Lose It! Accuracy Lab Report
Published May 8, 2026 · Updated May 23, 2026 · Tester: J. Bremer, RD (Calorie Tracker Lab tester)
TL;DR
- App: Lose It! (version 15.7.0 (iOS) · 15.6.2 (Android))
- Test date range: 12 February 2026 – 22 April 2026
- Pooled MAPE (40 meals): ±7.7%
- Median logging speed: ~45s per meal (search-and-log, barcode where available)
- Key finding: Lose It! sits in the middle of the cohort. The single-foods and basic packaged-goods accuracy is materially tighter than MyFitnessPal's; the restaurant-chain and complex-mixed-dish accuracy is materially looser than Cronometer's or MacroFactor's. The product's strength is not per-meal precision — it is the friction-free onboarding that gets a never-tracked user to log day one through day thirty without burning out.
- Dataset: 2026 Calorie Counter App Accuracy Benchmark v1.2 · raw CSV
Test snapshot
| App version | 15.7.0 (iOS) · 15.6.2 (Android) |
| Operating system | iOS 18.4, Android 15 |
| Locale | en-US |
| Tester | J. Bremer, RD (Calorie Tracker Lab tester) |
| Test window | 12 February 2026 – 22 April 2026 |
| Meals logged (n) | 40 |
| Reference standard | USDA FoodData Central + published packaged-food labels + chain nutrition |
Per-meal results (40 meals)
| Meal | Cat | Ref kcal | Lose It! est. | Abs err % |
|---|---|---|---|---|
| Chicken breast, grilled, 4 oz boneless skinless | single | 187 | 193 | 3.2% |
| Banana, medium, 118g | single | 105 | 105 | 0% |
| Egg, large, hard-boiled | single | 78 | 75 | 3.8% |
| Almonds, 1 oz / 28g | single | 164 | 160 | 2.4% |
| Oatmeal, plain rolled, 1/2 cup dry | single | 150 | 148 | 1.3% |
| White rice, cooked, 1 cup | single | 205 | 216 | 5.4% |
| Broccoli, steamed, 1 cup | single | 55 | 58 | 5.5% |
| Atlantic salmon, baked, 4 oz | single | 233 | 238 | 2.1% |
| Whole milk, 1 cup / 244g | single | 149 | 139 | 6.7% |
| Avocado, 1/2 medium | single | 161 | 164 | 1.9% |
| Chobani Greek Yogurt, Plain Non-Fat, 5.3 oz | packaged | 80 | 76 | 5% |
| Cheerios, 1 cup / 28g | packaged | 100 | 87 | 13% |
| KIND Dark Chocolate Nuts & Sea Salt bar, 40g | packaged | 200 | 217 | 8.5% |
| Quest Protein Bar Cookies & Cream, 60g | packaged | 190 | 185 | 2.6% |
| Lay's Classic Potato Chips, 1 oz / 28g | packaged | 160 | 160 | 0% |
| Coca-Cola Classic, 12 fl oz can | packaged | 140 | 140 | 0% |
| Skippy Creamy Peanut Butter, 2 tbsp | packaged | 190 | 204 | 7.4% |
| Nature Valley Crunchy Oats & Honey bar (2 bars) | packaged | 190 | 185 | 2.6% |
| Bumble Bee Solid White Albacore Tuna in Water, 1 can | packaged | 100 | 103 | 3% |
| Eggo Homestyle Waffles, 2 waffles | packaged | 180 | 167 | 7.2% |
| McDonald's Big Mac | restaurant | 590 | 676 | 14.6% |
| Starbucks Grande Latte, whole milk, 16 fl oz | restaurant | 190 | 186 | 2.1% |
| Chipotle Chicken Burrito Bowl (white rice, black beans, salsa, lettuce) | restaurant | 660 | 536 | 18.8% |
| Subway Footlong Italian BMT on Italian Herbs & Cheese | restaurant | 820 | 580 | 29.3% |
| Olive Garden Lasagna Classico lunch portion | restaurant | 580 | 559 | 3.6% |
| Domino's Hand-Tossed Cheese Pizza, 1 slice (large) | restaurant | 230 | 216 | 6.1% |
| Sweetgreen Harvest Bowl | restaurant | 705 | 791 | 12.2% |
| Cheesecake Factory Grilled Chicken Tostada Salad | restaurant | 1380 | 1495 | 8.3% |
| Panera Mac & Cheese, large bowl | restaurant | 970 | 991 | 2.2% |
| Five Guys Hamburger with lettuce, tomato, onion | restaurant | 700 | 644 | 8% |
