Effects of Sahaj Samadhi meditation on heart rate variability and depressive symptoms in patients with late-life depression – RETRACTED
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Late-life depression (LLD) is a disabling disorder and antidepressants are ineffective in as many as 60% of cases. Converging evidence shows a strong correlation between LLD and subsequent risk of cardiovascular disease. There is a need for new, well-tolerated, non-pharmacological augmentation interventions that can treat depressive symptoms as well as improve heart rate variability (HRV), an important prognostic marker for development of subsequent cardiovascular disease. Meditation-based techniques are of interest based on positive findings in other samples.AimsWe aimed to assess the efficacy of Sahaj Samadhi meditation (SSM), an underevaluated, standardised and manualised meditation intervention, on HRV and depressive symptoms. METHOD: Eighty-three men and women aged 60-85 years, with mild to moderate depression and receiving treatment as usual (TAU) were randomised to either the SSM or TAU arm. Those allocated to SSM attended 4 consecutive days of group meditation training, using personalised mantras followed by 11 weekly reinforcement sessions. HRV and Hamilton Rating Scale for Depression (HRSD; 17-item) score were measured at baseline and 12 weeks. RESULTS: All time and frequency domain measures of HRV did not significantly change in either arm. However, there was significant improvement in the SSM arm, compared with TAU, on the HRSD (difference in mean, 2.66; 95% CI 0.26-5.05; P = 0.03). CONCLUSIONS: Compared with TAU, SSM is associated with improvements in depressive symptoms but does not significantly improve HRV in patients with LLD. These results need to be replicated in subsequent studies incorporating a group-based, active control arm.Declaration of interestR.I.N. is the Director of Research and Health Promotion for the Art of Living Foundation, Canada and supervised the staff providing Sahaj Samadhi meditation. S.R. has received research funding from Satellite Healthcare for a mindfulness meditation trial in patients on haemodialysis. The remaining authors report no financial or other relationship relevant to the subject of this article.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".