Digital Diabetes Congress
DDC 2019 Mobile Application Contest Finalists
App Name: BlueStar
Company Name: Welldoc
Download Link: iOSDescription
BlueStar, powered by Welldoc, was created to help people manage their diabetes from improved outcomes. BlueStar is developed on a foundation of clinical expertise and for an optimal user experience. It seamlessly integrates with other devices and trackers. By connecting users to their existing care team, it is unlike any other digital therapeutic. BlueStar takes a holistic approach in understanding user’s multiple dimensions of health from their blood glucose readings, medication adherence, food, activity, sleep, psycho-social, and more. It streamlines this information and delivers it to their care teams for improved health outcomes. It simplifies the process for people living with diabetes so they can focus on living their life, rather than just managing their chronic disease.
App Name: Control:Diabetes
Company Name: Brigham and Women's Hospital
Download Link: iOS AndroidDescription
For many patients with diabetes, keeping blood glucose levels under control can be complicated. Blood glucose can be affected by multiple factors - food, exercise, medication, stress, illness, sleep deprivation, and others. It can be difficult to quantify their impact and put all of them together to understand which way blood glucose is going to go.
We are not solving this problem by having an electronic gizmo tell the patient what to do. The most powerful computer each of us has access to - every minute of our lives - is our brain. Control:Diabetes is a natural intelligence mobile app that aims to leverage this incredible resource to help patients with diabetes take charge of their disease.
The app does this by asking the user to predict their blood glucose at a particular date and time of their choosing (e.g. tomorrow at noon). Then, when this time comes, the user is asked to enter the actual blood glucose measurement. If there are significant differences between the original prediction and the measurement, the user is asked to enter the reasons for the difference. We believe that, by repeatedly challenging themselves to deeply analyze changes in their blood glucose, the patients will “teach” themselves how different external and internal factors affect their blood glucose and will be empowered to take control of their diabetes, and their lives. All of this can be accomplished with an app that anyone in the world who has a smartphone can use – not even requiring an Internet connection – saving healthcare costs at the same time as improving people’s lives.
We are not solving this problem by having an electronic gizmo tell the patient what to do. The most powerful computer each of us has access to - every minute of our lives - is our brain. Control:Diabetes is a natural intelligence mobile app that aims to leverage this incredible resource to help patients with diabetes take charge of their disease.
The app does this by asking the user to predict their blood glucose at a particular date and time of their choosing (e.g. tomorrow at noon). Then, when this time comes, the user is asked to enter the actual blood glucose measurement. If there are significant differences between the original prediction and the measurement, the user is asked to enter the reasons for the difference. We believe that, by repeatedly challenging themselves to deeply analyze changes in their blood glucose, the patients will “teach” themselves how different external and internal factors affect their blood glucose and will be empowered to take control of their diabetes, and their lives. All of this can be accomplished with an app that anyone in the world who has a smartphone can use – not even requiring an Internet connection – saving healthcare costs at the same time as improving people’s lives.
App Name: Diabetes Advisor
Company Name: UT Southwestern Medical Center
Download Link: iOSDescription
Management of type 1 diabetes even on “normal” days is full of myriad challenges and a tremendous cognitive burden for caregivers of children with diabetes. Add in everyday childhood illnesses to that, and that burden is only magnified, as even routine illnesses, for a child with diabetes, increase one’s risk for hypoglycemia, hyperglycemia, and diabetic ketoacidosis. Guidelines for sick day management in children with diabetes, on insulin, do exist, but although “sick day management” is a well-accepted part of most diabetes self-management education programs, often months can pass from when a family is provided such education and when their child next experiences a significant illness, making recall of that information a challenge and a contributor to the burden of illness.
Most approaches to sick day management emphasize a few basic principles:
• Never to stop insulin completely
• Illness should always be a cue to assess a child’s ketone status, regardless of whether a child is euglycemic, hypoglycemic, or hyperglycemic.
• The risk of diabetic ketoacidosis can be minimized by careful attention to adequate fluid intake, increased frequency of monitoring of glucose and ketone levels, and extra administration of insulin in an amount proportional to a child’s usual doses of insulin.
