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Vita AI: GLP-1 Assistant

Vita AI: GLP-1 Assistant

Blitzbuild Software

Health & Fitness免费v3.0.1
App Store
评分

5.0

14 条评分

星级

★★★★★

最近更新

2026年5月11日

发布日期

2025年12月16日

更新内容

v3.0.1

New in version 3.0.1: • Improved medication logging • Improved symptom tracking • Added medication reminder notifications • Updated UI for a cleaner and more intuitive experience This release also includes general bug fixes and performance improvements.

应用信息

开发者
Blitzbuild Software
分类
Health & Fitness
价格
免费
版本
3.0.1
App ID
6748570255

简介

Vita AI is your GLP-1 assistant for food, medication, symptoms, and progress tracking. Know what to eat, stay consistent with your routine, and better understand how your meals, medication, symptoms, and weight connect over time. Use Vita AI to: • Scan barcodes and analyze food photos • Chat with AI for personalized GLP-1 guidance • Log meals, symptoms, medications, and weight • View your history in a simple calendar • Track trends over time and stay on top of your routine Vita helps you make everyday decisions with more confidence. Quickly check whether a food fits your goals, discover better options, and build meals that better support protein intake, fullness, and overall progress on GLP-1. You can also track symptoms, medications, weight, sleep, exercise, and habits in one place. See patterns over time, review your daily logs, and generate simple summaries that help you stay organized and support more informed conversations with healthcare professionals. Vita also includes reminders, guided check-ins, and a progress system designed to help you stay consistent with healthy habits. Vita is not a medical device and does not provide medical diagnosis or treatment. Always consult a qualified healthcare professional before making medical decisions. Privacy Policy: https://www.myvita.health/privacy Terms of Use (EULA): https://www.apple.com/legal/internet-services/itunes/dev/stdeula/

下载量预测

专业 · 预览

预估总下载量

1K9332K
保守估计乐观估计

187

低 / 月

255

预估 / 月

400

高 / 月

基于14 条评分
假设评分率1.1%
应用年龄5 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。