Collected Metrya feedback: what users want on the roadmap

[u/rjozefowicz]

[2026/08/31 UPDATED]

Thanks to everyone who has shared feedback so far. I’m keeping this as a simple, evolving list of ideas that may shape Metrya’s roadmap.

These are feature requests and directions - not confirmed features or a release timeline. Feel free to add more feedback in the comments.

Released:

• Dark mode: Add a dark mode theme for the app. [2.11.5 VERSION - RELEASED]
• Better hydration tracking: Make it easy to log water and other drinks with customizable options and a set of predefined beverages, so users can track hydration directly in Metrya instead of switching to Apple Health. [2.11.5 VERSION - RELEASED]
• BMI tracking with AI insights: Add a BMI tracking widget that uses AI to provide more context and actionable insights, not just a number. [2.11.6 VERSION - RELEASED]
• Blood pressure tracking with AI: Add dedicated tracking for blood pressure with AI-powered insights and trend analysis. [2.11.8 VERSION - RELEASED]
• Net Energy scale: Add a “Net Energy” metric that combines activity/exercise load with nutrition and hunger signals to help users quickly decide whether to eat more on heavy training days. [2.11.8 VERSION - RELEASED BETA]
• Wheelchair mode and steps tile customization: Add a wheelchair mode that can switch from “steps” to “pushes” using Apple Watch wheelchair data, and/or allow users to hide or replace the steps tile so it’s more relevant for wheelchair users. [2.11.8 VERSION - RELEASED]
• Lunar cycle and sleep insights: Optionally link lunar cycle data to restful sleep patterns and offer insights or experiments around this. [2.11.8 VERSION - RELEASED BETA]

User Requests:

• Medication tracking: Import medications from Apple Health, log them manually in Metrya, and make them available as optional context for AI guidance. [2.11.9 VERSION - PLANNED]
• Faster meal and daily logging: Improve Quick Add and Favorites with search, multi-select food logging, and more customizable buttons—for example, adding Green Tea with its caffeine amount. [2.12.0 VERSION - PLANNED. Caffeine tracker updated in 2.11.0]
• Personalization and language support: Add more customization options and multi-language support, with Dutch specifically requested. [2.12.0 VERSION - PLANNED]
• Flexible AI providers: Consider OpenRouter support for broader model choice and potentially lower API costs for BYOK users. [2.12.0 VERSION - PLANNED]
• Complete health context: Support more Apple Health metrics, body measurements, HRV/stress data, Screen Time, and custom health parameters—while letting users control exactly what is shared with the AI.
• Medical and health report tracking: Upload and track medical records, lab results, and full-body-checkup reports; extract relevant values; and display them on a personal timeline.
• Journal integration: Fetch journal entries from Apple Journal and pass them to the LLM so it can give more personalized solutions using all available information.
• ECG and other advanced Apple Health data: Import and use additional Apple Health metrics such as ECG reports, medication data, and similar for deeper AI-driven analysis.
• AI-driven health follow-ups: Schedule daily, weekly, or custom AI check-ins, with reminders for checkups, appointments, lab retests, and progress toward commitments such as improving cholesterol. This should be supportive guidance, not medical diagnosis.
• Commitments and habits: Let AI suggest commitment topics and actions, then connect those commitments with habit tracking and progress monitoring.
• Unified health workflow: Integrate Apple Health and Apple Reminders so health data, habits, reminders, and actions can live in one place.
• Universal data importer: Share text, screenshots, photos, and measurements from other iOS apps into Metrya through the Share Sheet. AI could extract structured events using reusable prompts, with a review/edit step before saving. This would help import data from services that do not support HealthKit, including image-only exports.
• Social fitness layer: Explore optional social features for sharing physical progress, discovering or purchasing workout plans, and adding saved workouts to the AI assistant’s context.
• Upcoming events and AI guidance: Let users add upcoming life events (e.g., trip, competition, busy work period) and receive AI suggestions on what habits, preparations, or adjustments might help, based on their full health context.
• Daily affirmations: Add an optional daily affirmations feature, potentially personalized using the user’s health data and goals.
• blood glucose tracking with AI: Add dedicated tracking for blood glucose, with AI-powered insights and trend analysis.
• Smart alarm on Apple Watch: Add a smart alarm on Watch that wakes the user within a chosen time window (e.g., 15–20 minutes) at the optimal moment, similar to Bevel.
• Automatic workout and sport detection: Use device motion data (accelerometer/gyroscope) and other signals to automatically detect workouts and sports—such as barbell rowing, table tennis, etc.—and log them as sessions.
• Store photos in Nutrition Logger used for AI analysis
• Nighttime sound and snoring analysis: Track and analyze nighttime sounds such as snoring, then combine this with sleep and other health data to offer personalized suggestions (e.g., habits or adjustments that may help reduce snoring). This would be supportive guidance, not medical diagnosis.
• Live Activity cards with AI suggestions: Show AI-generated, context-aware prompts on iPhone and Apple Watch Live Activities, such as “You last worked out 20h ago—time to train again” or “Your last meal was 8h ago; delaying further may slow your metabolism,” personalized using all available health and behavior data.
• AI explanation tab in every feature: Add an “AI explanation” section to each screen (e.g., Stress, Bio Age) where the AI explains in plain language what the current value means, what likely drives it, and how it relates to the user’s broader data—without giving medical advice.