At Children’s Health in Dallas, we have long followed a sick day algorithm which we teach to our families which can be illustrated largely in tabular format: https://docs.google.com/spreadsheets/d/1EA0ohYQZXvMgBSRqPjNYJTiACrBJnzUVSX4sQq3THEI/edit?usp=sharing
Although we distribute this – in paper format – to our patients currently – we judged a couple years ago that this algorithm, if delivered in the form of a mobile decision-support tool, available on demand to a caregiver when a child is sick, would be well received and might decrease the burden of decision-making placed on caregivers of children with type 1 diabetes.
We thus developed the Diabetes Advisor.
Although it was envisioned to be a sick day decision support tool, which it is at its core, by the first release product we elected to add to it an education library which contains the educational materials that all of our families receive from us at their initial visit with our staff, along with links to selected mobile sites which we collated to guide families to high-yield resources to aid in their familiarity with the condition.
The highlight of the app, though, remains its sick day advisor functionality. Although the education library is available to the general public, the “ketone manager” is only accessible through an invitation code which our patients receive from us after completing their self-management education classes. After a brief registration step, families are invited to record their child’s age, weight and insulin doses.
At that point, the ketone manager can be activated on demand. When activated, the app guides families through the same set of questions that they might be asked if they were to contact our nurses or physicians directly. If signs of a significant illness are reported, the app helps the family call our clinic or guides them to call their local emergency department. If, however, a certain set of circumstances is described (a low risk child with ketones, who otherwise feels OK and is keeping liquids down) the app will query caregivers for the child’s current glucose and ketone readings, and based on that information, along with the patient’s age, weight, and total daily dose of insulin, will advise a sick day plan which includes instruction about how much fluid to drink, what to drink, how much insulin to administer, and utilizing their mobile device’s native notification functionality, reminders for when to recheck their child.
All of these activations are also recorded in the app, in a journal sort of functionality, so that families can quickly review what they did the last time their child was ill – and how well it worked – the next time that their child is sick.
Most approaches to sick day management emphasize a few basic principles:
• Never to stop insulin completely
• Illness should always be a cue to assess a child’s ketone status, regardless of whether a child is euglycemic, hypoglycemic, or hyperglycemic.
• The risk of diabetic ketoacidosis can be minimized by careful attention to adequate fluid intake, increased frequency of monitoring of glucose and ketone levels, and extra administration of insulin in an amount proportional to a child’s usual doses of insulin.
At Children’s Health in Dallas, we have long followed a sick day algorithm which we teach to our families which can be illustrated largely in tabular format: https://docs.google.com/spreadsheets/d/1EA0ohYQZXvMgBSRqPjNYJTiACrBJnzUVSX4sQq3THEI/edit?usp=sharing
Although we distribute this – in paper format – to our patients currently – we judged a couple years ago that this algorithm, if delivered in the form of a mobile decision-support tool, available on demand to a caregiver when a child is sick, would be well received and might decrease the burden of decision-making placed on caregivers of children with type 1 diabetes.
We thus developed the Diabetes Advisor.
Although it was envisioned to be a sick day decision support tool, which it is at its core, by the first release product we elected to add to it an education library which contains the educational materials that all of our families receive from us at their initial visit with our staff, along with links to selected mobile sites which we collated to guide families to high-yield resources to aid in their familiarity with the condition.
The highlight of the app, though, remains its sick day advisor functionality. Although the education library is available to the general public, the “ketone manager” is only accessible through an invitation code which our patients receive from us after completing their self-management education classes. After a brief registration step, families are invited to record their child’s age, weight and insulin doses.
At that point, the ketone manager can be activated on demand. When activated, the app guides families through the same set of questions that they might be asked if they were to contact our nurses or physicians directly. If signs of a significant illness are reported, the app helps the family call our clinic or guides them to call their local emergency department. If, however, a certain set of circumstances is described (a low risk child with ketones, who otherwise feels OK and is keeping liquids down) the app will query caregivers for the child’s current glucose and ketone readings, and based on that information, along with the patient’s age, weight, and total daily dose of insulin, will advise a sick day plan which includes instruction about how much fluid to drink, what to drink, how much insulin to administer, and utilizing their mobile device’s native notification functionality, reminders for when to recheck their child.
All of these activations are also recorded in the app, in a journal sort of functionality, so that families can quickly review what they did the last time their child was ill – and how well it worked – the next time that their child is sick.
