Model Testing of the Factors That Influence Performance of Function Focused Care and Function Among Assisted Living Residents

Abstract

The purpose of this study was to test a model of factors associated with participating in function focused care. Function focused care is a philosophy of care in which residents are encouraged to engage in functional and physical activities during care interactions. This was a secondary data analysis using data from the Dissemination and Implementation of Function Focused Care for Assisted Living Using the Evidence Integration Triangle (FFC-AL-EIT) study. Residents (n = 550) were recruited from 59 AL settings. The majority were female (n = 380, 69%) and White (n = 536, 97%). Model testing was done. Comorbidities, quality of interactions, environments, profit status, cognitive impairment, depression, and function were associated with function focused care and accounted for 17% of the variance. Next steps should include intervening on changeable factors (e.g., environments) and adding factors to better explain performance of function focused care such as motivation, resilience, and staff satisfaction.

Keywords

activities of daily living (ADLs)assisted living function/functional status

Introduction

The majority of residents in assisted living (AL) engage in limited amount of physical activity, and decline functionally more rapidly than their peers in nursing homes (Galik et al., 2015). Reasons for decline are multifactorial and include resident and setting factors. Although not always consistent, resident factors that have been associated with engaging in functional and physical activities include age, gender, cognition, behavioral and psychological symptoms associated with dementia (BPSD), comorbidities, underlying physical capability, pain, motivation, and medications (Lunney et al., 2019; Peng et al., 2020; Unhjem et al., 2019). Those who are older, are female, demonstrate more BPSD, have more comorbidities, have lower physical capability, have more pain, and receive more medications are less likely to participate in functional and physical activities such as walking, bathing, dressing, or going to exercise classes.

Setting-level factors that influence functional and physical activities of residents include policies and the physical and care environment within the setting. Physical environment and policy factors include such things as the height of the chairs and beds, which can influence residents’ ability to transfer; access to straight and clear pathways for walking; having pleasant destination areas; the presence of age-appropriate resources to optimize function (e.g., available and safe assistive devices such as walkers and age-appropriate weights for resistance exercise); and policies that prevent residents from walking outside or using the stairways (Resnick & Galik, 2015). Poorly designed physical environments can impose barriers that can lead to decreased levels of physical activity (Nordin et al., 2017) and functional decline among residents (Chaudhury et al., 2018). The care environment in AL settings tends to focus on task completion and meeting the basic needs of residents (Galik et al., 2009) and does not always facilitate optimal resident participation in their own bathing and dressing (Bowers et al., 2000). When care is provided by staff, residents sometimes misinterpret this care as threatening, and they may try to physically defend themselves against the perceived threat (Maren, 2005), resist or refuse care, or demonstrate other behavioral symptoms (Volicer et al., 2007). Staff working in these settings may not have the skills to prevent and/or deal with aggressive and resistive behaviors (Morgan et al., 2016). Facilities that are not for profit tend to provide better preventive care, including engaging residents in physical activity, than those that are for profit (Lum et al., 2014; Moghadamyeghaneh et al., 2019). Number of beds has also been shown to influence participation in functional activities, with a larger number of beds associated with more dependent function in residents (Palese et al., 2019) although better improvement of function over time in rehabilitation settings (Yeh & Lo, 2004).

Verbal and nonverbal communication between staff and residents has an impact on how residents respond during care interactions and whether residents will actively participate in functional activities such as bathing and dressing (Savundranayagam & Lee, 2017). Positive interactions can be primarily social or those that occur during physical care including encouragement and involvement of the resident in his or her personal care activities. Promoting maximum level of resident participation in their own care activities can decrease the risk of behavioral and affective symptoms while optimizing function (Jablonski et al., 2011). Use of cueing, gesturing, pantomime, and hand over hand care reduces fear and resistiveness to care and also promotes functional independence and physical activity (Chalmers, 2000). Conversely, negative interactions include those that are overly protective of residents and restrictive and generally are carried out purely for the ease of the staff (Proctor et al., 1998). The work to date evaluating staff–resident interactions has mainly focused on behavioral outcomes and prevention and management of behavioral symptoms (Morgan et al., 2016). Understanding how staff–resident interactions affect residents’ participation in functional activities may help to maintain and optimize function among AL residents.

