Background

Sound levels in the pediatric intensive care unit (PICU) are often above recommended levels, but few researchers have identified the sound sources contributing to high levels.

Objectives

To identify sources of PICU sound exposure.

Methods

This was a secondary analysis of continuous bedside video and dosimeter data (n = 220.7 hours). A reliable coding scheme developed to identify sound sources in the adult ICU was modified for pediatrics. Proportions of sound sources were compared between times of high (≥45 dB) and low (<45 dB) sound, during day (7 AM to 6:59 PM) and night (7 PM to 6:59 AM) shifts, and during sound peaks (≥70 dB).

Results

Overall, family vocalizations (38% of observation time, n = 83.9 hours), clinician vocalizations (32%, n = 70.6 hours), and child nonverbal vocalizations (29.4%, n = 64.9 hours) were the main human sound sources. Media sounds (57.7%, n = 127.3 hours), general activity (40.7%, n = 89.8 hours), and medical equipment (31.3%, n = 69.1 hours) were the main environmental sound sources. Media sounds occurred in more than half of video hours. Child nonverbal (71.6%, n = 10.2 hours) and family vocalizations (63.2%, n = 9 hours) were highly prevalent during sound peaks. General activity (32.1%, n = 33.2 hours), clinician vocalizations (22.5%, n = 23.3 hours), and medical equipment sounds (20.6, n = 21.3 hours) were prevalent during night shifts.

Conclusions

Clinicians should partner with families to limit nighttime PICU noise pollution. Large-scale studies using this reliable coding scheme are needed to understand the PICU sound environment.

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Sources of Sound Exposure in Pediatric Critical Care Units

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Sources of Sound Exposure in Pediatric Critical Care Units

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Notice to CE enrollees

Notice to CE enrollees

This article has been designated for CE contact hour(s). The evaluation demonstrates your knowledge of the following objectives:

  1. Identify sources of sound exposure in the pediatric intensive care unit (ICU).

  2. Explore differences in sound sources during day and night shift and times of high and low sound levels.

  3. Consider interventions to limit nighttime noise pollution and improve sleep quality in the pediatric ICU.

To complete the evaluation for CE contact hour(s) for activity A2451, visit https://aacnjournals.org/ajcconline/ce-articles. No CE fee for AACN members. See CE activity page for expiration date.

The American Association of Critical-Care Nurses is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation, ANCC Provider Number 0012. AACN has been approved as a provider of continuing education in nursing by the California Board of Registered Nursing (CA BRN), CA Provider Number CEP1036, for 1.0 contact hour.

Sleep is crucial for healing during critical illness, but the pediatric intensive care unit (PICU) is not conducive to restorative sleep.1,2  Sound exposure in the PICU is consistently above recommended levels.3,4  The Environmental Protection Agency recommends hospital sound levels below 45 dB,5  whereas the World Health Organization recommends levels less than 40 dB in hospital hallways, 35 dB at the bedside, and 30 dB at night.6 

In contrast, children in the PICU experience an average of 115 minutes per day of sound levels greater than 100 dB,4  with average sound levels ranging from 46 dB to 79.5 dB.4,7  Sleep disturbance can occur at levels above 30 dB, and the Environmental Protection Agency and the World Health Organization recommend hearing protection for sustained exposure to sound levels greater than 85 dB.5,6 

Parents frequently report that the PICU is too loud to allow their children to rest.8  Clinicians and parents identify medical equipment, monitor alarms, and clinical staff conversations as the main contributors to high PICU sound levels.4,9,10  Sound levels associated with mechanical ventilation, monitor alarms, and staff conversations have been recorded at 71, 81, and 91 dB, respectively.11  However, few researchers have combined measurement of PICU sound with continuous identification of the various sources contributing to sound levels. Recently, Naef et al12  created a protocol for measurement of sound levels and sound sources in the adult ICU, which provides a framework for identifying sources of high sound levels in the PICU. Identification of the sources of high PICU sound levels can help clinicians and researchers provide targeted interventions to improve sleep quality in the PICU.

The purpose of this observational study was to identify sources of PICU sound exposure during day (ie, 7 am to 6:59 pm) and night (ie, 7 pm to 6:59 am) shifts, during times of high (ie, ≥45 dB) and low (ie, <45 dB) sound levels, and during sound peaks (ie, ≥70 dB).

