DEVELOPMENT AND EVALUATION OF A PERSONALIZED MUSIC

INTERVENTION FOR DEMENTIA

EVAN G. SHELTON

Master of Arts in Psychology of Adult Development and Aging

Cleveland State University

May 2014

submitted in partial fulfillment of requirements for the degree of

DOCTOR OF PHILOSOPHY

at the

CLEVELAND STATE UNIVERSITY

DECEMBER 2018

We hereby approve this dissertation

For

Evan G. Shelton

Candidate for the Doctor of Philosophy degree.

for the Department of Psychology

And

CLEVELAND STATE UNIVERSITY

College of Graduate Studies by

_____________________________________ Katherine Judge, PhD, Dissertation Committee Chairperson

________________________________________________________

Department & Date

_____________________________________ Eric Allard, PhD, Dissertation Committee Member

________________________________________________________

Department & Date

_____________________________________ Toni Bisconti, PhD, Dissertation Committee Member

________________________________________________________

Department & Date

_____________________________________ Linda Francis, PhD, Dissertation Committee Member

________________________________________________________

Department & Date

_____________________________________ Harvey Sterns, PhD, Dissertation Committee Member

________________________________________________________

Department & Date

November 26, 2018

Student’s Date of Defense

ii

DEVELOPMENT AND EVALUATION OF A PERSONALIZED MUSIC

INTERVENTION FOR DEMENTIA

EVAN G. SHELTON

ABSTRACT

The use of personalized music as an intervention tool for dementia is garnering attention

in both research and practice. Personalized music aims to capitalize on the relatively

well-preserved pathways linking familiar music to memory. The use of personalized

music to engage persons with dementia (PWDs) represents a low-cost non-

pharmacological approach to improving well-being. Reductions in agitation and other

dementia-related behaviors often viewed as problematic have been reported in existing

personalized music interventions, and personalized music therapy poses little risk of side

effects in comparison to pharmacological interventions for dementia. There is currently a

scarcity of evidence in the academic literature detailing the dissemination of a music

therapy intervention to persons with dementia and family caregivers living in the

community. Moreover, few studies have examined the benefits of personalized music on

broad psychosocial constructs such as quality of life, mood, and relationship quality

between the CG and PWD.

This pilot study consisted of the development, implementation, and evaluation of

a personalized music intervention for PWDs living in the community. A randomized-

controlled trial with a two group (personalized music vs. unfamiliar music) pre-test and

post-test design was used to evaluate intervention efficacy. A one-month randomized-

controlled trial was conducted examining the effects of personalized music listening

relative to unfamiliar music listening on the PWD’s affect, anxiety, quality of life,

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behavioral expressions of dementia, and relationship strain between the PWD and an

informal CG. Results indicated that PWDs in the personalized music listening group

demonstrated significant reductions in anxiety (t(25) = -2.15, p = .04) and relationship

strain (t(25) = -2.61, p = .02) relative to PWDs in the unfamiliar music listening group.

Implications, acceptability/feasibility, limitations, and future directions are discussed.

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TABLE OF CONTENTS

ABSTRACT………………………………………………………………………. ii

CHAPTER

I. INTRODUCTION……………………………………………………... 1

1.1.Dementia Background................................................................ 2

1.2 The Illness Experience………………………………………... 5

1.2.1 Affect…………………………………………….. 7

1.2.2 Behavioral Expressions…………………………… 9

1.2.3 Anxiety…………………………………………….. 11

1.2.4 Quality of Life…………………………………….. 12

1.2.5 Relationship Strain………………………………… 14

1.3 Meeting the Demands of Dementia…………………………… 16

1.3.1 Pharmacological Approaches to Intervention for

PWDs………………………………………………. 16

1.3.2 Non-Pharmacological Approaches to Intervention for

PWDs………………………………………………. 18

1.4 Music and Memory…..……………………………………….. 20

1.4.1 The Neural Link Between Music and Memory……. 20

1.4.2 Familiar vs. Unfamiliar Music…………………….. 22

1.4.3 Music Intervention Impact on PWDs………...……. 23

1.4.4 Music Intervention and the Dyadic Relationship…...27

1.4.5 Cognitive Stimulation Therapy…………………….. 30

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1.4.6 Personalized Music as a Humanistic Mode of

Intervention………………………………………… 32

1.5 Hypotheses…….……………………………………………… 35

II. METHODS……….………………….………………………………… 37

2.1 Study Design…………………………………………………. 37

2.2 Measures……………………………………………………… 37

2.2.1 Demographic Information…………………………. 38

2.2.2 Mini-Mental State Examination……………………. 38

2.2.3 Diary Based Fidelity Assessment………………….. 39

2.2.4 Outcome Measures………………………………… 39

2.3 Inclusion Criteria……………………………………………… 44

2.4 Participant Recruitment………………………………………. 45

2.4.1 Sample Size………………………………………… 46

2.5 Participant Characteristics….…………………………………. 47

2.6 Benjamin Rose Institute on Aging……………………………. 49

2.7 Procedure……………………………………………………… 50

2.7.1 Screening Phone Call………………………………. 50

2.7.2 In-Person Baseline Meeting……………………….. 51

2.7.3 In-Person 1-Month Follow-Up…………………….. 55

III. RESULTS…………………………………………………………….. 57

3.1 Statistical Analyses and Data Treatment…………………….. 57

3.2 Efficacy Testing………………………………………………. 60

3.2.1 Mood………………………………………………. 61

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3.2.2 Anxiety…………………………………………… 62

3.2.3 Behavioral Expressions of Dementia…………….. 63

3.2.4 Relationship Strain……………………………….. 64

3.2.5 Quality of Life……………………………………. 67

3.3 Acceptability and Feasibility………………………………… 67

IV. DISCUSSION………………………………………………………… 69

4.1 Efficacy………………………………………………………. 69

4.2 Acceptability and Feasibility…………………………………. 73

4.3 Clinical Implications…………………………………………. 77

4.4 Limitations……………………………………………………. 78

4.5 Future Directions for Research……………………………….. 81

REFERENCES…………………………………………………………………….. 86

APPENDICES…………………………………………………………………….. 116

A. Demographic information…………………………………………….. 117

B. Mini-Mental State Examination………………………………………. 118

C. The Assessment of Personalized Music Preference (patient version)… 122

D. The Assessment of Personalized Music Preference (family version)… 124

E. Diary-Based Fidelity Assessment Form (weeks 1-3)…………………. 126

F. Diary-Based Fidelity Assessment Form (week 4)........……………….. 127

G. Dementia Quality of Life Instrument – Positive Affect and Negative Affect

Subscales………………………………………………………………. 129

H. Quality of Life in Alzheimer’s Disease………………………………. 130

I. Zung Self-Rating Anxiety Scale………………………………………. 132

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J. Cohen-Mansfield Agitation Inventory – Short Form………………….. 133

K. Dyadic Relationship Strain ……………………………………………. 135

L. Telephone Screener…………………………………………………….. 136

M. Personalized Music Assessment……………………………………….. 140

N. Control Group Playlist…………………………………………………. 141

O. CONSORT Diagram…………………………………………………… 142

P. Commonly Selected Genres and Preferred Artists of Personalized Music

Group Participants…………………………………………………….. 143

1

CHAPTER I

INTRODUCTION

“Personalized music” refers to music that is meaningful and familiar to an

individual. The songs that a person learns over the course of his/her life are tied heavily

to the autobiographical memories associated with those songs, and the emotions that

accompany those memories (Baumgartner, 1992; Jäncke, 2008). If, for example, a

person hears the song they danced to at their wedding, or the rock and roll anthem of their

teen years, the memories of those events and the emotions that accompany them are often

recalled. Thus, familiar music can serve as a cue to the retrieval of the long-term

memories and positive emotions (Haj, Fasotti, & Allain, 2012). The ability to integrate

familiar musical stimuli with autobiographical memories and emotions is a well-

preserved aspect of cognition, even in individuals in the later stages of dementia

(Gerdner, 1997). Thus, the well-preserved connection between personalized music,

memory, and emotion has led to a surge in attention toward the use of personalized music

as a therapeutic tool for improving the lives of persons with dementia (PWDs). This

study aimed to accomplish the following: (1) to provide an in-depth overview of the

literature on the use of personalized music and dementia, (2) to identify important gaps in

the personalized music literature, and (3) to develop, implement, and assess the efficacy

2

of a personalized music intervention using a randomized-controlled trial in a community-

dwelling population of PWDs.

1.1. Dementia Background

Personalized music interventions for dementia attempt to circumvent the memory

deficits of PWDs in order to improve upon psychosocial well-being, or reduce psychical

and behavioral difficulties associated with the dementing illness. To begin this

discussion, it is important to have a general understanding of dementia and its impact on

the lives of those it affects.

Dementia is not a disease in itself. Rather, “dementia” is an umbrella term that

includes a group of conditions which lead to a global decline in memory and cognitive

ability. The most common form of dementia is Alzheimer’s disease, which accounts for

between 60% and 80% of dementia cases in the United States (Alzheimer’s Association,

2018). Other forms of dementia include vascular dementia, Frontotemporal dementia,

dementia with Lewy-bodies, dementia resulting from Parkinson’s disease, and prion

diseases such as Creutzfeldt-Jakob disease.

These varying etiologies lead to similar but distinct sets of symptoms, including

cognitive decline, memory loss, decline in physical ability and decline in the ability to

accomplish activities of daily living. Alzheimer’s disease and related dementias are often

associated with behavioral changes that are perceived by the caregiver (CG), friends, and

family to be inappropriate and out of character (Fauth & Gibbons, 2014, Finkel, 2000).

These behaviors might include wandering (Cipriani, Lucetti, Nuti, & Danti, 2014),

aggression/agitation (Cohen-Mansfield, 1997; Cohen-Mansfield, Marx, & Werner, 1992),

3

inappropriate sexual behaviors (Mendez & Shapira, 2013), and repetitive question asking

(Bourgoeis, 2002; Hwang, et al., 2000), for example. Among these types of behaviors,

apathy, depression, and agitation are among the most common, with behaviors such as

irritability, agitation, and delusions causing the greatest distress for CGs (Fauth &

Gibbons, 2014; Cohen-Mansfield, 2008). The Stress-Process Model posits that these

behaviors are primary stressors for CGs, which negatively impact the CG’s role strain,

intrapsychic strain, and psychosocial well-being outcomes (Pearlin, Mullan, Semple, &

Skaff, 1990). In line with this model, evidence from the literature suggests that CGs

providing care to PWDs who exhibit these types of behaviors are at a greater risk of

experiencing caregiver burden as well as depression (Ballard, Lowery, Powell, O’Brien,

& James, 2000). Further research applying the Stress-Process Model to PWDs suggests

that behavior problems may negatively impact PWDs in a similar way, including greater

role and intrapsychic strain, as well as increased depression and anxiety, and lower

physiological well-being and quality of life (Judge, Menne, & Whitlatch, 2010).

