Resident WellnessNCT04304703
Remote Physiologic Monitoring of Resident Wellness and Burnout
An observational study of Resident Wellness and Resident Burnout, run by Milton S. Hershey Medical Center at 1 site in United States.
Trial summary
Resident wellness and physician burnout are under the spotlight more and more as data begins to show that there is a point of diminishing return on the number of hours in training. In 2003, resident work hours were restricted to less than 80 hours per week averaged over 4 weeks. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Resident Wellness, Resident Burnout, Sleep
- What is being tested?
- WHOOP strap 3.0
- Type of treatment
- Medical device
- How many people
- 38 participants
- Where
- 1 siteUnited States
- Study dates
- 3 Jul 2020to 30 Jun 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Internal Medicine Residents (categorical) at Penn State Hershey Medical Center (PGY-1 to PGY-3)
Who may be able to join
- Internal Medicine Residents of Penn State Milton S. Hershey Medical Center (PGY-1 to PGY-3; categorical residents only).
- Age greater than 18 years old.
- Willing to wear WHOOP device for at least 80% of the time.
- Willing to complete weekly surveys at least 80% of time.
- Willing to provide and return saliva samples for analysis of stress biomarkers.
- Own smart phone for pairing with WHOOP device.
Who cannot join
- Preliminary or Transition-Year (TY) Internal Medicine Residents of Penn State Milton S. Hershey Medical Center
Trial locations and contacts
1 site in 1 country.
United States1 site
Penn State Hershey Medical Center
Hershey, Pennsylvania, 17033
About this study
Resident wellness and physician burnout are under the spotlight more and more as data begins to show that there is a point of diminishing return on the number of hours in training. In 2003, resident work hours were restricted to less than 80 hours per week averaged over 4 weeks. This change was implemented in response to the robust body of evidence that increased work hours leads to decreased sleep, which in turn leads to medical errors and depression. These factors directly and indirectly lead to worse outcomes for patients. In residency, it is difficult objectively to assess when residents are beginning to experience burnout and depression. The investigators propose a study to determine whether tracking of certain heart rate parameters (resting heart rate and heart rate variability) as well as sleep can correlate to subjective assessment of resident wellness, burnout and depression. The investigators will also compare these measures to biomarkers of stress, such as salivary cortisol. The results of this study may lead to improved understanding of what truly causes burnout and may be an eventual target for intervention to help improve short- and long-term outcomes for resident physicians as well as their patients.
Read the detailed description
Sleep deprivation contributes to workplace burnout, a psychological work-related syndrome characterized by depersonalization, emotional exhaustion and feelings of decreased personal accomplishment [Montgomery, 2019]. Medical residency training is associated with decreased sleep and exercise as well as an increase in burnout, which may also be associated with depression [Kamblach, 2019]. Resident wellness has become a focal point of many residency programs in order to prevent depression and long-term physician burnout. Many previous studies tracking sleep have used self-reporting, which institutes a certain level of bias, and some newer technologies such as FitBit tracking have become more prevalent [Case, 2015; de Zambotti, 2018]. Real-time physiologic metric tracking, such as resting heart rate (RHR) and heart rate variability (HRV), in addition to accurate sleep tracking, could provide a far more accurate and objective assessment of resident wellness [Sekiguchi, 2019]. These metrics have not been compared directly to subjective assessments of wellness, burnout and depression, thus their true value in this realm is unknown [Mendelsohn, 2019; Kamblach, 2018]. However, having an objective assessment of resident wellness, stratified by specific rotation, could help identify, develop, and institute interventions to prevent burnout and depression and improve resident well-being.
