With the introduction of digital health tools, connected health devices, and wearable activity trackers, patients and care teams have access to more health data than ever before. But not all of this data is created equal.
People with type 2 diabetes are often told by their healthcare provider to check their blood glucose with their meter every morning prior to eating (fasting).
A person with type 2 diabetes came to see me for an initial visit. He regularly checks his blood glucose fasting at 7:30 am. In fact, he proudly showed me his records with 365 blood glucose results – all within a similar range. He was never told to check his glucose other times of the day to learn how food and activity might affect his glucose levels. So in essence we had lots of data, but it was of minimal use to adjust his diabetes management plan.
Conversely, less data that is collected wisely can become applicable information. For example, one of my patients, who has type 2 diabetes, provides me with glucose results from a mobile app he is using. I observe a blood glucose result of 153 mg/dL recorded at 10:52 am and another data point, a glucose of 89 mg/dL two hours earlier with data that he consumed 45 grams of carbohydrates at breakfast and took 8 units of insulin. This additional data paints a more complete picture of a specific time of day and becomes more applicable information with which I’m able to help Mr. Jones.
Unless people with diabetes are provided with guidance on when to check their glucose levels (data) intelligently they and their provider will not have data from which to review and revise their treatment plan. Even more importantly, their data can be put to good use by themselves and their healthcare provider if they’re using a digital health therapeutic that offers real time feedback and actions to, for example, treat a low blood glucose or prevent a glucose rise.
Dense Data vs. Sparse Data
Taking this point one step further, let’s consider what data science experts call “dense data” vs. “sparse data.” In the image below (left), you see a tracing from a Continuous Glucose Monitor (CGM). It contains many data points – a result every 5 minutes. From this vast data recorded day after day and observable in various profiles, the patient and healthcare provider can see that this person’s glucose level starts to rise after midnight. We can impute the time that the user ate a midnight snack. The density of this CGM data makes it quite informative to observe glucose patterns day after day.
On the converse, consider the example of sparse data in the figure to the right. This is far fewer glucose data points, perhaps as few as 3 to 5 per week in a person who does a minimum of blood glucose checking with a meter.
Adding More Data Can Paint Context
The addition of more types of data can help the user and healthcare provider paint context and provide value as more actionable information. Continuing with the example of diabetes, digital therapeutics should collect lifestyle data including medication doses and food intake with types, preparation and amounts consumed; and the timing and amount of physical activity.
But there must be a balance. The collection should not be overly intrusive. People with diabetes don’t want to spend hours collecting and imputing data. Digital therapeutics can relieve some of this burden with an integrated or connected glucose meter, activity tracker and food bar code scanner. And as noted, the more the provider uses the patient’s data to make changes in the treatment plan and demonstrate the value of this data to the patient, the more they are likely to take the time to collect it and share it in a meaningful way.
Optimize Patient/User Engagement
The more easily this valuable data can be collected and enhance and reward user engagement, the better. However, user engagement, both initially and ongoing, can be a significant challenge. In digital health, engagement can be defined as the frequency of interactions the user has with the mobile app.
The right level of engagement for one person and their disease management can be very different from the next. Clinicians must learn to strike a balance with patients. The patient and the provider must gain sufficient data and feedback to make the app valuable without being burdensome. Patients and their providers may need to set different goals with the digital therapeutic at different times along the course of managing the chronic disease. These decisions can be part of the shared-decision making process.
A Final Thought
Einstein said “If you give me 60 minutes to solve a problem, I will spend 55 minutes thinking about the problem and 5 minutes working on the solution.” Technology can do many things, but we must develop solutions with a deep understanding of the complexities of the problem. We don’t need more data points, we need more applicable and actionable information.