of living with this illness on a daily basis; heightened patient access to new technology; and development of a provider-patient partnership where providers discuss data meaningfully with patients. Survey participants largely found their touchpoints with the healthcare system to be lacking, highlighting their desire for efficient assistance in translating results into realizable goals aimed at improving their daily lives (this is hopefully a job for scalable, smart technology that augments already-overburdened HCPs). Respondents emphasized the need to feel greater respect from their providers, suggesting there is still much to be done to progress the patient-clinician relationship – we hope actionable data can be an independent part of this process. Participants also asked for more provider incorporation of positive reinforcement during visits to further boost motivation. (As Adam would say, “What are my Bright Spots?”) We expect the trend of combining high touch with high tech will continue to be a patient priority in the years to come. In fact, Ms. Tenderich considers in-depth use of data to be the next standard of care for diabetes, with patients often choosing providers based on their technological fluency.

  • In the award portion of the Diabetes Mine survey, Dexcom’s CGM won “people’s choice” and Abbott’s FreeStyle Libre was voted “best newcomer.” Participants were thrilled with Dexcom’s CGM (which recently incorporated AGP to Dexcom Clarity and launched on Android), citing it as a game-changer for their diabetes management and quality of life. One respondent wrote that “all future innovation…rests on this foundation.” Despite not yet being available in the US (submitted for FDA review in 3Q16), the release of Abbott’s FreeStyle Libre has been met with considerable excitement (300,000+ users worldwide at Q1’s end), especially amongst the diabetes online community.
  • Participants gave diabetes apps an overall thumbs down, calling for explicit benefits and utility. When rating apps, the ability to motivate the user was key, as was ease of use, with manual data entry deemed an immediate (and obvious) turnoff.
    • Respondents reported nutrition and fitness apps to be useful in meeting short-term goals and found motivation in visualizing real-time data; however, the nutrition apps were difficult to use when eating at home and most participants claimed the apps were poor motivators for achieving goals over extended periods of time. We agree logging food has to move beyond inputting meals and guesstimating portion size. Pictures are the frontier, in our view!
    • Logging apps and vendor software were met with enthusiasm for their ability to make sense of real data, but participants expressed the need for greater provider/technology assistance with engagement and applying the data to their lives. As we elsewhere in this report, it’s not about data for its own sake, but using the data to identify patterns and drive therapeutic/behavior change.
    • Respondents also expressed excitement regarding insulin dosing apps for non-pumpers and look forward to the development of more sophisticated versions. There are many to come – see our competitive landscape.
    • Lastly, participants found official recognition of apps by organizations such as the ADA to be helpful when choosing between similar tools. Most recently, One Drop received ADA recognition for diabetes education, while Livongo was accredited by AADE in 2015, and WellDoc partnered with AADE in January. We hope professional associations can keep up with this landscape and validate apps that add value.
  • Survey participants said that providers should equip patients with the tools to troubleshoot their disease, but also establish a partnership in which barriers are reduced and coaching is extended beyond the clinic. There is increasing evidence that health care providers who engage with data see better outcomes. However, there is still much to be done in the way of infrastructure, as an estimated 50% of endocrinology clinics today reportedly do not have device data downloading software (we didn’t catch the citation). It is vital for patients to have resources to interact with the healthcare system in between visits – Ms. Tenderich said data management platforms like Tidepool and Glooko, along with affordable remote coaching options, may be useful in achieving this goal moving forward. One of the most common patient complaints raised in the survey was that providers were not doing enough to help patients optimize a particular tool. Participants expressed a strong desire to feel empowered and respected by their physicians and to have the opportunity to discuss data meaningfully with health care providers. 

Tsunami of Digital Data – Impact on Clinical Practices

Geralyn Spollett, NP (Yale University, New Haven, CT)

Dr. Geralyn Spollet’s overview of digital health was positive about the future potential of data in healthcare but also stressed a number challenges she has experienced in the field, most notably problems with data overload, a lack of standardization, and privacy. We have heard this sentiment over the past few years, though we thought she summarized the view of many clinicians quite nicely in her opening words, “So much information, so little time.” She acknowledged that the right data in the right hands at the right moment is supremely valuable, but suggested that the field has gotten a bit ahead of itself. In particular, she cited her own experience at Yale, where the hospital’s technology partner was actually selling patient’s de-identified data to a third party to make a profit. She noted this would never have been a problem in older low-tech systems, and though she did not advocate going back to those methods, she stressed that our new relationship with data and “the cloud” means rethinking security and data oversight from the ground up. Ultimately, we felt she provided an important cautionary message as we move forward in digital health

Symposium: Using Technology to Improve Performance Outcomes and Care (Supported by a Grant from The Leona M. and Harry B. Helmsley Charitable Trust)

MITI: The Mobile Insulin Titration Intervention

Natalie Levy, MD (NYU School of Medicine, New York, NY)

Dr. Natalie Levy offered a glimpse at the cleverly-named MITI text message program that helps type 2 patients titrate their basal insulin remotely. The workflow is as follows: Every weekday morning, an automated text message asks patients to respond with their morning fasting blood glucose levels. MITI nurses check the texts on Monday, Tuesday, Wednesday, and Friday to ensure there are no alarm values, but on “Titration Thursday,” a nurse calls the patient and recommends a new dose based on a validated algorithm. Patients are enrolled in the program for 12 weeks unless (i) they achieve a fasting blood glucose less than 130 mg/dl; (ii) a maximum basal insulin dose of 50 units is reached; or (iii) the patient is lost to follow-up. The program has been a success thus far at Bellevue and Gouverneur Hospitals: MITI enrolled 128 patients in the first 13 months (average age 50), and only eight were lost to follow-up. Moreover, 85% of patients achieved optimal insulin doses in an average of 23 days – just 7.5% actually end up needing to complete all 12 weeks. In the 13 months of use, there have only been two mild hypoglycemia events, and no severe hypoglycemia. For the 69 patients who have had labs thus far, average A1c dropped 1.5% from a high baseline of 11.4% - obviously still a ways to go, but that is great progress in a tough population. In terms of engagement, over 2,100 text messages have been received thus far, a startling 90% of which have received a response. Just as notably, MITI allowed for ~500 clinic visits to be averted over the course of the year, and only requires 15 minutes of nurse time per patient-week, allowing the clinic to better allocate resources. Patient and staff feedback on the program was overwhelmingly positive, so Dr. Levy and team have created a dissemination guide and are working on expanding the phone-based intervention to other aspects of diabetes and chronic diseases (oral medication titration, hypertension, etc.).This trial really shows how much of a difference automatic insulin titration is going to make once a variety of apps/devices are available and in patients’ hands. (Of course, several are cleared, including Voluntis’ Insulia, Amalgam’s iSage Rx, and Sanofi’s My Dose Coach).

