Quality of Life in People With Severe Dementia and Its Association With the Environment in Nursing Homes: An Observational Study

Quality of Life in People With Severe Dementia and Its Association With the Environment in... Abstract Background and Objectives Theoretical models propose the environment as a factor influencing the quality of life (QoL) of nursing home residents with dementia. This study investigates whether the observed QoL differs depending on the type of care unit. Research Design and Methods DemenzMonitor is an exploratory, observational study involving annual data collection in German nursing homes (2012–2014). For this analysis, we selected residents with a recorded diagnosis of dementia and severe cognitive impairment. QoL was measured with the proxy assessment QUALIDEM. Four care unit types were investigated: large integrated, large segregated, small integrated, and small segregated. Results We did not find a significant difference between the care units. During the 2 years, the observed QoL was not affected by any of the care unit types in a statistically significant or clinically relevant manner. However, a significant interaction effect between time and care unit types was found. Discussion and Implications Structural and organizational characteristics of care units, which in turn have implications for residents characteristics and the quality of care, may influence the QoL of residents. This may explain the interaction we observed. Dementia, Environment, Observational study, Structural characteristics, Quality of life Background and Objectives Improvement in quality of life (QoL) is considered a major goal of the “culture change” movement that developed during the last 30 years in nursing homes (Grabowski et al., 2014). The change constitutes a renunciation from the former medical view on aging towards a more person-centered perspective that values each person as a whole. Primarily described in the United States, the implementation of the culture change movement can also be observed in other Western countries, such as Germany (Michell-Auli, Kremer-Preiss, & Sowinksi, 2010). The nursing home culture change movement was driven by the implementation of different care models that are characterized by an adaptation of the environment, among other things. It is believed that a physical and social environment that adapts to the needs of people with dementia can improve their QoL (Holmes, Teresi, & Ory, 2000). Among other characteristics, nursing home-based care facilities for people with dementia differ from other care facilities in the structure and size of the care unit. Typically, dementia care units are exclusively inhabited by residents who have been diagnosed with dementia (segregated concept). A small size is recommended for these care units to support a home-like living environment and to minimize distress (Fleming & Purandare, 2010). Dementia care units are usually integrated into traditional nursing homes and are not necessarily operated as discrete units (Maslow, 2001). Examples of these care models are Alzheimer’s special care units in the United States (Grant & Ory, 2000) and dementia group living facilities in Germany (Michell-Auli et al., 2010). Theoretical Background The connection among the environment, health and behavior is the subject of various ecological models (Richard, Gauvin, & Raine, 2011). In these models, the environment is considered an “open-ended concept, that includes all that is external to and potentially or actually influential upon an object of investigation” (McLaren & Hawe, 2005). Prevailing ecological models regard the environment as multidimensional and dynamic, and they take into account both distal and individual characteristics (Krieger, 2001; Richard et al., 2011). A similar ecological model used by Lawton and colleagues to study the process of aging (Lawton & Nahemow, 1973) is clearly recognizable in their later works in which the environment has been identified as a factor influencing QoL: “Quality of life is the evaluation, by both subjective and social-normative criteria, of the behavioral and environmental situation of the person” (Lawton, 1994). This theoretical work focused on the development of dementia-oriented design features in nursing homes and had a great influence on the development of dementia-specific care units in the United States (Lawton, 2001). Empirical Evidence on the Relationship Between the Environment and QoL A growing body of evidence supports the influence of the physical environment on residents with dementia in long-term care settings (Chaudhury, Cooke, Cowie, & Razaghi, 2017). Evidence from correlational studies supports a relationship between environmental characteristics and QoL in nursing home residents with dementia. Temperature, noise and lightning influences (Garre-Olmo et al., 2012) QoL, and an environment that facilitates activities, offers familiarity, and provides adequate space and opportunities to participate in domestic activities (Fleming, Goodenough, Low, Chenoweth, & Brodaty, 2016) affects health-related QoL. One study found that in dementia special care units, the staff were more permissive of problematic behavior, and they frequently encouraged activities and provided physical contact and positive attitudes (Zimmerman et al., 2005). Hence, a positive relationship was found in terms of the health-related QoL in nursing homes with specialized workers and more staff training. This study also showed that a better rated environment was positively related to the observed QoL but not to the QoL reported by caregivers (Zimmerman et al., 2005). Furthermore, in a cross-sectional study, Nakanishi et al. (2012) showed a higher QoL for residents from group living care units compared to that in residents from traditional care units (Nakanishi, Nakashima, & Sawamura, 2012). In contrast, Crespo et al., in their cross-sectional study, demonstrated a lower health-related QoL for residents from dementia-specific special care units than for residents from traditional care units (Crespo, Hornillos, & Gomez, 2013). However, Mjørud et al. (2014) found no influence of the type of care unit on the QoL of residents with dementia over ten months. The principal of living in small facilities resp. small groups is strongly emphasized for people with dementia, although clear evidence for an effect of facility size on residents outcomes is lacking (Fleming & Purandare, 2010). Evidence from longitudinal quasi-experimental studies comparing small group living environments with traditional care units found no clear superiority in the QoL of residents living in any one of these settings (de Rooij et al., 2012; te Boekhorst, Depla, de Lange, Pot, & Eefsting, 2009; Verbeek et al., 2010). In these studies, only single subscales of the QoL instruments differed between the groups. Because the size of a care unit is linked with other organizational characteristics like staff organization and care principles like providing a homelike atmosphere, the individual influence is difficult to evaluate (Fleming & Purandare, 2010). Previous investigations on German nursing homes showed differences in environmental factors between different care unit types such as staff ratios, organization of staff and quality of care (Palm, Bartholomeyczik, Roes, & Holle, 2014a; Palm, Sirsch, Holle, & Bartholomeyczik, 2017; Palm, Trutschel, Simon, Bartholomeyczik, & Holle, 2016). These differences may have an influence on the QoL of residents. Because randomized experimental studies cannot be performed to investigate these influences, we observed the QoL of nursing home residents from different care unit types. Aim and Research Question This manuscript reports findings on the association of the environment with the QoL of residents with severe dementia in German nursing homes observed over a period of 12–24 months. We focused on residents with severe dementia because the influence of environmental factors may be of particular relevance for this group: people with severe dementia are more strongly exposed to and influenced by their environment than fellow residents who are still able to move around, perform activities or more clearly express their needs (Fleming, Kelly, & Stillfried, 2015). We investigated data from residents from four care unit types differing in size (small vs large) and concept (integrated versus segregated). We sought to answer the research question, if the average QoL of residents with severe dementia is associated with the type of care unit they are living in. Because QoL is a multidimensional construct that is influenced by a range of factors, we developed a model to map our assumptions of correlations and guide our selection of variables (Figure 1). Based on our theoretical assumptions, we selected individual characteristics that can influence the QoL of residents but are not likely to be associated with the care unit type, such as age, gender, length of stay in the nursing home, and frequency of visits by relatives/friends. Based on empirical evidence, we selected individual characteristics that are likely to be associated with QoL, such as dependency (Beerens et al., 2014), cognition (Hoe et al., 2009) and behavior (Mjørud et al., 2014), for analysis. These variables can also be associated with the type of care unit. Figure 1. View largeDownload slide Research model. Figure 1. View largeDownload slide Research model. This study is part of the DemenzMonitor study (Palm, Köhler, Schwab, Bartholomeyczik, & Holle, 2013), which has an overarching aim of exploring resident- and facility-related factors and covariates that are associated with the behavior and QoL of residents with dementia living in German nursing homes. Research Design and Methods Study Design This observational longitudinal study involves annual data collection in an open cohort at three time points. Participants and Sampling Nursing homes were eligible to participate if they were approved by the German long-term care insurance. The nursing homes decided on the number of care units they wanted to offer for participation in the convenience sample. We aimed to include every resident with or without dementia in the care units. Because participation was voluntary, the inclusion of all residents could not be guaranteed. We selected the patient cohorts based on defined criteria. The inclusion criteria were as follows: (a) Dementia diagnosis: a documented medical diagnosis of dementia (b) Dementia severity: an advanced severity of dementia rated by assessors within the study period over all observed time points (c) Complete QoL assessment at a minimum of one time point We excluded residents based on the following criteria: (d) Missing data on care unit concept or size (e) Residents living in a care unit that changed its concept after the first measurement (f) Residents who moved to another care unit (g) Residents who lived in the care unit for fewer than 28 days Measurements Nursing Home Characteristics We assessed the variables of provider (profit/nonprofit) and size (large for > 100 number of beds/small for ≤ 100 number of beds). Care Unit Characteristics We collected data on the size