Sleep & Stress in Healthcare Providers After Defined Music Intervention Measure by 7-Tesla fMRI & Actigraphy
Study Details
Study Description
Brief Summary
This study plans to explore whether specially chosen relaxing music can help improve sleep, reduce stress, and prevent burnout in healthcare workers, many of whom are often sleep-deprived. The researchers will measure changes in brain activity, sleep patterns, and self-reported stress levels before, during, and after participants listen to this music. The novel approach includes using advanced brain scanning technology, sleep monitoring devices, and carefully selected music. Ultimately, the aim is to create a scientifically backed music intervention that can be used widely to help healthcare providers get better sleep and manage stress, potentially reducing burnout rates.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Detailed Description
Study Summary Sleep deprivation is a serious safety hazard in acute-stress, high-autonomy, high-immediacy, and high-risk work environments, where small errors in judgment or motor performance can deleteriously impact clinical performance and patient outcomes. A reported 65% of acute-care physicians suffer from acute and chronic sleep deprivation, and 50-70% of emergency-care physicians have reported symptoms of burnout. Sleep-deprived surgeons performed surgical tasks less competently and more slowly than intoxicated surgeons. Prior functional magnetic resonance imaging (fMRI) studies have linked sleep deprivation to an impaired ability to down-regulate negative emotions. Music serves as an effective and powerful modulator of human stress responses, effectively alleviating psychophysiological stress, enhancing functional connectivity in the emotional centers in the brain, and improving sleep by dynamically modulating the hypothalamic-pituitary-adrenal (HPA) axis, autonomic nervous system (ANS) and heart rate variability (HRV).
Our central hypothesis is that listening to validated stress-reducing music containing defined compositional elements of relaxation will enhance sleep quality and/or quantity, reduce self-reported psychological stress, and mitigate burnout in health care providers. We aim to test this hypothesis by measuring pre-, intra-, and post- intervention changes in brain activation, functional connectivity, and neurotransmitter releases using 7-Tesla fMRI; in qualitative and quantitative sleep patterns using clinically validated actigraphy straps; and in self-reported psychological stress and burnout using validated surveys. We seek to gather data essential for a formal intervention to improve bedtime routines among healthcare providers prone to acute and chronic stress, sleep deprivation, depression and burnout.
The novelty of our proposed study is threefold: (1) we will use high-resolution 7-Tesla fMRI technology to visualize neurological changes in the brain during and after the defined music intervention; (2) we will use state-of-the-art actigraphy to qualitatively and quantitatively measure music-induced changes in slow wave and rapid eye movement (REM) sleep; and (3) we will use sophisticated and precise repertoire-selection methodology developed by expert musicians on our team and aimed at increasing the reproducibility and scientific rigor in future music intervention studies. Gathering our team's interdisciplinary expertise of concert musicians, a Grammy-nominated composer, acute-care physicians, surgeons, and sleep experts at Houston Methodist, we seek to measure the impact of defined musical intervention on sleep, stress and burnout using repertoire ranging from the Baroque period to 21st Century and containing a combination of 16 universal Compositional Elements of Relaxation previously extracted and evaluated by our team.
Our long-term goal is to implement and disseminate defined and scientifically reproducible stress-reducing and sleep-enhancing music interventions at the clinic and policy level that may alleviate psychophysiological stress and reduce the severity and prevalence of sleep deprivation and burnout in health care providers.
Purpose of the Study / Objectives
Objective 1 To measure the impact of defined music intervention on quality and quantity of sleep in acute care surgeons using WHOOP-a clinically validated actigraphy device Our research objective is to evaluate both pre- and self-selected music interventions prior to bedtime as a low-cost, zero-risk, noninvasive, easy-to-implement, and non-pharmacological technique to enhance sleep qualitatively and quantitatively in acute care attendings, fellows, and residents as measured by clinically validated actigraphy strap (WHOOP).
Objective 2 To validate music-induced stress-reduction and emotion-regulating improvement via increased frontal-amygdala connection and using 7-Tesla fMRI We seek to investigate neural mechanisms underlying impaired emotional regulation as a result of sleep deprivation, and to develop a better understanding of the effects of music intervention on the human stress response. An algorithm for the graph-theoretical analysis of functional magnetic resonance imaging (fMRI) data will be developed. Emotional centers including the amygdala and bilateral insula activation patterns will be visualized. The impact of music on the frontal lobe-amygdala connection will be examined. Network clustering analysis will be performed to evaluate functional subunits with highest interactions induced by music exposure. 7-Tesla fMRI in connection with graph theoretical network analysis will be used to identify and differentiate functional subunits in the human brain when under prescribed and self-selected music intervention.
