Assessment of noise in a medical intensive care unit

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Summer 2016 Assessment of noise in a medical intensive care unit Kathryn J. Crawford University of Iowa Copyright 2016 Kathryn J Crawford This thesis is available at Iowa Research Online: Recommended Citation Crawford, Kathryn J.. "Assessment of noise in a medical intensive care unit." MS (Master of Science) thesis, University of Iowa, Follow this and additional works at: Part of the Occupational Health and Industrial Hygiene Commons

2 ASSESSMENT OF NOISE IN A MEDICAL INTENSIVE CARE UNIT by Kathryn J. Crawford A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Occupational and Environmental Health in the Graduate College of The University of Iowa August 2016 Thesis Supervisor: Professor Thomas M. Peters

3 Copyright by Kathryn J. Crawford 2016 All Rights Reserved

4 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL This is to certify that the Master's thesis of MASTER'S THESIS Kathryn J. Crawford has been approved by the Examining Committee for the thesis requirement for the Master of Science degree in Occupational and Environmental Health at the August 2016 graduation. Thesis Committee: Thomas M. Peters, Thesis Supervisor T. Renée Anthony Brian K. Gehlbach

5 ACKNOWLEDGEMENTS I am grateful for having the opportunity to work on this project with so many great people. This has truly been an amazing learning experience working. First and foremost, I would like to thank my adviser, Dr. Tom Peters for his support through the entire two years I have been in this program. His guidance, patience, and knowledge have steered me through writing this thesis and I am grateful. I also would like to thank Dr. Renée Anthony who has advised me in and out of class during this master s program and I look forward to working with her during the PhD program. I also would like to thank Dr. Brian Gehlbach and Dr. Lindsey Barnes from the UIHC who were willing to let me be part of their study and gave me the chance to begin learning how to work with data sets of this size and complexity. I would also like to thank Mr. Jeff Falk for his statistical expertise and his creative data analyses. A huge thank you to Dr. Kevin Kelly for being so generous with his time and helping me learn how to navigate through SAS code. I must also thank my lab group and fellow IH students for their support and encouragement throughout my time here, you all are wonderful people and I am grateful that I have gotten to know you and work with you. Lastly, but certainly never least, I want to thank my family, especially my wonderful husband for supporting my career choice and decision to go back to school. I could not have done this without you. ii

6 ABSTRACT Exposure to noise in hospital intensive care units (ICUs) can disrupt patients sleep and delay their recovery. In this intervention study, noise levels were measured in eight patient rooms of a medical ICU (MICU) every minute with sound level meters for eight weeks before and after an intervention. Implemented over six weeks, the intervention was designed to educate nurses and other staff members to reduce noise levels through behavior modification, including instituting a quiet time in the afternoons, encouraging patients to keep televisions off or at lower volumes, and speaking more quietly during conversations. Sound equivalent levels (Leq) were calculated from one-minute measurements for each hour in each room. These hourly Leq (Leq-H) values were compared by pod (group of rooms within the MICU), room position (in proximity to a central nurses station), occupancy status, and time of day. Days with more than ten hours of one-minute noise levels above 60 dba were flagged as the loudest time periods and compared to MICU activity logs. The intervention was ineffective with Leq-H values always above World Health Organization guidelines for ICUs (35 dba in day; 30 dba at night) before and after the intervention. Leq-H values frequently exceeded more modest project goals during the day regardless of the intervention (50% of Leq-H > 55 dba both pre- and post-intervention) and at night (68% and 62% of Leq-H > 50 dba pre- and post-intervention). Statistical analysis of the Leq-H suggests a general source is contributing to the high baseline noise in the MICU, most likely the heating, ventilation, and air-conditioning (HVAC) system. Our analysis of one-minute data indicated that high noise was often associated with high-volume respiratory-support devices. We concluded that our intervention focusing on administrative controls (e.g., education and iii

7 training) was not enough to reduce noise in the MICU but that an intervention designed with engineering controls (e.g., shielding, substitution) would be more effective. iv

8 PUBLIC ABSTRACT Loud noises in hospital intensive care units (ICUs) can keep patients from sleeping at night and resting during the day which may delay their recovery and lengthen their hospital stay. The World Health Organization (WHO) recommends that noise in ICUs stay below 35 decibels (dba) during the day and 30 dba at night. There are many sources of noise in ICUs including medical equipment, such as heart monitors and respirators. Other sources of noise include conversations, televisions, and airconditioners. With so many noise sources, hospitals are not able to reach noise levels that are as low as the WHO recommendations. This study measured noise for eight weeks in the ICU. Then for six weeks, nurses and other staff were trained on how to reduce noise. Measurements were taken for an additional eight weeks to see if noise decreased. Measurements were taken in different rooms to see if any areas were louder than others. The loudest time periods were compared to activity logs to see if there was certain equipment or events that were causing the loud noises. Training the ICU staff on how to reduce noise was not enough to lower noise levels in the ICU. Noise remained above both WHO recommendation and project goals. Our study indicated that the heating, ventilation, and air-conditioning system, along with medical equipment such as respirators were contributing to noise in the ICU. Reducing the noise coming from this equipment would be a better way to lower noise in the ICU and help patients sleep better. v

9 TABLE OF CONTENTS LIST OF TABLES... vii LIST OF FIGURES... viii CHAPTER I: LITERATURE REVIEW... 1 Overview... 1 Sound and noise... 2 Adverse health effects of noise in hospital ICUs... 2 Regulations and guidelines... 4 Summary of Noise Exposure Assessments in Hospitals... 5 Summary of Intervention Studies to Reduce Noise... 8 Shortcomings of the literature... 9 Objectives... 9 CHAPTER II: NOISE ASSESSMENT IN A MEDICAL INTENSIVE CARE UNIT Introduction Methods Site description Sampling protocol Noise intervention Data Analysis Results Hourly L eq analysis One-minute noise analysis Discussion Conclusions Tables Figures CHAPTER III: CONCLUSIONS APPENDIX Appendix A. Training Materials for Intervention Appendix B. SAS code for L eq-h data set Appendix C. SAS code for one-minute data set Appendix D. R code REFERENCES vi

10 LIST OF TABLES Table 1. Estimates from two-level repeated-measures model predicting L eq-h (dba) Table 2. Median L eq-h values (dba) by pod and occupancy status Table 3. L eq-h values (dba) for day and night time periods compared by intervention phase Table 4. One-minute measurements (dba) for day and night time periods compared by intervention phase vii

11 LIST OF FIGURES Figure 1. Layout of the MICU with Pods 1-5 numbered Figure 2. Boxplot comparing pre-and post-intervention L eq-h values by pod Figure 3. Cumulative frequency plots of L eq-h values for each pod pre-intervention (a) and postintervention (b) Figure 4. Boxplot comparing pre-and post-intervention L eq-h values by occupancy status Figure 5. Boxplot comparing L eq-h values in each pod during occupied (O) and unoccupied (U) time periods Figure 6. Boxplot comparing pre-and post-intervention L eq-h values by proximity to central nurses' stations (near or far) Figure 7. One-minute noise measurements over full days with medical interventions in use viii

