An estimated 30% of individuals with dementia live the end stages of their lives in assisted living facilities or nursing homes (Alzheimer’s Study Group, 2009). Changes that occur during the progression of dementia make it a leading cause of institutionalization among older adults, accounting for 60% to 80% of nursing home placement (Oh, Eom, & Kwon, 2004; Schreiner, Yamamoto, & Shiotani, 2000). Challenging behavioral symptoms may contribute to the decision to place individuals in nursing homes. Yet behavioral symptoms often remain unchanged after relocation to a nursing home.
Estimates of nursing home residents’ agitated behavior, exhibited on a weekly basis, range from 62% to 82% (Oh et al., 2004; Schreiner et al., 2000). The consequences of agitated behavior in the nursing home environment are not benign and need to be addressed. Agitation interferes with care delivery (Nowak & Davis, 2007) and quality of life for people with dementia (Winzelberg, Williams, Preisser, Zimmerman, & Sloane, 2005).
Environmental Effects on Agitation
Specialized dementia care units have been designed with the aim of compensating for the physical and cognitive changes associated with dementia. Some are designed to promote a home-like atmosphere, thereby reducing institutional ambience and promoting resident social interaction. Active participation is thought to promote overall well-being and may reduce aggressive/agitated behavior (Day, Carreon, & Stump, 2000; Kovach, Weisman, Chaudhury, & Calkins, 1997; Wilkes, Fleming, Wilkes, Cioffi, & Le Miere, 2005). Lighting, social settings, walking paths, and personal and communal space have all been found to be related to lower levels of agitated behavior, whereas lack of these architectural features seems to contribute to an increase in agitated behavior in people with dementia (Kovach et al., 1997). Furthermore, nursing home environments are commonly noisy (Hunter, 1994; McClaugherty & Burnette, 2001). However, there has been limited study of the relationship between noise and agitation.
Individuals with moderate to severe dementia may have a limited capacity to understand and interpret their environment (Baker et al., 2001; Zeisel et al., 2003). It is challenging to understand and provide proper stimulation for those with dementia. Many support the theory that too little or too much stimulation is often the underlying source of agitation and disruptive behavior (Kovach, 2000; Kovach & Wells, 2002; Rachal & Kunik, 2006). Coupled with this theory is the notion that adaptation to environmental stressors may be dependent on the individual’s level of competence (Lawton, 1986). One may suggest that both theoretical underpinnings can be combined to provide a framework to begin to understand this problem.
Although cognitive deficits associated with the progression of dementia may result in the display of challenging behaviors such as agitation, it is important to consider other factors that may be contributing to the distressed behavior. Several clinical education articles recommend altering care environments to accommodate for cognitive losses for those with dementia-related illness (Fick & Mion, 2008; Hilgers, 2003; Rowe, 2008). Sensory stimuli overload is believed to be overwhelming for those with dementia, making it difficult for them to make sense of the environment. Environmental changes proposed in the literature are designed to accommodate for the person’s cognitive losses. The aim has been to create an ambient environment through altering or managing stimuli.
Reducing sound levels is frequently recommended as a desired intervention, postulating that it may avert some of the behavioral consequences of the disease (Fick & Mion, 2008; Hilgers, 2003; Rowe, 2008). Recommendations, though well meaning and having great promise, have not been empirically evaluated regarding the specific contribution that sound may have on challenging behaviors and whether changing the variable of sound could make a difference. The aim of this study attempts to fill a gap in the knowledge on whether the environmental stimulus of sound and space affects the challenging behavior of agitation.
In sum, people who reside in nursing home environments may be exposed to overwhelming amounts of stimuli. Conventional nursing home environments may put stress on the cognitive abilities of those with dementia. Sound and space may very well contribute to the stress load for those who are impaired. Theories regarding adaptation in people with cognitive deficits suggest adaptation to stress is dependent on cognitive abilities, suggesting specialized care is needed for those with dementia. Specifically, designing care environments with the aim of compensating for the physical and cognitive changes that occur as the disease progresses is needed. Empirical evidence suggests that sound levels adversely affect the health of individuals (Castle, Xing, Warner, & Korsten, 2007; Clausen, Christensen, Lund, & Kristiansen, 2009; Willich, Wegscheider, Stallmann, & Keil, 2006). What remains unknown is the specific impact of sound levels and the spatial qualities of the sound environment on the behavioral health of those with dementia.