Previous studies have attempted to make an association between sleep hours, duty hours, exercise and wellness, burnout, depression; however, they have used primitive forms of physiologic tracking (i.e. counting steps as a surrogate for exercise and self-reporting of sleep), which is likely why the results have been relatively inconclusive [Mendelsohn, 2019; Kamblach, 2018; Poonja, 2018; Basner, 2017; Marek, 2019]. A systematic review and meta-analysis of studies attempting to identify factors associated with greater resident well-being showed that increased sleep and time away from work were the strongest influencers of improved resident wellness [Raj, 2016]. Objective, real-time assessment of sleep may identify a stronger association and the addition of RHR and HRV to this analysis could further validate subjective assessment of wellness.
HRV, or the fluctuation in the time intervals between adjacent heart beats, has never before been used to track resident well-being but it is an established metric for prediction and management of disease states such as heart failure [Jimenez-Morgan, 2017; Goessl, 2017, Shaffer, 2017; Bullinga; 2005; Tsuji, 1996]. HRV has been shown to predict mortality in Heart Failure with reduced Ejection Fraction (HFrEF) and new cardiac events (angina, myocardial infarction, coronary artery disease-related death, or HF) in the Framingham study, and it also correlates with improved hemodynamics in response to beta-blocker therapy for HF [Bullinga; 2005; Tsuji, 1996].
The investigators propose to use the WHOOP strap 3.0 for remote monitoring of residents to determine a relationship between its measured data (RHR, HRV, and sleep duration) and wellness using literature-validated surveys (Maslach Burnout Inventory, Mini-ReZ survey, Physician Well Being Index, Patient Health Questionnaire-9) [Montgomery, 2019; Linzer, 2016; Olson 2019; Kroenke, 2001; Levis, 2019]. There is no published literature or known ongoing studies investigating this relationship Recent studies have, however, validated the WHOOP device for sleep tracking and determined its efficacy to be nearly identical to that of the gold standard of polysomnography (PSG) [Berryhill, 2020]. This study also showed that the precision of HRV measurements using the wearable WHOOP device had less than 10% error when compared to continuous ECG monitoring, as part of PSG.
There is an established relationship between HRV and anticipated stress, quantified by salivary cortisol levels, yet there has not been studies linking salivary cortisol as a marker of stress, to subjective assessments in physicians nor against data from wearable devices. Biomarkers of stress (salivary cortisol and alpha-amylase) will compared at baseline and on different rotation considered to be associated with varying levels of stress (i.e. outpatient clinic and inpatient consult services versus the intensive care unit (ICU) setting) [Dickerson, 2004; Petrakova, 2015]. Saliva samples provided by subjects will allow the investigators to validate the WHOOP device as a novel tool to measure stress by allowing the team to assess the association between HRV and other device metrics and objective stress-based analytes found in saliva (e.g., cortisol and alpha amylase). These results will be correlated with each other and with work hours via duty logging to determine whether specific rotations in medical residency have better or worse objective and subjective metrics; these results will also be correlated to baseline (according to baseline characteristics survey).
- Conditions
- Resident Wellness, Resident Burnout, Sleep, Stress, Anxiety, Depression
- Being tested
- WHOOP strap 3.0
- Keywords
- Remote monitoring, Heart rate variability, Resident wellness, Resident burnout, WHOOP strap 3.0, Stress, Anxiety, Depression
- Medical subject headings
- Anxiety Disorders, Depression
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
WHOOP strap 3.0Device
WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability.
| Study group | Role | What they receive |
|---|---|---|
| Internal Medicine resident subjects | Subjects who are categorical Internal Medicine residents at Penn State Hershey Medical Center (PGY1-PGY3), and meet inclusion/exclusion criteria, will be enrolled in this study and wear the WHOOP strap 3.0 for real-time measurement of physiologic metrics. Receives: Device: WHOOP strap 3.0 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in Heart Rate Variability (HRV)
Heart Rate Variability will be objectively measured nightly. Average HRV (over two weeks) will be assessed for change every two weeks over the duration of the study.
