Cross-sectional analysis of frailty status and heart rate variability at rest and during exercise in older adult females
Bibliographic record
Abstract
Frailty is related to an imbalance of homeostasis within the body, accompanied by an accumulation of health deficits. A possible way to measure this imbalance, is with a non-invasive measure of the autonomic nervous system through heart rate variability (HRV). This study utilized a secondary, cross-sectional analysis of data collected in the Women’s Advanced Risk-Assessment in Manitoba (WARM) Hearts study. The purpose was to determine if there is a difference in HRV depending on frailty level measured by the WARM Hearts HRV Frailty Index (WH-HRV FI) and the Standardized Frailty Phenotype measured at rest and during moderate exercise; and to determine if there is an association between HRV and frailty when measuring frailty on a continuous scale both at rest and during moderate exercise. Data analysis included 125 females in the resting cohort, and 102 in the exercise cohort. Frailty was measured using two separate methods (WH-HRV FI and the Standardized Frailty Phenotype). HRV was measured using a Polar H7 heart rate monitor with a 5-minute sample at rest and a 2-minute sample during exercise. This analysis included various HRV indices including mean RR intervals (Mean RR), standard deviation of normal-normal intervals (SDNN), root mean square of successive intervals (RMSSD), low frequency (LF), high frequency (HF), total power (TP), low frequency to high frequency ratio (LF/HF), and approximate entropy (ApEn). Resting analysis revealed a trend towards a reduction in SDNNlog, TPlog, and LF/HFlog and a significant positive association in LFlog in those who were frail with no changes of RMSSDlog or HFlog indicating an impaired sympathetic nervous system (SNS). Exercise analysis revealed a significant difference and positive association between frailty and mean RRlog, and an inverse relationship between frailty and TPlog. This indicates a reduced ability to adapt to changes and a reduced global HRV for both frailty measures. There were inconsistent results regarding the direction of the association between LFlog and SDNNlog between frailty tools. Overall, there may be an impairment in SNS activity at rest and a reduced ability to adapt, along with decreased global HRV (TPlog) during exercise in those who have increased levels of frailty.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".