  • Dr. Levy was pleased with MITI, though she is under no illusion that it is the perfect program: “MITI can take patients from an F to a B, but it may not be able to get them to an A.” Getting patients with poor baseline control to a B and avoiding 500 clinic visits is an incredible feat, but we wonder if new insulin titration applications may lead to similar, if not better outcomes, and with even less provider time required. Apps will lack the human element ad accountability, which is greatly appreciated by enrolled patients, based on this presentation. We’d love to see a head-to-head comparing MITI to one of the already FDA-cleared apps – is the human element necessary? Would a titration app further cut down on clinic visits and nurse time? Which populations do better with which system? Of course, a skeptic might point out that outcomes could be worse with a titration app alone, since it removes the accountability and personalization to a nurse. Hmmm….
  • Patient feedback for MITI was overwhelmingly positive. One said “I think I’m going to do a lot better now because…not only am I going to be reminded to check my sugar, but at the same time, it’s going to remind myself to take my medication.” Others claimed that it reminded them to check their blood glucose, and in doing so, to eat healthier. Others were delighted that they didn’t have to come into the clinic. At the end of the day, MITI felt like a source of support for patients: “When you got somebody helping you, it’s easier.”

Questions and Answers

Q: Any impression what happens after the program ends? Are you considering comparing phone call to a text?

A: I don’t have a sense for the first question, but I hope so soon. I’m very into patient education, it’s such a wasted opportunity – when my patients ask me to look at their sugars, I don’t want them to be the messenger, I want them to internalize. In terms of texting instructions, we’ve started to think about that. I think they really like the phone call once a week, and it’s nice to have the nurse make that phone call, but that’s a consideration to give instructions through text.

Q: Folks that chose to participate – who didn’t choose and why? Any instances where nurses didn’t follow the algorithm? What’s your ability to make this an automatic, computerized intervention?

A: The majority of patients who were offered the choice chose texting, and those who do not are not tech savvy. Some don’t have cell phones. Sadly, a lot don’t have social support – they derive something warm and wonderful from support. They are happy to come to clinic because someone who cares about them is there. Very few times, nurses don’t follow an algorithm – they’ll tell us that it sounds like a patient didn’t take insulin or ate cake every day, so they felt that they should hold off another week. We provide support, but they go rogue sometimes because they should. 

Evaluating and Using Apps to Improve Health Care Provider Performance

Brandon Arbiter (Tidepool, San Francisco, CA)

Tidepool’s smart Mr. Brandon Arbiter presented a very encouraging case study suggesting that patients are more willing to upload data to the Tidepool data management platform than might be expected. At the pediatric UCSF Madison Clinic, 400 of 800 patients have Tidepool accounts. In this analysis, every single one of the 400 users had upload histories – 347 of the accounts had data uploaded by a clinician at some point, while 198 had data uploaded by patients themselves. Mr. Arbiter was very surprised that nearly half of the patients with accounts were uploading from home, and 53 of the accounts had patient-generated data input (e.g., notes). Even more telling, on a week-to-week basis, 50%-75% of reports uploaded were done so at home in 2017. Mr. Arbiter was forthcoming with limitations – the Madison Clinic is in a big and tech-savvy city, the patients in question are pediatric and therefore likely have highly engaged parents, etc. – but he ultimately concluded, “it seems like we’ve moved the needle in terms of making data upload easier.” Home uploading seems like a small thing, but doing so can save precious minutes of limited patient-provider time, as well as keep patients engaged with data in between visits. The fact that there are now several “data management platforms” shows just how far we’ve come in the past six years. When Mr. Arbiter was diagnosed with type 1 diabetes in 2011, he was asked to take manual notes and fax them in to the doctor’s office every day – talk about prehistoric!

  • Looking ahead, we see diabetes technology quickly moving past the patient/clinician “ upload” model – more and more connected devices are automatically sending data to cloud-based platforms (e.g., Dexcom’s G5, Abbott’s LibreLink, Medtronic’s Guardian and MiniMed  Connect, Roche’s Accu-Check Guide/Connect, Ascensia’s Contour Next One, Livongo, One Drop, etc.). Once uploaded automatically, patients and clinicians will be able to access data on-demand and use it for decision making (no manual sync or upload needed). Yep, it’s about time that diabetes gets some of the zero-hassle, “it just works like magic” that we’re all used to with consumer tech.
  • We look forward to seeing the rollout of Tidepool’s expanded mobile app, which will go beyond the current note-taking app, Blip Notes.

 

Technological Approaches to Achieve Pay-for-Performance Standards

Athena Philis-Tsimikas, MD (Scripps Health, La Jolla, CA)

