Individuals with dementia want to live well, and there was a strong recommendation from the 2017 National Dementia Care Research Summit to include outcomes that measure well-being in care, services, and supports for this population (Kolanowski et al., 2018). Well-being for individuals with dementia has been conceptualized as the extent to which psychological needs for comfort, attachment, inclusion, occupation, and identity are met (Kaufmann & Engel, 2016). In the general population, well-being is assessed by self-report or by observing states of emotionality, but the former approach is problematic when neurodegenerative diseases progress and verbal communication becomes difficult. For this reason, investigators often infer states of well-being in late-stage dementia by observing displays of affect (Lawton, Van Haitsma, & Klapper, 1996).
Affect balance is a concept developed by Bradburn (1969) who hypothesized that well-being is a judgement made by people when comparing the relative frequency of experiencing positive affect versus negative affect over a given time period. In this perspective, the mere absence of negative affect does not equate with well-being. A higher ratio of positive to negative affect enhances well-being and predicts engagement and resilience to adverse events (Fredrickson, 2001). Some investigators have proposed that a 3:1 ratio of experiencing positive to negative affect is indicative of high-level well-being (Fredrickson & Losada, 2005).
Early studies of affect in people with late stage dementia reported lack of affective expression (Galynker, Roane, Miner, Feinberg, & Watts, 1995), but more current work challenged those findings. Negative affect becomes more common as dementia progresses, but people with late-stage dementia also display positive affect (Kolanowski, Litaker, Catalano, Higgens, & Heineken, 2002; Magai, Cohen, Gomberg, Malatesta, & Culver, 1996). In fact, the loss of positive affect has been considered a late event (Albert et al., 2001).
In the current authors' work with nursing home residents, positive and negative affect were found to be only moderately correlated; the mean level of positive affect displayed, unlike negative affect, was associated with functional ability but was unrelated to mental status; and both positive and negative affect showed a significant amount of daily variation (Kolanowski, Hoffman, & Hofer, 2007). In another study of nursing home residents, the current authors found that the mean ratio of positive to negative affect was 1.31:1, and this ratio was positively and significantly associated with greater engagement in activities and other indicators of well-being, including self-reported mood and observed behavior (Kolanowski, Van Haitsma, Meeks, & Litaker, 2014).
Several conclusions can be drawn from this body of literature. First, a number of individual and environmental factors impact well-being in late-stage dementia. Second, the expression of positive affect may be more easily influenced by environmental factors and therefore more malleable than negative affect.
Historically, much of dementia care research focused on pharmacological approaches for reducing negative affect. The wisdom of using medications that sedate, such as antipsychotic agents, to suppress negative affect in this population has been questioned not only because of their adverse effects but because negative affect may be the only way for people with late-stage dementia to communicate an undesirable state, such as pain (Algase et al., 1996). Based on the concept of affect balance, person-centered approaches for improving well-being would aim to shift the affect balance in favor of more positive relative to negative affect by using interventions such as activities or pleasant interactions, not by suppressing appropriate negative affect. Person-centered approaches help achieve well-being in individuals with late-stage dementia, many of whom experience significant negative affect.
Nursing home staff often cite lack of time as a reason for not implementing person-centered approaches. Much of staff care, however, is relational and takes place in direct interaction with residents. The quality of that interaction has the potential to impact affect and is essentially time-neutral, making it an attractive target for improving resident well-being. In a pilot study, Gilmore-Bykovskyi, Roberts, Bowers, and Brown (2015) found that resident agitation was more likely to occur when nursing home staff engaged in task-focused care as opposed to more person-centered care. Direct care staff are not always knowledgeable of what constitutes person-centered care (Matthews, Stanhope, Choy-Brown, & Doherty, 2018) and knowledge does not automatically translate into a practice change, but it is a prerequisite to change. Whether the quality of caregiving interactions and knowledge of person-centered approaches are associated with affect balance in nursing home residents has not been examined.
Factors such as staffing hours, the quality of the physical environment, and policies that promote person-centered care reflect the nursing home's commitment to quality care and are likely to impact resident well-being. Prior research indicated that total nursing and RN staffing levels were associated with quality of care (Kim, Kovner, Harrington, Greene, & Mezey, 2009). As RN to licensed practical nurse (LPN) ratios increased, total deficiencies and serious deficiencies decreased (Kim, Harrington, & Greene, 2009). In addition to staffing mix and levels, the built environment can be a support or barrier to independent functioning, physical activity, and well-being (Calkins, 2018). Light, noise, and available seating areas all contribute to the fit between the facility and residents' cognitive and physical capabilities. A well-designed physical environment promotes residents' ability to achieve optimal independence, physical activity, and well-being (De Boer et al., 2018). Formal policies help ensure consistency in practice and approaches to care. Written policies that reflect a philosophy of person-centered care should translate into better practice and resident outcomes, although there is not always strong evidence to that effect (Ampe, Sevenants, Smets, Declercq, & Van Audenhove, 2016). The number of staff hours provided by the nursing home, the quality of the nursing home's physical environment, and its policies supportive of resident well-being have not been examined in relation to the effect on resident affect balance.
