Closed-loop digital meditation improves sustained attention in young adults


Attention is a fundamental cognitive process that is critical for essentially all aspects of higher-order cognition and real-world activities. Younger generations have deeply embraced information technology and multitasking in their personal lives, school and the workplace, creating myriad challenges to their attention. While improving sustained attention in healthy young adults would be beneficial, enhancing this ability has proven notoriously difficult in this age group. Here we show that 6 weeks of engagement with a meditation-inspired, closed-loop software program (MediTrain) delivered on mobile devices led to gains in both sustained attention and working memory in healthy young adults. These improvements were associated with positive changes in key neural signatures of attentional control (frontal theta inter-trial coherence and parietal P3b latency), as measured by electroencephalography. Our findings suggest the utility of delivering aspects of the ancient practice of focused-attention meditation in a modern, technology-based approach and its benefits on enhancing sustained attention.

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Fig. 1: MediTrain training curves.
Fig. 2: Improvements in sustained attention.
Fig. 3: Correlations between RTVar and neural markers of attention for experiment 3.
Fig. 4: Changes in mid-frontal theta ITC.
Fig. 5: Changes in P3b latencies.
Fig. 6: Improvements in visual discrimination and working memory.

Data availability

The data that support the findings of this study are available from the corresponding authors on reasonable request.

Code availability

The code used in the analysis of EEG data reported in this paper is available from the corresponding authors on reasonable request.


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We thank H. Cho, S. Corona, A. Ho, K. Huang, J. Kang, D. Kingsbrook, R. LoPilato, M. Kim, J. Martin, L. Martin, A. Recinos and M. Torres for help with data collection and T. Zanto for advice on EEG data analysis and interpretation. Thanks to A. Denison-Afifi, A. Speight, K. Stern, K. Weber and numerous other volunteers at for assistance in designing and building the MediTrain software and to A. Duanmu for critical programming support of the application during the study. We also thank R. Campusano, J. Gazzaley, A. Leggitt and H. Weng for helpful discussions. Thanks to all of our participants and to Apple who generously provided many of the iPads used in this study. Jamie Gates, Evan and Sara Williams, and NIH grants R21 AG041071 and R01 AG049424 provided financial support for this research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

D.A.Z., A.J.S., S.S., J.M., J.A.A. and A.G. designed the experiments. D.A.Z., J.R.J., J.K. and A.G. developed the MediTrain software. D.A.Z., A.J.S., S.S., J.J.V. and C.E.R. collected the data. D.A.Z., A.J.S., C.L.G., S.S., J.J.V. and J.A.A. analysed the data. D.A.Z., A.J.S. and A.G. wrote the paper. All authors discussed the results and contributed to editing the manuscript.

Correspondence to David A. Ziegler or Adam Gazzaley.

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Competing interests

A.G. is co-founder, shareholder, BOD member and advisor for Akili Interactive, a company that produces therapeutic video games. MediTrain and the apps used for the control condition are not currently associated with Akili. The other authors declare no competing interests.

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Supplementary Methods, Supplementary Figures 1–7 and Supplementary Table 1.

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