Internet-delivered cognitive behaviour therapy for chronic health conditions: a systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aims to evaluate the effectiveness of internet-delivered cognitive behavioural therapy (ICBT) on anxiety and depression among persons with chronic health conditions. A systematic database search was conducted of MEDLINE, CINAHL, PsycInfo, EMBASE, and Cochrane for relevant studies published from 1990 to September 2018. A study was included if the following criteria were met: (1) randomized controlled trial involving an ICBT intervention; (2) participants experienced a chronic health condition; (3) participants ≥ 18 years of age; and (4) effects of ICBT on anxiety and/or depression were reported. The Cochrane Risk of Bias tool was used to assess the risk of bias on the included studies. Pooled analysis was conducted on the primary and condition specific secondary outcomes. Twenty-five studies met inclusion criteria and investigated the following chronic health conditions: tinnitus (n = 6), fibromyalgia (n = 3), pain (n = 7), rheumatoid arthritis (n = 3), cardiovascular disease (n = 2), diabetes (n = 1), cancer (n = 1), heterogeneous chronic disease population (n = 1), and spinal cord injury (n = 1). Pooled analysis demonstrated small effects of ICBT in improving anxiety and depression. Moderate effects of therapist-guided approach were seen for depression and anxiety outcomes; while, self-guided approaches resulted in small effects for depression and moderate effects in anxiety outcomes. ICBT shows promise as an alternative to traditional face-to-face interventions among persons with chronic health conditions. Future research on long-term effects of ICBT for individuals with chronic health conditions is needed.Trial Registration PROSPERO registration number: CRD42018087292.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.034 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".