and concept (integrated or segregated). We defined the size of a care unit as large when more than 15 beds were offered. An integrated concept means that residents with and without dementia were living in those care units, and segregated means that only residents with dementia were living in those care units. In accordance with previous studies (Palm, Bartholomeyczik, Roes, & Holle, 2014b), we stratified the data into four care unit types based on their size and concept: 1. Large integrated 2. Large segregated 3. Small integrated 4. Small segregated For the analysis, we defined our study groups based on the care unit types and investigated their influence on the dependent variable, QoL. Resident Characteristics To measure QoL, we used the QUALIDEM questionnaire that was initially developed in the Netherlands and translated into German (Dichter et al., 2013; Ettema, Droes, de Lange, Mellenbergh, & Ribbe, 2007) (English version is available under: https://www.dzne.de/fileadmin/user_upload/editors/QUALIDEM_User_Guide_2016_final_30.06.2016.pdf). The instrument can be administered to people with mild to very severe dementia; for the latter group, a shorter version of the original instrument is recommended (Ettema et al., 2007). The QUALIDEM is a proxy-rating instrument that assesses the QoL of people with severe dementia with 18 items in six different subscales: “care relationship” (3), “positive affect” (4), “negative affect” (2), “restless tense behavior” (3), “social relations” (3), and “social isolation” (3). Each scale consists of a different number of items (in brackets); to calculate the total score, we divided the summed score from each subscale by the number of items and summarized them. Previous testing revealed satisfactory scalability and intra-rater reliability, but there were problems with internal consistency (Dichter et al., 2013; Dichter et al., 2014). We evaluated internal consistency with our data and found weak Cronbach’s α values (<0.5) for the subscale “social relations” (see Table A1 in the Supplementary Appendix). To achieve strong inter-rater reliability, more than one proxy-rater is recommended (Dichter et al., 2014). To rate dementia severity, we used the proxy-rating Dementia Screening Scale (DSS) that was developed in German for use in nursing homes (Köhler, Weyerer, & Schaufele, 2007). The DSS contains 7 items to assess orientation and memory skills (range 0–14; higher values indicate stronger impairment). The DSS was validated against three established dementia screening instruments (Mini-Mental Status Examination, Clinical Dementia Rating, and Dementia Scale of the Brief Assessment Schedule) in 598 nursing home residents. We used the recommended cutoff value to identify residents with severe dementia (DSS > 7) (Köhler et al., 2007). To assess impairments in physical functions and self-care abilities, we used the Physical Self Maintenance Scale (PSMS) (Lawton & Brody, 1969) (range 6–30). Neuropsychiatric symptoms were assessed with the Neuropsychiatric Inventory Questionnaire (NPI-Q) (Kaufer et al., 2000) (range 0–36). We also collected data on patient age, sex, length of stay in the nursing home (categorized as short [≤3 months] and long [>3 months] stay), the number of external visitors and the frequency of visits to residents during the week before assessment (visit score: range 0–16). More details regarding the measurements can be found in the study protocol (Palm et al., 2013). Data Collection Data collection was performed each year in May (from 2012 to 2014). The head nurses at the respective nursing homes collected the nursing home and care unit data. The assessments on residents were performed by the professional caregivers in the care units who were mostly involved in the care of the respective residents (registered nurses, certified nursing assistants). Based on the recommendations of Ettema et al. (2007) and Dichter et al. (2014), the assessment of the QUALIDEM questionnaire was performed by two professional caregivers of the care units. One staff member of each participating care unit received a one-day training on how to use the assessments; this person either assessed the data themselve or guided additional persons involved in the data collection. Statistical Analysis The baseline characteristics of nursing homes, care units and participants were described using relative frequencies or means (± standard deviations). To answer our research question, a linear mixed model was used to estimate the expected values of the dependent variable QUALIDEM total score and the subscales of the instrument (Brown & Prescott, 2006). Within the model, the fixed effects (independent variables) were time, study group, and the interaction Time × Study group. The random effects were clusters (care units) and participants, which were adjusted for cluster correlations and repeated measurements. Repeated measurement was adjusted by covariance patterns (Brown & Prescott, 2006) with compound symmetry (CS) structure. Backward algorithms (using a p value < .05) were used to select covariates and confounders that could explain the variation within the data. Following this procedure, the final model was adjusted for the remaining covariates (size of nursing home, length of stay, PSMS score, and NPI-Q score) in accordance with the literature. The variables age and DSS score were associated with the dependent variable in univariate models; however, as they were also correlated with other covariates, they were not included in the model. The expected values of the outcomes of time-study group strata were estimated by model-based least square means and presented graphically with 95% confidence intervals (CIs) (based on mean values of the covariates in the model). The analysis was repeated for all subscales of the instrument using the model of the primary outcome analysis. For the total QoL score only, type III ANOVA (two-sided) was performed for the primary fixed effects of time, study group, and their interaction and the covariates. The level of significance was set at 5%. For the subscales, we abstained from evaluating significance to avoid multiple testing. Because of the high number of nursing homes that dropped out of the study, we constructed a second sample for sensitivity analysis that included data only from residents of care units (n = 31) in nursing homes (n = 16) that participated at all three time points of the study within 2012–2014. All the described analyses were conducted for both samples. Statistical analyses were performed using R statistical software version 3.2.4 (www.R-project.org). Ethics The ethics committee of the German Society for Nursing Science reviewed and approved the study protocol (October 2011). The ethical considerations and procedures to obtain informed consent are described in the study protocol (Palm et al., 2013). Results Participants During the whole study period, we recruited 66 German nursing homes with 140 care units and 2,906 residents over a period of three years (including the number of replaced dropouts). We excluded residents without a diagnosis of dementia or severe cognitive impairment (n = 1,489), residents living in the nursing homes for less than 28 days or having an incomplete QoL assessment at every time point (n = 29) and residents with missing data or changes regarding the care unit (n = 20). Overall, the main analysis was performed in 1,368 (47.1%) residents in 134 care units. Of 134 care units, 31 participated at three times, 39 two times (1 of these participated not at consecutive data collections) and 64 care units participated one time. The sensitivity analysis was performed in 390 residents (13.4%) from 31 care units who participated at all three data collections. Figure 2 shows the evolution of the sample size over the three time points. Figure 2. View largeDownload slide Flowchart. Figure 2. View largeDownload slide Flowchart. The results from the descriptive analysis are shown in Table 1. Table 1. Characteristics of Nursing Homes, Care Units, and Participants Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Note: Data are reported as n (%) or mean (±SD). aBased on the first measurement. bMissing values PSMS score: n = 10. cMissing values NPI-Q score: n = 19. dNo missing data regarding QoL. View Large Table 1. Characteristics of Nursing Homes, Care Units, and Participants Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Note: Data are reported as n (%) or mean (±SD). aBased on the first measurement. bMissing values PSMS score: n = 10. cMissing values NPI-Q score: n = 19. dNo missing data regarding QoL. View Large The descriptive results from the sample that we used for the sensitivity analysis are shown in Table A2 in the Supplementary Appendix. Results of the QUALIDEM Total Score Main Analysis In Table 2, the model-based least square means for the QUALIDEM total score are reported for each time point in each study group. Table 2. Model-based Least Square Means for All Study Groups and Time Points (main analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.562 (0.148, 0.976)*; length of stay (long), 0.418 (0.076, 0.76)*; PSMS, −0.119 (−0.144, −0.095)**; and NPI-Q, −0.363 (−0.383, −0.342)**. Variance between cluster: 0.88; Variance within cluster: 0.84; Residual variance: 2.83 aWithout missing data. *Significant at 95% CI. **Significant at 99% CI. View Large Table 2. Model-based Least Square Means for All Study Groups and Time Points (main analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.562 (0.148, 0.976)*; length of stay (long), 0.418 (0.076, 0.76)*; PSMS, −0.119 (−0.144, −0.095)**; and NPI-Q, −0.363 (−0.383, −0.342)**. Variance between cluster: 0.88; Variance within cluster: 0.84; Residual variance: 2.83 aWithout missing data. *Significant at 95% CI. **Significant at 99% CI. View Large We did not find significant differences in the QUALIDEM total score between the study groups, but we observed a significant interaction between time and study group, indicating that the impact of time depends on the type of the care unit. As Figure 3 shows, the mean QoL in the group of residents in large integrated care units was higher in 2013 compared to that in 2012 and 2014 (roof-shaped graph). The other groups displayed lower mean values over the three time points. However, the difference between 2012 and 2014 was less than one point for each study group on a scale of 0–18; the largest negative difference was found in the small segregated group (−0.80; CI = −1.79, 0.19), and a positive difference was found in the large integrated group only (0.08; CI = −0.32, 0.47). Figure 3. View largeDownload slide Course of the total quality of life score in all study groups (main analysis). Figure 3. View largeDownload slide Course of the total quality of life score in all study groups (main analysis). Significant differences were observed between nursing homes of different sizes and for changes in the PSMS and NPI-Q scores. The model indicates that residents from large nursing homes (>100 beds) have a higher QUALIDEM total score than residents from small nursing homes; the estimate is 0.56 points (CI = 0.15, 0.98). Additionally, the QUALIDEM