Shiny Health 2.0 — The Ultimate Cross-Platform Health Library

Jun 15, 2026

Allan Ritchie

One of the guys who builds this

Reading health data on .NET MAUI means two completely different worlds: Apple HealthKit with its HKQuantityType/HKCategoryType/HKCorrelation zoo, and Android Health Connect with its Kotlin record types, coroutine APIs, and per-record permissions. Shiny Health collapses both behind a single IHealthService — and 2.0 is a big one: 30+ cross-platform data types, dedicated records for the non-numeric stuff, and a new package that turns your health data into tools an LLM can call.

This is our first proper write-up of the library, so let’s cover the whole thing.

One Interface, Two Platforms

dotnet add package Shiny.Health

public static MauiApp CreateMauiApp()
{
    var builder = MauiApp.CreateBuilder().UseMauiApp<App>().UseShiny();
    builder.Services.AddHealthIntegration();
    return builder.Build();
}

Inject IHealthService, request permissions, and query. Everything is async, cancellation-aware, and AOT-friendly (no reflection — the Android side bridges Kotlin coroutines through a hand-rolled IContinuation).

public class DashboardViewModel(IHealthService health)
{
    public async Task LoadAsync()
    {
        await health.RequestPermissions(DataType.StepCount, DataType.HeartRate, DataType.Calories);

        var end = DateTimeOffset.Now;
        var start = end.AddDays(-1);

        var steps = (await health.GetStepCounts(start, end, Interval.Hours)).Sum(x => x.Value);
        var hr = (await health.GetAverageHeartRate(start, end, Interval.Hours)).Average(x => x.Value);
        var kcal = (await health.GetCalories(start, end, Interval.Days)).Sum(x => x.Value);
    }
}

Numeric metrics are time-bucketed: you pick Interval.Minutes, Hours, or Days and get one result per bucket. Cumulative metrics (steps, calories, distance, hydration, sleep) you .Sum(); point-in-time metrics (heart rate, weight, body fat) you .Average().

30+ Data Types

2.0 expanded the catalog dramatically. Numeric metrics — each with its own Get… method and full read/write/observe support:

• Activity — step count, distance, active & basal energy, floors climbed, wheelchair pushes, speed, power
• Heart — average heart rate, resting heart rate, heart rate variability
• Body — weight, height, body fat %, lean body mass
• Vitals — blood pressure, oxygen saturation, blood glucose, body temperature, basal body temperature, respiratory rate, VO2 max
• Lifestyle — sleep duration, hydration

Blood pressure is special — it carries two values, so it gets its own result type:

var bp = await health.GetBloodPressure(start, end, Interval.Days); // BloodPressureResult
var avgSystolic = bp.Average(x => x.Systolic);   // mmHg
var avgDiastolic = bp.Average(x => x.Diastolic);

Reproductive & Cycle Tracking

These are categorical and event-based — no Interval bucketing, each with a dedicated record:

await health.RequestPermissions(DataType.MenstruationFlow, DataType.OvulationTest);

var flow = await health.GetMenstruationFlow(start, end);     // MenstrualFlow: None/Light/Medium/Heavy
var ovulation = await health.GetOvulationTests(start, end);  // Positive/Negative/High/Inconclusive
var mucus = await health.GetCervicalMucus(start, end);       // Dry/Sticky/Creamy/Watery/EggWhite
var activity = await health.GetSexualActivity(start, end);   // Protected/Unprotected
var spotting = await health.GetIntermenstrualBleeding(start, end);

// logging
await health.Write(new MenstruationFlowResult(DateTimeOffset.Now, DateTimeOffset.Now, MenstrualFlow.Medium, IsCycleStart: true));

Workouts & Nutrition

var workouts = await health.GetWorkouts(start, end);
foreach (var w in workouts)
    Console.WriteLine($"{w.Workout}: {w.TotalEnergyKilocalories} kcal over {w.End - w.Start}");

await health.Write(new WorkoutResult(start, end, WorkoutType.Running, TotalEnergyKilocalories: 420, TotalDistanceMeters: 7500));

await health.Write(new NutritionResult(
    DateTimeOffset.Now, DateTimeOffset.Now,
    Meal: MealType.Lunch, Name: "Chicken & rice",
    EnergyKilocalories: 550, ProteinGrams: 40, CarbohydratesGrams: 60, TotalFatGrams: 12
));

WorkoutType maps 21 activities that exist on both platforms (running, cycling, swimming, strength training, HIIT, yoga…); anything unmapped reads back as Other.

