Impacts of the COVID-19 Pandemic on Cardiac Rehabilitation Delivery around the World
Bibliographic record
Abstract
Abstract Background To investigate impacts of COVID-19 on CR delivery around the globe, including effects on providers and patients. Methods In this cross-sectional study, a piloted survey was administered to CR programs globally via REDCap from April-June/2020. The 50 members of the ICCPR and personal contacts facilitated program identification. Results Overall, 1062(18.3% program response rate) responses were received from 70/111(63.1% country response rate) countries in the world with existent CR programs. Of these, 367(49.1%) programs reported they had stopped CR delivery, and 203(27.1%) stopped temporarily (mean=8.3±2.8weeks). Alternative models were delivered in 322(39.7%) programs, primarily through low-tech modes (n=226,19.3%). 353(30.2%) respondents were re-deployed, and 276 (37.3%) felt the need to work due to fear of losing their job, despite the perceived risk of contracting COVID-19 (mean=30.0%±27.4/100). 266(22.5%) reported anxiety, 241(20.4%) were concerned about exposing their family, 113(9.7%) reported increased workload to transition to remote delivery, and 105(9.0%) were juggling caregiving responsibilities during business hours. Patients were often contacting staff regarding grocery shopping for heart-healthy foods (n=333,28.4%), how to use technology to interact with the program (n=329,27.9%), having to stop their exercise because they have no place to exercise (n=303,25.7%), and their risk of death from COVID-19 due to pre-existing cardiovascular disease (n=249,21.2%). Respondents perceived staff (n=488,41.3%) and patient (n=453,38.6%) personal protective equipment, as well as COVID-19 screening (n=414,35.2%) and testing (n=411,35.0%) as paramount to in-person service resumption. Conclusion Approximately 4400 programs ceased service delivery. Those that remain open are implementing new technologies to ensure their patients receive CR safely, despite the challenges. Highlights - COVID-19 has impacted cardiac rehabilitation (CR) delivery around the globe. - In this cross-sectional study, a survey was completed by 1062 (18.3%) CR programs from 70 (63.1%) countries. - The pandemic has resulted in cessation of ∼75% of CR programs, with others ceasing initiation of new patients, reducing components delivered, and/or changing of mode delivery with little opportunity for planning and training. - There is also significant psychosocial and economic impact on CR providers. - Alternative CR model (e.g. home-based, virtual) reimbursement advocacy is needed, to ensure safe, accessible secondary prevention delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".