total score significantly decreases with higher PSMS and NPI-Q scores. Residents who stayed longer than three months in the nursing home also had a higher QUALIDEM total score than residents with a shorter stay (estimate 0.41; CI = 0.07, 0.76). Sensitivity Analysis A significant interaction (Time × Study group) was found only in the main analysis but not in the sensitivity analysis that included only the care units participating at every time point. The stable number of participating care units and the unchanged composition of the four study groups may explain why there was no statistical significant interaction between time and study group. The small number of participants in each group and the resulting large CIs may explain the lack of significance. In the sensitivity analysis, the difference in QoL in large integrated care units is estimated to be comparable to that observed in the main analysis of the full sample (roof-shaped graph). In contrast to the main analysis, the large segregated group showed this trend in the sensitivity analysis. However, in the sensitivity analysis, all study groups had almost the same average QUALIDEM total score, but the CIs were variable. The results from the sensitivity analysis are shown in Table 3 and Figure 4. Table 3. Model-based Least Square Means for All Study Groups and Time Points (sensitivity analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.249 (−0.633, 1.131); length of stay (long), 0.648 (0.063, 1.232)*; PSMS, −0.104 (−0.141, −0.066)**; and NPI-Q, −0.354 (−0.389, −0.319)**. Variance between cluster: 0.92; Variance within cluster: 1.21; Residual Variance: 2.55. aWithout missing data. *Significant at 95% CI. **Significant at 99% CI View Large Table 3. Model-based Least Square Means for All Study Groups and Time Points (sensitivity analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.249 (−0.633, 1.131); length of stay (long), 0.648 (0.063, 1.232)*; PSMS, −0.104 (−0.141, −0.066)**; and NPI-Q, −0.354 (−0.389, −0.319)**. Variance between cluster: 0.92; Variance within cluster: 1.21; Residual Variance: 2.55. aWithout missing data. *Significant at 95% CI. **Significant at 99% CI View Large Figure 4. View largeDownload slide Course of the total quality of life score in all study groups (sensitivity analysis). Figure 4. View largeDownload slide Course of the total quality of life score in all study groups (sensitivity analysis). Results for the Subscales For the subscales, we show all results from both the main and sensitivity analyses in tables in the Supplementary Appendix (Tables A3–A8). Discussion and Implications In summary, we did not find significant differences among the groups, but we observed a significant interaction between time and study group, indicating that the impact of time on the observed QoL of residents with severe dementia depends on the care unit types favoring large integrated care units. Several other studies failed to show a clear effect of certain care unit types, such as small-scale living environments, on QoL (de Rooij et al., 2012; te Boekhorst et al., 2009; Verbeek et al., 2010). Additionally, our results could not confirm the assumption that the type of the care unit has any great influence on the QoL of residents. Another explanation for the lack of association between the type of care unit and QoL could be that certain features of care units—such as the attitude and education of the staff as well as the quality of care provided—probably have a greater impact on QoL than the care unit type (Lai, Yeung, Mok, & Chi, 2009). In addition, although empirical evidence supports the assumption that these features are more often present in segregated care units, we do not have a clear idea regarding the specifics of these features. For example, from a previous study, DemenzMonitor, we know that some segregated care units have a higher number of registered nurses and more educated managers (Palm et al., 2014b). We speculated that this observation could have influenced the care provided in these units. However, this assumption is difficult to prove, as our own data are not consistent in this respect (Palm et al., 2016; Palm et al., 2017). The data from studies in other countries on the other indicators of QoL with regard to the care provided in certain unit types are also inconsistent (Cadigan, Grabowski, Givens, & Mitchell, 2012). However, the significant interaction demonstrated by our results for large integrated care units indicate a slightly different trend in the observed QoL of residents than that for other care unit types. This finding requires an explanation, as it contradicts the current preference for small-scale specialized environments for people with dementia. In our study, QoL was assessed by caregivers. Their ratings can be influenced by several factors that are likely associated with their institution, such as work stress, distress, and burnout, as well as the perceived quality of care (Robertson et al., 2017). In a cross-sectional study, Pekkarinen et al. (2004) showed that work stressors, unit size, and QoL are related to one another, and they assumed that unit size has an indirect effect on QoL because it is correlated with work stressors. Thus, they concluded that the larger the care unit, the more prominent work stressors are and the poorer QoL is (Pekkarinen et al., 2004). In a subsequent study, Pekkarinen et al. (2006) showed that work stress related to the behavioral problems of residents can be reduced by specialization into segregated care units. However, our results point in another direction and indicate that caregivers from large units perceive the QoL of their residents with severe dementia as being slightly better than do caregivers from small units. Because we did not collect data on work stress or other work-related variables that may affect the staff’s perception of the residents’ QoL, we can only assume that work-related variables may explain our results. Another explanation for the unexpected finding may be that staff members in large care units are organized differently than those in small units (staff turnover, staff-resident ratios), which in turn has an impact on how well the staff knows the resident and performs the QoL rating. Limitations Because of the methodological shortcomings of the study, our findings must be interpreted with caution. The DemenzMonitor study is exploratory and does not aim to test statistical hypotheses. It can be used only to investigate associations, not to obtain causal inferences. Therefore, no power calculation was performed to determine the sample size required for the detection of certain effects. The nonsignificant results must also be interpreted with this perspective in mind. Another limitation of the study is the incomplete recruitment in the care units, primarily because of the lack of informed consent. Therefore, we had to exclude a number of residents in some of the care units. The DemenzMonitor was intentionally designed as a longitudinal study aiming to investigate the influence of environmental variables on the changes in QoL. Because of the high number of nursing homes that dropped out of the study, we were not able to calculate reliable estimates of changes (of n = 1,368 participants n = 352 participated at two and n = 154 at three data collections). Another limitation is that we selected our study cohort based on certain criteria, so our results cannot be generalized to all nursing home residents. The lack of remarkable differences in QoL detected may also be explained by the method of assessment of QoL. The QUALIDEM assesses overall QoL with a focus on psychosocial aspects. Previously, relationships between the environment and QoL were measured with models that assess health-related QoL (Fleming et al., 2016; Zimmerman et al., 2005). Other studies that used the QUALIDEM questionnaire also did not find significant group effects on the total QoL score (de Rooij et al., 2012; te Boekhorst et al., 2009; Verbeek et al., 2010). However, we must be critical of our study, because the internal consistency of the scales was rather low, possibly because of the small number of items that cause low alpha-values (Streiner, 2003). Moreover, our QoL measurement is limited by our lack of knowledge of who performed the assessment; thus, we could not estimate rater-associated bias. The lack of differences detected between small and large care units may also be explained by the organizational structure of small-scale living units. In Germany, small-scale units are generally not organizationally independent, but they usually operate within larger units. Registered nurses are not always present in small-scale units, as they provide care for several units (Palm et al., 2014b). This context may impact QoL ratings. Our findings regarding the dependence of QoL on the type of care unit need to be confirmed in future studies. These studies should investigate which factors of the care units contribute to the perceived positive QoL in residents with severe dementia and how these factors may be found more often in large integrated care units. Implications Further studies are needed to overcome the simple categorization we used for our study to define care unit types and use a typology that is based on certain features of care units. Such a typology has already been developed for the United States (Grant & Ory, 2000) but is still missing in Germany. Within such a typology, it would be of value to explore the relationship among the care unit size, specialization, work stressors, and observed QoL. Supplementary Data Supplementary data are available at The Gerontologist online. Funding The DemenzMonitor study was funded by the German Center for Neurodegenerative Diseases (DZNE), Witten. No industrial or other grants have been received. The DZNE is financed by public funds only. Conflict of Interest None reported. Author Contributions: Study design: R.P. and B.Ho. Development of the research question and methods: R.P., D.T., C.G.G.S., and M.N.D. Data collection and handling: R.P. and C.G.G.S. Data analysis: D.T., C.G.G.S., and B.Ha. Manuscript preparation: R.P., D.T., C.G.G.S., M.N.D., B.Ho., and B.Ha. Acknowledgments The authors would like to thank Bernd Albers and Jan Dreyer for the critical review of the manuscript prior to submission. References Beerens, H. C., Zwakhalen, S. M. G., Verbeek, H., Ruwaard, D., Ambergen, A. W., Leino-Kilpi, H.,… Hamers, J. P. H. ( 2014). Change in quality of life of people with dementia recently admitted to long-term care facilities. Journal of Advanced Nursing , 71, 1435– 1447. doi: 10.1111/jan.12570 Google Scholar CrossRef Search ADS PubMed  Brown, H., & Prescott, R. ( 2006). Applied mixed models in medicine . Das sollte stimmen. Wenn man es ganz genau nimmt, steht es so im Buch. John Wiley & Sons Ltd. ISBN: 978-0-470-02356-3 (HB). Google Scholar CrossRef Search ADS   Cadigan, R. O., Grabowski, D. C., Givens, J. L., & Mitchell, S. L. ( 2012). The quality of advanced dementia care in the nursing home: The role of special care units. Medical Care , 50, 856– 862. doi: 10.1097/MLR.0b013e31825dd713 Google Scholar CrossRef Search ADS PubMed  