App Name: diafyt
Company Name: diafyt MedTech
Download Link: AndroidDescription
Diabetic patients need to calculate insulin dosages multiple times a day. diafyt App assists patients in their daily diabetes management and individual bolus calculation. The principle of insulin dosage calculation is based on an algorithm that calculates the patient's individual insulin demand based on
• blood glucose levels
• insulin injections
• types of insulin used
• food intakes (carbohydrates)
• time of day
• parameter tuning functionality
Therefore the main innovative feature is an insulin calculation. The system adapts to the individual patient with machine learning. Software learns in less than 10 days individual dosage calculation. diafyt App will receive medical device certification in May 2019 (EU)
Typical use-case situation: calculate insulin dose prior to meal, The patient
1. starts the app
2. reads the glucose sensor data
3. estimates the amount of carbohydrates in the meal and records the value in the app
4. runs the insulin demand calculation and receives a directing value
5. makes a qualified decision about the dosage amount taking the directing value into consideration and records the chosen dosage amount
6. injects the chosen amount of insulin (bolus)
• blood glucose levels
• insulin injections
• types of insulin used
• food intakes (carbohydrates)
• time of day
• parameter tuning functionality
Therefore the main innovative feature is an insulin calculation. The system adapts to the individual patient with machine learning. Software learns in less than 10 days individual dosage calculation. diafyt App will receive medical device certification in May 2019 (EU)
Typical use-case situation: calculate insulin dose prior to meal, The patient
1. starts the app
2. reads the glucose sensor data
3. estimates the amount of carbohydrates in the meal and records the value in the app
4. runs the insulin demand calculation and receives a directing value
5. makes a qualified decision about the dosage amount taking the directing value into consideration and records the chosen dosage amount
6. injects the chosen amount of insulin (bolus)
App Name: Glooko
Company Name: Glooko
Download Link: iOS AndroidDescription
Glooko’s universal diabetes software products provide insights to improve outcomes for people with diabetes. The Glooko app syncs data from 80+ of the world’s most popular diabetes devices, major fitness and activity trackers and supplies personalized, timely patient data such as glucose level, food, insulin, medications, blood pressure, and weight data. Users of the Glooko app can also share their data with their care team between appointments, which allows collaboration on treatment decision that is based on data.
App Name: GlucoseZone
Company Name: Fitscript, LLC
Download Link: iOS AndroidDescription
GlucoseZone is a digital exercise platform designed to help people with diabetes reach their diabetes and fitness goals. Based on individual needs, GlucoseZone provides exercise guidance and support to help decrease weight, lower blood sugar, reduce medication(s), and manage diabetes.
Features and Innovations
1. Contemporaneous blood sugar testing that informs exercise guidance
2. Incorporates GlucoseZone predictor algorithm that mitigates risk of hyper and hypoglycemia
3. Integration with Apple Watch and Android Wear devices
4. Integration with Apple Health Records, specific blood glucose meters.
5. Currently in development integration with CGM(s) and InsulinPump(s)
6. In app messaging with a GlucoseZone certified coach / trainer. Message escalated to CDE if out of scope of practice of coach /trainer
7. Just for you - selection of appropriate workouts from our library based upon user profile information
8. Quick start - choice of two workouts based upon current glucose level and profile
9. Educational videos
10. Themed programmatic content
Features and Innovations
1. Contemporaneous blood sugar testing that informs exercise guidance
2. Incorporates GlucoseZone predictor algorithm that mitigates risk of hyper and hypoglycemia
3. Integration with Apple Watch and Android Wear devices
4. Integration with Apple Health Records, specific blood glucose meters.
5. Currently in development integration with CGM(s) and InsulinPump(s)
6. In app messaging with a GlucoseZone certified coach / trainer. Message escalated to CDE if out of scope of practice of coach /trainer
7. Just for you - selection of appropriate workouts from our library based upon user profile information
8. Quick start - choice of two workouts based upon current glucose level and profile
9. Educational videos
10. Themed programmatic content
App Name: Happy Bob
Company Name: Harald AI Oy
Description
Happy Bob is an app for children with T1D that rewards and motivates in daily diabetes care in a positive and personalised way. It uses data from child's own continuous glucose monitor, gives advice for more stable blood sugar based on own data and turns daily diabetes management into opportunities of fun moments and better health.
Happy Bob uses machine learning to predict blood sugar up to two hours ahead, using diabetic's own cgm data.
Happy Bob app is based on the following core ideas:
1) Living with diabetes demands already so much from a child that an app should not add on the cognitive load. Therefore we want to create an experience that follows your data real time, looks after your health in a fun way and gives you advice at the right time, but does not necessarily require your input.
