Non-Invasive Sleep Monitoring for Burnout and Retention Risk in Postgraduate Nurses

June 18, 2026 updated by: Kaohsiung Armed Forces General Hospital

Development of a Non-Invasive Sleep-Based Prediction Platform for Burnout and Retention Risk Among Postgraduate Nurses: A Psychophysiological and AI-Driven Approach for High-Stress Clinical Populations

Newly graduated nurses often experience high levels of psychological stress, sleep disturbance, fatigue, and burnout during the early transition into clinical practice. Early identification of burnout and retention risk may help improve mental well-being, workforce stability, and quality of patient care.

This longitudinal observational study aims to develop a non-invasive sleep-based prediction platform for assessing burnout and retention risk among postgraduate nurses. Participants will undergo repeated psychological assessments and non-contact sleep monitoring during the study period. Sleep-related physiological parameters, including sleep efficiency, sleep structure, heart rate variability, and respiratory variability, will be collected together with validated psychological questionnaires.

The study will further apply machine learning and artificial intelligence approaches to integrate longitudinal physiological and psychological data for risk prediction and early identification of burnout-related conditions. The findings may support future development of precision mental health monitoring and supportive management strategies for high-stress healthcare workers.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Postgraduate nurses frequently experience substantial psychological and physiological stress during the transition from academic training to clinical practice. Heavy workloads, rotating shifts, emotional demands, and adaptation to clinical environments may contribute to sleep disturbance, fatigue, burnout, and increased turnover intention. Previous studies have demonstrated significant associations between sleep quality, autonomic nervous system regulation, emotional distress, and occupational burnout among healthcare workers, particularly in shift-working nurses.

Current psychological assessments mainly rely on self-reported questionnaires and short-term evaluations, which may not adequately capture dynamic physiological changes over time. Recent advances in non-contact sleep monitoring technologies provide opportunities for continuous and low-burden collection of sleep-related physiological data in natural sleep environments. In addition, artificial intelligence and machine learning approaches may improve early identification of individuals at higher risk of burnout and retention problems.

This study is a prospective longitudinal observational study designed to investigate the relationship between sleep-related physiological characteristics, psychological status, burnout risk, and retention risk among postgraduate nurses during the early clinical transition period.

Eligible participants will include newly employed postgraduate nurses within three months of clinical employment. Participants will complete validated psychological questionnaires, including the Brief Symptom Rating Scale-5 (BSRS-5), Chinese Health Questionnaire-12 (CHQ-12), Pittsburgh Sleep Quality Index (PSQI), Karolinska Sleepiness Scale (KSS), and Copenhagen Burnout Inventory (CBI). In parallel, participants will undergo non-invasive and non-contact sleep monitoring under natural sleep conditions. Sleep-related physiological parameters including sleep efficiency, sleep stage distribution, deep sleep proportion, REM sleep stability, heart rate variability, and respiratory variability will be analyzed.

Repeated assessments will be conducted longitudinally at baseline, 3 months, and 6 months. Statistical analyses will include descriptive statistics, longitudinal analyses, generalized estimating equations, mixed-effects models, and survival-related analyses when applicable. Machine learning and deep learning approaches, including Random Forest, XGBoost, and longitudinal prediction models, will be applied to develop predictive models for burnout and retention risk.

The study does not involve therapeutic intervention, medication administration, or changes to work schedules. All collected data will be de-identified and managed according to institutional research ethics and privacy protection regulations. The results of this study may contribute to the future development of precision mental health monitoring systems and supportive management strategies for high-stress healthcare professionals.

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Taiwan
    • Kaohsiung City
      • Kaohsiung City, Kaohsiung City, Taiwan, 80284
        • Kaohsiung Armed Forces General Hospital
        • Contact:
        • Contact:
        • Principal Investigator:
          • Hui-Ru Lin, MS

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of newly employed postgraduate nurses during the early clinical transition period at a medical center in Taiwan. Participants will be followed longitudinally to evaluate sleep-related physiological characteristics, psychological stress, burnout risk, and retention risk using repeated questionnaires and non-invasive sleep monitoring.

