A National Survey of Data Currently being Collected by Adult Day Service Centers Across the United States

Abstract

An understanding of adult day service centers’ (ADC) impacts on clients’ health and well-being has been hampered by a lack of large-scale data. Standardizing data collection is critical to strengthening ADC programs, demonstrating their effectiveness, and enabling them to leverage additional funding streams beyond Medicaid. We distributed an electronic survey on current data collection efforts to ADCs nationally to determine categories of data ADCs are collecting related to clients’ health. In our sample (N = 248), only 32% of ADCs collected patient-level data for research and analysis—most commonly on activities of daily living, cognition, nutrition, and caregiver strain. However, validated assessment tools were used in less than 50% of the cases. ADCs are willing to collect data: More than 70% reported a willingness to participate in future studies. National studies piloting data collection protocols with uniform outcome measures are needed to advance the understanding of ADCs’ capabilities and impacts.

Keywords

adult day care health outcomes health services long-term services and supports policy

A National Survey of Data Currently Being Collected by Adult Day Service Centers Across the United States

The number of individuals served by adult day service centers (ADCs) increased 63% between 1990 and 2010 (Anderson et al., 2013). ADCs are nonresidential facilities that support the health and social needs of chronically ill adults in a professionally staffed group setting (Oliver & Foster, 2013). ADCs manage users’ physical and mental health, provide caregivers respite, and offer social activities that allow older adults to remain productively engaged while aging in place (Sadarangani et al., 2019).

Despite historical growth in the ADC sector, ADCs remain underutilized and underfunded relative to other forms of long-term care. According to the National Study of Long-Term Care Providers (Harris-Kojetin et al., 2019), in 2016, 286,300 adults utilized ADCs compared with 1,347,600 nursing home residents and 4,455,700 home health care users. One reason that may explain their underutilization is the lack of scientific evidence on the effectiveness of ADCs compared with other types of long-term care (Fields et al., 2014). For example, there have been no large-scale nationally representative studies of outcomes associated with ADC use. Scarce evidence restricts researchers’, policymakers’, and caregivers’ understanding of ADCs’ effectiveness and impact on users’ health and functional status. It also limits ADCs’ abilities to leverage additional funding streams beyond Medicaid, resulting in access barriers. Using high-quality data to strengthen community-based long-term care programs is especially critical in the aftermath of the COVID-19 pandemic—a time in which patients in nursing homes have fared poorly, causing more frail older adults to remain in their communities, and funding shortfalls have led to budgetary cuts to programs providing community-based health and social services (Mazumder et al., 2020).

A paper by Anderson et al. (2020) proposes a set of uniform outcome measures and data collection protocols for use by ADCs to assess their impact on users’ and caregivers’ well-being and to identify areas for quality improvement. Gaugler and Dykes (2019) also developed Adult Day Services Process and Use Measures to examine the mechanisms by which ADCs operate to benefit clients and caregivers. However, the implementation of these measures and protocols has been challenged by a lack of resources to support data collection within ADCs, such as personnel and electronic health records tracking systems. Moreover, mandates for data collection in ADCs vary considerably from state to state—with some states, such as Connecticut, having no data reporting requirements for ADC users and others, such as California and New York, having mandatory measures that must be completed every 6 months (O’Keeffe et al., 2014). Thus, to make data collection both practical and feasible for ADCs, an important step is aligning research-based recommendations, such as those by Anderson et al. (2020) or Gaugler and Dykes (2019), with data that centers are already collecting so as to minimize any additional burdens on the ADCs. The purpose of this article is to determine, based on a national cross-sectional survey of adult day service providers, what data points ADCs are already collecting, whether any measures are being used to collect data, and which processes ADCs are using to manage data. This initial step will be critical to future academic and community partnerships between researchers and ADC stakeholders to ensure data collection is meaningful, relevant, and feasible within a resource-constrained environment.

