Efficacy of internet-delivered cognitive behavioural therapy following an acute coronary event: A randomized controlled trial
Bibliographic record
Abstract
Depression and anxiety are common among people who have experienced an acute coronary event (e.g., heart attack). Multidisciplinary cardiac rehabilitation programs often focus on reducing risk factors associated with future cardiac events, however, mental health interventions are not routinely available. Given known difficulties with access to mental health treatment, the present study sought to explore the efficacy and acceptability of an Internet-delivered cognitive behavioural therapy program (Cardiac Wellbeing Course) among participants who experienced an acute coronary event. The five-lesson course was delivered over eight weeks and was provided with brief weekly contact, via telephone and secure email with a guide. Participants were randomized to the Cardiac Wellbeing Course (n = 25) or waiting-list control group (n = 28). Symptoms were assessed at pre-treatment, post-treatment, and four-week follow-up. Completion rates (84%) and satisfaction ratings (95%) were high. Statistically significant between-group improvements were observed for the treatment group on primary measures of general anxiety (Cohen's d = 1.62; 67% reduction), depression (Cohen's d = 1.09; 61% reduction), and physical activity levels (Cohen's d = 0.27; 70% increase). Statistically significant improvements were also observed on secondary measures of distress (Cohen's d = 0.98; 51% reduction), cardiac anxiety (Cohen's d = 0.92; 34% reduction), and mental-health quality of life (Cohen's d = 0.23; 24% improvement). The changes were maintained at four-week follow-up. The current findings add to the existing literature and highlight the potential of Internet-delivered cognitive behavioural therapy programs among participants who have experienced an acute coronary event.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".