Conceptual Model and Research Questions
The study was conceptualized using the environmental vulnerability theory. Specifically, the conceptual framework of the Environmental Press (EP) model built on Lawton’s (1982, 1986) well-known Environmental Docility Hypothesis guided this study. The model identifies two factors that have the potential to affect behavioral responses: (a) individual competencies, and (b) the environmental demand that is placed on the person. The individual competency specific to this study is cognitive skill, which is altered by the progression of dementia. Environmental factors include those external to the person, such as sound and space, as represented in the physical living environment.
Expanding on this notion, the Progressively Lowered Stress Threshold (PLST) model (Hall & Buckwalter, 1987) was also used to guide this study. The premise of the model suggests that progressive degenerative changes, as a result of Alzheimer’s and related dementias, produce a progressive decline in one’s ability to adapt to stress, which is referred to as stress threshold. This diminished capacity to cope with stressors may be exhibited as dysfunctional behavior if stressors exceed the individual’s threshold. The model posits that variations in the environment may be triggers for behavioral changes in those with less cognitive skill. It was hypothesized that variations in the environment, such as sound and space, may be triggers of agitation. Based on this understanding, the following research questions focused on these aspects of the physical environment and individuals’ cognitive skill for this study:
- Do acute sound levels and personal space predict agitated behavior in nursing home residents with dementia?
- Does the accumulation of sound and personal space exposure predict agitated behavior in nursing home residents with dementia?
This study used a descriptive, cross-sectional, correlational design. Individuals with dementia residing in nursing homes were observed in their natural living environment during 15-minute periods over the course of 1 day. The study and procedures were approved by dual institutional review boards at the university and facility level. Power of attorney designee provided proxy consent for the residents to participate in the study. In addition, resident assent was obtained before observations occurred. Resident assent was determined by an expression of approval. Therefore, if an expression of disapproval or a change in the person’s behavior occurred as a result of being observed, the resident was not included in the study.
Setting and Sample
The target population for this study was nursing home residents with moderate to severe dementia. Participants on eight different nursing units in four southeastern Wisconsin nursing home facilities were sampled. Study participants met the following criteria: lived in the nursing home for at least 1 month, no history of psychiatric illness beyond dementia, diagnosed with moderate to advanced cognitive impairment, scored <20 on the Mini-Mental State Examination (MMSE, Folstein, Folstein, & McHugh, 1975), and all consent requirements were met.
A MMSE score of 19 or below was used because this study was interested in the relationship between the target environmental stressors specifically in people with moderate to severe dementia. People with a history of other psychiatric illness were excluded to avoid psychiatric behavioral confounders. In addition, participants were required to reside in the nursing facility for at least 1 month to reduce the chance of behavioral symptoms due to relocation stress. Four weeks was chosen because it was a feasible time frame to control for relocation stress that may be present during the initial transition to a new living environment (Walker, Curry, & Hogstel, 2007). Participants (N = 53) were not included or excluded based on gender, age, or ethnicity.
Dependent and independent variables were collected simultaneously. Observations occurred in 15-minute sessions, eight times per day, during residents’ awake time (6:00 a.m. to 8:00 p.m.). Observation times included two mealtimes on different shifts and nonscheduled times on both day and evening shifts. The observation clusters allowed measures to be recorded during times where activities may vary from scheduled and nonscheduled times for the resident. Observation clusters were categorized as morning, midday, and evening. The timing of observations were randomly assigned but included all three clustered times of day for each participant.
Sound levels, sources of sound, location, and number of people in the location were collected concurrently during each 15-minute observation. Sound levels were recorded continuously during each observation period. In addition, at the end of each observation session, participants were observed for 3 minutes for levels of agitation. Associations between the environmental characteristics and agitation were estimated at the observation period. Three nonblinded RN research observers collected the data. To ensure accuracy in the measurements, observers were trained for 16 hours on the study protocol and use of each tool.