We used the Strengthening the Reporting of Observational Studies in Epidemiology guidelines as a framework for reporting study methods and results.13  See Kalvas et al14  for full protocol details.

Study Design

This study was a secondary analysis of continuous bedside video and dosimeter data (n = 220.7 hours) collected between December 2020 and July 2021 during an observational pilot study exploring associations among modifiable PICU environmental exposures (light and sound levels, caregiving patterns), sleep disruption, and delirium.14  We used these data to identify sources of PICU sound exposure in the present study.

Setting and Participants

After obtaining institutional review board approval (IRB #00000034), we collected data from a convenience sample of 12 critically ill children aged 1 to 4 years admitted to 3 PICUs (ie, medical, surgical, cardiothoracic) at a large academic children’s hospital in the midwestern United States. See Kalvas et al14  for inclusion and exclusion criteria. The children’s average age was 20.1 (SD, 9) months. Most participants (83.3%, n = 10) were admitted for respiratory failure. Children had a low severity of illness, with a median Pediatric Risk of Mortality III15  score of 1 (IQR, 0–4.3) and a mean PICU length of stay of 41.4 (SD, 25.3) hours. All children were hospitalized in private rooms. Enrollment occurred within 48 hours of PICU admission. All parents opted-in to our use of their child’s research data for future secondary analyses.

Parents report that the PICU is too loud to allow their children to rest.

Measures

Video and dosimeter recordings were initiated at enrollment and continued until PICU discharge. Signs were posted inside and outside the hospital room as reminders that recording was ongoing. A study team member checked the equipment position and the power supply every 12 hours. At the conclusion of the observation period, data were downloaded onto a secure research drive and then deleted from the research equipment.

Sound Levels

Sound levels were measured with an Etymotic Research Dosimeter (model ER-200DW7; Etymotic Research, Inc) attached to the head of the hospital crib or bed. Sound levels were measured in A-weighted dB (dBA) in 0.22-second intervals, and a continuous equivalent sound level was calculated in 3.75-minute intervals. The accuracy of this dosimeter is ±2.5 dBA.

Sound Source

A Handycam video recorder (Sony) was attached to the top of the foot of the hospital crib or bed with the camera directed toward the child. Recordings were coded by using the Noldus Observer XT software platform (Noldus Information Technology). A coding scheme was developed by using an instantaneous sampling method to identify sources of sound exposure in 1-minute intervals. Given the 3.75-minute dosimeter measurement interval, a 1-minute instantaneous sampling interval was deemed adequate to identify the sound sources contributing to each dosimeter measurement.16,17  At the end of each 1-minute interval, each potential source of sound was marked as present or not present in the previous 15 seconds of recording.

During coding, it became clear that medical equipment continuously created sound, so we redefined the code to include only acute, short sounds (eg, beeps, alarms).

The coding scheme is based on a template developed by Naef et al12  to identify sources of sound in the adult ICU through in-person observation. The first author (L.B.K.) adapted the template for video observation in the pediatric population and broadly separated sounds into human and environmental sources (Table 1). Categories and codes were simplified because most verbal speech and many clinical activities occurred off camera. Vocalizations were identified by source (ie, clinician, family, child), but the number of people contributing to conversation was not quantified. Given the young ages of the children, their vocalizations were classified as verbal or nonverbal, whereas adult (ie, clinician, family) vocalizations were not differentiated as verbal or nonverbal. Patient care activities were separated into caregiving that involved physical touch with the child (ie, patient touch care)14  and all other activities (ie, general activity). Toys were added as a potential sound source. Initially, the sound of PICU machines (eg, intravenous medication pump, telemetry) and the alarms associated with them were collapsed into one category. During coding, it became clear that medical equipment was continuously creating sound. Therefore, the equipment code was redefined as only pertaining to acute, short-lasting (eg, beeps, alarms) sounds, and videos were recoded. If a sound source could not be identified, even with contextual cues (eg, clinician explaining procedure to family), or was not included in the coding scheme, it was coded as “other.”

