Online mindfulness interventions in the care of people with physical and mental health conditions: a scoping review
Bibliographic record
Abstract
OBJECTIVE: With growing access to the internet, online mindfulness programmes have become more commonly used to manage physical and mental health conditions. This scoping review aims to determine the nature and extent of the literature, and key characteristics, of online mindfulness-based interventions (MBIs) for adults with physical or mental health conditions. DESIGN: A scoping review guided by the Joanna Briggs Institute framework. DATA SOURCES: MEDLINE, CINAHL, Embase, PsycINFO, Allied and Complementary Medicine and the Cochrane Central Register of Controlled Trials. ELIGIBILITY CRITERIA: Studies focusing on online MBIs, online mindfulness-based stress reduction and online mindfulness-based cognitive therapy (MBCT) in adults with a physical or mental health condition were included. DATA EXTRACTION AND SYNTHESIS: Study and participant characteristics, key intervention characteristics, outcome measures and results were abstracted. RESULTS: 84 studies were included. Online MBIs have been studied in many different physical and mental health conditions; however, 63 of the included studies were for physical health conditions. MBCT was the most common intervention type assessed, with 33 of the included studies assessing it. Regarding intervention characteristics, intervention duration was similar across intervention type at 8 weeks, with sessions led by therapists, clinicians or mindfulness instructors. Web-based and videoconferencing were the most common delivery formats. Intervention content generally remained similar to standardised MBIs, with the addition of psychoeducation and disease management. Many studies did not report on tailoring the intervention to the participant population. There was a lack of consistency in reporting intervention characteristics. CONCLUSIONS: This review highlights some evidence for online mindfulness programmes for both physical and mental health conditions. However, intervention componentry remains somewhat obscure, and reporting on tailoring appears relatively sparse. Greater consistency in reporting intervention componentry will improve knowledge and study in this area and enhance the translation of these interventions to clinical settings.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".