OJNI

Detection of delirium in community-dwelling persons with dementia

by

Melinda R. Steis, PhD, RN,

Vittal V. Prabhu, PhD,

Ann Kolanowski, PhD, RN, FAAN,

Yuncheol Kang,

Kathryn H. Bowles, PhD, RN,

Donna Fick, PhD, RN, FGSA, FAAN,

and Lois Evans, PhD, RN

This article was made possible by an educational grant from

Chamberlain College of Nursing

CITATION

Steis, M., Prabhu, V., Kolanowski, A., Kang, Yuncheol, Bowles, K., Fick, D., and Evans, L. (February 2012). Detection of delirium in community-dwelling persons with dementia. Online Journal of Nursing Informatics (OJNI), 16 (1), Available at  http://ojni.org/issues/?p=1274

Abstract

The purpose of this pilot study was to prospectively explore the feasibility of engaging family caregivers to electronically report observations of delirium symptoms in community-dwelling older adults with dementia. This study also sought to assess the ability to recruit and retain participants, gauge satisfaction with computerized communication using personal computers or smart phones and describe agreement between family observations of delirium (Family Confusion Assessment Method [FAM-CAM]) and researcher assessments (Confusion Assessment Method [CAM]).  Family caregivers accessed an electronic delirium assessment instrument via their personal computer or a study supplied smart phone daily to transmit data.  Compliance with daily data transmission of the FAM-CAM was 77%.  The family caregivers were generally satisfied with the technology and the task of answering the assessment questions.  Out of a total of 13 participants, there were 7 confirmed episodes of delirium in 3 participants in this study.  Overall, the CAM and FAM-CAM scores were positively correlated at 0.856 using the Pearson Correlation statistic (p=0.01).

Introduction

Of persons age 65 and over, more than 96% reside in the community (Centers for Disease Control, 2004) and many of them suffer from Alzheimer’s disease or other dementias.  Delirium is also a common occurrence in the older adult population, especially among those with pre-existing brain disease such as dementia.  Frequency of delirium superimposed on dementia (DSD) at the time of hospital admission is reportedly between four and 52% (Margiotta, Bianchetti, Ranieri, & Trabucchi, 2006; McCusker, Cole, Dendukuri, Belzile, & Primeau, 2001) and persons who experience DSD have a two-fold increase of death within one year (Bellelli, Speciale, Barisione, & Trabucchi, 2007).  Among hospitalized older adults, delirium results in increased risk of poor health outcomes, including complications during hospitalization, increased lengths of stay, nursing home placement, and death (Marcantonio, Ta, Duthie, & Resnick, 2002; McAvay, et al., 2006; Pitkala, Laurila, Strandberg, & Tilvis, 2005). Very little is known, however, about the incidence of delirium among older adults with dementia living in the community. In the only reported study, more than 13% in a community-dwelling older adult population with dementia experienced one or more incidents of delirium (Fick, Kolanowski, Waller, & Inouye, 2005).

Both delirium and dementia have cognitive impairment in common, often challenging clinicians to distinguish them.  Dementia is a chronic, slowly progressing neurodegenerative disorder that is rarely reversible (American Psychiatric Association, 2000). Delirium is an acute, fluctuating confusional state that often signals an emergent decline in health state (Inouye, et al., 1990).  Delirium is potentially preventable and the underlying cause treatable, especially when recognized early.  The Confusion Assessment Method (CAM) algorithm is the gold standard tool for the identification of delirium by trained professionals.  More recently, Dr. Inouye, the developer of the CAM, developed the Family Confusion Assessment Method (FAM-CAM) to be used with family caregivers (Inouye, et al., 1999; Inouye & Charpentier, 1996) in identifying delirium.

Caregivers of community dwelling older adults with dementia can become isolated from society, including health care providers, especially as the severity of their family member’s dementia progresses.  The use of mobile health or m-health, may facilitate remote access to health care protocols (Hurling, et al., 2007).  Telehealth (the use of technology to access health care services and information) has been shown to be useful clinically, have economic advantages and be highly satisfactory to providers and end users (Hebert, Korabek, & Scott, 2006; Lai, Kaufman, Starren, & Shea, 2009; Luptak, et al., 2010).  Mobile health is the use of mobile telecommunications such as smart phones and personal digital assistants to access health care services and information (Barton, 2010).  Clinically, use of telehealth in assessment and monitoring has decreased acute care hospitalizations, emergency department use and improved health outcomes for the community-dweller (Cardozo & Steinberg, 2010; Darkins, et al., 2008; Luptak, et al., 2010).  From an economic perspective, use of telehealth has improved utilization of services as well as proven to be a cost-effective means for delivering health care services to rural and home-bound persons in the community (Darkins, et al., 2008).   In an observational study utilizing an interdisciplinary team approach and case-managed telemedicine, 851 patients with chronic conditions such as congestive heart failure, hypertension, lung disease and diabetes mellitus were enrolled and followed for 60 days post-hospitalization (Cardozo & Steinberg, 2010).  The participants, 90% of whom were age 69 or older, were asked to transmit data daily using a telehealth device with an overall transmission compliance rate of 77%.  Patient satisfaction with the telehealth program was also very high (88%).