Methods

Overview

Discussions among stakeholders at the 2019 Annual Meeting of the National Adult Day Services Association (NADSA) in Minneapolis, MN, affirmed the need to capitalize on existing data collection efforts within ADCs to move toward the creation of a nationally representative database; this prompted the creation of our survey. Preliminary survey questions were generated through discussions with stakeholders, including ADC owners, program directors, and caregivers. The online survey was designed using Qualtrics Software and was distributed to 3,768 members of NADSA’s LISTSERV between February and April 2020. It should be noted that this listserv is not solely comprised of ADC providers, but also organizations and individuals who do not provide client services (e.g., academics, policymakers). Respondents provided consent electronically prior to beginning the survey. Approval for the study was given by the Institutional Review Board at the first author’s institution.

Sampling

The target audience for the survey were staff in ADCs who engage in data collection for purposes of research and analysis. Key NADSA board members and stakeholders based in the five major regions of the United States—Northeast, Midwest, Southeast, Northwest, and Southwest—championed the survey among ADCs. The survey link was made shareable to facilitate snowball sampling. No participant incentives were offered.

Survey Development

The survey questions used are listed as a supplemental file. Preliminary survey questions were intended to highlight differences between ADC programs (e.g., population served), the resources available for data collection (e.g., staff, electronic systems), state-mandated data-reporting requirements, and the specific tools, if any, used to collect data to understand how well they align with the measures recommended by Anderson et al. (2020). Space was given for participants to provide open-ended responses regarding their goals for data collection and to identify any standardized tools or measures that they use for data collection.

Statistical Analysis

Our analysis of the survey data was limited to descriptive statistics tabulated within IBM SPSS Software version 27.0, largely using frequencies to understand the percentage of responding centers that collect specific data points.

Results

The survey was distributed to the 3,768 entities on the NADSA listserv; 248 responded, resulting in a 6.58% response rate. Survey respondents (N = 248) represented 38 out of 50 states. A breakdown of survey respondents by state and center type is provided in Supplement 2. Some states, such as Ohio (n = 42), Tennessee (n = 15), New York (n = 14), and Connecticut (n = 14), were disproportionately represented within the survey, and others, such as Idaho, Hawaii, and Kansas, had no representation.

Data Collection and Management Processes Among Adult Day Centers

Of the respondents, only 32% reported routinely collecting client data for purposes of research and analysis; reasons for doing so included achieving quality improvement, gauging client satisfaction, and obtaining additional funding. A more substantial portion (70.1%) of participants expressed an interest in being part of future large-scale data collection efforts. Program directors and senior leadership at ADCs were primarily responsible for data collection (40.2%). Methods for managing data leveraged either Microsoft Office (including MS Word and Excel) or ADC-specific billing software (e.g., Dayware) (Table 1).

Table 1.

Data Collection and Management Processes Among Adult Day Centers.

Adult day center response	%
Self-reported engagement in systematic data collection via standardized outcome measures for research and analysis
No	67.6
Yes	32.4
Type of data collected
Both	74.8
Quantitative	13.7
Qualitative	11.5
Sample size for data collected by ADC
<15	18.2
15–50	46.2
51–75	13.6
76–100	6.8
100–150	15.2
Primary staff member responsible for collecting data
ADC director/Senior leadership	40.2
Nurses	18.2
Administrators	16.8
Social workers	10.8
Other	9.8
Unsure	4.2
Primary person responsible for data analysis
ADC internal staff member	80.9
Unsure	11.6
Outside consultant	7.5
Data storage platform
Paper charts	34.5
Microsoft Excel	18.4
Electronic health records	15.5
Microsoft Word	12.3
Other statistical software	6.1
Other electronic source	5.3
Google Documents	4.1
Unsure	2.3
Other non-electronic source	1.5

Note. Data are listed as a percentage of those who answered yes to “Self-reported engagement in systematic data collection via standardized outcome measures for research and analysis.” ADC = adult day service centers.