2) Gamification can improve the self-management of people with diabetes: with positive feedback and rewards we can create an encouraging loop for people who live with a condition that requires medication and attention 24/7.
3) Even though diabetes is a medical condition, the user experience does not need to be typical med tech. Instead, we want to create a UX and UI that delights user and turns diabetes management into opportunities of fun moments and better health.
The core features in Happy Bob are:
1) Gamification: cgm data is turned into stars. Whenever user is able to keep blood sugar within target range, he collects golden stars, which is also a currency that he can use to further customize own character, unlock new characters or to achieve targets set together with parents. Happy Bob also sends notifications when an action needs to be taken, but in a less serious way and with sounds user can himself pick.
2) Predictions: our machine learning algorithm, is used to turn cgm data into a prediction of future blood sugar up to two hours ahead.
3) Social features: in the Happy Bob scorecard user can follow friends and have friendly contests with each other to see who gets highest daily star scores each week
Children with T1D are easily forgotten with diabetes care. However, good diabetes care during childhood years is crucial for later health. We believe gamification should be more used with medical conditions and especially with children with type 1 diabetes.
Happy Bob is a nice, happy character in our app that is easy to approach and like. As Happy Bob is integrated to user's CGM (currently using data through Nightscout, more integrations to follow soon), Happy Bob does not require more data from the user. Instead, Happy Bob gives personalised advice for the actions user needs to take, if blood sugar is going high or low. Happy Bob also rewards for stable blood sugar, and for keeping blood sugar within target. The Happy Bob usability has been considered from the casual user's point of view, focusing on the fun UI and UX with the idea that diabetes management can be turned into opportunities of fun moments that also help achieving more stable blood sugar and better health.
Happy Bob uses machine learning to predict blood sugar up to two hours ahead, using diabetic's own cgm data.
Happy Bob app is based on the following core ideas:
1) Living with diabetes demands already so much from a child that an app should not add on the cognitive load. Therefore we want to create an experience that follows your data real time, looks after your health in a fun way and gives you advice at the right time, but does not necessarily require your input.
2) Gamification can improve the self-management of people with diabetes: with positive feedback and rewards we can create an encouraging loop for people who live with a condition that requires medication and attention 24/7.
3) Even though diabetes is a medical condition, the user experience does not need to be typical med tech. Instead, we want to create a UX and UI that delights user and turns diabetes management into opportunities of fun moments and better health.
The core features in Happy Bob are:
1) Gamification: cgm data is turned into stars. Whenever user is able to keep blood sugar within target range, he collects golden stars, which is also a currency that he can use to further customize own character, unlock new characters or to achieve targets set together with parents. Happy Bob also sends notifications when an action needs to be taken, but in a less serious way and with sounds user can himself pick.
2) Predictions: our machine learning algorithm, is used to turn cgm data into a prediction of future blood sugar up to two hours ahead.
3) Social features: in the Happy Bob scorecard user can follow friends and have friendly contests with each other to see who gets highest daily star scores each week
Children with T1D are easily forgotten with diabetes care. However, good diabetes care during childhood years is crucial for later health. We believe gamification should be more used with medical conditions and especially with children with type 1 diabetes.
Happy Bob is a nice, happy character in our app that is easy to approach and like. As Happy Bob is integrated to user's CGM (currently using data through Nightscout, more integrations to follow soon), Happy Bob does not require more data from the user. Instead, Happy Bob gives personalised advice for the actions user needs to take, if blood sugar is going high or low. Happy Bob also rewards for stable blood sugar, and for keeping blood sugar within target. The Happy Bob usability has been considered from the casual user's point of view, focusing on the fun UI and UX with the idea that diabetes management can be turned into opportunities of fun moments that also help achieving more stable blood sugar and better health.
App Name: mAID (Multivariable Automated Insulin Delivery)
Company Name: Illinois Institute of Technology
Description
Fully automated multivariable artificial pancreas Android application, with optimal bolus and basal insulin delivery and rescue carbohydrate suggestions. It integrates a continuous glucose monitor (CGM), an insulin pump and a physical activity sensing device (wristband). Its purpose is a fully automated insulin delivery system that does not require any manual announcements. However, manual announcements can be selected by the user and the automated system can act as safety net, for example for forgotten meal announcements.