Family caregivers have valuable information regarding the signs and symptoms of delirium by a family member that could be very useful in delivering care (Durston, 2006).  For example, they are likely to notice changes, especially subtle changes, in the mental status and behavior of their family members.  The Partners in Caring philosophy expresses key concepts in the vision of creating a family-friendly environment: “information is the foundation of healing; family and friends have valuable information if we listen; empowerment of patient and family is required; and commitment to partnership between patient, family, and healthcare team must be present (Durston, 2006, p. 106).”    The Partners in Caring philosophy (Durston, 2006) guided the aims of this study.

The purpose of this pilot study was to explore the feasibility of partnering with family caregivers to use a smart phone mobile health device or existing caregiver’s computer to electronically report observations of delirium symptoms in community-dwelling older adults with dementia.  Empowering family caregivers and prompting them to report symptoms by way of facilitating telehealth could facilitate the sharing of valuable information and subsequent early recognition of delirium in community-dwelling older adults with dementia.  Therefore, the aims were to 1) assess the ability to recruit and retain as participants community-dwelling persons with dementia and their family caregivers; 2) assess satisfaction with smart phone mobile health or computer communication; and 3) describe agreement between family observations of delirium and researcher assessments.

Research Design

     This pilot study used a prospective, exploratory feasibility design with family caregivers who accessed an electronic delirium assessment instrument daily via their personal computer or a study supplied smart phone mobile health device.

Methods

Setting and Participants

Initial recruitment was in collaboration with one hospital-based and two free-standing home health agencies in three adjacent north-central Pennsylvania counties; later, contact with community-based organizations who could provide access to family caregivers of persons with dementia and word-of mouth referrals were also used.  Community-based organizations included dementia caregiver support groups and adult day care centers.  Inclusion criteria for enrollment were persons with dementia age 65 years or older, English speaking and community-dwelling with a family caregiver willing to participate in the study.  Exclusion criteria were terminal stage of illness or no family caregiver consenting to participate in the study.  The initial recruitment goal was to enroll 20 participants.

Measures

Delirium

Delirium was operationally defined according to the validated CAM criteria and the Delirium Rating Scale (DRS-R-98).  The CAM features are 1) acute onset and fluctuating course, 2) inattention, and either 3) disorganized thinking, or 4) altered level of consciousness (Inouye, et al., 1990).  The CAM is a standardized screening tool allowing persons without formal psychiatric training to quickly and accurately identify delirium. The CAM was validated against geriatric psychiatrists’ ratings using the Diagnostic and Statistical Manual of Mental Illness criteria and was shown to have sensitivity of 94-100% and specificity of 90-95% (Inouye, S. K., et al., 1990a; Pompei, Foreman, Cassel, Alessi, & Cox, 1995; Wei, Fearing, Sternberg, & Inouye, 2008).

The research assistants (RAs) used the Mini-Mental Status Exam (MMSE) (Folstein, Folstein, & McHugh, 1975) to facilitate completion of the CAM.  The MMSE is a 30-item cognitive screen with established reliability.  The MMSE was used to measure baseline cognitive impairment and mental status over time in combination with the CAM (Inouye, S. K., et al., 1990b).

The DRS-R-98 is a 16-item clinician-rated scale with 13 severity items and 3 diagnostic items and has been validated against both delirium and dementia groups.  The severity score ranges from 0-39 to rate the severity of the delirium.  Sensitivity and specificity of the DRS-R-98 ranges from  85%-100% and 77%-93% respectively (Trzepacz, et al., 2001).