12 CHAPTER I: LITERATURE REVIEW Overview Exposure to noise in hospital intensive care units (ICUs) can disrupt patient sleep and delay recovery. Noise in ICUs poses a unique challenge for controlling exposure because of both the inherent high level of activity and the susceptible patient population within a hospital. Even though some noise in hospitals is unavoidable (e.g. alarms, ventilation), it is essential to keep noise levels as low as reasonably possible because there are known adverse health effects to loud and/or disturbing noise exposure, some of which are specific to patients undergoing critical care. The World Health Organization (WHO) reports that patients who are injured or ill have a diminished ability to cope with stress and are therefore more prone to sleep disturbance at night and annoyance during the day (Berglund, Lindvall, & Schwela, 1999). Research on reducing noise exposure in ICUs have typically involved recording noise levels for brief periods of time such as hours or days to establish a baseline. Noise is measured again after a targeted intervention has been established to determine if there has been any reduction. The problem with these studies is that noise is typically only measured for a brief period of time in one location. Limited sampling strategies do not allow researchers to fully characterize the noise and identify its patterns and sources, which, in turn limits the effectiveness of any intervention. This thesis describes part of a quality improvement (QI) study designed to reduce noise in a university medical ICU (MICU). Noise levels were measured in eight different patient rooms for eight weeks before and eight weeks after a targeted intervention. Data were analyzed to determine how noise levels compared to recommended guidelines, how noise varied throughout the different areas of the MICU, and whether noise levels were successfully reduced by the intervention. 1

13 Sound and noise Sounds occur over a range of frequencies which are typically divided into nine octave bands ranging from 31.5 hertz (Hz) to 8000 Hz (Bruce et al., 2011). One of the most common methods to measure sound levels over time is to calculate an equivalent sound level (L eq ), which represents the sound level equivalent to the total sound energy occurring over a selected period of time. The L eq integrates sound levels over a given period of time resulting in a generalized characterization of sound levels in an environment. These measurements are traditionally measured in decibels using an A-weighted scale (dba). The A-weighting adjusts lower frequencies in the octave band to provide an overall sound measurement that closely approximates human hearing. Other weighting scales include dbc, which minimally adjusts the highest and lowest octave bands, and dbz which is considered a flat response with no weighting. In addition to calculating sound averages, many researchers have quantified the occurrence and decibel level of sound peaks. Sound peaks can be measured as a single instantaneous sound pressure level as L peak, which is measured in C-weighted decibels (LC peak ) (Bruce et al., 2011). Often, the use of the word peak can be confused with max when describing extreme high sound levels, but in sound measurements, peak values and maximum sound levels (L max ) are different. The peak value is actually the highest point of the sound pressure level, and the L max is the highest sound level recorded. The L max should be recorded to capture loud sounds that can be hidden if integrated over a period of time. Because sounds are perceived differently by everyone, the term noise describes any unwanted sound, and noise at any level, high or low, has the potential to adversely affect human health. Adverse health effects of noise in hospital ICUs Continuous noise experienced throughout the day by ICU patients contributes to elevated stress and annoyance (Berglund, Lindvall, & Schwela, 1999). These mental and emotional reactions can then have a biological impact on patients by increasing heart rate, blood pressure, 2

14 and muscle tension (Overman-Dube et al., 2008). In this elevated state, patients are not able to rest, which is a critical part of the healing process. The effects of noise can be compounded because of the weakened state of the patient population. High noise levels may contribute to delirium (Qutub & El-Said, 2009) and delay the healing of wounds which has been reported in animal studies (Rafi, Khan, & Minhas, 2014). All of these adverse health effects can cause patients to have increased hospital stays (Fife & Rappaport, 1976). Critically in a hospital environment, exposure to high noise levels can interfere with patients sleep, which is integral in recovery. There are three crucial aspects to a sleep cycle: the amount, quality, and structure (Knauert et al., 2015). There are two main phases of the sleep cycle; the first, non-rapid eye movement (NREM), is further broken down into three stages (1, 2, and 3), and the second phase is rapid eye movement (REM). There is little health benefit during Stages 1 and 2 of the first phase of the sleep cycle and a person is easily roused at this time. Studies have shown that the majority of sleep that patients achieve in hospitals is in these first two stages (Delaney et al, 2015) and that the beneficial third stage of Phase 1 and the REM phase are either much reduced (Friese et al, 2007) or absent altogether due to the patients being aroused from sleep either from noise or medical interventions (Freedman et al., 1999). These studies also indicate that even if patients sleep for an appropriate amount of time (i.e., eight hours) the sleep is not beneficial if the time spent sleeping at each stage is not appropriate. Lack of proper sleep can affect a person s emotional state, their memory, and their ability to make decisions (Knauert et al., 2015). Lack of sleep can also disrupt biological functions and lead to adverse effects within the endocrine and pulmonary systems (Knauert et al., 2015) as well as the cardiovascular and immunological systems (Delaney et al., 2015). The body attempts to make up for poor sleep at night by sleeping during the day, and naps further disrupt normal circadian rhythms. The effect of altered circadian cycles can persist long after a normal sleep schedule is resumed (Delaney et al., 2015). 3

15 The WHO reports that noise levels above 55 decibels in the A-weighted scale (dba) can be disruptive for sleep (Berglund, Lindvall, & Schwela, 1999), and some studies report that levels should be kept even lower, less than 40 dba, for uninterrupted sleep (Freedman et al., 1999). Park et al. (2014) reported that 86% of patients in the internal medicine ward of a university hospital experienced disrupted sleep and found that the frequency of sleep disruption was directly related to increases in the L eq levels over a 24-hour period. Other studies have measured peak noise levels because even if the majority of noise levels are low, one brief loud sound can awaken someone from sleep (Aaron et al., 1996). The Environmental Protection Agency (EPA) illustrates this concept explaining that sleeping in a room, where the noise levels remained steady around 35 dba with no louder peaks, would be more beneficial than sleeping in a room with sound levels predominately at 25 dba but with high peaks above 35 dba even if they were infrequent in occurrence (EPA, 1974). Aaron et al. (1996) reported that the occurrence of peak levels >80 dba correlated with patients arousals from sleep in a respiratory ICU. Friese et al. (2007) found that ICU patients have as many as 6.2 arousals from sleep per hour. Regulations and guidelines Numerous governmental and professional agencies have established regulations and guidelines for noise. The Occupational Safety and Health Administration (OSHA) has established enforceable standards for noise levels in occupational settings. Workers cannot be exposed to noise levels greater than 90 dba over an eight-hour time-weighted average (OSHA, 2015). These standards apply only to the workers (e.g., staff in an ICU) and are based on the prevention of hearing loss. In an effort to address noise levels outside of the workplace, the United States federal government established the Noise Control Act (NCA) of The NCA focuses mainly on limiting the noise produced in the environment by vehicles and commercial appliances and machinery as well as to promote research into noise control (EPA, 2015). It does not however include enforceable standards for community noise levels. State and local 4

