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Food Tracker

The food tracker built for diabetes, not for dieting.

Photo meal logs. Carb counts trusted by 250,000+ users. Glucose context when you connect a CGM. AI coaching that understands T1D, T2D, GDM, prediabetes, and GLP-1 contexts.

SNAQ app on iPhone showing a photo-logged meal with CGM glucose curve overlay
WHAT IS A DIABETES FOOD TRACKER

What makes a food tracker built for diabetes different?

A diabetes food tracker is an app that records meals and their carbohydrate, protein, fat, and fibre content with a focus on glucose management rather than calorie counting. Generic food trackers optimise for weight loss. A diabetes-specific tracker puts carb accuracy, glucose context, and clinical relevance first. SNAQ connects meal photos directly to CGM glucose data, so you can see not just what you ate but how similar meals have tended to affect your glucose over time.

The problem

Generic food trackers were never built for diabetes.

Calorie-counting apps optimise for weight loss. Diabetes management often focuses on carb accuracy and glucose response. A food tracker built for diabetes puts those metrics first, not the scale.

Diabetes-first

Built around the metric many people with diabetes care about most: glucose response.

Every meal can be photo-logged and overlaid on your CGM curve to see patterns over time. When no CGM is connected, SNAQ still tracks meals and surfaces carb and nutrition patterns. The AI Coach understands diabetes context, not a generic weight-loss playbook.

T1D · T2D · GDM · prediabetes GLP-1 aware CGM overlay optional
SNAQ insights tab showing glucose and meal patterns over time
Why it works

Built on clinical evidence.

+6.6 pp

Time in Range

Time in Range (the proportion of time glucose stays within a healthy target range). Published RCT result in T1D users, measured by CGM, not self-report.

eClinicalMedicine, 2025
−38%

Fewer carb errors

Versus T1D self-estimates in a head-to-head accuracy study.

JDST, 2024
Pattern AI

Pattern detection across weeks

Spots the foods, timings, and combinations that move your numbers, automatically.

AI Nutritionist · always on
Clinical Evidence
+6.6 pp
Time in Range (the proportion of time glucose stays within a healthy target range) · eClinicalMedicine · 2025

One of the few consumer nutrition apps backed by peer-reviewed clinical evidence in diabetes.

Validated in a randomised controlled trial with T1D patients on automated insulin delivery. Participants spent more time in their blood sugar target range.

Source: eClinicalMedicine (The Lancet), 2025 · 53 T1D participants.

HOW IT COMPARES

Built for diabetes. Not retrofitted for it.

Generic food tracker SNAQ
Primary focus Calories Carbs and glucose response
Meal logging Manual search or barcode Photo, voice, barcode, label, favourites
Glucose context None CGM overlay (optional)
Coaching Generic nutrition tips Patterns from your own meal and glucose data
Clinical evidence No Published RCT and accuracy study

Real users. Real outcomes.

★★★★★
Really insightful and helpful with seeing glucose patterns related to the meals and foods he eats.
M.K., App Store, 5 stars (parent of child with T1D)
★★★★★
I want to know what my blood sugar did the last time I ate a certain meal, and the ability to pull up exactly that data when I need it is invaluable.
E.S., App Store, 5 stars

Common questions.

What makes SNAQ different from MyFitnessPal or Carb Manager?
Generic calorie trackers optimise for weight loss. SNAQ optimises for the metric that matters in diabetes: glucose response. Every photo-logged meal can be overlaid on your CGM curve, carb accuracy is RCT-validated, and the AI Coach coaches in a diabetes context, not a generic weight-loss playbook.
Does SNAQ work for T1D, T2D, gestational diabetes, prediabetes, and GLP-1 users?
Yes. SNAQ works across all these contexts. For T1D users, meals can be overlaid on pump and CGM data. For T2D and prediabetes, the focus is on meal and glucose patterns over time. For gestational diabetes, carb distribution per meal is tracked. For GLP-1 users, the app supports protein and meal composition awareness. Features vary by setup. Connect a CGM for deeper glucose context.
How accurate is the meal recognition?
In a peer-reviewed accuracy study (Journal of Diabetes Science and Technology, 2024), SNAQ reduced carbohydrate estimation errors by 38% compared to self-estimates by 53 adults with T1D. The underlying volumetric technology was validated across 48 meals (JMIR mHealth, 2020) with a mean absolute carb error of 5.5 g.
Do I need a CGM to use the food tracker?
No. SNAQ works camera-only, no sensor required. Photo meal logs, carb counts, and AI coaching all work without a CGM. If you do connect one (Dexcom, FreeStyle Libre, Stelo, Lingo), SNAQ overlays each meal on your glucose curve.
Free to download

The food tracker built for the way diabetes works.

Photo logs. Glucose context. AI coaching. 7-day free trial.

4.6 · 2.7K+ App Store ratings

iOS 16+ · Android 8+ · Free to start

Coming from MyFitnessPal or Carb Manager?

SNAQ's photo recognition + glucose context go beyond manual entry.

See comparison →
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