Apple Health Pro 是一款专为数据分析师、健康极客和开发者设计的跨平台桌面工具。它能够高效解析 Apple 健康导出的巨型 XML 压缩包，并将其转化为结构清晰、开箱即用的专业级数据集。
Apple Health Pro is a high-performance cross-platform desktop tool designed for data analysts, health enthusiasts, and developers. It efficiently parses massive Apple Health XML export archives and transforms them into organized, analysis-ready professional datasets.
采用 iterparse 空间优化技术。即使面对数 GB 级别的 export.xml，也能保证极低的内存占用，彻底告别程序崩溃。 Utilizing iterparse space optimization. It ensures extremely low memory usage even with multi-GB export.xml files, eliminating system crashes.
自动识别所有数据来源（包括 Apple Watch, iPhone 及第三方 App），支持用户按需选取特定来源进行精准提取，避免数据重复。 Automatically identifies all data sources (including Apple Watch, iPhone, and 3rd-party apps), allowing users to select specific sources for precise extraction and avoid data duplication.
开启隐私保护模式后，应用将自动抹除包含个人信息的硬件原始名称（如“某某的 Apple Watch”），并替换为标准化的专属匿名标签，全方位保障数据对外分享与分析的安全性。 Enable the privacy shield mode to automatically scrub original hardware device names containing personal information (e.g., "John's Apple Watch") and replace them with standardized anonymous tags, fully ensuring the security of data sharing and analysis.
引擎会在导入瞬间极速嗅探并自动填报数据的真实起止时间。结合全新设计的纯文本约束化输入框，用户可以极其高效、精准地截取并导出特定时间段内的健康记录。 The engine rapidly detects and auto-fills the true start and end dates of your data upon import. Combined with a newly designed masked text input, users can highly efficiently and precisely extract health records within any specific timeframe.
针对大规模时间序列数据（如分钟级心率），应用执行自动分片逻辑。当单文件超过 80 万行时自动拆分，确保 Excel 及各类 AI 模型可以流畅加载。 For large-scale time-series data, the app executes auto-sharding. Files are automatically split when exceeding 800,000 rows, ensuring smooth loading in Excel and AI models.
Apple Health Pro v8.5.0 utilizes a dual-tag parsing engine to seamlessly extract both raw data points (Record) and functional training logs (Workout), categorized into 15 professional dimensions:
| 分类序号 | 维度名称 (Category) | 导出文件名 (Exported CSV) | 核心指标涵盖 (Key Metrics Included) |
|---|---|---|---|
| 01 | 核心心血管 (Heart & Cardio) | 1_Heart_Cardio.csv |
心率、静息心率、HRV (心率变异性)、步行平均心率 |
| 02 | 身体成分 (Body Metrics) | 2_Body_Metrics.csv |
体重、BMI、体脂率、瘦体重、身体水分 |
| 03 | 日常基础消耗 (Daily Activity) | 3_Daily_Activity.csv |
步数、活动能量消耗、静息能量消耗、步行距离、爬楼层数 |
| 04 | 睡眠与恢复 (Sleep Recovery) | 4_Sleep_Recovery.csv |
睡眠分析 (核心、深度、快速动眼、清醒等状态) |
| 05 | 步态与行动力 (Mobility & Gait) | 5_Mobility_Gait.csv |
步行速度、步长、不对称性、双足支撑时间、步态稳定性 |
| 06 | 生殖与生理健康 (Reproductive) | 6_Reproductive.csv |
经期记录、排卵测试结果、宫颈粘液质量 |
| 07 | 生命体征 (Vitals & Respiratory) | 7_Vitals_Respiratory.csv |
血氧饱和度、呼吸率、体温、血压 |
| 08 | 跑步硬核动态 (Running Dynamics) | 8_Running_Dynamics.csv |
跑步功率、垂直振幅、触地时间、跑步步幅、跑步速度 |
| 09 | 骑行表现 (Cycling Stats) | 9_Cycling_Stats.csv |
骑行功率、踏频、骑行速度、功能性阈值功率 (FTP) |
| 10 | 游泳与水域 (Swimming & Water) | 10_Swimming_Water.csv |
游泳距离、划水次数、水下深度、水温 |
| 11 | 通用体能训练 (Workouts & Training) | 11_Workouts_Training.csv |
力量训练、瑜伽、HIIT、传统跑/骑/游等所有手动开启的运动记录 |
| 12 | 环境与感官 (Environment & Senses) | 12_Environment_Senses.csv |
日照时间、环境音量暴露、耳机音量暴露 |
| 13 | 营养与摄入 (Nutrition & Hydration) | 13_Nutrition_Hydration.csv |
膳食能量、碳水、蛋白质、饮水量、咖啡因摄入 |
| 14 | 心理状态与正念 (Mindfulness & Mental) | 14_Mindfulness_Mental.csv |
心理状态打卡、情绪追踪、正念冥想时间 |
| 15 | 症状与病史 (Symptoms & Illness) | 15_Symptoms_Illness.csv |
头痛、咳嗽、疲劳等手动打卡的各类症状记录 |
在 iPhone “健康” App 中点击头像 -> “导出所有健康数据”。获得 export.zip。
Export data in the iPhone "Health" App via Profile -> "Export All Health Data" to obtain export.zip.