     The FAM-CAM was developed as part of a larger cohort study as a means to detect delirium in elders; it relies on caregiver information to screen for the CAM features.  While the FAM-CAM is based on the original CAM, there are differences between the two tools.  The health care professional administering the CAM and employing observational skills, assesses the four main features of delirium directly.  In contrast, the FAM-CAM includes questions directed for the family member to help identify the cardinal signs of delirium as well as those sensitive to detect delirium (i.e., inattention, disorganized thinking, lethargy, disorientation, perceptual disturbances and inappropriate behavior/agitation).  According to the diagnostic algorithm, delirium is identified if the patient shows the presence of acute onset, fluctuating course, inattention, and either the presence of disorganized thinking or an altered level of consciousness.  Data from the present study, combined with that from a second contemporaneous study of community dwelling elders with cognitive impairment, will be used to determine beginning psychometric properties, as none have heretofore been reported (manuscript in review)

Dementia

For the purpose of this study, history of dementia symptoms for at least a 6-month period and a score of greater than 3 on the Modified Blessed Dementia Rating Scale (MBDRS) (Blessed, Tomlinson, & Roth, 1968) were used to operationalize dementia. Family caregiver participants were interviewed using the MBDRS, an 8-item scale for family informants that has been shown to discriminate between dementia and non-dementia in study participants by establishing pre-morbid cognitive functioning.  The MBDRS has been shown in the Helsinki Aging studies and others to discriminate between demented and non-demented subjects and has been used by delirium and dementia experts to establish pre-morbid cognitive functioning (Blessed, et al., 1968).  Dementia staging was determined using the Clinical Dementia Rating scale (CDR) (Hughes, Berg, Danziger, Coben, & Martin, 1982); a score of 0.5 to 2.0 indicates mild-moderate stage dementia.

  Demographic and other data

An investigator-developed form was used to document age, language, medical history, medications and treatments ordered.  Participant data were obtained either from the home health care records for the home health agency participants or via family caregiver interview for study participants.  The Charlson’s Co-morbidity Index (CCI) was used to obtain a baseline measurement of the enrollees’ co-morbid illnesses.  The CCI is a weighted index of the number and severity of a person’s comorbid conditions (Charlson, Pompei, Ales, & Mackenzie, 1987).  Researchers determined the CCI was the best single predictor of mortality rates in a group of older hospitalized patients with dementia.  The CCI was compared to socio-demographic characteristics, cognitive status, functional status, and nutritional status (Zekry, et al., 2008).

Satisfaction surveys

Guided by the Partnership in Caring philosophy, family caregivers were requested to complete a researcher modified survey (The Ministry of Health & Long-Term Care and Canada Health Infoway, 2007) soliciting their satisfaction with the FAM-CAM questions, the technology and any impact participating in the study may have had on their perceived caregiving competence when discharged from the study.  RAs were also prompted to complete a satisfaction survey for each study participant at the time they were discharged from the study.    The RA survey was specific to each study participant and solicited the RA’s satisfaction with the technology as well as any observations they had regarding the specific caregiver’s satisfaction or dissatisfaction during the study.  Satisfaction surveys were short (5-8 questions) with yes, no or don’t know answer choices and a free-text field for comments.

Ethical Considerations

Ethical approval for the study was obtained from the university and health system (for the hospital-based home health agency) Institutional Review Boards (IRB).  The two free-standing home health agencies and the community-based agencies agreed to allow the university IRB to be the IRB of record.  In addition, all three home health agencies required a written consent be obtained by the home care staff from the family caregiver before any contact information was released to the researchers to enable screening and enrollmen

  Procedures

In home health agencies, intake nurses receive all referrals, make inquiries about the anticipated care needs, communicate with referral source and insurance company representatives as well as handle calls to and from physicians for the nurses who are making the home visits. In this study, home health intake nurses reviewed new referrals for evidence of cognitive impairment or dementia and then flagged the new admission packet by including a ‘permission to contact’ form.   A voice mail was sent from the intake nurse to the admitting professional (registered nurse or physical therapist) informing them the preliminary review indicated the new admission may be appropriate for this study and requesting they attempt to gain written permission for study personnel to contact the family caregiver for screening.  To recruit participants from the other community-based organizations, key persons within the organizations reviewed their case-loads for persons with dementia; they then contacted the family caregiver to ask permission for study personnel to contact them for the purpose of screening for this study.

Two RAs were registered nurses and two RAs were senior level undergraduate students in health related majors.  RAs completed required training in research ethics as well as use of the instruments for this study by self-learning, didactic sessions, paired sessions of interviewers (mock interviews) with inter-rater assessment, and paired ratings of patients observed by a trained interviewer.   The inter-rater agreement ratings were consistently between 99-100% congruent.  Additionally, RAs were hosted to a day-long training session to become familiar and comfortable with the technology as well as with the engineering staff who would address technology-related questions during the course of the study.  The session included hands-on training with smart phones and laptops and use of the smart phones as modems for the laptops when wireless Internet was not available.

