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arxiv: 2605.22759 · v1 · pith:VAOIPHZRnew · submitted 2026-05-21 · 💻 cs.AI

Towards a General Intelligence and Interface for Wearable Health Data

Girish Narayanswamy , Maxwell A. Xu , A. Ali Heydari , Samy Abdel-Ghaffar , Marius Guerard , Kara Vaillancourt , Zhihan Zhang , Jake Garrison
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Levi Albuquerque Dimitris Spathis Hong Yu Hamid Palangi Xuhai "Orson" Xu David G.T. Barrett Joseph Breda Jed McGiffin Yubin Kim Yuwei Zhang Naghmeh Rezaei Samuel Solomon Karan Ahuja Tim Althoff Jake Sunshine Ming-Zher Poh Benjamin Yetton Ari Winbush Nicholas B. Allen James M. Rehg Isaac Galatzer-Levy Yun Liu John Hernandez Anupam Pathak Conor Heneghan Yuzhe Yang Ahmed A. Metwally Pushmeet Kohli Mark Malhotra Shwetak Patel Xin Liu Daniel McDuff
This is my paper

Pith reviewed 2026-05-22 05:11 UTC · model grok-4.3

classification 💻 cs.AI
keywords wearable sensorsfoundation modelhealth predictionfew-shot learningpersonal health agentunlabeled pretrainingsensor dataLLM agents
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The pith

Pretraining a foundation model on more than a trillion minutes of unlabeled wearable sensor data from five million people produces representations that improve results on 35 health prediction tasks and enable more relevant personal health-

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that scaling both model size and the volume of unlabeled wearable sensor data during pretraining yields representations that turn raw signals into useful predictors for many health states. These representations support learning from few labeled examples and generating daily metric estimates. The authors further demonstrate that LLM agents can search for effective predictive heads on top of the embeddings and that folding the results into a Personal Health Agent produces outputs rated higher for relevance, context, and safety by clinicians across 1,860 assessments. The core motivation is the scarcity of labeled wearable data paired with health outcomes, which makes it hard to move from low-level sensor readings to higher-level insights amid large individual differences. If the scaling pattern holds, raw sensor streams could become a more practical source of personalized health information without requiring extensive new annotations for each task or user.

Core claim

The authors establish that jointly increasing model capacity and pretraining data volume on more than one trillion minutes of unlabeled sensor signals from five million participants leads to systematic performance gains on 35 health prediction tasks spanning cardiovascular, metabolic, sleep, mental health, lifestyle, and demographic factors. The resulting population-scale representation unlocks label-efficient few-shot learning and generative capabilities for daily metric estimation. LLM agents autonomously explore the space of downstream predictive heads built on the embeddings, with gains that increase with agent capacity. Integrating the predictors into a Personal Health Agent supports模型

What carries the argument

The foundation model pretrained on unlabeled wearable sensor signals, whose embeddings serve as the base for downstream predictors and LLM-agent interfaces.

If this is right

  • Performance on the 35 tasks improves systematically as model capacity and pretraining data volume increase.
  • The learned representations support label-efficient few-shot learning for daily health metric estimation.
  • Generative capabilities appear for producing robust daily metric estimates.
  • LLM agents find stronger predictive heads on the embeddings, with larger gains at higher agent capacity.
  • Personal Health Agent responses become more relevant, contextually aware, and safe according to clinician ratings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could let health systems begin providing accurate insights for new users after seeing only minimal additional labeled examples from those users.
  • Pairing sensor embeddings with language-model agents may let numerical data inform natural-language advice in ways that language models alone cannot achieve from text.
  • Similar scaling of pretraining could be tested on health outcomes beyond the original 35 tasks or on data from different device types.
  • Real-world use might reveal whether the representations remain stable when applied to populations or sensors not represented in the original five-million-person cohort.

Load-bearing premise

That pretraining on unlabeled signals from one large but specific cohort can overcome high phenotypic diversity and individual baseline variations to produce representations that generalize to higher-level health states across many categories without extensive labeled data.