The purpose of the current study was to extend prior work by examining potentially modifiable factors associated with resident affect balance. It was hypothesized that more positive staff interaction during caregiving, greater staff knowledge of person-centered approaches for dementia care, higher staff hours of care, a more supportive physical environment, a greater number of person-centered policies, and higher resident function will be associated with higher affect balance (i.e., a higher ratio of positive to negative affect).
Participants and Setting
The current study is a secondary analysis of baseline data from the first two cohorts of an ongoing pragmatic clinical trial. In the trial, the effectiveness of an implementation strategy for improving staff uptake of behavioral approaches when responding to residents' behavioral symptoms is being tested. The study was approved by a university institutional review board and the protocol has been published (Resnick, Kolanowski, et al., 2018).
To reflect the real world of practice and increase external validity, nursing home and resident exclusion criteria were kept to a minimum. Briefly, 35 homes from two states on the East Coast were invited to participate in the study if they (a) agreed to actively partner with the research team on an initiative to change practice; (b) had at least 100 beds, or 50 beds if the facility had a dedicated dementia care unit; (c) identified a staff member to be an internal champion and work with the research team in the implementation process; and (d) were able to access e-mail and websites via a phone, tablet, or computer. Recruitment of nursing homes was done by mailing invitations to eligible facilities (i.e., sufficient bed-size) across the two states followed by telephone calls and site visits. Invitations were also posted on relevant websites such as state-based long-term care organizations. Once the nursing home agreed to participate, residents within that home were recruited.
Residents were eligible to participate if they (a) were living in a participating nursing home; (b) were age ≥55; (c) had cognitive impairment as determined by a score of 0 to 12 on the Brief Interview of Mental Status (BIMS) (Saliba et al., 2012); (d) were not enrolled in hospice; and (e) were not in the nursing facility for short-stay rehabilitation care. A list of all potentially eligible residents was obtained from the nursing home. These residents were initially approached for assent by study research assistants. If the resident did not assent, there was no further contact. If a resident did assent, an evaluation of his/her decisional capacity was performed using the Evaluation to Sign Consent (ESC) (Resnick et al., 2007). Residents' written/verbal consent was obtained if they demonstrated decisional capacity. If decisional capacity was impaired, the legally authorized representative was approached for consent. Residents were approached until 12 to 13 residents per nursing home were recruited.
The focus of the implementation study was to help staff use person-centered approaches to manage behavioral symptoms; consequently, all nursing staff participated in the project. All staff in the participating nursing homes received an initial training session on person-centered approaches for behavioral symptoms. Following this session, staff completed the Knowledge of Person-Centered Care Approaches for Dementia Care questionnaire (described below).
All data were collected by research evaluators who were blind to treatment assignment and who had prior experience working with nursing home residents. The baseline measures for this study were completed by direct observation of resident/staff interactions (quality of staff interaction), input from the nursing assistant who was providing care to the resident on the day of testing and who knew the resident well (affect balance and function), evaluator assessment (i.e., resident mental status, environmental assessment, and person-centered policies), and individual staff response (i.e., staff knowledge of person-centered approaches for dementia care). Demographic data and staff hours were obtained from medical chart review and the Nursing Home Compare (2019) website, respectively.
Resident demographics included age, gender, race, ethnic background, and marital status. These data were obtained from the medical chart.
Quality of staff interaction was measured using the Quality of Interactions Schedule (QuIS) (Clark & Bowling, 1989), an observational tool for rating staff/resident interactions. Behaviors that describe each interaction are provided: positive social (e.g., offering choices); positive care (e.g., explains care activities); neutral (e.g., undirected greeting); negative protective (e.g., “don't eat that”); and negative restrictive (e.g., being moved without warning). Research evaluators observe a staff/resident interaction over a 5-minute period and indicate if any of the interactions are observed using the responses: present, not present, or can't tell. For this study, the presence of positive social and/or positive care staff behaviors indicated high-quality staff interaction. The presence of negative protective and/or negative restrictive staff behaviors indicated poor quality staff interaction. The QuIS has demonstrated good reliability in clinical settings (McLean, Griffiths, Mesa-Eguiagaray, Pickering, & Bridges, 2017). An interrater reliability of r = 1.0 (p < 0.001) for the QuIS was achieved in the current study.
Staff knowledge was measured using a 10-item investigator-developed questionnaire, the Staff Knowledge of Person-Centered Care Approaches for Dementia Care. The questionnaire focuses on the most appropriate ways to prevent and manage challenging behaviors in nursing home residents without causing a decline in function and physical activity or restricting function or physical activity in any way. Six items address how to respond to a resident with a specific behavior (e.g., the resident resisting oral care), three items focus on ways to prevent challenging behaviors in residents with dementia (e.g., screaming), and one item addresses assessment of underlying capability of the resident to guide the development of appropriate person-centered care plans. The score is calculated as the number of items correct out of 10 (0 to 10). In the current trial, the authors found evidence of the questionnaire's reliability (item reliability index [alpha coefficient] = 0.99), construct validity with INFIT and OUTFIT statistics in the 0.6 to 1.4 range, and a significant correlation between scores on the questionnaire and positive care interactions (Resnick et al., in press). The mean score obtained in the facility was assigned to each participant in that facility.
