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Abstract 4369009: Near-Maximal Movement Captured by Accelerometry Offers Superior Mortality Risk Prediction Than Aggregate Movement in Heart Failure

2025· article· en· W4415793724 on OpenAlexaff
Abhinav Sharma, George Perlman, Orhun Köse, Elite Possik

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAccelerometerBiomarkerCohortPercentileHeart failureCohort studyMetric (unit)Confounding

Abstract

fetched live from OpenAlex

Background: There is a growing interest in understanding the impact of tested therapies on real-world functional outcomes as measured by wearable accelerometers (ACC) in heart failure (HF). However, there is a need to validate clinically meaningful ACC biomarkers as trials have shown limited efficacy in improving aggregate ACC measures such as daily steps to date. Here, we examine the association between an established aggregate ACC measure and a novel biomarker of near-maximal movement with the risk of all-cause and cardiovascular (CV) mortality in patients with HF. Research Question: How do measures of aggregate vs. near-maximal volitional movement captured via accelerometry compare in their prediction of mortality risk in patients with heart failure? Methods: Participants in the 2011–2012 and 2013–2014 waves of NHANES wore the Actigraph GT3X+ on their wrist for 7 days and were followed up to confirm mortality status and cause of death (ICD-10) in 2019. The sum of vector magnitudes (SVM) was calculated as the mean vector magnitude of acceleration for every 5 minutes. SVM values were ranked numerically and the 90 th percentile value was captured for each participant as a novel biomarker (i.e., SVM-90) of near-maximal movement. The aggregate ACC metric Monitor-independent movement summary (MIMS) was downloaded from the NHANES website. Cox proportional hazards models were employed to evaluate the relationship between SVM-90 and MIMS values above or below the cohort median and time to all-cause or CV mortality. Results: 291 participants with self-reported HF (50.9% female, mean (SD) age 66.8 (12.6), mean 5.4 years of follow-up) were included. Participants with SVM-90 below the median were older than their counterparts (age 70.6 (10.9) vs. 63.0 (13.1), p<0.001) while other demographics did not differ between groups. In models adjusted for MIMS above or below the median, the high SVM-90 group presented reduced risk of all-cause (HR 0.56 (0.38-0.84), p=0.00444, Fig.1A) and CV mortality (HR 0.43 (0.23-0.79), p=0.00676, Fig.1B) compared to the low SVM-90 group, while MIMS was not associated with risk of either outcome (all-cause: HR 0.72 (0.49-1.06), p=0.0959; CV: HR 0.81 (0.45-1.45), p=0.478). Conclusion: Here we provide evidence that a measure of near-maximal movement captured by ACC offers superior mortality prediction in HF compared to aggregate movement. ACC provides objective and patient-centric biomarkers that may serve as outcomes in HF trials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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