ArticleBMC nursing2024
The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.
Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05481138 (Wearable Augmented Prediction of Burnout in Nurses), which is not on this map. Cited by 13 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.
Wearable Augmented Prediction of Burnout in Nurses: A Synergy of Engineering, Bioethics, Nursing and Wellness Sciences
Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology.Cardiovascular and interventional radiology · 2026Review
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- The validation of measured and self-reported sleep duration and perceived sleep quality: an empirical study with three generations of smartwatches.Frontiers in psychiatry · 2026Article
- A Wearable-Based Program to Optimise Stress Regulation, Resilience, and Wellbeing in Emergency Care Settings: A Proof-of-Concept Study Protocol.Sensors (Basel, Switzerland) · 2025Article
- Passive AI Detection of Stress and Burnout Among Frontline Workers.Nursing reports (Pavia, Italy) · 2025Review
- Psychological Dimensions of Professional Burnout in Special Education: A Cross-Sectional Behavioral Data Analysis of Emotional Exhaustion, Personal Achievement, and Depersonalization.International journal of environmental research and public health · 2025Article
- Burnout crisis in Chinese radiology: will artificial intelligence help?European radiology · 2025Article
- Participant-Centered Engagement for Sustained Adherence to Smartwatches: A 12-Month Prospective Decentralized Digital Health Study.Clinical and translational science · 2025Article
- Climbing the ladder: a ranking approach to burnout prediction.Frontiers in digital health · 2025Article
- Seeing burnout coming: early signs and recognition strategies in health professionals.Frontiers in public health · 2025Review
- Toward burnout prevention with Bayesian mixed-effects regression analysis of longitudinal data from wearables: a preliminary study.Frontiers in digital health · 2025Article
- Artificial Intelligence in the Organization of Nursing Care: A Scoping Review.Nursing reports (Pavia, Italy) · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundWhen job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data.
methodsA total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices.
trial registrationNCT05481138.
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.