As dementia progresses, PWDs are more likely to have cognitive difficulties

related to executive function (Swanberg, Tractenberg, Mohs, Thal, & Cummings, 2004),

planning and problem solving (Allain et al., 2007; Piquard, Derouesné, Lacomblez, &

Siéroff, 2004), and orientation to time and place (Pai & Jacobs, 2004). The social well-

being of PWDs is often negatively impacted by increasing language difficulty and

embarrassment about memory problems (Ostwald, Duggleby, & Hepburn, 2002). This

often leads PWDs to withdraw from work and social activities (Ryan, Bannister, & Anas,

2009; Aminzadeh, Byszewski, Molnar, & Eisner, 2007). PWDs are also more likely to

4

experience changes in mood, increased depression, agitation, anxiety, and disturbances in

sleep (Alzheimer’s Association, 2018).

Although Alzheimer’s disease is not considered a part of normal aging, advancing

age is the greatest risk factor for Alzheimer’s (Thies & Bleiler, 2012). In 2018, the

number of individuals aged 65 and older with Alzheimer’s disease and other related

dementias in the United States is estimated at 5.7 million, and this number is projected to

grow to 8.4 million in 2030 and 13.8 million in 2050 (Alzheimer’s Association, 2018).

This drastic increase is projected to come as a result of a growing number of adults aged

65 and over as the baby boom generation enters later life. This “graying of the

population” has tremendous implications not only for those individuals who will develop

a dementing illness, but also for the healthcare systems and CGs who must meet the

challenge of providing quality care to PWDs. In the US, 83% of all older adults needing

care are cared for by an informal CG at home (Friedman, Shih, Langa, & Hurd, 2015).

Earlier estimates of PWD care place the proportion of PWDs cared for by a family or

friend at home between 65% and 75% (Aneshensel, Pearlin, Mullan, Zarit, & Whitlatch,

1995). In 2017, it was estimated that more than 16 million informal CGs provided

approximately 18.4 billion hours of unpaid care (Alzheimer’s Association, 2018). The

financial burden of caring for a PWD is not only represented in unpaid hours spent

providing care, but also in the cost of healthcare and, when it becomes necessary, long-

term care and hospice. The Alzheimer’s Association (2018) estimates that $277 billion

will be spent on these types of services for PWDs in 2018, with $60 billion of this cost

spent out-of-pocket.

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1.2. The Illness Experience

Alzheimer’s disease remains a highly stigmatized condition due to its negative

impact on cognitive functioning and its association with loss of independence (Harman &

Clare, 2006). The negative impact of Alzheimer’s disease and related dementias on

social well-being has been linked to changes in self-identity (Clare, 2003). Moreover,

Clare (2003) suggested that the way PWDs perceive their illness (i.e., their “illness

representation”) has an impact on the PWD’s ability to cope with and adjust to the illness.

The subjective nature of the experiences of dementia translate into meaningful

information that cannot be obtained with confidence using observational measures or

proxy report data. Regarding the experience of dementia and quality of life, Testa and

Simonson (1996) state, "the patients' subjective perceptions and expectations translate

that objective assessment into the actual quality of life experienced."

Although CGs may have an accurate understanding of the feelings of the PWD,

the illness representations, coping, and the PWD’s illness experience in general are best

understood by the PWD. The use of self-report methodology to understand the illness

experience of PWDs, for example, indicates that embarrassment about memory loss is a

strong predictor of anxiety while depressive symptomology is better predicted by

physical health strain and role captivity (Dawson, Powers, Krestar, Yarry, & Judge,

2013). Although studies have long used CG proxy reports to obtain information about

the PWD, literature suggests that this proxy report data is often inaccurate. For example,

CG proxy reporters tend to have limited knowledge about the PWD’s care-related

preferences (Hawkins, Ditto, Danks, & Smucker, 2005), especially in the later stages of

the disease (Carpenter, Kissel, & Lee, 2007). Similarly, CG proxy reports often under-

6

estimate the importance of the PWD’s care values compared to self-report data from the

PWD (Reamy, Kim, Zarit, & Whitlatch, 2011; Reamy, Kim, Zarit, & Whitlatch, 2013).

In the development of the Dementia Quality of Life Assessment (DQoL; Brod, Stewart,

Sands, & Walton, 1999), the authors emphasize that self-report data from the perspective

of the PWD is the “gold standard” for assessing quality of life. Indeed, a recent study has

found that discrepancies do exist in proxy versus PWD reports of quality of life (Gomez-

Gallego, Gomez-Garcia, & Ato-Lozano, 2015). In a study of proxy and PWD reports of

anxiety, results showed that both self-report and proxy reports were accurate in predicting

clinically significant levels of anxiety, though proxy respondents tended to rate symptoms

as being more extreme that the PWDs (Bradford et al., 2013).

In general, the use of self-report data from PWDs is becoming more widely

advocated in the literature as a means of avoiding overestimation and underestimation

biases that exist in the use of proxy-report methodology for PWDs (Dawson et al., 2013;

Snow et al., 2005). Given that discrepancies are common between proxy report and self-

report data, the present study used PWD self-report methodology to assess the PWD’s

experience of anxiety, relationship strain, quality of life, and mood. Behavioral

expressions of dementia, in contrast, were measured using CG report. This study used

the Cohen-Mansfield Agitation Inventory-Short Form (Werner, Cohen-Mansfield,

Koroknay, & Braun, 1994), which has been used and validated for CGs. It is important

to note that the assessment of behavioral expressions of dementia from the CG’s

perspective does not require the CG to make judgments on behalf of the PWD. That is,

CGs are not asked to provide insights about the illness experience on behalf of the PWD.

Thus, the CG responses used in this study are not considered proxy-style responses.

7

Instead, this outcome, as it is used in this study, will represent the change in the CG’s

perception of behavioral expressions from baseline to post-test.

The use of self-report methodology for PWDs has been well-supported by the

literature as a valid and reliable means of obtaining data from persons with early to

moderate stage dementia (Moyle, Murfield, Griffiths, & Venturato, 2012; Brod, Stewart,

Sands, & Walton, 1999; Clark, Tucke, & Whitlatch, 2008; Whitlatch, Feinberg, & Tucke,

2005; Krestar, Looman, Powers, Dawson, & Judge, 2012). Moreover, the measures

chosen for this study have been developed and validated for use in populations with

dementia. Details about each measure are discussed fully in Chapter II.

This study sought to understand the impact of a personalized music intervention

on the above mentioned domains of psychosocial well-being: affect (overall mood),

anxiety, quality of life, and relationship strain, as well as behavioral expressions of

dementia. These domains were selected based on evidence from intervention research in

dementia as well as from our understanding of the psychosocial impact of dementia on

the PWD (discussed at length in the following sections of this Chapter). Each of the

following subsections details the psychosocial impact of dementia within the domains of

affect, behavioral expressions, anxiety, quality of life, and relationship strain. The

purpose of these sections is to call attention to the prevalence and importance of these

experiences from the perspective of the PWD, and to underscore the benefits of reducing

the negative psychosocial impact of dementia through targeted interventions such as the

personalized music intervention reported here.

1.2.1. Affect

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Negative affect, mood, and mood disorders such as depression are common in

dementia (Zahodne, Ornstein, Cosentino, Devanand, & Stern, 2015). In the early stages,

and especially after receiving a dementia diagnosis, negative reactions such as fear,

anger, frustration, feelings of uncertainty, depression and embarrassment are common

(Cotrell & Schulz, 1993; Bamford & Bruce, 2000; Aminzadeh, et al., 2007). Worries

about the future and about memory function contribute to negative affect, as well

concerns including the loss of independence/autonomy, communication difficulty and

loss of control (De Boer et al., 2007). Harman and Clare (2006) found that individuals in

the early stages of dementia tended to have a concrete understanding of the fact that

dementia is a progressive illness with no medical cure. Anosognosia, or the lack of

awareness about one’s own condition, is more common in the middle and later stages of

dementia and has been negatively correlated with depressive symptomology (Mograbi &

Morris, 2013). In a study comparing affect across individuals with Alzheimer’s disease,

mild cognitive impairment, and healthy older adults, persons with Alzheimer’s disease

reported more negative emotions and confusion than the other groups (Ready, Carvalho,

Green, Gavett, & Stern, 2011).

In persons experiencing greater degrees of memory loss, positive and negative

mood resulting from emotional stimuli may persist well past the time that the stimulus is

forgotten (Feinstein, Duff, & Tranel, 2010). Two separate studies including participants

with amnesia (Feinstein et al., 2010) and individuals with probable Alzheimer’s disease

(Guzmán-Vélez, Feinstein, & Tranel, 2014) found that emotional responses to sad and

happy stimuli remained even after participants had forgotten the stimuli. These findings

have two important implications to consider if, in fact, personalized music listening does

9

have a positive impact on mood. First, experiences that are distressing for PWDs such as

confusion related to a situation or environment can have negative lasting effects on mood

even after the initial experience is forgotten. Second, the influence of positive emotional

experiences on mood, such as those associated with enjoyable music, may have lasting

effects, even after the actual stimulus is gone and forgotten. If sustained improvements in

mood can be achieved through personalized music listening, CGs may see further

benefits in ease of providing care with care tasks—even after the music has stopped. The

provision of help with personal activities of daily living such as bathing, toileting, and

eating, which may be uncomfortable for both the CG and the PWD, could benefit

substantially from improved mood of the PWD.

1.2.2. Behavioral Expressions

As dementia progresses, behaviors such as wandering, pacing, verbal agitation,

aggression, and repetitious mannerisms, become more likely (Lyketsos et al., 2002; Fauth

& Gibbons, 2014). These behaviors have been referred to as problem behaviors,

disruptive behaviors, disturbing behaviors, agitation (Cohen-Mansfield, 2001), and

behavioral expressions. For the purposes of this paper, the term “behavioral expressions”

will be used. This term has been most recently advocated in the literature as an

alternative to other terms which may imply that behaviors are a direct result of a

dementing illness (“symptoms”), or that the root problem leading to such behaviors lies

with the PWD rather than the environment or the provision of care, for example (Caspi,

2013).

The behavioral expressions associated with dementia often increase the suffering

of the PWD (Ballard, Day, Sharp, Wing, & Sorensen, 2008; Gilley, Whalen, Wilson, &

10

Bennett, 1991) and increase CG burden (van der Lee, Bakker, Duivenvoorden, & Dröes,

2014; Cohen-Mansfield, 2001). Moreover, PWDs who exhibit behaviors perceived to be

problematic by a CG are more likely to be institutionalized (Afram et al., 2014). While

not completely understood, research indicates that behavioral expressions of dementia

may stem from a combination of factors, including brain changes, pain, physical disease,

and an attempt to communicate unmet needs (Husebo, Ballard, & Aarsland, 2011;

Cohen-Mansfield & Werner, 1995). Several models exist to explain the nature of

problematic behaviors, but the perhaps the most supported model is the Unmet Needs

Model (Cohen-Mansfield, 2000; Cohen-Mansfield, Dakheel-Ali, Marx, Thein, & Regier,

2015; Cohen-Mansfield & Werner, 1995). This theory states that the needs of PWDs are

likely to go unmet as communication becomes more difficult and the ability to provide

for oneself diminishes. A variety of factors contribute to the development of behavioral

expressions, including personality (especially neuroticism and pre-morbid aggression;

Osborne, Simpson, & Stokes, 2010), environment, as well as physical and mental states

(Cohen-Mansfield et al., 2015). The Unmet Needs Hypothesis posits that behavioral

expressions are a way of attempting to fulfill needs, attempting to communicate, or an

outcome of frustration or negative affect combined with decreased inhibition (Cohen-

Mansfield, Dakheel-Ali, & Marx, 2009; Cohen-Mansfield et al., 2015). Personalized

music may facilitate improvement within several of these domains. Connecting with a

CG through music, for example, may improve communication between care partners.