Function Focused Care

Function focused care is a philosophy of care that has been used to engage residents in functional activities and thereby maintain or improve function and physical activity (Galik et al., 2015; Resnick et al., 2011). Function focused care incorporates a step approach to address the environment; teach and motivate staff, particularly the direct care workers, to evaluate older adults’ underlying capability with regard to function and physical activity; and implement interventions to optimize their participation in all activities. Examples of function focused care interactions include role modeling behavior for residents (e.g., oral care, eating), providing verbal cues during dressing, walking with a resident to the dining room rather than transporting him or her via a wheelchair, or motivating and helping a resident get to and participate in an exercise class.

The purpose of this study was to test a model of the factors that are associated with participating in function focused care among residents in AL settings. Understanding these factors and the relationship between these factors via model testing can be used to guide interventions and thereby help to maintain and optimize function among AL residents. As shown in Figure 1, it was hypothesized that resident age, gender, comorbidities, medications, physical function, depression, agitation, resistiveness to care, pain, number of beds, profit status, environments, and policies related to optimizing function would be directly and/or indirectly associated with participation in function focused care activities.

Figure 1.

Full hypothesized model.

Method

Design

This was a secondary data analysis using baseline data from the second and third cohorts of the study titled, “Dissemination and Implementation of Function Focused Care for Assisted Living Using the Evidence Integration Triangle (FFC-AL-EIT).” The cohorts were recruited annually over the course of 3 years, and Year 2 and Year 3 cohorts included 59 different AL settings in Maryland, Pennsylvania, and Massachusetts. The study was approved by a university-based institutional review board. Participants were invited to participate if they (a) had at least 25 beds, (b) identified a nurse (a direct care worker, licensed practical nurse, or registered nurse) to be the champion and work with the study team in the implementation of FFC-AL-EIT, and (c) were able to access email and websites via a phone, tablet, or computer.

Study Participants

Residents were eligible to participate in this study if they were 65 years of age or older; able to speak English; lived in a participating AL setting in Maryland, Pennsylvania, or Massachusetts at the time of recruitment; and were able to recall at least one out of three words as per the Mini-Cog (Borson et al., 2003). Residents were excluded from the study if they were enrolled in hospice. Potentially eligible participants were identified by the staff in the AL setting and were randomly approached until 10 residents per setting were recruited. Potentially eligible residents were given the Evaluation to Sign Consent, a five-item questionnaire evaluating the individual’s understanding of participation in the research project (Resnick et al., 2007). A total of 963 residents were screened in these two cohorts, and 955 residents were eligible based on age and not being enrolled in hospice. Of the 955 residents, 398 residents (42%) refused and 557 residents (58%) consented. Seven individuals were ineligible due to their Mini-Cog score and 550 residents were enrolled in the study.

Procedures

Following consent, demographic and descriptive information was obtained from residents’ charts including age, gender, race, medications, and comorbidities based on the Cumulative Illness Rating Scale for Geriatrics (Linn et al., 1968). In addition, assessments were done with regard to function, BPSD (resistiveness to care, anxiety, and depression), pain, and the quality of interactions between the resident and staff.

Measures

Measures were completed at baseline by study evaluators who had experience working with older adults with cognitive impairment in AL settings. With the exception of the completion of the Saint Louis University Mental Status Examination (SLUMS; Morley & Tumosa, 2002), which was done based on resident interview, data were obtained via direct observation of residents or input from staff during daytime hours. Facility measures included number of beds and profit status obtained from facility staff, and the Function Focused Environment Assessment (FFC-EA) and Function Focused Policy Assessment (FFC-PA) in AL completed by research evaluators. The following measures were used.

Function

The Barthel Index (Mahoney & Barthel, 1965) was used to measure function. This is a 10-item measure of activities of daily living (e.g., bathing, dressing, ambulation) that was completed based on input from the caregiver working with the resident on the day of testing. The items are weighted to account for the amount of assistance required. A score of 100 indicates complete independence. Prior testing of this measure provided support for internal consistency, inter-rater reliability, and validity based on correlations with the Functional Inventory Measure (Mahoney & Barthel, 1965).

Pain

Pain was evaluated using the Pain Assessment in Advanced Dementia (PAINAD) Scale (Warden et al., 2003), which is an observational measure of pain. The PAINAD Scale evaluates five behaviors that are commonly noted among individuals with pain. These include breathing independent of vocalization, negative vocalization, facial expression, body language, and consolability. Observations were done during periods of activity such as transferring or ambulating. For each behavior, scoring options range from 0 to 2. A total score of 1 to 3 is indicative of mild pain, 4 to 6 is moderate pain, and 7 to 10 is severe pain. There is evidence of inter-rater reliability of the PAINAD (DeWaters et al., 2008) and validity based on a correlation between the PAINAD and the Numeric Pain Scale and the Discomfort Scale for Dementia of the Alzheimer’s Type (DeWaters et al., 2008).