Dependent Variable. The Wisconsin Agitation Inventory (Kovach et al., 2004), a visual analog scale, was used to measure levels of agitation. This observational instrument yields an overall summed agitation score and is adopted from Cohen-Mansfield’s 29 characteristic behaviors of agitation (Cohen-Mansfield & Werner, 1999). The score is derived from the presence and number of behaviors, duration of the behaviors, and intensity of behaviors. The visual analog scale method requires 3 minutes of direct observation, during which severity of agitation is based on the intensity parameters. The score ranges from 0 (no agitation) to 100 (the most severe level of agitation). The instrument has demonstrated an interrater reliability range of 0.95 to 0.98 following training sessions (Kovach et al., 2004). The three observers were trained on the use of the instrument using prerecorded vignettes. Interrater reliability prior to data collection yielded a 0.92 agreement with three observers. Recheck reliability at 6 months yielded 0.96 agreement with two observers.
Agitation scores were obtained for each observation session, during which special attention was given to the dignity of the participants. Participants were not observed while they were receiving personal hygiene care. Other examples include having the rater discontinue observation and leave the room if the participant changed his or her behavior or reacted negatively to the rater’s presence. Also, if at any time during the observation the participant requested not to be observed, the rater left the room and subsequent observations were agreed on by the participant prior to any repeat observations.
Independent Variables. Choice of independent variables was driven by the EP model that postulates a relationship between environmental stressors, cognitive strength, and behavior. The variables of interest were sound levels and space. Sound levels, sources of sound, location of participant, and number of occupants in the observed space were collected though direct observation during each 15-minute observation session.
Sound levels were measured using SoundPro™ DL 2 (Quest Technologies, 2008) sound decibel meter using A-weighted measure on a slow response interval. An A-weighted sound level is designed to attenuate the lower frequencies that correspond with the frequencies of human speech, and higher frequencies are weighted more heavily because they are more irritating to the human ear (Behar, Chasin, & Cheesman, 1999). Slow impulse mode was used to record the readings that measure the steady state of sound during 1-second observations. The meter was factory calibrated to all specifications of the National Institute of Standards and Technology and manually calibrated before each use according to the manufacturer’s protocol and records sounds ranging from 30 to 130 dBA with measurement accuracy of ±1.5 dBA (Quest Technologies, 2008). To ensure accuracy of the measurement, study procedures were closely followed, and the meter was checked for accuracy of settings at the beginning of each measurement cycle. The meter was mounted on a tripod for stability and adjusted to the participant’s ear level. The meter ran for 15 minutes during the observation session, and the values were digitally recorded on the meter. Minimum, maximum, mean, and peak sound level sums were auto-calculated for each session. The mean sums for each session were used for all regression analyses.
Personal space, which will be referred to as space variable, was established using the following procedure. Location of the participant, room type, size, and number of occupants of the room were documented during each observation period. The space variable depicts, theoretically, the amount of space each individual would be allocated during the observed time. Room size was determined through “as-built drawings” or actual room measurements. The space score was calculated by dividing the room size in square feet by the number of occupants in the space for each observation period.
Control Variables. Three control variables were included in the model based on a review of the literature: mental status, hearing impairment, and visual impairment. Mental status was examined using the MMSE (Folstein et al., 1975), a widely used instrument that measures long- and short-term memory, orientation, attention, calculation, registration, language, praxis, and copying of a shape. Originally the MMSE was created as a tool to differentiate organic from functional organic disorders (Folstein et al., 1975). Subsequently, the tool has been used as a quantitative measure of cognitive impairment and has well-established validity and reliability in research settings.
Hearing and vision data were obtained from existing Minimum Data Set (MDS) documentation collected from the participants’ medical record. This tool was developed by the Health Care Financing Administration to help nursing home facilities develop a comprehensive care plan for each resident and provides a framework for uniform assessment categories, including resident’s hearing and vision status (Rand Corporation, 2008). The instrument categorizes hearing as hears adequately, minimal difficulty, hears with tone adjustments, and highly impaired. Vision categories include adequate, impaired, moderately impaired, highly impaired, and severely impaired.
Data were analyzed using SPSS version 16. Analysis included descriptive statistics, correlational analysis, and hierarchical regression. Data were examined to meet various assumptions necessary for statistical analyses. The data were examined to determine the extent the data approximated a normal distribution. Assessment of normality, linearity, and homoscedasticity were evaluated for each combination of variables. Normality was demonstrated through one-sample Kolmogorov-Smirnov test dependent and independent variables. Test for skewness in distributions were performed, and the MMSE score was the only variable not normally distributed. The raw data demonstrated a positively skewed distribution, with a disproportionate number of participants (n = 17) scoring 0 on the scale. The decision was made to dichotomize the variable into categories based on level of dementia and used as a dummy code. Seventy percent (n = 37) of the sample was severely impaired, while 30% (n = 16) were considered moderately impaired. Bivariate correlations and tolerance values of less than 0.1 for independent variables indicated that multicollinearity was not present in variables included in the model. A p value of 0.05 was adopted as the standard for all two-tailed significance testing.