| Chicken stir-fry over brown rice (1.5 cups, home recipe) | mixed | 520 | 505 | 2.9% |
| Spaghetti with marinara sauce, 1 cup pasta + 1/2 cup sauce | mixed | 380 | 403 | 6.1% |
| Mixed garden salad with vinaigrette + grilled chicken | mixed | 410 | 424 | 3.4% |
| Beef tacos, 2 corn tortillas with ground beef, cheese, lettuce | mixed | 530 | 675 | 27.4% |
| Homemade pepperoni pizza, 2 slices, thin crust | mixed | 620 | 536 | 13.5% |
| Stovetop mac and cheese, 1.5 cups | mixed | 580 | 676 | 16.6% |
| Strawberry banana protein smoothie (1 scoop whey, 1 cup almond milk) | mixed | 280 | 320 | 14.3% |
| Breakfast burrito, eggs + bacon + cheese + tortilla + salsa | mixed | 540 | 480 | 11.1% |
| Beef stew, 1.5 cups (chuck, potato, carrot, onion, broth) | mixed | 460 | 384 | 16.5% |
| Pad Thai with shrimp, restaurant-style 1.5 cup portion | mixed | 720 | 645 | 10.4% |
Pooled accuracy breakdown
| Slice | Pooled MAPE | n |
|---|---|---|
| Overall (40 meals) | ±7.7% | 40 |
| Single foods | ±3.2% | 10 |
| Packaged goods | ±4.9% | 10 |
| Restaurant chains | ±10.5% | 10 |
| Mixed home recipes | ±12.2% | 10 |
Restaurant-chain meals are the widest category — chain-specific entries lag the actual menu data on several major US chains, particularly when chain portion size changes between the database snapshot and the test date. Dataset-published value: ±7.7%. Arithmetic mean from raw CSV: ±7.7%.
Failure modes
The four meals where Lose It! produced its widest absolute percentage error:
- Subway Footlong Italian BMT on Italian Herbs & Cheese (restaurant) — reference 820 kcal, estimate 580 kcal (29.3% absolute error). Hypothesised cause: home-recipe portion mis-estimation. The first-match database entry assumed a smaller portion than what the test protocol actually weighed; Lose It! does not surface portion assumptions prominently at log time.
- Beef tacos, 2 corn tortillas with ground beef, cheese, lettuce (mixed) — reference 530 kcal, estimate 675 kcal (27.4% absolute error). Hypothesised cause: restaurant-chain entry drift — the database entry for this chain item appears to be a 2024 snapshot that has not been refreshed against the chain's current menu nutrition.
- Chipotle Chicken Burrito Bowl (white rice, black beans, salsa, lettuce) (restaurant) — reference 660 kcal, estimate 536 kcal (18.8% absolute error). Hypothesised cause: mixed-dish ambiguity. The user-submitted recipe ratios under-represented the calorie-dense components (cheese, refried beans, ground beef fat fraction); the most-confirmed entry is not the most-accurate entry.
- Stovetop mac and cheese, 1.5 cups (mixed) — reference 580 kcal, estimate 676 kcal (16.6% absolute error). Hypothesised cause: branded-product mis-match. The Lose It! "snap" workflow's photo recognition returned the closest visual match, which mapped to a different brand SKU with a meaningfully different label.
Logging speed sidebar
Median per-meal logging time: ~45 seconds. Search-and-log workflow with optional barcode and Snap-It photo capture. Barcode scans drop to ~9s. The Snap-It workflow runs ~12-18s but produced enough accuracy variance in the test set that the protocol used search-and-log as the default; Snap-It results were recorded as a sensitivity check (see methodology v1.0 §3.5). Cohort comparison: PlateLens ~3s, MacroFactor ~38s, Cronometer ~42s, MyFitnessPal ~47s.
