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Record W4413030538 · doi:10.2196/69343

Analysis of a Medically Certified, Wrist-Worn Sensor for the Assessment of Heart Rate and Energy Expenditure During Daily Activities in Patients With Chronic Heart Failure or Coronary Artery Disease and Recreational Athletes: Validation Study

2025· article· en· W4413030538 on OpenAlexvenueno aff
I L J De Lathauwer, Valerie A. A. van Es, Mayke van Leunen, Steven Onkelinx, R W M Brouwers, Danny AJP van de Sande, Mathias Funk, Hareld Kemps

Bibliographic record

VenueJMIR Cardio · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureCardiologyCoronary artery diseaseInternal medicineAthletesEnergy expenditurePhysical therapyHeart rateWristSurgeryBlood pressure

Abstract

fetched live from OpenAlex

Background: Exercise capacity and lifestyle have proven to be important prognostic factors for cardiovascular patients. Both can be ameliorated through different preventive interventions. Cardiac rehabilitation and remote patient monitoring have been proven to reduce cardiac events and cardiovascular mortality. One of the most important goals of cardiac rehabilitation and remote patient monitoring is improving physical fitness and monitoring of cardiovascular parameters, which could predict cardiac deterioration. In order to monitor cardiac patients successfully, reliable and nonobtrusive devices to assess physical activity and cardiovascular parameters need to be available. Objective: This validation study aims to determine the accuracy of the Philips Health Band (PHB), a noninvasive, wrist-worn, medically certified device, for the assessment of heart rate (HR) and energy expenditure (EE) in patients with chronic cardiovascular diseases and recreational athletes (RAs). Methods: The assessment of HR and EE by the PHB was compared with indirect calorimetry (Oxycon Mobile [OM; CareFusion GmbH]) during an activity protocol consisting of daily activities. Three groups were assessed: patients with heart failure with reduced ejection fraction (HFrEF), patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction, and RAs. Results: A total of 57 patients were included: 19 with CAD, 19 with HFrEF, and 19 RAs. HR assessment in the HFrEF and CAD groups was significantly underestimated over the entire protocol by the PHB as compared to the OM, with poor and fair reliability, respectively. No significant difference in HR was found between the PHB and OM over the entire protocol for the RA group, with good reliability (HFrEF: mean difference 3.0; P<.001; intraclass correlation coefficient [ICC] 0.36; CAD: mean difference 2.7; P<.001; ICC 0.55; RA: mean difference 0.8; ICC 0.60). Assessment of EE showed an underestimation over the entire protocol for the RA and CAD group, with poor and fair reliability, respectively. The HFrEF group showed no significant difference in EE assessment over the entire protocol, with poor reliability (HFrEF: mean difference 0.09; ICC 0.32; CAD: mean difference 0.29; P<.001; ICC 0.46; RA: mean difference 0.79; P<.001; ICC 0.26). The responsiveness to detect within-patient changes in activity intensity of the PHB was moderate for the HFrEF and CAD groups and acceptable for the RA group. Conclusions: HR and EE assessment of a medically certified noninvasive sensor using a photoplethysmogram and accelerometer showed poor accuracy and moderate responsiveness during an activity protocol reflecting daily living activities in patients with stable CAD and chronic HFrEF. Accuracy of HR in RAs was good and the responsiveness for both HR and EE was acceptable. This research confirms previous research and stresses the need for better patient-specific algorithms in noninvasive sensors, taking cardiovascular pathology and medication usage into account, for assessing HR and EE prior to their implemention in patient care.

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)0.000
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 score0.022
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.275
Teacher spread0.265 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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