Halthy: A privacy-first bridge between Apple Health and Home Assistant for workout tracking and long-term analytics

Table of Contents

1. Key Highlights
2. Introduction
3. What Halthy is and why it matters
4. How Halthy moves data: architecture and data flow
5. Home Assistant integration: what appears in your server
6. Why InfluxDB matters for long-term, sample-level analysis
7. Privacy design: no cloud accounts, no trackers
8. Use cases and real-world scenarios
9. Features that matter day-to-day
10. Installing and setting up Halthy
11. Multi-user handling and household workflows
12. Comparison with commercial services (Strava, Komoot, etc.)
13. Limitations and trade-offs
14. Advanced usage patterns: Grafana, automation triggers, and research
15. Exporting and sharing: GPX, CSV, and PDFs
16. Practical advice: tokens, security, and troubleshooting
17. Where Halthy is heading: potential features and roadmap ideas
18. Troubleshooting common issues
19. How Halthy fits into a data-responsible fitness workflow
20. Community, contribution, and where to find Halthy
21. Final considerations before adopting Halthy
22. FAQ

Key Highlights

• Halthy sends selected Apple HealthKit data and workouts directly from an iPhone to a self-controlled Home Assistant or InfluxDB instance with no intermediary cloud, tracking, or analytics.
• The app creates Home Assistant sensors, calendar entities, and a workout dashboard (with maps, statistics, replay and exports); it also supports writing Home Assistant sensor values back into HealthKit and raw-sample export to InfluxDB for detailed historical analysis.
• Multi-user support, secure token storage in the iPhone Keychain, HTTPS connections, GPX/CSV/PDF exports, and TestFlight availability make Halthy a practical option for users who want control over their fitness data.

Introduction

Many people rely on third-party platforms to store and visualize fitness and health data. Those services work well but often require trust in an external provider and sometimes a recurring subscription. For users who already run a local server and Home Assistant, relying on a remote cloud to store personal health records can feel unnecessary and uncomfortable.

Halthy emerged from that gap. Built by an engineer who wanted full control of their metrics, this iOS app moves HealthKit data directly to systems users control. It avoids a middleman cloud, preserves privacy, and integrates tightly with Home Assistant and InfluxDB to enable both everyday dashboards and high-resolution historical analysis. The result is an architecture that treats fitness data as personal property — readable and writable by the device owner, and usable inside a server environment for long-term trend analysis, visualization, and sharing under the owner’s terms.

This article explains how Halthy works, what it adds to Home Assistant workflows, how it handles privacy and security, which real-world use cases benefit most, and practical steps for getting started. It compares Halthy to mainstream fitness apps, examines technical trade-offs, and outlines advanced setups for users who want sample-level control and long-term storage with tools like Grafana.

What Halthy is and why it matters

Halthy is an iOS application that acts as a direct conduit between Apple Health (HealthKit) and self-hosted services: Home Assistant for immediate automation and visualization, and InfluxDB for sample-level time-series storage. The core design principle is data ownership. Instead of uploading runs or heart-rate records to a third-party cloud, Halthy transmits the chosen HealthKit data from the phone straight to an endpoint you control.

Why that matters:

• Control: Users decide exactly what data is exported and where it is stored.
• Privacy: No Halthy account, no Halthy cloud, and no embedded third-party analytics or advertising SDKs.
• Extensibility: Data enters the same ecosystem as home sensor data, enabling cross-correlation (e.g., indoor air quality vs. sleep quality).
• Flexibility: Options to push detailed HealthKit samples to InfluxDB preserve raw data for granular dashboards and advanced analytics.

The app addresses the common tension between convenience and privacy: it sacrifices the convenience of cloud services for the guarantee that sensitive data never leaves your infrastructure.