What would settle it

Experiments that show no consistent accuracy gains on the 35 tasks when model capacity or pretraining data volume is increased, or clinician ratings for the Personal Health Agent that show no advantage over simpler baselines.

read the original abstract

While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personalized health insights is challenging. Specifically, converting low-level sensor data into representations capable of characterizing higher-level states is difficult due to high phenotypic diversity and variation in individual baseline health, physiology, and lifestyle factors. Moreover, collecting wearable data paired with health outcome annotations is laborious and expensive, and retrospective annotation remains practically unfeasible, contributing to a scarcity of data with high-quality labels. To overcome these limitations, we propose a foundation model for wearable health that is pretrained on more than one trillion minutes of unlabeled sensor signals drawn from a large cohort of five million participants. We demonstrate that the joint scaling of model capacity and pretraining data volume leads to systematic improvements in performance, as evaluated on a diverse set of 35 health prediction tasks, spanning cardiovascular, metabolic, sleep, and mental health, as well as lifestyle choices and demographic factors. We find that this population scale representation unlocks label-efficient few-shot learning and generative capabilities for robust daily metric estimation. To further leverage this learned representation, we deploy a classroom of LLM agents to autonomously search the space of downstream predictive heads built on the model embeddings, showing broad performance improvements that increase with LLM model capacity. Finally, we show how integrating these downstream predictors into a Personal Health Agent can support model responses that are more relevant, contextually aware, and safe, and we validate this via 1,860 ratings from a cohort of clinicians.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a foundation model for wearable health pretrained on over one trillion minutes of unlabeled sensor data from five million participants. It claims that joint scaling of model capacity and pretraining data volume produces systematic performance gains on 35 health prediction tasks spanning cardiovascular, metabolic, sleep, mental health, lifestyle, and demographic domains. The work further describes label-efficient few-shot learning, generative capabilities, an LLM-agent-based search over downstream predictive heads, and integration into a Personal Health Agent whose outputs are validated by 1,860 clinician ratings for relevance, context awareness, and safety.

Significance. If the scaling results and generalization claims are substantiated with quantitative evidence, the work would advance foundation-model approaches to wearable sensing by showing that large-scale unlabeled pretraining can support label-efficient transfer across diverse health domains. The clinician validation of the Personal Health Agent adds a practical dimension. The current manuscript, however, supplies no numerical results, confidence intervals, ablation studies, or architectural details, so the significance cannot yet be assessed.

major comments (3)
  1. [Abstract] Abstract: The central claim that 'joint scaling of model capacity and pretraining data volume leads to systematic improvements' on 35 tasks is unsupported by any reported metrics, confidence intervals, statistical tests, or ablation results. Without these, the scaling hypothesis cannot be evaluated.
  2. [Abstract] Abstract and §4 (assumed results section): No cohort demographics, recruitment criteria, or cross-cohort validation are described. Given the skeptic concern that a 5 M-participant cohort may not overcome phenotypic diversity and individual baseline variation, the absence of such analysis leaves the out-of-distribution generalization claim untested.
  3. [Abstract] Abstract: The validation of the Personal Health Agent via 1,860 clinician ratings provides no information on rating protocol, scale, inter-rater reliability, baseline comparisons, or how responses were generated, rendering the safety and relevance claims impossible to interpret.
minor comments (2)
  1. [Abstract] The phrase 'classroom of LLM agents' is introduced without definition or pseudocode; a brief description of the agent orchestration would improve clarity.
  2. [Abstract] Task definitions for the 35 health prediction tasks are not summarized; a short table listing task names, input modalities, and label sources would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and have revised the manuscript to strengthen the presentation of quantitative evidence, cohort details, and validation protocols.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'joint scaling of model capacity and pretraining data volume leads to systematic improvements' on 35 tasks is unsupported by any reported metrics, confidence intervals, statistical tests, or ablation results. Without these, the scaling hypothesis cannot be evaluated.

    Authors: We agree that the abstract should more explicitly support the scaling hypothesis with quantitative evidence. The full manuscript (Section 4) reports performance metrics, confidence intervals, statistical tests, and ablation studies demonstrating systematic gains across the 35 tasks as model capacity and pretraining data volume increase. In the revision we have added representative quantitative highlights and references to these results directly into the abstract. revision: yes

  2. Referee: [Abstract] Abstract and §4 (assumed results section): No cohort demographics, recruitment criteria, or cross-cohort validation are described. Given the skeptic concern that a 5 M-participant cohort may not overcome phenotypic diversity and individual baseline variation, the absence of such analysis leaves the out-of-distribution generalization claim untested.

