Decision 3 of 10

Why we track added sugar, and what we mean by it

Snapkin says “added sugar” everywhere, because that is the phrase people use. The definition underneath is the World Health Organization's free sugars, which covers more.

Written and maintained by Mattias Geniar. Last updated 10 September 2026.

What the app does: Counts WHO free sugars and sets the daily limit at 10% of your calorie target. No streak on it, no reward for staying under.
How much we believe it: Less than anything else the app shows. It is a rough budget, not a measurement, which is why it has no confidence badge.

What “added sugar” means here

The definition underneath is the World Health Organization's free sugars: sugar added by a manufacturer, a cook or the person eating, plus honey, syrups, and fruit juice and juice concentrates. It does not include the sugar in whole fruit, in vegetables, or in plain milk and yoghurt.

The “Added Sugars” line on a US nutrition label (21 CFR 101.9) is not the same thing. Total sugars, the figure a European label declares, is a third thing again. The three disagree in exactly one place.

Three definitions of sugar, and the one row where they disagree What each one counts, for the same food.

Counted as sugar?	Snapkin (WHO free sugars)	US label “Added Sugars”	EU label “of which sugars”
Sugar stirred in, or added by a manufacturer	Yes	Yes	Yes
Honey, syrups, molasses	Yes	Yes	Yes
100% fruit juice and its concentrates	Yes	No	Yes
Sugar in whole fruit and vegetables	No	No	Yes
Lactose in plain milk and yoghurt	No	No	Yes

A glass of orange juice is 0 g of “added sugar” on an American label and roughly 21 g in Snapkin. We chose the wider definition on purpose, because juice is exactly what people mean when they say they are cutting sugar. Total sugars is the wrong number for this job: a limit that turns amber for a bowl of berries is a limit people stop reading.

The WHO guideline recommends keeping free sugars below 10% of energy, and suggests going below 5% if possible. Snapkin sets the daily limit at 10% of your calorie target.

How the number is worked out

Free sugars are not printed on any European label, so they have to be estimated. There is a published method for exactly this, and we follow it rather than inventing our own. Louie et al. (2015) set out ten steps, starting from hard evidence and ending with guesses by food category. Kibblewhite et al. (2017) restated them for WHO free sugars. Scapin et al. (2021) adapted them for a country whose labels declare neither total nor added sugars.

Scapin checked the method against FDA-declared values on 930 products and reached an agreement score (ICC) of 0.98. An independent test of Louie's method (Davies et al., 2022) put it at R² 0.97 with a mean absolute error of 1.26 g per 100 g. In both cases that was one specialist working through packets with the ingredient list in hand.

0.98 ICC against FDA-declared values, 930 products One specialist, ingredient lists in hand

1.26 g mean absolute error per 100 g, independent test Davies et al. 2022, R² 0.97

4.6 g standard deviation between two trained researchers, per 100 g Louie, Lei & Rangan 2016, the same 5,740 foods

That last number shows what the method is really worth. Two people applying the same written steps to the same 5,740 foods differed with a standard deviation of 4.6 g per 100 g. That is three and a half times the error the validation studies report.

The evidence against, and there is a lot of it

Sugar does almost nothing at matched calories

Te Morenga et al. (BMJ 2013): eating less sugar changes weight by −0.80 kg (95% CI −0.39 to −1.21) and eating more by +0.75 kg (0.30 to 1.19). But there is no evidence of any effect when total calories are kept the same. Cutting sugar works because it removes easy calories and because it is a rule you can follow, not because sugar does something special to your body. We present the limit as a rule of thumb for that reason.

Estimating sugar from a photograph is the weakest thing this app does

In the whole research literature there is one peer-reviewed study that reports a sugar figure from food photographs: O'Hara et al. (2025), 114 photographs of weighed meals. Nobody anywhere has separated added sugar from total sugar in a photograph. Other research groups refuse to analyse sugar at all and say why: it hides in sauces and processed foods and cannot be seen.

Sugar is the nutrient a photograph gets most wrong How far each nutrient was out, over 114 photographs of weighed meals.

Sugar 32% low

The single largest error in the study, and the only sugar figure in the literature

Protein 2.7% low

Same photographs, same models

Energy 0.1% high

Almost exact on average, which is what makes the sugar row worth stating

0 35% out

Energy is almost exactly right and a third of the sugar is missing, in the same study, from the same pictures. One encouraging detail in the same result: sugar was the worst nutrient by amount but among the best by ranking, with a rank correlation of 0.75 by amount and 0.84 as a share of energy. The model knows which meals are sugary. It guesses too low on how much.

“Carbs that turn into sugar” is a different thing, and we do not track it

That is glycaemic load, not sugar. Zeevi et al. (Cell 2015) put 800 people on continuous glucose monitors across 46,898 meals and found that different people respond very differently to the same meal. There is no single number per food to put in a table, so we do not pretend to have one.

So the sugar limit is a rough budget rather than a measurement, and it carries no confidence badge. The eating disorders page has the other half of this decision: there is no streak on the sugar limit, and no celebration for staying under it.