How Halthy moves data: architecture and data flow

Halthy’s architecture emphasizes direct connections and minimal intermediaries. Conceptually, the flow looks like this:

1. HealthKit (on iPhone) — Halthy reads only user-authorized health metrics and workout records.
2. Halthy — Formats and sends data over HTTPS to either:
   • Home Assistant’s REST API (or WebSocket) where data becomes entities, calendar events, and historical statistics; or
   • InfluxDB, where raw HealthKit samples are stored as time-series points.
3. Home Assistant — Offers dashboards, cards, calendar entities, and automation triggers. Halthy also provides a custom integration that simplifies entity creation and a workout dashboard card for workout replay and browsing.
4. Optional reverse sync — Halthy can read specified Home Assistant sensor values and write them back into HealthKit when the user permits (useful for devices unsupported by HomeKit, such as certain scales).

Technical points:

• Authentication: Access tokens for Home Assistant are stored securely in the iPhone Keychain. Users can create dedicated Home Assistant tokens or service accounts to limit scope.
• Transport security: Communications use HTTPS. Self-hosted servers should run TLS (Let’s Encrypt or similar) and, where necessary, configured to accept the app’s connections.
• No intermediary cloud: Halthy does not persist or process data on any third-party server other than the recipient Home Assistant/InfluxDB instance.

This straightforward model reduces attack surfaces associated with third-party cloud storage and gives owners full control over retention policies and backups.

Home Assistant integration: what appears in your server

Halthy’s Home Assistant integration translates HealthKit content into entities and features that fit naturally into Home Assistant.

Key integration features:

• Entities for selected health metrics: Halthy creates sensors for the metrics you choose to sync. Typical metrics include duration, distance, active energy (calories), average and max heart rate, speed, and cadence when available from a workout. These entities behave like any other Home Assistant entity and can be used in automations.
• Workout dashboard card: An integrated card shows workout routes on standard, satellite, and 3D maps, displays main statistics, and includes a calendar-based selector for browsing past workouts. The card supports route replay.
• Calendar entities for workouts: Each workout becomes a calendar entry, making it easy to see a training timeline in Home Assistant’s calendar UI. The calendar entries are also queryable by other parts of Home Assistant and can trigger automations.
• Historical statistics: Halthy produces its own historical statistic entities to support trend following within Home Assistant, providing per-day aggregates tailored to the selected metrics.
• Multi-user support: Each person sets up their own Halthy connection, producing separate entities and calendar histories. This is convenient for households that want to consolidate multiple family members’ fitness records into a single Home Assistant instance without mixing data.

These integration features make Halthy more than a data pump. It becomes a first-class citizen in a Home Assistant setup, allowing users to combine workout data with environmental sensors, energy consumption, or schedule-driven automations.

Why InfluxDB matters for long-term, sample-level analysis

Home Assistant stores stateful entity values and offers long-term statistics, but its historical groups are often aggregated (hourly buckets for many statistics). That works for trend recognition but discards sample-level detail. Runners, cyclists, and researchers frequently want every GPS point, every heart-rate sample, or every cadence reading preserved for precise replay, split analysis, or external visualization tools like Grafana.

Halthy supports pushing raw HealthKit samples directly into InfluxDB. Advantages of that approach:

• Sample resolution: Every recorded HealthKit sample can become a point in InfluxDB, preserving timestamps and fine-grained values for accurate replay and analysis.
• Retention and downsampling control: InfluxDB allows explicit retention policies and continuous queries for downsampling, giving administrators control over storage and data lifecycle.
• Advanced dashboards: Grafana connects to InfluxDB to create customized, high-resolution charts such as per-lap heart-rate curves, grade-adjusted speed profiles, or combined environmental overlays (temperature, humidity) against workout performance.
• Export workflows: Storing in InfluxDB simplifies building export pipelines to other tools or backups.

Real-world example: use cases for InfluxDB with Halthy

• An endurance coach retains every heart-rate sample for athlete profiling over months and uses Grafana to detect overtraining patterns via subtle trends in night-time HRV.
• A triathlete correlates swim pace and pool temperature across hundreds of sessions to identify environmental impacts on swim performance.
• A researcher analyzing the impact of indoor air quality on sleep quality can store every sleep-sensor reading and every overnight heart-rate sample for statistical analysis.

For users who do not require sample-level retention, Home Assistant’s historical statistics suffice and reduce the need for additional infrastructure.