Positive affect, as discussed in the previous section, may further translate into reduced

behavioral expressions, and frustration stemming from confusing and/or unfamiliar

stimuli might be circumvented by providing a stimulus that is familiar and enjoyable.

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1.2.3. Anxiety

Anxiety is common across several different types of dementia, including

frontotemporal dementia, vascular dementia, and Alzheimer’s disease (Porter et al.,

2003). In Alzheimer’s disease, anxiety has been linked to individuals with greater levels

of cognitive impairment (Porter et al., 2003), with anxiety symptoms present in as many

as 70% of PWDs (Teri et al., 1998), though clinically significant anxiety is only prevalent

in about 7% of PWDs (Ferretti, McCurry, Logsdon, Gibbons, & Teri, 2001). Given the

high prevalence, anxiety symptomology is a major concern for research and practice in

dementia. Anxiety has been linked to reduced quality of life, reduced ADL ability,

increased behavioral expressions of dementia, and greater likelihood for nursing home

placement (Seignourel, Kunik, Snow, Wilson, & Stanley, 2008). The relationship

between anxiety and cognitive impairment is somewhat nuanced, with some studies

finding that higher levels of impairment are actually less likely to be associated with

anxiety (Kaiser et al., 2014). One explanation for these findings may be that anxiety is

greatest in the moderate stages of dementia when memory problems are present and

particularly troublesome compared to the early stages when symptoms are less noticeable

and the late stages when PWDs may be less aware of their memory deficits. Thus, it is

possible that PWDs in the middle stages of dementia may be able to derive the most

benefit from the use of personalized music in terms of anxiety reduction.

Findings from a small preliminary study suggest that the correlates of anxiety in

dementia may be different for those who develop early-onset Alzheimer’s disease

compared to late-onset Alzheimer’s disease. Those with early-onset Alzheimer’s disease

are more likely to present with anxiety symptomology if they are separated from their CG

12

and if they are male, while anxiety in late-onset Alzheimer’s disease is associated more

strongly with behavioral expressions of dementia (Kaiser et al., 2014). In both of these

demographics, however, the experience of anxiety in dementia is consistent with

Gerdner’s “Mid-Range Theory” of personalized music in dementia. This theory posits

that individuals with cognitive impairment may have more trouble and become more

confused when interpreting external stimuli from their environment, leading to anxiety

(Gerdner, 1997; more on this theory in the later sections of this Chapter). Indeed,

behavioral expressions of dementia and decreased ability to accomplish activities of daily

living have been identified as significant predictors of anxiety symptoms among PWDs

(Teri et al., 1998).

1.2.4. Quality of Life

Dementia poses a substantial obstacle to quality of life for PWDs. A long-

standing model in gerontology from Rowe & Kahn (1997) conceptualized successful

aging as (1) the avoidance of disability, (2) the maintenance of physical functioning, and

(3) engagement with life. Considering the life course of PWDs, the ominous trajectory of

dementia poses a substantial threat to each of these criteria. So, how can PWDs find

satisfaction and high quality of life in spite of these potential declines? Based on the

symptom profile of a PWD, it seems reasonable to turn to measures of cognitive

impairment and memory ability as indicators of quality of life. However, though

dementia is defined by general cognitive decline and memory impairment, objective

measures in such domains have provided little insight into the well-being of the PWD

(Logsdon, Gibbons, McCurry, & Teri, 2002; Banjeree et al., 2006).

13

There are several conceptualizations of quality of life in dementia, with most

including factors related to affect, self-esteem, appraisal of physical function, social

functioning, the environment, and health (for an overview, see Ettema et al., 2005). More

recently, O’Rourke, Duggleby, Fraser, & Jerke (2015) further conceptualized quality of

life as including: relationships with others, agency in life today (i.e., purposefulness), the

PWD’s wellness perspective, and sense of place. Indeed, findings from the qualitative

literature indicate that the social aspects of dementia and an individual’s coping abilities

are better predictors of quality of life than cognitive impairment. Maintaining autonomy

over the disease course and contributing to others and the community in a positive and

meaningful way have been identified as some of the most important factors influencing a

positive quality of life for PWDs (Moyle et al., 2011; Everard, Lach, Fisher, & Baum,

2000).

At a functional level, declines in physical and cognitive ability threaten autonomy

by making activities of daily living (ADLs) more difficult. ADLs include a wide range of

personal (PADLs) and instrumental (IADLs) activities such as dressing, ambulating,

bathing, toileting, etc. for the former, and banking, shopping, cooking, doing laundry, etc.

for the latter. ADLs tend to decline differentially over the course of the illness, with

greater levels of ADL impairment present in populations with greater cognitive

impairment (Giebel, Sutcliffe, & Challis, 2015; Giebel et al., 2014). Giebel and

colleagues (2015) reported that difficulty with PADLs such as toileting, continence, and

feeding were significant predictors of quality of life for PWDs.

Among the various factors encompassing our understanding on quality of life in

dementia, personalized music may have a positive impact on quality of life through its

14

impact on affect, social functioning, and the environment. Cognitive and physical

impairment may interfere with the PWD’s ability to derive quality of life from the same

activities and experiences that they did before their illness. One possibility of using

personalized music for PWDs is that music listening will create new opportunities and

experiences from which the PWD can derive quality of life.

1.2.5. Relationship Strain

Improving the relationship between the PWD and the CG is an especially

important goal for non-pharmacological interventions in dementia given the tremendous

impact of social and relationship factors on the well-being of both the PWD and the CG.

Based on research linking high affective similarity in marital pairs (Bookwala & Schulz,

1996; Tower & Kasl, 1996), Schulz and Martire (2004) hypothesized that negative affect

experienced by either the CG or the PWD may have an adverse impact on the other care

partner. Thus, negative affect experienced by either care partner may adversely impact

the CG/PWD relationship as a whole.

The responsibility for providing care to a PWD typically falls on a family member

or friend who has not received formal training on how best to provide care (Schulz &

Martire, 2004; Brodaty & Donkin, 2009). Thus, CGs are at risk of experiencing strain

and stress from their increased workload and unfamiliar roles. PWDs also experience

role change, increased dependency, and the experience of the illness itself that threaten

the quality of the relationship with the CG. The progression of dementia and the role

acquisition of the CG and the PWD into a care-giver and care-receiver, respectively,

marks a drastic change in the familial or friend relationship. Informal CGs are typically

spouses, adult children, or children-in-law of the PWD (Pinquart & Sorensen, 2011)

15

whose role with respect to the PWD has traditionally been either a recipient of care

(parent-child) or a partner (spouse). Becoming a provider of care for the PWD requires

significant adjustment with respect to role change, with CGs often feeling trapped in their

new role (Brodaty & Donkin, 2009). Indeed, research suggests that CGs who are very

close to the PWD experience significantly higher levels of caregiver burden than CGs

who are not as close (Cantor, 1983). Spouses are typically the first to assume the CG role

if they are able to do so (Brody, 1981; Pinquart & Sorenson, 2011). Spousal CGs tend to

experience greater levels of caregiver burden than adult children or children–in-law,

including greater financial burden, physical burden, and relationship strain (Pinquart &

Sorensen, 2011), but are also less likely to place the PWD in a nursing home than an

adult child CG (Montgomery & Kosloski, 1994). Moreover, lower levels of marital

intimacy before and after the onset of dementia is predictive of higher levels of spousal

CG depression and strain (Morris, Morris, & Britton, 1988).

Family and friends—those individuals who are most likely to provide informal

care—typically represent a key source of social support and quality of life for PWDs

(Moyle et al, 2011). This further emphasizes the importance of a strong relationship

between the PWD and the CG. For PWDs, the maintenance of emotional social support

in late life has beneficial effects in a wide array of domains, including cardiovascular

function, endocrine function, immune function, and even mortality (Everard et al., 2000;

Uchino, Cacioppo, & Kiecolt-Glaser, 1996; Berkman & Syme, 1979). Social support

also serves as a strong predictor of life satisfaction, whereas social isolation and marital

status (being unmarried) is associated with lower life satisfaction (Eshkoor, Hamid,

16

Nudin, & Mun, 2014). Thus, improvement within the CG/PWD relationship may lead to

further improvement in broader domains of well-being.

1.3. Meeting the Demands of Dementia

Given the extraordinary prevalence and impact of dementia on those who will

develop a dementing illness, the individuals who will provide care for PWDs, and on the

health care system, efficacious treatments for dementia are in high demand. Approaches

to treating dementia typically fall within one of two categories: pharmacological

treatments and non-pharmacological treatments. While personalized music interventions

for dementia fall soundly within the non-pharmacological realm of treatment, it is

important to highlight the efficacy (or lack thereof) of pharmacology as a means of

addressing the problems associated with dementia in order to emphasize the important

role and therapeutic potential of non-pharmacology.

1.3.1 Pharmacological Approaches to Intervention for PWDs

Despite a tremendous medical effort over the past three decades to develop a cure

for Alzheimer’s disease and related dementias, no current pharmacological approaches to

treating dementia have been found to be curative. A meta-analysis of randomized,

controlled drug trials of several cholinesterase inhibitors (donepezil, galantamine,

rivastigmine, and tacrine) which were FDA-approved for treating dementia found

statistically significant but clinically marginal improvements in cognition and global

dementia assessment (Raina et al., 2008). Another recent meta-analysis (Matsunage,

Kishi, & Iwata, 2015) examining the efficacy of combination therapy using a

cholinesterase inhibitor and memantine, an NDMA receptor antagonist, resulted in

17

statistically significant improvements for individuals with moderate to advanced

dementia on some cognitive measures (Severe Impairment Battery [SIB; Panisset et al.,

1994]), but no improvements were found on other measures (Alzheimer’s Disease

Assessment Scale cognitive subscale [ADAS-cog; Rosen, Mohs, & Davis, 1984]; Mini-

Mental State Examination [MMSE; Folstein, Folstein, & McHugh, 1975]). While such

pharmacological treatments are arguably beneficially for PWDs, the currently available

medications only address the cognitive symptoms of dementia and do not alter the course

of the disease (Yiannopoulou & Papageorgiou, 2013). Recently developed medications

aimed at modifying the disease course have not been efficacious, though several

pharmacological treatments are currently undergoing clinical trials (Yiannopoulou &

Papageorgiou, 2013; Imbimbo & Giardina, 2011).

