Remote Physiologic Monitoring of Resident Wellness and Burnout

Depression Clinical Trial

Official title:

Remote Physiologic Monitoring of Resident Wellness and Burnout

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04304703
• Other study ID #: STUDY14522
• Status: Completed
• Start date: July 3, 2020
• Est. completion date: June 30, 2021

Study information

• Verified date: November 2021
• Source: Milton S. Hershey Medical Center
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 38
• Est. completion date: June 30, 2021
• Est. primary completion date: June 30, 2021
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Exclusion Criteria:

• Preliminary or Transition-Year (TY) Internal Medicine Residents of Penn State Milton S. Hershey Medical Center

Related Conditions & MeSH terms

• Anxiety
• Burnout, Psychological
• Depression
• Resident Burnout
• Resident Wellness
• Sleep
• Stress

Intervention

Device:
• WHOOP strap 3.0
WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability.

Locations

Country	Name	City	State
United States	Penn State Hershey Medical Center	Hershey	Pennsylvania

Sponsors (1)

Lead Sponsor	Collaborator
Milton S. Hershey Medical Center

Country where clinical trial is conducted

United States, 

References & Publications (26)

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). doi: 10.1093/sleep/zsx027. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Dickerson SS, Kemeny ME. Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. Psychol Bull. 2004 May;130(3):355-91. — View Citation

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. — View Citation

Jiménez 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. Review. — View Citation

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). doi: 10.1093/sleep/zsw073. — View Citation

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. — View Citation

Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606-13. — View Citation

Levis B, Benedetti A, Thombs BD; DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 2019 Apr 9;365:l1476. doi: 10.1136/bmj.l1476. Erratum in: BMJ. 2019 Apr 12;365:l1781. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Petrakova L, Doering BK, Vits S, Engler H, Rief W, Schedlowski M, Grigoleit JS. Psychosocial Stress Increases Salivary Alpha-Amylase Activity Independently from Plasma Noradrenaline Levels. PLoS One. 2015 Aug 6;10(8):e0134561. doi: 10.1371/journal.pone.0134561. eCollection 2015. — View Citation

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. — View Citation

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. Review. — View Citation

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. — View Citation

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. Review. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

* Note: There are 26 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	12 months, change measured every 2 weeks
Primary	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.	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every 2 weeks
Secondary	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).	12 months, change measured every 2 weeks
Secondary	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.	12 months, change measured every week
Secondary	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.	12 months, change measured every week
Secondary	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).	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)

External Links

•   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.