BurnoutNCT07666633
Non-Invasive Sleep Monitoring for Burnout and Retention Risk in Postgraduate Nurses
An observational study of Burnout and Sleep Disturbance, run by Kaohsiung Armed Forces General Hospital at 1 site in Taiwan.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 20 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Burnout, Sleep Disturbance, Occupational Stress
- What is being tested?
- Non-Invasive Sleep Monitoring
- How many people
- 100 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Aug 2026to 23 Apr 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
Kaohsiung Armed Forces General Hospital
Kaohsiung City, Kaohsiung City, 80284
Yung-Kuo Lee, PhD, +886910977485, yungkuolee@gmail.com
Chih-Hsuan Chang, MS, +886905163699, abstyle0204@gmail.com
About this study
Official title: 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.
Read the 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.
- Conditions
- Burnout, Sleep Disturbance, Occupational Stress, Mental Health, Nurse Retention, Fatigue
- Being tested
- Non-Invasive Sleep Monitoring
- Keywords
- Postgraduate Nurses, Non-Invasive Sleep Monitoring, Burnout Risk, Retention Risk, Artificial Intelligence, Machine Learning, Sleep Quality, Heart Rate Variability, Shift Work, Psychological Stress
- Medical subject headings
- Burnout, Psychological, Parasomnias, Occupational Stress, Psychological Well-Being, Fatigue, Sleep Initiation and Maintenance Disorders, Stress, Psychological
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Non-Invasive Sleep MonitoringDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Non-Invasive Sleep Monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
Copenhagen Burnout Inventory (CBI) Score
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.
Measured: Baseline, 3 months, and 6 months
Pittsburgh Sleep Quality Index (PSQI) Score
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.
Measured: Baseline, 3 months, and 6 months
Brief Symptom Rating Scale-5 (BSRS-5) Score
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.
Measured: Baseline, 3 months, and 6 months
Chinese Health Questionnaire-12 (CHQ-12) Score
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.
Measured: Baseline, 3 months, and 6 months
Karolinska Sleepiness Scale (KSS) Score
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.
Measured: Baseline, 3 months, and 6 months
Secondary outcomes (4)
Sleep Efficiency
Assessment of sleep efficiency derived from non-invasive sleep monitoring. Higher values indicate better sleep efficiency.
Measured: Baseline, 3 months, and 6 months
Deep Sleep Proportion
Assessment of deep sleep proportion derived from non-invasive sleep monitoring. Higher values indicate a greater proportion of deep sleep during total sleep time.
Measured: Baseline, 3 months, and 6 months
REM Sleep Stability
Assessment of REM sleep stability derived from non-invasive sleep monitoring.
Measured: Baseline, 3 months, and 6 months
Heart Rate Variability
Assessment of autonomic nervous system regulation using heart rate variability derived from non-invasive sleep monitoring.
Measured: Baseline, 3 months, and 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jun 2026
- Study start1 Aug 2026 (estimated)
- Primary completion23 Apr 2027 (estimated)
- Study completion23 Apr 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BurnoutStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Kaohsiung Armed Forces General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yung-Kuo Lee, PhD, Principal investigator, Kaohsiung Armed Forces General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07666633
- Sponsor's protocol ID
- KAFGHIRB 115-007
- Status last verified
- May 2026
- First submitted
- 18 May 2026
- Last updated
- 24 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 24 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07666633
- Acronym / sponsor ID
- KAFGHIRB 115-007
- Conditions
- Burnout, Sleep Disturbance, Occupational Stress, Mental Health, Nurse Retention, Fatigue
- Keywords
- Postgraduate Nurses, Non-Invasive Sleep Monitoring, Burnout Risk, Retention Risk, Artificial Intelligence, Machine Learning, Sleep Quality, Heart Rate Variability
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Kaohsiung Armed Forces General HospitalUniversity, hospital or other
- Study start
- 1 Aug 2026 (estimated)
- Primary completion
- 23 Apr 2027 (estimated)
- Study completion
- 23 Apr 2027 (estimated)
- First posted
- 24 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Postgraduate Nurses (PGNs) | – | Diagnostic Test: Non-Invasive Sleep MonitoringNewly 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. |
Who may qualify
- Ages
- 20 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Non-Invasive Sleep MonitoringParticipants 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. | Diagnostic test | Postgraduate Nurses (PGNs) |
Primary outcome
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.
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.
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.
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.
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.
4 secondary outcomes
Sleep Efficiency
Time frame: Baseline, 3 months, and 6 months
Deep Sleep Proportion
Time frame: Baseline, 3 months, and 6 months
REM Sleep Stability
Time frame: Baseline, 3 months, and 6 months
Heart Rate Variability
Time frame: Baseline, 3 months, and 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Jun 2026.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Yung-Kuo Lee, PhD principal investigator
Kaohsiung Armed Forces General Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Burnout and Sleep Disturbance, run by Kaohsiung Armed Forces General Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 24 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yung-Kuo Lee, PhD (+886910977485, yungkuolee@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Copenhagen Burnout Inventory (CBI) Score (time frame: Baseline, 3 months, and 6 months). The study lists 5 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Jun 2026.