Measured: 12 months, change measured every 2 weeks
Change in Maslach Burnout Inventory score (3 subscales: 0-54, 0-30, 0-48)
Maslach Burnout Inventory - Human Services Survey for Medical Personnel (MBI-HSS (MP)). The MBI-HSS (MP) is a variation of the MBI-HSS adapted for medical personnel. The most notable alteration is this form refers to "patients" instead of "recipients". The MBI-HSS (MP) scales are Emotional Exhaustion (9 questions), Depersonalization (5 questions), and Personal Accomplishment (8 questions).
Maslach Burnout Inventory score will be assessed every two weeks in survey format. Each question is scored 0-6, thus the subscale ranges are 0-54, 0-30, 0-48, respectively, with higher scores signifying higher levels of burnout for the emotional exhaustion and depersonalization subscales and lower scores signifying higher levels of burnout for the personal accomplishment subscale.
Measured: 12 months, change measured every 2 weeks
Secondary outcomes (9)
Change in Sleep (hours per night)
Sleep will be objectively measured nightly. Sleep (hours per night) will be assessed for change every two weeks over the duration of the study (average sleep per night over two weeks). Subscales for sleep will include duration of rapid eye movement (REM), slow wave sleep (SWS), and light sleep. Time in bed and naps will also be recorded.
Measured: 12 months, change measured every 2 weeks
Change in Resting Heart Rate (RHR)
Resting Heart Rate will be objectively measured nightly. Average RHR (over two weeks) will be assessed for change every two weeks over the duration of the study.
Measured: 12 months, change measured every 2 weeks
Change in Average Weekly Duty Hours
Weekly duty hours will be self-reported every two weeks, individually as week 1 hours and week 2 hours. Week 1 and week 2 hours will be averaged for each two-week block.
Measured: 12 months, change measured every 2 weeks
Change in Mini ReZ score (15-76 scale)
The Mini-Z comprises 15 items which assess satisfaction, stress, burnout, work control, chaos, values alignment, teamwork, documentation, time pressure, excess electronic medical record (EMR) use at home, and EMR proficiency. It is scored on a scale of 15-76. A total score greater 60 represents a positive learning environment. Subscale 1 - Supportive Work Environment (questions 1-5): range 6-26 (greater than 20 is a highly supportive work environment). Subscale 2 - Work pace and EMR Stress (questions 6-10: range 5-25 (greater than 20 is an environment with good pace and manageable EMR stress). Subscale 3 - Resident Experience (questions 11-15): range 5-25 (greater than 20 is a positive and healthy resident experience).
Mini-ReZ will be assessed every two weeks in survey format.
Measured: 12 months, change measured every 2 weeks
Change in Physician Well-Being Index (PWBI) (0-7 scale)
The Physician Well Being Index is a 7 question survey, scored 0-7, with lower scores indicative of better physician well being.
Measured: 12 months, change measured every 2 weeks
Change in Patient Health Questionnaire-9 (PHQ-9) score (0-27 scale)
The PHQ-9 is a 9-question instrument given to subjects in a primary care setting to screen for the presence and severity of depression. PHQ-9 score will be assessed every two weeks in survey format. It is scored on a 0-27 scale, with higher scores signifying higher levels of depression. The PHQ-9 scores indicate mild (<4) to severe (20+) severe PD. The PHQ-9 also has a self-report item for suicidal ideation (SI).
Measured: 12 months, change measured every 2 weeks
Change in Hospital Anxiety and Depression Subscale (HADS) score (0-42 scale, Anxiety: 0-21 subscale, Depression 0-21 subscale)
The HADS consists of two scales; A (anxiety) - with 7 items [Cronbach's alpha = 0.78] and D (depression) - with 7 items [Cronbach's alpha = 0.71], each with scores ranging from 0-21; total scale scores range from 0-42, with higher scores indicating more distressing symptoms. The HADS has been validated with primary care patients.
Measured: 12 months, change measured every week
Change in Perceived Stress Scale (PSS-4) score (0-16 scale)
The PSS-4 consists of 4 items that assess perceived stress. The items are scored on a 4-point scale (Score range: 0-16; higher scores reflect greater perceived stress. The measure demonstrates strong internal consistency with a Cronbach's alpha of .88.