Cognition

The SLUMS (Morley & Tumosa, 2002), which is a 30-point screening measure that tests for orientation, memory, attention, and executive function, was used to evaluate cognitive function. Scores of 0 to 20 are indicative of cognitive impairment. Prior testing provided evidence of reliability and convergent validity when compared with the Mini Mental Status Examination (MMSE; Stewart et al., 2012). There were 69 individuals (13%) who refused to complete the SLUMS, and for these individuals, evidence of cognitive impairment was based on the screening Mini-Cog, with a score of 1 to 2 out of 3 recall indicative of cognitive impairment (Borson et al., 2003).

Resistiveness to care

Resistiveness to care was measured using the Resistiveness to Care Scale (Mahoney et al., 1999), which is based on 13 items that assess residents’ behaviors during activities of daily living. The evaluator observed care interactions over a 5- to 15-min period for evidence of any of the following behaviors: screaming, crying, pushing away, grabbing an object or a person, pulling at an object or a person, clenching the mouth, turning, adducting, hitting or kicking, pushing or pulling, refusing care, or threatening the caregiver. Evidence of resistive behavior was noted and summed to get the total number of behaviors in which the resident was resistive to care. Prior testing of the scale provided evidence of internal consistency (Galik et al., 2017; Mahoney et al., 1999) and construct validity by factor analysis and Rasch analysis, both of which supported the structure of the Resistiveness to Care Scale (Galik et al., 2017; Mahoney et al., 1999).

Agitation

Agitation was evaluated using the Short Cohen-Mansfield Agitation Inventory (CMAI; Cohen-Mansfield, 1986). The Short CMAI is completed based on input from the caregiver providing care to the resident on the day of testing. The measure includes 14 items with a 5-point Likert-type response scale and addresses the frequency of symptoms indicative of agitation. Prior use supported evidence of internal consistency, inter-rater reliability (Cohen-Mansfield, 1986), and validity based on factor analysis and convergent validity (Zare et al., 2012).

Depression

The Cornell Scale for Depression in Dementia (CSDD; Alexopoulos et al., 1988) was used to evaluate 19 symptoms of depression and was also based on input from the caregiver providing care to the resident on the day of testing. Prior research supported evidence of inter-rater reliability and construct validity based on comparisons with other measures of depression (Barca et al., 2015). Higher scores were indicative of more depressive symptoms.

The quality of care interactions

The Quality of Care Interactions Scale (QuIS; Dean et al., 1993) is a descriptive measure of positive care, positive social, neutral, negative protective, and negative restrictive interactions that can occur between staff and residents. Interactions were observed by evaluators for 15 min during care-related activities. Prior testing of the QuIS using this descriptive approach provided evidence of inter-rater reliability (Dean et al., 1993) and validity based on significant relationships between QuIS findings and positive patient experiences (Dean et al., 1993). Quantification of the QuIS has also been done with scores ranging from 0 to 7 with higher scores indicating a better, or more positive, quality of interaction between the staff member and the resident (Marcantonio et al., 2001).

Performance of function focused care

The Function Focused Care Behavioral Checklist for Residents is a 19-item observation measure that assesses the resident’s performance of function focused care during routine care interactions. For example, the tool evaluates whether the resident engages in the care activity (e.g., walking to the bathroom) or whether she refuses to participate when encouraged to do so. Prior testing provided evidence of inter-rater reliability and construct validity (Resnick & Simpson, 2006).

FFC-EA and FFC-PA in AL

The FFC-EA includes 16 items focusing on environmental factors such as whether or not there were areas for residents to walk that were free of clutter. The FFC-PA includes 15 items addressing policies such as access to outside areas or access to devices that optimize function and physical activity. Items are scored as being present or not present and coded so that higher scores on both measures are indicative of environments and policies that are better for optimizing function and physical activity. The scores are summed and range from 0 to 16 on the FFC-EA and 0 to 15 on the FFC-PA.