Hierarchical regression was used to test the associations between both sound and space, with agitation at the observation period level. The following confounding variables were controlled in the regression analysis: mental status, hearing impairment, and visual impairment. To address the research question related to acute exposure, three separate hierarchical regression models were analyzed using the total average sound level exposure for morning, midday, and evening time frames. The total average space variable was also calculated for the corresponding time frames (morning, midday, evening). Next, there was an interest in examining whether the accumulation of sound and space throughout the day influences agitation. To evaluate the effects of cumulative exposure to sound and space, the means for each of the data collection sessions were summed and analyzed with the mean agitation sum for each session.
Fifty-three participants were included in the study (Table 1). The study population consisted of 79% women (n = 42), with a mean age of 86.53 (SD = 9.32 years, age range = 61 to 103). Mean MMSE score was 6.83 (SD = 6.67, range = 0 to 19). Specifically, 70% (n = 37) of the sample scored <13 (severe dementia), and 30% (n = 16) scored 13 to 20 (moderate dementia). Participants lived in the nursing home on average 30.92 months (SD = 32.27, range = 2 to 126), and 34 (64%) lived on a designated dementia unit.
Table 1: Characteristics of the Sample (N = 53)
Overall, participants were observed in three discrete study locations: dining room, bedroom, and shared spaces. Sound levels varied by location: The bedroom space was the quietest space observed, with an average sound level of 51.84 dBA (SD = 6.88), and the dining room was the loudest (60.43 dBA, SD = 4.14), followed by the shared spaces 58.99 dBA (SD = 4.27). Mean sound levels during the eight times of day ranged from 52.91 dBA (SD = 5.44) to 59.46 dBA (SD = 5.19). Space allocated for each participant ranged from 12 to 238 square feet per person. The evening time period showed the lowest space allocation of 64.6 square feet per person (SD = 25.53), whereas midday had the greatest space per person, with an average of 88.3 square feet per person (SD = 33.43). The average space person for all time periods was 78.8 square feet per person (SD = 31.4). Table 2 provides an overview of study variables categorized by time frame. Descriptive details of the environment are reported elsewhere (Joosse, 2011).
Table 2: Descriptive Statistics for Sound, Space, and Agitation
Acute Exposure to Sound and Space
A series of hierarchical linear regression analyses were performed to determine the contribution of variables in predicting agitated behavior. Three separate models were analyzed using morning, midday, and evening time frames of acute sound levels and space. Bivariate correlations and tolerance values of less than 0.1 for independent variables indicated that multicollinearity was not present. There was no evidence of nonlinearity from the scatterplots for each model.
In the first model, mental status, hearing impairment, and visual impairment were entered into the model first as control variables, followed by space, and then morning sound levels. When entered into the model alone, mental status, hearing impairment, and visual impairment accounted for 8% of the variance in agitation (adjusted R2 = 0.076, p = 0.076). Space decreased the adjusted R2 to 6%; however, this change was not statistically significant (ΔR2 = 0.02, p = 0.744). After controlling for mental status, hearing impairment, and visual impairment, morning sound and morning space did not significantly predict morning agitation, F(5, 47) = 1.976, p = 0.100, and adjusted R2 = 0.086.
The second model also used mental status, hearing impairment, and visual impairment as control variables and were entered first, followed by space and midday sound levels (Table 3). When entered into the model alone, mental status, hearing impairment, and visual impairment accounted for 4% of the variance in agitation (ΔR2 = 0.043, p = 0.407). This was not statistically significant. Space increased the adjusted R2 to 0.5%; however, this change was not statistically significant (ΔR2 = 0.024, p = 0.253). Midday sound, after controlling for mental status, hearing impairment, visual impairment, and space, did significantly predict midday agitation, F(5, 47) = 4.312, p ⩽ 0.005, and adjusted R2 = 0.242. Step 3 of the model showed that 19.2% of the variance in midday agitation was uniquely accounted for by midday sound level. This was a statistically significant contribution (ΔR2 = 0.192, p ⩽ 0.001).