Staff hours were obtained from the Nursing Home Compare website (access https://www.medicare.gov/nursinghomecompare/search.html). The staffing data reflect the number of hours facility staff are paid to work each day and are reported electronically from site payroll or other auditable data. The mean RN, LPN, and certified nurse assistant (CNA) hours (reported as minutes) per resident per day for the facility was assigned to each participant in that facility.
The Environment Assessment is an investigator-developed instrument that includes 24 items related to the quality of the environment that impact care of residents (e.g., outdoor spaces are available with rest spots, walking paths, noise is controlled). Items are scored as present or not present and summed. Scores can range from 0 to 24, with higher scores indicating a more supportive physical environment. There is evidence of the instrument's inter-rater reliability (r = 0.99) and construct validity based on Rasch analysis and INFIT and OUTFIT statistics in the range of 0.6 to 1.4. (Resnick et al., 2019). The facility score for environmental assessment was assigned to each participant in that facility.
Person-Centered Policies is an investigator-developed instrument that includes 24 items reflecting policies that support person-centered behavioral approaches (e.g., policies on use of restraints, unlimited visiting hours, food preferences). Items are scored as present or not present and summed. Scores can range from 0 to 24, and higher scores indicate more person-centered policies. There is evidence of the instrument's interrater reliability (r = 0.81) and construct validity based on Rasch analysis and INFIT and OUTFIT statistics in the range of 0.6 to 1.4 (Resnick et al., 2019). The facility score for Person-Centered Policies was assigned to each participant in that facility.
Function was measured using the Barthel Index (BI), a 10-item measure of performance of activities of daily living with evidence of internal consistency, interrater reliability, and validity (Mahoney & Barthel, 1965). Items such as self-care (e.g., feeding, dressing, grooming) and mobility (e.g., walking, stairs, transfer ability) are weighted to account for the amount of assistance required. Scores range from 0 (complete dependence) to 100 (complete independence).
Mental status was measured using the BIMS (Saliba et al., 2012). The BIMS includes recall (e.g., “What were the three words that I asked you to repeat?”) and orientation (e.g., “Please tell me what year it is right now.”) questions. Scores range from 0 to 15, and higher scores indicate greater cognitive ability.
Affect balance was measured using items for positive affect and negative affect that emerged after conducting a principal components analysis (PCA) of the Quality of Life in Late-stage Dementia Scale (QUALID) (Weiner et al., 2000). The QUALID was developed specifically for individuals with late-stage dementia and those who cannot communicate coherently. Eleven observable affective expressions thought to be indicative of quality of life are included such as smiles, appears sad, and cries. Assessments were provided by knowledgeable direct care staff using a 5-point Likert scale reflecting the amount of time each day over the past 1 week the resident displayed the affect (once or more times to rare if at all). Scores can range from 11 to 55, with higher scores indicating worse quality of life.
Initial psychometric testing of the QUALID provided evidence of internal consistency (alpha coefficient = 0.77), test–retest reliability (intraclass correlation = 0.81), and interrater reliability (intraclass correlation = 0.83). There was also evidence of validity based on significant correlations between scores on the QUALID and Geriatric Depression Scale (r = 0.36, p = 0.04) and Neuropsychiatric Inventory (r = 0.40, p = 0.01). In the current study, continued support was found for the reliability and validity of the QUALID scale using Rasch analysis (Resnick, Galik, et al., 2018).
To construct a measure of affect balance, a PCA was first conducted with varimax rotation to determine the factor structure of the QUALID. The positive items were reverse scored, so that a higher score on any item (positive or negative) indicates more of that affect. The PCA resulted in two components that accounted for 37.5% of the variance. The first component was labeled “negative affect” and included four items with loadings ≥0.6: appears sad, cries, facial expression of discomfort, and statements of discomfort. The second component was labeled “positive affect” and included three items with loadings ≥0.56: smiles, enjoys touching, and enjoys interacting. Four QUALID items were eliminated because their loadings were <0.6: appears physically uncomfortable, is irritable, enjoys eating, and appears calm and comfortable.
For each participant, the three positive items and four negative items were averaged separately using the QUALID 5-point scoring system and these results were used to calculate a ratio of positive to negative affect. A higher ratio indicates better affect balance.
All variables were summarized with frequencies and percentages or with means, medians, and standard deviations prior to any analysis being performed to check the distributions of the variables. The association of positive and negative affect scores from the PCA of the QUALID were also examined to determine their degree of association and relative independence, thereby justifying the use of affect balance rather than relying solely on either affect as a measure of well-being.
The primary outcome variable, affect balance, was skewed. Several attempts were made to transform it to a more normally distributed state, but none were successful. Because of this, a nonparametric analysis using quantile regression of the median was used instead of the usual parametric methods. A bivariate analysis of each potential predictor and covariate versus the outcome variable was implemented first to determine which of these variables had a significant effect on affect balance. The statistically significant variables from the bivariate analysis (i.e., staff knowledge and RN and CNA hours) in addition to a known clinically important variable (i.e., quality of staff interaction) and variables that reached the p = 0.10 level of significance (function and gender) were then combined together into a multivariable model to determine if significance was maintained when adjusted for the effect of the other variables. This approach was used because even though some clinically important variables may be insignificant in bivariate analyses, once they are examined in the context of other important variables, they can reach significance when interacting with these important variables (Heinze & Dunkler, 2017). Because quality of care varies from one facility to another, the nursing home was included as a covariate in the multivariable model to adjust for any impact it might have on affect balance.