Data Analysis

Descriptive analyses were performed to describe the sample. Structural equation modeling was used in this analysis to consider both the direct and indirect relationships between variables. Model testing was performed using structural equation modeling and the AMOS (SPSS, IBM) statistical program (Arbuckle, 1997). The sample covariance matrix was used as input and a maximum likelihood solution sought. The chi-square statistic and Steiger’s root mean square error of approximation (RMSEA) were used to estimate model fit. The larger the probability associated with the chi-square, the better the fit of the model to the data (Bollen, 1989). Because the chi-square statistic is sample size dependent, the chi-square is divided by degrees of freedom (df) to control for sample size effects. A ratio of less than or equal to 3 is considered an acceptable fit of the model to the data (Bollen, 1989). The RMSEA is insensitive to sample size. An RMSEA of <0.10 is considered good, and <0.05 is very good (Bollen, 1989). Path significance (i.e., significance of the Lambda values) was based on the critical ratio (CR). A CR > 2 in absolute value was considered significant. A squared multiple correlation (R²) was calculated for each outcome. The R² indicates the proportion of variance in the dependent variable accounted for by the set of independent variables in the model. The model was also tested using Mplus to control for facility and there were no differences noted in the paths, so the unadjusted findings are reported.

Results

The majority of the participants were female (n = 380, 69%) and White (n = 536, 97%) with the remaining Black (n = 10, 2%) or Other (n = 4, 1%). Only seven (1%) individuals were Hispanic. As shown in Table 1, the mean age of the participants was 89.30 years (SD = 7.64 years), they had 5.17 (SD = 1.87) comorbidities, and the majority were noted to have cognitive impairment (n = 407, 74%). Overall, the participants were generally independent with activities of daily living based on a mean score on the Barthel index of 81.48 (SD = 20.87). There was little evidence of pain with a mean PAINAD score of 0.24 (SD = 0.77), little evidence of agitation with a mean score of 14.69 (SD = 2.11) on the CMAI, little evidence of depressive symptoms with a mean score on the CSDD of 1.79 (SD = 2.97). The residents participated in a mean of 7.57 (SD = 2.70) out of a total of 19 function focused care activities evaluated. The quality of the care interactions between the staff and residents was generally positive with a mean score of 5.96 (SD = 1.44) out of a total possible score of 7. The majority of participants were exposed to positive social interactions (87%) and positive care interactions (63%). Only 22% of participants were exposed to neutral interactions, 3% were exposed to negative protective interactions, and 1% were exposed to negative restrictive interactions. With regard to facility descriptives, the mean number of beds was 98 (SD = 74) and the majority were for profit (n = 37, 63%). The mean environmental assessment was 15.29 (SD = 1.66) and the mean policy assessment was 10.56 (SD = 3.63).

Table 1.

Sample Descriptive Data of Model Variables (n = 550).

Variable	Minimum	Maximum	M	SD
Quality of interaction	0.00	7	5.96	1.44
Performance of function focused care	1.00	16.00	7.57	2.70
Age	65.00	105.00	89.30	7.64
Cognition (Saint Louis University Mental Status Examination)	1.00	24.00	15.90	4.74
Resistiveness to care	0.00	6.00	0.10	0.51
Agitation	14.00	35.00	14.69	2.11
Pain	0.00	6.00	0.24	0.77
Comorbidities	1.00	11.00	5.17	1.87
Function	5.00	100.00	81.48	20.87
Depressive symptoms	0.00	28.00	1.79	2.97
Total medications	0.00	12.00	5.15	2.52

The full model of factors associated with performance of function focused care (Figure 1) showed a poor fit of the data to the model with a χ²/df of 8.20 and an RMSEA of 0.10. Only 19 paths out of the 40 hypothesized paths were significant (Table 2). A revised model with significant paths only was tested (Figure 2, Table 3). Comorbidities, quality of interactions, environments that supported function, profit status, cognitive impairment, depression, and function were all directly associated with performance of function focused care of residents. Environment and profit status also were associated with function focused care indirectly through quality of care interactions. Together, these variables accounted for 17% of the variance in performance of function focused care. Residents with fewer comorbidities, more depressive symptoms, less cognitive impairment, more positive quality of care interactions, better function, and those who were living in for-profit facilities and in facilities with environments that supported function and physical activity performed or participated in more function focused care activities. The fit indices of the revised model still indicated a poor fit of the model to the data with a χ²/df = 9.20 and an RMSEA of 0.10.

Table 2.

Standardized Regression Weights for Performance of Function Focused Care.