Chaudhury, H., Cooke, H. A., Cowie, H., & Razaghi, L. ( 2017). The Influence of the physical environment on residents with dementia in long-term care settings: A review of the empirical literature. Gerontologist , 00, 1– 13 doi: 10.1093/geront/gnw259 Crespo, M., Hornillos, C., & Gómez, M. M. ( 2013). Dementia special care units: A comparison with standard units regarding residents’ profile and care features. International Psychogeriatrics , 25, 2023– 2031. doi: 10.1017/S1041610213001439 Google Scholar CrossRef Search ADS PubMed  de Rooij, A. H., Luijkx, K. G., Schaafsma, J., Declercq, A. G., Emmerink, P. M., & Schols, J. M. ( 2012). Quality of life of residents with dementia in traditional versus small-scale long-term care settings: A quasi-experimental study. International Journal of Nursing Studies , 49, 931– 940. doi: 10.1016/j.ijnurstu.2012.02.007 Google Scholar CrossRef Search ADS PubMed  Dichter, M. N., Dortmann, O., Halek, M., Meyer, G., Holle, D., Nordheim, J., & Bartholomeyczik, S. ( 2013). Scalability and internal consistency of the German version of the dementia-specific quality of life instrument QUALIDEM in nursing homes - a secondary data analysis. Health and Quality of Life Outcomes , 11, 91. doi: 10.1186/1477-7525-11-91 Google Scholar CrossRef Search ADS PubMed  Dichter, M. N., Schwab, C. G., Meyer, G., Bartholomeyczik, S., Dortmann, O., & Halek, M. ( 2014). Measuring the quality of life in mild to very severe dementia: Testing the inter-rater and intra-rater reliability of the German version of the QUALIDEM. International Psychogeriatrics , 26, 825– 836. doi: 10.1017/S1041610214000052 Google Scholar CrossRef Search ADS PubMed  Ettema, T. P., Dröes, R. M., de Lange, J., Mellenbergh, G. J., & Ribbe, M. W. ( 2007). QUALIDEM: development and evaluation of a dementia specific quality of life instrument. Scalability, reliability and internal structure. International journal of geriatric psychiatry , 22, 549– 556. doi: 10.1002/gps.1713 Google Scholar CrossRef Search ADS PubMed  Fleming, R., Goodenough, B., Low, L. F., Chenoweth, L., & Brodaty, H. ( 2016). The relationship between the quality of the built environment and the quality of life of people with dementia in residential care. Dementia (London, England) , 15, 663– 680. doi: 10.1177/1471301214532460 Google Scholar PubMed  Fleming, R., Kelly, F., & Stillfried, G. ( 2015). ‘I want to feel at home’: Establishing what aspects of environmental design are important to people with dementia nearing the end of life. BMC Palliative Care , 14, 26. doi: 10.1186/s12904-015-0026-y Google Scholar CrossRef Search ADS PubMed  Fleming, R., & Purandare, N. ( 2010). Long-term care for people with dementia: environmental design guidelines. International Psychogeriatrics , 22, 1084– 1096. doi: 10.1017/S1041610210000438 Google Scholar CrossRef Search ADS PubMed  Garre-Olmo, J., López-Pousa, S., Turon-Estrada, A., Juvinyà, D., Ballester, D., & Vilalta-Franch, J. ( 2012). Environmental determinants of quality of life in nursing home residents with severe dementia. Journal of the American Geriatrics Society , 60, 1230– 1236. doi: 10.1111/j.1532-5415.2012.04040.x Google Scholar CrossRef Search ADS PubMed  Grabowski, D. C., O’Malley, A. J., Afendulis, C. C., Caudry, D. J., Elliot, A., & Zimmerman, S. ( 2014). Culture change and nursing home quality of care. Gerontologist , 54 ( Suppl 1), S35– S45. doi: 10.1093/geront/gnt143 Google Scholar CrossRef Search ADS PubMed  Grant, L. A., & Ory, M. ( 2000). Alzheimer special care units in the United States. In Holmes D., Teresi J. A., & Ory M. (Eds.), Special care units  (pp. 19– 45). Paris: Serdi Publisher. Hoe, J., Hancock, G., Livingston, G., Woods, B., Challis, D., & Orrell, M. ( 2009). Changes in the quality of life of people with dementia living in care homes. Alzheimer Disease and Associated Disorders , 23, 285– 290. doi: 10.1097/WAD.0b013e318194fc1e Google Scholar CrossRef Search ADS PubMed  Holmes, D., Teresi, J. A., & Ory, M. (Eds.). ( 2000). Special care units . Paris, New York: Serdi Publisher, Springer Publishing Company. Kaufer, D. I., Cummings, J. L., Ketchel, P., Smith, V., MacMillan, A., Shelley, T.,… DeKosky, S. T. ( 2000). Validation of the NPI-Q, a brief clinical form of the neuropsychiatric inventory. The Journal of Neuropsychiatry and Clinical Neurosciences , 12, 233– 239. doi: 10.1176/jnp.12.2.233 Google Scholar CrossRef Search ADS PubMed  Köhler, L., Weyerer, S., & Schäufele, M. ( 2007). Proxy screening tools improve the recognition of dementia in old-age homes: Results of a validation study. Age and Ageing , 36, 549– 554. doi: 10.1093/ageing/afm108 Google Scholar CrossRef Search ADS PubMed  Krieger, N. ( 2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology , 30, 668– 677. doi:10.1093/ije/30.4.668 Google Scholar CrossRef Search ADS PubMed  Lai, C. K., Yeung, J. H., Mok, V., & Chi, I. ( 2009). Special care units for dementia individuals with behavioural problems. Cochrane Database Syst Rev , CD006470. doi: 10.1002/14651858.CD006470.pub2 Lawton, M. P. ( 1994). Quality of life in Alzheimer disease. Alzheimer Disease and Associated Disorders , 8( Suppl 3), 138– 150. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P. ( 2001). The physical environment of the person with Alzheimer’s disease. Aging & Mental Health , 5( Suppl 1), S56– S64. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P., & Brody, E. M. ( 1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist , 9, 179– 186. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P., & Nahemow, L. ( 1973). Ecology and the ageing process. In Eisdorfer C. & Lawton M. P. (Eds.), The psychology of adult development and aging . (pp. 619– 674). Washington, D.C.: American Psychological Association. Maslow, K. O. M. ( 2001). Review of a decade of dementia special care unit research: Lessons learned and future directions. Alzheimer’s Care Quaterly , 2, 10– 16. McLaren, L., & Hawe, P. ( 2005). Ecological perspectives in health research. Journal of Epidemiology and Community Health , 59, 6– 14. doi: 10.1136/jech.2003.018044 Google Scholar CrossRef Search ADS PubMed  Michell-Auli, P., Kremer-Preiss, U., & Sowinksi, C. ( 2010). Akteure im Quartier - füreinander, miteinander. Pro Alter , November/Dezember, 30– 35. Mjørud, M., Røsvik, J., Rokstad, A. M., Kirkevold, M., & Engedal, K. ( 2014). Variables associated with change in quality of life among persons with dementia in nursing homes: A 10 months follow-up study. PLoS ONE , 9, e115248. doi: 10.1371/journal.pone.0115248 Google Scholar CrossRef Search ADS PubMed  Nakanishi, M., Nakashima, T., & Sawamura, K. ( 2012). Quality of life of residents with dementia in a group-living situation: An approach to creating small, homelike environments in traditional nursing homes in Japan. [Nihon koshu eisei zasshi] Japanese Journal of Public Health , 59, 3– 10. doi: 10.11236/jph.59.1_3 Google Scholar PubMed  Palm, R., Bartholomeyczik, S., Roes, M., & Holle, B. ( 2014a). Structural characteristics of specialised living units for people with dementia: A cross-sectional study in German nursing homes. International Journal of Mental Health Systems , 8, 39. doi: 10.1186/1752-4458-8-39 Google Scholar CrossRef Search ADS   Palm, R., Bartholomeyczik, S., Roes, M., & Holle, B. ( 2014b). Structural characteristics of specialised living units for people with dementia: A cross-sectional study in German nursing homes. International Journal of Mental Health Systems , 8, 39. doi: 10.1186/1752-4458-8-39 Google Scholar CrossRef Search ADS   Palm, R., Köhler, K., Schwab, C. G., Bartholomeyczik, S., & Holle, B. ( 2013). Longitudinal evaluation of dementia care in German nursing homes: The “DemenzMonitor” study protocol. BMC Geriatrics , 13, 123. doi: 10.1186/1471-2318-13-123 Google Scholar CrossRef Search ADS PubMed  Palm, R., Sirsch, E., Holle, B., & Bartholomeyczik, S. ( 2017). [Standardised pain assessment in cognitively impaired nursing home residents: Comparing the use of assessment tools in dementia care units and in integrated care units]. Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen , 122, 32– 40. doi: 10.1016/j.zefq.2017.04.008 Google Scholar CrossRef Search ADS PubMed  Palm, R., Trutschel, D., Simon, M., Bartholomeyczik, S., & Holle, B. ( 2016). Differences in case conferences in dementia specific vs traditional care units in German Nursing Homes: Results from a cross-sectional study. Journal of the American Medical Directors Association , 17, 91.e99– 13. doi: 10.1016/j.jamda.2015.08.018 Google Scholar CrossRef Search ADS   Pekkarinen, L., Sinervo, T., Elovainio, M., Noro, A., Finne-Soveri, H., & Leskinen, E. ( 2006). Resident care needs and work stressors in special care units versus non-specialized long-term care units. Research in Nursing & Health , 29, 465– 476. doi: 10.1002/nur.20157 Google Scholar CrossRef Search ADS PubMed  Pekkarinen, L., Sinervo, T., Perälä, M. L., & Elovainio, M. ( 2004). Work stressors and the quality of life in long-term care units. The Gerontologist , 44, 633– 643. doi:10.1093/geront/44.5.633 Google Scholar CrossRef Search ADS PubMed  Richard, L., Gauvin, L., & Raine, K. ( 2011). Ecological models revisited: Their uses and evolution in health promotion over two decades. Annual Review of Public Health , 32, 307– 326. doi: 10.1146/annurev-publhealth-031210-101141 Google Scholar CrossRef Search ADS PubMed  Robertson, S., Cooper, C., Hoe, J., Hamilton, O., Stringer, A., & Livingston, G. ( 2017). Proxy rated quality of life of care home residents with dementia: A systematic review. International Psychogeriatrics , 29, 569– 581. doi: 10.1017/S1041610216002167 Google Scholar CrossRef Search ADS PubMed  Streiner, D. L. ( 2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment , 80, 99– 103. doi: 10.1207/S15327752JPA8001_18 Google Scholar CrossRef Search ADS PubMed  te Boekhorst, S., Depla, M. F., de Lange, J., Pot, A. M., & Eefsting, J. A. ( 2009). The effects of group living homes on older people with dementia: A comparison with traditional nursing home care. International Journal of Geriatric Psychiatry , 24, 970– 978. doi: 10.1002/gps.2205 Google Scholar CrossRef Search ADS PubMed  Verbeek, H., Zwakhalen, S. M., van Rossum, E., Ambergen, T., Kempen, G. I., & Hamers, J. P. ( 2010). Dementia care redesigned: Effects of small-scale living facilities on residents, their family caregivers, and staff. Journal of the American Medical Directors Association , 11, 662– 670. doi: 10.1016/j.jamda.2010.08.001 Google Scholar CrossRef Search ADS PubMed  Zimmerman, S., Sloane, P. D., Williams, C. S., Reed, P. S., Preisser, J. S., Eckert, J. K.,… Dobbs, D. ( 2005). Dementia care and quality of life in assisted living and nursing homes. The Gerontologist , 45 Spec No 1, 133– 146. doi:10.1093/geront/45.suppl_1.133 Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Gerontologist Oxford University Press

Quality of Life in People With Severe Dementia and Its Association With the Environment in Nursing Homes: An Observational Study

Loading next page...
 
/lp/ou_press/quality-of-life-in-people-with-severe-dementia-and-its-association-tn3rxG0HPW
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
0016-9013
eISSN
1758-5341
D.O.I.
