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Calorie Counter - EasyFit

Calorie Counter - EasyFit

Mario Herzberg

Health & Fitness免费v3.7
App Store
评分

4.4

1,564 条评分

星级

★★★★☆

最近更新

2024年12月16日

发布日期

2019年5月7日

更新内容

v3.7

- Barcode Scanner Feature. - Use existing food as template to create a custom food.

应用信息

开发者
Mario Herzberg
分类
Health & Fitness
价格
免费
版本
3.7
App ID
1460863487

简介

EasyFit calorie counter tracks your food, exercises, weight loss progress and macros. See animated statistics, make your own food and achieve all your fitness goal. Use Easy Fit to lose weight, gain muscles or simply improve your fitness. ----- Very Effective ----- - No hundreds of lists of same food. Just pick the food and add it. All calorie estimation are carefully calculated and very well tested to produce great results. - Search function for all the foods and exercises is integrated. - Create your own food in addition to +1500 default foods and let EasyFit calculate the total calories and macros automatically. ----- 100% Privacy ----- - NO shady permissions. NO data collecting/selling like your contacts or location. Everything is saved locally. Your privacy is guaranteed! ----- Statistics ----- -Many animated & zoomable statistics about your calories, exercise time, macros and weight loss. - Set your custom daily macro percentages that you would like to achieve. ----- Personalisation ----- - This food diary has 42 beautiful themes to choose from and put your own feel in this beautiful and originally designed app. ----- Apple Health ----- Apple Health App Integration. Enjoy the ability to have our calorie counter app send your diet data such as dietary energy, protein, carbohydrates, fats, weight loss statistics to the apple health app. ----- Website ----- http://www.easyfit-caloriecounter.de ----- Note from Developer ----- Please write an email about any opinion/wish you have. I gladly communicate with my users :) Email: easyfit@easyfit-caloriecounter.de

下载量预测

专业 · 预览

预估总下载量

142K104K223K
保守估计乐观估计

1K

低 / 月

2K

预估 / 月

3K

高 / 月

基于1,564 条评分
假设评分率1.1%
应用年龄85 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。