Path			Weight	CR	P *
Quality of interaction	←	Age	.09	2.35	.02
Quality of interaction	←	Gender	−.02	−0.35	.72
Quality of interaction	←	Comorbidities	.04	0.85	.40
Pain	←	Medications	−.08	−1.85	.07
Pain	←	Comorbidities	.18	4.05	.01
Pain	←	Gender	−.02	−0.49	.62
Pain	←	Age	−.02	−0.36	.72
Pain	←	Depression	.11	2.23	.03
Pain	←	Agitation	.10	2.13	.03
Pain	←	Resistiveness to care	.39	9.78	.01
Quality of interaction	←	Number of beds	−.08	−0.94	.34
Quality of interaction	←	Profit status	−.15	−3.12	.01
Quality of interaction	←	Environment	.18	4.09	.01
Quality of interaction	←	Policies	.06	1.50	.13
Function focused care performed	←	Age	.02	0.53	.59
Function focused care performed	←	Gender	.05	1.57	.12
Function focused care performed	←	Comorbidities	−.18	−3.63	.01
Function focused care performed	←	Medications	.03	0.99	.32
Function focused care performed	←	Pain	−.06	−1.30	.19
Function focused care performed	←	Quality of interaction	.21	4.92	.01
Function focused care performed	←	Policies	.01	0.05	.96
Function focused care performed	←	Environment	.17	4.84	.01
Function focused care performed	←	Profit status	−.10	−3.58	.01
Function focused care performed	←	Number of beds	−.04	−1.20	.23
Function focused care performed	←	Cognitive impairment	−.10	−2.56	.01
Function focused care performed	←	Depression	.15	3.00	.01
Function focused care performed	←	Agitation	.05	1.15	.25
Function focused care performed	←	Resistiveness to care	.05	1.14	.26
Function focused care performed	←	Function	.10	2.74	.01
Function	←	Policies	.02	0.66	.51
Function	←	Environment	−.03	−0.69	.49
Function	←	Profit status	.11	3.15	.01
Function	←	Number of beds	−.03	−0.95	.34
Function	←	Cognitive impairment	−.10	−3.08	.01
Function	←	Depression		−4.32	.01
Function	←	Agitation		−1.98	.05
Function	←	Resistiveness to care		−0.95	.34
Function	←	Quality of interaction		−1.29	.20

Note. *p < .05 level. CR = critical ratio.

Figure 2.

Significant paths only.

Table 3.

Standardized Regression Weights for Revised Model for Performance of Function Focused Care.

Path			Weight	CR	P *
Quality of interaction	←	Age	.09	2.15	.03
Quality of interaction	←	Profit status	−.14	−3.38	.01
Quality of interaction	←	Environment	.20	4.65	.01
Pain	←	Comorbidities	.15	3.69	.01
Pain	←	Depression	.10	2.45	.01
Pain	←	Agitation	.10	2.55	.01
Pain	←	Resistiveness to care	.39	9.85	.01
Function	←	Pain	−.14	−3.30	.01
Function	←	Cognitive impairment	−.10	−2.57	.01
Function	←	Depression	−.15	−3.56	.01
Function	←	Agitation	−.08	−1.84	.05
Function	←	Profit status	.11	−3.30	.01
Function focused care performed	←	Comorbidities	−.13	−3.80	.01
Function focused care performed	←	Quality of interaction	.21	5.23	.01
Function focused care performed	←	Environment	.20	5.08	.01
Function focused care performed	←	Profit status	−.11	−3.32	.01
Function focused care performed	←	Cognitive impairment	−.10	−2.45	.01
Function focused care performed	←	Depression	.15	3.76	.01
Function focused care performed	←	Function	.10	2.74	.01

Note. *p < .05 level. CR = critical ratio.

Discussion

The hypothesized model describing the factors associated with performance of function focused care did not have a good fit of the data to the model and only 19 out of 40 paths were significant. The revised model with significant paths only did not improve the fit of the model to the data. Lack of model fit may be due to discrepancies between an empirically and theoretically based model as was hypothesized and the sample-specific real-world findings in the study (Boomsma, 2000). It is anticipated that lack of model fit in this sample was due to the lack of variance in the CSDD, CMAI, resistiveness to care, and PAINAD measures. The model explained only 17% of the variance in performance of function focused care. To improve model fit, other factors to include are leadership values and commitment to function focused care (Fryer et al., 2018), organizational factors such as staff satisfaction and morale (Chaudoir et al., 2013), beliefs among the staff and residents about the benefits or risks associated with performing function focused care activities (Resnick et al., 2012), resident characteristics including motivation and physical resilience (Colón-Emeric et al., 2019), and physical factors such as waist circumference and inflammation (Tay et al., 2019).