Description

Inclusion Criteria:

  • Newly employed postgraduate nurses within the first 3 months of clinical practice
  • Age 20 to 65 years
  • Full-time clinical nursing staff
  • Able to read and complete Chinese questionnaires
  • Willing to participate in repeated psychological assessments and non-invasive sleep monitoring
  • Able to provide written informed consent

Exclusion Criteria:

  • Diagnosed severe sleep disorders
  • Diagnosed severe psychiatric disorders
  • Current use of medications that significantly affect sleep or autonomic nervous system function
  • Inability to comply with longitudinal follow-up procedures
  • Inability to complete repeated sleep monitoring assessments

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Postgraduate Nurses (PGNs)
Newly employed postgraduate nurses within the first three months of clinical practice will be enrolled and followed longitudinally. Participants will complete repeated psychological assessments and undergo non-invasive sleep monitoring during the study period. Sleep-related physiological parameters, including sleep efficiency, sleep structure, heart rate variability, and respiratory variability, will be analyzed to evaluate burnout risk, psychological stress, fatigue, and retention risk during the early clinical transition period.
Diagnostic test: Non-Invasive Sleep Monitoring
Participants will undergo non-invasive and non-contact sleep monitoring under natural sleep conditions. The monitoring system will collect sleep-related physiological signals and estimate sleep parameters, including sleep efficiency, sleep stage distribution, deep sleep proportion, REM sleep stability, heart rate variability, and respiratory variability. This procedure is used for observational data collection only and does not involve treatment or changes to clinical work schedules.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Copenhagen Burnout Inventory (CBI) Score
Time Frame: Baseline, 3 months, and 6 months
Assessment of burnout severity using the Copenhagen Burnout Inventory (CBI), a validated questionnaire for evaluating personal and work-related burnout symptoms. The CBI score ranges from 0 to 100, with higher scores indicating greater burnout severity.
Baseline, 3 months, and 6 months
Pittsburgh Sleep Quality Index (PSQI) Score
Time Frame: Baseline, 3 months, and 6 months
Assessment of sleep quality using the Pittsburgh Sleep Quality Index (PSQI), a validated questionnaire for evaluating subjective sleep quality and sleep disturbance. The PSQI global score ranges from 0 to 21, with higher scores indicating poorer sleep quality.
Baseline, 3 months, and 6 months
Brief Symptom Rating Scale-5 (BSRS-5) Score
Time Frame: Baseline, 3 months, and 6 months
Assessment of psychological distress using the Brief Symptom Rating Scale-5 (BSRS-5), a validated questionnaire for evaluating anxiety, depression, hostility, interpersonal sensitivity, and insomnia symptoms. The BSRS-5 total score ranges from 0 to 20, with higher scores indicating greater psychological distress.
Baseline, 3 months, and 6 months
Chinese Health Questionnaire-12 (CHQ-12) Score
Time Frame: Baseline, 3 months, and 6 months
Assessment of mental health status using the Chinese Health Questionnaire-12 (CHQ-12), a validated questionnaire for evaluating psychological well-being and minor psychiatric morbidity. Higher scores indicate poorer mental health status.
Baseline, 3 months, and 6 months
Karolinska Sleepiness Scale (KSS) Score
Time Frame: Baseline, 3 months, and 6 months
Assessment of subjective sleepiness using the Karolinska Sleepiness Scale (KSS). The KSS score ranges from 1 to 9, with higher scores indicating greater subjective sleepiness.
Baseline, 3 months, and 6 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sleep Efficiency
Time Frame: Baseline, 3 months, and 6 months
Assessment of sleep efficiency derived from non-invasive sleep monitoring. Higher values indicate better sleep efficiency.
Baseline, 3 months, and 6 months
Deep Sleep Proportion
Time Frame: Baseline, 3 months, and 6 months
Assessment of deep sleep proportion derived from non-invasive sleep monitoring. Higher values indicate a greater proportion of deep sleep during total sleep time.
Baseline, 3 months, and 6 months
REM Sleep Stability
Time Frame: Baseline, 3 months, and 6 months
Assessment of REM sleep stability derived from non-invasive sleep monitoring.
Baseline, 3 months, and 6 months
Heart Rate Variability
Time Frame: Baseline, 3 months, and 6 months
Assessment of autonomic nervous system regulation using heart rate variability derived from non-invasive sleep monitoring.
Baseline, 3 months, and 6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Kaohsiung Armed Forces General Hospital

Investigators

  • Principal Investigator: Yung-Kuo Lee, PhD, Kaohsiung Armed Forces General Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

August 1, 2026

Primary Completion (Estimated)

April 23, 2027

Study Completion (Estimated)

April 23, 2027

Study Registration Dates

First Submitted

May 18, 2026

First Submitted That Met QC Criteria

June 18, 2026

First Posted (Actual)

June 24, 2026

Study Record Updates

Last Update Posted (Actual)

June 24, 2026

Last Update Submitted That Met QC Criteria

June 18, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • KAFGHIRB 115-007

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

No individual participant data (IPD) will be shared because the study involves sensitive psychological and physiological data collected from healthcare workers, and data sharing is restricted to protect participant privacy and confidentiality in accordance with institutional research ethics regulations.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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