Pharmacological treatments aimed at addressing the behavioral expressions

associated with dementia are also common. The most frequently used medications for

PWDs include antipsychotics, antidepressants, and benzodiazepines (Seitz, et al., 2013),

though the most commonly used pharmacological treatments of problematic behavioral

expressions for PWDs are antipsychotics (Seitz, et al., 2013). The safety of using

antipsychotics for PWDs is a point of contention among many, with some studies

indicating that the most highly utilized antipsychotics increase risk of death (Huybrechts,

et al., 2012; Schneider, Dagerman, & Insel, 2005; Wang et al., 2005; Gill et al., 2007),

stroke (Herrmann, Mamdani, & Lanctot, 2004; Gill et al., 2005), and falls (Hien Le et al.,

2005), among other potentially dangerous adverse events (Seitz et al., 2012). Further, the

logistics of administering medications and maintaining medication adherence for PWDs

poses a challenge, particularly for PWDs living in the community (Arlt, Lindner, Rösler,

18

& von Renteln-Kruse, 2008). Researchers focusing on the use of antipsychotics for

treating the behavioral expressions associated with dementia consistently advocate the

use of non-pharmacological strategies for addressing behaviors before resorting to the use

of antipsychotics (Sink, Holden & Yaffe, 2005; Seitz et al., 2012). However, a major

barrier to the use of non-pharmacological approaches—and consequently the over-

administration of antipsychotic medications—is a relatively low understanding of and/or

unwillingness to implement non-pharmacological strategies of dementia care (Cohen-

Mansfield & Jensen, 2008). Since behavioral expressions may represent an underlying

problem (e.g., pain, interpersonal strain, environmental problems), it is important to rule

out such possibilities before resorting to the use of antipsychotics (Cohen-Mansfield &

Mintzer, 2005; Sink et al., 2005; Seitz et al., 2012). The widespread use of antipsychotics

in managing behavioral expressions of PWDs has garnered sharp criticism and opposition

from some, who maintain that behavioral expressions are attempts at communication

which can be addressed through rational non-pharmacological strategies. Perhaps the

most forward argument against the use of such medications comes from a humanistic

perspective of care:

“The medicalization of dementia has created a self-fulfilling prophecy, in which

persons with dementia are assigned a “sick person role,” are treated as children,

and then are medicated when they “act out” in rebellion to this treatment.” (Camp,

2017).

1.3.2. Non-Pharmacological Approaches to Intervention for PWDs

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Non-pharmacological approaches to treating dementia are extremely varied in

scope and intention. The focus of these interventions and models ranges from policy-

level reforms of the healthcare system (Maslow, Fazio, Ortigara, Kuhn, & Zeisel, 2013;

Maslow, 2103) to support and skills training for CGs (Nichols, Martindale-Adams,

Burns, Graney, & Zuber, 2011; Judge, Yarry, & Orsulic-Jeras, 2010), cognitive

rehabilitation for PWDs (Bahar-Fuchs, Clare & Woods, 2013; Aguirre, Woods, Spector,

& Orrell, 2013; Clare & Woods, 2004), management of problematic behaviors (Solai,

Schultz, & Kunik, 2015; Livingston et al., 2014; Cohen-Mansfield, 2015; Ayalon, Gum,

Feliciano, & Areán, 2006), and training for staff in professional care settings (Spector,

Orrell, & Goyder, 2013; Noguchi, Kawano, & Yamanaka, 2013; Kuske et al., 2007).

Many different types of models and interventions now exist which aim to improve upon

the circumstances of the PWD and/or the CG. Within the domain of cognitive outcomes,

for example, non-pharmacological cognitive training techniques may lead to

improvements in cognitive outcomes. A systematic review by Sitzer and colleagues

(2006) revealed an overall effect size for cognitive training studies of Cohen’s d = .47,

with especially large improvements observed in domain specific outcomes of verbal and

visual learning (Cohen’s d = 2.16). Sitzer and colleagues (2006) argued that non-

pharmacological cognitive training and cognitive rehabilitation interventions may also

lead to improvements in depression, ADL ability, and general self-rated functioning,

though these non-cognitive outcomes of cognitive interventions were less well-supported

by a subsequent systematic review (Bahar-Fuchs, Clare, & Woods, 2013). Non-

pharmacological interventions aimed at improving upon the psychosocial well-being of

PWDs as well as behavioral expressions of dementia have demonstrated moderate

20

efficacy (for a review see: Livingston et al., 2014a; Livingston et al., 2014b; Cooper et

al., 2012), as will be discussed at length in the following sections.

Non-pharmacological interventions for dementia take multiple approaches,

including several different modes of intervention to improve upon the well-being of both

the PWD and the CG. While many non-pharmacological interventions have been

successful at improving key outcomes related to dementia, these types of interventions

are often expensive, require extensive training of staff or family members, and only target

one specific issue and one part of the illness continuum. In contrast, the use of

personalized music as a non-pharmacological tool for improving the lives of PWDs may

represent a low-cost yet efficacious approach to improving the lives of those with

dementia that can be implemented across the entire illness continuum. The subsequent

sections focus on the use of non-pharmacological interventions for dementia as a means

of improving psychosocial well-being (particular focus will be paid to quality of life,

affect, and anxiety), and behavioral expressions of dementia (including wandering,

agitation, etc.). In the following sections, discussion will begin with personalized music

interventions for dementia, and will draw on evidence from the broader non-

pharmacological intervention literature where the personalized music literature is lacking.

1.4. Music and Memory

1.4.1. The Neural Link between Music and Memory

Research indicates that memory for familiar music is well-preserved in

Alzheimer’s disease until the very late stages (Vanstone & Cuddy, 2009; Vanstone,

Cuddy, Duffin, & Alexander, 2009, Vanstone et al., 2012; Simmons-Stern, Budson, &

Ally, 2010). Several brain regions have been identified as important for musical

21

memory. The medial prefrontal cortex (mPFC) is a brain structure associated with

judgments regarding self-relevance and affect, as well as the integration of emotion,

autobiographical memory and music (Janata, 2009). Evidence from functional magnetic

resonance imaging (fMRI) suggests a “tonality structure” in the mPFC, such that changes

in tonal space are associated with a topographical activation patterns within the mPFC

(Janata, et al., 2002). Music that is familiar to the listener elicits a stronger response in the

mPFC than unfamiliar music (Plailly et al., 2007), and unfamiliar pleasant music elicits a

stronger mPFC response than unfamiliar unpleasant music (Blood et al., 1999). The

greater activation of the mPFC for familiar music relative to other forms of music may

indicate familiar music elicits a stronger (more positive) affective response than other

forms of music at an observable neural level. This underscores the importance of the

“personalized” aspect of music for dementia, which is discussed at greater length in the

following section.

In a comprehensive neuroimaging study, Jacobsen and colleagues (2015) used

fMRI to examine differences in brain activation regions while listening to familiar and

unfamiliar music. The regions identified by this study included the anterior cingulate

gyrus as well as the pre-supplementary motor area, which shares extensive connections

with the mPFC. While not traditionally thought to be associated with musical memory,

there is a growing body of evidence linking the pre-supplementary motor area and the

anterior cingulate gyrus to music familiarity (Pereira et al., 2011; Janata, 2009; Groussard

et al., 2010). Jacobsen and colleagues (2015) conducted a follow-up study in which they

examined grey matter atrophy, hypometabolism, and amyloid-β deposition in PWDs

using structural MRI and positron emission tomography (PET). Findings indicated that

22

the pre-supplementary motor area and the anterior cingulate gyrus are among the most

well-preserved brain regions in Alzheimer’s disease, with both regions demonstrating

significantly less grey matter atrophy and lower rates of hypometabolism than other brain

areas. While these areas contained levels of amyloid-β deposition similar to other

cortical areas, this accumulation of amyloid-β is representative of an earlier stage in the

disease process and a precursor to both hypometabolism and cortical atrophy. These

findings are supported by other neuroimaging studies of Alzheimer’s disease (Frisoni et

al., 2007; Benzinger et al., 2013; Jack & Holtzmann, 2013), and may help to explain the

preserved connection between music and memory for PWDs. These findings lend

support to the use of personalized music in dementia over the entire course of the

dementing illness—even into the later stages of the disease.

1.4.2. Familiar vs. Unfamiliar Music

The personalized aspect of musical interventions for dementia is underscored both

by theoretical models of music therapy (Gerdner, 1997) as well as the neuroscientific

evidence as discussed above. The articles discussed in the previous sectioned revealed a

difference in brain activation patterns when given familiar and unfamiliar music. From

the theoretical literature on music interventions for dementia, Gerdner hypothesized that

agitation was a result of a lowered stress threshold in dementia (1997). This lowered

stress threshold is thought to result from the increased difficulty of receiving and

processing sensory stimuli from the environment. As dementia progresses, external

stimuli may become confusing, and environmental cues may make it difficult for a PWD

to orient to time and place. Termed the “Mid-Range Theory,” it was hypothesized that

personalized music can serve as an attentional focus for PWDs, allowing them to focus

23

on something familiar and interpretable while filtering out more distressing external

stimuli (Gerdner, 1997). Thus, music that is familiar, interpretable, and tied to positive

memories and emotions should provide the best attentional focus for PWDs. Conversely,

novel, unfamiliar music which does not activate the well-preserved anterior cingulate

gyrus and pre-supplementary motor area is unlikely to capitalize on the link between

music, memory and emotion. Later biological support for the Mid-Range Theory came

from a randomized-controlled trial examining the impact of a personalized music

intervention on salivary chromogranin A, a biomarker of stress found in saliva, which

was significantly reduced among individuals in the personalized music condition relative

to a control condition (Suzuki, Kanamori, Nagasawa, Tokiko, & Takayuki, 2004;

Gerdner, 2010). A recent fMRI study provided further support for this theory, with

results suggesting increased functional connectivity immediately following the

presentation of personalized music (King et al., 2018).

The focus of the following sections will be on personalized music interventions

for dementia. While personalized music has garnered a substantial amount of evidence

for proximal outcomes such as agitation, behavioral expressions, and engagement, it is

important to note that the literature examining distal outcomes such as quality of life,

affect, and anxiety is sparse and methodologically inconsistent. Thus, where the

literature on personalized music is lacking, evidence will be drawn from literature on

music therapy (i.e., singing, dancing, live music, etc.), as well as the broader cognitive

stimulation literature.

1.4.3. Music Intervention Impact on PWDs

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The preservation of musical memory in Alzheimer’s disease was recognized by

the scientific community as early as the mid-1980s (Crystal, Grober, and Masur, 1989

Norberg, Melin & Asplund, 1986). Some of the earliest studies of the effect of

personalized music on dementia focused on agitation in nursing home settings. Agitated

behaviors, or “behavioral expressions” as they are referred to in this paper, are

particularly relevant in this population. Behaviors that are verbally aggressive, repetitive,

or socially inappropriate are not only distressing for CGs, but are also associated with an

increased likelihood of the PWD being placed in a long-term care facility (Cohen-

Mansfield, Marx, & Rosenthal, 1989). Decreasing these types of behaviors may lead to

PWDs remaining in the home longer, reduce the cost of care, and reduce the burden

experienced by the CG.