The main features of the application are:
• Managing the Bluetooth connection and data streaming of the physical activity sensing device: Empatica E4. The incoming data types are blood volume pulse, inter beat interval, 3D accelerometer, galvanic skin response and skin temperature.
• Managing the Bluetooth connection, data reception and calibration of the CGM: Dexcom G5/G6.
• Managing the Bluetooth connection and issuing the commands to an insulin pump: Tandem t:slim (insulin pump) – the commands send by the application are 1)bolus request, 2)temporal basal rate and 3)status request.
• Allowing the user to input their personal data, including: (1) body measurements (bodyweight, height and waist), (2) insulin-related constants and ratios (total daily insulin, total daily insulin bolus, carbohydrate-to-insulin ratio and correction actor) and (3) daily basal profile.
• Running a multivariable model predictive control algorithm with a recursively updated model that makes predictions based on the current metabolic state of the user. The controller is initialized with the user personal data and uses the newest and historical input data from the sensors (CGM, wristband) to predict the future glucose values. Based on that prediction it will issue two outputs to maintain the glucose within range: (1) optimum insulin commands (bolus insulin or temporal basal rate) and (2)future hypoglycemia prediction alerts. Other outputs computed are plasma insulin concentration (PIC), physical activity values, including metabolic equivalent (MET) and exercise detection flag.
• Storing information in a local database: Dexcom measurements, Empatica measurements, control algorithm inputs and outputs, user personal data.
• Displaying the incoming data from the sensors.
• Displaying last issued insulin commands and whether they have been acknowledged by the pump or not.
• Displaying the insulin dose decisions of the control algorithm.
• Issuing various alerts when user interaction is needed: device disconnection, CGM calibration.
• Issuing a local alarm when hypoglycemia is detected.
• Login into the application system. The innovations of the application are:
• Being the first multivariable and fully automated artificial pancreas.
• Being the first AP integrating a physical activity sensing device (wristband) with real-time streaming data. The wristband itself, Empatica E4, is also a new component, which has been developed to monitor epilepsy but not been used in the diabetes field.
• The control algorithm is a unique algorithm developed on IIT. Its main innovative features are:
o Using the the wristband (Empatica E4) data, it can compute energy expenditure, to detect the presence of physical activity, its duration and its intensity. This enables insulin dose adjustments for spontaneous physical activities of daily living as well as scheduled exercise.
o It makes use of current and historical CGM and insulin infusion data to compute plasma insulin concentration (PIC), instead of relying on generic insulin on board values, to optimize insulin dosage.
o It includes meal estimates based on CGM data.
o It includes hypoglycemia early detection (30 minutes ahead prediction of possible hypoglycemic episodes).
o The control algorithm itself has been tested in MATLAB, using the UVa/Padova Simulator, and exported to the Android platform - to ensure the correct functioning of the algorithm in Android. It has also been tested with a set of TestUnits that we developed to verify the results are not changed from one platform to another.
The main features of the application are:
• Managing the Bluetooth connection and data streaming of the physical activity sensing device: Empatica E4. The incoming data types are blood volume pulse, inter beat interval, 3D accelerometer, galvanic skin response and skin temperature.
• Managing the Bluetooth connection, data reception and calibration of the CGM: Dexcom G5/G6.
• Managing the Bluetooth connection and issuing the commands to an insulin pump: Tandem t:slim (insulin pump) – the commands send by the application are 1)bolus request, 2)temporal basal rate and 3)status request.
• Allowing the user to input their personal data, including: (1) body measurements (bodyweight, height and waist), (2) insulin-related constants and ratios (total daily insulin, total daily insulin bolus, carbohydrate-to-insulin ratio and correction actor) and (3) daily basal profile.
• Running a multivariable model predictive control algorithm with a recursively updated model that makes predictions based on the current metabolic state of the user. The controller is initialized with the user personal data and uses the newest and historical input data from the sensors (CGM, wristband) to predict the future glucose values. Based on that prediction it will issue two outputs to maintain the glucose within range: (1) optimum insulin commands (bolus insulin or temporal basal rate) and (2)future hypoglycemia prediction alerts. Other outputs computed are plasma insulin concentration (PIC), physical activity values, including metabolic equivalent (MET) and exercise detection flag.
• Storing information in a local database: Dexcom measurements, Empatica measurements, control algorithm inputs and outputs, user personal data.
• Displaying the incoming data from the sensors.