Once participant referrals were received, an RA initiated communication with the family caregiver via telephone and began the screening process using the inclusion criteria.  The study was explained to the caregiver and verbal permission to further screen and potentially enroll them and their family member was sought.  The RA then visited the home and finalized screening, enrollment and consenting processes (Table 1).  During the enrollment visit, the RA interviewed the family caregiver to collect study data.   If possible, the study data were transmitted electronically either via smart phone or laptop using the smart phone as a modem.  The RAs were prepared with paper and pencil in case unable to connect to the Internet, in which case the RAs would transmit later in the day when Internet access was available.  The RAs then reviewed the home health record (if a home health agency patient).

At Enrollment

At Subsequent Home Visits

Daily by Caregiver

When Discharged from Study

Confusion Assessment Method (CAM)

X

X

Delirium Rating Scale (DRS-R-98)

X

X

Mini-Mental State Exam (MMSE)

X

X

Modified Blessed Dementia Rating Scale (MBDRS)

X

Clinical Dementia Rating Scale (CDR)

X

Charlson Comorbidity Index

X

Family Confusion Assessment Method (FAM-CAM)

X

Caregiver Satisfaction Survey

X

Research Assistant Satisfaction Survey

X

Demographics

X

Table 1: Measurement Table

The RA taught the family caregiver, 1) about dementia and delirium using a referenced educational handout, 2) how to access the eCare for Eldercare website, and 3) how to complete the FAM-CAM, using either their personal computer or the smart phone. For participants referred by a home health agency, researchers informed the home health nurses of the enrollment. Given this was a feasibility study, there was no attempt to influence the plan of care developed in conjunction with the physician.

The family caregiver was instructed to observe their family member daily and transmit their observations in the FAM-CAM using the eCare for Eldercare secure web application. eCare for Eldercare was architected to electronically collect the study information from the RAs and family caregivers as shown in Figure 1. Each eCare for Eldercare user category had role-specific functionality, such as family caregiver, RA, and researchers. In particular, the eCare for Eldercare system had the flexibility to collect information through various interfaces such as the web and smart phones. This flexibility in data collection and transmission allowed families without an Internet connected personal computer to access eCare for Eldercare using a smart phone mobile health device.

Figure 1: An overview of eCare for Eldercare software architecture

Figure 1: An overview of eCare for Eldercare software architecture

NOTE: RA=Research Assistant

     To ensure privacy and security within the eCare for Eldercare application, users’ login was a unique ID and password, and the data were encrypted while being transferred over the Internet using the standard Secure Sockets Layer protocol (Bellazzi, Montani, Riva, & Stefanelli, 2001).  As the data were securely received by the eCare for Eldercare web server, they were stored in a relational database for efficient and easy processing.  These data were automatically backed up daily using network-attached storage to protect against loss due to hard disk failure.  An uninterruptable power supply was used to protect against short-term power failure to prevent data loss and damage to the server.

One self-identified ‘responsible caregiver’ was designated to complete and electronically transmit the daily observations.  Caregivers were requested to transmit their observations before 11:00 AM to enable an RA to visit the same day if warranted.  If the assessment was positive for delirium, several actions followed.  First, an email was automatically generated to notify the RAs; second, the software prompted the family caregivers to communicate their observations to their health care provider; third, a new screen appeared asking the family caregiver questions about the possible causes of the delirium to possibly facilitate early treatment and reversal of the delirium; and last, if a home health agency patient, an email was automatically sent to the intake nurses informing them that the patient had a positive delirium screen (follow-up of the nurses’ response was not an aim of this study).

The RAs visited the participants weekly and additionally, within 24-hours of a positive delirium screen.  During face-to-face visits, RAs assessed the participants’ for delirium using the CAM and the Delirium Rating Scale (DRS-R-98) (Trzepacz, et al., 2001) (Table 1).  Study participants were followed for 30 days.  If the participant experienced delirium, the RAs continued to follow them for up to 30 additional days.

   Data Analysis

All statistical analyses using de-identified data were conducted using SPSS (version 17.0).  The ability to recruit and retain study participant data (aim 1), satisfaction survey results (aim 2) and socio-demographic and clinical characteristics of the sample were described using means, standard deviations, ranges and frequency distributions.  Agreement between the CAM and the FAM-CAM results (aim 3) were analyzed using the Pearson correlation statistic.

     Findings

There were 13 patient participants in this pilot study.  All were Caucasian, mean age 80, sixty-nine percent were female and mean years of education was 11 (Table 2).  Participants were enrolled from only two of the three cooperating home health agencies (N=5), from adult day-care programs (N= 6) and from dementia caregiver support groups (N= 2).  Caregivers were adult children (N=8), spouses (N=4) and siblings (N=1).  Eight caregivers used their own computers and five used study supplied smart phones.

