The Impact of Mindfulness-Based Interventions on Objective Physiological Measures of Autonomic Function for Individuals With Medical Conditions: A Review of the Evidence
Bibliographic record
Abstract
OBJECTIVE: Autonomic dysregulation is common in many medical conditions and can have a widespread, negative impact on multiple bodily systems, leading to poorer health outcomes. Thus, addressing autonomic dysregulation as part of a comprehensive treatment plan is important. The goal of this study was to gain a better understanding of the physiological benefits of a mindfulness-based intervention (MBI) for a population with medical conditions, using validated, objective measures of autonomic functioning. METHODS: We conducted a review of the literature and followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocols. Studies were included if a) participants were diagnosed with a medical condition, b) an MBI was used, and c) objective pre/post measurements of autonomic nervous system function were collected. Medical conditions were included as a category for this review when a minimum of three articles met the criteria for inclusion. RESULTS: Ten articles met the criteria and included oncology, cardiac, and chronic pain conditions. Clinical recommendations were made based on the Clinical Practice Guideline Process Manual, 2017 Edition by the American Academy of Neurology. CONCLUSIONS: Based on level of evidence, only oncology met the criteria for "possibly effective." However, there was some evidence of the benefit of MBIs for all three medical conditions, based on individual study findings.
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 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".