Before creating the multivariable model, the predictors and covariates were tested for multicollinearity using variance inflation factors (VIF) statistics, but none was found using a cut-off of 4. The final model fit was assessed using a histogram of the standardized residuals from the model and a Q-Q plot of the residuals from the model. Both showed a good fit. The parameter estimates from the model were used to determine the magnitude and direction of any significant effects of the predictor variables. All analyses were performed using SAS version 9.4.
The 35 participating nursing homes were a mix of forprofit and non-profit facilities and had a mean bed size of 149 (SD = 78 beds). Participant demographics and clinical characteristics are in Table 1. The 325 participants in the current sample had a mean age of approximately 83, the majority were female (69%), Caucasian (75%), and widowed (46%). These participants had moderate to severe cognitive (mean BIMS = 4.3 [SD = 3.6]) and functional (mean BI = 34 [SD = 25]) impairments.
Participants' Demographic and Clinical Characteristics (N = 325)
Means and standard deviations for the baseline measures are in Table 2. In the initial bivariate analyses, a small negative correlation was found between positive and negative affect items in the QUALID (rs = −0.32), indicating that positive and negative affect are not highly associated and justifies using a ratio of positive to negative affect as a measure of well-being.
Baseline Measures of Predictor and Outcome Variables
The mean QUALID score for the sample was 19.7 (SD = 7.9), indicating overall good quality of life for participants. Similarly, the mean affect ratio (2.98:1) indicates that participants were experiencing a positive affect balance. Over 1 week, they expressed 2.98 times more positive than negative affect. Univariate analyses (frequencies), however, indicated that most participants (55.1%) were below the affect ratio of 3:1, which is considered indicative of high-level well-being by some investigators (Fredrickson & Losada, 2005).
The majority of observed staff interactions with residents were rated as positive only (n = 263); 12 interactions were rated as solely negative and another 50 interactions were rated as including both positive and negative staff behaviors. Staff in the 35 facilities made correct responses to an average of eight of 10 items on the Knowledge of Person-Centered Approaches for Dementia Care. As expected, CNA hours per resident per day (reported in minutes) were highest, followed by RN and LPN hours. The results of the Environmental Assessment indicated that the physical environments in the facilities were designed to be somewhat supportive of residents' needs (mean score = 18 [of 24]). This finding was also true of the Person-Centered Policies (mean score = 18 [of 24]).
The bivariate analyses for each potential predictor variable and covariate versus the outcome variable are in Table 3. The following predictors were positively and significantly associated with resident affect balance: staff knowledge, RN hours, and CNA hours. Nursing home (covariate) had a significant effect on affect balance. Function (BI) and gender were not significant, but there was a trend for those with better function and males to have higher affect balance. Age, mental status (BIMS), quality of staff interaction (QuIS), LPN hours, environmental assessment, and person-centered policies were not significantly associated with affect balance.
Bivariate Analyses of Predictor Variables With Affect Balance Using Quantile Regression of the Median
The results of the quantile multivariable regression analysis, including parameter estimates and 95% confidence intervals, are in Table 4. The model indicated that quality of staff interaction and function were independently significant after controlling for the other variables.
Quantile Multivariable Regression Model of Affect Balance
Staff in nursing homes are challenged to go beyond basic custodial care and help people live well with dementia. The assessment of progress toward this goal requires using measures from a positive rather than a deficit framework. Affect balance is one such measure and captures well-being in people with late-stage dementia.
In this secondary analysis, it was hypothesized that more positive staff interaction during caregiving, greater staff knowledge of person-centered approaches for dementia care, higher staff hours of care, a more supportive physical environment, a greater number of person-centered policies, and higher resident function will be associated with higher affect balance. The authors controlled for resident demographics and mental status and found that the quality of staff interaction and resident function, two factors that can be modified, were independently and significantly associated with resident well-being as measured by affect balance.
The demographic characteristics of the 325 participants in the sample indicate that these residents were typical of most nursing home residents in the United States: they were female, Caucasian, and had significant cognitive and functional impairments. Staff hours (reported in minutes) available per resident per day were also similar to U.S. averages as reported on the Nursing Home Compare website (RN = 49 minutes, LPN = 50 minutes, and CNA = 148 minutes). The only exception was CNA hours, which at 134.4 minutes, were lower. Staff had good knowledge of person-centered approaches to care as shown by their mean score of 8 of 10 on the knowledge questionnaire. Predominantly positive interactions were also observed between staff and residents, indicating that staff used appropriate approaches in practice, at least during observation times. Although there was room for improvement, the environment and policies in the 35 facilities were supportive of residents' independence and function and endorsed the use of person-centered approaches.
The bivariate analyses confirmed what others have found (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000) and indicate that positive and negative affect were not highly associated. As a result, a ratio of positive to negative affect was used as a measure of well-being and factors that were associated with that ratio were examined in an effort to determine potential targets for improving well-being.