Measured: 12 months, change measured every week
Change in Salivary Stress Biomarkers (cortisol, alpha-amylase)
Saliva samples will be collected during baseline assessment (2 consecutive days) and during outpatient clinic/inpatient consult services (low stress) and ICU (high stress) rotations (weekly for 4 weeks, every Friday). Each collection day will have 3 collection times: wake-up (t=0), wake-up time + 30 min (t=30), night time (just prior to sleep) (NT).
Measured: 12 months; baseline during week 1 of study (2 consecutive collection days), clinic/consult rotations (4 consecutive weeks, every Friday), ICU (4 consecutive weeks, every Friday)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Mar 2020
- Study start3 Jul 2020
- Primary completion30 Jun 2021
- Study completion30 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (27)Papers the sponsor has linked to this study.
- Other trials for Resident WellnessStudies of the same condition, open and under way.
- More trials run by Milton S. Hershey Medical CenterThe sponsor's other registered studies.
- 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
- 38 participants (actual)
Sponsor and investigators
- Lead sponsor
- Milton S. Hershey Medical Center
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Andrew Tinsley, Milton S. Hershey Medical Center
- Investigators
- Andrew Tinsley, MD, Principal investigator, Milton S. Hershey Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04304703
- Sponsor's protocol ID
- STUDY14522
- Status last verified
- Nov 2021
- First submitted
- 5 Mar 2020
- Last updated
- 5 Nov 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Montgomery A, Panagopoulou E, Esmail A, Richards T, Maslach C. Burnout in healthcare: the case for organisational change. BMJ. 2019 Jul 30;366:l4774. doi: 10.1136/bmj.l4774. No abstract available.
PubMed 31362957Kalmbach DA, Arnedt JT, Song PX, Guille C, Sen S. Sleep Disturbance and Short Sleep as Risk Factors for Depression and Perceived Medical Errors in First-Year Residents. Sleep. 2017 Mar 1;40(3):zsw073. doi: 10.1093/sleep/zsw073.
PubMed 28369654Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015 Feb 10;313(6):625-6. doi: 10.1001/jama.2014.17841. No abstract available.
PubMed 25668268de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2 compared with polysomnography in adults. Chronobiol Int. 2018 Apr;35(4):465-476. doi: 10.1080/07420528.2017.1413578. Epub 2017 Dec 13.
PubMed 29235907Mendelsohn D, Despot I, Gooderham PA, Singhal A, Redekop GJ, Toyota BD. Impact of work hours and sleep on well-being and burnout for physicians-in-training: the Resident Activity Tracker Evaluation Study. Med Educ. 2019 Mar;53(3):306-315. doi: 10.1111/medu.13757. Epub 2018 Nov 28.
PubMed 30485496Kalmbach DA, Fang Y, Arnedt JT, Cochran AL, Deldin PJ, Kaplin AI, Sen S. Effects of Sleep, Physical Activity, and Shift Work on Daily Mood: a Prospective Mobile Monitoring Study of Medical Interns. J Gen Intern Med. 2018 Jun;33(6):914-920. doi: 10.1007/s11606-018-4373-2. Epub 2018 Mar 14.
PubMed 29542006Jimenez Morgan S, Molina Mora JA. Effect of Heart Rate Variability Biofeedback on Sport Performance, a Systematic Review. Appl Psychophysiol Biofeedback. 2017 Sep;42(3):235-245. doi: 10.1007/s10484-017-9364-2.
PubMed 28573597Goessl VC, Curtiss JE, Hofmann SG. The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis. Psychol Med. 2017 Nov;47(15):2578-2586. doi: 10.1017/S0033291717001003. Epub 2017 May 8.
PubMed 28478782Basner M, Dinges DF, Shea JA, Small DS, Zhu J, Norton L, Ecker AJ, Novak C, Bellini LM, Volpp KG. Sleep and Alertness in Medical Interns and Residents: An Observational Study on the Role of Extended Shifts. Sleep. 2017 Apr 1;40(4):zsx027. doi: 10.1093/sleep/zsx027.