Dr. Athena Philis-Tsimikas opened her discussion of digital health in diabetes with a simple but candid assessment of the current landscape: “Lots of hype. But where are the outcomes?” She acknowledged that the commotion around digital solutions – and their ability to enable a safer and more cost effective system – has been warranted, but also noted that as talk has grown louder in recent years, we’ve also lost track of the end goal of these systems: namely, as facilitators of better care delivery as opposed to solutions in and of themselves. She broke down her analysis in terms of hospital and ambulatory care, noting that digital solutions can improve outcomes in both environments if applied appropriately. On the hospital side, she referenced a study from her team at Scripps being presented at ADA (68-OR) showing in a pilot RCT that remote glucose monitoring in high-risk hospitalized patients is not only feasible and safe, but is also associated with reduced hypo- and hyperglycemia. No surprise there – we see enormous potential for CGM moving into the hospital over the next few years, now that sensors are more accurate and reliable! Meanwhile, on the ambulatory side, Dr. Philis-Tsimikas pulled from a recently published study (Fortmann et al. Diabetes Care 2017) – also from her team – to demonstrate how mHealth interventions (e.g., educational text messaging) can improve diabetes control, adherence, and communication, especially in low income, low health literacy groups. She noted that a lot of the work on this front has used "one-size-fits-all" messaging (e.g., the same message sent to various patients), which has been seen as a limitation despite positive results. With that in mind, she acknowledged that adaptive text messaging and notifications are the next frontier (some are already here, like  Livongo) and expressed hope that positive results will further convince clinicians that digital health is scalable and sustainable. Scripps will soon start the Dulce Digital-Me trial, an three-arm study for underserved hispanics with diabetes. The 414-patient trial is expected to start this month and wrap up in four years! The trial design is quite fascinating:

Symposium: Venturing Beyond the Bricks, Mortar, and Books

A Social Media Learning Collaborative Approach to Competency-Based Training in Diabetes

Donald Simonson, MD (Brigham & Women’s Hospital, Boston, MA)

Dr. Donald Simonson introduced an in-development online tool that aims to customize therapy based on patient data to maximize the probability of reducing A1c. The software pulls from 19 randomized clinical trials that total 7,000 patients and electronic health records from 233,000 patients in the Partners Health system to estimate which treatment options are most likely to lead to A1c drops for a given patient. This particular tool is still in testing, but Dr. Simonson hoped that, someday, providers could access the website (he didn’t comment on cost/business model), input patient data, and observe the probability of various treatments achieving set A1c goals for a specific patient. We would be remiss if we didn’t suggest that a next-gen version should include the ability to customize desirable outcomes to include time in range, hypoglycemia, or even patient-reported outcomes. But decision support is still in its infancy, and we can’t wait until this launches! This reminds us of what IBM Watson has done in oncology, and hopefully, what it will do in diabetes with the Medtronic and Novo Nordisk partnerships.

  • The need for such a precision medicine algorithm is evident: (i) clinical trials are excellent at dictating how to treat the “average patient,” which is never who providers care for in practice; (ii) while the mean success rates for achieving A1c goals are 27% for patients on diet & exercise and 67% for patients on sulfonylureas, there is a variable response curve and some patients will succeed and fail with either intervention; and (iii) the breadth of tools available to clinicians is too great. There have been over 40 diabetes drugs approved in the past decade, and combination therapy further complicates the scenario by adding a significant multiplier of permutations. Clinical decision support is a very real need!

Symposium: Big Data, Small Data, and Type 1 Diabetes – Practice Redesign at the Intersection of Families, Providers, Value-Based Reimbursement, and Technology

Findings from the Field — Results of a Randomized Pilot Study of Intensive Remote Monitoring among Children with Type 1 Diabetes

Aylin Altan, PhD (OptumLabs, Cambridge, MA); Deneen Vojta, MD (UnitedHealth Group, Minneapolis, MN)

Dr. Aylin Altan presented a pilot study of intensive remote monitoring (using an activity tracker and text message reminders to upload data/come in for doctor’s appointments) for children and adolescents with type 1 diabetes (n=117). After six months, patients randomized to intensive remote monitoring (n=60) experienced a 0.34% A1c decline vs. 0.05% for patients randomized to conventional care (n=57) – all participants started with a high baseline A1c between 8%-10.5%. Dr. Altan described how this effect was mediated by engagement, in that participants with ≥24 downloads experienced 0.7% mean A1c-lowering vs. 0.08% mean A1c-lowering for participants with ≤20 downloads over the course of the study. (Yep, if you don’t use the technology, you don’t benefit from it.) She also presented a subgroup analysis of children age 8-12 vs. adolescents age 13-17, showing how intensive remote monitoring is most beneficial for the older pediatric cohort, who experienced a mean 0.5% A1c drop vs. 0.15% among younger children. This subgroup data may suggest that engaged adolescents are an ideal target for this kind of remote monitoring intervention, while pediatrics and those who won’t engage may need a different approach. There was no significant difference in hypoglycemia between the groups, although there were two events in the intensive arm and none in the conventional care arm. The positive effects of intensive remote monitoring on A1c dissipated somewhat after cessation of the intervention, as mean A1c increased 0.1% after three months of follow-up. Dr. Altan acknowledged that this was a small pilot study and that a larger/longer trial would provide more conclusive evidence on the benefits to intensive remote monitoring. We wonder if there could be randomization within the intervention group in subsequent trial to compare different types or frequencies of alerts. We believe the killer app for this population will be automated insulin delivery/MDI dose titration, paired with connected CGM and automated remote monitoring. Such a system would flag providers of patients falling out of range, but otherwise operate autonomously using software to inform dosing.

  • Patient-reported quality of life improved markedly more in the intensive group vs. the conventional care group: On average, PedsQL score grew from 74.6 at baseline to 80.82 after six months of intensive remote monitoring (6.22 improvement), and grew from 77.24 at baseline to just 78.55 after six months of conventional care (1.31 improvement).
  • UnitedHealth Group Vice President of R&D Dr. Deneen Vojta, who chaired this session, spoke to some of new questions that arise with an effective remote monitoring solution: How many provider visits are optimal? What will the workflow look like for this system in a clinical setting? How will this be reimbursed (both for the patient, and for the provider)? She underscored that even though this was a randomized controlled trial (RCT), it was still proof-of-concept, designed to show that a remote monitoring intervention could work well in supporting children and adolescents with type 1 diabetes. There's substantial evidence demonstrating the value of activity trackers and reminders for medications/doctor's visits in chronic disease management, but how to disseminate these features to a widespread patient population is a distinct challenge, one that's beginning to be addressed with pilot studies like this. In speaking to why intensive remote monitoring may have shown a more profound positive effect in older participants, Dr. Vojta described how adolescents are just starting to gain independence, which opens up the perfect opportunity for a support tool like the one investigated. She also shared anecdotally how this intervention improved quality of life for the parents of children/adolescents with type 1 diabetes, who faced a lower decision-making burden in the remote monitoring group vs. the conventional care group.