Participants had a mean affect ratio of 2.98:1, which indicates a positive balance but below the high-level well-being ratio of 3.0:1. This ratio is higher than what was found in prior research (Kolanowski et al., 2014) of nursing home residents but lower than the affect ratio found in other samples of nursing home residents and community-dwelling older adults (Diehl, Hay, & Berg, 2011; Meeks, Van Haitsma, Kostiwa, & Murrell, 2012). The current findings and those of others indicate that across nursing home residents there is variability in affect balance. More than one half (55.1%) of the current sample had affect ratios below 3.0:1, indicating that more can be done to improve their well-being. Methods to improve positive affect and simultaneously reduce negative affect include engaging residents in activities they enjoy and providing care according to their preferences. Determining the best approach for achieving the goal of high-level well-being is especially important for high-risk residents who exhibit significant negative affect and who are frequently isolated and not included in activities that could improve their positive affect.
In the bivariate analyses, staff knowledge of person-centered approaches for dementia care and the number of RN and CNA hours (reported as minutes) of care provided per resident per day were found to be positively and significantly associated with affect balance. These findings make intuitive sense and suggest that positive resident outcomes can be achieved when staff are educated on person-centered care approaches (Resnick et al., in press) and when there is sufficient time for staff to implement these approaches (Kim, Harrington, et al., 2009). The current findings also suggest that providing more RN vs. LPN hours of care may be advantageous, the former having more educational preparation to manage residents' complex needs.
In the multivariable model, the quality of staff interaction (QuIS) and function (BI) were independently significant after controlling for the other variables. Interactions that were positive versus those having a combination of positive and negative interactions and better function were associated with higher affect balance. The authors did not find that negative interactions resulted in lower affect balance and believe this is a function of the small number of negative episodes observed (n = 12).
The manner in which staff interact and communicate with residents contributes to their well-being (Wiechula et al., 2016). Studies support the association of positive staff communication with improved resident affect (Tappen & Williams, 2009). The current findings provide additional evidence for including communication training in all staff educational programs as a critical component for improving positive affect, raising the affect balance, and ensuring the well-being of residents. A recent systematic review of communication skills training for nursing staff found, however, that the content of these programs requires greater specificity for real-world practice (Machiels, Metzelthin, Hamers, & Zwakhalen, 2017). Nursing staff need explicit guidance on how to improve their communication skills so that their interactions foster resident well-being. Role modeling this behavior and demonstrating the positive impact it can have on residents may be a helpful way to augment education.
In prior research, the current authors found that positive affect was associated with resident functional ability but not mental status (Kolanowski et al., 2007). The findings in the current study are similar. Higher functional ability may contribute to well-being by making it possible for residents to engage in activities they enjoy and to perform activities of daily living rather than having others “do” for them in what they may perceive as an intrusion. The findings underscore the importance of maintaining residents' functional abilities for their overall well-being. Function-focused care, an approach that encourages resident participation in all aspects of direct care (Galik, Resnick, Hammersla, & Brightwater, 2013), may have benefits beyond functional competence to include well-being. The authors note that successful implementation of function-focused care requires some of the same factors found to be significant in the bivariate analyses: adequate RN and CNA staffing and staff knowledge of person-centered care.
The current cross-sectional observational study has limitations. The study was a secondary analysis of baseline data from a large pragmatic clinical trial and that study was not powered to detect small sized effects linked to affect balance. There are other variables that likely effect affect balance that were not measured and controlled for, such as psychoactive medications and staff job satisfaction/turnover. Despite these limitations, the study has notable strengths: the sample size was large, the research evaluators were well-trained and had prior experience working with nursing home residents, the study measures were validated for use with nursing home residents, and they demonstrated good to excellent reliability. There are implications for practice and research that can be made based on these findings.
Implications and Conclusion
Because the quality of staff interaction and function are important for resident well-being, the bivariate results may give some guidance on how these factors can be improved. There is an urgent need to improve the right kind of staffing levels in nursing homes, that is, professional nursing staff who have leadership skills and expertise in geriatric assessment and intervention. These individuals can function as role models for direct care staff to prevent functional loss and promote positive resident interactions. The need for staff sufficient in number and preparation in person-centered care is consistent with the Alzheimer's Association workforce principle for assuring quality dementia care (Gilster, Boltz, & Dalessandro, 2018).
The findings also have heuristic value for future research. Frameworks that focus on behavioral symptoms operate from a deficit perspective and may not be appropriate for modeling positive outcomes such as affect balance. Strong conceptual frameworks from a strength perspective are needed to guide future research on affect balance and are being developed (Van Haitsma et al., 2019). Future studies with larger samples of nursing homes might provide greater variance on some of the predictor variables and the ability to show a stronger effect on affect balance. More research is needed to design and evaluate staff communication interventions and their effect on affect balance. There is also a need to examine affect balance as a quality indicator and its relationship with other salient resident (e.g., depression, use of psychoactive medications, pain, nutritional status, physical activity) and staff (e.g., turnover, job satisfaction) outcomes.