PubMed 28329124Marek AP, Nygaard RM, Liang ET, Roetker NS, DeLaquil M, Gregorich S, Richardson CJ, Van Camp JM. The association between objectively-measured activity, sleep, call responsibilities, and burnout in a resident cohort. BMC Med Educ. 2019 May 21;19(1):158. doi: 10.1186/s12909-019-1592-0.
PubMed 31113435Raj KS. Well-Being in Residency: A Systematic Review. J Grad Med Educ. 2016 Dec;8(5):674-684. doi: 10.4300/JGME-D-15-00764.1.
PubMed 28018531Zahrai A, Bhandari M, Varma A, Rennie WR, Kreder H, Stephen D, McKee MD, Waddell JP, Schemitsch EH. Residents' quality of life during an orthopedic trauma rotation: a multicentre prospective observational study. Can J Surg. 2008 Jun;51(3):190-6.
PubMed 18682757West CP, Shanafelt TD, Cook DA. Lack of association between resident doctors' well-being and medical knowledge. Med Educ. 2010 Dec;44(12):1224-31. doi: 10.1111/j.1365-2923.2010.03803.x.
PubMed 21091761Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Front Public Health. 2017 Sep 28;5:258. doi: 10.3389/fpubh.2017.00258. eCollection 2017.
PubMed 29034226Bullinga JR, Alharethi R, Schram MS, Bristow MR, Gilbert EM. Changes in heart rate variability are correlated to hemodynamic improvement with chronic CARVEDILOL therapy in heart failure. J Card Fail. 2005 Dec;11(9):693-9. doi: 10.1016/j.cardfail.2005.06.435.
PubMed 16360965
Study at a glance
From the registry record, last updated by the sponsor on 5 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Remote Physiologic Monitoring of Resident Wellness and Burnout
- ClinicalTrials.gov ID
- NCT04304703
- Acronym / sponsor ID
- STUDY14522
- Conditions
- Resident Wellness, Resident Burnout, Sleep, Stress, Anxiety, Depression
- Keywords
- Remote monitoring, Heart rate variability, Resident wellness, Resident burnout, WHOOP strap 3.0, Stress, Anxiety, Depression
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 38 participants (actual)
- Lead sponsor
- Milton S. Hershey Medical CenterUniversity, hospital or other
- Study start
- 3 Jul 2020 (actual)
- Primary completion
- 30 Jun 2021 (actual)
- Study completion
- 30 Jun 2021 (actual)
- First posted
- 11 Mar 2020 (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.
Resident wellness and physician burnout are under the spotlight more and more as data begins to show that there is a point of diminishing return on the number of hours in training. In 2003, resident work hours were restricted to less than 80 hours per week averaged over 4 weeks. This change was implemented in response to the robust body of evidence that increased work hours leads to decreased sleep, which in turn leads to medical errors and depression. These factors directly and indirectly lead to worse outcomes for patients. In residency, it is difficult objectively to assess when residents are beginning to experience burnout and depression. The investigators propose a study to determine whether tracking of certain heart rate parameters (resting heart rate and heart rate variability) as well as sleep can correlate to subjective assessment of resident wellness, burnout and depression. The investigators will also compare these measures to biomarkers of stress, such as salivary cortisol. The results of this study may lead to improved understanding of what truly causes burnout and may be an eventual target for intervention to help improve short- and long-term outcomes for resident physicians as well as their patients.