16 governments are in charge of establishing their own noise ordinances for the community. The EPA recommends noise levels in residential homes not exceed 55 dba and that non-residential indoor areas, such as schools and hospitals, should not exceed 45 dba as a 24-hour average (1974). In 1999, the WHO published Guidelines for Community Noise that includes specific guidelines for hospitals which are frequently referenced in the literature (WHO, 1999). WHO recommends that noise levels in a hospital, measured as L eq, do not exceed 35 dba during the day and 30 dba during the night. The WHO further recommends that noise levels do not exceed 40 dba as an L max at night (1999). The addition of the maximum noise level guideline is included to account for discrete loud noise events, which can disrupt sleep even if average noise levels are low. A range of 30 to 35 dba is very quiet considering that a quiet room typically measures around 40 dba (ASHA, 2015). For reference, the threshold of human hearing is considered to be 0 dba, sound levels in busy traffic measure at about 70 dba and a running chain saw measures around 110 dba (ASHA, 2015). Summary of Noise Exposure Assessments in Hospitals There is a general consensus that noise in hospitals exceeds the WHO guidelines. Konkani & Oakley (2012) reported that out of 29 ICU noise studies, all of them exceeded WHO guidelines. Christensen (2007) found that in an open, nine-bed ICU all noise levels exceeded 50 dba over the course of the three-day sampling period. Busch-Vishniac et al. (2005) found noise throughout Johns Hopkins Hospital averaged around dba. Cordova et al. (2012) focused specifically on a burn-icu and reported that average noise, measured for 10 separate days during a month-long period, ranged from 59.6 dba to 65.0 dba in patient rooms. Johansson et al. (2012) reported ICU patient rooms average noise levels were dba. Wang et al., (2013) reported an average baseline noise level in unoccupied rooms of a neo-natal ICU (NICU) as 49 dba. Similarly, in a two-night study, Walder et al. (2000) determined a baseline noise 5

17 measurement of 43.2 dba achieved in an unoccupied room with the doors closed and only the air conditioner in operation. Darbyshire & Young (2013) reported that the only way noise levels compliant with WHO guidelines could be reached during their two-week study was by measuring the noise in an unoccupied patient room with all equipment shut off. In an effort to identify the frequency and magnitude of the loudest noise in hospitals, some researchers have reported peak levels as well. Darbyshire and Young, (2013) reported that peak levels above 85 dba occurred on average during 25 minutes of every hour. Based on these levels, they expected patients to be disturbed every 7 to 16 minutes overnight. Similarly, Aaron et al. (1996) reported an average of 150 to 200 peaks above 80 dba from midnight to six am. Elliott et al. (2013) reported that during the day the median number of sound peaks greater than 80 dbc was 416. They also reported that at night there were approximately 90 sound peaks per hour greater than 80 dbc. Studies designed to investigate the temporal variability of noise in hospitals help identify how the different times of day may affect noise levels. The methods used to determine these temporal differences in noise levels have ranged from quantitative studies measuring noise with sound level meters (Christensen, 2007) to more qualitative studies involving the interviewing of staff and patients about their perceptions of the noise (Overman-Dube, 2008). Mornings (7am to 12pm) were typically perceived as noisiest (Overman-Dube et al., 2008). Christensen (2007) also found significant differences in the noise levels between the morning, afternoon, and night shifts. Wang et al. (2014) reported significant differences in the median noise levels for day shifts (52.8 db) and night shifts (51.5 db) as well as between weekdays (52.2 db) and weekends (51.9 db). These results indicate that although temporal variability in noise levels may be found to be statistically significant, the magnitude of the difference may be small and difficult for the human ear to distinguish. It does also suggest that different activities occurring throughout the day (e.g., shift changes, cleaning schedules, and visiting hours) may contribute to changes in noise level. 6

18 Targeted interventions to reduce noise in ICUs have attempted to modify activities that serve as noise sources. Regardless of time of day, noise levels in ICUs may be high because of the frequent interventions from medical staff. Patients must be routinely monitored and medicines must be administered at scheduled intervals to ensure recovery. Park et al. demonstrated that noise levels in one ICU were strongly correlated with APACHE II scores of patients being admitted to the ICU (2015). The APACHE II is an acute physiological and chronic health score assigned to incoming patients as a predictor for ICU mortality (Park et al., 2015). The results of the Park et al. (2015) study suggests that patients in more critical condition may be exposed to more noise because their condition requires more frequent and intensive medical attention. Since medical treatments are necessary, it is important to identify other noise sources within the ICU that may be more amenable to intervention. Kahn et al. (1998) identified sources of peak levels in the ICU greater than 80 dba and found that 49% were attributed to television (23%) and conversation (26%). The study also identified sources less amenable to changes such as alarms and ventilators, 20% and 8% respectively. Overman-Dube et al. (2008) reported that one-third of the patients and staff they interviewed believed that voices were the main cause of noise in the hospital. Elander & Hellström (1995) reported that staff conversation was the most common cause of loud noise in their study as it contributed to 62% of all the sound measurements taken. Similar results were found by Freedman et al. (1999) in their study where the patients perceived staff communication to be more disruptive than alarms or any other sources. Speech communication is certainly crucial in any setting, especially a hospital where doctors and nurses must communicate with each other and their patients. Based on the reported noise levels in hospitals, if a typical conversation between two people measures around 45 dba to 50 dba, then hospital staff must raise their voices to be heard over background levels (Busch- Vishniac et al., 2005). Conversation will continue to get louder as the surrounding environment gets louder; this is considered an involuntary reflex referred to as the Lombard effect (Delaney et 7

19 al., 2015). There is also a necessity for medical alarms in hospitals to produce sound levels that are 10 to 15 db higher than background levels in order for them to be heard (Bruce et al., 2011). Summary of Intervention Studies to Reduce Noise Many authors report that through identification of noise sources they were able to significantly reduce sound peaks with a targeted intervention program. Kahn et al. (1998) identified television and conversation as major noise sources and implemented a behavioral modification intervention to train staff and patients to control these sources by lowering television volume and speaking more quietly. Noise measurements were taken every 15 seconds for 10 consecutive minutes on 16 different occasions and compared to similar measurements taken after a two-week intervention. The authors reported a significant reduction in noise after the intervention (p=0.0001) however the change was minimal (80.0 ± 0.1 dba to 78.1 ± 0.1 dba) (Kahn et al. 1998). Similar results have been reported in other studies with interventions that focused on raising staff awareness. Walder et al. (2000) measured noise for two nights before and after nighttime behavior guidelines were distributed to hospital staff. They reported that nighttime noise levels were reduced from 51.3 dba to 48.3 dba after their intervention and average peak levels were reduced from 74.9 dba to 70.8 dba. Richardson et al. (2009) targeted staff behavior through training programs and the display of sleep promotion posters. Peak noise measurements taken over a 24-hour period before and after the intervention were reported to have significantly reduced from dba to dba (p < 0.01), while L eq measurements increased from 46.9 dba to 49.7 dba (p < 0.01). Tsunemi et al. (2012) measured NICU L eq levels ranging from 71.0 dba to 59.0 dba before an educational intervention and 80.4 dba to 52.6 dba after but the differences were not found to be significant (p>0.176 for all comparisons). Elander & Hellström (1995) used video and audio recordings to educate staff members on noise sources and controls. Results indicated that the mean noise levels were decreased, though not significantly, from 57 8