- Albert, S.M., Jacobs, D.M., Sano, M., Marder, K., Bell, K., Devanand, D. & Stern, Y. (2001). Longitudinal study of quality of life in people with advanced Alzheimer's disease. American Journal of Geriatric Psychiatry, 9, 160–168. http://doi.org/10.1097/00019442-200105000-00008 doi:10.1097/00019442-200105000-00008 [CrossRef]11316620
- Algase, D.L., Beck, C., Kolanowski, A., Whall, A., Berent, S., Richards, K. & Beattie, E. (1996). Need-driven dementia-compromised behavior: An alternative view of disruptive behavior. American Journal of Alzheimer's Disease, 11(6), 10–19. http://doi.org/10.1177/153331759601100603 doi:10.1177/153331759601100603 [CrossRef]
- Ampe, S., Sevenants, A., Smets, T., Declercq, A. & Van Audenhove, C. (2016). Advance care planning for nursing home residents with dementia: Policy vs. practice. Journal of Advanced Nursing, 72, 569–581. http://doi.org/10.1111/jan.12854 doi:10.1111/jan.12854 [CrossRef]
- Bradburn, N.M. (1969). The structure of psychological well-being. Chicago, IL: Aldine.
- Calkins, M.P. (2018). From research to application: Supportive and therapeutic environments for people living with dementia. The Gerontologist, 58(Suppl. 1), S114–S128. http://doi.org/10.1093/geront/gnx146 doi:10.1093/geront/gnx146 [CrossRef]29361065
- Carstensen, L.L., Pasupathi, M., Mayr, U. & Nesselroade, J.R. (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79, 644–655. http://doi.org/10.1037/0022-35220.127.116.114 doi:10.1037/0022-3518.104.22.1684 [CrossRef]11045744
- Clark, P. & Bowling, A. (1989). Observational study of quality of life in NHS nursing homes and a long-stay ward for the elderly. Ageing & Society, 9, 123–148. http://doi.org/10.1017/S0144686X00013520 doi:10.1017/S0144686X00013520 [CrossRef]
- De Boer, B., Beerens, H., Katterbach, M., Viduka, M., Willemse, B. & Verbeek, H. (2018). The physical environment of nursing homes for people with dementia: Traditional nursing homes, small-scale living facilities, and green care farms. Healthcare (Basel), 6, 137. http://doi.org/10.3390/healthcare6040137 doi:10.3390/healthcare6040137 [CrossRef]
- Diehl, M., Hay, E.L. & Berg, K.M. (2011). The ratio between positive and negative affect and flourishing mental health across adulthood. Aging & Mental Health, 15, 882–893. http://doi.org/10.1080/13607863.2011.569488 doi:10.1080/13607863.2011.569488 [CrossRef]
- Fredrickson, B.L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56, 218–226. http://doi.org/10.1037/0003-066X.56.3.218 doi:10.1037/0003-066X.56.3.218 [CrossRef]
- Fredrickson, B.L. & Losada, M.F. (2005). Positive affect and the complex dynamics of human flourishing. American Psychologist, 60, 678–686. http://doi.org/10.1037/0003-066X.60.7.678 doi:10.1037/0003-066X.60.7.678 [CrossRef]16221001
- Galik, E., Resnick, B., Hammersla, M. & Brightwater, J. (2013). Optimizing function and physical activity among nursing home residents with dementia: Testing the impact of function-focused care. The Gerontologist, 54, 930–943. http://doi.org/10.1093/geront/gnt108 doi:10.1093/geront/gnt108 [CrossRef]24092822
- Galynker, I.I., Roane, D.M., Miner, C.R., Feinberg, T.E. & Watts, P. (1995). Negative symptoms in patients with Alzheimer's disease. American Journal of Geriatric Psychiatry, 3, 52–59. http://doi.org/10.1097/00019442-199524310-0000728530958
- Gilmore-Bykovskyi, A.L., Roberts, T.J., Bowers, B.J. & Brown, R.L. (2015). Caregiver person-centeredness and behavioral symptoms in nursing home residents with dementia: A timed-event sequential analysis. The Gerontologist, 55(Suppl. 1), S61–S66. http://doi.org/10.1093/geront/gnu164 doi:10.1093/geront/gnu164 [CrossRef]26055782
- Gilster, S.D., Boltz, M. & Dalessandro, J.L. (2018). Long-term care workforce issues: Practice principles for quality dementia care. The Gerontologist, 58(Suppl. 1), S103–S113. https://doi.org/10.1093/geront/gnx174 doi:10.1093/geront/gnx174 [CrossRef]29361072
- Heinze, G. & Dunkler, D. (2017). Five myths about variable selection. Transplant International, 30, 6–10. http://doi.org/10.1111/tri.12895 doi:10.1111/tri.12895 [CrossRef]
- Kaufmann, E.G. & Engel, S.A. (2016). Dementia and well-being: A conceptual framework based on Tom Kitwood's model of needs. Dementia, 15, 774–788. http://doi.org/10.1177/1471301214539690 doi:10.1177/1471301214539690 [CrossRef]
- Kim, H., Harrington, C. & Greene, W.H. (2009). Registered nurse staffing mix and quality of care in nursing homes: A longitudinal analysis. The Gerontologist, 49, 81–90. http://doi.org/10.1093/geront/gnp014 doi:10.1093/geront/gnp014 [CrossRef]19363006
- Kim, H., Kovner, C., Harrington, C., Greene, W. & Mezey, M. (2009). A panel data analysis of the relationships of nursing home staffing levels and standards to regulatory deficiencies. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 64, 269–278. http://doi.org/10.1093/geronb/gbn019 doi:10.1093/geronb/gbn019 [CrossRef]