Detailed description
Sleep deprivation contributes to workplace burnout, a psychological work-related syndrome characterized by depersonalization, emotional exhaustion and feelings of decreased personal accomplishment [Montgomery, 2019]. Medical residency training is associated with decreased sleep and exercise as well as an increase in burnout, which may also be associated with depression [Kamblach, 2019]. Resident wellness has become a focal point of many residency programs in order to prevent depression and long-term physician burnout. Many previous studies tracking sleep have used self-reporting, which institutes a certain level of bias, and some newer technologies such as FitBit tracking have become more prevalent [Case, 2015; de Zambotti, 2018]. Real-time physiologic metric tracking, such as resting heart rate (RHR) and heart rate variability (HRV), in addition to accurate sleep tracking, could provide a far more accurate and objective assessment of resident wellness [Sekiguchi, 2019]. These metrics have not been compared directly to subjective assessments of wellness, burnout and depression, thus their true value in this realm is unknown [Mendelsohn, 2019; Kamblach, 2018]. However, having an objective assessment of resident wellness, stratified by specific rotation, could help identify, develop, and institute interventions to prevent burnout and depression and improve resident well-being.
Previous studies have attempted to make an association between sleep hours, duty hours, exercise and wellness, burnout, depression; however, they have used primitive forms of physiologic tracking (i.e. counting steps as a surrogate for exercise and self-reporting of sleep), which is likely why the results have been relatively inconclusive [Mendelsohn, 2019; Kamblach, 2018; Poonja, 2018; Basner, 2017; Marek, 2019]. A systematic review and meta-analysis of studies attempting to identify factors associated with greater resident well-being showed that increased sleep and time away from work were the strongest influencers of improved resident wellness [Raj, 2016]. Objective, real-time assessment of sleep may identify a stronger association and the addition of RHR and HRV to this analysis could further validate subjective assessment of wellness.
HRV, or the fluctuation in the time intervals between adjacent heart beats, has never before been used to track resident well-being but it is an established metric for prediction and management of disease states such as heart failure [Jimenez-Morgan, 2017; Goessl, 2017, Shaffer, 2017; Bullinga; 2005; Tsuji, 1996]. HRV has been shown to predict mortality in Heart Failure with reduced Ejection Fraction (HFrEF) and new cardiac events (angina, myocardial infarction, coronary artery disease-related death, or HF) in the Framingham study, and it also correlates with improved hemodynamics in response to beta-blocker therapy for HF [Bullinga; 2005; Tsuji, 1996].
The investigators propose to use the WHOOP strap 3.0 for remote monitoring of residents to determine a relationship between its measured data (RHR, HRV, and sleep duration) and wellness using literature-validated surveys (Maslach Burnout Inventory, Mini-ReZ survey, Physician Well Being Index, Patient Health Questionnaire-9) [Montgomery, 2019; Linzer, 2016; Olson 2019; Kroenke, 2001; Levis, 2019]. There is no published literature or known ongoing studies investigating this relationship Recent studies have, however, validated the WHOOP device for sleep tracking and determined its efficacy to be nearly identical to that of the gold standard of polysomnography (PSG) [Berryhill, 2020]. This study also showed that the precision of HRV measurements using the wearable WHOOP device had less than 10% error when compared to continuous ECG monitoring, as part of PSG.
There is an established relationship between HRV and anticipated stress, quantified by salivary cortisol levels, yet there has not been studies linking salivary cortisol as a marker of stress, to subjective assessments in physicians nor against data from wearable devices. Biomarkers of stress (salivary cortisol and alpha-amylase) will compared at baseline and on different rotation considered to be associated with varying levels of stress (i.e. outpatient clinic and inpatient consult services versus the intensive care unit (ICU) setting) [Dickerson, 2004; Petrakova, 2015]. Saliva samples provided by subjects will allow the investigators to validate the WHOOP device as a novel tool to measure stress by allowing the team to assess the association between HRV and other device metrics and objective stress-based analytes found in saliva (e.g., cortisol and alpha amylase). These results will be correlated with each other and with work hours via duty logging to determine whether specific rotations in medical residency have better or worse objective and subjective metrics; these results will also be correlated to baseline (according to baseline characteristics survey).