Symposium: ADA Diabetes Care Symposium – Diabetes Care and Research Through the Age

What Does the Future Hold?

Ele Ferrannini, MD (University of Pisa, Italy)

The great Dr. Ele Ferrannini offered his perspective on the value and relative speed of development in current major areas of research focus within diabetes. Overall, he foresees the greatest value in organ-level research into the role of the heart, kidney, and brain in diabetes pathophysiology, ranking this area a 9/10 in terms of value. Tissue-level research (into beta-cell plasticity, adipose tissue plasticity, and gut factors including the microbiome) also ranked highly in terms for value for Dr. Ferrannini – a 7/10. Pharmacology (new drugs and new treatment strategies) and genome level research (genetics, epigenetics, and “omics”) received a modest but respectable 6/10 value ranking. On the other hand, Dr. Ferrannini was much less optimistic about the value of environment-level research (diet/exercise, toxic environmental factors, and infections; an overall 3 ranking) and was particularly pessimistic about information technology research (including sensors, electronic health records, and big data). Information technology received a 2/10 value assessment from Dr. Ferrannini, though he noted that this is the fastest and most active area of development within his list – in terms of speed, information technology scored a 9/10 on his scale. Indeed, the speed of development in this area underscores that massive interest and value many in diabetes and the broader healthcare field see in digital health – which made Dr. Ferrannini’s pessimism all the more surprising. As he put it, the concept of precision medicine – powered by massive amounts of population-level health data – has gone “viral.” In his view, this focus on gathering large amounts of health data and mining it for insights has flipped the traditional model of medical research, which traditionally has focused on the generation of hypotheses and use of clinical data to evaluate the validity of a priori hypotheses. As a clinical trial purist, we’re not overtly surprised by Dr. Ferrannini’s stance, though we do think there’s value in generating insights from population-level data that would have been near-impossible (and extremely costly) to arrive at through randomized, controlled trials. We certainly think there’s room for both kinds of studies in science and medicine!

  • Taking the impact of sensors and big data one step further, Dr. Ferrannini cautioned that the drive toward digital could fundamentally change the doctor-patient relationship in a negative way. Dr. Ferrannini characterized the current model of healthcare as one in which (i) the patient feels unwell; (ii) the patient visits the doctor; (iii) the doctor assesses the patient’s symptoms, history, etc.; and (iv) the doctor provides advice based on his or her knowledge and expertise. In the near future, however, Dr. Ferrannini forecasted the patient visit could be replaced by health data generated through sensors (CGM, blood pressure monitors, etc.) and the doctor could be replaced by a clinical decision-making app. Further in the future, he suggested that patients could be replaced by anonymous ID numbers, in order to better collect their information for “big data” repositories. Ultimately, Dr. Ferrannini suggested that the healthcare system will fundamentally change to “feed this black hole of big data.” Further, he pointed out that a key consideration will be who “owns” each big data repository and how they might use and manipulate the data. While Dr. Ferrannini’s concerns are well-taken (and the high value he places on the doctor-patient relationship underscores what an incredible clinician he is), we feel he may have a bit of an extreme, doomsday-scenario view of digital health. In certain cases, telemedicine, clinical decision support, and other tools can help support increasingly busy physicians, expanding their capacity to serve more patients and focus their energies on the most complex cases. Further, the use of sensors and centralized collection and interpretation of individual health data plays an important role in empowering and engaging patients in their diabetes or broader health care. While some thoughtfulness is clearly warranted on how best to use the wealth of data that we now have the ability to collect (so as not to fall prey to information overload), we continue to believe that digital health represents an immense opportunity for the diabetes and healthcare fields.
  • Dr. Ferrannini also highlighted pharmacology as a relatively fast area of research, scoring a 7/10 on his scale. Organ- and tissue-level research scored a modest 5/10 for speed, while genome-level research scored a 4 and environment research scored a 3.

Symposium: mHealth – Friend or Bystander in Diabetes Management

Mobile Technologies for Data Collection

Mark Clements, MD, PhD (Children’s Mercy Hospital, Kansas City, MO)

In a balanced analysis, Dr. Mark Clements ultimately argued in favor of mobile health interventions, even though solid RCT evidence supporting their efficacy is lacking. Dr. Clements sees a number of reasons to adopt mHealth anyway: (i) substantial and growing smartphone adoption; (ii) a shortage of skilled clinicians in the diabetes field; and (iii) the potential of mHealth to cut costs related to transportation, inefficiency, diagnosis time, and hospital visits. (To that, we’d add that the RCT-> peer-reviewed publication model cannot possibly keep up with digital health.) Regarding efficacy amongst those with type 2 diabetes, Dr. Clements cited one systematic review and meta-analysis, which found an A1c reduction of 0.4% associated with diabetes app use. Evidence for smartphone app efficacy in the type 1 population is more limited, with Dr. Clements noting only two studies, neither of which detected any significant differences in clinical outcomes between those using apps and those not. That said, companies like Livongo, mySugr, Glooko, WellDoc, One Drop, and many others are investing in building an evidence base, particularly through real-world data. Earlier this year at the Clinical Diabetes Technology Meeting in Houston, Dr. Clements noted that software RCTs are difficult to conduct because apps have different modules and can therefore be used in completely different manners from patient to patient. He suggested introducing smart, adaptive designs – i.e., segmenting patients depending on their usage of different features within the app. We think the progress in apps is far outpacing the ability of the literature to keep up – companies seem to be focusing more on pilots and demonstrating data with payers/employers, rather than publishing a lot of results in peer-reviewed journals. (In other words, the absence of peer-reviewed publications does not mean apps don’t work.) Moreover, apps are only now starting to add insulin-dosing decision support, which we see as a real driver of efficacy. It’s no surprise to us that manual logging doesn’t improve clinical outcomes.