20 dba (±4.4 dba) to 49 dba (±7.6 dba). The authors also reported that conversation accounted for 62% of the noise before intervention and was reduced to 14% in the post-intervention phase (n=144 measurements). Shortcomings of the literature A review of the literature indicates many interventions are ineffective in reducing noise in ICUs. It is possible that part of the problem is that measurements taken in hospital noise studies rarely take place over an extended period of time. With short-term samples of noise (e.g. 10-minute samples, one 24-hour time period), it is difficult to determine any long-term patterns that might be present. Taking more measurements over longer sampling periods may allow for the identification of patterns and noise sources in the ICU. With more thorough sampling, new sources could be identified and interventions could be re-designed to target these specific sources. There are also few studies that have sampled in multiple locations of the ICU which could help to identify variability within the same location. Objectives This thesis summarizes an intervention study conducted by researchers at the University of Iowa Hospital to reduce noise in the medical ICU (MICU). Noise measurements were logged every minute for an eight-week period in eight patient rooms. Two rooms in four separate pods were chosen based on their proximity to the central nurses station (nearest and farthest). Following the initial eight-week sampling period, a targeted intervention was implemented over a six-week learning period. The intervention focused on education staff about the sources of noise, its adverse effects on patient health, and ways to reduce noise in their daily activities. After the intervention, noise levels were measured for an additional eight-week period. Hourly Leq values (L eq-h ) were calculated from all the one-minute data and then analyzed to evaluate the effectiveness of the intervention. Both the L eq-h and one-minute data were used to identify determinants of patient noise exposure in the MICU. 9

21 CHAPTER II: NOISE ASSESSMENT IN A MEDICAL INTENSIVE CARE UNIT Introduction Exposure to noise in hospital intensive care units (ICUs) can disrupt patient sleep and delay recovery. Continuous noise exposure throughout the day contributes to elevated stress in ICU patients (Berglund, Lindvall & Schwela, 1999). Exposure to noise can lead to changes in heart rate, blood pressure, and muscle tension (Overman-Dube et al., 2008) and may contribute to delirium (Qutub & El-Said, 2009). Animal studies have indicated that exposure to noise delays the healing of wounds (Rafi, Khan, & Minhas, 2014). Noise exposure during the night can have even greater detrimental effects on patient recovery because it disrupts patients sleep. Studies have shown that ICU patients do not spend enough time at restorative sleep stages and are easily awakened by noise even if it occurs at brief intervals (Delaney et al., 2015, Friese et al., 2007, Freedman et al., 1999). The World Health Organization (WHO) has reported that noise levels greater than 55 dba can be disruptive for sleep (Berglund, Lindvall, & Schwela, 1999), and Freedman et al. (2014) has reported that levels should be kept below 40 dba to ensure uninterrupted sleep. The WHO established guidelines for noise in hospitals to promote patient sleep and recovery. These guidelines are expressed as in terms of a sound equivalent level (L eq ), which is an average of the individual A-weighted decibel (dba) measurements over a given period of time, equivalent to the total sound energy over that time period. In hospitals, the WHO recommend that L eq remain below 35 dba throughout the day and below 30 dba overnight (Berglund, Lindvall & Schwela, 1999). The WHO further recommends that maximum sound levels do not exceed 40 dba overnight to ensure no brief loud noises interrupt patient sleep. For reference, the threshold of human hearing is approximately 0 dba, quiet conversation measures dba, traffic on the road measures around 70 dba, and many hand-held power tools can measure up to 100 dba (ASHA, 2015). 10

22 In an effort to protect people from nuisance and harmful levels of noise, some federal agencies in the United States have established regulations for noise. The Occupational Safety and Health Administration (OSHA) require that employees not be exposed to 90 dba as calculated by an 8-hour time-weighted average (OSHA, 2015). In hospitals, this standard applies only to staff, not patients. The Noise Control Act of 1972 was passed by the and the Environmental Protection Agency (EPA) to regulate noise in the environment caused by vehicles and commercial appliances and does not apply to hospitals. The EPA published recommendations that nonresidential indoor areas, including hospitals, do not exceed 45 dba as a 24-hour average (EPA, 1974). The WHO guidelines, although not enforceable, remain the most applicable recommendations for patients in hospitals. Descriptive studies have shown that noise in ICUs often exceeds WHO guidelines (Konkani & Oakley, 2012). Christensen (2007) reported that all noise measured in the ICU over a three-day period was above 50 dba and that the loudest noises occurred between 8 am and 11 am. Overman-Dube et al. (2008) identified mornings (7 am to 12 pm) as the noisiest time of day through a subjective analysis of noise in the ICU by surveying patients and staff. Short et al. (2010) reported that all noise recorded over a 24-hour period exceeded 50 dba and that the loudest noises (<80 dba) occurred between 6 am and 2 pm. The quietest time periods Short et al. (2010) recorded occurred overnight between 10 pm and 6 am. Darbyshire & Young (2013) measured noise in five ICUs over a two-week period and found the quietest times in a 24-hour period occurred between 4 am and 5 am, however noise levels were always above 45 dba exceeding WHO guidelines. Researchers have attempted to identify the determinants of noise in ICUs by measuring at specific locations (e.g., patients rooms and nurses stations) (Cordova et al., 2013) or during specific activities (e.g., conversation and television) (Kahn et al., 1998; Elander & Hellström, 1995). In most cases, noise was monitored over a short period, limiting the ability to identify 11

23 long-term patterns and sources of noise. Cordova et al. (2013) measured noise for 10 days over a one-month period at both a central nurses station and within randomly-selected patient rooms. They found no significant difference between the noise levels at the different locations. Kahn et al. (1998) measured peak sound levels for 10-minute periods near specific noise sources and reported mean peak levels as 79.1 dba (±0.5 dba) for televisions, 79.0 dba (±0.7 dba) for monitor alarms, 83.7 dba (±2.1 dba) for the intercom, and 84.6 dba (±0.7 dba) for conversations. Elander & Hellström (1995) measured the noise levels of routine activities in a neonatal ICU (NICU) for 24 hours; noise sources included loud talk (65 dba), incubator alarms (70 dba), and laughter (80 dba). Intervention studies have been designed to educate staff on how to reduce noise generated from specific sources or specific activities (Walder et al., 2000; Richardson et al, 2009). Walder et al. (2000) established guidelines during an intervention period that included closing doors, limiting nursing interventions, and restricting conversation, phone and television use during nighttime hours. They compared nighttime noise measured for 13 nights before and 11 nights after this intervention and found no significant change between the pre-intervention L eq (51.3 dba ±2.8 dba) and post-intervention L eq (48.3 dba ±1.4 dba). Richardson et al. (2009) implemented an intervention program to educate staff on noise reduction behaviors (e.g., decreasing phone ring volume and wearing soft-soled shoes) and displayed posters that listed noise, its effect on patient health, and tips for promoting sleep in the hospital. Noise was significantly higher in the post-intervention phase (49.69 dba ±7.93 dba) than the preintervention phase (46.87 dba ±5.67 dba) (p <0.01). These intervention studies typically took place over longer periods of time than descriptive studies, but the data used for analysis were measurements taken over one day to one week. The goals of this study were to determine the effectiveness of a quality improvement (QI) intervention to reduce noise and to identify determinants of noise in a MICU. One-minute noise 12

