Background: Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout.

Objective: In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app–based methods may have the potential to enhance self-compassion and mindfulness in health care professionals.

Methods: In this study, we designed and implemented a scalable, digital app–based, brief mindfulness and compassion training program called “WellMind” for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated preand postintervention neural activity in core brain networks using electroencephalography source imaging as an objective
neurophysiological training outcome.

Results: Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=–0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=–0.368; P=.04).

Conclusions: Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention.

Participants

A total of 43 human subjects participated in the study (mean age: 28.77 ± 4.13, range: 23-43 years, 20 males). All subjects were fluent in English. Each participant gave written informed consent in accordance with the Declaration of Helsinki before participating in the experiment. All the experimental procedures were approved by the Institutional Review Board of University of California San Diego (UCSD) (protocol #180140). Participants were recruited from the UCSD School of Medicine during Spring 2021-Fall 2022 via email advertisements and campus flyers.

Participants provided demographic data with regards to age, gender and ethnicity. All participants were healthy adults, i.e., did not have any current medical diagnosis nor were taking any current psychotropic medications. Healthy status and affiliation to the UCSD School of Medicine were the only eligibility criteria.
Participants completed the Maslach Burnout Inventory (MBI) at time of screening; MBI scores did not reflect high burnout in our sample as all scores were less than the mid-score of the MBI score range.

Study Design

The study design was interventional and cluster randomized. Of the total 43 study participants, 22 enrolled in the digital WellMind intervention group and 21 were part of the no-contact control group. Participants were cluster randomized based on the quarter of enrollment to the WellMind or control group. This was done because individuals within each academic quarter (but not across quarters) were working/studying together and hence, knew each other professionally and could reveal components of the study intervention to each other. The WellMind group participants received the digital app intervention and had periodic email contact from our research team during intervention, at about once every two weeks frequency, to ensure compliance and help troubleshoot any issues faced by the participants. On the other hand, the no-contact control group had no interaction with the study research team nor any digital training resource provided to them between their pre and post assessment time points.

Sample Size and Power

The sample size within each group was powered to detect medium effect size pre/post differences (Cohen’s d >0.6), at beta power of 0.8 and alpha level of 0.05. Between-group differences met criteria for investigating only large effect size outcomes (Cohen’s d >0.8) at beta power of 0.8 and alpha level of 0.05. Effect sizes were calculated a priori using the G*Power software.

Intervention

The WellMind digital intervention was deployed on the BrainE© platform implemented in Unity and available on both iOS and Android phone devices. This digital program is HIPAA-compliant and secured by password-protection, and each user interacts via an alphanumeric study ID that is not linked to any personal health information. Participants accessed the app in their own free time and engaged in breath-focused mindfulness training with each session of 5-10 minutes duration for up to 60 sessions. The training was delivered in a game-like format and was performance-adaptive. Specifically, individuals were requested to close their eyes, pay attention to their breathing and tap the mobile screen after a specific number of breaths. The app monitored consistency of tap responses. If the user was distracted based on low consistency of breath monitoring taps, a gentle chime reminded the user to let go of the distraction and revert their attention back to mindful breathing. Initially, at level 1, participants tapped the screen after each breath. If they were able to do this consistently for three repeats of level 1 of 1 minute duration each, they graduated to level 2 and tracked two breaths at a time for 2 minutes, and so on. Thus, in the performance-adaptive task, the level reflected the number of minutes spent at that level and the number of breaths the participant was requested to repeatedly monitor. The max achievable level was level 10, i.e. monitoring 10 breaths at a time for up to 10 minutes. When the user graduated to the max level, they stayed at this level until end of all assigned sessions, i.e. 60 sessions. Also within the game-like format, when the participant opened their eyes at the end of a level, a peaceful nature scene would slowly unveil as a form of training reward.

Overall, this digital meditative practice is considered closed-loop because of its performance-adaptive feature. Consistent attention to breathing is emphasized over say other types of breathing techniques like deep breathing. The moment-to-moment performance tracking further allows quantification of the attentive focus during each session that is not possible with traditional non-digital meditation.