  • Dr. Clements discussed the ongoing Dav2id project, which aims to develop a software architecture permitting storage of aggregated device data in a diabetes center. A rules engine capable of sending alerts to the user is incorporated and designed to respond to various trends and patterns in collaboration with the patient and provider. We wonder how this will interface with other growing data management platforms, such as Glooko, Tidepool, Dexcom Clarity, Medtronic CareLink, etc. There are a lot of platforms out there and we hope some convergence happens over time.
  • Looking toward the future, Dr. Clements anticipates that fitness trackers, in particular those that facilitate sleep tracking, will become increasingly integrated into diabetes management. We agree. In addition, he believes new wearables will gain traction, including smart watches, glasses, and garments (socks and underwear!) capable of passively monitoring events. GPS will also have a firm hold in the field, he continued, given its ability to provide location context. We see huge potential for consumer smartphones and smartwatches to drive this area, since they can track all of these metrics in one already-used device. On the other hand, dedicated tracking devices – at least Fitbit – seem to be reaching a point of commercial saturation/slower growth.
  • Dr. Clements called for change in the mHealth field, advocating for more patient reported outcomes, better models to capture mental health changes, and introduction of ecological momentary assessments (micro-surveys). He believes these measures will be critical in fleshing out a more complete picture of the individual experience moving forward. Hear, hear! These are crucial diabetes outcomes beyond A1c that do hold value, but also need to be standardized and collected in a systematic way.
  • On a tangentially-related note, Dr. Clements is heavily involved in a new project to integrate predictive analytics (via machine learning) to proactively identify and manage at-risk type 1 patients at Children’s Mercy. The very cool collaboration, with the Helmsley Charitable Trust (funder), Cyft Inc. (technology-provider), and the Joslin Diabetes Clinic (other participating clinic), will begin in mid-2017 and last three years. It’s great to see this work on risk stratification + remote monitoring, given the potential to prioritize HCP time and focus on those most at risk of severe events.

NDEP Symposium – Shared Decision-Making – Strategies for Improvements in Patient-Provider Communication

Shared Decision Making in Pediatrics: Development and Evaluation of Web-Based Decision Aids for Candidates for Insulin Pump or CGM

Tim Wysocki, PhD (Center for Health Care Delivery Science, Jacksonville, FL)

In one of the few randomized controlled trials assessing shared decision-making efficacy, Dr. Wysocki and his team assessed the benefits of their recently-developed multi-media, internet-based shared decision-making platform for adolescents making choices regarding pumps and CGM. While shared decision-making models are quickly gaining ground, there is still much to be done to determine who takes on how much responsibility, when, and how. The study is still in its early stages, but preliminary data is promising: Out of a cohort of 153 adolescents with type 1 diabetes and their parents, 44 adolescents (67%) and 53 parents (80%) logged into the platform at least once. The initial data suggest that supervised use of the site in the clinic may drive more effective engagement than providing instruction for initiation at a later date – of course, the whole point of the website is to educate adolescents while reducing provider burden, so we wonder if this trend translates to unsupervised, at-home use of the site. The platforms (one for pumps and one for CGM) are free and available for public use on the National Diabetes Education Program website – their stated goal is to help teens think about how a device might fit into their lives. This is absolutely crucial when it comes to expectation management – patients should ideally know about the demands and common pitfalls of wearing a CGM or pump (or closed loop system, for that matter), before initiating.

  • Dr. Wysocki described adolescents with diabetes as a marginalized population requiring focused efforts to improve their decision-making capacity. Given a number of studies showing that adolescents benefit less from the use of advanced technology, better shared decision making might go a long way in reducing these issues. To this end, Dr. Wysocki engaged healthcare providers, patients, parents, and media experts alike to develop a platform aimed at facilitating informed decision making, crediting the program’s success to this diverse array of individuals. The program guides users through a series of quizzes, activities, information, and videos about diabetes technology. Pro and con lists for different devices are supplied, as well as personal stories, demonstrations, and tips for talking with family members and providers. This seems like a great resource – adding technology on top of diabetes can be intimidating, and understanding the benefits relative to the costs is not always clear. 
  • Dr. Wysocki emphasized the need to be cognizant of parental coercion and adolescent submission, highlighting the role of healthcare providers in promoting open and frank discussions, anticipating barriers, and building adolescent-parent consensus. In one poignant anecdote, Dr. Wysocki noted an incident in which a patient changed his decision regarding the use of a device, only to report that he had done so because he had been promised the latest Game Boy. Adolescents may be uncomfortable with actively participating in healthcare decisions and may feel reluctant to confront or challenge their parents during disagreements. To combat these tendencies, Dr. Wysocki calls for systematic intervention – we couldn’t agree more and believe this new focus on shared decision-making is an excellent first step.

Mini-Symposium: What’s App? Opportunities and Challenges in Diabetes Mobile Technologies

Translating Behavioral Science for mHealth Systems

Shelagh Mulvaney, PhD (Vanderbilt University, Nashville, TN) and Sara Krugman, (Tidepool, San Francisco, CA)

Dr. Shelagh Mulvaney described the three key advantages of mHealth as: enhancing data access, improving proximity, and facilitating insight in context. She anticipates that these benefits will allow for better precision and pattern recognition, thereby strengthening our ability to assess behavioral variability and to develop ubiquitous means of identifying risk factors. Dr. Mulvaney and Ms. Krugman cautioned that mobile platforms have a micro-impact, changing “how” but not “what” we do. Given the plethora of variables that accompany diabetes, the tension lies in choosing which ones to tackle in a single app. Therefore, it will be necessary to clarify and narrow the rationale behind each tool, developing an implementation plan with a user in mind. Thus, the mantra that culminated the discourse: less is more, simple is hard, shorter takes time. The speakers raised several guiding questions to facilitate the pursuit of these aims, including: (i) What is the cognitive burden of the tool? (ii) What are the emotional associations? (iii) What are the learning paths? and (iv) What are the environments in which this technology will be used? These questions will be critical in establishing positive patient relationships with mHealth services, boosting adherence in a potentially rich field that has yet to live up to the promise. We urge developers in the field to ask these tough questions and focus on demonstrated, real needs in the diabetes community.