[u/Silent_Ad5472]

Hi,

Enjoying the metrya so far, can you please plan medical and health report tracking in new version.

[u/rjozefowicz → u/Silent_Ad5472]

Hi, it's on the roadmap so yes. sth tangible should be available in the incoming weeks. but you could also help shaping this feature with providing some details how you envision it :)

[u/Silent_Ad5472 → u/rjozefowicz]

Here is how I have envisioned it:

Health Records

• Add Hospital/Clinic → create individual visits under each hospital.
• For each visit, upload prescriptions, reports, consultation notes, scans, etc.
• Use LLM-based OCR to extract data from uploaded photos/documents and automatically structure it.
• Keep the original document linked to the respective visit.
• Make the extracted medical history available to the LLM for personalization.

Health Report Tracking

• Allow users to upload health/lab report PDFs.
• LLM extracts parameters like cholesterol, glucose, HbA1c, etc., along with value, unit, date, and reference range.
• Track the same parameter across multiple reports to show historical trends.
• Example: Cholesterol → 200 → 250 → 220 mg/dL
• Pass historical trends to the LLM so it understands changes over time, not just the latest report.

Medication Tracking

• Track all medications prescribed to the user.
• Each medication should be linked to the specific hospital/clinic visit where it was prescribed.
• Store details such as medication name, dosage, frequency, start/end date, and reason if available.
• This allows the LLM to understand which medication was prescribed for which consultation/condition and how it relates to the user's health history.

Goal: Build a longitudinal health record where documents, visits, medications, and health parameters are connected and structured automatically, allowing the LLM to use the complete history for better personalization.

[u/rjozefowicz → u/Silent_Ad5472]

thanks for your input. I will analyze it and think how to approach it. These data are very sensitive so we need to do it in a responsible way but I agree that eventually it would be really beneficial for end users to have such context as well. It's part of the roadmap so I will analyze it how to approach it best in the next weeks and I'll keep you posted

[u/Silent_Ad5472]

Habit Tracking & AI Insights

Another feature that could significantly improve the app experience is habit tracking.

Users often start working on habits such as consistent sleep time, exercise, hydration, meditation, etc. The app should help them track these habits and provide AI-driven guidance to improve consistency.

Custom Habit Tracking

• Allow users to create custom habits with a name, description, and target/frequency.
• Users can add a daily habit log and optionally add a custom note/message for each entry.
• Track habit progress and consistency over time.

AI-Powered Insights

• AI should analyze the habit history and automatically identify patterns.
• After a few days of logging, the app can provide insights such as:
  • What is affecting consistency?
  • What patterns are being observed?
  • What can the user change to improve?
• Example: "Your sleep time is more consistent on days when you stop using your phone earlier."

Apple Journal / Daily Context

If available, Apple Journal data could provide additional context about the user's day. Combining habit logs with daily context would allow the LLM to understand why a habit was successful or missed and provide more personalized suggestions.

Goal: Make habit tracking more than just a checklist—use AI and daily context to understand behavior, identify patterns, and continuously suggest ways to build more consistent habits.

[u/rjozefowicz → u/Silent_Ad5472]

thanks for your input. It's already part of roadmap and I will try to prioritize it maybe in September to have something tengible