Privacy design: no cloud accounts, no trackers

Privacy is central to Halthy’s design. The app deliberately avoids server-side data collection and common tracking mechanisms that appear in many consumer apps.

Key privacy choices:

• No Halthy account: There is no user account on Halthy servers because there are no Halthy servers. Authentication occurs directly with your Home Assistant instance using a token you control.
• No analytics or advertising SDKs: The app does not include third-party analytics trackers or ad networks that could create hidden data flows.
• Token storage: API tokens live in the iPhone Keychain rather than in local files or less secure storage.
• End-to-end control: All data flows from the iPhone to destinations you own and manage. You set retention, backups, and access rules.

Practical security recommendations:

• Create a dedicated Home Assistant user and token for Halthy with limited permissions to minimize impact if credentials leak.
• Use TLS with a valid certificate for your Home Assistant endpoint to avoid exposing tokens over insecure channels.
• If using a public endpoint, consider IP filtering, VPN access, or cloud-flare-like fronting depending on your threat model.

These practices ensure the privacy claims align with secure deployment.

Use cases and real-world scenarios

Halthy fits multiple real-world scenarios where people want to blend fitness data with other personal or household datasets.

Personal training and performance tracking

• A cyclist tracks every ride route and heart-rate trace, storing samples in InfluxDB. The cyclist uses Grafana to compare power and cadence trends across seasons and to visualize improvements over specific training blocks.
• A runner uses Home Assistant dashboards to see weekly mileage and long-run distributions while the workout calendar helps plan taper weeks.

Household tracking and health monitoring

• Two partners share a Home Assistant instance. Each installs Halthy and writes to separate calendars and entities. This lets a household track collective activity metrics (e.g., combined weekly outdoor time) without outsourcing data to a third party.
• Family caregivers use Halthy for remote monitoring of an elderly relative’s activity patterns, integrating movement data with smart home alerts for missed walks or irregular sleep patterns.

Integrating non-HomeKit devices

• A bathroom scale that reports to Home Assistant but lacks HomeKit integration can still contribute weight entries to HealthKit via Halthy. The weight readings then appear in Apple Health apps and any other iOS apps that read HealthKit. This two-way sync closes a common gap for devices that integrate with the home server but not Apple frameworks.

Research and quantified-self projects

• An academic project combines indoor air sensor readings with overnight heart-rate metrics stored in InfluxDB to study correlations between ventilation and sleep quality.
• A quantified-self enthusiast records detailed training sessions, exports GPX for route analytics, and builds custom dashboards for cadence and stride length over time.

Small organization or team management

• A cycling club runs a shared Home Assistant instance and allows consenting members to push training sessions to the club’s database for group analysis without requiring members to create accounts on a commercial site.

These scenarios emphasize control, cross-correlation, and long-term analysis—advantages of owning the data storage and pipeline.

Features that matter day-to-day

Halthy offers several practical features that make it useful beyond data transport. These features serve both casual users and power users.

Workout visualization and replay

• Workout routes display on map tiles: standard, satellite, or 3D. Seeing a route’s gradient or context helps interpret performance.
• Replay functionality: The app and Home Assistant card support replaying a workout, animating the route and showing metrics over time. Replay is useful for post-session analysis and for creating short highlight videos.

Exports: GPX, CSV, PDF

• GPX export preserves full workout routes for use in mapping software, route-analysis tools, or for sharing with coaches.
• CSV export provides sample-level data useful for ad-hoc analysis in spreadsheets or for importing into other systems.
• PDF export offers a snapshot of a session for sharing in messages or via printed logs.

Adding weather context to workouts

• Halthy can annotate workouts with weather data. Weather overlays contextualize performance—running in hot, humid conditions versus cool, dry days yields different physiological loads.

Workout-replay videos

• The app can create short workout-replay videos suitable for social sharing. These videos combine map animation and key metrics into a concise media asset.

Calendar-based browsing

• The Home Assistant workout card includes a calendar selector. Browsing workouts by date simplifies trend inspection and helps identify consistent training blocks or gaps.

