This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based mindfulness training with experience sampling may lessen the association between craving and smoking, an effect that did not lead to reduced smoking abstinence rates compared with control but may be meaningful to support quitting and prevent relapse in the longer-term.

Background Meditation is increasingly showing beneficial effects for psychiatric disorders. However, learning to meditate is not straightforward as there are no easily discernible outward signs of performance and thus no direct feedback is possible. As meditation has been found to correlate with posterior cingulate cortex (PCC) activity, we tested whether source-space EEG neurofeedback from the PCC followed the subjective experience of effortless awareness (a major component of meditation), and whether participants could volitionally control the signal. Methods Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators were briefly trained to perform a basic meditation practice to induce the subjective experience of effortless awareness in a progressively more challenging neurofeedback test-battery. Experienced meditators performed a self-selected meditation practice to induce this state in the same test-battery. Neurofeedback was provided based on gamma-band (40–57 Hz) PCC activity extracted using a beamformer algorithm. Associations between PCC activity and the subjective experience of effortless awareness were assessed by verbal probes. Results Both groups reported that decreased PCC activity corresponded with effortless awareness (P<0.0025 for each group), with high median confidence ratings (novices: 8 on a 0–10 Likert scale; experienced: 9). Both groups showed high moment-to-moment median correspondence ratings between PCC activity and subjective experience of effortless awareness (novices: 8, experienced: 9). Both groups were able to volitionally control the PCC signal in the direction associated with effortless awareness by practicing effortless awareness meditation (novices: median % of time =77.97, P=0.001; experienced: 89.83, P<0.0005). Conclusions These findings support the feasibility of using EEG neurofeedback to link an objective measure of brain activity with the subjective experience of effortless awareness, and suggest potential utility of this paradigm as a tool for meditation training.

Introduction This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. Methods Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4–8 Hz), alpha (8–13 Hz) and lower beta (13–20 Hz) frequency bands. Results Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P=0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P=0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P=0.016 and P=0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. Conclusion These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

BackgroundTobacco use is responsible for the death of about 1 in 10 individuals worldwide. Mindfulness training has shown preliminary efficacy as a behavioral treatment for smoking cessation. Recent advances in mobile health suggest advantages to smartphone-based smoking cessation treatment including smartphone-based mindfulness training. This study evaluates the efficacy of a smartphone app-based mindfulness training program for improving smoking cessation rates at 6-months follow-up.Methods/DesignA two-group parallel-randomized clinical trial with allocation concealment will be conducted. Group assignment will be concealed from study researchers through to follow-up. The study will be conducted by smartphone and online. Daily smokers who are interested in quitting smoking and own a smartphone (n = 140) will be recruited through study advertisements posted online. After completion of a baseline survey, participants will be allocated randomly to the control or intervention group. Participants in both groups will receive a 22-day smartphone-based treatment program for smoking. Participants in the intervention group will receive mobile mindfulness training plus experience sampling. Participants in the control group will receive experience sampling-only. The primary outcome measure will be one-week point prevalence abstinence from smoking (at 6-months follow-up) assessed using carbon monoxide breath monitoring, which will be validated through smartphone-based video chat.DiscussionThis is the first intervention study to evaluate smartphone-based delivery of mindfulness training for smoking cessation. Such an intervention may provide treatment in-hand, in real-world contexts, to help individuals quit smoking.Trial registrationClinicaltrials.gov NCT02134509. Registered 7 May 2014.

Quantitative analysis of left ventricular deformation can provide valuable information about the extent of disease as well as the efficacy of treatment. In this work, we develop an adaptive multi-level compactly supported radial basis approach for deformation analysis in 3D+time echocardiography. Our method combines displacement information from shape tracking of myocardial boundaries (derived from B-mode data) with mid-wall displacements from radio-frequency-based ultrasound speckle tracking. We evaluate our methods on open-chest canines (N=8) and show that our combined approach is better correlated to magnetic resonance tagging-derived strains than either individual method. We also are able to identify regions of myocardial infarction (confirmed by postmortem analysis) using radial strain values obtained with our approach.