24 levels were measured in patient rooms for an eight-week period before and after the intervention. The intervention was unique in that it was a six-week education program using staff meetings, one-on-one training sessions, and weekly progress reports to help staff learn ways to reduce noise in the MICU. Hourly L eq values (L eq-h ), calculated from one-minute data, were used to evaluate the effectiveness of the intervention. The L eq-h and one-minute data were used to identify key determinants of patient exposure to noise in the MICU. Methods Site description The study was conducted in the MICU at the University of Iowa Hospital (UIHC) between November 2014 and April The study design was approved by the Institutional Review Board (IRB). The MICU layout with the location of the sampled rooms and the nurses stations identified are illustrated in Figure 1. The MICU consisted of 26 patient rooms separated into five pods (Pod 1 through Pod 5). Each pod contained four to six patient rooms, and although similar in size and style, they were not uniformly constructed. A central nurses station was located near the entrance of each pod and minor nursing stations were located within each pod. Two rooms (one nearest and one farthest from the central nurses station) within each of four pods (Pod 1, Pod 3, Pod 4, and Pod 5) were included in this study (2 rooms x 4 pods = 8 rooms). Pod 2 was only used for overflow patients and was excluded from the study due to low occupancy. Sampling protocol Noise levels from 30 dba to 130 dba were logged every minute in each of the selected rooms with a sound level meters (SLM, SDL 600, Extech Instruments, Nashua, NH) set to A- weighting, slow response. SLMs were attached to the wall within six feet of the head of the patient and pre-calibrated to dba, using the calibrator provided by the manufacturer (407766, Extech Instruments, Nashua, NH). Once a week, weekly data were uploaded manually 13

25 from the SLM to a central database. Baseline noise measurements were collected for eight weeks (11/3/14-12/31/14) to characterize the noise environment of the MICU prior to the intervention, which was then implemented for six weeks (1/1/15-2/8/15). Sound level meters were still in position and logging data during the intervention period but were not used in the analysis. Postintervention measurements were collected for an additional eight weeks after the intervention (2/9/15-4/17/15). Noise intervention During the intervention, project researchers and designated nursing champions provided one-on-one education for MICU staff (nurses, physicians, respiratory therapists, and unit clerks) and visitors on the adverse health effects of noise, how to recognize sources of noise, and how to reduce noise from those sources. Nurses were the main targets for the educational program because they have the most contact time with patients. At the study MICU, nurses typically rotate through three to four 12-hour shifts per week. Posters that outlined the intervention project goals and contained helpful tips on how to reduce noise were displayed in the MICU in nursing work rooms, restrooms, patient rooms, and family waiting rooms. Some posters were designed specifically to target staff while others were designed to target visitors. Examples of these posters are included in Appendix A. Project staff constructed weekly time-series plots of one-minute noise levels with project goals super imposed (<55 dba during the day and <50 dba during the night). These plots were distributed by to nursing staff and delivered in hard copy to the three main nursing stations. An example of a weekly plot is shown in Appendix A. Staff were encouraged to relate their activities to the plots to raise awareness to their own contributions to noise in patient rooms. The intervention also included weekly rounds by project leaders who performed random room spot checks and one-on-one education with nurses. Rooms for spot checks were selected using a 14

26 random number generator. Posters remained on display and the weekly plots were distributed throughout the intervention and post-intervention phases. Data Analysis All one-minute data were downloaded and imported into SAS version 9.3 (SAS, Cary, NC) for analysis. These data included date, time, and noise in dba to one decimal place, pod number (1, 3, 4, and 5), position (near, far), day of week (Sunday through Saturday), period (day, dusk, night), and occupancy status (occupied, unoccupied). Additional variables were coded into the dataset in SAS as follows: time period described measurements recorded during the day (07:00-22:59) or at night (23:00-06:59); intervention signified whether measurements were recorded during the pre- or post-intervention phases of the study; study hour represented a the cumulative hour for each room during the entire study. Less than 2% of the 1,452,255 oneminute data collected during the pre- and post-intervention phases were eliminated from further analysis because they were missing or deemed unusable (missing room occupancy status or instrument malfunction). Hourly Leq analysis et al., 2011): Hourly L eq (L eq-h ) values were calculated from one-minute noise measurements as (Bruce 10log10 Equation 1 Consolidating the noise measurements in this way allowed us to compare to goals of the intervention and WHO recommended guidelines. Descriptive statistics and normality tests were performed (SAS, PROC UNIVARIATE). Cumulative frequency plots and boxplots displaying quartile distributions of the L eq-h were generated to compare the pods, positions, and time periods by intervention phase. Due to the large sample size (n=24095), we used a probability plot to display the L eq-h data to visually assess normality. The data were displayed as following very 15

27 closely to a straight line (Appendix B). We therefore modeled the data set as if it had a normal distribution. A two-level nested mixed effects model for repeated measures (SAS, PROC Mixed) was used to identify predictors of L eq-h in the MICU and estimate variance between and within the rooms in our study. The model is illustrated in Equation 2 (Rappaport et al., 1999): μ Equation 2 for i = 1, 2, k rooms, and for j = 1, 2, n i study hours of the i-th room for m= 1, 2, p covariates The significance level was set at α=0.05. An intraclass correlation coefficient was calculated using the variance estimates for level-1 (study hour) and level-2 predictors (rooms) (Model 1) (Appendix B). Covariates (pod, position, intervention, time period, and interaction terms) were added to a full model (Model 2). Backwards elimination was used to exclude nonsignificant terms. One-minute noise analysis The one-minute noise data were queried to identify 22 days with extended periods of elevated noise defined as >10 hours with noise >60 dba. For these flagged days, MICU activity logs were reviewed to identify times when specific medical procedures took place. Procedures included dialysis, and respiratory support using oxygen (O 2 ) delivery systems that operate at a range of flow rates measured in liters per minute (LPM). Delivery systems included bilevel positive airway pressure (BiPAP), continuous positive airway pressure (CPAP), face mask (>10 Lpm), nasal cannula (NC) (1-10 Lpm), and high flow cannula (15-50 Lpm). For comparison purposes, logs were also reviewed to identify medical procedures for the same room on the day prior to that which was flagged. 16