启动程序，点击 SELECT DATA ARCHIVE (.ZIP)。系统执行索引构建。 Launch the app and click SELECT DATA ARCHIVE (.ZIP). The system builds the data index.
在 IDENTIFIED SOURCES 列表中勾选目标源。默认状态为“全不选”。 Check target sources in the IDENTIFIED SOURCES list. Default is set to deselect all.
系统已自动填报该数据包的真实起止时间。如有需要，可直接在文本框中输入修改，以精准截取特定时间段。 The system automatically fills in the true start and end dates of the archive. You can directly edit the text fields to precisely extract a specific timeframe if needed.
（可选）勾选 Mask Device Names 选项，应用将自动抹除并匿名化原始硬件名称，全方位保护您的隐私安全。 (Optional) Check the Mask Device Names option to automatically scrub and anonymize original hardware names for comprehensive privacy protection.
点击 EXECUTE EXPORT。生成的 CSV 将存储于原压缩包同级目录。 Click EXECUTE EXPORT. CSV files will be saved in the same directory as the source zip.
你是一名具备运动生理学、 心血管医学 和健康数据建模能力的专业分析师。我将提供Apple Health原始数据（CSV），请基于数据进行接近专业体检级别的分析，并严格按照以下结构输出：首先给出【一句话结论】，直接判断整体健康状态（健康 / 亚健康 / 风险状态），不得模糊；然后进行【生理系统拆解分析】，从心血管系统（心率、HRV、静息心率）、神经系统（基于HRV分析交感/副交感平衡）、睡眠恢复系统、代谢与活动水平四个层面分析，必须解释背后的生理机制而非表象；接着进行【趋势建模】，基于时间序列判断是否存在周期性波动、长期改善或恶化趋势
You are a professional analyst with expertise in exercise physiology, cardiovascular medicine, and health data modeling. I will provide raw Apple Health data (CSV). Please conduct a professional, clinical-grade analysis based on this data, adhering strictly to the following structure:
[One-Sentence Conclusion]: Provide a direct assessment of the overall health status (Healthy / Sub-healthy / At-risk) without ambiguity.
[Physiological System Breakdown Analysis]: Analyze four key areas—the cardiovascular system (Heart Rate, HRV, Resting Heart Rate), the nervous system (Sympathetic/Parasympathetic balance based on HRV), the sleep recovery system, and metabolism/activity levels. You must explain the underlying physiological mechanisms rather than merely reporting surface-level observations.
[Trend Modeling]: Based on time-series data, identify periodic fluctuations and determine whether there are long-term trends of improvement or deterioration.
无需配置 Python 环境，直接下载构建完成的二进制包： No Python environment required. Download the pre-built binary packages directly:
# AppImage — 下载即用 / Download and run directly
chmod +x HealthPro-8.4.0-x86_64.AppImage
./HealthPro-8.4.0-x86_64.AppImage
# Debian/Ubuntu — deb 安装 / Install with dpkg
sudo dpkg -i healthpro_8.4.0_amd64.deb
sudo apt-get install -f # 自动修复依赖 / Fix dependencies if neededThis project exists thanks to all the people who contribute. Special thanks to the following developers for their outstanding contributions to Apple Health Pro: 特别感谢以下开发者对本项目的杰出贡献：
- @CybDnb - For building the Linux native support (AppImage & deb) and the automated CI/CD packaging pipeline. (为本项目构建了完整的 Linux 原生支持与自动化打包流水线。)
I'm LEEcDiang, a developer passionate about health data and studio-grade tools. If you find this tool helpful, check out more of my thoughts and tutorials on my blog: 👉 leecdiang.github.io