Table 3: Midday Sound Levels and Space as Predictors of Agitation (N = 53)
The third model used the same control variables and subsequent evening space variable, followed by evening sound levels. When entered into the model alone, mental status, hearing impairment, and visual impairment accounted for 5% of the variance in agitation (adjusted R2 = 0.050, p = 0.139). This change was not statistically significant. Space increased the adjusted R2 to 7.3%; however, this change was not statistically significant (ΔR2 = 0.040, p = 0.143). Evening sound increased the adjusted R2 to 16.4%. This change was not statistically significant (ΔR2 = 0.020, p = 0.298). Evening sound, after controlling for mental status, hearing impairment, visual impairment, and evening space, did not significantly predict evening agitation, F(5, 47) = 1.848, p = 0.122, and adjusted R2 = 0.164.
Cumulative Exposure to Sound and Space
Next, the study sought to examine the extent that accumulation of sound and space affects agitation. Similarly, hierarchical regression was used to analyze the data specific to the proposed cumulative effects. Mental status, hearing impairment, and visual impairment were entered into the model first as control variables, followed by space, and then cumulative sound level (Table 4). When entered into the model alone, mental status, hearing impairment, and visual impairment accounted for 11% of the variance in agitation (adjusted R2 = 0.110, p = 0.608). Mental status was the only predictor that emerged in this step. Space decreased the overall adjusted R2 to 9%, but this change was not statistically significant (Δ R2 = 0.000, p = 0.956). Cumulative sound, after controlling for mental status, hearing impairment, visual impairment, and space, did significantly predict positive agitation, F(5, 47) = 4.520, p ⩽ 0.002, and R2 = 0.325, adjusted R2 = 0.253. Step 3 of the model showed that 16% of the variance in agitation was uniquely accounted for by cumulative sound level. This was a statistically significant contribution (ΔR2 = 0.163, p ⩽ 0.002).
Table 4: Accumulation of Sound and Space as Predictors of Agitation (N = 53)
Results of this study support the relationship posited in the EP and PLST models, suggesting that those with lower cognitive strength may be less able to adapt to environmental stressors. The exception is the variable of space, where data were insufficient to demonstrate a contribution. After controlling for potential confounding variables of mental status, hearing impairment, and visual impairment, sound was found to contribute to agitation.
Consistent with Vance et al. (2003), hearing and vision were accounted for in the analysis. While Vance et al. (2003) found a significant association with hearing and vision to agitation, the findings of this study did not support this association. Further exploration of this relationship is needed.
This study provides insight into the relationship between environmental stimuli and agitation in people with dementia. Specifically, it contributes to our understanding of the significance of sound and its impact on agitated behavior, as hypothesized through the environmental vulnerability models. The findings of this study suggest that sound levels are an important variable to include in the study of agitation in people with dementia and should be considered when addressing the complexities of challenging behaviors.
Gerdner, Buckwalter, and Hall (2005) examined case profiles using the PLST model and found that environmental sound is a probable cause for increased agitation in select individuals with dementia. These findings suggested a potential relationship between sound and agitation. However, when agitation occurs during a 24-hour period has received little attention in the literature. Hall and Buckwalter (1987) hypothesized that as the day progresses, people with dementia are less able to cope with stressors from external stimuli. Some refer to this phenomenon as sundown syndrome. This syndrome is widely recognized and is thought to be related to the presence of dementia. Symptoms such as reduced attention, altered sleep-wake cycles, and disturbed psychomotor behavior seem to be more evident in the evening hours (Drake, Drake, & Curwen, 1997). According to Dewing (2000), there is a commonly observed tendency for people with dementia to become more confused in late afternoon to evening hours. However, little consensus has been achieved regarding the cause. Acknowledging the observed phenomena of evening agitation and the findings of this study, the relationship between accumulation of sound and agitation in the evening hours requires further study.
There is no known previous study of the relationship supported in the current study. One study examined stress effects of sound on young (ages 24 to 54) healthy individuals and found that 50% (n = 21) of the participants were not able to compensate for the sound exposure over a 6.5-hour experiment (Ising & Michalak, 2004). Lack of compensation was measured by increased levels of noradrenaline. These findings suggest that healthy people may have difficulty with cumulative exposure to sound. Although the findings are subject to interpretation, it argues that individual adaptation to the stressor of sound over time needs further exploration, particularity in the older adult population.