The coding scheme was developed iteratively through review of study videos and discussion with the second author (T.M.H.), who has expertise in observational video coding. A research assistant trained in use of the Noldus Observer XT software and coding scheme and oriented to the PICU environment performed all video coding. Interrater reliability was established before independent coding. Reliability was measured with Cohen’s kappa statistic.18,19  Given the high level of interrater reliability identified by Naef et al12  for the original coding scheme, a cutoff value of 0.70 was considered acceptable. To ensure ongoing agreement, the first author (L.B.K.) independently coded 13.7% (n = 30.2 hours) of randomly selected video segments.20  During these checks, the first author (L.B.K.) identified systemic discrepancies in coding and reviewed these with the research assistant to ensure ongoing accuracy in video coding.

Statistical Analysis

Video and dosimeter data were imported into the Noldus Observer XT software and time-aligned for video coding and analysis. Videos were coded in segments of 2 to 3 hours. The proportion of intervals in which each source of sound exposure was present was calculated. Videos were separated into day (7 am to 6:59 pm) and night (7 pm to 6:59 am) shifts and proportions were compared between periods of high (ie, ≥45 dBA) and low (ie, <45 dBA) sound levels. Video segments that contained both day and night shifts were assigned to the shift that made up the majority of the video segment. Finally, proportions were calculated for intervals in which sound levels peaked, reaching a level of 70 dBA or higher.

Overall, the most common human sources of sound in the PICU were family vocalizations (38% of observation time, n = 83.9 hours), clinician vocalizations (32%, n = 70.6 hours), and child nonverbal vocalizations (29.4%, n = 64.9 hours; see Figure; Table 2). The most common environmental sources of sound in the PICU were media (57.7%, n = 127.3 hours), general activity (40.7%, n = 89.8 hours), and medical equipment (31.3%, n = 69.1 hours). Other sources of sound were identified in 1.5% of observation time (n = 3.3 hours) and commonly included traffic sounds from outside the hospital.

Day Shift

Video recordings included 116.1 day shift hours (52.9% of observation time). Of these hours, 39.3% (n = 45.7 hours) had sound levels of at least 45 dBA and 8.1% (n = 9.4 hours) had sound peaks of at least 70 dBA. When sound levels were 45 dBA or higher, family vocalizations (66.6%, n = 30.4 hours), child non-verbal vocalizations (61%, n = 27.9 hours), and clinician vocalizations (50.3%, n = 23 hours) were the main sources of human sound (Table 2). When sound levels were below 45 dBA, all human sound sources decreased in prevalence. However, child nonverbal vocalizations (19%, n = 13.4 hours) and family vocalizations (35.5%, n = 41.2 hours) decreased the most.

When sound levels were 45 dBA or higher, media (68.1%, n = 31.1 hours), general activity (57.6%, n = 26.3 hours), and medical equipment (46.5%, n = 21.3 hours) were the main sources of environmental sound. When sound levels were below 45 dBA, all environmental sound sources decreased in prevalence, except for media, which remained at a similarly high percentage of observation time (68.3%, n = 48.1 hours). However, when sound levels were low, environmental sound sources did not decrease in prevalence as much as child nonverbal vocalizations and family vocalizations did.

Although prevalence decreased at night, general activity, clinician vocalizations, and medical equipment sounds were common during night shift.

Night Shift

Video recordings included 103.4 night shift hours (47.1% of observation time). Of these hours, 21.9% (n = 22.7 hours) had sound levels of at least 45 dBA, and 4.7% (n = 4.9 hours) had sound peaks of at least 70 dBA. When sound levels were 45 dBA or higher, child nonverbal vocalizations (56.9%, n = 12.9 hours), family vocalizations (54.4%, n = 12.3 hours), and clinician vocalizations (39.9%, n = 9.1 hours) were the main sources of human sound (Table 2). When sound levels were below 45 dBA, all human sound sources decreased in prevalence. However, child nonverbal vocalizations (14%, n = 11.3 hours) and family vocalizations (20.8%, n = 16.8 hours) decreased the most.

When sound levels were 45 dBA or higher, media (53.5%, n = 12.1 hours), general activity (52.8%, n = 12 hours), and medical equipment (41.3%, n = 9.4 hours) were the main sources of environmental sound. When sound levels were below 45 dBA, all environmental sound sources decreased in prevalence, but not as much as child nonverbal vocalizations and family vocalizations.