Writing Data

Every numeric metric writes through NumericHealthResult — pass the DataType and value in its documented unit:

await health.RequestPermissions(PermissionType.Write, DataType.Weight, DataType.Hydration);

await health.Write(new NumericHealthResult(DataType.Weight, DateTimeOffset.Now, DateTimeOffset.Now, 75.0));   // kg
await health.Write(new NumericHealthResult(DataType.Hydration, start, end, 0.5));                              // liters
await health.Write(new BloodPressureResult(DateTimeOffset.Now, DateTimeOffset.Now, 120, 80));                  // mmHg

Permissions are granular: PermissionType.Read, Write, or ReadWrite, requestable uniformly or per-metric in a single call.

Real-Time Observation

Observe streams new samples as they’re recorded via IAsyncEnumerable<HealthResult> — push-based HKAnchoredObjectQuery on iOS, Health Connect change-token polling on Android (interval configurable):

using var cts = new CancellationTokenSource();
await foreach (var result in health.Observe(DataType.HeartRate, cancelToken: cts.Token))
{
    if (result is NumericHealthResult n)
        Console.WriteLine($"❤️ {n.Value} bpm at {n.Start:T}");
}

New in 2.0: AI Tools

This is the headline feature. Shiny.Health.Extensions.AI exposes IHealthService as Microsoft.Extensions.AI tool functions, so an LLM agent can answer “How did I sleep last week?” or “Log a 45-minute run” by calling your health store directly.

It uses a small set of parameterized tools — one get_health_metric tool covers all numeric metrics via a metric enum argument, instead of 25 separate tools — which keeps the model’s tool list short and its selection accurate. Read-only by default; write is opt-in per area.

dotnet add package Shiny.Health.Extensions.AI

builder.Services.AddHealthIntegration();
builder.Services.AddHealthAITools(tools => tools
    .AddAllMetrics()                                          // read every numeric metric
    .AddMetric(DataType.Weight, HealthAICapabilities.ReadWrite)
    .AddBloodPressure(HealthAICapabilities.ReadWrite)
    .AddCycleTracking()
    .AddWorkouts(HealthAICapabilities.ReadWrite)
    .AddNutrition()
);

// hand the tools to any IChatClient
var tools = sp.GetRequiredService<HealthAITools>().Tools;
var response = await chatClient.GetResponseAsync(messages, new ChatOptions { Tools = [.. tools] });

Only the areas you opt-in to are visible to the model, and enum arguments are constrained to exactly what you allowed. The whole thing is IsAotCompatible — schemas are hand-built and results are emitted as JsonNode, so there’s no reflection in the tool path. Full details on the AI Tools page.

Honest About the Platforms

A unified API shouldn’t pretend the platforms are identical. Where they differ, we document it rather than hide it:

• Heart rate variability is SDNN on iOS and RMSSD on Android — both milliseconds, but different computations, so don’t compare the numbers across platforms.
• Speed/Power are generic on Health Connect; HealthKit has no generic equivalents, so Speed maps to walking speed and Power to cycling power.
• A WorkoutResult’s energy/distance are null on Android read — Health Connect stores those as separate records from the exercise session.
• MenstrualFlow.None and the IsCycleStart flag are iOS-only.

See Platform Notes for the full list.

Get Started

dotnet add package Shiny.Health
dotnet add package Shiny.Health.Extensions.AI   # optional — for LLM agents

Then head to Reading Data, Writing Data, and AI Tools. One interface, both platforms, 30+ metrics, and now an agent that can read and write them for you.

Tags:

• health
• release
• AI

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