Variable

%

Mean (SD) (Range)

Age

80 (7)(70-90)

Years of Education

11(3)(6-15)

MBDRS

9 (4)(4-16)

CDR

2 (1)(0.5-3)

CCI

3 (2)(1-7)

Gender (% Female)

69%

Marital Status (% Widowed)

77%  or 10

Caregiver: % Adult child

62%  or 8

     % Spousal caregivers

31% or 4

     % Used smart phones

38% or 5

     % Lives with patient

85% or 11

Table 2: eCare Patient Participant Sample Statistics (N=13)

NOTE:

MBDRS = Modified Blessed Dementia Rating Scale;

CDR = Clinical Dementia Rating scale;

CCI = Charlson Comorbidity Index.

Aim 1: Explore the ability to recruit and retain study participants

Recruitment commenced on May 10, 2010 and the first study participant was enrolled June 2, 2010.  Two more participants were enrolled the following week.  In four full weeks of recruitment efforts in three home health agencies in three different communities, only three participants were enrolled from two of the sites.  Recruitment was then expanded to include community based referrals from other sources in the same geographic areas. i.e., dementia caregiver support group and adult day care center leaders.  In total, 21 persons with dementia were referred for screening; two (9%) were ineligible, three (14%) refused, and two (9%) were unable to be reached.  Of those eligible and approached, 62% enrolled.  The reasons for refusal were not wanting to be burdened, not wanting to deal with the technology and thinking their family member would not want to participate.

Once enrolled in the study, overall compliance with daily data transmission of the FAM-CAM was 77%.  Reasons for variations in transmission compliance were not formally assessed although, anecdotally, when caregivers were distracted by other activities such as holiday/family gatherings or personal health issues, transmission of study data was neglected.  There was no difference in levels of transmission between the adult children or the spouses, each was 77%.  Those who used smart phones transmitted 86% of the enrolled days compared to 71% from those who used their personal computers, a difference not statistically significant.  The range of days participants were enrolled in the study was 2 to 45 (M=24; SD=13).  Days skipped without data transmission ranged from 0 to 12 (M=5; SD=4).  The range of days of transmitted data per patient was 1 to 43 (M=19; SD=12).

Aim 2: Family caregiver and RA satisfaction with computerized communication 

For an unknown reason, one caregiver did not complete the satisfaction survey.  All other family caregivers surveyed (N=12, 92%) felt comfortable answering the FAM-CAM questions (Table 3).  Thirty-eight percent of caregivers (N=5) answered that they would continue to use these questions after the completion of the study.  Sixty-two percent of the caregivers (N=8) indicated that using the FAM-CAM helped them feel more confident in caring for their family member.  Less than 25% of caregivers (N=2) expressed dissatisfaction with the technology.  Negative comments regarding family caregivers’ technology use originated mostly from RAs’ feedback.  Specifically, dissatisfaction was due to difficulty reading the screen, dexterity challenges, visual and navigation difficulties and lack of comfort level with technology.  One participant used dial-up internet connection and was dissatisfied with the slow connection.  Two participants requested early discharge from the study due to frustration with the technology.  One of these opted out of the study after 2 days and one opted out after 4 days.

Survey Question Yes No DK
  1. Were you confident in answering the questions?
12
  1. Have you performed assessments like these before?
3 9
  1. Will you continue to use the assessment questions after the study ends?
5 4 3
  1. Did answering the questions make you feel more confident caring for family member?
8 2 2
  1. Did this assessment help your family member recover from their illness?
1 9 2

Table 3: Family Satisfaction Survey Results

NOTE:DK=don’t know

There were 13 RA satisfaction surveys completed by 3 RAs (Table 4), one for each participant upon discharge from the study.  All RAs perceived that the on-line data collection method was “easy to use,” they had adequate training, the format and layout of the software was acceptable and the online security was acceptable.   The majority (77%) of RAs indicated that the technology integrated well with their workflow.  Anecdotal comments were related to need for improvement in functionality of the smart phones (software issues).

Survey Question Yes No DK
  1. Was the online data collection easy to use?
13
  1. Did the use of technology integrate well with your workflow?
10 3
  1. Was the online security acceptable?
13
  1. Was the online transmission reliable?
11 2
  1. Were the format and layout of the software acceptable?
13
  1. Was the training adequate?
13
  1. Was on-going technical support acceptable?
9 4
  1. Did family caregiver communicate any positive or negative experiences with the technology?  If yes, describe:
8 5

Table 4: Research Assistant Satisfaction Survey Resultss

NOTE:DK=don’t know

 Aim 3: FAM-CAM and CAM agreement

Family caregivers transmitted FAM-CAMs daily.  RAs visited weekly and when FAM-CAMs were positive for delirium.  Therefore, overall there were 248 FAM-CAM transmissions; 40 of these were paired with RA administered CAMs. Of the 40 paired readings, 8 of the FAM-CAM transmissions were positive for delirium (20%, 4 participants); 7 (87%) incidents of delirium were corroborated in 3 participants by RA-performed CAM assessments.  One RA CAM did not agree with the FAM-CAM by the caregiver.  This was the caregiver’s first transmission; thus the RA re-educated the caregiver regarding use of the FAM-CAM during the face-to-face visit.  Including this one disagreement, the FAM-CAM and CAM scores were positively correlated at 0.856 using the Pearson Correlation statistic (p=0.01).