Type of Data Collected by Adult Day Centers

Demographics and clinical measures

The following were the most common demographic measures collected by centers: age (16.4%), race (13.5%), and preferred language (11.1%). Of the respondents, 25.9% specialized in the care of persons with multiple chronic conditions, yet they collected clinical measurements infrequently. For example, 12.7% of the centers collected serial blood pressure measurements. Just five centers (2.0%) collected clients’ hemoglobin A1c levels and body mass index (BMI) (Table 2).

Table 2.

Clinical and Demographic Data Collected by Adult Day Centers.

Category	%
Demographics
Age	16.4
Race	13.5
Language	11.1
Insurance	9.4
Ethnicity	9.3
Participation in other government	8.2
Programs
Income	8.1
Educational level	7.8
Household size	6.3
English proficiency	4.3
Clinical measures
Current diagnoses	17.7
Weight	13.1
Blood pressure	12.7
Vital signs	11.7
Falls in the center	13.5
Falls outside the center	9.0
Height	8.6
Vaccination record	4.7
Fasting blood glucose	2.6
Body mass index	2.3
HbA1c	2.0
Unsure	1.2
Complete blood count	0.8
Serum cortisol	0.1

Screenings

Cognitive impairment

Among the ADCs surveyed, 28.3% specialized in the care of persons with cognitive impairment, and 34.4% of respondents reported that their states required routine screening of clients for cognitive impairment. An evidence-based tool was used to assess cognitive impairment in a majority (57.5%) of the centers, and the one most commonly cited was the Mini-Mental State Examination (Table 3).

Table 3.

Type and Frequency of Screenings Conducted by Adult Day Centers.

Type of Screening	% of centers conducting screenings^a	% reporting screening is state-required^b	Conducted every 3 months^b (%)	Conducted every 6 months^b (%)	Conducted every 12 months^b (%)	Conducted as needed or on a case-by-case basis^b (%)	Other frequency of screening^b (%)
Function
ADLs	88.10	47.80	19.60	23.20	21.00	25.40	10.90
Fall risk	74.70	26.90	16.50	21.50	4.10	45.50	12.40
IADLs	72.30	48.60	18.90	23.40	23.40	22.50	11.70
Cognitive impairment	63.20	34.40	15.80	31.60	10.50	27.40	14.70
Quality of life
Caregiver well-being	49.30	22.50	9.30	20.00	18.70	41.30	10.70
Depression	48.70	22.70	11.80	29.00	13.20	32.90	13.20
Pain level	35.80	38.50	18.90	17.00	0.00	50.90	13.20
Loneliness	34.20	23.50	11.30	30.20	11.30	35.90	11.30
Quality of life	18.80	25.00	19.40	19.40	12.90	29.00	19.40
Lifestyle
Nutritional risk	49.40	37.80	16.50	25.30	13.90	32.90	11.40
Substance use disorders	21.30	29.00	6.30	37.50	6.30	25.00	25.00
Other	31.60	10.50	15.80	26.30	15.80	15.80	26.30

Note. ADLs = activities of daily living; IADLs = instrumental activities of daily living.

Data are listed as a percentage of those who answered yes to “Self-reported engagement in systematic data collection via standardized outcome measures for research and analysis.” ^bData for “Use of evidence-based tool or state-required form to assess measure,” “Measure collected is state-required,” and “Screening frequency” are listed as a percentage of those who answered yes to “Self-reported engagement in systematic data collection via standardized outcome measures for research and analysis” and subsequently answered yes to collection of the measure in question (fall risk, depression, nutritional risk, etc.).

Functional status

Among the ADCs, 88.1% screened for users’ abilities to carry out activities of daily living (ADLs) and 72.3% screened for instrumental activities of daily living (IADLs); these screenings were state-mandated in 47.8% of cases. A large proportion, 74.6%, screened for fall risks, but most did not track actual falls. Tools used to screen for ADLs, IADLs, and falls were largely embedded within state-mandated assessment tools, especially in ADCs based in New York and Virginia. In states that did not mandate data reporting, the Hendrich II Assessment (Hendrich et al., 2003) for fall risks and the Katz Index for ADLs (Brorsson & Asberg, 1984) were commonly used.