Results from several studies demonstrated a reduction in agitation during a 30

minute music session and for 60 minutes immediately after listening (Gerdner, 1992;

Gerdner, 2005). A separate study found that participants with advanced dementia living

in a nursing home setting in a personalized music condition demonstrated significantly

reduced agitation compared to a classical music condition while listening and 30-minutes

after listening (Gerdner, 2000). The impact of music interventions of agitation for PWDs

has been well-supported in subsequent music intervention studies with few side effects

(Sung, Chang, & Abbey, 2006; Janata, 2012; Garland, Beer, Eppingstall, & O’Connor,

2007). The majority of studies assessing the impact of music on agitation in PWDs use

either the Cohen-Mansfield Agitation Inventory (Cohen-Mansfield, 1986) or the

Disruptive Behavior Scale (DBS; Beck et al., 1997) to assess agitation levels.

25

In several early studies, PWDs with late-stage dementia were found to benefit

from singing, as evidenced by behavioral observation of engagement and participation

(Norberg, Melin, & Asplund, 1986; Clair & Bernstein, 1990a; Clair & Bernstein, 1990b;

Clair, 1996). Engagement is also a consistent finding in music programming for nursing

home settings, demonstrating increased group singing participation, socializing, and

vocalizing (Olderog-Millard & Smith, 1989; Pollack & Namazi, 1992; Koger, Chaping,

& Brotons, 1999). Moreover, music has been identified in Validation Therapy as an

effective means of connecting with and engaging unresponsive late-stage PWDs (Feil,

2014), though little experimental evidence exists to support validation therapy as a whole

(Neal & Briggs, 2002).

Music intervention in dementia initially focused on proximal outcomes

(immediately after listening) rather than distal outcomes (sustained benefits over a period

of time). More recent studies have examined distal outcomes as a result of music

interventions for dementia. Unfortunately, there is very little consistency across these

interventions in terms of protocol and measurement, and very few studies of personalized

music have been conducted in the community. A recent meta-analysis of music use in

institutional settings concluded that the results of music interventions are promising, but

that further research is needed due to poor methodological quality of existing studies

(Blackburn & Bradshaw, 2014).

While the majority of studies examine music therapy in an institutional setting,

music interventions implemented in the community may pose similar benefits for the

PWD as well as benefits for the CG. Of the interventions that have been done in

community populations, one study found no impact on caregiver burden or behavioral

26

and psychological symptoms of dementia after 6, 12, and 24 months using a combined

music therapy and CG support group intervention (Berger et al., 2004). However, these

results may be due to insufficient dosage (1 hour long session per week), the lack of CG

involvement in the music portion of the intervention, minimal use of personalized music

during music therapy sessions, small sample size, and a large attrition rate. The use of

personalized music in community settings poses several benefits that are not applicable

(or less applicable) in institutional settings. For example, music’s ability to reconnect a

PWD and an informal family CG around shared musical memories from the course of

their lives is not possible in institutionalized populations. Any reductions seen in

behavioral expressions of dementia in the home may delay institutionalization and serve

as a major source of relief to a family caregiver who does not have formal training to care

for a person with dementia. Difficult behaviors that are more easily contained in long-

term care facilities such as wandering may be especially important outcomes for those

living in community settings.

Interventions in institutional settings may be more common due to the

convenience of recruiting participants and the ease of conducting music therapy sessions

in a group format. Two manuscripts from one larger study which used a 40 minute live

group music program administered three times a week for eight weeks found no positive

effects for agitation or anxiety for PWDs (Cooke, Moyle, Shum, Harrison, & Murfield,

2010a), but significant improvements in quality of life and depression for participants

with high baseline depression scores (Cooke, Moyle, Shum, Harrison, & Murfield,

2010b). A separate live music intervention conducted in Iceland using familiar songs

demonstrated a significant reduction in anxiety, aggressiveness, and activity disturbance

27

at a 6-week post-test, though results were not maintained four weeks post-intervention

(Svansdottir & Snaedal, 2006). In one nursing home study, PWDs were split into

personalized music and standard care conditions. After listening to personalized music

twice a week for six weeks, participants in the music condition demonstrated

significantly reduced levels of anxiety from baseline to week six relative to the control

condition (Sung, Chang, & Lee, 2010). One personalized music program examined the

impact of personalized music at different times of the day for residents in long-term care

(Janata, 2012). This study found that music intervention may reduce depressive

symptomology most in the late afternoon and evening.

1.4.4. Music Intervention and the Dyadic Relationship

Substantially less literature exists examining the impact of personalized music on

informal family CGs and their relationship with the PWD. To date, only one published

study has sought to examine the CG’s benefits of using mp3 players with personalized

music for PWDs living at home (Lewis, Bauer, Winbolt, Chenco, & Hanley, 2015). This

study was a pre-post design conducted in a community setting in Australia. CGs were

primarily spouses (71%), primarily female (69%), and nearly all CGs lived with the PWD

(94%). Significant quantitative outcomes for the informal family/friend CGs included

increased self-efficacy for managing dementia and reduced psychological distress,

evaluated using the Kessler-10 measure of Psychological Distress (anxiety and

depression; Kessler et al., 2002). Non-significant outcomes included general health and

life satisfaction, as well as caregiving stress. Significant reductions were observed from

Time 1 to Time 2 on a single item measure of relationship quality. It is likely that this

finding is due to the illness progression over the course of the study rather than mp3

28

usage, but the lack of control group makes it difficult to draw inferences about this

outcome. Qualitative items from the same study indicated that CGs felt that using mp3

players gave them a break from caregiving, helped to put the PWD in a better mood, and

CGs viewed the mp3 players as useful tools for providing care. A second study by Baker

and colleagues (2012) of spousal CGs and PWDs in the community setting included the

use of music as a therapeutic tool, but required the involvement of the CG in each music

session. For example, CGs were trained by a music therapist to utilize music techniques

such as singing familiar songs to the PWD, encouraging the PWD to move to music, and

having the PWD listen to quiet, relaxing music with closed eyes. Sessions were

approximately 20-30 minutes each, and CGs were instructed to ask the PWDs to recall

memories after listening to the music. Each CG kept track of sessions and responded to

qualitative questions using a diary during the 6-week program. Quantitative measures of

depression, anxiety, relationship quality, and satisfaction with the caregiving role did not

change significantly from Time 1 to Time 2, though this may be due to ceiling/floor

effects at Time 1, as well as a very small sample size (5 dyads). Qualitative interviews of

the participants indicated that CGs found the music activities to be relaxing for

themselves and the PWD, improved the quality of the time spent together, enabled the

CG and PWD to be more intimate, and increased satisfaction of the caregiving role. The

authors hypothesized that quantitative measures did not indicate any change from Time 1

to Time 2 because CGs were not presenting with depressive symptoms, anxiety, or

negative perceptions of their relationship with the PWD at Time 1. A third study

examining the effect of personalized music in the home measured CG reports of agitation

before, during and after a 30 minute listening session (Park & Pringle Specht, 2009).

29

PWDs were over age 60, scored below 25 on the MMSE, and were exhibiting agitation.

CG demographics and inclusion criteria were not reported. As predicted, CGs reported

significantly reduced PWD agitation during and after listening as compared to before

listening. Though the authors mention that agitation is a direct contributor to caregiver

burden, there was no measure of caregiver burden to gauge the effect of reduced agitation

from music listening on CGs. The use of a more rigorous measure of relationship quality

in this study as well as the use of a control group to gauge the effects of the intervention

should provide much clearer insight into the impact of a personalized music intervention

on the CG/PWD relationship.

The benefits of general music therapy and music interventions for CGs have been

substantially more studied than personalized music, though the methodology used in

these studies varies widely. A recent systematic review of music therapy interventions

concluded that music therapy may reduce agitation and distressed behaviors in PWDs

while improving the quality of interactions between PWDs and CGs (Blackburn &

Bradshaw, 2014). The American Musical Therapy Association (2006) claims that music

therapy helps with the management of pain, stress, and anxiety for both the CG and the

PWD, provides opportunities for emotional intimacy, and opportunities for respite for the

CG. A musical therapy retreat study indicated that CGs felt less anxious and more than

half of CG participants reported improvements in the PWD’s social and emotional state

(Brotons & Marti, 2003). A 10-week group singing intervention found that quality of life

for the PWD and the CG remained stable over the course of the study despite steady

decline in cognitive status, behavioral and psychological symptoms of dementia, and

ADL ability (Camic, Williams, & Meeten, 2011). While not directly emblematic of a

30

“personalized” approach to music intervention in dementia, the results from the general

music therapy literature are encouraging. These results indicate that a musical approach

to intervention may be beneficial for both members of the dyad.

1.4.5. Cognitive Stimulation Therapy

The relatively sparse body of literature linking personalized music in dementia to

improvement within psychosocial domains warrants evidence from the broader cognitive

stimulation literature. Specifically, the personalized music intervention literature lacks

evidence that these types of interventions can result in sustained (as opposed to

immediately after the intervention is administered) well-being outcomes like quality of

life. Personalized music interventions are, arguably, cognitive stimulation interventions

at their core. The goal of personalized music is to engage the PWD cognitively while

providing an enjoyable and familiar attentional stimulus. The neuroscientific evidence

for personalized music captures a network of emotion and memory (discussed earlier in

this chapter) that may not be tapped in other cognitive stimulation approaches.

Nevertheless, there is evidence to be drawn from the cognitive stimulation literature

which may help to build the rationale and support the efficacy of personalized music

interventions for PWDs.

Cognitive stimulation approaches to intervention aim to engage the PWD in

activities that use cognitive resources, often in a social context (Aguirre, Woods, Spector,

& Orrell, 2013; Clare & Woods, 2004). Cognitive stimulation therapy relies on the

philosophy that cognitive stimulation can serve as a buffer against cognitive decline, and

that the lack of cognitive activity can, conversely, exacerbate this decline. Moreover,

31

cognitive stimulation therapy is based on the assumption that engagement in cognitively

stimulating activities is more enjoyable than disengagement.

Instead of targeting specific cognitive modalities, cognitive stimulation therapy

aims to benefit general cognitive functioning (Clare & Woods, 2004; Spector, Orrell, &

Woods, 2010). The types of activities commonly used in cognitive stimulation therapy

studies include discussion of past and recent events, engaging activities such as baking or

gardening, games and puzzles, as well as musical activities (Woods, Aguirre, Spector, &

Orrell, 2012). Intervention research on cognitive stimulation often utilizes several

different types of activities over the course of the study. For example, one RCT study

used a 14-session design in which participants engaged in physical games, sound,

childhood (reminiscence), food, current affairs, faces/scenes, word association, being

creative, categorizing objects, orientation, using money, number games, word games, and

team quizzes (Apóstolo, Cardoso, Rosa & Paúl, 2014). This study reported

improvements in cognition (using the Montreal Cognitive Assessment), but saw no

improvement in depression or independence related to ADLs. In addition to possible

cognitive benefits, some studies have found evidence for improvement of quality of life.

