Why it matters
It defines the starting state before the next response begins.
How it Works
One clear sequence turns daily checking into something usable.
GlucoMove is built around one consistent reading order: Pre-meal → Medication → Meal → Activity → Post-meal. In daily use, the minimum useful dataset is pre-meal glucose, activity, and post-meal glucose. Meal and medication sharpen interpretation when available.
Pre-meal
Medication
Meal
Activity
Post-meal
The full order and the minimum dataset are not the same thing.
Communication across the whole site should always use the same full order. But for a daily dataset to become useful, the required points are pre-meal glucose, activity, and post-meal glucose. Meal and medication are valuable context, not mandatory every time.
Full order
Use the same order everywhere in UI, content, and guidance.
Minimum set
Pre-meal · Activity · Post-meal are the daily essentials.
Extra context
Meal level and medication settings improve interpretation.
01 · Pre-meal
Start with a clear baseline.
Pre-meal glucose shows where the event begins. It is the anchor point that gives later change its meaning.
What it supports
Meaningful comparison against the later post-meal value.
Without it
The later number becomes harder to interpret as change.
02 · Medication
Medication is contextual guidance, not universal daily input.
Medication appears in coaching when users enable medication reminders in settings. The app supports oral medication, GLP-1, and insulin, with insulin structured into basal, mealtime, and premixed types, simplified through common combination patterns.
Settings-based reminders.
Medication is not always shown. It becomes visible in coaching when the user has medication reminders configured.
Reduce repeated burden.
Instead of forcing complex drug entry every time, GlucoMove uses a configured pattern that can trigger relevant reminders at the right time.
03 · Meal
Meals are logged by realistic levels, not perfect nutrition math.
GlucoMove does not try to make users enter exact nutrients or precise portion weights every day. Meals are simplified into five carbohydrate levels so people can choose quickly and stay consistent. Users can also attach meal photos that appear later as thumbnails in Records.
Level 1 / 2 / 3 / 4 / 5
Carbohydrate-based levels make meal entry fast, light, and repeatable.
Why levels
Precise food input is hard to sustain and hard to measure accurately in real life.
Photo support
Meal photos can be reviewed later in Records alongside the log itself.
04 · Activity
Activity is the most practical daily control variable.
Users can choose from more than 30 activity types, with seven recommended activities highlighted for real-life glucose spike control. Intensity is shown with three colored dot levels, and activity can be logged by 5-minute duration blocks or repetition counts. Photos can also be attached.
30+ types
Structured enough to capture real-life differences between activities.
7 recommended
Designed around activities that are practical and effective for daily spike control.
3 intensity levels
Visualized with dots and color so intensity is easy to read quickly.
Time or repetitions
Users can log movement in the way that best matches the activity itself.
05 · Post-meal
Post-meal completes the event and reveals the change.
Ordinary datasets should talk about the change and whether the rise looked smaller. They should not claim an exact mg/dL reduction from activity because meal composition and quantity are not captured with laboratory precision in everyday use.
Read the change.
Look for whether the post-meal rise was smaller, more stable, or more predictable.
Avoid exact reduction claims.
Ordinary daily datasets are strong for pattern reading, not for exact effect claims.
Why activity matters enough to sit inside the core loop.
On difficult meal days, people may still be able to act through movement. That is why GlucoMove centers activity as a practical lever for making the rise feel smaller and the pattern more repeatable.
Realistic
It can be repeated in ordinary life.
Visible
Users can feel and see that the post-meal change looked smaller.
Trainable
Repeated sets make the routine easier to trust and reuse.