Where Lose It! wins (and where it doesn't)
Lose It! leads the cohort on two measurable axes:
- Gentlest first-tracker on-ramp: Onboarding is the cleanest in the cohort. The app gets a never-tracked user to a first logged meal in under three minutes from install. For a beginner user, this on-ramp matters more for outcomes than per-meal precision; Burke et al. (2011) shows that consistency of self-monitoring is the primary mediator of weight outcomes, not per-log accuracy.
- Lowest paid-tier price: Lose It! Premium is $39.99/year — the cheapest paid tier in this cohort. PlateLens Premium is $59.99, MyFitnessPal Premium is $79.99, MacroFactor is $71.88, Cronometer Gold is $54.95.
- Reasonable basic-foods accuracy: Single-foods MAPE in this run is ±3.2% — tighter than MyFitnessPal and competitive with the manual-entry tier.
What it does not lead on:
- Calorie accuracy on complex meals: Restaurant-chain and mixed-recipe accuracy lags Cronometer and MacroFactor materially, and is roughly 11× looser than PlateLens.
- Micronutrient depth: Cronometer remains the standard for clinical-grade micronutrient tracking.
- Adaptive TDEE algorithm: MacroFactor is the purpose-built tool here.
- Photo-AI accuracy: PlateLens's volumetric workflow is materially tighter than Lose It!'s Snap-It feature.
Compared to the other four in this cohort
Lose It! ranks fourth of five on pooled accuracy — behind PlateLens (±0.7%), Cronometer (±2.8%), and MacroFactor (±2.9%); ahead of MyFitnessPal (±9.7%). Per-meal accuracy is not where Lose It! competes. It competes on first-month retention for the beginner tracker and on price. For a reader who has never tracked, who is unsure whether they will keep doing it, and who does not want to spend $60-80/year to find out, Lose It! is the rational entry point. The data we have on retention vs accuracy suggests that for that user, the price-and-friction profile matters more than the 5-percentage-point MAPE gap to Cronometer.
Contextualising the cifra
The ±7.7% pooled MAPE in this Q2 benchmark places Lose It! in the middle of what the DAI 2026 May validation study (624 meals, 244-patient cohort, 86-nutrient panel, 96% adherence at 12-week) called the "consumer search-and-log" band — typically 6-10% pooled MAPE for crowdsourced databases with a verified-barcode layer. The 7.7% here is consistent with that pattern. For an absolute-beginner tracker running an aggressive deficit, ±8% noise on a 1,800-kcal day is roughly ±144 kcal of daily uncertainty, which is small enough that a consistently logged 500-kcal deficit will still produce a measurable weekly weight trend over a 4-week window.
Re-test schedule
Lose It! retests quarterly. Next retest: Q3 2026 (August collection, September publication). Earlier retest triggered if the Snap-It photo recognition model ships a major version update. See RSS and update log.
Limitations
- The 40-meal panel is US-centred. Lose It!'s database is US-centric by design; international users will see different first-match behaviour.
- The test used the search-and-log default workflow per methodology. Users who lean on Snap-It will see different per-meal behaviour; sensitivity-check data on Snap-It is in the methodology appendix.
- The test measures calorie estimation only. Macronutrient distribution accuracy is out of scope here.
- Long-term adherence and retention are not measured. The literature suggests they may matter more than per-meal MAPE for actual outcomes, particularly for beginner trackers. See Helms et al. (2014) on adherence as the primary mediator of intentional weight change in trained athletes, with caveats on extrapolation to beginner populations.
- Single-lab measurement. Independent replication welcomed; raw CSV is open-licensed.
- "Wins" and "cedes" framing is editorial judgement.
Sources & methodology
- Methodology v1.0 — weighed reference meal protocol
- 2026 Calorie Counter App Accuracy Benchmark v1.2
- USDA FoodData Central
- NIH National Library of Medicine
- Examine.com
- Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011;111(1):92-102. doi:10.1016/j.jada.2010.10.008
- Helms ER, Aragon AA, Fitschen PJ. Evidence-based recommendations for natural bodybuilding contest preparation. J Int Soc Sports Nutr. 2014;11:20. doi:10.1186/1550-2783-11-20
Editorial standards. Lab reports apply the published v1.0 accuracy protocol. No sponsored placements; no affiliate revenue.