Here's the idea. Please, tell me what you think. Do you know people who could do this? (I'm less interested in regulatory feasibility than in technical feasibility.)
PROBLEM: Every day, several times a day, we take our lives in our hands when we decide on insulin, food or exercise activities. No one is smart enough, certainly not me, to use even the limited data sets we have to effectively or accurately treat ourselves. Poring over charts and graphs is not my forte. There are way too many variables (e.g., food type, amount, mix, recency; time of day; exercise type, amount, recency; stress level; insulin type, amount on-board, basal levels, bolus levels; personal data like weight, BMI, and work environment, etc.), they are all in constant flux and their relationships are often (apparently) inconsistent.
SOLUTION: Crowdsource, anonymize and aggregate all that data from Type 1 geeks and smartphone users globally. Analyze it for patterns, and feed relevant insights back to individuals in the crowd for them to use to help them decide what to do now - in the situation they find themselves in. Should they eat, bolus, walk, run? And how much of each?
APPROACH: We could collect much of this data: some passively, some actively. Let's assume that there are smartphone apps that can passively gather some exercise data (pedometers, Nike+), that the new Sanofi/AgaMatrix can gather BG test data, and that appropriately incentivised users would employ exercise and diet apps apps to share food, stress, personal and exercise data points. With all this data from thousands of people posting more data every day, I have to believe that we would all benefit.
Could we do this? How? What are the hurdles? How could we get over them?
I have also been frustrated by the perpetual challenge of adjusting insulin dosages to match meals and changes in activity level, stress, etc., but I don't think crowd sourcing is the answer. I think a better approach is to follow the lead of the artificial pancreas projects and feed data from a single person into a predictive model for glucose. In the same way that we (and/or your endocrinologist) look at graphs of glucose, carbohydrate intake, and insulin dosages to adjust current parameters and strategies, a predictive model could use these data to suggest parameters like insulin-carbohydrate ratio, correction factor, and insulin-activity time, as well as anticipate future changes. Such an analysis could be performed anytime that glucose trends have been getting out of hand.
To do this you would need accurate CGM data, accurate carbohydrate logs, and a good model. A lot of diabetics collect CGM data and log carbohydrates. There are several models in the medical literature that seem to do a nice job of modeling CGM data. Or, if we are just looking for suggestions of insulin ratios, automating the analysis methods in Pumping Insulin may work just fine.
Of course, error in the input data and the robustness of the model could dramatically affect the reliability of parameter calculations. So, you would still have to be use your own brain when making decisions based on this semi-automated method. For this and many other reasons, it is unlikely that a home-based version of this approach would ever make it through regulators. Like you though, I have only been interested in asking, "could this work?" If I can ever find the time (I'm finishing my PhD this year), I'd like to give this method a shot. Appealing to the open-source community may be the best way to start working toward this sort of solution.
I suppose that what I'm looking for is a system that acts as the nurse educator or endo 24/7, combining my recent data and the experience of hundreds of other patients to make a recommendation. Without learning from other patients, our diabetes team would be no better at predicting results than the rules they originally learned when they trained. Similarly, a system that combines input from thousands of users and millions of data points should be pretty doggone accurate.
As well, a fixed algorithm or a single-patient-model assumes that 1) I'm like this 'average' user, which no one is, and 2) it doesn't learn from experience.
It seems to me that crowdsourcing solves all those problems. You could definitely start with a base algorithm, but as data accumulates, the system should adjust and iterate. (It may learn, as a tiny example, that people over 188 pounds consistently require 8% more insulin than the average user.) Over time, when a user queries the system as to "What do I do now?", he/she would get a set of examples that are increasingly representative of his specific variables. It's a neural network. It's more a Google approach than an expert system approach.
I do agree that the system should only suggest, and would require that the user decide what to do. Also, having a large base of data reduces the chance that an error in data input would dramatically affect reliability. In fact, the system could suggest that you recheck your input as it seems not to align with patterns among the other 2,000 users.
Finally, it seems to me that data presented in this kind of context would be much more accessible to the average user (some of whom are teenagers with very poor judgment!) than are charts and graphs available today.
I haven't read Pumping Insulin, but I just ordered it on Amazon.