- Kolanowski, A., Fortinsky, R.H., Calkins, M., Devanand, D.P., Gould, E., Heller, T. & Zimmerman, S. (2018). Advancing research on care needs and supportive approaches for persons with dementia: Recommendations and rationale. Journal of the American Medical Directors Association, 19, 1047–1053. http://doi.org/10.1016/j.jamda.2018.07.005 doi:10.1016/j.jamda.2018.07.005 [CrossRef]30145171
- Kolanowski, A., Hoffman, L. & Hofer, S.M. (2007). Concordance of self-report and informant assessment of emotional well-being in nursing home residents with dementia. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 62, P20–P27. http://doi.org/10.1093/geronb/62.1.P20 doi:10.1093/geronb/62.1.P20 [CrossRef]
- Kolanowski, A., Litaker, M.S., Catalano, P.A., Higgins, P.A. & Heineken, J. (2002). Emotional well-being in a person with dementia. Western Journal of Nursing Research, 24, 28–48. http://doi.org/10.1177/01939450222045699 doi:10.1177/01939450222045699 [CrossRef]11829272
- Kolanowski, A., Van Haitsma, K., Meeks, S. & Litaker, M. (2014). Affect balance and relationship with well-being in nursing home residents with dementia. American Journal of Alzheimer's Disease & Other Dementias, 29, 457–462. http://doi.org/10.1177/1533317513518657 doi:10.1177/1533317513518657 [CrossRef]
- Lawton, M.P., Van Haitsma, K. & Klapper, J. (1996). Observed affect in nursing home residents with Alzheimer's disease. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 51, P3–P14. http://doi.org/10.1093/geronb/51B.1.P3 doi:10.1093/geronb/51B.1.P3 [CrossRef]
- Machiels, M., Metzelthin, S.F., Hamers, J.P. & Zwakhalen, S.M. (2017). Interventions to improve communication between people with dementia and nursing staff during daily nursing care: A systematic review. International Journal of Nursing Studies, 66, 37–46. http://doi.org/10.1016/j.ijnurstu.2016.11.017 doi:10.1016/j.ijnurstu.2016.11.017 [CrossRef]
- Magai, C., Cohen, C., Gomberg, D., Malatesta, C. & Culver, C. (1996). Emotional expression during mid-to late-stage dementia. International Psychogeriatrics, 8, 383–395. http://doi.org/10.1017/S104161029600275X doi:10.1017/S104161029600275X [CrossRef]
- Mahoney, F. & Barthel, D. (1965). Functional evaluation: The Barthel index. Maryland State Medical Journal, 14, 61–65.14258950
- Matthews, E.B., Stanhope, V., Choy-Brown, M. & Doherty, M. (2018). Do providers know what they do not know? A correlational study of knowledge acquisition and person-centered care. Community Mental Health Journal, 54, 514–520. http://doi.org/10.1007/s10597-017-0216-6 doi:10.1007/s10597-017-0216-6 [CrossRef]29313172
- McLean, C., Griffiths, P., Mesa-Eguiagaray, I., Pickering, R.M. & Bridges, J. (2017). Reliability, feasibility, and validity of the quality of interactions schedule (QuIS) in acute hospital care: An observational study. BMC Health Services Research, 17, 380. http://doi.org/10.1186/s12913-017-2312-2 doi:10.1186/s12913-017-2312-2 [CrossRef]28569151
- Meeks, S., Van Haitsma, K., Kostiwa, I. & Murrell, S.A. (2012). Positivity and well-being among community-residing elders and nursing home residents: What is the optimal affect balance?Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 67, 460–467. http://doi.org/10.1093/geronb/gbr135 doi:10.1093/geronb/gbr135 [CrossRef]
- Nursing Home Compare. (2019). Nursing Home Compare datasets. Retrieved from https://www.medicare.gov/nursinghomecompare/search.html
- Resnick, B., Galik, E., Kolanowski, A., Van Haitsma, K., Boltz, M., Ellis, J. & Flanagan, N.M. (2018). Reliability and validity testing of the quality of life in late-stage dementia scale. American Journal of Alzheimer's Disease & Other Dementias, 33, 277–283. http://doi.org/10.1177/1533317518765133 doi:10.1177/1533317518765133 [CrossRef]
- Resnick, B., Gruber-Baldini, A.L., Pretzer-Aboff, I., Galik, E., Buie, V.C., Russ, K. & Zimmerman, S. (2007). Reliability and validity of the evaluation to sign consent measure. The Gerontologist, 47, 69–77. http://doi.org/10.1093/geront/47.1.69 doi:10.1093/geront/47.1.69 [CrossRef]17327542
- Resnick, B., Kolanowski, A., Van Haitsma, K., Galik, E., Boltz, M., Ellis, J. & Zhu, S. (2018). Testing the evidence integration triangle for implementation of interventions to manage behavioral and psychological symptoms associated with dementia: Protocol for a pragmatic trial. Research in Nursing and Health, 41, 228–242. http://doi.org/10.1002/nur.21866 doi:10.1002/nur.21866 [CrossRef]29485197
- Resnick, B., Kolanowski, A., Van Haitsma, K., Galik, E., Boltz, M., Ellis, J. & Zhu, S. (2019). Reliability and validity of the environment and policy assessments for person centered care and management of BPSD. Manuscript submitted for publication.