When a FAM-CAM screen for delirium was positive, an additional set of questions exploring possible triggers for delirium were electronically presented to the caregivers.  Although caregivers were prompted to answer the etiology questions following each of the positive delirium FAM-CAMs, family caregivers did so on only 4 occasions (50%).  In 3 of the 4 occasions, family caregivers responded yes when asked, “Do you think their problems are from not drinking enough water or fluids?”  Three of the delirium episodes were within one week and for the same home health patient participant who was recovering from surgery.  The spousal caregiver of this patient made the decision to transition the spouse to a nursing home.  No acknowledgement of the electronic notification of delirium for this participant was documented in the record by the home health agency staff.  Another confirmed episode of delirium was transmitted after the study participant and family had spent a very warm summer day at an amusement park.  The caregiver promptly removed the study participant from the heat and the stimulation of the amusement park and provided hydration and rest.  By the next day the study participant had returned to baseline mental status.  Finally, a third participant had three confirmed episodes of delirium over a two-week period.  The adult child caregiver was in contact with the physician but no action other than vigilance was taken during the course of the study.

Discussion

This is the first study to use the FAM-CAM in the community setting with computer generated alerts.  Approximately 3.7 million Americans with dementia live in the community and 80% of them receive their care from family caregivers (Alzheimer Association, 2011).  Family caregivers who participated in this study were eager to help in an effort that had any possibility of helping their family member suffering with dementia.  More than half (62%) of the caregivers in this study stated they felt more confident in caring for their family member as a result of using the FAM-CAM.  Similar studies have also reported bolstered confidence in caregiving (Cardozo & Steinberg, 2010; Liddy, et al., 2008).

Previous telehealth studies reported similar sources of caregiver dissatisfaction with technology as reported in this study (Lai, et al., 2009; Mitzner, et al., 2010).  Dissatisfaction derived from use of the smart phones and one participant who had dial-up internet service.  Past research results suggest that a touch screen is more effective for older adults who often have difficulty operating a mouse or similar manipulating tool (Lai, et al., 2009).  The smart phone used in the study did have a small touch screen (2.4″) and although the choices of answers were dichotomous radio buttons, in order to add comments, they needed to use the small letter keys.  Technical support was required minimally in the current study: once during the initial set-up the caregiver had a difficult time connecting to the eCare for Eldercare web site and there were times when the RAs had difficulty with the smart phones maintaining Internet connection during data collection.

The FAM-CAM:CAM agreement was exceptional in this study.  The CAM was designed to be administered by trained clinicians but not psychiatry experts.  The FAM-CAM is the CAM translated into lay terminology and mapped to the CAM algorithm.  Family caregivers are the most qualified persons to report any acute or subtle changes occurring in the person with dementia.  With that in mind, FAM-CAM:CAM agreement was expected.

Twenty-five percent of Medicare beneficiaries who received Medicare-covered home health services had a diagnosis of Alzheimer’s disease or other dementia (U. S. Centers for Medicare and Medicaid Services, 2008); therefore, we were surprised to find that recruiting our target population from home health agencies posed significant challenges.   There are a number of possible reasons for the lack of successful recruitment in this setting. First, home health intake nurses are the ‘nerve center’ of the agency, having multiple and competing responsibilities.  Recruitment of subjects for research does not take priority and may only add to existing burdens. Second, very often home health agencies receive little information about the patient being referred.  If dementia is not the primary or secondary diagnosis, which is often the case, staff may not be able to identify potential participants.  Third, home health agencies were understandably concerned about possible violations of HIPAA regulations and would release contact information only after obtaining written permission from the caregiver. Obtaining caregiver permission was difficult for the reasons cited above. The task of obtaining consent to release contact information to the researchers was further complicated because it was delegated to various nurses and therapists who were already performing the cumbersome home care admission assessments and documentation. Fortunately, the same barriers were not experienced in the non-home care community settings.  One of the dementia support group facilitators was the social worker for a local adult day care center and offered to facilitate recruitment at the center.  The administrative staff at the adult day care center were enthusiastic and allowed waiver of documentation for consent to release contact information, a decision supported by the Code of Federal Regulations (45 CFR 46) (Department of Health and Human Services, 2009), the federal policy governing the protection of human subjects.

