CLAWHEALTH

Agent-native precision health, from data to action.

ClawHealth turns wearable data, nutrition, mood, goals, readiness, and evidence-backed context into services the user’s own Agent can call. The Health Intelligence Panel keeps the full context visible when deeper control is needed.

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Personal health context

live context

Readiness

74/100

Protein progress

82/120g

Energy remaining

320 kcal

Personal baselineimproving

Health management should not stop at data.

Viewing a chart is only the beginning. Wearables record the body, and Agents can talk to users, but health guidance needs a service layer that turns signals into context and context into action. ClawHealth gives the Agent composable services: read the body, record habits, compare baselines, explain change, suggest action, and open the panel when deeper control is needed.

Synced signals

Sleep rhythmlate type

HRV vs baseline-8%

Meal patternlarger dinner

Training loadlight

Latest insight

For this person, a later rhythm is normal, but HRV is below their own recent baseline today.

Habit context

Meal size, timing, mood, and soreness can be compared with sleep and recovery instead of judged by generic rules.

Next action

Ask for an adjustment plan, then open the panel for charts, baselines, explanations, and references.

Connect body signals

The iOS app keeps a rolling 90-day health context from authorized HealthKit data: sleep, HRV, heart rate, activity, workouts, vitals, glucose-ready metrics, and nutrition signals.

Add life context

Food intake, mood, stress, soreness, profile, and goals can be recorded through Agent workflows, then compared with the person’s own wearable baseline.

Act through the Agent

Every service is callable: reports, nutrition targets, recovery support, readiness, mood tracking, feedback, and an intelligence panel for details.

Ask naturally. ClawHealth turns the question into data-backed action.

The user can ask in plain language. ClawHealth selects the relevant service, reads the latest health context, applies the right model layer, and returns an answer with visible data, assumptions, and next steps.

01Health context

Authorized device signals become a rolling health context that can be queried by service modules.

02Agent call

The Agent calls ClawHealth services when the user asks, without needing to keep raw records inside the chat.

03Model layer

Backend models compare the person with their own longitudinal baseline, then combine recovery, nutrition, readiness, and anomaly-context logic.

04Traceable answer

The Agent receives structured outputs, guideline boundaries, and a panel link so the answer can be inspected.

Selected service

N-of-1 recovery comparison

Data-backed

How am I doing overall right now?

ClawHealth response

Based on current context and personal baseline

Today looks stable, but not like a peak-performance day. Sleep duration is adequate, while HRV is below this person’s recent baseline and training load does not need to increase. The practical suggestion is to keep training lighter, protect recovery routines, and compare tomorrow’s signal before pushing intensity.

Recovery readiness

74/100

HRV vs baseline

-8%

Sleep rhythm

late-normal

Recovery

Moderate readiness, not a high-load day.

Keep the day lighter and re-check tomorrow.

Habit context

Potential contributor, not a single-cause claim.

Protect meal timing and recovery routines.

Traceability

Readable answer with inspectable backing.

Open the panel for charts and baseline history.

Open the data behind this answer

Shows which data was used, which service handled the question, and what assumptions shaped the answer.

Data used

Sleep, HRV, resting HR, activity

Model layer

Recovery and readiness baseline

Use boundary

Trend interpretation and safety boundary

Real health guidance is different in different moments.

The same person can need very different advice on different days. ClawHealth connects what happened, what the body is showing, and what the user wants next, then lets the Agent turn that context into action.

Training readiness

Should I train hard today?

Before training, the Agent can compare sleep, HRV, soreness, mood, and recent load. A ready day may become a progressive session; a low-readiness day may become technique work, mobility, or rest.

Ready

Push the planned session

Not ready

Lower load or recover

Nutrition context

What should I eat next?

A meal photo or short description can become calories, protein, carbs, fat, and fiber estimates. If today is over target, the Agent can suggest a lighter next meal; if protein is short, it can suggest what to add.

Over target

Plan a lighter dinner

Under target

Add protein or fiber

Sleep rhythm

What is normal for me?

ClawHealth does not assume everyone needs the same bedtime or sleep duration. It learns the person’s rhythm, then watches how sleep timing, meals, training, and stress affect the next day.

Early rhythm

Protect the evening window

Late rhythm

Compare with personal baseline

A simple stack: data, reports, actions, and the panel.

The product is intentionally modular. ClawHealth does not replace the user’s Agent; it gives that Agent a reliable service layer, while the panel shows charts, baselines, references, profile settings, and data availability.

Data context

The iOS app keeps an authorized 90-day health context across sleep, HRV, heart rate, activity, workouts, vitals, body metrics, glucose-ready signals, blood pressure, and nutrition types.

Reports and explanations

Agent-readable reports and latest insights turn the current window into plain-language patterns, personal context, evidence notes, and next actions.

