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.
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 Type
OBSERVATIONAL
Enrollment
38
WHOOP strap 3.0, a photodiode-based device that tracks sleep, resting heart rate, and heart rate variability.
Penn State Hershey Medical Center
Hershey, Pennsylvania, United States
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.
Time frame: 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.
Time frame: 12 months, change measured every 2 weeks
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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).
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)
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.
Time frame: 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.
Time frame: 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).
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)
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