Mental health, pain, and quality of life

Depression (48.7%), quality of life (18.8%), loneliness (34.2%), and pain level (35.8%) were among the measures collected by the ADCs. Tools used for these assessments included the Older People’s Quality of Life Questionnaire (Bilotta et al., 2011) and the Geriatric Depression Scale (Hoyl et al., 1999).

Caregiver well-being

Caregiver well-being was assessed by 49.3% of the centers and was state-mandated for 22% of the respondents. Only 26.8% of the centers used an evidence-based tool for this purpose. When caregiver well-being was not part of a state-mandated screening, the Zarit Caregiver Burden Scale (Zarit et al., 1980) was frequently cited as being used for assessment.

Lifestyle and miscellaneous screenings

Of the centers surveyed, 49.4% screened for nutritional risk and 21.3% screened for substance use disorders. Evidence-based tools were used 37.3% of the time for nutritional risk and 37.5% of the time for substance use disorders—most common among them was the Mini Nutritional Assessment (Guigoz et al., 2002). Other categories of data the ADCs assessed that were not listed on the survey form included cultural information, interests, hobbies, voter registration status, and social skills.

Discussion

The intent of this study was to gain a better understanding of the current state of data collection in ADCs across the United States. Although previous researchers have speculated that data collection in ADC programs is fragmented and disjointed (e.g., Anderson et al., 2020), this is actually the first attempt to survey ADC programs and characterize the national picture on data collection in this increasingly important platform of home and community-based services. The data reveal two primary themes:

The majority of ADC programs are not using standardized outcome measures for clients and caregivers.

Data collection in ADC programs appears to be largely limited to basic, yet important, areas of client and caregiver well-being, but certain clinical measures are being overlooked.

Of the 248 ADC programs that responded to the survey, only 32% reported the use of standardized outcome measures for clients and caregivers. This is consequential and somewhat disappointing, given the importance of using standardized measures that are psychometrically stable, valid, and reliable. Possible reasons that ADC programs are not using standardized measures include cost, the measures not being in the public domain, and the time and training required to administer the tools. The Mini-Mental State Examination, for example, has historically been one of the most widely used cognitive screening measures, yet its developers began enforcing copyright permission and charging fees for its usage in 2000 (Feldman & Newman, 2013). ADC programs that formerly used this measure may have reverted to developing their own cognitive screenings once fees were implemented. Second, standardized measures can be lengthy; difficult to administer, score, and interpret; and may require special training. For example, the hemoglobin A1c test used to screen for and diagnose diabetes and prediabetes requires a blood draw and laboratory analysis. Although detecting and controlling diabetes is critically important and well within the purview of ADC programs, many may lack the trained staffing to carry out such screenings.

ADC programs need to be made aware of the existence of standardized measures that are in the public domain and that are simple to administer with existing or easily augmented staffing. The Montreal Cognitive Assessment (Nasreddine et al., 2005) and the Saint Louis University Mental Status examination (Stewart et al., 2012) that screen for cognitive impairment meet these criteria and would be appropriate for use in ADCs. Researchers have recommended these measures (Anderson et al., 2020; Jarrott & Ogletree, 2019), yet the recommendations often reside in restricted-access academic journals, and the availability of this information may be limited. Informing and educating providers require additional steps, such as providing open-access summaries of pertinent journal articles and linking ADCs with national organizations (e.g., NADSA) to alert providers to the existence of these free standardized measures. Overcoming the barrier of specialized training is more difficult; however, ADC programs can play a role. The hemoglobin A1c test, for example, is available at no cost to those covered by Medicare (Centers for Medicare and Medicaid Services, n.d.) and many other health care insurance programs. ADC programs can educate clients and caregivers and then play a role in monitoring blood glucose via simple and cost-effective self-tests.