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Internal Medicine resident subjects | – | Device: WHOOP strap 3.0Subjects who are categorical Internal Medicine residents at Penn State Hershey Medical Center (PGY1-PGY3), and meet inclusion/exclusion criteria, will be enrolled in this study and wear the WHOOP strap 3.0 for real-time measurement of physiologic metrics. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Internal Medicine Residents (categorical) at Penn State Hershey Medical Center (PGY-1 to PGY-3)
Key inclusion criteria (6 listed)
- Internal Medicine Residents of Penn State Milton S. Hershey Medical Center (PGY-1 to PGY-3; categorical residents only).
- Age greater than 18 years old.
- Willing to wear WHOOP device for at least 80% of the time.
- Willing to complete weekly surveys at least 80% of time.
- Willing to provide and return saliva samples for analysis of stress biomarkers.
Key exclusion criteria (1 listed)
- Preliminary or Transition-Year (TY) Internal Medicine Residents of Penn State Milton S. Hershey Medical Center
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 |
|---|---|---|
| WHOOP strap 3.0WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability. | Device | Internal Medicine resident subjects |
Primary outcome
Change in Heart Rate Variability (HRV)
Time frame: 12 months, change measured every 2 weeks
Heart Rate Variability will be objectively measured nightly. Average HRV (over two weeks) will be assessed for change every two weeks over the duration of the study.
Change in Maslach Burnout Inventory score (3 subscales: 0-54, 0-30, 0-48)
Time frame: 12 months, change measured every 2 weeks
Maslach Burnout Inventory - Human Services Survey for Medical Personnel (MBI-HSS (MP)). The MBI-HSS (MP) is a variation of the MBI-HSS adapted for medical personnel. The most notable alteration is this form refers to "patients" instead of "recipients". The MBI-HSS (MP) scales are Emotional Exhaustion (9 questions), Depersonalization (5 questions), and Personal Accomplishment (8 questions). Maslach Burnout Inventory score will be assessed every two weeks in survey format. Each question is scored 0-6, thus the subscale ranges are 0-54, 0-30, 0-48, respectively, with higher scores signifying higher levels of burnout for the emotional exhaustion and depersonalization subscales and lower scores signifying higher levels of burnout for the personal accomplishment subscale.
9 secondary outcomes
Change in Sleep (hours per night)
Time frame: 12 months, change measured every 2 weeks
Change in Resting Heart Rate (RHR)
Time frame: 12 months, change measured every 2 weeks
Change in Average Weekly Duty Hours
Time frame: 12 months, change measured every 2 weeks
Change in Mini ReZ score (15-76 scale)
Time frame: 12 months, change measured every 2 weeks
Change in Physician Well-Being Index (PWBI) (0-7 scale)
Time frame: 12 months, change measured every 2 weeks
Change in Patient Health Questionnaire-9 (PHQ-9) score (0-27 scale)
Time frame: 12 months, change measured every 2 weeks
Change in Hospital Anxiety and Depression Subscale (HADS) score (0-42 scale, Anxiety: 0-21 subscale, Depression 0-21 subscale)
Time frame: 12 months, change measured every week
Change in Perceived Stress Scale (PSS-4) score (0-16 scale)
Time frame: 12 months, change measured every week
Change in Salivary Stress Biomarkers (cortisol, alpha-amylase)
Time frame: 12 months; baseline during week 1 of study (2 consecutive collection days), clinic/consult rotations (4 consecutive weeks, every Friday), ICU (4 consecutive weeks, every Friday)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Nov 2021.
- Background: PubMed 31362957Montgomery A, Panagopoulou E, Esmail A, Richards T, Maslach C. Burnout in healthcare: the case for organisational change. BMJ. 2019 Jul 30;366:l4774. doi: 10.1136/bmj.l4774. No abstract available.
- Background: PubMed 28369654Kalmbach DA, Arnedt JT, Song PX, Guille C, Sen S. Sleep Disturbance and Short Sleep as Risk Factors for Depression and Perceived Medical Errors in First-Year Residents. Sleep. 2017 Mar 1;40(3):zsw073. doi: 10.1093/sleep/zsw073.
