How to Improve Time-in-Range After Meals

How to Improve Time-in-Range After Meals

More clinicians and people with diabetes are becoming aware of Time-in-Range, a powerful metric to manage your diabetes. You might have started tracking Time-in-Range with a CGM and noticed a pattern: spikes in blood sugar levels after meals. This is a common challenge - right after meals is usually the hardest time to keep glucose levels in your target range. Today, we’ll be breaking down the basics of Time-in-Range and diving into how to stay in-range after meals, using a diabetes food tracker app.

What’s Time-in-Range? And Why’s It So Important? Let’s cover the basics first. Time-in-Range is a diabetes management metric, which indicates the percentage of time per day a person’s glucose level is in a target range. Ideally, people with diabetes should spend as much Time-in-Range as possible. Now, this target range is different for everyone, and you should speak to your doctor to determine your ideal range. Factors that affect your range include diabetes type, food you have access to, and the amount of exercise you can commit to.
Why Apps are not Free

Why Apps are not Free

As much as we all enjoy free stuff, it is very rare that something is truly free. That also applies to apps. Although the additional cost for one additional user is usually very low, developing and maintaining an app that people like is still very difficult, expensive, and requires many iterations. Let’s dive into the phases of developing an app and why there are no free apps.

Research & Development (R&D) Not all apps contain a tech component requiring R&D. But if the product does, it brings certain challenges. The complexity of problems requiring R&D to find solutions will make these activities difficult to plan. The relationship between input (time and other resources) and outcome (e.g. working solution) is not linear. A problem that occurs can be solved within days or within months or, in the worst case, never.
Learnings from Releasing the App to the Public

Learnings from Releasing the App to the Public

In this second blog post about our journey as a start up, we highlight some of the key learnings from releasing the app to the public. We’ll be talking about how the product evolved in a technical way but also how we as a company had to adjust our vision of how our app was going to help our users.

For SNAQ the first moment of truth was when the app was released to the public in 2019. We quickly got a few hundred users but the app was not yet what people expected. Many basic product features where missing — as is the usual case with an MVP. Additionally, we learned that we needed to work on managing our users’ expectations in a better way. Moving from an MVP to a usable product took about 100 releases.
Food Recognition with Little Data

Food Recognition with Little Data

This blog post talks about the inner workings of our app and gives some insight on how our app does what it does.

Food classification is a straightforward problem: identify the fruit, vegetable, or meal in front of you. For humans, this is easy, yet for our phone, this is quite a complex task. One reason is that a large degree of visual variation exists between images of the same dish. It’s not only a problem of comparing apples and oranges, but also one of comparing apples and apples — and identifying them as the same.
The Start of our Journey and Learnings Made Along the Way

The Start of our Journey and Learnings Made Along the Way

In this first blog post about our journey, we will dive into the early workings of SNAQ and how it all began. Read on to find out, where we came from and how we got to where we are today. How we went from just analysing foods you eat to importing and incorporating your blood sugar levels.

We started SNAQ after the partner of one of our founders got diagnosed with Type 1 Diabetes. This made us experience many of the everyday challenges. Today we support people with diabetes and also without diabetes to take the guesswork out of mealtime decisions*. Our app helps to count carbs, protein, and fat content of meals by snapping a photo and provides insights on what keeps glucose levels in range after meals.