Harnessing AI for Hypothyroidism Monitoring Using Wearable-Derived Heart Rate Data: Retrospective Cohort Study

413 participants (76% women, mean age 43.1), 1,677 paired observations of serum thyroid tests (free T4, TSH) with Fitbit sleep HR from the 15 nights before each draw. LightGBM combining a baseline thyroid test with statistical and temporal sleep-HR features; patient-level leave-one-out CV. Euthyroid vs hypothyroid subset (744 pairs): accuracy 74.6%, sensitivity 82%, specificity 73.5% with one reference pair; 78.1% / 81.8% / 77.7% with three.

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Background

Hypothyroidism is a prevalent endocrine disorder characterized by an insidious onset and nonspecific symptoms, often leading to significant diagnostic delays. While biochemical thyroid function tests (TFTs) are the gold standard, their invasive nature and cost make them impractical for continuous, proactive screening of at-risk populations. Thyroid hormones exert direct chronotropic effects on the cardiovascular system, and their levels are closely correlated with heart rate (HR). Wearable devices capable of continuous HR monitoring offer a novel opportunity for noninvasive, digital phenotype–based detection.

Objective

This study aimed to develop and validate an AI-assisted system that uses wearable-derived sleep HR data as a digital monitoring tool for individuals at risk of hypothyroidism, helping identify those who warrant further confirmatory thyroid testing.

Methods

This retrospective cohort study used 1677 paired observations consisting of sleep HR data and serum TFT results, including free thyroxine (T4) and thyroid-stimulating hormone levels, from 413 participants (mean age 43.1, SD 9.7 y; women: n=314, 76%). Participants wore consumer-grade wearables (Fitbit) that recorded HR and sleep data. Each TFT result was paired with sleep HR data extracted from the 15 consecutive nights preceding the blood test. An AI system was developed using a gradient boosting algorithm (LightGBM) to classify thyroid status by integrating baseline TFT results with statistical and temporal HR features. The model’s performance was evaluated using patient-level leave-one-out cross-validation. To examine the benefit of longitudinal data, performance was compared using 1, 2, or 3 prior paired observations as reference points.

Results

To ensure clinical relevance and avoid performance inflation from hyperthyroid cases, we prioritized the analysis of a subset containing only euthyroid and hypothyroid data (n=744 paired observations). In this clinically challenging subset, the system achieved an accuracy of 74.6%, with a sensitivity of 82% and a specificity of 73.5% using a single-reference data pair. Incorporating longitudinal HR patterns further enhanced performance; using 3 reference pairs improved accuracy to 78.1% (sensitivity 81.8% and specificity 77.7%). In the overall dataset (n=1677 paired observations), the system demonstrated an accuracy of 80.8%, with a sensitivity of 82% and a specificity of 80.8% using a single-reference pair. Notably, the system achieved high clinical safety by detecting 96.3% of severe overt hypothyroidism cases (free T4 Conclusions

This AI-assisted system demonstrates the feasibility of a sensitive, noninvasive longitudinal monitoring tool for hypothyroidism, robustly identifying a high proportion of hypothyroid cases—particularly severe ones—to prioritize at-risk individuals for confirmatory blood tests. Given the predominantly female iatrogenic-withdrawal cohort, prospective sex-balanced validation in primary hypothyroidism is warranted.

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