  • Both Dr. Mulvaney and Ms. Sara Krugman emphasized the need to focus on patient engagement with mobile technologies, highlighting the importance of features that make technology accessible and fun to use. To this end, Dr. Mulvaney has designed two personalized mobile tools, YourWay and SuperEgo, aimed at improving self-management and mitigating psychosocial challenges for adolescents with diabetes.
  • A big challenge, we’d point out, is that digital technology should ultimately drive reduced burden or outcomes that far outweigh the additional burden they impose. Most apps will require some level of patient/HCP interaction, but the key will be getting the balance right – what outcomes are possible with the minimum amount of usage?

Questions and Answers

Sara Krugman (Tidepool, San Francisco, CA) and Shelagh Mulvaney, PhD (Vanderbilt University, Nashville, TN)

Q: We have an app for exercise guidance based on real-time glucose levels. We just released it. What is your experience in patient acquisition of new apps that will help with lifestyle change? Our app helps people with behavioral change. What is your experience with working with positive behavior change around diabetes self-management?

Dr. Mulvaney: I’ll ask another question. Trying to ask a question that broad can be frustrating. Reverse engineer that question and think backwards from your endpoint to where you start with an interaction with someone. Think about all the possible steps and the types of change you want to see in each step. This would be an important process to map out. So it kind of depends.

Ms. Krugman: It’s also important to assess what motivates that person. Focusing in on that helps. Focusing in on the use case of one person would be fruitful, rather than looking at a broad group of people. We’re now in an industry and we’re figuring out that there are so many use cases – it’s hard to focus on one. And focusing on one is such a small percentage. How do we find a solution that works at that scale for everyone? I don’t think we have the tools for this yet. The language we’re using back to that person should address them. So being smart about very basic things like language and tonality makes a huge impact. But it’s about defining who the user group is.

Q: When you’re trying to study a mobile app, it’s not like studying a medication. Every app has multiple components. Apps can also link to offline resources. So my question is to what degree do you think we should go into smart design and assess the dose effects of these components – so we can better understand what portions of this are working and not working?

Ms. Krugman: Smart design is something that will be increasingly used in identifying uses – what works for whom, etc. It’s really designed for resource allocation purposes. We want to start with the configuration of features and look at what resources are most feasible for your system and the individuals. The design is in two to three stages of randomization. Each stage bifurcates into those who do and do not respond to treatment. It’s a very useful way that probably will be utilized a lot more. But, the problem is that the features are difficult to disentangle. Most apps and behavioral interventions have many different components. What’s the smallest number of components that can work together? We could do an RCT with just that portion – advancing it into that data is a problem solving process. What gets tricky is the networking component, which maps onto everything. When you take that out, you’re messing with the glue. So that’s the base challenge to me. But I think smart designs are important.

Q: Also what are your thoughts on the importance of attention controls?

Ms. Krugman: Attention controls have a great place in the research design world but they’re difficult to do well. It’s easy to overstep what attention means. You want an active control, without overpowering it or making it into a comparative effectiveness trial. Attention control for mobile apps is difficult.

Q: Could you expand on the experiences of patients and families using Tidepool?

Ms. Krugman: One thing is that families and doctors engaging together has been a really useful and motivated group of people. There has been high engagement on that end. Otherwise, it’s pretty hard to get individuals who are not highly motivated to engage in data usage.

I think there’s a lot to talk regarding language and approach. One day I was sitting in clinic, watching people sign up. And we have a question that asks, “Is this for you or for someone you care for?” A teenager stopped at that question – and it was really hard for him to answer this, when understanding and thinking about identity. It can be a really loaded question. So there are nuances like that.

I see the data we’re showing as more of a communication and education tool. Another example is looking at the trend of data between 12 pm and 1:30 pm. With our tools, the question between parents and teenagers is more about the dots in the data, rather than being high during that time period. That social experience can have a rippling contextual impact, so that’s a real problem that can be solved. That family can have a real conversation rather than something antagonistic.

Q: What kind of data do you have on why people lost engagement? Why do people drop off from using? Anything from family members on this topic?

Ms. Krugman: It’s mostly around “I don’t have time” or “I got an error and moved onto the next thing.” I don’t have data on what makes people leave when. But it’s more of a web of experiences around having to sit down and upload data. It’s an intense use of time. People are motivated to do this with their doctor and people doing that outside of the caregiving experience are highly motivated. We’re always invested in improving things, even if it’s not as fast as we want it to be.

Q: In the Precision Medicine Initiative, mHealth was said 2,700 times. How are we going to hold the government accountable to reach communities with this technology?

Dr. Mulvaney: The Precision Medicine Initiative uses mobile technology, but not really for behavioral change. It’s really being used to document behaviors and to look at people in their cohorts. The government can’t be the main answer. There is a lack of push from patients themselves. I think it’s important to get tech into the hands of patients themselves. It’s important to get something back. That’s a big part of why things don’t get used. I do feel that there has to be some sort of push from the bottom up – some driving factor from the market.

Diabetes Mine D-Data Exchange

Highlights: Insulet Shows Cool Horizon Screens; FDA’s Strong Call for Interoperability Between Companies; Dexcom API This Year

At DiabetesMine’s D-Data Exchange, we enjoyed seeing the DIY community and industry in what we felt was the most productive discussion yet. Highlights included Insulet’s Dr. Trang Ly on the OmniPod Horizon system (first screenshots from near-final design – major investment here), FDA’s Dr. Stayce Beck on why device interoperability really matters for automated insulin delivery progress (FDA may even have a workshop), Dr. Nate Heintzman gave an update on Dexcom’s APIs for retrospective data at developer.dexcom.com (launching later this year), and an artificial pancreas progress panel included views from Medtronic’s Dr. Fran Kaufman, Bigfoot’s Bryan Mazlish, Insulet’s Dr. Ly, Tandem’s John Sheridan, and our own Adam Brown. MIT innovation professor Dr. Eric Von Hippel gave an outstanding keynote, emphasizing that the lead-user (DIY) community is a critical driver of innovation in all fields – diabetes is no exception. The HOW of this for industry is still an interesting question – how should/can/will industry create open sandboxes to enable innovation for the DIY community and build it into commercial products? We felt the conversation dynamic shifted this year and was more patient-friendly, “how-to,” and pro-DIY than ever before.