Model testing did support some of the proposed relationships between variables. Depressive symptoms were associated with better performance of function focused care activities. Associations between depressive symptoms and performance of function vary, with most studies noting that depressive symptoms were associated with less functional performance (Chen et al., 2019; Kilinç et al., 2019), whereas others noting that individuals with depressive symptoms show better functional performance (Ellis et al., 2019; Jung et al., 2020; Smith et al., 2019). It is possible that individuals with depression engage in physical activity as a way in which to manage their depression.

Other resident factors that were directly associated with performance of function focused care activities included having a greater number of comorbidities, impaired cognition, and better function. This suggests that there may be a need to educate staff on ways to help residents, who are more medically complex and cognitively impaired, participate in function focused care. For example, interventions that engage residents with cognitive impairment to participate in functional and physical activities such as providing role modeling and cueing may increase participation in function focused care and thereby improve or maintain function. Educational and intervention resources for staff are available on websites such as www.functionfocusedcare.org or www.nursinghometoolkit.com.

In addition to resident factors, facility factors also influenced resident performance of function focused care. Exposure to more positive and less negative or neutral care interactions, such as giving explanations for how to complete a task versus telling a resident to “sit down he or she might fall,” can help to facilitate performance of function focused care. Given the importance of these interactions on performance of function focused care, it may be helpful to work with staff to eliminate negative and neutral care interactions and provide more positive care and social interactions.

Profit status was also associated with performance of function focused care. Living in for-profit settings was associated with better performance of function focused care. Although not-for-profit settings tend to be seen as providing better quality of care than those that are for profit (Frey et al., 2019; Friedman et al., 2018), the for-profit settings may have had a different philosophy of care that resulted in better support of function focused care and an appreciation of the benefits associated with function focused care including improved residents outcomes, decreased risk of adverse events, and better resident/family satisfaction. The relationship between profit status and actual function of residents was quite different. Living in for-profit settings was associated with lower function among residents. The reason for this association is not clear, although it is anticipated that it may be a self-selection bias, in that, individuals with lower function tended to select for-profit settings. In for-profit settings, individuals with lower function can often pay for additional services to help them with personal care activities versus having to be discharged to a higher level of care (Paying for Senior Care, 2020).

The physical environment influenced whether or not participants performed function focused care activities. Prior research with residents in AL has likewise noted both inside and outside environments influence participation in function and physical activity (Holt et al., 2016; Park et al., 2019). Environment alone, however, does not assure participation in function focused care–related activities (Holt et al., 2016; Horowitz & Vanner, 2010). Environments may be built to facilitate function and physical activity and have walking paths and numerous exercise classes and opportunities to perform functional tasks offered but the individual has to have the motivation and willingness to engage in these activities and resilience to overcome challenges such as pain and fatigue.

Study Limitations

This study was limited by being a secondary data analysis and not developed to answer the proposed research questions. Although the study included a large sample of AL residents, they were from only three states and the variance in many of the outcome measures was limited, which affected model fit. In addition to being from only three states, the sample was from larger AL settings that were quite similar in terms of size, and environments and policies. We cannot assume the findings can be generalized to all AL residents or replicated with other AL samples. Future research should focus on including more heterogeneous settings and a more heterogeneous sample with more pain and behavioral symptoms.

Conclusion

Despite these limitations, the study provides some guidance for potential factors that may influence performance of function focused care and raises questions about the direction of some associations such as those between depressive symptoms and profit status with performance of function focused care. Future research attempting to replicate these associations is needed. Optimizing function, managing comorbidities, encouraging positive care interactions, and developing environments that support participation in an active lifestyle will likely help residents to engage in function and physical activities. In addition, establishing a philosophy of care, regardless of profit status, in which health promotion, functional independence, and physical activity are encouraged is critically important to optimizing performance of function focused care and helping to maintain function among residents living in AL.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the National Institute of Aging (5R01AG050516-03).

Ethical Approval

The study was approved by the University of Maryland, Baltimore Institutional Review Board: Protocol HP-00067128.

ORCID iDs

Barbara Resnick

Sarah Holmes

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