28 Results Hourly L eq analysis Table 1 displays the parameter estimates the mixed models applied to the L eq-h data. Without the addition of any covariates (Model 1), the grand mean L eq-h was 54.0 dba and the interclass correlation coefficient was 0.23 (i.e., 23% of the variability in L eq-h was between the rooms). In the final model (Model 2), significant (p < 0.05) predictors of the variability in L eq-h included intervention (Post:-1.4 dba), pod (-2.7 dba), occupancy (Occupied: 3.2 dba), time period (Day: 2.2 dba). Although significant, the estimates were small. Position was found to be nonsignificant but was kept in the model because there were significant interaction terms including position (Table 2). Noise was significantly louder in Pod 1 than in Pods 3, 4, and 5 (p <0.01) in both preand post-intervention phases (Figures 2, 3). The median L eq-h in Pod 1 was approximately 58 dba for both intervention phases. As shown in Figure 4, the L eq-h was consistently higher in occupied rooms (n=18810, median = 54.9 dba) than in unoccupied rooms (n=5285, median= 49.6 dba). Unoccupied rooms in Pod 1 had a higher median L eq-h (56.5 dba) than all of the occupied rooms in other pods (53-54 dba) (Figure 5). Median daytime L eq-h values were approximately 55 dba in both phases of the study compared to the nighttime medians of approximately 52 dba (Table 3). Approximately 50% of the daytime L eq-h in both phases exceeded the project goal of 55 dba, whereas 68% of the nighttime pre-intervention L eq-h and 62% of the post-intervention L eq-h exceeded the goal of 50 dba. No significant differences were found between the L eq-h in the near and far room positions (Figure 6). One-minute noise analysis The one-minute noise measurements during the day ranged from 31.8 dba to dba with a median of 53.1 dba pre-intervention and from 32.4 dba to 94.9 dba with a median of 53.0 dba post-intervention (Table 4). No difference in the percent of noise measurements below 17

29 the day and night goals was observed between intervention phases. Approximately 37% of the daytime noise levels were greater than 55 dba, and 58% of the nighttime noise levels were greater than 50 dba. These percentages were observed during both phases of the study. One-minute noise measurements over 24 hours are plotted for three of the flagged days (out of 22) with elevated noise in Figure 7 (Panels B, D, and F) along with the reference days (Panel A, C, and E). These plots also show when and what type of medical procedure occurred on these days. Noise was highest when the dialysis machine was used at the same time as a 15-Lpm face mask (~68 dba; Figure 7B). MICU staff reported that the dialysis machine can be very loud and can alarm frequently during its two to four hour operation period. Noise levels at this time were much higher to those measured just prior when the patient was wearing a 5L respirator instead (~50 dba; Figure 7B). In comparison, the day before in the same location noise was relatively low when the 4Lpm and 1Lpm NCs were in use (~50 dba) and when the room was unoccupied (~45 dba; Figure 7A). Similarly the remaining plots indicate a trend toward higher noise levels when the oxygen delivery devices in use were operated at higher flow rates. Noise was higher (~60 dba) when the 10Lpm face mask was in operation (Figures 7C, 7D) compared to when the 5Lpm NC was in use (~55 dba; Figure 7C). Noise was even higher (~65 dba to 70 dba) when the 15Lpm NC or 15Lpm face mask were used (Figures 7D, 7E, 7F). Discussion The behavioral intervention applied in this study was ineffective in reducing noise in the MICU. In the post-intervention phase, all hourly Leq-H exceeded WHO recommendations and frequently exceeded project goals during the day (50% of Leq-H > 55 dba) and at night (62% of Leq-H > 50 dba) (Table 3). Although significant, our model indicated that the intervention reduced the L eq-h by only ~1dBA (Table 1). Median L eq-h remained at approximately 55 dba for daytime periods and 53 dba for nighttime periods during both phases of the intervention (Table 3). Maximum L eq-h values were reduced from 83.4 dba to 77.3 dba during 18

30 the day and from 79.1 dba to 69.8 dba during the night. However, the range of L eq-h values between the 5 th and 95 th percentiles indicated changes between intervention phases of no more than 1 dba during the day and 1.5 dba at night (Table 3). The reductions of L eq-h in our study were not substantial and unlikely to be discernible by the human ear. Tsunemi et al. (2012) also implemented an education program to serve as an intervention to reduce noise in a NICU. They reported minimal differences in noise between the intervention phases (63.7 dba to 62.9 dba in the mornings, 66.1 dba to 67.0 dba in the afternoons, and 60.2 dba to 62.1 dba in the evenings). Richardson et al. (2009) trained nurses on recognizing and reducing noise and also displayed sleep promotion posters at nursing stations for their intervention. They reported a significant increase in noise between intervention phases (46.9 dba and 49.7 dba; <0.01). Our initial hypotheses were that the main contributors to noise in the MICU patient rooms were human sources, either generated from the central nurses stations or from the patients themselves through conversation or television use. Therefore, we expected to see substantial reductions in MICU noise since staff was trained on how to limit noise in their activities and in the patient rooms. We also expected to see marked differences in L eq-h between the rooms nearest and the rooms farthest from the nursing stations. Our analysis, however, indicated that position (near to or far from the nursing station) was not a predictor of L eq-h values (Table 1). Since we did not see any substantial differences between the L eq-h in the near and far rooms (Figure 6), it suggests that nursing staff are not the main contributors of noise in the MICU. Staff conversation has been documented in literature as a common noise source. Elander & Hellström (1995) reported that 62% of the noise measurements they recorded were attributed to staff conversation and Overman-Dube et al. (2008) reported that approximately 33% of their interviewed patients and staff believed conversation was the main cause of noise in the hospital. Kahn et al. (1998) reported that talking accounted for 26% of their measurements and had high 19

31 mean peak levels (84.6 dba +/- 0.7 dba). Since staff conversation has been frequently reported as a noise source, studies, including ours, have tried to quantify its contribution to noise by comparing measurements from highly staffed areas (e.g., nursing stations) to measurements from patient areas. Similar to our results, Cordova et al. (2013) reported no significant difference in noise levels between nursing stations and patient rooms. Elander & Hellström (1995) educated nurses on noise and trained them to speak more quietly when working in the NICU and reported a decrease in the percent time conversation occurred during the pre- (62%) and post-intervention phases (14%). Even though conversations occurred less frequently, there was no significant difference found between noise levels in each intervention phase (57 dba +/- 4.4 dba and 49 dba +/- 7.6 dba). Analysis of these data with respect to co-variates allows us to make some statements on sources of noise in the MICU. While our initial hypothesis regarding staff contributions to noise may have been incorrect, our analysis has identified other potential determinants of noise exposure. Our findings indicate a general source (e.g., heating, ventilation, and air-conditioning, HVAC, system and standard medical interventions) dominates the high baseline L eq-h within the MICU. L eq-h values were higher in occupied rooms than unoccupied rooms but not substantially (Figure 4) suggesting an ongoing high baseline L eq-h in patient rooms even when patients and staff were not present. In general, L eq-h was highest in Pod 1 in both phases of the study (Figure 2), and furthermore, unoccupied rooms in Pod 1 were consistently louder than all of the occupied rooms in other the other pods (Table 2). The MICU nurse manager reported that the HVAC system in Pod 1 was part of the oldest construction in the MICU. All of the systems in the other pods have been renovated more recently. It is possible that the HVAC system contributed to the overall higher noise levels in this pod. If true, it serves as at least a partial explanation as to why even the unoccupied time periods in Pod 1 were louder than the rest of the MICU and supports our findings that most noise is coming from a general source. 20

