The Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Pulmonary Hypertension Patients
Bibliographic record
Abstract
Abstract Patients with pulmonary arterial hypertension (PAH) have quality of life (QoL) limitations, decreased exercise capacity, and poor prognosis if left untreated. Standard exercise testing is routinely performed for the evaluation of patients with PAH but may be limited in its ability to monitor activity levels in daily living. We evaluated the validity of the commercial Fitbit Charge HR as a tool to assess real time exercise capacity as compared to standard exercise testing in patients with PAH. Ambulatory pediatric and adult PAH patients were enrolled and given a Fitbit with instructions to continuously wear during waking hours. Subjects underwent a 6-minute walk test (6MWT), cardiopulmonary exercise test (CPET) and an SF-36 QoL survey on the day of enrollment and follow-up. Twenty-seven ambulatory subjects with PH were enrolled and 21 had sufficient data for analyses (median age 25, range 13-59, 14 F) were enrolled. Daily steps measured by the Fitbit had a positive correlation with 6MWT distance (r = 0.72, p = 0.03) and an inverse correlation with WHO functional class. On the QoL survey, 77% reported improvement in energy/fatigue (p = 0.055). At follow up there was a strong correlation between Fitbit steps and role limitations due to physical problems (r = 0.88, p = 0.020) and weaker correlations with less related QoL markers. These findings suggest activity monitors may have potential as a simple/novel method of assessing longitudinal exercise capacity and activity levels in PAH patients. Further study in larger cohorts of patients is warranted to determine the best accelerometric correlates with outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".