Importing Home Assistant sensors into HealthKit

• Halthy can write Home Assistant sensor values into HealthKit. The primary use case is integrating devices that report to Home Assistant but lack HomeKit compatibility, such as certain smart scales, custom sensors, or garage-door trackers used for activity logging.

These features make Halthy useful immediately while also enabling deeper analytics through InfluxDB or third-party tools.

Installing and setting up Halthy

Halthy is currently available via TestFlight and as a custom repository for HACS. Full App Store release or HACS distribution may follow as the project matures.

Basic setup steps:

1. Install Halthy via TestFlight: Join the TestFlight link (provided by the developer) and install the app on an iPhone with HealthKit data.
2. Authorize HealthKit access: When launching the app, grant Halthy permission only to the HealthKit types you want to share. Fine-grained selection prevents unwanted data transfer.
3. Configure Home Assistant:
   • Option A: Use Home Assistant’s REST API—create a dedicated long-lived access token for Halthy. Store the token in the app’s settings.
   • Option B: Point Halthy to an InfluxDB endpoint and configure database credentials and retention policies.
4. Add Halthy’s integration to Home Assistant:
   • Add the custom repository to HACS or copy the integration files manually to your Home Assistant custom_components directory.
   • Restart Home Assistant and configure the integration to accept data from your device.
5. Verify entities and calendar creation: After a workout or a manual sync, check that the entities, workout calendar entries, and dashboard card appear in Home Assistant.

Practical tips for reliability:

• Use a dedicated Home Assistant service account with minimal privileges.
• Use a valid TLS certificate and avoid self-signed certificates unless you configure the app to accept them.
• If using external access, ensure your router forwards ports securely or rely on a reverse proxy with authentication.
• If integrating with InfluxDB, configure appropriate retention and downsampling strategies to avoid unbounded storage growth.

The developer also recommends keeping the app up-to-date via TestFlight for bug fixes and feature additions.

Multi-user handling and household workflows

Halthy supports multiple users sending their health data to the same Home Assistant instance. Each user has their own separate setup and corresponding entities, calendars, and history.

Household coordination scenarios:

• Partner A and Partner B each install Halthy and connect to the same Home Assistant. Each person’s entities are namespace-separated so that dashboards reflect individual metrics without cross-contamination.
• A family member who prefers not to share is not required to participate. Halthy requires explicit HealthKit permissions for each installation.
• For shared automations, Home Assistant can aggregate or filter across users. For example, automations can detect when any household member completed a workout to trigger group celebrations (lights flash, music plays), or calculate the household’s total active minutes per week.

This model preserves individual privacy while enabling collective insights.

Comparison with commercial services (Strava, Komoot, etc.)

Commercial fitness platforms offer convenience, social features, and polished apps. Halthy targets a different audience: people who prioritize data sovereignty or who want to integrate fitness data into a broader home-server ecosystem.

Where commercial services excel:

• Community features (leaderboards, segments, social sharing).
• Polished, widely-used mobile interfaces with extensive platform integrations.
• Advanced route planning and discovery features (Komoot’s route recommendations, Strava segments).

Where Halthy outperforms for its niche:

• Data ownership: All data goes to infrastructure you control.
• Integration: Tight coupling with Home Assistant and InfluxDB enables cross-domain insights (fitness versus indoor air quality, environmental sensors, smart lights, and more).
• No subscriptions for server-side features: There’s no requirement to pay a third-party cloud service to access your historical data.
• Privacy: No centralized platform collecting aggregates or social metrics unless you choose to export them.

A practical hybrid approach: use both Some users adopt a hybrid workflow. They keep an account on a social/training platform for community interaction but also run Halthy to retain a private archive. This approach preserves social features while guaranteeing a private, long-term backup.

Limitations and trade-offs

Halthy is not a universal replacement for every fitness need. Understanding its limits helps decide whether it fits a user’s workflow.

