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Burnout & Depression: Development of an early warning system and AI advisor for healthcare workers to assess and manage stress, monitor longterm trajectories, and predict outcomes using multivariate and AI-based extraction of specific biomarkers and mHealth technologies.

Burnout & Depression: Development of an early warning system and AI advisor for healthcare workers to assess and manage stress, monitor longterm trajectories, and predict outcomes using multivariate and AI-based extraction of specific biomarkers and mHealth technologies. - StressPredict4Nurses

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
DRKS
Registry ID
DRKS00037767
Enrollment
120
Registered
2025-09-08
Start date
2025-09-24
Completion date
Unknown
Last updated
2025-10-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Stress levels in nursing staff

Interventions

Group 1: Nursing staff at Jena University Hospital, organized into two observational cohorts – pilot cohort (n = 60) and validation cohort (n = 60). The study will be conducted at Jena University Hosp

Sponsors

Universitätsklinikum Jena
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 65 Years

Inclusion criteria

Inclusion criteria: • Healthy nursing staff between the ages of 18 and 65 with no known chronic illnesses. • No acute illnesses or infections at the time of the study. • No medications that could influence physiological parameters. • Willingness to participate in the study and ability to give consent. • Regular nursing duties to reflect typical working conditions.

Exclusion criteria

Exclusion criteria: • Presence of cardiovascular disease, diabetes, or other chronic conditions. • Pregnancy or breastfeeding. • Acute infections or fever. • Use of medications that affect heart rate, blood pressure, or other physiological parameters. • Physical limitations that could interfere with the measurements (e.g., skin injuries at measurement sites).

Design outcomes

Primary

MeasureTime frame
The primary objective of the study is the development of a digital early warning system to detect increased stress that raises the risk of burnout and depression among nurses in their daily work. The system is based on a multivariate analysis of physiological parameters (e.g., heart rate variability, sleep quality, activity patterns) recorded using mHealth technologies in 24-hour monitoring. The goal is to identify patterns and thresholds associated with critical stress states and an elevated risk for burnout or depressive symptoms. The primary endpoint is the identification of recurring physiological patterns that correlate with increased subjective stress and can prognostically indicate health-relevant strain. Validation of these patterns forms the basis for the algorithm-supported development of a digital system for the early detection and prevention of work-related stress, burnout, and depressive symptoms among nursing staff.

Secondary

MeasureTime frame
A secondary objective of the study is a comprehensive analysis of 24-hour stress exposure in nurses, with a particular focus on recovery, sleep behavior, and stress coping. Both physiological measurements and subjective assessments are systematically collected and analyzed in relation to each other. In addition, qualitative interviews aim to provide deeper insight into individual experiences of stress, perceived stressors, coping strategies, as well as potential signs of burnout or depression. The secondary endpoints include: -Frequency and intensity of subjectively perceived stress episodes over time -Changes in sleep quality in relation to elevated stress levels, based on physiological measurements and self-report -Qualitative data on work-related stressors, perceived sources of stress, coping mechanisms, and potential burnout symptoms -Correlations between work- and leisure-related factors (e.g., working hours, free time) and physiological stress parameters

Countries

Germany

Contacts

Public ContactGeorg Seifert

Charité Competence Center for Traditional and Integrative Medicine (CCCTIM) Charité - Universitätsmedizin Berlin AG Prävention, Integrative Medizin und Gesundheitsförderung in der Pädiatrie Campus Virchow-Klinikum

georg.seifert@charite.de+49 30 450 666087

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026