Objective 3 To evaluate the impact of defined music intervention on self-reported psychological stress, quality of sleep and burnout in acute-care surgeons using validated surveys
Our objective is to measure the impact of defined music intervention on self-reported psychological stress using the State-Trait Anxiety Inventory (STAI), on quality and quantity of sleep using the Pittsburg Sleep Quality Index (PSQI), and on self-reported burnout syndrome using the Maslach Burnout Inventory (MBI). We also aim to examine potential differences between the self-selected music and pre-selected music groups, and test the hypothesis that pre-selected music repertoire containing defined compositional elements of relaxation will achieve comparable or superior results at enhancing sleep and reducing self-reported stress. Additionally, a Brief Musical Experience Questionnaire (BMEQ) will be collected as information for the participants' musical background to investigate potential link between susceptibility to music intervention and prior musical training, preference and exposure.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: Prescribed Week 1 was used to collect baseline fMRI and sleep data for all participants. The participants underwent a baseline fMRI brain. Average, WHOOP baseline sleep measurements were recorded, as well as pre-intervention surveys: State-Trait Anxiety Inventory (STAI) with two forms, Pittsburgh Sleep Quality Index (PSQI), and Maslach Burnout Inventory (MBI) with three sections. During weeks 2-5, participants in the prescribed music group underwent a minimum of 15 minutes (mandatory) of nightly self-administered music intervention immediately before bedtime. Sleep measurements were recorded by WHOOP and accessed through WHOOP Application Programming Interface (API). During week 6, each participant underwent a post-intervention fMRI scan. Post-intervention STAI, MBI, and PSQI surveys were completed and scored. |
Other: Music Therapy
All music pieces selected for the prescribed intervention contain compositional elements of relaxation analyzed, prepared, and recorded by professional concert artists and a Grammy-nominated composer. The compositional elements included Accentuation, Articulation, Dynamic Range, Familiarity, Interpretive Expertise, Melodic Shape, Meter, Recording Quality, Repetition, Register, Rubato, Tempo, Texture, Timbre, Transition, and Tonality. These 15 repertoire-selecting parameters were used to choose the prescribed music playlist for this study. Music used for the study was downloaded from a password-protected Google Drive accessible to the study participants assigned to the "prescribed music" intervention group.
|
| Experimental: Self-selected Week 1 was used to collect baseline fMRI and sleep data for all participants. The participants underwent a baseline fMRI brain. Average, WHOOP baseline sleep measurements were recorded, as well as pre-intervention surveys: State-Trait Anxiety Inventory (STAI) with two forms, Pittsburgh Sleep Quality Index (PSQI), and Maslach Burnout Inventory (MBI) with three sections. During weeks 2-5, participants in the self-selected music group underwent a minimum of 15 minutes (mandatory) of nightly self-administered music intervention immediately before bedtime. Sleep measurements were recorded by WHOOP and accessed through WHOOP Application Programming Interface (API). During week 6, each participant underwent a post-intervention fMRI scan. Post-intervention STAI, MBI, and PSQI surveys were completed and scored. |
Other: Music Therapy
All music pieces selected for the prescribed intervention contain compositional elements of relaxation analyzed, prepared, and recorded by professional concert artists and a Grammy-nominated composer. The compositional elements included Accentuation, Articulation, Dynamic Range, Familiarity, Interpretive Expertise, Melodic Shape, Meter, Recording Quality, Repetition, Register, Rubato, Tempo, Texture, Timbre, Transition, and Tonality. These 15 repertoire-selecting parameters were used to choose the prescribed music playlist for this study. Music used for the study was downloaded from a password-protected Google Drive accessible to the study participants assigned to the "prescribed music" intervention group.