• Displaying last issued insulin commands and whether they have been acknowledged by the pump or not.
• Displaying the insulin dose decisions of the control algorithm.
• Issuing various alerts when user interaction is needed: device disconnection, CGM calibration.
• Issuing a local alarm when hypoglycemia is detected.
• Login into the application system. The innovations of the application are:
• Being the first multivariable and fully automated artificial pancreas.
• Being the first AP integrating a physical activity sensing device (wristband) with real-time streaming data. The wristband itself, Empatica E4, is also a new component, which has been developed to monitor epilepsy but not been used in the diabetes field.
• The control algorithm is a unique algorithm developed on IIT. Its main innovative features are:
o Using the the wristband (Empatica E4) data, it can compute energy expenditure, to detect the presence of physical activity, its duration and its intensity. This enables insulin dose adjustments for spontaneous physical activities of daily living as well as scheduled exercise.
o It makes use of current and historical CGM and insulin infusion data to compute plasma insulin concentration (PIC), instead of relying on generic insulin on board values, to optimize insulin dosage.
o It includes meal estimates based on CGM data.
o It includes hypoglycemia early detection (30 minutes ahead prediction of possible hypoglycemic episodes).
o The control algorithm itself has been tested in MATLAB, using the UVa/Padova Simulator, and exported to the Android platform - to ensure the correct functioning of the algorithm in Android. It has also been tested with a set of TestUnits that we developed to verify the results are not changed from one platform to another.
App Name: METACARDIO
Company Name: MetaCardio,Inc.
Description
THE APP: METACARDIO – YOUR SENSIBLE LIFESTYLE HEALTH APP, BECAUSE YOUR LIFE IS NOT JUST A DIET.
Now in beta testing, this new smart mobile app harnesses digital media’s enormous power of changing public behavior to coach overweight adults into adopting a guideline-based healthy lifestyle. We persuade the user to willfully make personal healthy choices in diet, exercise and stress management through a fundamental change in heart and mind about wellness for life.
It is on iOS platform downloadable from the Apple App Store for free. Engagement is for 10-20 minutes a day for at least 3 months.
PURPOSE: HEALTHY LIFESTYLE COACHING
To educate, coach, engage and nudge adults with pre-diabetic and diabetic traits and risk factors to follow clinical guideline advocated healthy lifestyle measures of (1) nutrition (2) activity and (3) wellness as well as stress management.
FEATURES: SIMPLE, DELIGHTFUL, ENGAGING MINI-DOSES
1. Simple page-flipping.
2. Frequent one-liner key messages and nudges.
3. Motivating semi-gamification.
4. Audio-visual storytelling.
5. Daily stress resilience training.
6. Personalized progress tracking—results cumulative, private but benchmarked against a community of like-users, thus difficult to quit after starting.
7. Master website link—for literature references and for user suggestions, feedback and community sharing.
INNOVATIONS: DIGITAL MEDIA’S BEHAVIOR CHANGING TECHNOLOGY
1. Adoption of today’s mobile app Behavioral Design and Persuasive Technology.
2. Neuroscientific habit-loop and gamification strategies.
3. Mini-dose practical information conveyed by a variety of mini-series videos spread out throughout the day, each under 3 minutes and ending in cliff-hangers.
4. Plethora of humor and motivation.
5. Appeal to a wide diversity of user identities using diverse cartoon and avatar personalities as teachers and classmates.
6. Varying educational formats including (1) conversations in group video conferences, (2) clever and funny one-liner educational flashcards, and (3) thought-provoking audio and video storytelling.
7. Holistic body-mind-spirit wellness nurturing approach.
Now in beta testing, this new smart mobile app harnesses digital media’s enormous power of changing public behavior to coach overweight adults into adopting a guideline-based healthy lifestyle. We persuade the user to willfully make personal healthy choices in diet, exercise and stress management through a fundamental change in heart and mind about wellness for life.
It is on iOS platform downloadable from the Apple App Store for free. Engagement is for 10-20 minutes a day for at least 3 months.
PURPOSE: HEALTHY LIFESTYLE COACHING
To educate, coach, engage and nudge adults with pre-diabetic and diabetic traits and risk factors to follow clinical guideline advocated healthy lifestyle measures of (1) nutrition (2) activity and (3) wellness as well as stress management.
