Using digital meditation to conquer burnout in healthcare workers

In the demanding world of healthcare, professionals often face the challenge of burnout. This study explores the effectiveness of a digital meditation program named “WellMind”. The idea is to address the high levels of stress and burnout among healthcare workers.

Additionally, show the benefits of mindfulness practice using objective measures such as EEG. For recording brain activity, researchers used mbt’s portable EEG system – SMARTING.

Healthcare worker doing meditation in an office, to handle high-stress environment

Studies show a 44% burnout rate among physicians, higher than in other professions. This impacts not only the well-being of healthcare providers but also the quality of patient care. The study explores if a digital program could reduce stress and enhance mindfulness.

Study overview

Researchers recruited 43 participants from UCSD School of Medicine. They divided participants into two groups: the WellMind group and control group.

The control group had no digital training  or interaction with the research team between pre- and postintervention time points.

WellMind group had to practice mindfulness every day using a meditation app. Both groups had to come in for assessment 3 times during the course of the study.

Training app shown on the left, and the study design shown on the right.
(A) The WellMind app with instructions for 10 levels of training. (B) The entire study design showing pre- and post-training assessments. This includes collecting behavioral and neurophysiological data for both groups.

WellMind training program

WellMind’s digital platform that replaces a devoted teacher. It keeps users interested by giving feedback after each session. It offered an innovative approach to mindfulness training. The program focuses on breathing exercises, which are important in traditional meditation.

Yet, traditional meditation doesn’t offer instant feedback or levels of progress based on how you’re doing. Also, results can vary depending on the instructor.

One training session

The training was like a game and changed based on the performance of the participant. They had to close their eyes and focus on breathing. After counting a certain number of breaths, they had to tap the phone screen.

Also, sessions included audio and text prompts for practicing compassion. These prompts included guidance in settling the mind, compassion for a loved one, oneself and embracing common humanity. Participants received app notifications each day, reminding them to complete their training.

Data of interest

Data collection is divided into 3 time periods – T1 (beginning of the study), T2 (after meditation training) and T3 (check-up after 3 months).

Behavioral data collected in the study consists of self-report scales on self-compassion and mindfulness. Behavioral measures were taken at all 3 time points in the study.

Neurocognitive assessment task

EEG data was collected at T1 and T2 time points.

While recording EEG, participants were instructed to close their eyes, breathe and respond every 2 breaths by tapping on the spacebar. Keyboard taps were synchronized with EEG using LabStreamingLayer (LSL) protocol.

Researches focused on certain brain areas in the study. To do this, they needed to apply source localization analysis on the recorded EEG data.

Areas of interest were:

  • Default Mode Network (DMN) which has been shown to be modulated by meditation;
  • Fronto-parietal Network (FPN);
  • Cingulo-Opercular Network (CON).
fMRI scan of default mode network areas
fMRI scan showing regions of the default mode network (DMN).
Image is by John Graner, Neuroimaging Department, National Intrepid Center of Excellence, Walter Reed National Military Medical Center, 8901 Wisconsin Avenue, Bethesda, MD 20889, USA., Public domain, via Wikimedia Commons.

What can we draw from the results

Changes in Self-compassion and mindfulness ratings from 2 participant groups across the study

The study observed improvement in self-compassion and mindfulness as the number of completed training sessions grew.

Correlation results between self-reported ratings and number of completed trainings
(C) Self-compassion ratings are significantly correlated with the rising number of completed sessions (r = 0.52) and (D) Mindfulness ratings show positive, but non-significant correlation with number of sessions (r = 0.38)

EEG data backs up mindfulness enhancement with meditation

By looking at source-localized neural activity, we can see significant changes in DMN network for WellMind group in the pre- and post-training phase. Compared to that, control group didn’t show these changes:

Brain activity across different network areas, showing the effect of mindfulness training during the study period
(C) Highlighted brain areas for FPN network in red, CON in green and DMN in blue. (D) Source activity changes for all 3 networks. We can observe difference in activity for both groups, comparing T1 (pre-) and T2 (post-training) phase.

The next step was analyzing the correlation between brain and behavioral measures. The study found significant relation between change in DMN and self-compassion ratings. Specifically, individuals who showed the largest improvement in self-compassion also showed the greatest DMN suppression.

Correlation between changes in brain activity in DMN areas and self-compassion ratings.

Paving the Way for Mental Wellness in Healthcare

This study focuses on burnout, which is a critical healthcare issue. Researchers introduce a promising solution through using digital mindfulness training. The WellMind program has an innovative approach while effectively using EEG to support its success. It shows how mindfulness can change brain function and improve mental well-being in healthcare workers.

These results pave new paths for research and encourage the application of mindfulness-based interventions. Such interventions are useful in high-stress environments. They lead to a healthier, more resilient healthcare workforce.

Original publication source: https://mental.jmir.org/2024/1/e49467

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