Patient experiences of implantable cardiac monitoring in hypertrophic cardiomyopathy: an exploratory study
Bibliographic record
Abstract
AIMS: Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease. Insertable cardiac monitors (ICMs) are increasingly used in this population to provide closer monitoring, with the potential for notification systems. However, little is known regarding the psychological impact this information may have on patients. The Abbott Confirm Rx™ ICM has the capability of connecting to the patient's smartphone to enable active participation in their care, as well as two-way communication between the patient and their care providers. This study aimed to explore individuals' experiences of having a smartphone-enabled ICM to monitor for arrhythmias in HCM. METHODS AND RESULTS: Semi-structured interviews were conducted with 10 participants. Utilizing a grounded theory approach, the interview guide was modified based on emerging themes throughout the study. Reflexive thematic analysis was applied to categorize interview data into codes and overacting themes, with each interview independently coded by two study members. Analysis revealed three key themes: (i) psychological impact, (ii) educational needs, and (iii) technology expectations. Participants reported that receiving feedback from ICM transmissions resulted in improved symptom clarity, providing reassurance, and aiding implantable cardioverter defibrillator decision-making. Some participants reported uncertainty regarding when to send manual transmissions. Lastly, participants reported the app interface did not meet expectations with regard to the amount of data available for patients. CONCLUSION: Overall, utilizing a smartphone app to facilitate two-way communication of ICM transmissions was well accepted. Future directions include addressing gaps in educational needs and improvements in the patient interface with increased access to data.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".