Hey Nate, in my first reply I addressed every point you made except the most important one. Is there any skill, anything or anyone you could contribute to making this idea come to life? I'm definitelynot expert in any of this, but I'm guessing we'd need (at the least) someone to design a database, plug-in some pattern recognition software, a way to generate algorithms and add some sort of simple AI capability to analyze new, incoming data and feed back new rules into the algorithm. And that's before we deal with issues like sourcing some of the data from existing apps, designing a frictionless input interface and finally, getting people to sign up and try it.
If you're a scientist at Woods Hole, I'm thinking some of these people we need may be friends of yours. If not, do you know how, as you mentioned, to appeal to the open-source community (i.e., via what channels)?
Anybody else have ideas?
That's a cool idea.I really like the idea of the bolus being reduced if your BG is trending up and vice versa. And the idea of counteracting insulin with glucagon is also interesting. I wonder if anyone's tried that. Since our natural glucagon delivery system is still working and responding to drops in BG levels, I wonder how one could calculate the proper dosage to counter the insulin over-delivery.
There are two reasons my concept is not connected directly to the pump. One is that I'm not sure I trust the algorithm to actually deliver my bolus. I kind of like to be able to say "go" or "no go." The second is that it requires cooperation with the pump manufacturer and is likely to be regulated. The idea I'm proposing would be grass roots development, open source code and on a peer-to-peer network, so that it doesn't risk anybody getting sued. People can us the info if they want, but if they do, it's not a company providing the info; it's all of us.
OK folks. 31 of you have viewed this post. Can anybody actually contribute to building it? Design the database? Develop the initial algorithms? Build the app? Help find funding?
Hi Craig, I have just read your idea and I like your analytic approach using statistics & crowd sourcing. Now, for these statistics to be useful, I guess, ethnicity, age, weight, age at which diagnosed, etc, etc need also to be considered.
I am ready to spare some of my time working on this in partnership with someone to take a more educated approach..(technically qualified to design the algorithms, etc).
Sai (from India)
I agree with your point about other data that may need to be collected. The beauty of this idea is that the data that actually drives accurate predictions will become apparent as more predictions are generated and results fed back. For example, if "age at diagnosis" show no correlation with predictive accuracy, we learn that it's irrelevant. Or, we may find that it's only relevant during the first 2 years after diagnosis, or after 24 years, or among women. We don't know until we gather the data and feed in the results.
What kind of skills do you have, Sai?
I can help with the programming part. I am Software engineer by profession and right now I have spare time. I am not sure if someone could come up with a "Design Document" based on your analysis (posted above) which is a pretty good start (I guess!). One of the things useful could be building a Database first...Moving this onto a Cloud or other platform could be the secondary step. I can probably work on the database design for now (probably using open source database like mysql or something similar).
From a statistical standpoint having data from many diabetics will lead to more stable predictions. Could the data be collected in the cloud?
Jann, I completely agree that the more data, the greater the stability and the higher the confidence levels regarding the predictions.
if you mean could this be a service that sits in the cloud rather than a piece of software and database on your smartphone, absolutely. That's the idea. The are many cloud services providers who could accommodate this service, either as a private cloud or on shared servers.
Check out the presentations by John Walsh at diabetesnet.com (he wrote Pumping Insulin, the 5th edition is out now) : Diabetes Presentations
In this poster: DiabTech2007Poster.pdf he presents anonymous data from over 500 Cozmo pump users and the data on the pump settings is very interesting. Most of the settings are not normally distributed as would be expected. A large number of pump users have "magic number" parameters and therefore are likely not optimized.
I think that this kind of data is good for getting a 1st approximation of settings for an individual user (total daily dose, correction factor, carb factor, etc. for a given weight, BMI, age, sex, time after diagnosis), but beyond that I think that optimum parameters are extremely specific to an individual, and that is where the value of a software tool lies.
I would imagine a careful iterative process of say two weeks of data analyzed, and then small adjustments to basal programs, carb factors, and correction doses, followed by another 2 week period and adjustments.
Another issue that Walsh addresses is that the different pump manufacturers have different ways of calculating bolus recommendations: AACE2007Poster.pdf therefore a software tool would also be pump specific.
And these free papers:
And not free (contact me):