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Record W4386862273 · doi:10.2196/49345

Outcomes of a Remote Cardiac Rehabilitation Program for Patients Undergoing Atrial Fibrillation Ablation: Pilot Study

2023· article· en· W4386862273 on OpenAlex
Satish Misra, Karen Niazi, Kamala Swayampakala, Amanda Blackmon, M. Lang, Elizabeth Davenport, Sherry J. Saxonhouse, John M. Fedor, Brian D. Powell, Joseph Thompson, John W. Holshouser, Rohit Mehta

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Cardio · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersAtrium Health Foundation
KeywordsAtrial fibrillationMedicineRehabilitationAblationCardiologyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Risk factor modification, in particular exercise and weight loss, has been shown to improve outcomes for patients with atrial fibrillation (AF). However, access to structured supporting programs is limited. Barriers include the distance from appropriate facilities, insurance coverage, work or home responsibilities, and transportation. Digital health technology offers an opportunity to address this gap and offer scalable interventions for risk factor modification. OBJECTIVE: This study aims to assess the feasibility and effectiveness of a 12-week asynchronous remotely supervised exercise and patient education program, modeled on cardiac rehabilitation programs, in patients with AF. METHODS: A total of 12 patients undergoing catheter ablation of AF were enrolled in this pilot study. Participants met with an exercise physiologist for a supervised exercise session to generate a personalized exercise plan to be implemented over the subsequent 12-week program. Disease-specific education was also provided as well as instruction in areas such as blood pressure and weight measurement. A digital health toolkit for self-tracking was provided to facilitate monitoring of exercise time, blood pressure, weight, and cardiac rhythm. The exercise physiologist remotely monitored participants and completed weekly check-ins to titrate exercise targets and provide further education. The primary end point was program completion. Secondary end points included change in self-tracking adherence, weight, 6-minute walk test (6MWT), waist circumference, AF symptom score, and program satisfaction. RESULTS: and CHADs2VASC (Congestive Heart Failure, Hypertension, Age [≥75 years], Diabetes, Stroke/Transient Ischemic Attack, Vascular Disease, Age [65-74 years], Sex [Female]) of 1.5. A total of 11/12 (92%) participants completed the program, with 94% of expected check-ins completed and 2.9 exercise sessions per week. Adherence to electrocardiogram and blood pressure tracking was fair at 81% and 47%, respectively. Significant reductions in weight, waist circumference, and BMI were observed with improvements in 6MWT and AF symptom scores (P<.05) at the completion of the program. For program management, a mean of 2 hours per week or 0.5 hours per patient per week was required, inclusive of time for follow-up and intake visits. Participants rated the program highly (>8 on a 10-point Likert scale) in terms of the impact on health and wellness, educational value, and sustainability of the personal exercise program. CONCLUSIONS: An asynchronous remotely supervised exercise program augmented with AF-specific educational components for patients with AF was feasible and well received in this pilot study. While improvements in patient metrics like BMI and 6MWT are encouraging, they should be viewed as hypothesis generating. Based on insights gained, future program iterations will include particular attention to improved technology for data aggregation, adjustment of self-monitoring targets based on observed adherence, and protocol-driven exercise titration. The study design will need to incorporate strategies to facilitate the recruitment of a diverse and representative participant cohort.

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.

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)0.001
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 score0.042
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.370
Teacher spread0.314 · 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