There was an interest in examining and controlling for levels of discomfort in this study. Data were collected using the Discomfort Scale for Dementia of the Alzheimer’s Type. The scale was developed as an objective tool for measuring discomfort in noncommunicative people with advanced Alzheimer’s disease (Hurley, Volicer, Hanrahan, Houde, & Volicer, 1992). The tool is an observation assessment tool that uses a visual analog scale composed of nine behavioral indicators of discomfort. Each indicator is evaluated by the observer on a scale of 0 (no observed discomfort) to 75 (highest level of discomfort observed) based on the frequency, duration, and intensity of the behavior. A level of discomfort for each item is derived from the value attributed from the three previously described components. Observing and assessing the intensity parameters for each of the nine items on the tool yields a quantitative discomfort score. The tool was tested longitudinally for 6 months with 82 participants at two sites (Hurley et al., 1992). Internal consistency yielded Cronbach’s alpha coefficients from 0.86 to 0.89. The interrater reliability of 0.86 to 0.98 and test-retest reliability of Pearson’s r = 0.60 (p < 0.001) was established. Additional psychometric testing has been conducted with similar findings (Miller et al., 1996).
This study was limited in that the sample was small and no control group was used. A larger sample with a control group may lend itself for more robust findings. Convenience sampling was used in this study, which limits generalizability. A cross-sectional design was also used, which involved the collection of data at one point in time. Single-occasion data capture may make it difficult to establish a causal relationship. Future studies may be needed to examine the relationship over time and under different conditions. Non-blinded research observers may bias the findings.
Eligibility criteria for the study included those living in the nursing home longer than 4 weeks to allow for initial transition to a new living environment (Walker et al., 2007). While not all of the relocation stress may have been controlled through a 4-week time frame, declines in cortisol levels begin as early as 4 weeks, suggesting that adjustment to a move to a nursing home has begun (Hodgson, Freedman, Granger, & Erno, 2004). However, others have suggested that assimilation to the new environment may exceed this time frame (Dimond, McCance, King, Benoliel, & Chang, 1987; Kao, Travis, & Acton, 2004). Future studies may consider extending this time to control for the potential of relocation stress behavioral responses.
Although the constructs of agitation and discomfort have been considered separate constructs, there may be some overlap. In this study, there was a moderately strong (r = 0.704, p < 0.0001) and significant correlation between agitation scores and discomfort scores with an established assessment tool for people with dementia. The findings suggest the possibility that the two scales maybe measuring similar factors that could be present in both agitation and pain. Therefore, the decision was made not to include both variables in the model.
Kong (2005) attempted to clarify the concept of agitation. Based on the findings from an extensive review of the literature, Kong suggested there are five critical attributes of agitation in dementia: excessive, inappropriate, repetitive, nonspecific, and observable. However, the boundaries of these attributes have been blurred or expanded by including aggression, restlessness, resistance to care, behavior associated with sleep disorders, and abusive behavior in the evaluation of agitation. Some of the expanded definitions may pose problems when attempting to use established tools. Some evidence suggests that discomfort may be a mediating antecedent of agitation (Ragneskog, Gerdner, Josefsson, & Kihlgren, 1998). It should be considered that well-established tools, which measure discomfort and agitation, may very well measure common constructs. A recent study that measured pain and agitation discovered that the Pittsburgh Agitation Scale and the Discomfort Scale for Dementia of the Alzheimer’s Type may be measuring similar constructs (Zieber, Hagen, Armstrong-Esther, & Aho, 2005). They found that both tools shared some common items, such as motor restlessness and vocal agitation. These common factors may pose significant measurement error when attempting to quantify distinct variables in a study.
Based on the findings presented in this study and emerging literature on the constructs of agitation and discomfort, future studies are needed to examine well-known and well-used scales for these common constructs. Items such as motor restlessness, tension, and vocalization, which could represent agitation alone, may be agitation as an expression of pain. The similarity of the constructs may have overlapping contributions with the independent and dependent variable, causing results of some studies to be skewed. Future research is needed to explore the contribution of overlapping symptoms of pain and agitation—with tools that provide greater sensitivity and specificity—in order to examine true differences in the data.
