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Automated digital counselling promotes therapeutic change in mental health in patients with chronic heart failure or chronic kidney disease: ODYSSEE-vCHAT study

2024· article· en· W4403806225 on OpenAlexaff
Robert P. Nolan, H. Ross, Charles K. Chan, Valeria E. Rac, Ayub Akbari, Bourne L. Auguste, Ella Huszti, J Arcand, Jillianne Code, Juan Duero Posada, Janusz Kaczorowski, Steven A. Grover, Michael E. Farkouh, Robert Maunder, Stephanie Poon

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversité de MontréalUniversity of British ColumbiaHealth Sciences CentreSunnybrook Health Science CentreUniversity Health NetworkUniversity of TorontoOttawa HospitalMcGill UniversityTed Rogers Centre for Heart Research
Fundersnot available
KeywordsMedicineHeart failureKidney diseaseDiseaseChronic renal failureIntensive care medicineChronic diseaseMental healthInternal medicinePhysical therapyCardiologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background The efficacy of digital programs to improve the management of chronic heart failure (CHF) and chronic kidney disease (CKD) is not established. Building on related studies in digital health, behavioural counselling, and social support, we evaluated whether an automated digital counselling program with social network support (ODYSSEE-vCHAT) could improve mental health and health-related quality of life in patients with CHF or CKD. Purpose The primary objective was to examine whether engagement with ODYSSEE-vCHAT was associated with a minimal clinically important difference for improvement in the Mental Component Summary (MCS) of the SF-36 at end-of-study. Secondary outcomes included: ENRICHD Social Support Index (ESSI), Patient Health Questionnaire for Depression (PHQ-9) Godin-Shephard Leisure Time Physical Activity Scale (GSLTPAQ), and an index of the frequency to which patients engaged in goal-directed activities for living well (EUROIA). Methods ODYSSEE-vCHAT was a multi-centre, single group, open label study with assessments at baseline and end-of-study (approximately 11 months). Inclusion criteria were: ≥18 years of age, diagnosis of CHF (reduced ejection fraction (EF) ≤ 40%, mid-range EF, 41-49%, or preserved EF ≥ 50%); or CKD diagnosis (>10% risk for dialysis using the 4-variable, 2-year Kidney Failure Risk Equation, or on dialysis). Exclusion was based on inability to participate due to co-morbidities. The digital counselling protocol taught self-care skills to improve mental health, quality of life, and adherence to medications, exercise, diet, and smoke-free living (Figure 1). Patient engagement with ODYSSEE-vCHAT was defined as Not Engaged (0 logon minutes) vs. Engaged (> 0 logon minutes). The primary outcome was a minimal clinically important difference for MCS change: ΔMCS ≥ 3.8 or MCS ≥ 65 at baseline and study endpoint. It was assessed using multivariable binary logistic regression, controlling for exposure to COVID-19 and health literacy. Secondary outcomes were assessed using multivariable linear regression analyses with the same covariates. Results 215 patients were enrolled. There were 7 deaths and 34 withdrew. In our sample of 174 patients, mean age = 54.4 years (95% Confidence Interval, CI, 25, 81), gender identity as women, n = 68 (39%), ethno-racial group other than White, n = 83, (48%), primary diagnosis, CHF, n = 68 (39%), and CKD, n = 106 (61%). Duration of enrolment to end-of-study: median = 352 days, (95% CI, 159, 496). Engagement with ODYSSEE was associated with therapeutic change on the MCS (for both CHF and CKD patients, and CHF patients alone), GSLTPAQ, and EUROIA, but not the PHQ9 or ESSI (Figure 2). Conclusions Automated digital counselling with social network support promotes improvement in indices of mental health for patients with CHF and CKD. These findings support a follow-up randomized controlled trial.Figure 1.ODYSSEE-vCHAT Study ProtocolFiFigure 2.ODYSSEE-vCHAT Study Outcomes

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.047
GPT teacher head0.374
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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