10.1093/geront/gny017
Publisher site
See Article on Publisher Site

Abstract

Abstract Background and Objectives Theoretical models propose the environment as a factor influencing the quality of life (QoL) of nursing home residents with dementia. This study investigates whether the observed QoL differs depending on the type of care unit. Research Design and Methods DemenzMonitor is an exploratory, observational study involving annual data collection in German nursing homes (2012–2014). For this analysis, we selected residents with a recorded diagnosis of dementia and severe cognitive impairment. QoL was measured with the proxy assessment QUALIDEM. Four care unit types were investigated: large integrated, large segregated, small integrated, and small segregated. Results We did not find a significant difference between the care units. During the 2 years, the observed QoL was not affected by any of the care unit types in a statistically significant or clinically relevant manner. However, a significant interaction effect between time and care unit types was found. Discussion and Implications Structural and organizational characteristics of care units, which in turn have implications for residents characteristics and the quality of care, may influence the QoL of residents. This may explain the interaction we observed. Dementia, Environment, Observational study, Structural characteristics, Quality of life Background and Objectives Improvement in quality of life (QoL) is considered a major goal of the “culture change” movement that developed during the last 30 years in nursing homes (Grabowski et al., 2014). The change constitutes a renunciation from the former medical view on aging towards a more person-centered perspective that values each person as a whole. Primarily described in the United States, the implementation of the culture change movement can also be observed in other Western countries, such as Germany (Michell-Auli, Kremer-Preiss, & Sowinksi, 2010). The nursing home culture change movement was driven by the implementation of different care models that are characterized by an adaptation of the environment, among other things. It is believed that a physical and social environment that adapts to the needs of people with dementia can improve their QoL (Holmes, Teresi, & Ory, 2000). Among other characteristics, nursing home-based care facilities for people with dementia differ from other care facilities in the structure and size of the care unit. Typically, dementia care units are exclusively inhabited by residents who have been diagnosed with dementia (segregated concept). A small size is recommended for these care units to support a home-like living environment and to minimize distress (Fleming & Purandare, 2010). Dementia care units are usually integrated into traditional nursing homes and are not necessarily operated as discrete units (Maslow, 2001). Examples of these care models are Alzheimer’s special care units in the United States (Grant & Ory, 2000) and dementia group living facilities in Germany (Michell-Auli et al., 2010). Theoretical Background The connection among the environment, health and behavior is the subject of various ecological models (Richard, Gauvin, & Raine, 2011). In these models, the environment is considered an “open-ended concept, that includes all that is external to and potentially or actually influential upon an object of investigation” (McLaren & Hawe, 2005). Prevailing ecological models regard the environment as multidimensional and dynamic, and they take into account both distal and individual characteristics (Krieger, 2001; Richard et al., 2011). A similar ecological model used by Lawton and colleagues to study the process of aging (Lawton & Nahemow, 1973) is clearly recognizable in their later works in which the environment has been identified as a factor influencing QoL: “Quality of life is the evaluation, by both subjective and social-normative criteria, of the behavioral and environmental situation of the person” (Lawton, 1994). This theoretical work focused on the development of dementia-oriented design features in nursing homes and had a great influence on the development of dementia-specific care units in the United States (Lawton, 2001). Empirical Evidence on the Relationship Between the Environment and QoL A growing body of evidence supports the influence of the physical environment on residents with dementia in long-term care settings (Chaudhury, Cooke, Cowie, & Razaghi, 2017). Evidence from correlational studies supports a relationship between environmental characteristics and QoL in nursing home residents with dementia. Temperature, noise and lightning influences (Garre-Olmo et al., 2012) QoL, and an environment that facilitates activities, offers familiarity, and provides adequate space and opportunities to participate in domestic activities (Fleming, Goodenough, Low, Chenoweth, & Brodaty, 2016) affects health-related QoL. One study found that in dementia special care units, the staff were more permissive of problematic behavior, and they frequently encouraged activities and provided physical contact and positive attitudes (Zimmerman et al., 2005). Hence, a positive relationship was found in terms of the health-related QoL in nursing homes with specialized workers and more staff training. This study also showed that a better rated environment was positively related to the observed QoL but not to the QoL reported by caregivers (Zimmerman et al., 2005). Furthermore, in a cross-sectional study, Nakanishi et al. (2012) showed a higher QoL for residents from group living care units compared to that in residents from traditional care units (Nakanishi, Nakashima, & Sawamura, 2012). In contrast, Crespo et al., in their cross-sectional study, demonstrated a lower health-related QoL for residents from dementia-specific special care units than for residents from traditional care units (Crespo, Hornillos, & Gomez, 2013). However, Mjørud et al. (2014) found no influence of the type of care unit on the QoL of residents with dementia over ten months. The principal of living in small facilities resp. small groups is strongly emphasized for people with dementia, although clear evidence for an effect of facility size on residents outcomes is lacking (Fleming & Purandare, 2010). Evidence from longitudinal quasi-experimental studies comparing small group living environments with traditional care units found no clear superiority in the QoL of residents living in any one of these settings (de Rooij et al., 2012; te Boekhorst, Depla, de Lange, Pot, & Eefsting, 2009; Verbeek et al., 2010). In these studies, only single subscales of the QoL instruments differed between the groups. Because the size of a care unit is linked with other organizational characteristics like staff organization and care principles like providing a homelike atmosphere, the individual influence is difficult to evaluate (Fleming & Purandare, 2010). Previous investigations on German nursing homes showed differences in environmental factors between different care unit types such as staff ratios, organization of staff and quality of care (Palm, Bartholomeyczik, Roes, & Holle, 2014a; Palm, Sirsch, Holle, & Bartholomeyczik, 2017; Palm, Trutschel, Simon, Bartholomeyczik, & Holle, 2016). These differences may have an influence on the QoL of residents. Because randomized experimental studies cannot be performed to investigate these influences, we observed the QoL of nursing home residents from different care unit types. Aim and Research Question This manuscript reports findings on the association of the environment with the QoL of residents with severe dementia in German nursing homes observed over a period of 12–24 months. We focused on residents with severe dementia because the influence of environmental factors may be of particular relevance for this group: people with severe dementia are more strongly exposed to and influenced by their environment than fellow residents who are still able to move around, perform activities or more clearly express their needs (Fleming, Kelly, & Stillfried, 2015). We investigated data from residents from four care unit types differing in size (small vs large) and concept (integrated versus segregated). We sought to answer the research question, if the average QoL of residents with severe dementia is associated with the type of care unit they are living in. Because QoL is a multidimensional construct that is influenced by a range of factors, we developed a model to map our assumptions of correlations and guide our selection of variables (Figure 1). Based on our theoretical assumptions, we selected individual characteristics that can influence the QoL of residents but are not likely to be associated with the care unit type, such as age, gender, length of stay in the nursing home, and frequency of visits by relatives/friends. Based on empirical evidence, we selected individual characteristics that are likely to be associated with QoL, such as dependency (Beerens et al., 2014), cognition (Hoe et al., 2009) and behavior (Mjørud et al., 2014), for analysis. These variables can also be associated with the type of care unit. Figure 1. View largeDownload slide Research model. Figure 1. View largeDownload slide Research model. This study is part of the DemenzMonitor study (Palm, Köhler, Schwab, Bartholomeyczik, & Holle, 2013), which has an overarching aim of exploring resident- and facility-related factors and covariates that are associated with the behavior and QoL of residents with dementia living in German nursing homes. Research Design and Methods Study Design This observational longitudinal study involves annual data collection in an open cohort at three time points. Participants and Sampling Nursing homes were eligible to participate if they were approved by the German long-term care insurance. The nursing homes decided on the number of care units they wanted to offer for participation in the convenience sample. We aimed to include every resident with or without dementia in the care units. Because participation was voluntary, the inclusion of all residents could not be guaranteed. We selected the patient cohorts based on defined criteria. The inclusion criteria were as follows: (a) Dementia diagnosis: a documented medical diagnosis of dementia (b) Dementia severity: an advanced severity of dementia rated by assessors within the study period over all observed time points (c) Complete QoL assessment at a minimum of one time point We excluded residents based on the following criteria: (d) Missing data on care unit concept or size (e) Residents living in a care unit that changed its concept after the first measurement (f) Residents who moved to another care unit (g) Residents who lived in the care unit for fewer than 28 days Measurements Nursing Home Characteristics We assessed the variables of provider (profit/nonprofit) and size (large for > 100 number of beds/small for ≤ 100 number of beds). Care Unit Characteristics We collected data on the size and concept (integrated or segregated). We defined the size of a care unit as large when more than 15 beds were offered. An integrated concept means that residents with and without dementia were living in those care units, and segregated means that only residents with dementia were living in those care units. In accordance with previous studies (Palm, Bartholomeyczik, Roes, & Holle, 2014b), we stratified the data into four care unit types based on their size and concept: 1. Large integrated 2. Large segregated 3. Small integrated 4. Small segregated For the analysis, we defined our study groups based on the care unit types and investigated their influence on the dependent