Observational studyIndian heart journal
Psychological problems and burnout among healthcare workers: Impact of non-pharmacological lifestyle interventions.
Observational study in Indian heart journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Lifestyle and Psychosocial Determinants of Type 2 Diabetes Risk Across Adulthood: The Role of Burnout in a Large Occupational Cohort.Medical sciences (Basel, Switzerland) · 2026Article
- Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.NPJ digital medicine · 2026Article
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate role of rajyoga meditation (RYM) versus stress management counselling (SMC) in addressing burnout syndrome and resultant improvement in electrocardiogram (ECG) so as to automate burnout prediction from raw ECG data with machine learning (ML).
methodsHealthcare providers were assigned to two groups: RYM (n = 100) or SMC (n = 102). Subjects in RYM received rajyoga for 3 months including one week offline and thereafter, virtual mode. SMC group received counselling for 1 day in offline mode and thereafter, received positive thoughts on a weekly basis. All subjects were assessed for psychological (depression, anxiety, stress scale-21 (DASS-21) and burnout syndrome (Mini Z questionnaire) along with 12-lead ECG at baseline after 4 weeks, and after 12 weeks. Based on response on question 3 of the Mini-Z questionnaire, participants were classified either as burnout or satisfied.
resultsRYM group showed significant reduction in depression, anxiety, and stress in comparison to SMC group. Burnout results display significant reduction in the RYM group in comparison to SMC group. Reduction in burnout and enhancement in satisfaction from visit-1 to visit-3: burnout visit-1 (27.2 %), visit-2 (23.8 %), visit-3 (19.3 %) and, satisfaction visit-1 (72.8 %), visit-2 (76.2 %), and visit-3 (80.7 %). ML algorithms could identify burnout patients using the raw ECG data with time-series features based classifier performing better than Ultra Short HRV features based ML classifier model.
conclusionAI based early diagnosis of heart's healthy status using ECG analysis may prevent development of cardiovascular disorder in the long run.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.