The second theme that emerged from the findings is that ADC programs appear to be capable of collecting data on outcome measures for clients and outcomes, albeit only in a few basic, yet critically important, domains. Almost nine (88.1%) out of 10 of the ADC programs we surveyed assessed functional ability and impairment (e.g., ADLs), and approximately half assessed for cognitive impairment (57.5%), depression (48.7%), nutritional risks (49.4%), and caregiver burden/well-being (49.3%). These findings are promising as they demonstrate the capacity and capability of ADC programs to collect outcome data on clients and caregivers. Whereas some ADC programs are mandated by their respective state bodies to collect these data, many ADC programs appear to be collecting these data of their own volition. This demonstrates a desire to collect outcome data, and it appears that ADC programs are increasingly realizing the power of data for influencing programming, policymaking, and funding. Less promising is the fact that approximately half of the ADC programs in this sample did not collect data on clients’ and caregivers’ basic well-being. It may be the case that ADC programs are caught in limbo—unable to collect outcome data due to limited resources and unable to leverage additional resources due to the lack of evidence on their programs’ effectiveness. Again, researchers can play a role in developing and testing simple yet effective protocols for collecting outcome data in ADC programs. Although such protocols have been proposed (Anderson et al., 2020; Jarrott & Ogletree, 2019), they have yet to be implemented and tested in large-scale national studies.

Reflecting on “what data are being collected” and “what data should be collected,” it is worthwhile to compare the findings from this study with recently proposed batteries of outcome measures. Before doing so, it is important to reiterate that less than one-third of ADCs in this study used standardized outcome measures. Functional status, cognitive impairment, and socioemotional well-being are consistently cited as three areas where ADCs can have a positive impact, and as such, researchers have called for outcome measures to evaluate the effectiveness of ADCs (Anderson et al., 2020; Jarrott & Ogletree, 2019). Of the 32% of ADCs reporting the collection of standardized outcome measures, 88.1% collected data on ADLs, 57.5% performed cognitive screenings, and 48.7% and 34.2% collected data on depression and loneliness, respectively. These percentages are encouraging to a degree, but in no way are they reflective of the services and benefits that ADCs claim to provide (e.g., physical activity, cognitive stimulation, socialization) and the centrality of these measures that researchers have stated. Data collection in other important domains, such as caregiver well-being and health care utilization, was either inadequate or non-existent. For example, only 27.8% used an evidence-based tool to assess caregiver well-being and few if any centers recorded the number of falls, emergency department visits, and/or hospitalizations. The latter measures have all been strongly suggested by researchers (Anderson et al., 2020) and may be critically important to demonstrating the potential of ADCs in reducing health care utilization and costs and to leveraging funding.

The collection of outcome data in other sectors of long-term care can also guide the development of outcome measures in ADCs. Nursing homes have long used the Minimum Data Set (MDS), and some providers in the home care and assisted living sectors have adopted other standardized batteries of outcome measures (see https://www.interrai.org/). As we begin to develop standardized outcomes measures in ADCs, we should reflect upon these existing efforts and compare and, if appropriate, align the outcome domains and specific measures used in these other settings. We should also appreciate the fact that the services and intentions of ADCs are often unique. Measures should be tailored to capture outcomes that are most relevant to this sector of long-term care.

The development of a standard set of outcome measures that are relevant to ADC providers, funders, and families is an ongoing pursuit. This study represents one of the initial steps in this process, and we intend to build off of these findings in future work. The post-pandemic landscape has changed for ADCs and it will be interesting to see whether outcomes and, more importantly, the priority of certain outcomes (e.g., respite, social and emotional well-being) have also changed. Future studies should also consider the use of a more targeted sampling strategy (e.g., lists of consisting solely of ADC providers) and incentivizing participation to increase response rates. This was an unfunded study, and as such, incentives were not provided to participants. In addition, qualitative inquiry into what outcomes are most meaningful to ADC users and their caregivers is necessary to ensure data collection is patient-centered and relevant.

