Remote Physiologic Monitoring of Resident Wellness and Burnout
NCT ID: NCT04304703
Last Updated: 2021-11-05
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Total Enrollment
38 participants
Study Classification
OBSERVATIONAL
Study Start Date
2020-07-03
Study Completion Date
2021-06-30
Brief Summary
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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
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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).
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
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Resident Wellness
Resident Burnout
Sleep
Stress
Anxiety
Depression
Keywords
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Remote monitoring
Heart rate variability
Resident wellness
Resident burnout
WHOOP strap 3.0
Stress
Anxiety
Depression
Study Design
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Observational Model Type
COHORT
Study Time Perspective
PROSPECTIVE
Study Groups
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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.
WHOOP strap 3.0
Intervention Type
DEVICE
WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability.
Interventions
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WHOOP strap 3.0
WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability.
Intervention Type
DEVICE
Eligibility Criteria
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Inclusion Criteria
* 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.
* 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.
Exclusion Criteria
* Preliminary or Transition-Year (TY) Internal Medicine Residents of Penn State Milton S. Hershey Medical Center
Minimum Eligible Age
18 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
Yes
Sponsors
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Milton S. Hershey Medical Center
OTHER
Sponsor Role
lead
Responsible Party
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Andrew Tinsley
Associate Professor, Gastroenterology
Responsibility Role
PRINCIPAL_INVESTIGATOR
Principal Investigators
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Andrew Tinsley, MD
Role: PRINCIPAL_INVESTIGATOR
Milton S. Hershey Medical Center
Locations
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Penn State Hershey Medical Center
Hershey, Pennsylvania, United States
Site Status
Countries
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United States
References
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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.
Reference Type
BACKGROUND
Kalmbach 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.
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BACKGROUND
Case 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.
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BACKGROUND
de 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.
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BACKGROUND
Mendelsohn 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.
Reference Type
BACKGROUND
Kalmbach 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.
Reference Type
BACKGROUND
Jimenez 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.
Reference Type
BACKGROUND
Goessl 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.
Reference Type
BACKGROUND
Basner 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.
Reference Type
BACKGROUND
Marek 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.
Reference Type
BACKGROUND
Raj 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.
Reference Type
BACKGROUND
Zahrai 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.
Reference Type
BACKGROUND
West 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.
Reference Type
BACKGROUND
Shaffer 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.
Reference Type
BACKGROUND
Bullinga 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.
Reference Type
BACKGROUND
Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, Levy D. Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation. 1996 Dec 1;94(11):2850-5. doi: 10.1161/01.cir.94.11.2850.
Reference Type
BACKGROUND
Berryhill S, Morton CJ, Dean A, Berryhill A, Provencio-Dean N, Patel SI, Estep L, Combs D, Mashaqi S, Gerald LB, Krishnan JA, Parthasarathy S. Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study. J Clin Sleep Med. 2020 May 15;16(5):775-783. doi: 10.5664/jcsm.8356. Epub 2020 Feb 11.
Reference Type
BACKGROUND
Sekiguchi Y, Adams WM, Benjamin CL, Curtis RM, Giersch GEW, Casa DJ. Relationships between resting heart rate, heart rate variability and sleep characteristics among female collegiate cross-country athletes. J Sleep Res. 2019 Dec;28(6):e12836. doi: 10.1111/jsr.12836. Epub 2019 Mar 6.
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BACKGROUND
Poonja Z, O'Brien P, Cross E, Bryce R, Dance E, Jaggi P, Krentz J, Thoma B. Sleep and Exercise in Emergency Medicine Residents: An Observational Pilot Study Exploring the Utility of Wearable Activity Monitors for Monitoring Wellness. Cureus. 2018 Jul 12;10(7):e2973. doi: 10.7759/cureus.2973.
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Tawfik DS, Profit J, Morgenthaler TI, Satele DV, Sinsky CA, Dyrbye LN, Tutty MA, West CP, Shanafelt TD. Physician Burnout, Well-being, and Work Unit Safety Grades in Relationship to Reported Medical Errors. Mayo Clin Proc. 2018 Nov;93(11):1571-1580. doi: 10.1016/j.mayocp.2018.05.014. Epub 2018 Jul 9.
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BACKGROUND
Linzer M, Poplau S, Babbott S, Collins T, Guzman-Corrales L, Menk J, Murphy ML, Ovington K. Worklife and Wellness in Academic General Internal Medicine: Results from a National Survey. J Gen Intern Med. 2016 Sep;31(9):1004-10. doi: 10.1007/s11606-016-3720-4. Epub 2016 May 2.
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BACKGROUND
Olson K, Sinsky C, Rinne ST, Long T, Vender R, Mukherjee S, Bennick M, Linzer M. Cross-sectional survey of workplace stressors associated with physician burnout measured by the Mini-Z and the Maslach Burnout Inventory. Stress Health. 2019 Apr;35(2):157-175. doi: 10.1002/smi.2849. Epub 2019 Jan 21.
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Reference Type
DERIVED
Related Links
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http://www.whoop.com/experience/
WHOOP measures 3 physiological markers: Heart Rate Variability (HRV), Resting Heart Rate (RHR), and sleep. HRV measures the variation in time between each heart beat. RHR is measured during sleep each night for consistent, controlled readings.
Other Identifiers
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STUDY14522
Identifier Type: -
Identifier Source: org_study_id