Action layer

Nutrition logging, goal updates, readiness checks, mood tracking, recovery support, feedback, and panel creation are exposed as callable Agent services.

Health Intelligence Panel

The Health Intelligence Panel shows charts, baselines, sleep architecture, nutrition targets, service readiness, references, profile settings, and the state behind Agent answers.

Services designed around real health decisions.

ClawHealth organizes connected health data into practical services users can ask for through their Agent: metabolic and cardiovascular context, food-response learning, mental-health self-checks, nutrition targets, readiness, and habit feedback. Some services open gradually as the required data and review flow become available.

Metabolic risk context

When profile, labs, CGM, and meal response data are available, ClawHealth can help users understand whether their metabolic pattern deserves earlier attention.

References and method notes

Built from established metabolic modelling ideas and precision-nutrition studies showing large differences in post-meal glucose response between people.

Matthews et al., HOMA model Zeevi et al., Cell 2015 Berry et al., Nature Medicine 2020

Cardiovascular context

ClawHealth can connect standard cardiovascular inputs with daily trends such as resting heart rate, activity, recovery, and cardiorespiratory fitness, so prevention is easier to discuss.

References and method notes

Built from validated cardiovascular risk frameworks. Wearable trends are used as context, not as a replacement for blood pressure, lipids, history, or clinician assessment.

SCORE2, European Heart Journal 2021 USPSTF statin prevention guidance

Personal food response

ClawHealth can help users test how meals affect their own energy, sleep, recovery, training, and glucose-led metabolic signals, instead of relying only on generic food rules.

References and method notes

Built from precision-nutrition research showing that the same meal can create different responses in different people. That is why N-of-1 tracking matters.

Zeevi et al., personalized nutrition PREDICT 1, Nature Medicine 2020

Mental health self-check

Users will be able to describe how they feel in natural language, then receive structured self-check scales and clear guidance on when to seek timely support.

References and method notes

Built from validated brief scales such as PHQ-9, GAD-7, and WHO-5. These tools support screening and help-seeking, not diagnosis.

PHQ-9 validation, J Gen Intern Med GAD-7 validation, Arch Intern Med WHO-5 systematic review

The goal is to understand what changes health for one person.

Some people sleep late and still perform well; others need an earlier rhythm. Some tolerate a larger dinner; others see recovery or glucose change quickly. There is no single perfect bedtime, sleep duration, meal size, or training load for everyone. What matters is your pattern, your goal, and what reliably changes your outcomes.

Evidence loop

Evidence, personal baselines, habits, abnormal signals, goals, and next actions stay connected instead of becoming separate app screens.

Multidisciplinary team

The team combines health machine learning, healthcare service design, nutrition, sports rehabilitation, and real-world data backgrounds.

N-of-1 model

Context network

Personal pattern

Personal baseline

Sleep, HRV, resting heart rate

Daily habits

Meals, training, mood

Health goals

Weight and performance

Clinical markers

Labs, glucose, lipids

Outcome feedback

Change after action

The system keeps comparing what happened, how the body changed, whether the goal is closer, and whether the action improved the next signal.

Data connections

One health context across every device and service.

ClawHealth already supports iOS through Apple Health. Fitbit, Google Health, and Garmin Connect are next. The long-term plan is simple: connect wearable, phone, app, food, lab, and coaching data so the user’s Agent can reason over the whole picture instead of one disconnected source.

Supported now

Apple Health

Available now on iOS. Users grant permission in the app, and ClawHealth syncs the latest 90 days from the Apple Health app.

In development

Fitbit

Fitbit device and app data will be connected through the supported Fitbit / Google Health data path after account authorization.

Coming soon

Google Health

Google Health and Health Connect support will help Android and Google ecosystem users bring activity, sleep, vitals, nutrition, and related health signals into the same context.

Coming soon

Garmin Connect

Garmin Connect integration will add endurance, training, activity, sleep, heart-rate, and daily health summaries for Garmin users.

From personal data to an Agent-native precision health network.

Personal health data layer

The user chooses which wearable and lifestyle signals to connect, then sees which signals are available and how complete the current picture is.

Callable health services

Latest insights, optional reviews, readiness, nutrition logging, profile updates, goals, recovery support, and feedback are available as callable Agent services.

Nutrition, metabolism, and N-of-1 loops

Food photos, meal notes, glucose-led metabolic signals, macro targets, and body goals can be connected into one loop that explains how habits affect outcomes.

Privacy-first personalization network

Future personalization can combine privacy-preserving federated learning, transfer learning, and auditable on-chain model updates so population discovery can improve individual guidance without exposing raw records.

Try the Agent-first health workflow.

Connect health data once, then let your Agent read insight requests, write back useful updates, log habits, set nutrition goals, check readiness, support recovery, and explain anomaly context. The Health Intelligence Panel is there when you want the charts, baselines, explanations, and settings behind the answer.

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