32 The median L eq-h of approximately 55 dba (occupied rooms, Table 2) measured in this study is consistent with observations of others. Busch-Vishniac et al. (2005) reported 24-hour L eq in Johns Hopkins Hospital between 50 dba and 60 dba. Cordova et al. (2012) reported a range of 30-minute L eq in patient rooms from 59.6 dba to 65.0 dba, and Johansson et al. (2012) reported a range of 24-hour L eq values from 55 dba to 66 dba. Our unoccupied rooms had a median L eq-h of approximately 51 dba that is substantially higher than the 43.2 dba value reported by Walder et al. (2000). However their measurement was taken at night in a room with door closed and only the air conditioner in operation. Kahn et al. (1998) reported the air conditioner was one of the 12 individual sources they found to contribute to overall noise levels and, although it was only in operation for 2% of the time they were measuring, it had mean peak levels of 74.8 dba +/- 1.2 dba. Further analysis of the one-minute data provided insight into additional sources of noise (e.g., medical interventions) and alternative interventions that could reduce noise in the MICU. Twenty-two days with more than 10 hours of noise levels above 60 dba were flagged as the loudest time periods. A preliminary investigation of three of these days indicated the noisiest time periods were during high-flow respiratory support operation (Figure 7). It might be possible to target this equipment in a future noise reduction program. It is unlikely that the isolation of noise sources would be effective in the MICU because the equipment is kept near the patient and the patient must be easily accessible to staff. Doors to patient rooms usually remain open to increase accessibility and cannot be used as a control so another option could be the installation of some form of shielding over noisy equipment. A shield in place over a device may reduce the level of noise produced at the source. This would only be possible however if the shield did not interfere with patient care. By reducing the loudest noise sources in the ICU, we may be able to reduce overall noise levels more effectively and in turn create a better environment to promote patient sleep and 21

33 recovery. If two sources producing the same level of noise are in operation next to each other, the noise energy is effectively doubled resulting in a 3 dba increase (e.g., two dialysis machines running at 80 dba each would result in the noise level of the room to measure 83 dba). If one of the same of the same dialysis machines is running at 80 dba and a respirator was operating at 60 dba, the noise level in the room would still be approximately 80 dba. This suggests that if we cannot reduce the loudest noise in the ICU patient rooms we cannot reduce patient exposure. Educating the staff on ways to reduce their own contributions to noise is important but it is unlikely to succeed if the staff has no control over the loud equipment sources. Since the equipment in use is dependent on patient status, it could be that patient status is a good predictor of noise level in the MICU. Park et al. (2015) reported strong correlations between APACHE II scores with noise levels. Future investigations of the noise data in this study could involve using patient status and census data to model noise level. Patient status and census count could both be added to our model as predictors for overall L eq-h values in the MICU. If patient status does have a strong correlation with noise levels it becomes doubly critical to try and reduce noise because studies have shown that patients in the poorest health are more susceptible to the adverse health effects of noise. The limitations in this study include omitting peak sound level measurements and octaveband analyses. Collecting this information could give more insight into the frequencies over which the noise in the MICU is occurring. These data would help in the implementation of any engineering controls such as shielding noisy equipment, changing ventilation systems, or installing sound-absorbing materials in celling or floor tiles. Different materials can absorb and reflect sound at different frequencies, so knowing where the most noise is occurring would allow to us to choose the most appropriate shields. We also did not evaluate specific sources of noise. Some other studies have recorded noise levels for conversations, televisions, and other equipment found in the ICU. Completing spot checks with a hand-held sound level meter could help better 22

34 identify noise sources and estimate their contribution to overall noise. As this quality improvement study is an ongoing project, there is ample opportunity to incorporate our results with ideas for improvements in order to continue working toward reducing noise levels in the MICU. Conclusions A behavioral intervention consisting of educating nursing staff on ways to limit noise during their activities was ineffective in reducing noise in the MICU. Although a significant reduction in L eq-h was observed between intervention phases, the reduction was minimal (1.4 dba). All L eq-h exceed WHO recommendations and frequently exceeded project goals during the day (50% >55 dba) and at night (62% > 50 dba). Analysis of both L eq-h and one-minute data allowed us to better understand the determinants of patient noise exposure. We originally hypothesized that nursing staff, through conversation and daily activities, were major contributors to noise in the MICU, but our analysis indicates a more general source instead. We now believe the main determinants of noise in the MICU are the HVAC system and some of the medical intervention procedures (e.g., dialysis, high-flow respiratory support). This suggests a more effective intervention to reduce MICU noise would be to use engineering controls to modify the HVAC system and to replace or shield noisy equipment. Future work will involve characterizing these newly identified sources more completely by taking peak measurements and performing octave band analyses. 23

35 Tables Table 1. Estimates from two-level repeated-measures model predicting L eq-h (dba) Model 1 Model 2 Fixed Effects Intercept (grand mean Leq) 54.0* 53.5* (0.92) (1.3) Intervention (0: Pre; 1: Post) -1.4* (0.1) Pod (0: Pod 1; 1: Pod 3; 2: Pod 4; 4: Pod5) -2.7* (0.6) Occupancy (0: Unoccupied; 1: Occupied) 3.2* (0.2) Time period (0: Night; 1: Day) 2.2* (0.1) Position (0: Near; 1: Far) -0.8 (1.4) Intervention*Position 0.6* (0.1) Intervention*Occupied 0.7* (0.1) Pod*Occupied 0.9* (0.1) Pod*Day 0.3* (0.1) Position*Occupied -0.6* (0.1) Error Variance Level * 17.5* (0.21) (0.2) Level-2 Intercept 6.74* 3.7 (3.58) (2.3) Model Fit AIC BIC n = Note: * Statistically significant, p<0.05; ICC=0.23 Values based on SAS PROC Mixed. Entries show parameter estimates with standard errors below in parentheses. 24

36 Table 2. Median Leq-H values (dba) by pod and occupancy status Occupancy Pod Occupied Unoccupied Table 3. Leq-H values (dba) for day and night time periods compared by intervention phase Day (goal: < 55 dba) Night (goal: < 50 dba) Quantile PRE (n=7446) POST (n=8673) PRE (n=3661) POST (n=4315) 5% % % % %

37 Table 4. One-minute measurements (dba) for day and night time periods compared by intervention phase Day (goal: < 55 dba) Night (goal: < 50 dba) Quantile PRE (n=440876) POST (n=510822) PRE (n=217920) POST (n=256380) 5% % % % %

38 Figures Figure 1. Layout of the MICU with Pods 1-5 numbered. Pod 2 was excluded due to low census. Sound Level Meters were placed in a near (N) room and a far (F) room in the remaining pods. Central nurses' stations are shown as black stars; minor stations are shown as open stars. The middle group of rooms at the bottom consists of offices and call stations. 27

39 Figure 2. Boxplot comparing pre-and post-intervention Leq-H values by pod. The horizontal lines in the middle of each box represent the median value and the diamonds represent the mean. The edges of each box represent the interquartile range (the 25 th through 75 th percentiles). Vertical lines at each end of the box extend to the minimum and maximum values, while the circle beyond represent outliers. 28

40 Figure 3. Cumulative frequency plots of Leq-H values for each pod pre-intervention (a) and post-intervention (b). Figure 4. Boxplot comparing pre-and post-intervention Leq-H values by occupancy status. 29

41 Figure 5. Boxplot comparing Leq-H values in each pod during occupied (O) and unoccupied (U) time periods. 30

42 Figure 6. Boxplot comparing pre-and post-intervention Leq-H values by proximity to central nurses' stations (near or far). 31