Current limitations:

• iOS-only by design: Halthy hooks into Apple HealthKit, so it is limited to iPhones and Apple Watch ecosystems.
• Self-hosting requirement: This benefits privacy but requires a Home Assistant server or an InfluxDB instance and some technical ability to maintain them.
• TestFlight and HACS state: The app is available on TestFlight, and the integration is pending in the official HACS store; some manual steps may be required for less technical users.
• No out-of-the-box social features: If comparing to Strava’s social model, Halthy focuses on private data control rather than social sharing.
• Reliant on HealthKit permissions: The app only receives what the user authorizes, which is a design feature but may be perceived as friction for users who want broad automatic sync.

Operational trade-offs:

• Maintenance and backups: Owning the data requires maintaining the server and backup strategies. A user must implement InfluxDB backups and Home Assistant snapshots if long-term retention matters.
• Storage costs: Sample-level storage in InfluxDB can grow rapidly; administrators must configure retention policies and downsampling.
• Setup complexity: Initial setup requires steps that are more technical than signing up for a SaaS platform.

These trade-offs explain why Halthy appeals more to privacy-conscious, tech-savvy users and households that already run Home Assistant.

Advanced usage patterns: Grafana, automation triggers, and research

Halthy opens doors to advanced analytics and automation workflows that combine fitness data with home automation logic.

Grafana dashboards

• Query InfluxDB for sample-level visualizations: overlay heart-rate with elevation or cadence, compute normalized power metrics for cyclists, and display moving averages for weekly load.
• Build comparative dashboards: show year-over-year progress by aligning workouts on a calendar axis or using annotations for events like races or injuries.

Automations based on activity

• Post-workout routines: trigger a warm-down sequence—start recovery music, set the home thermostat to a recovery-friendly temperature, or flash lights to celebrate a session.
• Rest-day alarms: if weekly volume exceeds thresholds, automatically suggest reduced-intensity activities via a notification or dashboard prompt.
• Safety automations: detect long, unusual inactivity after a high-intensity session and send a check-in notification.

Research workflows

• Export sample sets for statistical analysis using CSV or direct queries to InfluxDB.
• Merge Home Assistant environmental datasets with HealthKit samples to study correlations (e.g., noise levels influencing sleep fragmentation).

These advanced patterns demonstrate how owning the data enables bespoke workflows not possible on many consumer platforms.

Exporting and sharing: GPX, CSV, and PDFs

Halthy includes export capabilities that make it easy to take data out of your private system when you need to.

GPX exports

• Preserve routes for mapping tools or for import into other training platforms.
• Useful for archival or for creating route-centric media.

CSV exports

• Export sample-level activity data for spreadsheets, custom analysis, or to feed other local tools.
• Good for coaches who prefer raw data for external processing.

PDF exports

• Generate printable or shareable session summaries with key metrics and route snapshots.
• Useful for sending race reports to coaches or for maintaining a physical logbook.

Create-replay videos

• Short animated videos of a workout are handy for social sharing without exposing full datasets or relying on a third-party site.

These exports let users selectively share while retaining ownership of the primary dataset.

Practical advice: tokens, security, and troubleshooting

A few practical details make a Home Assistant + Halthy deployment reliable and secure.

Tokens and permissions

• Create a dedicated Home Assistant user for Halthy with scoped permissions. Avoid using a personal admin token.
• Use long-lived access tokens for persistent connection; rotate tokens periodically as part of security hygiene.

TLS and certificates

• Serve Home Assistant over HTTPS. Let’s Encrypt certificates are a low-friction option.
• If you use a reverse proxy or cloud fronting service, ensure proper header forwarding and that authentication remains secure.

Firewall and access control

• If exposing Home Assistant to the internet, use layered defenses: VPN access, reverse proxies, or Cloudflare-style protections.
• Consider IP filtering or conditional access rules if the server must be externally reachable for mobile clients.

Logging and debugging

• Check Home Assistant logs and Halthy’s app logs (when available) for failed connections or authentication errors.
• For self-signed certificates, configure the app to trust them only after careful risk assessment; prefer valid certificates.

Retention and storage

• Configure InfluxDB retention policies to avoid unbounded growth.
• Use Home Assistant snapshots and InfluxDB backups for recovery.

These practices make the system resilient without undermining Halthy’s privacy-first stance.

