Cited by 3Mindfulness and Compassion InterventionsHealthcare professionals’ stress and burnoutStress and Burnout Research

Digital Mindfulness Training for Burnout Reduction in Physicians: Clinician-Driven Approach

Open access
Antico, Lia, Brewer, Judson·2025·JMIR Formative Research

Published version of a preprint

Abstract

Background: Physician burnout is widespread in health care systems, with harmful consequences on physicians, patients, and health care organizations. Mindfulness training (MT) has proven effective in reducing burnout; however, its time-consuming requirements often pose challenges for physicians who are already struggling with their busy schedules. Objective: This study aimed to design a short and pragmatic digital MT program with input from clinicians specifically to address burnout and to test its efficacy in physicians. Methods: Two separate nonrandomized pilot studies were conducted. In the first study, 27 physicians received the digital MT in a podcast format, while in the second study, 29 physicians and nurse practitioners accessed the same training through a free app-based platform. The main outcome measure was cynicism, one dimension of burnout. The secondary outcome measures were emotional exhaustion (the second dimension of burnout), anxiety, depression, intolerance of uncertainty, empathy (personal distress, perspective taking, and empathic concern subscales), self-compassion, and mindfulness (nonreactivity and nonjudgment subscales). In the second study, worry, sleep disturbances, and difficulties in emotion regulation were also measured. Changes in outcomes were assessed using self-report questionnaires administered before and after the treatment and 1 month later as follow-up. Results: Both studies showed that MT decreased cynicism (posttreatment: 33% reduction; P≤.04; r≥0.41 and follow-up: 33% reduction; P≤.04; r≥0.45), while improvements in emotional exhaustion were observed solely in the first study (25% reduction, P=.02, r=.50 at posttreatment; 25% reduction, P=.008, r=.62 at follow-up). There were also significant reductions in anxiety (P≤.01, r≥0.49 at posttreatment; P≤.01, r≥0.54 at follow-up), intolerance of uncertainty (P≤.03, r≥.57 at posttreatment; P<.001, r≥0.66 at follow-up), and personal distress (P=.03, r=0.43 at posttreatment; P=.03, r=0.46 at follow-up), while increases in self-compassion (P≤.02, r≥0.50 at posttreatment; P≤.006, r≥0.59 at follow-up) and mindfulness (nonreactivity: P≤.001, r≥0.69 at posttreatment; P≤.004, r≥0.58 at follow-up; nonjudgment: P≤.009, r≥0.50 at posttreatment; P≤.03, r≥0.60 at follow-up). In addition, the second study reported significant decreases in worry (P=.04, r=0.40 at posttreatment; P=.006, r=0.58 at follow-up), sleep disturbances (P=.04, r=0.42 at posttreatment; P=.01, r=0.53 at follow-up), and difficulties in emotion regulation (P=.005, r=0.54 at posttreatment; P<.001, r=0.70 at follow-up). However, no changes were observed over time for depression or perspective taking and empathic concern. Finally, both studies revealed significant positive correlations between burnout and anxiety (cynicism: r≥0.38; P≤.04; emotional exhaustion: r≥0.58; P≤.001). Conclusions: To our knowledge, this research is the first where clinicians were involved in designing an intervention targeting burnout. These findings suggest that this digital MT serves as a viable and effective tool for alleviating burnout and anxiety among physicians. Trial Registration: ClinicalTrials.gov NCT06145425; https://clinicaltrials.gov/study/NCT06145425

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2196/63197

Cites

25 of 25 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Authors
Antico, L., & Brewer, J.
Affiliations
Brown University (US)
Corresponding author
Antico, Lia
Venue
JMIR Formative Research
Published
2025-01-24
Volume
9
Pages
e63197
ISSN
2561-326X (ISSN-L)
OpenAlex
W4403142021
Cited by
3
References
25
Citations by year
2026: 1 · 2025: 2
FWCI
1.50
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Formative Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Science Foundation
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (202924)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
  • Health Sciences › Health Professions › General Health Professions › Healthcare professionals’ stress and burnout
  • Social Sciences › Psychology › Social Psychology › Stress and Burnout Research
Keywords
physician burnout, mindfulness training, digital interventions, podcasts, mobile application, cynicism, emotional exhaustion, self-compassion, empathy, anxiety, depression, intolerance of uncertainty, emotion regulation, sleep disorders, nurse practitioners, physician well-being, burnout prevention, self-determination theory, self-efficacy, perceived behavioral control, perceived autonomy
MeSH terms
Adult, Burnout, Professional, Empathy, Female, Humans, Male, Middle Aged, Physicians, Pilot Projects, Surveys and Questionnaires, Mindfulness

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review