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Record W4389429183 · doi:10.1186/s40359-023-01457-z

The effects of cognitive behavioral therapy on health-related quality of life, anxiety, depression, illness perception, and in atrial fibrillation patients: a six-month longitudinal study

2023· article· en· W4389429183 on OpenAlexaboutno aff
Zheng Minjie, Xie Zhijuan, Bai Xinzhu, Shan Qu

Bibliographic record

VenueBMC Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAnxietyQuality of life (healthcare)Depression (economics)DistressGeeAtrial fibrillationClinical psychologyPsychologyCognitionMedicineCognitive behavioral therapyGeneralized anxiety disorderPhysical therapyGeneralized estimating equationPsychiatryInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Atrial fibrillation (AF) often leads to an impaired Health-Related Quality of Life (HRQoL) in many patients. Moreover, psychological factors such as depression, anxiety, and illness perception have been found to significantly correlate with HRQoL. This study aims to evaluate the long-term effectiveness of Cognitive Behavioral Therapy (CBT) in enhancing HRQoL and mitigating psychological distress among AF patients. METHODS: Employing a prospective, open design with pseudo-randomization, this study encompassed pre-tests, post-treatment evaluations, and a 6-month follow-up. A total of 102 consecutive patients diagnosed with paroxysmal AF were initially enrolled. Out of these, 90 were assigned to two groups; one to receive a 10-week CBT treatment specifically focusing on anxiety, and the other to receive standard care. Outcome measures were evaluated using tools such as the Item Short Form Health Survey (SF-12), General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), University of Toronto Atrial Fibrillation Severity Scale (AFSS), and Brief Illness Perception Questionnaire (BIPQ). These assessments were conducted at pre-treatment, post-treatment, and at the 6-month follow-up mark. We explored the effectiveness of CBT using Generalized Estimating Equations (GEE). RESULTS: Our analysis revealed a notable improvement in the CBT group relative to the control group. All metrics displayed consistent improvement across a 6-month duration. At the 6-month checkpoint, the CBT group exhibited a more favorable SF-12 Mental Component Score (MCS) (50.261 ± 0.758 vs. 45.208 ± 0.887, p < 0.001), reduced GAD-7 (4.150 ± 0.347 vs. 8.022 ± 0.423, p < 0.001), BIPQ (34.700 ± 0.432 vs. 38.026 ± 0.318, p < 0.001), and AFSS (9.890 ± 0.217 vs. 10.928 ± 0.218, p = 0.001) scores when compared to the TAU group. Conversely, the SF-12 PCS (44.212 ± 0.816 vs. 47.489 ± 0.960, p = 0.139) and PHQ-9 scores (8.419 ± 0.713 vs. 10.409 ± 0.741, p = 0.794) manifested no significant difference between the two groups. CONCLUSION: The findings suggest that CBT is effective in improving HRQoL and reducing psychological distress among patients with AF at 6 month follow-up. This highlights the potential benefits of integrating CBT into the therapeutic regimen for AF patients. TRIAL REGISTRATION: Retrospectively registered with ClinicalTrials.gov (NCT05716828). The date of registration : 5 June 2023.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.118
GPT teacher head0.447
Teacher spread0.329 · 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 designObservational
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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractyes

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