Attentional and affective consequences of technology supported mindfulness training: a randomised, active control, efficacy trial
Bibliographic record
Abstract
BACKGROUND: Mindfulness training (MT) programs represent an approach to attention training with well-validated mental health benefits. However, research supporting MT efficacy is based predominantly on weekly-meeting, facilitator-led, group-intervention formats. It is unknown whether participants might benefit from neurofeedback-assisted, technology-supported MT (N-tsMT), in which meditation is delivered individually, without the need for a facilitator, travel to a training site, or the presence of a supportive group environment. Mirroring the validation of group MT interventions, the first step in addressing this question requires identifying whether N-tsMT promotes measurable benefits. Here, we report on an initial investigation of a commercial N-tsMT system. METHODS: In a randomized, active control trial, community-dwelling healthy adult participants carried out 6 weeks of daily practice, receiving either N-tsMT (n = 13), or a control condition of daily online math training (n = 13). Training effects were assessed on target measures of attention and well-being. Participants also completed daily post-training surveys assessing effects on mood, body awareness, calm, effort, and stress. RESULTS: Analysis revealed training effects specific to N-tsMT, with attentional improvements in overall reaction time on a Stroop task, and well-being improvements via reduced somatic symptoms on the Brief Symptom Inventory. Attention and well-being improvements were correlated, and effects were greatest for the most neurotic participants. However, secondary, exploratory measures of attention and well-being did not show training-specific effects. N-tsMT was associated with greater body awareness and calm, and initially greater effort that later converged with effort in the control condition. CONCLUSIONS: Preliminary findings indicate that N-tsMT promotes modest benefits for attention and subjective well-being in a healthy community sample relative to an active control condition. However, the findings would benefit from replication in a larger sample, and more intensive practice or more comprehensive MT instruction might be required to promote the broader benefits typically reported in group format, facilitated MT. TRIAL REGISTRATION: Current Controlled Trials ISRCTN43629398 . Retrospectively registered on June 16, 2016.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".