|
| No Intervention: Control Week 1 was used to collect baseline fMRI and sleep data for all participants. To that end, the participants underwent a baseline fMRI brain. Average, WHOOP baseline sleep measurements were recorded, as well as pre-intervention surveys: State-Trait Anxiety Inventory (STAI) with two forms, Pittsburgh Sleep Quality Index (PSQI), and Maslach Burnout Inventory (MBI) with three sections. During weeks 2-5, participants in the no-music group continued as usual. Sleep measurements were recorded by WHOOP and accessed through WHOOP Application Programming Interface (API). During week 6, each participant underwent a post-intervention fMRI scan. Post-intervention STAI, MBI, PSQI surveys were completed and scored. If for any reason a mandatory, pre-bedtime music listening session was missed, or if the participant had problems with WHOOP device, any remaining day/days in week 6 were used to fulfill and complete the full intervention. |
Outcome Measures
Primary Outcome Measures
- Mean change in quantity of sleep (total sleep duration, hours) [6 weeks]
Hours of sleep recorded by the WHOOP device pre to post intervention
- Mean change in quality of sleep (light sleep duration, hours) [6 weeks]
Hours of light sleep recorded by the WHOOP device pre to post intervention
- Mean change in quality of sleep (rapid eye movement sleep duration, hours) [6 weeks]
Hours of rapid eye movement sleep recorded by the WHOOP device pre to post intervention
Secondary Outcome Measures
- Mean change in resting heart rate, beats per minute [6 weeks]
change in resting heart rate recorded by the WHOOP device pre to post intervention
- Mean change in heart rate variability root mean square of successive rate rhythm interval differences [6 weeks]
change in heart rate variability recorded by the WHOOP device pre to post intervention
- Mean change in response to Intensive Care Unit noise [6 weeks]
change in response assessed as functional connectivity in the insula region recorded by fMRI pre to post intervention
- Mean change in empathetic response [6 weeks]
change in response assessed as functional connectivity in the anterior cingulate region recorded by fMRI pre to post intervention
Eligibility Criteria
Criteria
Inclusion Criteria:
• attending surgeon at Houston Methodist Hospital, Houston TX
Exclusion Criteria:
-
diagnosed sleep apnea
-
hearing impairment
-
cognitive impairment
-
large metal implants
-
Self-reported regular use of prescribed sleep medications
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Houston Methodist Hospital | Houston | Texas | United States | 77030 |
Sponsors and Collaborators
- The Methodist Hospital Research Institute
Investigators
None specified.Study Documents (Full-Text)
None provided.More Information
Publications
- Deneva T, Ianakiev Y, Keskinova D. Burnout Syndrome in Physicians-Psychological Assessment and Biomarker Research. Medicina (Kaunas). 2019 May 24;55(5):209. doi: 10.3390/medicina55050209.
- Dyrbye LN, West CP, Satele D, Boone S, Tan L, Sloan J, Shanafelt TD. Burnout among U.S. medical students, residents, and early career physicians relative to the general U.S. population. Acad Med. 2014 Mar;89(3):443-51. doi: 10.1097/ACM.0000000000000134.
- Jackson-Koku G, Grime P. Emotion regulation and burnout in doctors: a systematic review. Occup Med (Lond). 2019 Feb 7;69(1):9-21. doi: 10.1093/occmed/kqz004. Erratum In: Occup Med (Lond). 2019 Jun 24;69(4):304.
- McPherson T, Berger D, Alagapan S, Frohlich F. Active and Passive Rhythmic Music Therapy Interventions Differentially Modulate Sympathetic Autonomic Nervous System Activity. J Music Ther. 2019 Aug 13;56(3):240-264. doi: 10.1093/jmt/thz007.
- Nowak J, Dimitrov A, Oei NYL, Walter H, Adli M, Veer IM. Association of naturally occurring sleep loss with reduced amygdala resting-state functional connectivity following psychosocial stress. Psychoneuroendocrinology. 2020 Apr;114:104585. doi: 10.1016/j.psyneuen.2020.104585. Epub 2020 Jan 24.
- Oskrochi Y, Maruthappu M, Henriksson M, Davies AH, Shalhoub J. Beyond the body: A systematic review of the nonphysical effects of a surgical career. Surgery. 2016 Feb;159(2):650-64. doi: 10.1016/j.surg.2015.08.017. Epub 2015 Oct 1.
- 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.
- Shanafelt TD, Boone S, Tan L, Dyrbye LN, Sotile W, Satele D, West CP, Sloan J, Oreskovich MR. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med. 2012 Oct 8;172(18):1377-85. doi: 10.1001/archinternmed.2012.3199.
- Strine TW, Chapman DP. Associations of frequent sleep insufficiency with health-related quality of life and health behaviors. Sleep Med. 2005 Jan;6(1):23-7. doi: 10.1016/j.sleep.2004.06.003.
- Zentner M, Grandjean D, Scherer KR. Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion. 2008 Aug;8(4):494-521. doi: 10.1037/1528-3542.8.4.494.
- PRO00024163