variable, QoL. Resident Characteristics To measure QoL, we used the QUALIDEM questionnaire that was initially developed in the Netherlands and translated into German (Dichter et al., 2013; Ettema, Droes, de Lange, Mellenbergh, & Ribbe, 2007) (English version is available under: https://www.dzne.de/fileadmin/user_upload/editors/QUALIDEM_User_Guide_2016_final_30.06.2016.pdf). The instrument can be administered to people with mild to very severe dementia; for the latter group, a shorter version of the original instrument is recommended (Ettema et al., 2007). The QUALIDEM is a proxy-rating instrument that assesses the QoL of people with severe dementia with 18 items in six different subscales: “care relationship” (3), “positive affect” (4), “negative affect” (2), “restless tense behavior” (3), “social relations” (3), and “social isolation” (3). Each scale consists of a different number of items (in brackets); to calculate the total score, we divided the summed score from each subscale by the number of items and summarized them. Previous testing revealed satisfactory scalability and intra-rater reliability, but there were problems with internal consistency (Dichter et al., 2013; Dichter et al., 2014). We evaluated internal consistency with our data and found weak Cronbach’s α values (<0.5) for the subscale “social relations” (see Table A1 in the Supplementary Appendix). To achieve strong inter-rater reliability, more than one proxy-rater is recommended (Dichter et al., 2014). To rate dementia severity, we used the proxy-rating Dementia Screening Scale (DSS) that was developed in German for use in nursing homes (Köhler, Weyerer, & Schaufele, 2007). The DSS contains 7 items to assess orientation and memory skills (range 0–14; higher values indicate stronger impairment). The DSS was validated against three established dementia screening instruments (Mini-Mental Status Examination, Clinical Dementia Rating, and Dementia Scale of the Brief Assessment Schedule) in 598 nursing home residents. We used the recommended cutoff value to identify residents with severe dementia (DSS > 7) (Köhler et al., 2007). To assess impairments in physical functions and self-care abilities, we used the Physical Self Maintenance Scale (PSMS) (Lawton & Brody, 1969) (range 6–30). Neuropsychiatric symptoms were assessed with the Neuropsychiatric Inventory Questionnaire (NPI-Q) (Kaufer et al., 2000) (range 0–36). We also collected data on patient age, sex, length of stay in the nursing home (categorized as short [≤3 months] and long [>3 months] stay), the number of external visitors and the frequency of visits to residents during the week before assessment (visit score: range 0–16). More details regarding the measurements can be found in the study protocol (Palm et al., 2013). Data Collection Data collection was performed each year in May (from 2012 to 2014). The head nurses at the respective nursing homes collected the nursing home and care unit data. The assessments on residents were performed by the professional caregivers in the care units who were mostly involved in the care of the respective residents (registered nurses, certified nursing assistants). Based on the recommendations of Ettema et al. (2007) and Dichter et al. (2014), the assessment of the QUALIDEM questionnaire was performed by two professional caregivers of the care units. One staff member of each participating care unit received a one-day training on how to use the assessments; this person either assessed the data themselve or guided additional persons involved in the data collection. Statistical Analysis The baseline characteristics of nursing homes, care units and participants were described using relative frequencies or means (± standard deviations). To answer our research question, a linear mixed model was used to estimate the expected values of the dependent variable QUALIDEM total score and the subscales of the instrument (Brown & Prescott, 2006). Within the model, the fixed effects (independent variables) were time, study group, and the interaction Time × Study group. The random effects were clusters (care units) and participants, which were adjusted for cluster correlations and repeated measurements. Repeated measurement was adjusted by covariance patterns (Brown & Prescott, 2006) with compound symmetry (CS) structure. Backward algorithms (using a p value < .05) were used to select covariates and confounders that could explain the variation within the data. Following this procedure, the final model was adjusted for the remaining covariates (size of nursing home, length of stay, PSMS score, and NPI-Q score) in accordance with the literature. The variables age and DSS score were associated with the dependent variable in univariate models; however, as they were also correlated with other covariates, they were not included in the model. The expected values of the outcomes of time-study group strata were estimated by model-based least square means and presented graphically with 95% confidence intervals (CIs) (based on mean values of the covariates in the model). The analysis was repeated for all subscales of the instrument using the model of the primary outcome analysis. For the total QoL score only, type III ANOVA (two-sided) was performed for the primary fixed effects of time, study group, and their interaction and the covariates. The level of significance was set at 5%. For the subscales, we abstained from evaluating significance to avoid multiple testing. Because of the high number of nursing homes that dropped out of the study, we constructed a second sample for sensitivity analysis that included data only from residents of care units (n = 31) in nursing homes (n = 16) that participated at all three time points of the study within 2012–2014. All the described analyses were conducted for both samples. Statistical analyses were performed using R statistical software version 3.2.4 (www.R-project.org). Ethics The ethics committee of the German Society for Nursing Science reviewed and approved the study protocol (October 2011). The ethical considerations and procedures to obtain informed consent are described in the study protocol (Palm et al., 2013). Results Participants During the whole study period, we recruited 66 German nursing homes with 140 care units and 2,906 residents over a period of three years (including the number of replaced dropouts). We excluded residents without a diagnosis of dementia or severe cognitive impairment (n = 1,489), residents living in the nursing homes for less than 28 days or having an incomplete QoL assessment at every time point (n = 29) and residents with missing data or changes regarding the care unit (n = 20). Overall, the main analysis was performed in 1,368 (47.1%) residents in 134 care units. Of 134 care units, 31 participated at three times, 39 two times (1 of these participated not at consecutive data collections) and 64 care units participated one time. The sensitivity analysis was performed in 390 residents (13.4%) from 31 care units who participated at all three data collections. Figure 2 shows the evolution of the sample size over the three time points. Figure 2. View largeDownload slide Flowchart. Figure 2. View largeDownload slide Flowchart. The results from the descriptive analysis are shown in Table 1. Table 1. Characteristics of Nursing Homes, Care Units, and Participants Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Note: Data are reported as n (%) or mean (±SD). aBased on the first measurement. bMissing values PSMS score: n = 10. cMissing values NPI-Q score: n = 19. dNo missing data regarding QoL. View Large Table 1. Characteristics of Nursing Homes, Care Units, and Participants Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Characteristics of nursing homes (NHs)     Numbers of NHs  65   Small NH (≤100 beds)  32 (49.2%)   Nonprofit provider  47 (72.3%)   Number of participating care units  2.1 (±1.3)  Characteristics of care units   Numbers of care units  134   Large care unit (>15 beds)  105 (78.4%)   Integrated care unit as concept  82 (61.2%)  Characteristics of the participantsa   Numbers of participants  1368   Measurements per participant  1.4 (±0.6)   Living in a large integrated care unit  555 (40.6%)   Living in a large segregated care unit  616 (45.0%)   Living in a small integrated care unit  90 (6.6%)   Living in a small segregated care unit  107 (7.8%)   Living in a small NH (≤100 beds)  611 (44.7%)   Living in a home of a nonprofit provider  1,004 (73.4%)   Female  1,046 (76.5%)   Age in years  83.5 (±7.9)   Length of stay > 3 months  1,226 (89.6%)   Frequency of visits (Visit-Score) (0–16)  3.9 (±2.3)   Dementia severity (DSS-Score) (8–14)  11.4 (±2.0)   Physical function and self-care (PSMS-Score)b (6–30)  21.6 (±4.0)   Neuropsychiatric symptoms (NPI-Q-Score)c (0–36)  4.9 (±4.8)   QoLd (QUALIDEM total score) (0–18)  12.9 (±2.8)    “Care relationship” (0–9)  6.8 (±2.1)    “Positive affect” (0–12)  8.8 (±2.8)    “Negative affect” (0–6)  4.6 (±1.5)    “Restless tense behavior” (0–9)  5.0 (±2.7)    “Social relations” (0–9)  7.0 (±1.9)    “Social isolation” (0–9)  6.6 (±2.2)  Note: Data are reported as n (%) or mean (±SD). aBased on the first measurement. bMissing values PSMS score: n = 10. cMissing values NPI-Q score: n = 19. dNo missing data regarding QoL. View Large The descriptive results from the sample that we used for the sensitivity analysis are shown in Table A2 in the Supplementary Appendix. Results of the QUALIDEM Total Score Main Analysis In Table 2, the model-based least square means for the QUALIDEM total score are reported for each time point in each study group. Table 2. Model-based Least Square Means for All Study Groups and Time Points (main analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.562 (0.148, 0.976)*; length of stay (long), 0.418 (0.076, 0.76)*; PSMS, −0.119 (−0.144, −0.095)**; and NPI-Q, −0.363 (−0.383, −0.342)**. Variance between cluster: 0.88; Variance within cluster: 0.84; Residual variance: 2.83 aWithout missing data. *Significant at 95% CI. **Significant at 99% CI. View Large Table 2. Model-based Least Square Means for All Study Groups and Time Points (main analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 1,368  n = 675a  n = 839a  n = 297a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.4, 13.1)  13.5 (13.1, 13.8)  12.8 (12.4, 13.2)  .06  .94  <.001  Large segregated  13.0 (12.7, 13.4)  12.9 (12.6, 13.3)  12.5 (11.9, 13.2)  Small integrated  13.3 (12.5, 14.1)  12.7 (12.0, 13.5)  12.8 (12.0, 13.5)  Small segregated  13.5 (12.8, 14.2)  12.8 (12.1, 13.6)  12.7 (11.6, 13.7)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.562 (0.148, 0.976)*; length of stay (long), 0.418 (0.076, 0.76)*; PSMS, −0.119 (−0.144, −0.095)**; and NPI-Q, −0.363 (−0.383, −0.342)**. Variance between cluster: 0.88; Variance within cluster: 0.84; Residual variance: 2.83 aWithout missing data. *Significant at 95% CI. **Significant at 99% CI. View Large We did not find significant differences in the QUALIDEM total score between the study groups, but we observed a significant interaction between time and study group, indicating that the impact of time depends on the type of the care unit. As Figure 3 shows, the mean QoL in the group of residents in large integrated care units was higher in 2013 compared to that in 2012 and 2014 (roof-shaped graph). The other groups displayed lower mean values over the three time points. However, the difference between 2012 and 2014 was less than one point for each study group on a scale of 0–18; the largest negative difference was found in the small segregated group (−0.80; CI = −1.79, 0.19), and a positive difference was found in the large integrated group