FEATURES: SIMPLE, DELIGHTFUL, ENGAGING MINI-DOSES
1. Simple page-flipping.
2. Frequent one-liner key messages and nudges.
3. Motivating semi-gamification.
4. Audio-visual storytelling.
5. Daily stress resilience training.
6. Personalized progress tracking—results cumulative, private but benchmarked against a community of like-users, thus difficult to quit after starting.
7. Master website link—for literature references and for user suggestions, feedback and community sharing.
INNOVATIONS: DIGITAL MEDIA’S BEHAVIOR CHANGING TECHNOLOGY
1. Adoption of today’s mobile app Behavioral Design and Persuasive Technology.
2. Neuroscientific habit-loop and gamification strategies.
3. Mini-dose practical information conveyed by a variety of mini-series videos spread out throughout the day, each under 3 minutes and ending in cliff-hangers.
4. Plethora of humor and motivation.
5. Appeal to a wide diversity of user identities using diverse cartoon and avatar personalities as teachers and classmates.
6. Varying educational formats including (1) conversations in group video conferences, (2) clever and funny one-liner educational flashcards, and (3) thought-provoking audio and video storytelling.
7. Holistic body-mind-spirit wellness nurturing approach.
App Name: My Dose Coach
Company Name: Sanofi
Download Link: iOS AndroidDescription
My Dose Coach titration application is a digital dosing resource, combining a web portal for clinicians and the iOS and Android Apps for patients who are taking once-daily long-acting basal insulin.
The web portal allows setting an individualized dose plan for the patients taking once-daily long-active basal insulin. You can review the treatment progress of the patients and adjust their plans if needed.
The Apps provide patients with type 2 diabetes with regular basal insulin dose suggestions according to their fasting blood glucose readings and dose adjustment instructions pre-set by the healthcare professionals.
The web portal allows setting an individualized dose plan for the patients taking once-daily long-active basal insulin. You can review the treatment progress of the patients and adjust their plans if needed.
The Apps provide patients with type 2 diabetes with regular basal insulin dose suggestions according to their fasting blood glucose readings and dose adjustment instructions pre-set by the healthcare professionals.
Benefits to Contest Entrants
- Finalists featured on the DTS website
- Finalists highlighted in the #DDC2019 Meeting Report by DTS in Journal of Diabetes Science and Technology in 2019
- Opportunity for rapid, open-access publication by DTS in Journal of Diabetes Science and Technology with access charges waived for the winner
Categories and Prizes
Applicants will be divided into two categories: 1) new apps (i.e. no data or limited outcome data validating the app); or 2) established apps (i.e. outcome data is available for the current version).
A winner will be determined for each category judged by a global panel of experts in diabetes care and technology.
The developer of each of the two winning apps will be invited to showcase their app at #DDC2019 in front of an audience representing academia, industry, people with diabetes, and clinician stakeholders in San Francisco May 14-15, 2019.
A panel of digital health experts at the meeting will critique these two winners and invite contributions from the audience.
How will apps be judged?
Scores will be assigned by a panel of judges that includes:
- David Ahn, MD
Mary & Dick Allen Diabetes Center at Hoag - Korey Hood, PhD
Stanford University - Chris Memering
American Association of Diabetes Educators - Aaron Neinstein, MD
UCSF - John Nolan, MD
European Diabetes Forum - Keesha Perkins-Crosby
Tri-Guard Risk Solutions - Stephan Ponder, MD
American Diabetes Association - Shahid Shah
Netspective
Intellectual Property and Ownership
Materials used in the app, including drawings, images, sounds, and computer code, must be original. It is the responsibility of the individual making the submission to secure necessary permissions and/or licenses for the use of any auxiliary materials that include the work of third-party individuals or organizations. The intellectual and commercial property rights will remain with the entrants.
Entry Requirements
All submission materials must be in English.
Functionality: The App must be ready to install and operate on the platform for which it is intended.
Platforms: Submitted Apps must be compatible with Android and/or iOS mobile devices, such as phones, tablets, and/or watches. All necessary accompanying servers or sensors must also be available in case the developers are invited to demonstrate their app at #DDC2019.
What does the submission process include?
You will be asked to answer five questions about your project and you will have an opportunity to add supporting documents.
How long do I have to make my submission?
Submissions open on February 4, 2019. We encourage you to get your entries in as soon as possible. Submit your entry by April 5, 2019.
How much does it cost to enter?
Entries are free of charge.
The mobile app contest is sponsored by Ascensia Diabetes Care.