Strengths and Limitations

Strengths of this pilot study include the education of caregivers.  First, delirium and dementia education accompanied the intervention in the form of an educational handout; second, by asking the caregivers to repeatedly answer the FAM-CAM questions they were educated regarding important changes in symptomatology; and third, if the FAM-CAM algorithm was positive for delirium, an additional screen prompted the caregivers to answer questions leading them to potentially discover what triggered the delirium onset.  This study invites family caregivers of community-dwelling persons with dementia to participate in assessing their family members using a physician developed algorithm with minimal burden.

This pilot study had significant limitations due to the small sample size that limits generalizations that may be made from the results.  Geographically, the mountainous terrain which may interfere with communication technology was challenging since the study required the use of the Internet via smart phone or personal computer.  Such conditions are to be expected when conducting research in the geographic terrain of Pennsylvania and similar mountainous regions.  The participants, however, were able to transmit data successfully but the RAs sometimes needed to use their paper and pencil back up to record data in the home environment and then enter the data onto the secure eCare for Eldercare website at a later time.

Future Considerations

Investigators recruiting community-dwelling older adults with dementia may want to consider approaching family caregivers directly instead of recruiting through sources that depend on non-research staff to recruit participants.  We also suggest a multi-pronged approach to reach a cross-section of community-dwellers, i.e., support groups, churches, fliers strategically placed where home health supplies are sold, adult day care centers, and senior centers.  If the target is a sicker population, older persons with dementia may be identified upon discharge from acute care or skilled nursing homes.  Recruitment through home health agencies may be successful if a member of the research team is also an employee of the agency, thereby, ensuring direct access to potential enrollees for screening.

Overall, the family caregivers’ satisfaction with technology was positive.  The following factors should be considered when planning to use technology with family caregivers: training needs, complexity of the technology device, possible sensory deficits, tactile or functional impairments and burden (Morgan, et al., 2011).  Family caregiver satisfaction is also worthy to measure.  Although not often a study variable, family caregiver satisfaction with this technology has been reported by others as positive and caregivers’ sense of security is reportedly bolstered by use of the telehealth (Liddy, et al., 2008; Luptak, et al., 2010).

Posing the etiology questions to the family caregivers after a positive FAM-CAM screen for delirium was novel and a notable effort to further empower caregivers to explore potential causes of the observed changes.  Their subsequent actions or inactions may, however, be an indication that more explicit efforts to educate family caregivers regarding the significance of reporting mental status changes in persons with dementia to their healthcare team are warranted.

The FAM-CAM has far-reaching possibilities such as inclusion in a toolkit for caregivers to access when changes are noted or to partner with family caregivers when they present with their family member at the emergency department or other points of access to formal services.  Additionally, the FAM-CAM may be used to facilitate longitudinal study of community-dwelling persons with cognitive impairment.

Conclusion

     Although we experienced difficulty enrolling participants initially, we learned a great deal from this pilot study.  Once enrolled, family caregivers were generally satisfied with the technology and the task of answering the FAM-CAM questions daily.  Scores from the CAM and FAM-CAM were highly correlated in this pilot.  Thus, we have confidence in its utility in research and potentially in care partnerships.  We are currently finalizing the analysis of a larger study from which we will publish psychometric properties validating the FAM-CAM in this population and setting.

References

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Author Bios

 

Melinda R. Steis, PhD, RN, HSR&D

Principal and Corresponding author: Melinda R. Steis, PhD, RN, HSR&D Postdoctoral Research Fellow, James A. Haley Veterans’ Hospital Research Center of Excellence, 8900 Grand Oak Circle, Tampa, FL 33637-1022; Melinda.Steis@va.gov; 813-558-3988 (telephone), 813-558-7616 (fax).  Mindy Steis is an experienced home health nurse whose research interest is to facilitate caregivers of community-dwelling persons with dementia to better manage their family members at home, especially to recognize vital changes in mental status that signal acute health events.

Vittal V. Prabhu, PhD,

Professor of Industrial and Manufacturing Engineering, The Pennsylvania State University, 310 Leonhard Building, University Park, PA 16802;  (814) 863-3212; prabhu@engr.psu.edu. Vittal Prabhu received his Ph.D. in Mechanical Engineering from the University of Wisconsin-Madison. He is currently a Professor of Industrial Engineering at Penn State University. Professor Prabhu works in the area of distributed control systems with a focus on manufacturing and service enterprises. The goal of his research is to develop a unified mathematical and computational framework that enables engineering of distributed control systems consisting of discrete-events, physical processes, and service processes. He teaches courses in manufacturing systems, information systems, retail services, financial services, and distributed control systems.