Where Halthy is heading: potential features and roadmap ideas

The project is evolving; potential future capabilities could broaden its appeal.

Possible future directions

• Official HACS listing and App Store release to simplify installation and discoverability.
• Expanded reverse-write capabilities to allow more Home Assistant sensor types into HealthKit, with granular mapping templates.
• End-to-end encryption of payloads beyond HTTPS for users with particular threat models.
• Native support for other time-series databases or cloud targets for hybrid backup strategies.
• Expanded mapping features: slope analysis, Strava-like segment creation (local-only, without sharing), or vector-tile overlays.
• Automation templates or dashboards geared toward athletes (training load estimators, performance indexes).

The project’s open-source orientation invites contributions and customizations from the Home Assistant community.

Troubleshooting common issues

Some common user issues have straightforward solutions.

No entities appearing in Home Assistant

• Verify the token is valid and the URL is correct.
• Confirm Home Assistant custom integration is installed and the server has been restarted after adding files.
• Check Home Assistant logs for rejected requests or permission errors.

Workouts not uploading

• Confirm Halthy has HealthKit permissions for workout types.
• Verify the iPhone has network connectivity at the time of sync.
• If using Home Assistant on a LAN-only address, ensure the iPhone can reach that address over the same network or via VPN.

InfluxDB writes fail

• Confirm database credentials and that the InfluxDB API endpoint is reachable.
• Check InfluxDB logs for authentication or authorization errors.
• Ensure the InfluxDB user has write permissions for the target bucket.

Privacy concerns

• Double-check that no third-party integrations on your Home Assistant instance share data externally unless you explicitly allow them.
• Use dedicated service users and minimal permissions for Halthy to limit accidental exposure.

Following these checks resolves most setup problems.

How Halthy fits into a data-responsible fitness workflow

Halthy suits users who want to retain lasting control of their personal metrics. It turns a smartphone into a secure data source sending to a destination the user controls. That model supports experimentation and deeper analytics without surrendering data custody.

A suggested workflow:

• Use Halthy as the primary exporter from HealthKit into your Home Assistant and InfluxDB.
• Create a Grafana dashboard for fine-grained analytics and reuse Home Assistant dashboards for quick summaries inside the home interface.
• Keep a selective social account for public sharing when desired, while relying on Halthy’s private archive for research or long-term evidence of progress.
• Regularly backup InfluxDB and Home Assistant snapshots and apply retention policies to balance storage and analysis needs.

This approach balances privacy, control, and the occasional need to share highlights publicly.

Community, contribution, and where to find Halthy

Halthy originated as a personal project but has been made available to the broader Home Assistant community. The app currently accepts TestFlight testers and includes a GitHub repository for the integration and source code. The integration is pending inclusion in HACS; until then, users can install through HACS custom repositories or manual integration file copy.

For those familiar with open-source contributions, the repository provides an opportunity to help:

• Test the app across iOS versions and report issues.
• Refine the Home Assistant integration and dashboard card.
• Propose or contribute new export formats and mappings for additional Home Assistant sensor types.

Active community feedback will shape the app’s next steps and determine which enhancements appear first.

Final considerations before adopting Halthy

Halthy addresses a clear niche: people who want privacy-preserving access to HealthKit data and advanced analytics integrated with their home server. It requires a modest level of technical investment and is most attractive to Home Assistant users or those willing to run an InfluxDB instance. The benefits—no third-party cloud, secure token handling, integration with smart-home data, and sample-level exports—make it compelling for users who value data control and customizable analytics.

The practical trade-offs are straightforward: more setup and maintenance in exchange for ownership and flexibility. For many users, that trade is worthwhile.

FAQ

Q: Which HealthKit data types can Halthy read and send? A: Halthy can read and send workout records (routes and statistics) and individual HealthKit metrics that you authorize. Commonly included metrics are duration, distance, active energy (calories), heart rate (average and max), speed, and cadence. The app requests explicit HealthKit permissions; only the types you grant are accessible.