Limitations

This study has a few limitations to note. First, the response rate was relatively low. The response rate for this study was a modest 6.58%. This relatively low rate may have been influenced by several factors. The sampling frame was not solely comprised of ADC providers, and the survey may not have been relevant to the work of academics and policymakers. ADCs that do not collect outcome data may also have felt that the survey was irrelevant. Finally, ADCs may have been struggling with the COVID-19 pandemic during the time of the survey and prioritized other activities (e.g., remaining in business) over completing the survey. Second, data analysis was limited to simple, descriptive statistics. This is reflective of the intentions of this study—to simply describe current data collection efforts in ADCs. We also felt that a deeper quantitative analysis (e.g., correlations) would not yield useful results due to the incompleteness of the sample. For example, some states were overrepresented in the sample. Correlations may indicate that ADCs in these states are especially interested in certain outcomes, but the actual case may simply be that data collection is mandated by funding agencies in these states. Future studies should consider the use of mixed-methods approaches to better address the “why” questions associated with data collection. For example, qualitative data would allow us to understand the facilitators and barriers to data collection and the driving forces behind data collection in certain states and in certain ADC centers. Finally, there may be questions as to why ADC participants and family members were not included in this study, especially given the importance of person-centered care. The intention of this study was to capture a picture of what data “are” being collected, not what data “should” be collected. As reflected in the literature, research on the development of outcome data should certainly incorporate those outcomes that are most important to care recipients and family members (Forsythe et al., 2019; Zimmerman, 2021).

Conclusion

Outcome data are a driving force in health care, influencing services, programming, policies, and research. Among health care platforms for older adults (e.g., nursing homes, home health care), ADCs are late to the game in collecting outcome data, and the results of this study reflect that fact. Outcome data collection in ADCs can best be characterized as inconsistent regarding the outcome variables and the methods and measures used to quantify them. This reflects the high levels of variability that exist within the ADC industry in terms of center size, staffing, funding, populations served, and existence or lack of existence of state-mandated data collection. Although a considerable proportion of ADCs in this study did not collect data in important domains of well-being, it does appear that many ADCs are capable of and willing to collect data. More importantly, more than 70% of the ADCs in this study reported that they would be willing to participate in future large-scale data collection. National studies piloting data collection protocols are needed to advance the understanding of ADC capabilities and impacts. Data from these studies would provide a translational link between services, policies, and funding and provide much-needed guideposts for the future evolution of ADCs.

Supplemental Material

sj-pdf-1-jag-10.1177_07334648211013974 – Supplemental material for A National Survey of Data Currently being Collected by Adult Day Service Centers Across the United States

Supplemental material, sj-pdf-1-jag-10.1177_07334648211013974 for A National Survey of Data Currently being Collected by Adult Day Service Centers Across the United States by Tina Sadarangani, Keith Anderson, Paayal Vora, Lydia Missaelides and William Zagorski in Journal of Applied Gerontology

Supplemental Material

sj-pdf-2-jag-10.1177_07334648211013974 – Supplemental material for A National Survey of Data Currently being Collected by Adult Day Service Centers Across the United States

Supplemental material, sj-pdf-2-jag-10.1177_07334648211013974 for A National Survey of Data Currently being Collected by Adult Day Service Centers Across the United States by Tina Sadarangani, Keith Anderson, Paayal Vora, Lydia Missaelides and William Zagorski in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

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

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

IRB Approval

This study was approved by the University Committee on Activities Involving Human Subjects (UCAIHS), New York University, Approval Number IRB-FY2020-3957.

ORCID iD

Tina Sadarangani

Supplemental Material

Supplemental material for this article is available online.

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