 

Ann Kolanowski, PhD, RN, FAAN,

Elouise Ross Eberly Professor of Nursing and Professor of Psychiatry at the College of Medicine, Penn State University, 201 HHDE, University Park, PA 16802; (814) 863-9901; amk20@psu.edu.  For the past two decades she has conducted research on factors associated with behavioral symptoms exhibited by persons with dementia. Her work has been supported by grants from the National Institute of Health, the Alzheimer’s Association and the Neuroscience Nursing Foundation. She is currently the principal investigator (with Dr. Donna Fick) on a 5-year NIH funded project, “Reserve For Delirium Superimposed on Dementia.” This study is testing the use of cognitive activities for promoting cognitive reserve and reducing the severity, duration and health care costs of delirium in post-acute care patients with dementia. Dr. Kolanowski has published extensively in her area of research. She is a fellow in the American Academy of Nursing and the Gerontological Society of America, and Director of the Hartford Center of Geriatric Nursing Excellence at Penn State University.

Yuncheol Kang, Doctoral Candidate,

The Pennsylvania State University School of Industrial Engineering; 596 Blair Rd., State College, Pa 16801; (814) 689-9327; kang.yuncheol@gmail.com.  He holds a master’s degree in Industrial Engineering from Seoul National University. His current research interest is the use of information technology in health care and prevention education.

Kathryn Bowles, PhD, RN,

Associate Professor of Nursing and Ralston House Endowed Term Chair in Gerontology, Associate Director of the NewCourtland Center for Transitions and Health, and the Beatrice Renfield Visiting Scholar for the Visiting Nurse Service of New York, Room 340 Fagin Hall, 418 Curie Blvd., Philadelphia, Pennsylvania 19104-4217; (215) 898-0323; bowles@nursing.upenn.edu.  Her program of research focuses on the use of information technology to improve the care of older adults. Dr. Bowles has led or been co-investigator on several multi-site, decision support, telehealth, and transitional care studies funded by NIH, Robert Wood Johnson Foundation, the Centers for Disease Control, and several foundations. She is an expert in the development of decision support for discharge referral decision making and in implementation and testing of telehealth technology in home care.

Donna Fick, PhD, RN, FGSA, FAAN,

Professor at The Pennsylvania State University School of Nursing and Co-Director of the Penn State Hartford Center for Geriatric Nursing Excellence, The Pennsylvania State University, 201 HHDE, University Park, PA 16802; (814) 865-9325; dmf21@psu.edu.  She was the lead researcher of an interdisciplinary panel to update the Beers Criteria for inappropriate medication use in older adults published in 2003 in Archives of Internal Medicine. Her other research efforts focus on knowledge of delirium superimposed on dementia (DSD), the outcomes, costs and health care utilization associated with DSD, and examination of strategies to detect and manage delirium utilizing non-drug approaches. She is currently co-chairing a panel to update the Beer’s Criteria with the American Geriatrics Society; and is PI on a C-RCT to decrease the duration and severity of DSD in hospitalized older adults.  Her work is funded by the National Institute of Nursing (NINR).

Lois K. Evans, PhD, RN, FAAN,

van Ameringen Professor in Nursing Excellence, University of Pennsylvania, Room 419 Fagin Hall, 418 Curie Blvd., Philadelphia, Pennsylvania 19104-4217; (215) 898-2140; evans@nursing.upenn.edu.  Dr. Evans is an internationally renowned scholar in gerontologic and geropsychiatric nursing. Building on her seminal study of Sundown Syndrome, she produced with her colleague, Dr. Neville Strumpf, a program of path-breaking research lessening the use of restraints with frail elders in nursing homes and hospitals. This work shaped national public policy, dramatically changing practice across the globe.  Dr. Evans’ research, practice and educational projects have received substantial federal and foundation support. From 1995-2001, Dr. Evans led  development of the School’s academic practice mission, demonstrating, among others, the benefits of integrating advanced practice mental health nursing services in primary care and specialized practices for frail elders. As Director of the Family & Community Health Division in the School, she facilitated the institution of a formal faculty mentorship program. Dr. Evans is the consummate teacher-scholar. In recognition of her work with over 40 students and junior faculty, she has received the University’s Lindback Award, the doctoral students’ Lowery Mentoring Award, and the Division’s Lynaugh Mentoring Award. Currently, she works to improve the mental health of older Americans by enhancing the competencies of nurses.

 

 

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