Spector and colleagues (2004) reported significantly improved quality of life for

participants in a cognitive stimulation therapy group compared to a treatment as usual

group. A study of the long-term effects of cognitive stimulation therapy found that

participants in a “maintenance” intervention group who participated in a weekly session

over the course of 6 months reported higher quality of life at month 3, and had higher

proxy ratings of quality of life, activities of daily living, and better mood at month 6

compared to a treatment as usual group (Orrell et al., 2014). Using a similar design,

32

Chapman and colleagues (2004) found that using a combination of donepezil and

cognitive stimulation therapy improved quality of life and slowed cognitive decline

relative to a donepezil only condition. In a Parkinson’s disease study, participants with

cognitive impairment in an individualized cognitive stimulation therapy intervention

showed some cognitive benefits, as measured by the Montreal Cognitive Assessment, as

well as improvements in quality of life, and ADL ability (Farzana et al., 2015). Based on

these findings, it stands to reason that the benefits of cognitive engagement observed

from this wider body of literature may be present also in a cognitively engaging

personalized music intervention. The link between cognitive engagement and quality of

life is especially relevant from this body of literature, as the personalized music

interventions which examine quality of life as an outcome are generally lacking in

methodological control.

1.4.6. Personalized Music as Humanistic Mode of Intervention

Personalized music interventions in dementia are built upon the philosophy that

PWDs are able to live well with dementia. Rather than taking a purely biomedical

approach that focuses on the illness at its detriments, personalized music aims to

circumvent the impairments of the PWD altogether and build instead upon the abilities of

the PWD. Bearing this in mind, the methodology desccribed in Chapter II is informed by

two approaches to care for persons with dementia: the “strength-based approach” and the

“person-centered approach.”

The philosophy behind the strength-based approach to intervention is to focus on

the strengths of the individuals rather than on weaknesses (Warchol, 2006; Judge, Yarry,

& Orsulic-Jeras, 2010; Yarry, Judge, & Orsulic-Jeras, 2010). A medicalized approach to

33

intervention for any chronic condition risks placing too heavy an emphasis on the disease

itself, its negative symptoms, and the limitations it places on the person with the disease.

The strength-based approach, conversely, places the emphasis entirely on the adaptive

and successful characteristics of each person individually. Though the efficacious

approaches to intervention discussed in this chapter are consistent with the strength-based

approach, the translation of the strength-based approach to intervention research was not

made until recently. The strength-based approach to intervention grew from the Solution-

focused approach to empowering clients within a counsellor-patient relationship (White

& Epston, 1990; Iveson, 2002). More recently, the strength-based approach has been

advocated as a humanistic framework for intervention for persons living with HIV/AIDs

(Orsulic-Jeras, Shepherd, & Britton, 2003) and for PWDs (Judge et al., 2010; Yarry et al.,

2010). The use of the strength-based model is particularly apropos for these populations

because it affords interventionists the ability to empower individuals who may feel

handicapped and stigmatized by their circumstances.

The “person-centered” approach to care is a term that has long been used and

advocated in research and in practice, though it lacks an agreed upon definition (Kogan,

Wilber, & Mosqueda, 2016; Lai, 2016). A recently published White Paper concerning

the quality of dementia care in the United States set out to provide a concrete outline for

what person-centered care is and is not, as well as to advocate for sweeping reforms

toward a person-centered model of healthcare (National Dementia Initiative, 2013). This

White Paper integrates the values espoused by various researchers on the topic of person-

centered care into a single framework. Thus, the person-centered approach, as defined by

the National Dementia Initiative (2013), is as follows:

34

(1) The core values and philosophy that we adopt must serve to foster a

meaningful relationship with the PWD, develop an understanding of the PWD’s

unique personhood, focus on strengths rather than weaknesses, and attempt to

enter the world of the PWD.

(2) We must build the structural elements of person-centered care within the long-

term care environment, including relationships within the community (a sense of

belonging), active and involved governance, reliable leadership (staff

empowerment and staff retention), care partners (staff training in core values of

person-centered care), services, meaningful engagement, a positive environment,

and accountability of person-centered care in practice.

(3) We must operationalize core and structural values in the development of broad

practices of person-centered care.

(4) Personalized practices must focus on specific ways of providing care and

embracing the personhood of each unique individual.

The National Dementia Initiative (2013) argues that health care in the United

States is fragmented and focuses too heavily on a biomedical approach. Instead, person-

centered healthcare aims to foster well-being in physical, psychological, social, and

spiritual domains.

The design and protocol of this intervention were made with careful consideration

given to the philosophies of both the person-centered approach as well as the strength-

based approach to dementia. In consideration of the strength-based approach, this

personalized music intervention relies on the remaining abilities of individuals with

dementia. As outlined in the previous sections, the ability to recognize familiar music

35

from throughout one’s life and draw on long-term memories and emotions is a remaining

strength of PWDs. The use of this type of music as a therapeutic tool may serve as a

means of circumventing the negative experiences associated with the dementing illness.

Thus, personalized music for dementia is strength-based insofar as it shifts focus away

from disease and disability and focuses instead on the abilities that remain over the

course of the disease. Consistent with the person-centered philosophy, the protocol of

this study includes the use of self-report methodology (discussed in Chapters I and II).

This approach aims to capture the perspective of the PWD from the perspective of the

PWD. Instead of using proxy report methodology or observational measures, the PWD is

treated as a valid and reliable respondent in the research process. Finally, the

personalized music aspect of this intervention capitalizes on the unique experiences,

preferences, and life circumstances of each participant.

1.5. Hypotheses

The development of the hypotheses presented in this section was informed by two

conceptual models. First, the Mid-Range Theory, as discussed earlier in this chapter,

links personalized music to positive affect (H1), reduced anxiety (H2), and reduced

behavioral expressions of dementia (H3) (Gerdner 1997; Gerdner, 2000). Second, the

Stress-Process Model, as discussed in Chapter I, posits that external resources or

“mediators” can alleviate the impact of dementia-related stressors (e.g., cognitive status,

functional problems, behavioral problems, perceived dependency, role captivity, and

perceived distress) on role strain and well-being (Judge et al., 2010). By treating

personalized music listening as an external resource for PWDs in the Stress-Process

Model, it was hypothesized that improvements would be seen in dyadic relationship

36

strain (H4, conceptualized as a secondary strain in the Stress-Process Model) as well as

quality of life (H5, a well-being outcome of the Stress-Process Model).

H1. Listening to personalized music over a four week period will improve the

overall mood of PWDs relative to participants in a control group.

H2. Listening to personalized music over a four week period will lead to reduced

anxiety for PWDs relative to participants in a control group.

H3. Listening to personalized music over a four week period will lead to fewer

behavioral expressions of dementia for PWDs relative to participants in a control group

H4. Listening to personalized music over a four week period will improve the

dyadic relationship between the PWD and the CG relative to dyads in the control group.

H5. Listening to personalized music over a four week period will improve the

quality of life of PWDs relative to participants in a control group.

An additional research question was to examine the acceptability and feasibility

of the intervention protocol. This was assessed using an acceptability questionnaire

(described in detail in the following sections), and by tracking dosage (amount of music

listening), adverse events, and attrition rates.

37

CHAPTER II

METHODS

2.1. Study Design

The current study was a one-month randomized, controlled trial with pre-test and

post-test assessments. Groups included one experimental (personalized music) condition

and one control (novel music) condition. After listening to novel music for one month,

the control condition participants were given the option of changing to a personalized

playlist following post-test assessments. The methodology described in this section was

approved by the Institutional Review Boards at Cleveland State University and at

Benjamin Rose Institute on Aging.

2.2. Measures

For the purposes of examining the impact of this personalized music intervention

on PWDs, self-report methodology was used to obtain the most valid insights into the

PWD’s perspective. All measures reported in the following sections were chosen based

on their reliability and validity for assessing populations with dementia. Affect, quality

of life, anxiety, and relationship strain measures were administered to the PWD in-person

by an interviewer. Response cards with visual response options were used to facilitate

responses to items for each measure. The following sections detail each of the measures

38

used in the study followed by a detailed account of the procedure for both the

experimental group and the control group.

2.2.1. Demographic Information

Initial eligibility information was collected via telephone. Information from this

call included details about the PWD’s memory loss (including when the memory

problems were first noticed, what the PWD’s memory-related symptoms are, whether or

not they have talked to a doctor about memory problems, and if the PWD is taking

medication for memory problems), how many days per week the CG provides care, when

the CG began providing assistance, the living arrangements of the CG and PWD, and

whether the CG has primary responsibility for the PWD. For a full account of these

questions, see Appendix I. If participants were eligible based on the information

provided via the initial telephone call, further demographic information was collected at

the first in-person meeting such as age, race/ethnicity, gender, marital status, relationship

between CG/PWD, employment status, income, and education level. Demographic

information was collected from both the PWD and the CG via a self-report questionnaire

(see Appendix A).

2.2.2. Mini-Mental State Examination

The Mini-Mental State Examination (MMSE) is a widely used assessment to

screen for cognitive impairment related to working memory, attention, orientation, and

language (see Appendix B; Folstein, Folstein, & McHugh, 1975). The MMSE is a brief

30-item assessment administered by an interviewer. Scores on the MMSE range from 0

to 30, with scores below 24 considered abnormal. In the present study, the MMSE will be

used to exclude participants scoring below 10. This cutoff point was chosen because of

39

the potentially diminished ability of individuals with severe dementia to self-report

reliably using a range of Likert response options on a variety of measures. While the

individuals were excluded from this RCT, individuals scoring below 10 on the MMSE

were referred for participation to the community rollout of Music & Memory offered

through the Benjamin Rose Institute on Aging.

2.2.3. Diary-Based Fidelity Assessment

All participants were given a goal “dosage” of 20 minutes of music listening at

least five days per week for four weeks. In order to account for dosage, a self-report

diary method was used to track time spent listening to music. These assessments were

provided to CGs at the in-person baseline meeting to be completed by CGs after each

listening session. This information was obtained via telephone on a weekly basis

throughout the duration of the study. This measure was administered to CGs because

CGs were typically the ones initiating music listening for the PWD. The diary-based

fidelity assessment was identical for the first three weeks of participation (see Appendix

E). The fourth and final diary-based fidelity assessment included all items from the first

three weeks in addition to a series of questions assessing the acceptability and feasibility

of the intervention (see Appendix F).

2.2.4. Outcome Measures

Affect

Overall affect of the PWD was measured using the positive affect subscale and

negative affect subscales of the Dementia Quality of Life Instrument (DQoL; see

Appendix G) (Brod, Stewart, Sands, & Walton, 1999). The positive and negative affect

subscales have indicated good reliability for use with PWDs, with Cronbach’s alpha for

40

the positive affect subscale of .83, and .89 for the negative affect subscale (Brod et al.,

1999). Taken as a whole, the five subscales of the DQoL (self-esteem, positive affect,

negative affect, feelings of belonging, and sense of aesthetics) measure the quality of life

of PWDs. While it is widely agreed upon in the literature that mood is an important

factor influencing quality of life (Logsdon, McCurry, & Teri, 2007; Thorgrimsen et al.,

2003; Banjeree et al., 2006), the DQoL Assessment places a substantially greater

emphasis on mood than other measures of quality of life (such as the QoL-AD, discussed

below; see Appendices G and H for a list of items). This has led researchers to use one or

both affect subscales from the DQoL independently from the remaining subscales of the

DQoL (Zarit, Femia, Kim & Whitlatch, 2010; Sabol et al, 2011; Shelton, Orsulic-Jeras,

Whitlatch, & Szabo, 2017). Indeed, a factor analysis of the DQoL indicated that at least

two separate factors of the DQoL exist: negative affect and the remaining items,

indicating that the affect items of the DQoL may be substantively unique from the

remaining subscales in their measurement (Edelman, Fulton, Kuhn, & Chang, 2005).