Q: Does Halthy store my data in any cloud operated by the developer? A: No. Halthy does not use a developer-operated cloud. Data flows directly from your iPhone to your Home Assistant instance or to an InfluxDB endpoint you control. There is no Halthy account, no remote persistence on developer servers, and no analytics SDKs.

Q: Can Halthy sync data the other way — from Home Assistant into Apple Health? A: Yes. Halthy can write selected Home Assistant sensor values into HealthKit when you permit it. This is useful for devices that report to Home Assistant but lack native HomeKit integration, such as some scales or custom sensor setups. Write permissions are explicit and controlled by the user.

Q: How is authentication handled when connecting Halthy to Home Assistant or InfluxDB? A: Halthy uses API tokens for Home Assistant; tokens are stored in the iPhone Keychain. Communications use HTTPS to protect tokens in transit. For InfluxDB, standard API credentials or tokens are used, depending on the database configuration.

Q: Is Halthy suitable for athletes who want high-resolution analytics? A: Yes, particularly when paired with InfluxDB. Halthy can push raw HealthKit samples to InfluxDB so every GPS point, heart-rate sample, and cadence reading is preserved. Grafana or similar tools can then produce high-resolution visualizations and comparisons.

Q: What platforms does Halthy support? A: Halthy runs on iOS and integrates with Apple HealthKit. It does not support Android or non-HealthKit ecosystems. On the server side, it integrates with Home Assistant and InfluxDB, which run on various platforms.

Q: How do I install the Home Assistant integration? A: The integration is available via a custom repository on GitHub. You can add the custom repository in HACS or copy the integration files manually to your Home Assistant custom_components directory. The project is pending addition to the HACS store for simpler installation.

Q: What export formats are available? A: Halthy supports exporting workouts as GPX (route), CSV (sample data), and PDF (session summary). The app can also produce short workout-replay videos for sharing.

Q: Can multiple people use the same Home Assistant instance with Halthy? A: Yes. Halthy supports multiple users. Each person configures their own Halthy installation, producing separate entities, workout calendar entries, and history within the same Home Assistant instance.

Q: What security practices should I follow when using Halthy? A: Use a dedicated Home Assistant user with limited permissions for Halthy. Serve Home Assistant over HTTPS with valid certificates. Rotate tokens periodically and maintain backup strategies (Home Assistant snapshots and InfluxDB backups). If the server is publicly accessible, apply additional protections such as VPN access, reverse-proxy authentication, or IP filtering.

Q: Will Halthy appear in the official HACS store or the App Store? A: At the time of this writing, Halthy is available on TestFlight and via a custom HACS repository. The integration is pending addition to the HACS store; an App Store release may follow as the project matures.

Q: What are the main limitations to consider? A: Halthy is iOS-only and requires a Home Assistant server or InfluxDB to store data locally. Setup is more technical compared to signing up for a SaaS fitness platform, and the app currently lacks built-in social features.

Q: Where can I provide feedback or contribute? A: The project is hosted on GitHub. TestFlight participants and Home Assistant community members are encouraged to file issues, suggest features, and contribute code or translations.

Q: Can I use Halthy alongside commercial services like Strava? A: Yes. You can keep a Strava or Komoot account for social features while using Halthy to maintain a private backup and engage in more advanced analytics.

Q: How do I handle long-term storage if I send data to InfluxDB? A: Configure InfluxDB retention policies and downsampling (continuous queries or tasks) to manage storage growth. Back up your InfluxDB periodically and define a retention policy that balances storage cost with analytic needs.

Q: What happens if I lose my phone or uninstall Halthy? A: If you’ve been uploading data to your Home Assistant or InfluxDB instance, the historical records remain on those servers. Losing the phone does not erase the server-side archive. Reinstalling Halthy and reconnecting to the same destination will resume uploads; you may need to reauthorize HealthKit access.

Q: Does the app include trackers or analytics? A: No. Halthy intentionally excludes third-party analytics and tracking SDKs.

Q: Can Halthy replay workouts on the Home Assistant dashboard? A: Yes. The workout dashboard card supports route display and replay, showing metrics over time as the route animates.

If your question is not answered above, check the project’s GitHub repository for the latest documentation and community discussion.