  • Insulet’s Dr. Trang Ly (VP & Medical Director) gave a first glimpse of the Horizon hybrid closed loop user interface on the Dash PDM – wow is Insulet investing heavily in user experience! The screenshots below are “close to the finish line” of the design process, with a prominent display of the CGM value and trend, IOB, and bold use of colors. Other information has been kept to a minimum, a good move in our view. In addition to the clinical trials testing the algorithm, Insulet has already performed six usability studies (31 unique participants to date, including some 670G users) to get feedback on the Horizon’s user interface and data display. Dr. Ly also highlighted Insulet’s cool use of Lightning Labs – condensed user experience design processes that occur in a short period of time with cross functional teams. The group designs and iterates quickly based on user feedback. Insulet even invited six members of the OpenAPS community and spent hours hearing about their experiences – yes! She noted that user interface’s use of colors is “controversial” (some like it, some don’t), and the company is also debating how to display the system’s insulin dosing graphically (microboluses vs. basal rate can be confusing). Dr. Ly shared sincere commitment to getting lots of patients’ feedback (including from MDIs), and Insulet is clearly investing deeply in this area. She reiterated the expectation for a pivotal trial of the Horizon product next year. As previously described, the algorithm will be embedded on the pod and talk directly to the Dexcom G6 CGM, meaning users will remain in closed loop even when the PDM is out of range.
    • To date, Insulet has completed studies of the algorithm in 82 patients, collecting ~3,500 hours of closed-loop data: “The algorithm is doing what it should be doing – reducing mean glucose and increasing time-in-range without causing more hypoglycemia. We’re well on our way to creating a commercial artificial pancreas product.”

  • FDA’s Dr. Stayce Beck hoped for an interoperable future where AID systems have multiple interchangeable components made by different manufacturers (“plug-and-play”). The Agency “hopes” to have a workshop on this topic and seems highly committed to continuing this discussion. Nice! Noted Dr. Beck, “The anticipated pace of AID innovation challenges the current regulatory framework for medical devices. Every time one of the components is modified, a company has to come in to FDA with a new submission. Users can’t mix and match systems that meet their needs. We’re at a point where technical solutions can be implemented.” We’ve heard this in theory before from FDA’s Dr. Courtney Lias, but it sounded like things have progressed internally at FDA, and Dr. Beck said the solution is “under construction” –  the entire community now must come together to help the FDA figure out how to do this. For instance, the field must nail down the interoperability, connectivity, and data approaches (the HOW), enabling devices from different companies to seamlessly and safely talk to one another. We see enormous innovation potential and patient choice enabled by plug-and-play systems – using one company’s pump, another company’s sensor, a third company’s algorithm, and being able to swap components in and out as desired. We hope a workshop does indeed happen and it drives progress and standardization in the field. Dr. Beck also commented on dosing insulin from a smartphone, sharing that the agency is open to it and very platform/device agnostic – companies must simply demonstrate the device works as intended and is robust to different failure scenarios (e.g., the mobile medical app will be prioritized when battery is low, a game is being played, etc.). We loved her opening comment, “We don’t see mHealth as a ‘challenge,’ but as an ‘opportunity.’”
  • Dexcom’s Director of Data Partnerships Dr. Nate Heintzman shared that the company’s open API for retrospective data will launch “later this year” at developer.dexcom.com. As a reminder, this will allow third parties to access Dexcom’s APIs (retrospective data, three-hour-delay), create and manage pre-commercial (prototype) apps, play with simulated (sandbox) data, learn how to become a Dexcom data partner, and even submit an app for commercial approval. This important effort was first announced at the D-Data Exchange last fall, but clearly there are a lot to details to work out – the “early 2017” planned launch has slipped a bit, but things sounded very close to launch at this point. Dr. Heintzman shared a “top 10 things to know list,” noting that 500+ people have already signed up for email updates on developer.dexcom.com. He reminded attendees that developers will have access to quite a bit of information to develop novel retrospective data apps, including glucose data, statistics, device info, and calibrations. Like Dexcom Clarity, this platform is a class I medical device (retrospective CGM data), and Dexcom will support data partners and share best practices through the developer portal. This is clearly a huge internal investment from Dexcom and we hope it drives an ecosystem of innovation and novel ways to use retrospective CGM data.
  • A lively panel discussion featuring closed loop industry members (Insulet’s Dr. Trang Ly, Medtronic’s Dr. Fran Kaufman, Bigfoot’s Mr. Bryan Mazlish, and Tandem’s Mr. John Sheridan) and moderated by our own Mr. Adam Brown showed encouraging industry openness to engaging with the DIY and broader patient communities. From the audience, Dr. Eric von Hippel called for data to be readily available to patients, providing what Adam called “a sandbox” for innovators to play in and access devices. “It’s crazy when 90% of the effort of the user community has to go to hacking into devices, just to get access to the data. Life is slightly unsafe. If you ensure safety, you also ensure slow progress. There must be a way – in every other field, users can take risk on themselves. If I want to build a rocket-powered car, I can do it, even though it’s unsafe. There must be a way to sign rights that I want access to this damn thing and it’s no longer your [industry’s] problem.” Another audience member emphasized that remote monitoring on a pump is absolutely not an optional feature – kids with diabetes are sent to school, where there are no or few trained professionals, and parents have the right and need to see how their child is doing. Still others asked for companies to re-think and improve infusion sets, the traditional four-year pump warranty cycle, and algorithm transparency. To our delight, none of the panelists once responded “No, we can’t do that.” Of course there is work to be done on the best vehicle for the DIY community to interact with industry productively, but panelists were very open to hearing the audience – Dr. Kaufman even asked attendees to “go on a date” with Medtronic so they can get as much input as possible. We find this level of progress quite remarkable, since this dialogue didn’t exist even two years ago – we owe a hearty thanks to these industry leaders and the patient innovators who have pushed the field ahead.
  • MIT Sloan School of Management’s very smart Dr. Eric von Hippel, speaking to a room full of DIYers and lead users, encouraged some collaboration with industry…but not too much! In collaborating with companies too closely, non-industry innovators may end up with “indicia of commerciality,” which could result in a product that FDA can regulate (under the Commerce Clause, FDA cannot regulate non-commercial medical innovations). According to Dr. von Hippel, there’s more to lose than, for example, the OpenAPS community’s right to use their DIY closed-loop systems. He explained that truly patient-led, grassroots innovation systems interact quite robustly with the producer innovation paradigm to the profit of both parties: During a producer’s R&D phase, it will offer innovation support to free innovators, who in turn churn out innovation designs upon which manufacturers can base products. It’s a difficult line to toe, however, as too close an interaction – a blended system – could lead to FDA involvement, and the scaffold could crumble.
    • According a study from Dr. von Hippels’ group, the number of medical patient innovators outnumber producers by over 100:1. Holy moly! Innovation has traditionally been seen as something that just producers do, and this is perpetuated by the fact that innovation is only accounted for in government statistics until a product is formally introduced onto the market – the user innovator is typically invisible. In the US alone, there are an estimated ~384,000 individuals working on medical consumer innovations.
  • According to Companion Medical CEO Sean Saint, Apple added insulin doses into Health Kit as of the Worldwide Developer Conference earlier during ADA. It will be available starting in iOS 11 this fall, and the beta is out now – see the iOS 11 developer page here. This has been long awaited and could be a nice data ecosystem enabler – especially as connected pens and connected pumps come out.