Thus, the two affect subscales from the DQoL served as an indicator of general mood and

are not thought to be redundant or indicative of global quality of life. Global quality of

life was assessed using a separate measure (discussed below). However, it may be

important to note that the quality of life measure discussed below includes a single item

related to overall mood in its conceptualization of quality of life.

Psychometric testing of PWDs’ responses to the DQoL in the present study

indicated excellent reliability at baseline and follow-up, with Chronbach’s alpha scores of

.86 and .88, respectively. As analyses were conducted with each of the mood subscales,

reliability analyses were conducted independently one the positive and negative affect

41

subscales as well as the full scale. Results for the positive DQoL subscale indicated a

Chronbach’s alpha of .84 and .86 at baseline and follow-up, respectively. The negative

DQoL subscale demonstrated Chronbach’s alpha reliability scores of .87 and .84 at

baseline and follow-up, respectively.

Quality of Life

Quality of Life of the PWD was assessed using the 13-item Quality of Life in

Alzheimer’s Disease questionnaire (QoL-AD; see Appendix H) (Logsdon, Gibbons,

McCurry, & Teri, 1999). The QoL-AD questionnaire is a brief self-report measure that

has demonstrated good acceptability, reliability, and validity for use with PWDs with

MMSE scores between 10 and 28 (Logsdon et al., 1999; Logsdon, McCurry, Gibbons, &

Teri, 2002). Thorgrimsen and colleagues (2003) have reported good reliability, with a

Cronbach’s alpha level of .83 for a sample of 261 respondents with a wide range of

dementia severity. The QoL-AD questionnaire includes items related to physical health,

energy, mood, social relationships, independence and ADL ability, and life as a whole

(for a full account of items, see Appendix H).

Psychometric testing of PWDs’ responses to the QOL-AD in the present study

indicated excellent reliability at baseline and follow-up, with Chronbach’s alpha scores of

.84 and .90, respectively.

Anxiety

Anxiety was measured using the Self-Rating Anxiety Scale (SAS; see Appendix

I) (Zung, 1971). The SAS is a widely used 20-item self-report questionnaire on which

participants rate how often they experience anxiety-related symptoms using a four point

Likert scale response format. Relatively fewer studies exist utilizing this scale as a self-

42

report questionnaire for PWDs. Studies from one sample reported good reliability

(Cronbach’s alpha of .73) using a 4-item version of the SAS (Dawson et al., 2013) and

acceptable reliability (Cronbach’s alpha of .68) using a 12-item version of the SAS

(Shelton, 2014). The SAS includes items about overall anxiety such as “I feel more

nervous and anxious than usual” and “I feel like I’m falling apart and going to pieces” as

well as items related to somatic concerns that are often indicative of anxiety such as “I

can feel my heart beating fast” and “My arms and legs shake and tremble.”

Psychometric testing of PWDs’ responses to the SAS in the present study

indicated good reliability at baseline and follow-up, with Chronbach’s alpha scores of .80

and .81, respectively.

Behavioral Expressions of Dementia

The frequency of behavioral expressions of dementia was assessed using the short

form of the Cohen-Mansfield Agitation Inventory (CMAI-SF; see Appendix J) (Werner,

Cohen-Mansfield, Koroknay, & Braun, 1994). This instrument was administered to CGs,

and asked about the frequency of 14 behaviors. CGs responded on a 5-point Likert scale

indicating how often the PWD has exhibited each behavior within the past two weeks.

Items on the CMAI-SF include behaviors such as verbal aggression, hitting/kicking,

pacing/wandering, repetitive question asking, hoarding, etc. The short form of the CMAI

was chosen over the long-form measure because the short form measure utilizes a 5-point

Likert-scale rather than a 7-point Likert scale. The simplicity of the 5-point Likert scale

makes remembering response options easier for CGs who may also have some degree of

memory difficulty. Fu, Moyle, & Cooke (2013) reported excellent reliability of the

43

CMAI-SF over five longitudinal measurements, with Cronbach’s alphas ranging from .87

to .91.

Psychometric testing of CGs’ responses to the CMAI-SF in the present study

indicated acceptable (albeit lower than previously reported) reliability at baseline and

follow-up, with Chronbach’s alpha scores of .71 and .79, respectively.

Relationship Strain

Relationship strain was measured using the Dyadic Relationship Scale (DRS; See

Appendix K) (Sebern & Whitlatch, 2007). The DRS includes 10 items with responses

recorded on a 4-point Likert scale. Two subscales are included in the DRS: a positive

dyadic interaction subscale and a dyadic strain subscale. The positive subscale includes

items such as “I felt that my relationship with my care partner has improved” and the

dyadic strain subscale includes items like “I felt angry toward him/her.” Sebern and

Whitlatch (2007) reported excellent reliability for CGs (Cronbach’s alphas of .85 for

positive interaction subscale and .89 for the dyadic strain subscale) as well as for

cognitively intact care recipients (Cronbach’s alphas of .86 for positive interaction

subscale and .84 for the dyadic strain subscale).

Chronbach’s alpha values for the PWDs’ scores on the DRS were .73 and .87 at

baseline and follow-up, respectively. Prior to the imputation of missing values in the

DRS, the reliability at baseline was .69—just below the widely accepted standard of .70

(Nunnally, 1978). There are two likely explanations for the low reliability prior to

imputation for the DRS. First, there were a higher number of missing values on items

comprising this scale relative to other measures. Two items in particular, I felt closer to

him/her than I have in a while, and I felt that communication between me and my care

44

partner improved were not answered at baseline by 7 and 6 participants, respectively.

Second, the DRS is composed of two distinct subscales, Positive Interaction and Role

Strain. When separated into its subscales, reliability for these domains was .81 (positive

interaction) and .83 (role strain) at baseline—a substantial improvement over the full-

scale reliability coefficient. These results indicate that the DRS is potentially more

informative when separated into its subscales. For the purposes of this study, analyses

were conducted both ways; with the full-scale DRS and with each individual subscale.

2.3. Inclusion Criteria

In order to be eligible for participation in the study, participants had to meet the

following criteria:

(1) The PWD must be 60 years of age or older. There was no age requirement for

the CG.

(2) Regarding memory impairment and dementia diagnosis, the goal of this study

was not to confirm or diagnose specific memory-related conditions, but to make

reasonable assumptions about inclusion. The inclusion criteria for the present study

mirrored closely the inclusion criteria of past research on personalized music

interventions for dementia (for an overview of samples used in personalized music

intervention studies, see Sung & Chang, 2005). In order to participate, PWDs had to

score 10 or above on the Mini-Mental State Examination (MMSE; Folstein, Folstein, &

McHugh, 1975). A score of 10 has been identified as a cutoff point for reliable self-

report responding on quality of life (Logsdon, Gibbons, McCury, & Teri, 2002; Mozley

et al., 1999) and was chosen to help to ensure that PWD self-report responses are reliable

45

(Feinberg & Whitlatch, 2001). Participants who scored above 27 on the MMSE must

have been diagnosed with dementia to be included in the study. This cutoff was chosen

based on literature suggesting that the conventional cutoff of 24 has a high degree of

specificity, but low sensitivity to detecting cognitive impairment (Kukull et al., 1994;

O’Bryant et al., 2008). These articles suggest an alternative score of 27 while noting that

differences in education levels may further impact performance.

(3) The PWD must live at home and receive care primarily from an informal

caregiver. There were no exclusions based on the relationship of the CG to the PWD

(e.g., spouse, son/daughter, friend, etc.). To be included, CGs must live with or visit the

PWD in-person at least five days out of the week. Since CGs were expected to help

initiate listening, this ensured that each CG will be able to provide the required music

listening dosage to the PWD.

2.4. Participant Recruitment

Several methods of recruitment were used to identify individuals to be screened

for participation. The predominant method of recruitment was through attendance at

caregiver support groups within the targeted geographical area. The principal researcher

attended 16 such support group meetings aimed at supporting caregivers of persons in the

early-to-moderate stages of dementia. Additionally, recruitment was conducted through

local adult day programs. Three adult day programs agreed to help identify and refer

appropriate participants for participation based on the eligibility requirements of the

study. Each of these organizations first contacted their clients to inform them of the

study and obtain a verbal agreement to share their contact information with the principal

46

researcher for a follow-up call. In addition to these recruitment methods, several other

recruitment efforts were made which yielded substantially fewer contacts. These

methods included outreach to local religious organizations, community talks about music

and memory, flyer postings at local establishments, outreach to senior apartment

buildings and home health care providers, and a description of the study in a local

homecare assistance newsletter.

2.4.1. Sample Size

Through the above recruitment efforts, 71 dyads expressed interest in

participating in the study. Of those 71 dyads, 46 were able to be contacted via telephone

and completed the telephone screener. Following the telephone screener, 8 participants

were excluded due to ineligibility (n = 1), declining to participate (n = 2), or other

reasons (n = 5). Thus, 38 dyads met with the principal investigator for the baseline

meeting. Of these, 4 did not meet MMSE eligibility requirements. The remaining 34

participants were randomly assigned to either the control group (N = 16) or the

experimental group (N = 18). Following assignment, 4 dyads withdrew from the study for

a total N-size of 30: 15 participants in the control group and 15 in the experimental

group. One of these dyads withdrew due to lack of interest. The other three dyads

withdrew due because they felt that the study required more time than their schedules

allowed. For a full CONSORT diagram detailing recruitment, see Appendix O.

An analysis was conducted to determine if there were any group differences in

baseline scores on outcome measures between completers and non-completers. Results

indicated no differences between completers and non-completers on mood, quality of life,

47

agitation, or relationship strain. However, participants who withdrew had lower baseline

anxiety scores on average (M = 1.25, SD = .04) than completers (M = 1.46, SD = .36).

2.5. Participant Characteristics

Potential participants were contacted by telephone and screened for inclusion and

exclusion criteria using the Telephone Screener (Appendix L). Informed consent was

obtained from both the CG and the PWD at the in-person baseline meeting.

For a full account of demographic information, see Table 1. Ten of the PWDs in

the control group were men, and 7 of the PWDs in the personalized music group were

men. Average age for participants in the control group was 73.5 (SD = 6.9) and 79.4 (SD

= 10.4) in the personalized music group. In the control group, 11 PWDs were white non-

Hispanic, 1 PWD was white Hispanic, and 3 PWDs were black. In the personalized

music group, 8 PWDs were white non-Hispanic and 7 PWDs were black. Three PWDs in

the control group and 4 PWDs in the personalized music group were veterans. In the

control group, 14 PWDs were married and 1 had never married. In the personalized

music group, 6 PWDs were married, 7 were widowed, 1 was divorced, and 1 had never

married.

Twelve PWDs in the control group received care from a spouse, 1 from an adult

child, and 2 from a non-relative. In the personalized music group, 6 PWDs received care

from a spouse, 7 from an adult child, 1 by another relative, and 1 by a non-relative.