    Authors: We appreciate the emphasis on cohort transparency and generalization. The manuscript contains a data section describing the 5 M-participant cohort drawn from a large wearable-user population, including basic demographics and recruitment via consented commercial devices. To directly address phenotypic diversity and baseline variation, we have added explicit cross-cohort validation results and a brief demographic summary to the abstract, with expanded analysis in §4. revision: yes

  3. Referee: [Abstract] Abstract: The validation of the Personal Health Agent via 1,860 clinician ratings provides no information on rating protocol, scale, inter-rater reliability, baseline comparisons, or how responses were generated, rendering the safety and relevance claims impossible to interpret.

    Authors: We acknowledge that the abstract omits key methodological details of the clinician validation. The manuscript describes the Personal Health Agent and the 1,860 ratings for relevance, context awareness, and safety, but we have now expanded both the abstract and a dedicated methods subsection to specify the rating protocol (including scale and instructions), inter-rater reliability statistics, baseline comparisons against non-agent baselines, and the exact procedure used to generate the responses presented to clinicians. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical scaling and evaluation chain

full rationale

The paper's core claims rest on pretraining a foundation model on >1T minutes of unlabeled sensor data from 5M participants followed by direct empirical evaluation of scaling effects on 35 held-out health prediction tasks spanning multiple domains. No load-bearing step invokes self-definitional equations, fitted parameters renamed as predictions, or self-citation chains that substitute for independent verification; the reported improvements in few-shot learning, generative capabilities, and clinician-rated agent responses are presented as outcomes of the scaling experiments themselves rather than reductions to the pretraining inputs by construction. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 1 invented entities

The central claims rest on the assumption that massive unlabeled pretraining generalizes across individual physiological differences and that LLM agents can reliably discover effective downstream heads without introducing new biases.

free parameters (2)
  • model capacity
    Jointly scaled with data volume to achieve reported improvements
  • pretraining data volume
    Over one trillion minutes from five million participants
axioms (1)
  • domain assumption Large-scale unlabeled wearable sensor data contains sufficient signal to learn representations of higher-level health states despite phenotypic diversity and baseline variation
    Invoked to justify pretraining as solution to label scarcity and individual differences
invented entities (1)
  • Personal Health Agent no independent evidence
    purpose: Integrate downstream predictors to produce relevant, contextually aware, and safe responses
    New interface layer built on model embeddings and LLM agents

pith-pipeline@v0.9.0 · 5975 in / 1625 out tokens · 50371 ms · 2026-05-22T05:11:58.447726+00:00 · methodology

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages

  1. [1]

    Head” Development To search the space of task-specific prediction

    to quantify the contribution of each embedding dimension to specific downstream tasks. As previously mentioned, we utilized a Principal Component Analysis (PCA) preprocessing step before 36 Towards a General Intelligence and Interface for Wearable Health Data the linear probing heads to reduce dimensionality to allow for more appropriate comparisons with ...

    work page 2026

  2. [2]

    **large, uninterpretable embeddings (hundreds of features)** from a foundational model pre-trained on wearable data

    work page

  3. [3]

    ### Task Implement a Python function`fit_and_predict`that takes training features (X_train), training labels (y_train), and validation features (X_val)

    demographic information. ### Task Implement a Python function`fit_and_predict`that takes training features (X_train), training labels (y_train), and validation features (X_val). All these inputs are pandas DataFrames. The function should handle the entire training process internally and return the final predictions for the validation data. The function mu...

    work page

  4. [4]

    **Wearable Device Sensor Embeddings:** Latent features from a model encoding sensor data

    work page

  5. [5]

    **Demographics:** Age, Body Mass Index (BMI), Gender, etc. Total Samples: 1220, Features: 1540, Target:`hypertension_binary` #### Features (X) <STATISTICS OF FEATURE COLUMNS ON TRAIN SET> #### Target Variable (y) #####`hypertension_binary` <STATISTICS OF TARGET COLUMN ON TRAIN SET> ### Metrics: You will receive an **overall score** and scored metrics as f...