only (0.08; CI = −0.32, 0.47). Figure 3. View largeDownload slide Course of the total quality of life score in all study groups (main analysis). Figure 3. View largeDownload slide Course of the total quality of life score in all study groups (main analysis). Significant differences were observed between nursing homes of different sizes and for changes in the PSMS and NPI-Q scores. The model indicates that residents from large nursing homes (>100 beds) have a higher QUALIDEM total score than residents from small nursing homes; the estimate is 0.56 points (CI = 0.15, 0.98). Additionally, the QUALIDEM total score significantly decreases with higher PSMS and NPI-Q scores. Residents who stayed longer than three months in the nursing home also had a higher QUALIDEM total score than residents with a shorter stay (estimate 0.41; CI = 0.07, 0.76). Sensitivity Analysis A significant interaction (Time × Study group) was found only in the main analysis but not in the sensitivity analysis that included only the care units participating at every time point. The stable number of participating care units and the unchanged composition of the four study groups may explain why there was no statistical significant interaction between time and study group. The small number of participants in each group and the resulting large CIs may explain the lack of significance. In the sensitivity analysis, the difference in QoL in large integrated care units is estimated to be comparable to that observed in the main analysis of the full sample (roof-shaped graph). In contrast to the main analysis, the large segregated group showed this trend in the sensitivity analysis. However, in the sensitivity analysis, all study groups had almost the same average QUALIDEM total score, but the CIs were variable. The results from the sensitivity analysis are shown in Table 3 and Figure 4. Table 3. Model-based Least Square Means for All Study Groups and Time Points (sensitivity analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.249 (−0.633, 1.131); length of stay (long), 0.648 (0.063, 1.232)*; PSMS, −0.104 (−0.141, −0.066)**; and NPI-Q, −0.354 (−0.389, −0.319)**. Variance between cluster: 0.92; Variance within cluster: 1.21; Residual Variance: 2.55. aWithout missing data. *Significant at 95% CI. **Significant at 99% CI View Large Table 3. Model-based Least Square Means for All Study Groups and Time Points (sensitivity analysis) Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Study group  Time point of measurement  Time  Study Group  Time × Study Group    2012  2013  2014    n = 390  n = 208a  n = 223a  n = 237a      LS-Means (confidence interval 95%)  p values  Large integrated  12.7 (12.1, 13.3)  13.3 (12.7, 13.9)  12.5 (12.0, 13.1)  .06  .75  .17  Large segregated  12.4 (11.2, 13.5)  13.4 (12.3, 14.6)  12.5 (11.4, 13.7)  Small integrated  12.8 (11.7, 14.0)  12.2 (11.1, 13.4)  12.4 (11.3, 13.5)  Small segregated  12.1 (10.6, 13.6)  12.3 (10.8, 13.8)  12.0 (10.5, 13.5)  Note: Included covariates (estimates [CI]): nursing home size (large), 0.249 (−0.633, 1.131); length of stay (long), 0.648 (0.063, 1.232)*; PSMS, −0.104 (−0.141, −0.066)**; and NPI-Q, −0.354 (−0.389, −0.319)**. Variance between cluster: 0.92; Variance within cluster: 1.21; Residual Variance: 2.55. aWithout missing data. *Significant at 95% CI. **Significant at 99% CI View Large Figure 4. View largeDownload slide Course of the total quality of life score in all study groups (sensitivity analysis). Figure 4. View largeDownload slide Course of the total quality of life score in all study groups (sensitivity analysis). Results for the Subscales For the subscales, we show all results from both the main and sensitivity analyses in tables in the Supplementary Appendix (Tables A3–A8). Discussion and Implications In summary, we did not find significant differences among the groups, but we observed a significant interaction between time and study group, indicating that the impact of time on the observed QoL of residents with severe dementia depends on the care unit types favoring large integrated care units. Several other studies failed to show a clear effect of certain care unit types, such as small-scale living environments, on QoL (de Rooij et al., 2012; te Boekhorst et al., 2009; Verbeek et al., 2010). Additionally, our results could not confirm the assumption that the type of the care unit has any great influence on the QoL of residents. Another explanation for the lack of association between the type of care unit and QoL could be that certain features of care units—such as the attitude and education of the staff as well as the quality of care provided—probably have a greater impact on QoL than the care unit type (Lai, Yeung, Mok, & Chi, 2009). In addition, although empirical evidence supports the assumption that these features are more often present in segregated care units, we do not have a clear idea regarding the specifics of these features. For example, from a previous study, DemenzMonitor, we know that some segregated care units have a higher number of registered nurses and more educated managers (Palm et al., 2014b). We speculated that this observation could have influenced the care provided in these units. However, this assumption is difficult to prove, as our own data are not consistent in this respect (Palm et al., 2016; Palm et al., 2017). The data from studies in other countries on the other indicators of QoL with regard to the care provided in certain unit types are also inconsistent (Cadigan, Grabowski, Givens, & Mitchell, 2012). However, the significant interaction demonstrated by our results for large integrated care units indicate a slightly different trend in the observed QoL of residents than that for other care unit types. This finding requires an explanation, as it contradicts the current preference for small-scale specialized environments for people with dementia. In our study, QoL was assessed by caregivers. Their ratings can be influenced by several factors that are likely associated with their institution, such as work stress, distress, and burnout, as well as the perceived quality of care (Robertson et al., 2017). In a cross-sectional study, Pekkarinen et al. (2004) showed that work stressors, unit size, and QoL are related to one another, and they assumed that unit size has an indirect effect on QoL because it is correlated with work stressors. Thus, they concluded that the larger the care unit, the more prominent work stressors are and the poorer QoL is (Pekkarinen et al., 2004). In a subsequent study, Pekkarinen et al. (2006) showed that work stress related to the behavioral problems of residents can be reduced by specialization into segregated care units. However, our results point in another direction and indicate that caregivers from large units perceive the QoL of their residents with severe dementia as being slightly better than do caregivers from small units. Because we did not collect data on work stress or other work-related variables that may affect the staff’s perception of the residents’ QoL, we can only assume that work-related variables may explain our results. Another explanation for the unexpected finding may be that staff members in large care units are organized differently than those in small units (staff turnover, staff-resident ratios), which in turn has an impact on how well the staff knows the resident and performs the QoL rating. Limitations Because of the methodological shortcomings of the study, our findings must be interpreted with caution. The DemenzMonitor study is exploratory and does not aim to test statistical hypotheses. It can be used only to investigate associations, not to obtain causal inferences. Therefore, no power calculation was performed to determine the sample size required for the detection of certain effects. The nonsignificant results must also be interpreted with this perspective in mind. Another limitation of the study is the incomplete recruitment in the care units, primarily because of the lack of informed consent. Therefore, we had to exclude a number of residents in some of the care units. The DemenzMonitor was intentionally designed as a longitudinal study aiming to investigate the influence of environmental variables on the changes in QoL. Because of the high number of nursing homes that dropped out of the study, we were not able to calculate reliable estimates of changes (of n = 1,368 participants n = 352 participated at two and n = 154 at three data collections). Another limitation is that we selected our study cohort based on certain criteria, so our results cannot be generalized to all nursing home residents. The lack of remarkable differences in QoL detected may also be explained by the method of assessment of QoL. The QUALIDEM assesses overall QoL with a focus on psychosocial aspects. Previously, relationships between the environment and QoL were measured with models that assess health-related QoL (Fleming et al., 2016; Zimmerman et al., 2005). Other studies that used the QUALIDEM questionnaire also did not find significant group effects on the total QoL score (de Rooij et al., 2012; te Boekhorst et al., 2009; Verbeek et al., 2010). However, we must be critical of our study, because the internal consistency of the scales was rather low, possibly because of the small number of items that cause low alpha-values (Streiner, 2003). Moreover, our QoL measurement is limited by our lack of knowledge of who performed the assessment; thus, we could not estimate rater-associated bias. The lack of differences detected between small and large care units may also be explained by the organizational structure of small-scale living units. In Germany, small-scale units are generally not organizationally independent, but they usually operate within larger units. Registered nurses are not always present in small-scale units, as they provide care for several units (Palm et al., 2014b). This context may impact QoL ratings. Our findings regarding the dependence of QoL on the type of care unit need to be confirmed in future studies. These studies should investigate which factors of the care units contribute to the perceived positive QoL in residents with severe dementia and how these factors may be found more often in large integrated care units. Implications Further studies are needed to overcome the simple categorization we used for our study to define care unit types and use a typology that is based on certain features of care units. Such a typology has already been developed for the United States (Grant & Ory, 2000) but is still missing in Germany. Within such a typology, it would be of value to explore the relationship among the care unit size, specialization, work stressors, and observed QoL. Supplementary Data Supplementary data are available at The Gerontologist online. Funding The DemenzMonitor study was funded by the German Center for Neurodegenerative Diseases (DZNE), Witten. No industrial or other grants have been received. The DZNE is financed by public funds only. Conflict of Interest None reported. Author Contributions: Study design: R.P. and B.Ho. Development of the research question and methods: R.P., D.T., C.G.G.S., and M.N.D. Data collection and handling: R.P. and C.G.G.S. Data analysis: D.T., C.G.G.S., and B.Ha. Manuscript preparation: R.P., D.T., C.G.G.S., M.N.D., B.Ho., and B.Ha. Acknowledgments The authors would like to thank Bernd Albers and Jan Dreyer for the critical review of the manuscript prior to submission. References Beerens, H. C., Zwakhalen, S. M. G., Verbeek, H., Ruwaard, D., Ambergen, A. W., Leino-Kilpi, H.,… Hamers, J. P. H. ( 2014). Change in quality of life of people with dementia recently admitted to