Therapeutic components of an automated digital counselling intervention for chronic heart failure
Bibliographic record
Abstract
Abstract Background/Introduction Task force statements advocate digital health interventions to promote self-care behaviour and health-related quality of life (HRQL) in patients with chronic heart failure (CHF). There is a need to identify therapeutic components of digital interventions to improve the efficacy and replicability of these CHF interventions. Purpose The Canadian e-Platform to PrOmote BehavioRal Self-ManagemenT in Chronic Heart Failure trial (CHF-CePPORT) evaluated the efficacy of automated digital counseling to improve HRQL at 12 months, using the Kansas City Cardiomyopathy Questionnaire-Overall Summary (KCCQ-OS). Our aim was to identify therapeutic components of the CHF-CePPORT protocol that were independently associated with KCCQ-OS endpoint. Methods CHF-CePPORT was a multicenter, randomized controlled trial with a 2-parallel group, double blind design, and assessments at baseline, 4- and 12-months. This substudy focused on patients randomized to the automated digital counseling arm of CHF-CePPORT. Ordinal logistic regression was used to identify components of the protocol that predicted higher KCCQ-OS tertile at 12-months, according to schedule of automated digital contact, modality of content, and clinical content theme – see Figure. Results From the sample enrolled in CHF-CePPORT (n=230), 117 patients were included in this substudy: female, n=24 (20.5%), median age=60 years (IQR, 52, 69), New York Heart Association Class 1, n=45 (38.5%), Class 2, n=48 (41.0%), and Class 3, n=16 (13.7%). Baseline KCCQ-OS was median=82.3 (IQR, 67, 93). Patient engagement with the digital counseling platform over 12 months was as follows: Median (IQR) total logons = 79 (24, 133), Total logon time = 5.8 hours (1.6, 9.8). Total logon time during the initial phase of the trial (sessions 1–16), with weekly scheduled sessions was independently associated with a higher 12-month KCCQ-OS tertile score (p=0.003). Subsequent sections (sessions 17–24, and 25–28) were not independently associated with the 12-month KCCQ-OS (p=0.56 and p=0.91 respectively). Within sessions 1–16, the 12-month KCCQ-OS was associated with the use of counseling and dramatic videos (p=0.04) and e-tools/trackers (p=0.007), but not conventional information/education pages (p=0.80). Content themes associated with the 12-month KCCQ-OS included motivational counseling (sessions 5–8) with self-assessment tools and trackers (p=0.04). Cognitive behavioural guidelines for HF self-care were also associated with 12-month KCCQ-OS tertiles when presented by expert and dramatic videos (p=0.02) as well as self-assessment etools/etrackers (p=0.02). Self-assessment tools and trackers for HRQL (sessions 15–16) were also associated with higher KCCQ tertiles at 12 months (p=0.01) – see Table. Conclusion(s) The results of this study confirm the importance of using key components from evidence-based, clinically organized protocols of behavioural counseling to promote HRQL for patients with CHF. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Canadian Institutes of Health Research
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".