- Background: PubMed 25668268Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015 Feb 10;313(6):625-6. doi: 10.1001/jama.2014.17841. No abstract available.
- Background: PubMed 29235907de Zambotti M, Goldstone A, Claudatos S, Colrain IM, Baker FC. A validation study of Fitbit Charge 2 compared with polysomnography in adults. Chronobiol Int. 2018 Apr;35(4):465-476. doi: 10.1080/07420528.2017.1413578. Epub 2017 Dec 13.
- Background: PubMed 30485496Mendelsohn D, Despot I, Gooderham PA, Singhal A, Redekop GJ, Toyota BD. Impact of work hours and sleep on well-being and burnout for physicians-in-training: the Resident Activity Tracker Evaluation Study. Med Educ. 2019 Mar;53(3):306-315. doi: 10.1111/medu.13757. Epub 2018 Nov 28.
- Background: PubMed 29542006Kalmbach DA, Fang Y, Arnedt JT, Cochran AL, Deldin PJ, Kaplin AI, Sen S. Effects of Sleep, Physical Activity, and Shift Work on Daily Mood: a Prospective Mobile Monitoring Study of Medical Interns. J Gen Intern Med. 2018 Jun;33(6):914-920. doi: 10.1007/s11606-018-4373-2. Epub 2018 Mar 14.
- Background: PubMed 28573597Jimenez Morgan S, Molina Mora JA. Effect of Heart Rate Variability Biofeedback on Sport Performance, a Systematic Review. Appl Psychophysiol Biofeedback. 2017 Sep;42(3):235-245. doi: 10.1007/s10484-017-9364-2.
- Background: PubMed 28478782Goessl VC, Curtiss JE, Hofmann SG. The effect of heart rate variability biofeedback training on stress and anxiety: a meta-analysis. Psychol Med. 2017 Nov;47(15):2578-2586. doi: 10.1017/S0033291717001003. Epub 2017 May 8.
- Background: PubMed 28329124Basner M, Dinges DF, Shea JA, Small DS, Zhu J, Norton L, Ecker AJ, Novak C, Bellini LM, Volpp KG. Sleep and Alertness in Medical Interns and Residents: An Observational Study on the Role of Extended Shifts. Sleep. 2017 Apr 1;40(4):zsx027. doi: 10.1093/sleep/zsx027.
- Background: PubMed 31113435Marek AP, Nygaard RM, Liang ET, Roetker NS, DeLaquil M, Gregorich S, Richardson CJ, Van Camp JM. The association between objectively-measured activity, sleep, call responsibilities, and burnout in a resident cohort. BMC Med Educ. 2019 May 21;19(1):158. doi: 10.1186/s12909-019-1592-0.
- Background: PubMed 28018531Raj KS. Well-Being in Residency: A Systematic Review. J Grad Med Educ. 2016 Dec;8(5):674-684. doi: 10.4300/JGME-D-15-00764.1.
- Background: PubMed 18682757Zahrai A, Bhandari M, Varma A, Rennie WR, Kreder H, Stephen D, McKee MD, Waddell JP, Schemitsch EH. Residents' quality of life during an orthopedic trauma rotation: a multicentre prospective observational study. Can J Surg. 2008 Jun;51(3):190-6.
- Individual participant dataSharing planned: No
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. This study is not recruiting at the moment.
Andrew Tinsley, MD principal investigator
Milton S. Hershey Medical Center
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 Resident Wellness and Resident Burnout, run by Milton S. Hershey Medical Center at 1 site in United States.
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 1 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: finished, as of the sponsor's last update on 5 Nov 2021. 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 United States. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in Heart Rate Variability (HRV) (time frame: 12 months, change measured every 2 weeks). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04304703, which also lists 27 linked publications.
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 5 Nov 2021; AzTrials last refreshed its copy on 8 Nov 2026.