long-term care facilities. Journal of Advanced Nursing , 71, 1435– 1447. doi: 10.1111/jan.12570 Google Scholar CrossRef Search ADS PubMed  Brown, H., & Prescott, R. ( 2006). Applied mixed models in medicine . Das sollte stimmen. Wenn man es ganz genau nimmt, steht es so im Buch. John Wiley & Sons Ltd. ISBN: 978-0-470-02356-3 (HB). Google Scholar CrossRef Search ADS   Cadigan, R. O., Grabowski, D. C., Givens, J. L., & Mitchell, S. L. ( 2012). The quality of advanced dementia care in the nursing home: The role of special care units. Medical Care , 50, 856– 862. doi: 10.1097/MLR.0b013e31825dd713 Google Scholar CrossRef Search ADS PubMed  Chaudhury, H., Cooke, H. A., Cowie, H., & Razaghi, L. ( 2017). The Influence of the physical environment on residents with dementia in long-term care settings: A review of the empirical literature. Gerontologist , 00, 1– 13 doi: 10.1093/geront/gnw259 Crespo, M., Hornillos, C., & Gómez, M. M. ( 2013). Dementia special care units: A comparison with standard units regarding residents’ profile and care features. International Psychogeriatrics , 25, 2023– 2031. doi: 10.1017/S1041610213001439 Google Scholar CrossRef Search ADS PubMed  de Rooij, A. H., Luijkx, K. G., Schaafsma, J., Declercq, A. G., Emmerink, P. M., & Schols, J. M. ( 2012). Quality of life of residents with dementia in traditional versus small-scale long-term care settings: A quasi-experimental study. International Journal of Nursing Studies , 49, 931– 940. doi: 10.1016/j.ijnurstu.2012.02.007 Google Scholar CrossRef Search ADS PubMed  Dichter, M. N., Dortmann, O., Halek, M., Meyer, G., Holle, D., Nordheim, J., & Bartholomeyczik, S. ( 2013). Scalability and internal consistency of the German version of the dementia-specific quality of life instrument QUALIDEM in nursing homes - a secondary data analysis. Health and Quality of Life Outcomes , 11, 91. doi: 10.1186/1477-7525-11-91 Google Scholar CrossRef Search ADS PubMed  Dichter, M. N., Schwab, C. G., Meyer, G., Bartholomeyczik, S., Dortmann, O., & Halek, M. ( 2014). Measuring the quality of life in mild to very severe dementia: Testing the inter-rater and intra-rater reliability of the German version of the QUALIDEM. International Psychogeriatrics , 26, 825– 836. doi: 10.1017/S1041610214000052 Google Scholar CrossRef Search ADS PubMed  Ettema, T. P., Dröes, R. M., de Lange, J., Mellenbergh, G. J., & Ribbe, M. W. ( 2007). QUALIDEM: development and evaluation of a dementia specific quality of life instrument. Scalability, reliability and internal structure. International journal of geriatric psychiatry , 22, 549– 556. doi: 10.1002/gps.1713 Google Scholar CrossRef Search ADS PubMed  Fleming, R., Goodenough, B., Low, L. F., Chenoweth, L., & Brodaty, H. ( 2016). The relationship between the quality of the built environment and the quality of life of people with dementia in residential care. Dementia (London, England) , 15, 663– 680. doi: 10.1177/1471301214532460 Google Scholar PubMed  Fleming, R., Kelly, F., & Stillfried, G. ( 2015). ‘I want to feel at home’: Establishing what aspects of environmental design are important to people with dementia nearing the end of life. BMC Palliative Care , 14, 26. doi: 10.1186/s12904-015-0026-y Google Scholar CrossRef Search ADS PubMed  Fleming, R., & Purandare, N. ( 2010). Long-term care for people with dementia: environmental design guidelines. International Psychogeriatrics , 22, 1084– 1096. doi: 10.1017/S1041610210000438 Google Scholar CrossRef Search ADS PubMed  Garre-Olmo, J., López-Pousa, S., Turon-Estrada, A., Juvinyà, D., Ballester, D., & Vilalta-Franch, J. ( 2012). Environmental determinants of quality of life in nursing home residents with severe dementia. Journal of the American Geriatrics Society , 60, 1230– 1236. doi: 10.1111/j.1532-5415.2012.04040.x Google Scholar CrossRef Search ADS PubMed  Grabowski, D. C., O’Malley, A. J., Afendulis, C. C., Caudry, D. J., Elliot, A., & Zimmerman, S. ( 2014). Culture change and nursing home quality of care. Gerontologist , 54 ( Suppl 1), S35– S45. doi: 10.1093/geront/gnt143 Google Scholar CrossRef Search ADS PubMed  Grant, L. A., & Ory, M. ( 2000). Alzheimer special care units in the United States. In Holmes D., Teresi J. A., & Ory M. (Eds.), Special care units  (pp. 19– 45). Paris: Serdi Publisher. Hoe, J., Hancock, G., Livingston, G., Woods, B., Challis, D., & Orrell, M. ( 2009). Changes in the quality of life of people with dementia living in care homes. Alzheimer Disease and Associated Disorders , 23, 285– 290. doi: 10.1097/WAD.0b013e318194fc1e Google Scholar CrossRef Search ADS PubMed  Holmes, D., Teresi, J. A., & Ory, M. (Eds.). ( 2000). Special care units . Paris, New York: Serdi Publisher, Springer Publishing Company. Kaufer, D. I., Cummings, J. L., Ketchel, P., Smith, V., MacMillan, A., Shelley, T.,… DeKosky, S. T. ( 2000). Validation of the NPI-Q, a brief clinical form of the neuropsychiatric inventory. The Journal of Neuropsychiatry and Clinical Neurosciences , 12, 233– 239. doi: 10.1176/jnp.12.2.233 Google Scholar CrossRef Search ADS PubMed  Köhler, L., Weyerer, S., & Schäufele, M. ( 2007). Proxy screening tools improve the recognition of dementia in old-age homes: Results of a validation study. Age and Ageing , 36, 549– 554. doi: 10.1093/ageing/afm108 Google Scholar CrossRef Search ADS PubMed  Krieger, N. ( 2001). Theories for social epidemiology in the 21st century: An ecosocial perspective. International Journal of Epidemiology , 30, 668– 677. doi:10.1093/ije/30.4.668 Google Scholar CrossRef Search ADS PubMed  Lai, C. K., Yeung, J. H., Mok, V., & Chi, I. ( 2009). Special care units for dementia individuals with behavioural problems. Cochrane Database Syst Rev , CD006470. doi: 10.1002/14651858.CD006470.pub2 Lawton, M. P. ( 1994). Quality of life in Alzheimer disease. Alzheimer Disease and Associated Disorders , 8( Suppl 3), 138– 150. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P. ( 2001). The physical environment of the person with Alzheimer’s disease. Aging & Mental Health , 5( Suppl 1), S56– S64. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P., & Brody, E. M. ( 1969). Assessment of older people: Self-maintaining and instrumental activities of daily living. The Gerontologist , 9, 179– 186. Google Scholar CrossRef Search ADS PubMed  Lawton, M. P., & Nahemow, L. ( 1973). Ecology and the ageing process. In Eisdorfer C. & Lawton M. P. (Eds.), The psychology of adult development and aging . (pp. 619– 674). Washington, D.C.: American Psychological Association. Maslow, K. O. M. ( 2001). Review of a decade of dementia special care unit research: Lessons learned and future directions. Alzheimer’s Care Quaterly , 2, 10– 16. McLaren, L., & Hawe, P. ( 2005). Ecological perspectives in health research. Journal of Epidemiology and Community Health , 59, 6– 14. doi: 10.1136/jech.2003.018044 Google Scholar CrossRef Search ADS PubMed  Michell-Auli, P., Kremer-Preiss, U., & Sowinksi, C. ( 2010). Akteure im Quartier - füreinander, miteinander. Pro Alter , November/Dezember, 30– 35. Mjørud, M., Røsvik, J., Rokstad, A. M., Kirkevold, M., & Engedal, K. ( 2014). Variables associated with change in quality of life among persons with dementia in nursing homes: A 10 months follow-up study. PLoS ONE , 9, e115248. doi: 10.1371/journal.pone.0115248 Google Scholar CrossRef Search ADS PubMed  Nakanishi, M., Nakashima, T., & Sawamura, K. ( 2012). Quality of life of residents with dementia in a group-living situation: An approach to creating small, homelike environments in traditional nursing homes in Japan. [Nihon koshu eisei zasshi] Japanese Journal of Public Health , 59, 3– 10. doi: 10.11236/jph.59.1_3 Google Scholar PubMed  Palm, R., Bartholomeyczik, S., Roes, M., & Holle, B. ( 2014a). Structural characteristics of specialised living units for people with dementia: A cross-sectional study in German nursing homes. International Journal of Mental Health Systems , 8, 39. doi: 10.1186/1752-4458-8-39 Google Scholar CrossRef Search ADS   Palm, R., Bartholomeyczik, S., Roes, M., & Holle, B. ( 2014b). Structural characteristics of specialised living units for people with dementia: A cross-sectional study in German nursing homes. International Journal of Mental Health Systems , 8, 39. doi: 10.1186/1752-4458-8-39 Google Scholar CrossRef Search ADS   Palm, R., Köhler, K., Schwab, C. G., Bartholomeyczik, S., & Holle, B. ( 2013). Longitudinal evaluation of dementia care in German nursing homes: The “DemenzMonitor” study protocol. BMC Geriatrics , 13, 123. doi: 10.1186/1471-2318-13-123 Google Scholar CrossRef Search ADS PubMed  Palm, R., Sirsch, E., Holle, B., & Bartholomeyczik, S. ( 2017). [Standardised pain assessment in cognitively impaired nursing home residents: Comparing the use of assessment tools in dementia care units and in integrated care units]. Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen , 122, 32– 40. doi: 10.1016/j.zefq.2017.04.008 Google Scholar CrossRef Search ADS PubMed  Palm, R., Trutschel, D., Simon, M., Bartholomeyczik, S., & Holle, B. ( 2016). Differences in case conferences in dementia specific vs traditional care units in German Nursing Homes: Results from a cross-sectional study. Journal of the American Medical Directors Association , 17, 91.e99– 13. doi: 10.1016/j.jamda.2015.08.018 Google Scholar CrossRef Search ADS   Pekkarinen, L., Sinervo, T., Elovainio, M., Noro, A., Finne-Soveri, H., & Leskinen, E. ( 2006). Resident care needs and work stressors in special care units versus non-specialized long-term care units. Research in Nursing & Health , 29, 465– 476. doi: 10.1002/nur.20157 Google Scholar CrossRef Search ADS PubMed  Pekkarinen, L., Sinervo, T., Perälä, M. L., & Elovainio, M. ( 2004). Work stressors and the quality of life in long-term care units. The Gerontologist , 44, 633– 643. doi:10.1093/geront/44.5.633 Google Scholar CrossRef Search ADS PubMed  Richard, L., Gauvin, L., & Raine, K. ( 2011). Ecological models revisited: Their uses and evolution in health promotion over two decades. Annual Review of Public Health , 32, 307– 326. doi: 10.1146/annurev-publhealth-031210-101141 Google Scholar CrossRef Search ADS PubMed  Robertson, S., Cooper, C., Hoe, J., Hamilton, O., Stringer, A., & Livingston, G. ( 2017). Proxy rated quality of life of care home residents with dementia: A systematic review. International Psychogeriatrics , 29, 569– 581. doi: 10.1017/S1041610216002167 Google Scholar CrossRef Search ADS PubMed  Streiner, D. L. ( 2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment , 80, 99– 103. doi: 10.1207/S15327752JPA8001_18 Google Scholar CrossRef Search ADS PubMed  te Boekhorst, S., Depla, M. F., de Lange, J., Pot, A. M., & Eefsting, J. A. ( 2009). The effects of group living homes on older people with dementia: A comparison with traditional nursing home care. International Journal of Geriatric Psychiatry , 24, 970– 978. doi: 10.1002/gps.2205 Google Scholar CrossRef Search ADS PubMed  Verbeek, H., Zwakhalen, S. M., van Rossum, E., Ambergen, T., Kempen, G. I., & Hamers, J. P. ( 2010). Dementia care redesigned: Effects of small-scale living facilities on residents, their family caregivers, and staff. Journal of the American Medical Directors Association , 11, 662– 670. doi: 10.1016/j.jamda.2010.08.001 Google Scholar CrossRef Search ADS PubMed  Zimmerman, S., Sloane, P. D., Williams, C. S., Reed, P. S., Preisser, J. S., Eckert, J. K.,… Dobbs, D. ( 2005). Dementia care and quality of life in assisted living and nursing homes. The Gerontologist , 45 Spec No 1, 133– 146. doi:10.1093/geront/45.suppl_1.133 Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Journal

The GerontologistOxford University Press

Published: Mar 16, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off