Remote Exercise Engagement Among Individuals With Cardiovascular Disease
Bibliographic record
Abstract
PURPOSE: To systematically review the literature regarding barriers and facilitators to remote exercise among individuals with cardiovascular diseases (CVD). REVIEW METHODS: Six databases were searched (inception-December 2023). Studies including barriers and facilitators for remote exercise reported by individuals living with CVD were considered. Quality was rated using the Mixed Methods Assessment Tool (MMAT). Results were synthesized following a thematic analysis approach. Characteristics of interventions were reported using the Template for Intervention Description and Replication (TIDieR). SUMMARY: From an initial pool of 8543 records, 18 studies met the inclusion criteria. Among these, 13 were qualitative studies, with individual interviews being the most frequent data extraction method. Nine studies received 5/5 MMAT scores (ie, high quality). Twelve studies reported remote exercise interventions, mostly delivered within cardiac rehabilitation, by physiotherapists, nurses, and dietitians, predominantly via telephone calls or videoconferences, with only one study incorporating text messages. Five key themes influencing remote exercise interventions were identified: technology (encompassing access and literacy, self-monitoring/motivation, and security concerns), individual health considerations (health/personal aspects and coronavirus disease-2019), social factors (support from health care team and family/social support), environment (facilities), and logistical factors (time and displacement). Several themes served as both facilitators and barriers. Key insights for remote exercise interventions included mobile accessibility, interactive chat sessions, minimal click interactions, short sessions, personalized feedback, and flexible timing. Results emphasize the importance of promoting social interaction and support between participants and health care teams to mitigate barriers and enhance facilitators in remote exercise interventions for individuals with CVD.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".