Day vs Night Shift

Compared with the night shift, there was a higher percentage of hours with sound levels above 45 dBA (21.9% vs 39.3%) and 70 dBA (4.7% vs 8.1%) during the day shift. During the day, clinician vocalizations (40.4% vs 22.5%), family vocalizations (47.7% vs 28.2%), media (68.2% vs 45.4%), and general activity (48.6% vs 32.1%) were more frequent than at night. Although prevalence decreased at night, general activity (32.1%, n = 33.2 hours), clinician vocalizations (22.5%, n = 23.3 hours), and medical equipment sounds (20.6%, n = 21.3 hours) were common during the night shift.

Sound Peaks

Video recordings included 14.3 hours (6.5%) of sound peaks above 70 dBA. During sound peaks, child nonverbal vocalizations (71.6%, n = 10.2 hours), family vocalizations (63.2%, n = 9 hours), and clinician vocalizations (55%, n = 7.9 hours) were the main sources of human sound (Table 2). Media (59.2%, n = 8.5 hours), general activity (53.7%, n = 7.7 hours), and medical equipment (51%, n = 7.3 hours) were the main sources of environmental sound. During sound peaks, child nonverbal vocalizations (71.6% vs 59.2%), clinician vocalizations (55% vs 46.6%), medical equipment (51% vs 44.1%), and patient touch care (16.3% vs 10.7%) increased the most in prevalence compared with when sound levels were 45 dBA or greater.

Interrater Reliability

At the completion of video coding, average overall interrater reliability between the first author (L.B.K.) and the research assistant was near perfect (κ, 0.98; SD, 0.02). See Table 3 for agreement on individual codes, which ranged from substantial (eg, media: κ, 0.82; SD, 0.40) to near perfect (eg, toys: κ, 1; SD, 0).

Similar to previous studies of PICU sound exposure,3,4  in the present study, sound levels rose frequently above the recommended maximum of 45 dBA. Regardless of shift or sound level, family vocalizations, clinician vocalizations, and child nonverbal vocalizations were the main human sources of PICU sound, whereas media, general activity, and medical equipment were the main environmental sources of PICU sound. Of all sound sources, child nonverbal vocalizations and family vocalizations decreased the most when sound levels were below 45 dBA compared with when sound levels rose above 45 dBA. This finding suggests that families and children were the main sources of PICU sound exposure in the current study. This result is in contrast with previous studies in which clinicians and parents identified family as an infrequent contributor to high PICU sound levels.4,9,10  Family vocalizations may be a therapeutic source of PICU sound, because these sounds indicate family presence and potential interaction with the child for care or comfort. Child nonverbal vocalizations include coughing and crying, and thus the high frequency of these nonverbal cues may be related to the child’s illness or discomfort. Clinicians should empower families to provide comforting interventions to manage distressing symptoms (eg, discomfort, delirium) in their children.21,22 

When sound levels peaked above 70 dBA, child nonverbal vocalizations, clinician vocalizations, medical equipment, and patient touch care increased in prevalence the most compared with when sound levels were 45 dBA or higher. This cluster of sound sources suggests that child discomfort (eg, crying), clinician vocal and physical interventions, and medical equipment alarms are contributing to the highest sound levels reached in the PICU. This finding aligns with the results of previous studies in which clinicians and parents identified medical equipment, monitor alarms, and clinician conversation as main contributors to high PICU sound levels.4,9,10 

There was evidence of day–night sound cycling in the PICU, as there was a lower percentage of hours at 45 dBA or greater during the night shift than during the day shift. Clinicians decreased levels of general activity (eg, emptying trash, opening and shutting drawers), medical equipment alarms, vocalizations, and patient touch care during nighttime hours. However, general activity still occurred during one-third of night shift hours, and medical equipment alarms and clinician vocalizations were present during almost one-quarter of night shift hours. Interventions to decrease high sound peaks and nighttime PICU sound include limiting clinician and family bedside conversation, grouping care activities, offering ear plugs, closing the child’s door if possible, and setting phones or pagers to silent,23  as well as ensuring suitable alarm parameters and discontinuing continuous monitoring when appropriate.24  When possible given the critical nature of illness treated in the PICU, these measures can result in significant reductions in nighttime PICU noise pollution.23 