Special Event: U.S. Diabetes Exchange & Experience event (dX2) (Sponsored by Abbott)

Visualizing the Future of Diabetes Management

Jo Boaler, PhD (Stanford University, Palo Alto, CA), George Grunberger, MD (AACE, Bloomfield Hills, MI), Joel Goldsmith (Abbott, San Francisco, CA)

Abbott’s Mr. Joel Goldsmith boiled down the digital transformation of diabetes care to three main trends: The shifts from strips to sensors, from proprietary handheld devices to connected consumer electronics as the preferred user interface, and from desktop application analytics to cloud-based services. CGM is becoming the standard of care, making the capture of dense glucose data almost effortless and much more cost effective – this dense data makes it easier to visualize trends and patterns. [He took the opportunity to remind attendees that FreeStyle Libre consumer is currently available in 35 countries and used by 300,000+ patients, though is still under review by FDA.] Similarly, smartphones are becoming intertwined with standards of medical care – they are pervasive, and are more and more an integral part of traditionally highly-regulated medical devices. Not only do they offer a familiar user interface and a constant source of connectivity, but they reduce the burden associated with carrying additional devices on one’s person. Finally, moving to the cloud is enabling instantaneous, widespread sharing and new forms of advanced data analytics, “helping to deliver on the promise of precision medicine.” Taken together, these three shifts are lowering the barrier to both insight generation and access, and are beginning to deliver outcomes. Mr. Goldsmith concluded by explaining why diabetes may be the “perfect candidate” for machine learning and AI: It is data-intensive, largely self-managed (increasingly through connected consumer electronics), and a growing global epidemic. We are seeing signs of life in this turf – automated retinopathy screening, Medtronic/IBM Watson’s Sugar.IQ (more on this in an oral session tomorrow), and One Drop just laid the foundation for future AI intervention this morning with its Amazon Alexa integration.

Company Announcements

One Drop Integrates with Amazon Alexa Voice Technology; Brings Dr. Nicole Johnson on to the Advisory Board

One Drop announced a slew of updates on the first day of ADA, most notably integration with Amazon’s Alexa voice technology. This new will allow patients to track blood glucose, food, and activity, as well as to access their data, all with voice. This represents a sizable leap forward in improving on manual entry, particularly for those who may be physically or visually impaired. The integration is set to launch on June 15th through the Amazon Alexa Skills Store (presumably for free in the One Drop app) and even allows caregivers in separate locations to access patient data summaries (i.e., “Alexa, did mom take her medication today?”). We love the commitment to simplifying data logging in all modalities and can’t wait to try the voice component out. Could voice become the new user interface in diabetes? It is relatively uncharted territory in diabetes, though one with serious potential (see Merck/Amazon’s competition) – In the same accessibility vein, One Drop introduced “Light Mode,” which is designed to improve readability for the visually impaired. The announcement also includes news of two new One Drop Experts in-app education programs – “Overcoming Diabetes Burnout” and “Advanced Carb Counting” – and the addition of Dr. Nicole Johnson to the One Drop Experts Advisory Board (joining Dr. David Marrero, Ms. Martha Funnell, Dr. Carla Miller, Ms. Heather Gabel, and Doug Warren). We look forward to digging into One Drop’s three new sources of outcomes data (two posters, one late-breaking poster) on Sunday and Monday – Jeff Dachis, Dr. Chandra Osborn, and co. already have two published retrospective posters demonstrating improved outcomes from use of One Drop Mobile and the Experts program.

Ascensia + Voluntis Partner to Connect Contour Next and Plus One BGMs with Insulia Basal Titration App; Launch in 4Q17

During ADA, Ascensia announced a global partnership to connect Voluntis’ Insulia basal insulin titration app for type 2s with Contour Next One and Plus One BGM systems. The integration is expected to go live for people with type 2 diabetes in 4Q17. We’re not sure exactly how the combo will be deployed – Insulia is a prescribed app, but we’re not sure how that will come to market in combination with Ascensia’s BGMs. Will the products be bundled and sold together directly to patients? Will providers prescribe the two together? Or will the commercialization be separate, with Ascensia simply feeding data into Insulia as an integrated device? Voluntis has moved quickly, establishing partnerships with Livongo, Sanofi, and now Ascensia after announcing FDA clearance for Insulia in December. Ascensia is fresh off announcing development agreements with Insulet and Glooko and is clearly doubling down on digital diabetes care and connectivity. Notes CEO Mr. Michael Kloss in the press release, “[This] is our first partnership in the area of medication management, which is a critical component of integrated diabetes management…” We’re glad to see both companies partnering widely and look forward to seeing the business model and how Insulia comes to market at scale.

 

-- by Melissa An, Adam Brown, John Erdman, Helen Gao, Varun Iyengar, Brian Levine, Payal Marathe, Maeve Serino, and Kelly Close