Nursing home environments are multifaceted, and this study only addressed one facet of the complexity of sound in this type of environment. The extent to which differences in environmental features, facility operations, and quality of sound could potentially influence agitation levels has yet to be explored. Other facets include, but are not limited to, organizational flow, acoustical ambience, organizational structure, policy/procedures, and human influences. Additional exploration may be needed to fully understand the implications of sound and space in a nursing home environment.
This exploratory study has potential implications for nursing. The findings raise the possibility that the expression of agitation may be a method of communicating that the environment is too loud. Agitated behavior may be a response to the sound stimuli. Therefore, it would seem important for nursing staff to evaluate levels of sound and the potential impact sound may have on levels of agitation. Nursing staff need to increase their awareness on how the sounds they create may affect levels of agitation in those for whom they are providing care. Nursing education linking the two concepts for frontline caregivers may help in reducing unwarranted sound levels.
The findings of this study provide support for the relationship between sound and agitation among nursing home residents with dementia. Furthermore, the findings also suggest that the accumulation of sound throughout the day predicts agitation. This study offers support for ameliorating sound levels in the nursing home setting. It appears plausible that the timing and accumulation of sound may affect individuals differently. Subtle agitated behavior may be a clue that can inform practitioners when the stress threshold of a resident with dementia has been exceeded. In addition to providing well-established interventions such as pain management, special consideration is needed to reduce levels of sound with the aim of preventing or reducing agitation. As the numbers of nursing home residents with dementia increase, more nursing research is needed on the important issue of sound in the environment where people with dementia reside. In particular, experimental studies that could provide more evidence regarding acceptable versus toxic levels of sound are needed. Interventional studies, which may provide insight into the potential benefits of tailored interventions, with the aim of controlling or managing sound for those affected, are desperately needed for this problem and this population.
Older adults with dementia deserve a care environment that matches and accommodates for the cognitive losses they experience. We owe it to these older adults to explore and better manage the cognitive losses and noncognitive behavioral symptoms associated with the debilitating diseases found within the dementia spectrum. However, additional research is necessary to clarify potential environmental changes that could make a difference.
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Characteristics of the Sample (N = 53)
|Characteristic||Mean (SD), Range|
|Age||86.53 (9.32), 61 to 103|
|Length of stay (months)||30.92 (32.27), 2 to 126|
|MMSE score||6.83 (6.67), 0 to 19|
|Resides in dementia unit|
| Yes||34 (64)|
| No||19 (36)|
| Severely impaireda||37 (70)|
| Moderate impairedb||16 (30)|
| Adequate||39 (74)|
| Minimal difficulty||9 (17)|
| Hears with tone adjustment||3 (6)|
| Highly impaired||2 (4)|
| Adequate||36 (68)|
| Impaired||6 (11)|
| Moderately impaired||3 (6)|
| Highly impaired||7 (13)|
| Severely impaired||1 (2)|
Descriptive Statistics for Sound, Space, and Agitation
|Variable||Time||Mean (SD), Range|
|Sound (dBA)||Morning||55.6 (10.78), 24 to 89.6|
|Midday||57 (10.67), 27.2 to 88.7|
|Evening||58 (13.97), 25.3 to 87.8|
|Space (sq. ft.)a||Morning||83.49 (36.80), 15 to 170|
|Midday||88.33 (33.43), 35 to 180|
|Evening||64.61 (25.53), 24 to 128|
|Agitationb||Morning||57.19 (25.92), 0 to 100|
|Midday||50.94 (30.99), 0 to 100|
|Evening||70.85 (29.63), 0 to 100|
Midday Sound Levels and Space as Predictors of Agitation (N = 53)
|Step and Predictor Variable||AdjustedR2||ΔR2||β||t||pValue|
| Space middayb||0.049||0.024||−0.158||−1.157||0.253|
| Space middayb||−0.109||−0.886||0.380|
| Sound middayc||0.242||0.192*||0.463||3.629||0.001|
|F(5, 47) = 4.312, p ⩽ 0.005|
Accumulation of Sound and Space as Predictors of Agitation (N = 53)
|Step and Predictor Variable||AdjustedR2||ΔR2||β||t||pValue|
|F(5, 47) = 4.520, p ⩽0.002|