43 A. Room 1; Comparison day Noise Level (dba) L NC 1L NC unoccupied B. Room 1; Flagged day Hour of Day Noise Level (dba) Resp 5L O2 Admitted x-ray hip asp x-ray dialysis Resp 40% face mask 15L O2 ABG cpap Hour of Day Figure 7. One-minute noise measurements over full days with medical interventions in use. Time periods shown in Panels B, D, and F were identified as having >10 hours of noise >60 dba. Panels A, C, and E show the same rooms one day prior as a baseline comparison. Interventions include oxygen delivery systems such as a face mask, nasal cannula (NC), CPAP, and BIPAP device which are shown in liters per minute (Lpm) and/or oxygen content (%). 32

44 C. Room 2; Comparison day Noise Level (dba) BIPAP 5L NC 10L face mask 40%O D. Room 2; Flagged day Hour of Day Noise Level (dba) _Resp 15L face mask_ Resp 10L O2 Resp 15L O2 BIPAP_ Hour of Day Figure 7 (continued). One-minute noise measurements over full days with medical interventions in use. Time periods shown in Panels B, D, and F were identified as having >10 hours of noise >60 dba. Panels A, C, and E show the same rooms one day prior as a baseline comparison. Interventions include oxygen delivery systems such as a face mask, nasal cannula (NC), CPAP, and BIPAP device which are shown in liters per minute (Lpm) and/or oxygen content (%). 33

45 E. Room 3; Comparison day Noise Level (dba) L NC unoccupied 15L 95% +4L +2L +6L F. Room 3; Flagged day Hour of Day Noise Level (dba) L NC 8L NC 10L NC 10L NC 15L NC 15L NC + 40L 95% face mask Hour of Day Figure 7 (continued). One-minute noise measurements over full days with medical interventions in use. Time periods shown in Panels B, D, and F were identified as having >10 hours of noise >60 dba. Panels A, C, and E show the same rooms one day prior as a baseline comparison. Interventions include oxygen delivery systems such as a face mask, nasal cannula (NC), CPAP, and BIPAP device which are shown in liters per minute (Lpm) and/or oxygen content (%). 34

46 CHAPTER III: CONCLUSIONS A behavioral intervention consisting of educating the nursing staff to limit noise in their activities was ineffective in reducing noise in the MICU. Although a significant reduction in noise levels was observed, the reduction was minimal (-1.4 dba). After the intervention, L eq-h values were above project goals 50% of the time in day (goal = 55 dba) and 62% of the time at night (goal = 50 dba). L eq-h values pre- and post-intervention at the 5 th, 25 th, 50 th, 75 th, and 95 th percentiles differed minimally (less than 1.5 dba). All of the post-intervention L eq-h values were higher than WHO guidelines of 35 dba during the day and 30 dba at night. The minimum L eq-h we measured during our study was 36.4 dba (pre-intervention) during the day and 35.3 dba (post-intervention) at night. Currently, most interventions to reduce noise levels in hospitals involve measuring noise for short periods of time. By logging data for 20 weeks we have tried to overcome some of the limitations presented by small sample size. Our large sample size ultimately led to statistical significance in our analysis which could be misleading because it suggests a marked difference. However the differences were minimal and therefore the intervention cannot be considered effective. The large sample size did, however, allow us to examine a vast range of time periods. Our analysis of hourly L eq-h values over a 20-week period allowed us to better understand the determinants of noise exposure in the MICU and our preliminary assessment into the one-minute data revealed a possibility for a new control strategy: the shielding of loud equipment. Analysis of the L eq-h data indicated that noise in the MICU was a result of a general source, likely the HVAC system, as opposed to our original theory that noise was a result of staff activity. We found that proximity to the nursing stations did not significantly contribute to patient noise exposure based on the fact that we observed no difference in L eq-h between the near and farm rooms. We found that unoccupied rooms had median L eq-h values > 46 dba suggesting a noise source within the rooms that was not a result of staff activity. Furthermore, Pod 1, which 35

47 houses the oldest HVAC system in the MICU, had median L eq-h values >56 dba which is higher than occupied room values in other pods. Analysis of the one-minute noise data indicated the loudest time periods occurred when high-flow respiratory support devices and a dialysis machine were in use. In comparison, high noise levels were not observed on the previous day when this equipment was not in use. Perhaps an effective intervention could be designed to shield this equipment. If this type of equipment is a significant source of noise in the MICU then patient status could be an important predictor of overall noise level as it dictates the equipment used for treatment. Future work needs to look into the association between noise levels and this equipment more extensively. The activity logs should be used to identify any time periods and rooms in which these devices were in use. The noise levels for those identified times need to then be examined to determine if the high levels we found in this analysis are consistent throughout the times where the equipment was used. Sound level meters should be used to measure these devices while they are in operation to record noise levels at the source. Octave band analyses should also be conducted to better characterize in which frequencies noise is occurring. This will greatly help in designing controls for these devices since different materials reflect and absorb noise across frequencies differently. Finally, based on these further observations, efforts should be made to shield the noise from the dialysis machine and the high-flow respiratory support devices. Of course, such efforts would need to ensure that shielding not compromise device effectiveness. Perhaps the behavior modification aspect of our intervention was not successful because staff and visitors are not the main contributors to overall noise levels. Since our loudest times were associated with certain equipment, the next targeted intervention that takes place in the ICU should focus on how to reduce the noise produced by those devices. Since the devices are right in the room with the patient, we know that if we can reduce the device noise we can reduce patient 36

48 noise exposure which should help them to rest more comfortably and heal more quickly which has always been the ultimate goal of this study. 37

49 APPENDIX Appendix A. Training Materials for Intervention A1. Poster displayed in MICU bathrooms targeting staff to raise awareness for QI study 38

50 To our patients and their families We are conducting a quality improvement project in the medical ICU to help our patients have better sleep and wake cycles. We hope this helps you or your loved one recover faster and experience less confusion in the ICU. In the daytime Our ICU team will wake patients up and turn the lights on. Most patients receive physical therapy Monday Friday. Naps are OK but should not last all day. In the night time We try to have our patients bathed and ready for bed by 11 PM. TVs should be turned off. Sleep aides are available, such as white noise machines, ear plugs, and eye masks. What you can do Provide gentle re orientation if your loved one is confused. Limit visitors and children during quiet time. Turn the TV off at night or if it is not being watched. Suggest music or other activities to enjoy. Bring hearing aides or glasses from home. Appendix A 1. Poster displayed in MICU targeting patients and families to raise awareness for QI study 39

51 Appendix A 2. Example of weekly report ed to MICU staff with weekly tips and sample graphs of noise data measured in comparison to project goals 40

52 Appendix B. SAS code for Leq-H data set Descriptive statistics and normality tests Code: options ls=80 ps=90 nocenter date number pageno=1; libname x 'M:\Peters\Research\Projects\2014-Noise- Hospital\01_Analysis\01_one-min'; data temp; set xx.hourleq; proc univariate data=temp plots normal; title 'Hour Leq'; var leq; Output: 41

53 42