Media (eg, television, mobile phone, tablet) was the most common source of PICU sound and media sound was present in more than half of all observed hours. However, the percentage of observation time with media sound was not highly different when sound levels were high or low. Although screen media were highly pervasive in the present study, they may not have contributed to high sound levels. Similarly, clinicians and parents report media (eg, television, radio) as an infrequent source of PICU sound.4,9  Nevertheless, screen time is associated with sleep disturbance in children, including prolonged sleep latency and reduced sleep duration.25  Researchers have identified a high level of screen time exposure in hospitalized children,2628  and this exposure should be investigated in future research as a potential contributor to sleep disruption during pediatric critical illness. Clinicians should encourage families and children to limit screen time during nighttime hours.

Regardless of shift or sound level, family, clinician, and child nonverbal vocalizations were the main human sound sources and media, general activity, and medical equipment were the main environmental sound sources.

Similar to the method of Naef et al,12  this modified coding scheme for identifying sources of PICU sound through video observation had a high level of interrater reliability. This finding indicates transferability of the coding scheme from the adult to the pediatric population. Benefits of bedside video observation for sound source identification include decreased clinician, family, and child reactivity to observation, because the video camera was out of eyesight at the top of the foot of the bed, as well as the ability to rewatch video to ensure identification of all sound sources. Limitations of video recording include limited ability to identify sounds occurring off screen. We developed a simplified coding scheme compared with that used by Naef and colleagues for in-person observation. We deemed a 1-minute instantaneous sampling interval to be adequate to sample each 3.75-minute dosimeter measurement interval, but this sampling interval may have missed identification of some sound sources. The timing of sound sources within each sampling point was not recorded, which limits our ability to understand how sound sources are temporally related (eg, family vocalizations frequently follow child nonverbal vocalizations). Placement of the dosimeter at the head of the hospital crib or bed did not account for times when children were out of bed (eg, held in chair). Finally, data were collected at the bedside of 12 young children with low criticality at one large, academic children’s hospital. Large-scale studies with a wider age range of children with differing diagnoses and severity of illness at multiple institutions may better capture the PICU sound environment. Vehicle traffic outside the hospital was a newly identified source of sound that may apply to hospitals located in urban areas and should be included in future versions of the coding scheme. Future investigations should systematically consider whether identified sources of sound are modifiable or therapeutic to better direct targeted interventions to limit PICU noise pollution.

Although PICU sound exposure is known to be consistently above recommended levels, few researchers have combined measurement of PICU sound with continuous identification of the various sound sources contributing to sound levels. In this secondary analysis, we used bedside video and dosimeter data from an observational pilot study to identify sources of PICU sound exposure during day and night shifts, during times of high and low sound levels, and during sound peaks. Family, clinician, and child nonverbal vocalizations were the main human sources of PICU sound, whereas media, general activity, and medical equipment alarms were the main environmental sources of PICU sound. Media was a pervasive sound source, occurring in more than half of video hours. Of all sound sources, child nonverbal vocalizations and family vocalizations decreased the most when sound levels were low, compared with when sound levels were high. During sound peaks, clinician vocalizations and medical equipment were highly prevalent. Although there was some evidence of day–night sound cycling, general activity occurred during one-third of night shift hours, and medical equipment alarms and clinician vocalizations were present during one-quarter of night shift hours. Clinicians should partner with families to engage in interventions to limit nighttime noise pollution in the PICU. Large-scale studies using this highly reliable coding scheme are needed to fully understand the PICU sound environment.

The authors thank Selin Kirbas for her assistance with video coding.

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Footnotes

This article is followed by an AJCC Patient Care Page on page 210.

 

FINANCIAL DISCLOSURES

This work was supported by the National Institute of Nursing Research (F32NR020579, F31NR018586, and T32NR014225) and the National Center for Advancing Translational Sciences (TL1TR002735) of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research was funded by the American Association of Critical-Care Nurses, Council for the Advancement of Nursing Science, Midwest Nursing Research Society Foundation, Ohio Nurses Foundation, Sigma Theta Tau International, and The Ohio State University Graduate School.

 

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