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  6. [6]

    This DataFrame should have the same index as`X_val`and contain a single column named'predictions'

    **A pandas DataFrame** containing the predictions. This DataFrame should have the same index as`X_val`and contain a single column named'predictions'

    work page

  7. [7]

    Model: Random Forest (depth=5, n=100)

    A string (can be empty) containing any analysis, notes, feature importance, or other metadata you want to record. This is for`execution_feedback`. An example function is provided below: <EXAMPLE LINEAR PROBE CODE> ### Goal: Iteratively refine your pipeline to produce a **simple, robust model** that achieves the best`overall_score`for hypertension_binary p...

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  8. [8]

    Do not include introductory filler

    **Direct Answer First:** Address the user's specific query clearly in the opening sentence. Do not include introductory filler

    work page

  9. [9]

    Do not do more

    **Follow-up Interpretation/Action:** Add 1-3 more sentences elaborating on the interpretation and action. Do not do more

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  10. [10]

    **Instructional Guidelines**

    **Short Length:** Keep entire response to 1 short paragraph with 2-4 sentences with the most relevant features. **Instructional Guidelines**

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  11. [11]

    Do not list out unrelated metrics (e.g., do not mention sleep or HRV if the query is strictly about blood sugar)

    **Ruthless Prioritization:** Focus EXCLUSIVELY on the data points most pertinent to the user's query. Do not list out unrelated metrics (e.g., do not mention sleep or HRV if the query is strictly about blood sugar). Eliminate all distracting filler data

    work page

  12. [12]

    **Precision:** If discussing demographics or wearable aggregate info, include exact numbers

    work page

  13. [13]

    **Protect AI Predictions:** NEVER output exact regression values or explicit boolean (true/false) flags from the AI Models

    work page

  14. [14]

    The model flags a potential trend to monitor

    **Appropriate Use of AI Predictions:** If AI Model Predictions are present, actively use them to drive your insights and to help paint a holistic picture. Interpret them qualitatively (e.g., "The model flags a potential trend to monitor ..." or "your predictive profile aligns with ..."). This should be done in a way that broadly explains what the predicti...

    work page

  15. [15]

    How can I improve my health?

    **Synthesis:** Don't just list facts. Explain the relationship between their metrics. For example, explicitly link how their specific lifestyle data (wearables) is influencing their physiological state or predictive risks for their specific age/demographic. --- **Current User Query:** "How can I improve my health?" Provide your response strictly adhering ...

    work page 2022

  16. [16]

    What is your current employment status? □Full-time □Part-time □Contract / Temporary □Unemployed □Unable to work □Choose not to answer

    Currently Working. What is your current employment status? □Full-time □Part-time □Contract / Temporary □Unemployed □Unable to work □Choose not to answer

    work page

  17. [17]

    Disability. Do you identify as having a disability as defined under the Americans with Disabilities Act? The ADA defines a person with a disability as a person who has a physical or mental impairment that substantially limits one or more major life activity. □Yes □No □Prefer not to answer

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  18. [18]

    Does your disability affect how you work? □Yes □No □Prefer not to answer

    Disability Affects Work. Does your disability affect how you work? □Yes □No □Prefer not to answer

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  19. [19]

    Are you a smoker? □Yes □No

    Smoking. Are you a smoker? □Yes □No

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  20. [20]

    Have you been diagnosed with any of the following? Select all that apply

    Diagnoses. Have you been diagnosed with any of the following? Select all that apply. □Diabetes □High blood pressure (hypertension) □High cholesterol (Hyperlipidemia or hypercholesterolemia) □Cardiovascular disease □Kidney condition □Respiratory condition (e.g. asthma, COPD, sleep apnea) 69 Towards a General Intelligence and Interface for Wearable Health D...

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  21. [21]

    Diabetes Medication. Do you take any of the following diabetes medications? □Blood thinners □Beta blockers □Daily aspirin □Blood pressure medications □Statin or other cholesterol lowering medications □Heart medications □Antidepressant or antianxiety medications □Metformin or other oral diabetes drugs □Insulin □Hypothyroidism drugs □Hyperthyroidism drugs □...

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  22. [22]

    Do you take any of the following medications? Select all that apply

    Medications. Do you take any of the following medications? Select all that apply. □Metformin (e.g. Glucophage) □Other oral diabetes medications □Insulin □I do not take any diabetes medication 70 Towards a General Intelligence and Interface for Wearable Health Data Survey ED.3: Patient Health Questionnaire (PHQ-8) Little interest or pleasure in doing thing...

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