- Resnick, B., Kolanowski, A., Van Haitsma, K., Galik, E., Boltz, M., Ellis, J. & Madrigal, C. (in press). Reliability and validity of the knowledge of person-centered behavioral approaches for BPSD test. Journal of Nursing Measurement.
- Saliba, D., Buchanan, J., Edelen, M.O., Streim, J., Ouslander, J., Berlowitz, D. & Chodosh, J. (2012). MDS 3.0: Brief interview for mental status. Journal of the American Medical Directors Association, 13, 611–617. https://doi.org/10.1016/j.jamda.2012.06.004 doi:10.1016/j.jamda.2012.06.004 [CrossRef]22796362
- Tappen, R.M. & Williams, C.L. (2009). Therapeutic conversation to improve mood in nursing home residents with Alzheimer's disease. Research in Gerontological Nursing, 2, 267–275. http://doi.org/10.3928/19404921-20090428-02 doi:10.3928/19404921-20090428-02 [CrossRef]
- Van Haitsma, K., Abbott, K.M., Arbogast, A., Bangerter, L.R., Heid, A.R., Behrens, L.L. & Madrigal, C. (2019). A preference-based model of care: An integrative theoretical model of the role of preferences in person-centered care. The Gerontologist. Advance online publication. doi: https://doi.org/10.1093/geront/gnz075 doi:10.1093/geront/gnz075 [CrossRef]31152589
- Weiner, M.F., Martin-Cook, K., Svetlik, D.A., Saine, K., Foster, B. & Fontaine, C. (2000). The quality of life in late-stage dementia (QUALID) scale. Journal of the American Medical Directors Association, 1, 114–116. http://doi.org/10.1037/t00432-000
- Wiechula, R., Conroy, T., Kitson, A. L., Marshall, R.J., Whitaker, N. & Rasmussen, P. (2016). Umbrella review of the evidence: What factors influence the caring relationship between a nurse and patient?Journal of Advanced Nursing, 72, 723–734. http://doi.org/10.1111/jan.12862 doi:10.1111/jan.12862 [CrossRef]
Participants' Demographic and Clinical Characteristics (N = 325)
|Age (mean, SD) (range) (years)||82.7 (10.3) (55 to 102)|
|Gender (n, %)|
| Female||224 (69)|
| Male||101 (31)|
|Race (n, %)|
| Caucasian||245 (75.4)|
| African American||79 (24.3)|
| Mixed race||1 (0.3)|
|Ethnic background (n, %)|
| Not Hispanic or Latino||318 (97.8)|
| Hispanic or Latino||7 (2.2)|
|Marital status (n, %)|
| Widowed||151 (46.4)|
| Never married||64 (19.6)|
| Married||54 (16.6)|
| Divorced/separated||36 (11)|
| Refused to answer/don't know||20 (6.1)|
|BIMS (mean, SD) (range)||4.3 (3.6) (0 to 12)|
|Barthel Index (mean, SD) (range)||34 (24.9) (3 to 80)|
Baseline Measures of Predictor and Outcome Variables
|Variable||Mean (SD) (Range)|
|Overall QUALID score||19.7 (7.9) (11 to 55)|
|Affect balancea||2.98:1 (0.2:1 to 5.0:1)|
|Knowledge Test||8 (0.9) (6 to 10)|
|Staff hours per resident per day (minutes)|
| RN||48.1 (17.2) (16 to 80)|
| LPN||49.3 (12.9) (18 to 74)|
| CNA||134.4 (20.4) (91 to 198)|
|Environment assessment||18.5 (4.1) (8 to 24)|
|Policies||17.7 (5.2) (5 to 24)|
|Quality of Interactions Schedule|
| Both||50 (15.4)|
| Positive only||263 (80.9)|
| Negative only||12 (3.7)|
Bivariate Analyses of Predictor Variables With Affect Balance Using Quantile Regression of the Median
|Variable||Estimate||95% CI||p Value|
| Negative||0.33||[−0.96, 1.63]||0.61|
| Positive||0.33||[−0.36, 1.03]||0.34|
| Both||0.00||[0.00, 0.00]|
|Staff knowledge||0.32||[0.03, 0.62]||0.03|
| RN||0.01||[0.00, 0.04]||0.04|
| LPN||−0.01||[−0.02, 0.02]||0.85|
| CNA||0.02||[−0.03, 0.01]||<0.01|
| Female||−0.47||[−1.05, 0.09]||0.10|
| Male||0.00||[0.00, 0.00]|
|Mental status||0.02||[−0.06, 0.10]||0.61|
|Nursing Home Comparea,b||<0.001|
Quantile Multivariable Regression Model of Affect Balance
|Variable||Estimate (95% CI)||p Value|
|Knowledge||0.925 [−83,145.5, 83,147.4]||1.0|
| Positive only||0.493 [0.109, 0.877]||0.01|
| Negative only||0.913 [−0.231, 2.057]||0.12|
| RN||−0.019 [−26,329.8, 26,329.76]||1.0|
| CNA||0.0 [−226,020.4, 226,020.4]||1.0|
|Function||0.005 [0.0, 0.010]||0.04|
| Female||−0.208 [−0.478, 0.063]||0.13|