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Record W4411300885 · doi:10.2196/70835

The Validation and Accuracy of Wearable Heart Rate Trackers in Children With Heart Disease: Prospective Cohort Study

2025· article· en· W4411300885 on OpenAlexvenueno aff
Hidde J. Hardon, Yara N. Van Kerkhof, Beatrijs Bartelds, Janneke A.E. Kammeraad, Arend W. van Deutekom

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintActivity trackerProspective cohort studyMedicineCohortWearable computerPhysical therapyInternal medicineComputer sciencePhysical activityEmbedded systemWorld Wide Web

Abstract

fetched live from OpenAlex

Background: Wearables are increasingly used in pediatric cardiology for heart rate (HR) monitoring due to advantages over traditional HR monitoring, such as prolonged monitoring time, increased patient comfort, and ease of use. However, their validation in this population is limited. Objective: The objective of this paper was to assess HR accuracy and validity from 2 wearables, the Corsano CardioWatch bracelet and the Hexoskin smart shirt, in children attending the pediatric cardiology outpatient clinic, exploring factors that influence accuracy, the Hexoskin shirt's arrhythmia detection efficacy, and patient satisfaction. Methods: Children with an indication for 24-hour Holter monitoring were equipped with a 24-hour Holter electrocardiogram (ECG; gold standard), together with both wearables. HR accuracy was defined as the percentage of HRs within 10% of Holter values, and agreement was assessed using Bland-Altman analysis. Subgroup analyses were conducted based on BMI, age, and time of wearing, among other factors. The association between accelerometry (expressed in gravitational units, g) and HR accuracy was analyzed to assess the impact of bodily movement on measurement accuracy. A blinded pediatric cardiologist analyzed Hexoskin shirt data for rhythm classification. Patient satisfaction was measured using a 5-point Likert scale questionnaire. Results: A total of 31 participants (mean age 13.2, SD 3.6 y; n=14, 45% female) and 36 participants (mean age 13.3, SD 3.9 y) were included for the CardioWatch and Hexoskin measurements, respectively. Mean accuracy was 84.8% (SD 8.7%) for the CardioWatch and 87.4% (SD 11%) for the Hexoskin shirt. Hexoskin shirt accuracy was notably higher in the first 12 hours (94.9%, SD 7.4%) compared to the latter 12 (80%, SD 16.7%; P<.001). Higher accuracy was observed at lower HRs (low vs high HR: CardioWatch: 90.9%, SD 9.3% vs 79%, SD 10.6%; P<.001 and Hexoskin shirt: 90.6%, SD 14% vs 84.5, SD 11.8%; P<.001). Both wearables demonstrated good agreement in their HR measurement with Holter readings (CardioWatch bias: -1.4 beats per minute [BPM]; 95% limits of agreement [LoA] -18.8 to 16.0. Hexoskin shirt bias: -1.1 BPM; 95% LoA -19.5 to 17.4). HR measurement accuracy declined during more intense bodily movements. Correct classification of the Hexoskin's shirt rhythm recordings was achieved in 86% (31/36) of cases. Patient satisfaction scores were significantly higher for both the CardioWatch (median 3.8, range 3.5-4.3; P<.001) and Hexoskin shirt (median 3.7, range 3.0-4.0; P<.001) compared to the Holter (median 2.6, range 2.1-3.2). Conclusions: The Corsano CardioWatch and Hexoskin shirt demonstrate good accuracy in pediatric HR monitoring and provide higher patient comfort than conventional monitoring. Both wearables show good agreement in relation to the gold standard device. However, measurement accuracy declines with increasing bodily movement and higher heart rates. More research is needed to explore the underlying causes for these inaccuracies and how to counteract them. The Hexoskin shirt also shows potential in arrhythmia detection. While further development is warranted, these wearables show promise in enhancing diagnostics, therapeutic monitoring, and patient safety in pediatric cardiology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.336
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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