Mindfulness-based interventions and cognitive function in cancer survivors: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: Deterioration in cognitive function is common among cancer survivors undergoing treatment. These problems may persist for several years after completion of treatment and can adversely affect cancer survivors' treatment adherence and quality of life. The cause of cognitive changes in cancer survivors is unclear, although it is likely a complex interaction of disease-related, treatment-related, and psychological factors. Mindfulness-based interventions (MBIs) are one promising intervention for cancer survivors to alleviate unwanted and burdensome side effects, including disruptions in cognitive function. The aim of the current review was to synthesize the literature on MBIs and cognitive function in cancer survivors. Methods: We searched five databases from inception on May 27, 2021 (original search), and May 4, 2022 (updated search): PubMed, MEDLINE Ovid, EMBASE Ovid, PsycInfo Ovid, CINAHL EBSCO, and Web of Science. Articles were screened at the abstract and full-text level by two reviewers. Results: A total of 1916 records were retrieved, and 24 unique studies met the inclusion criteria. There was significant variability across studies regarding type of MBIs investigated, types of cognitive outcome measures used, and study assessment timelines. Eleven studies were included in a meta-analysis of self-reported cognitive function, significantly favoring MBIs over inactive controls (ie, usual care) (standardized mean difference = 0.86; 95% confidence interval = 0.32–1.41). A similar model, including four studies, compared MBIs with active controls (ie, music listening, metacognition treatment, fatigue education and support, walking program); this model also demonstrated a statistically significant pooled effect (standardized mean difference = 0.61; 95% confidence interval = 0.23–0.99). Owing to a small number of studies, meta-analysis could not be completed for objectively assessed cognitive function; a narrative summary for this outcome revealed mixed results. Conclusions: MBIs demonstrated evidence for improving cognitive function among cancer survivors and particularly self-reported cognitive function. However, most studies demonstrated a high risk of bias and significant concerns regarding study quality. Further research is needed to determine the effects of MBIs on both self-reported and objectively assessed cognitive function for cancer survivors, as well as optimal intervention structure and timing.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".