Average MMSE score for PWDs in the control group was 22.7 (SD = 4.9), and 17.3 (SD

= 6.2) for PWDs in the personalized music group.

48

The type of music that was preferred by participants varied in genre. The most

commonly preferred genre of music was Gospel (n = 6), followed by Rock and Roll (n =

5). For a more detailed account of music preferences, see Appendix P.

Table 1

PWD Demographic Information

Characteristic Control (N = 15) Experimental (N = 15) Total (N = 30)

Gender

Female 5 (33%) 8 (53%) 13 (43%)

Age (Mean ± SD) 73.5 ± 6.9 79.4 ± 10.4 76.2 ± 9.2

Race

White Non-Hispanic 11 (73%) 8 (53%) 19 (63%)

Black/African American 3 (20%) 7 (47%) 10 (33%)

White Hispanic 1 (7%) 0 (0%) 1 (3%)

Marital Status

Married/Partnered 14 (93%) 6 (40%) 20 (67%)

Widowed 0 (0%) 7 (47%) 7 (23%)

Divorced 0 (0%) 1 (7%) 1 (3%)

Never Married 1 (7%) 1 (7%) 2 (7%)

Children

Yes 14 (93%) 11 (73%) 25 (83%)

No 1 (7%) 3 (20%) 4 (13%)

Relationship to Care Partner

Spouse 12 (80%) 6 (40%) 18 (60%)

Parent 1 (7%) 7 (47%) 8 (27%)

Other/Non-relative 2 (13%) 2 (13%) 4 (13%)

Level of Education

Some college or less 3 (20%) 11 (73%) 14 (47%)

College graduate or higher 12 (80%) 4 (27%) 16 (53%)

Household income

≤$40k per year 2 (15%) 11 (79%) 13 (48%)

>$40k per year 11 (85%) 3 (21%) 14 (52%)

MMSE Score (Mean ±SD) 22.67 ± 4.87 17.33 ± 6.20 20.00 ± 6.11

Efforts were made to schedule post-test meetings as close to the end of the one-

month intervention block as possible. The average number of days between baseline and

post-test for the control group participants was 34.9 days (SD = 7.7) and 47.9 days (SD =

49

23.2) for those in the experimental group. The average number of days between pre and

post-test for individuals in the experimental group was inflated due to three dyads. Two

PWDs in this group were hospitalized during the intervention. The time between pre and

post-test for these individuals was 106 days and 88 days. One additional post-test

meeting with a dyad was delayed due to health problems of a CG. The time between pre

and post-test for this dyad was 77 days. Between subjects differences were examined for

these individuals using the same approach as the final analyses (see Section 3.1 for

details). Rather than group assignment, a “delayed” variable was created and used as the

key predictor in each regression model. There were no significant differences between

these “delayed” participants and the rest of the experimental group in mood, quality of

life, anxiety, relationship strain, or behavioral expressions of dementia (all p’s > .05).

Thus, all participants were included in the final analyses.

2.6. Benjamin Rose Institute on Aging

This study was done with the support of an implementation study grant obtained

by Benjamin Rose Institute on Aging from the Ohio Department on Aging. This project

aimed to provide music equipment and music to PWDs living in the community in

Northeast Ohio. This dissertation study used a subset of the participants from the

implementation study to conduct a more rigorous examination of the benefits of

personalized music in dementia. In order to satisfy the terms of the grant, there are a few

methodological parameters that this dissertation study was required to meet. First, the

personalized music intervention was four weeks. This is the prescribed length of time

between pre-test and post-test deemed by the Ohio Department on Aging. This timeline

50

is consistent with previous personalized music interventions in dementia, which allow for

between 2 and 6 weeks between baseline and post-test measurements (Park & Pringle

Specht, 2009; Lewis et al., 2015; Gerdner, 2000). Second, Quality of Life was assessed

using the Quality of Life – Alzheimer’s Disease (QOL-AD) measure. Third, the

demographics assessment used in this study included all items required by the grant, and

was administered to both the PWD and the CG. Finally, this study used Apple® products

including iPod Shuffles® and iTunes® as the platform for obtaining and listening to

music.

2.7. Procedure

The following sections detail the study procedure for both the experimental and

control conditions throughout the study. To examine the acceptability and feasibility of

this protocol, pilot testing was conducted with the first three participants recruited, who

were automatically assigned to the experimental condition. This subsection of

participants allowed for the development and refinement of several key study

components, including questions used to assess personalized music (see Appendix M),

the procedure for training participants and setting up the music-related hardware and

software, and music tracking via the use of the diary-based fidelity assessments. The final

study protocol within each of these areas was finalized following interviews with the

initial three participants. Procedures and materials were adapted using insights gained

from these participants.

2.7.1. Screening Phone Call

51

Once a PWD or CG expressed interest in the program through community

recruitment efforts, they were contacted via telephone. A phone screener script and

questionnaire was utilized to facilitate the process of screening participants for eligibility

(for a full account of items and the script used in the screener, see Appendix L).

Caregivers contacted via telephone were asked about their care situation to determine

eligibility. The caller verified that the CG provides care for a person exhibiting memory

problems at least five days per week, that the PWD lives in the community, and is cared

for by a family CG. Additionally, participants were asked about their familiarity with

Apple® products, and if they have a computer with internet access that can store their

music. The goal of these questions was to identify how much assistance each dyad would

need to set up a personalized music database, create a playlist, and begin using a personal

music device. This helped determine what types of accommodations needed to be made

in terms of meeting location (for example, if participants plan to use a desktop computer

to house music located at the home, meetings should take place in this location).

All participants received an iPod Shuffle® with the accessories needed to begin

listening to personalized music (e.g., charger, charging cable, headphones, headphone

splitter, iTunes® gift card). Participants who did not have a home computer capable of

housing iTunes® and personalized music were given a pre-loaded iPod Shuffle® with

music identified as personally relevant through a personalized music assessment process

(discussed below). This method is used as an alternative to setting up an iTunes® account

for each participant because the ability to add, remove, or modify music in playlists is

easier if the device can be connected to a native desktop with iTunes®. Diary-based

52

fidelity questionnaires included an item to monitor whether technology complications

have prevented or deterred participants from engaging in music listening each week.

2.7.2. In-Person Baseline Meeting

If the participants met these inclusion criteria as determined in the screening call,

the researcher set up a time to meet with the CG and the PWD together in-person.

Options were provided to meet with the dyad in their home, at Benjamin Rose Institute

on Aging, or at another preferred location.

At the baseline meeting, a researcher obtained informed consent to participate in

the study from both the CG and the PWD. PWDs were administered the MMSE

(Appendix B) and demographic information was collected for both the PWD and the CG

(Appendix A). If participants met all eligibility requirements from the screening call and

the PWD scored 10 or above on the MMSE, the baseline assessment was administered.

Caregivers were asked to complete the following questionnaires independently: the

CMAI-SF (Appendix J), and the DRS (Appendix K). At the same time, an interviewer

administered the following questionnaires to the PWD in a separate room: DQoL affect

subscales (Appendix G), the QoL-AD (Appendix H), the SAS (Appendix I), and the DRS

(Appendix K). At this point, participants were randomly assigned to either the

experimental condition or the control condition. Blocked randomization was used to

ensure that each group contained approximately the same number of participants after

attrition.

Control Group – Baseline Meeting

If the participant is assigned to the control group of the intervention, the

researcher provided an iPod Shuffle® that had been preloaded with novel music.

53

Participants were given a goal music listening dosage of 20 minutes per day for at least

five days per week to match the dosage prescribed to the experimental condition. The

primary researcher provided 4 diary-based fidelity assessments (1 per week) to

participants in the control condition in order to capture actual amount of time spent

listening to music and to track any technology-related issues experienced by participants.

The primary researcher determined a weekly time to call participants to obtain diary-

based fidelity information via telephone. Participants in the control condition were asked

not to add any of their own music to the iPod® for the first month of listening. The

researcher explained that they will meet again in 4 weeks to follow up about the

questions they were asked at baseline. Finally, a researcher provided information and

resources related to the use of the iPod Shuffle® and participants were given study-

related information and contact information for a project researcher.

Experimental Group – Baseline Meeting

If the participant was assigned to the experimental condition, a personalized

music assessment was administered to determine the music preferences of the PWD. The

instrument used to assess personalized music preferences was based upon the Assessment

of Personal Music Preference questionnaire (APMP; Gerdner, Hartsock, & Buckwalter,

2000). Gerdner and colleagues developed two versions of the APMP; one version for

patients (see Appendix C), and one version for family members (see Appendix D). The

family version of the APMP used the CG as a proxy to respond on behalf of the PWD. A

single assessment was used in the present study (Appendix M), and was administered to

both the CG and the PWD jointly. This allowed the PWD to respond to questions about

54

favorite music themselves, and allowed CGs to weigh in with additional suggestions. A

summarized list of participants’ favorite music can be found in Appendix P.

Information obtained by the personalized music assessment helped the researcher

develop a playlist of 8-10 songs (or the amount of music that can be purchased using a

$10 iTunes® gift card). If the participants wished to add previously purchased music to

their playlist or add money to their iTunes account to purchase more music, the

researcher offered to help with this as well. Information was provided about how to add

music in the future if the participants so desire (more on this in the following paragraph).

If the participants owned a computer with internet access, the baseline meeting also

included setting up an iTunes account, purchasing music with the iTunes® gift card,

creating a playlist on the computer, transferring the music to an iPod Shuffle®, and

showing the participants how to use the device. If participants did not have a computer

that could store iTunes® and purchased music, the primary researcher had a laptop

available with internet access to download and transfer music to the participant’s iPod®.

As a final component of the baseline meeting, CGs in the experimental condition

were given a detailed information packet to serve as a resource for them after the

researcher left. This included the same information given to the participants in the control

group, additional information about the use and potential benefits of personalized music,

as well as information about how to use iTunes®. The researcher who met with the dyad

for the baseline session talked the CG through this information packet. In sum, this

information included:

The potential benefits of personalized music listening, including helping

with behavioral expressions that occur during certain times of the day or

55

during certain activities, decreasing anxiety and improving mood,

increasing quality of life, and providing an opportunity for the CG and the

PWD to reconnect around familiar music

How to use the iPod Shuffle®, iTunes®, and a list of resources available

to help with any technical or study-related questions.

A reminder about the amount of time PWDs and CGs should spend

listening to personalized music each week.

Finally, CGs were given the diary-based fidelity assessment forms (see

Appendices E and F) to track usage. As in the control group, a researcher set up a weekly

time to call participants to obtain diary-based fidelity information via telephone.

Participants were given the same goal “dosage” of 20 minutes per day of music listening,

at least five days per week over a period of 4 weeks. Actual dosage was tracked in the

diary-based fidelity assessment. The prescribed and actual dosage was captured and used

as a covariate in analyses.

2.7.3. In-Person 1-Month Follow-Up

Experimental Group – Follow-Up Meeting

At the end of the third week of the intervention, participants were contacted via

telephone to set up a final in-person meeting time. The meeting time was scheduled for

as close to one month from the baseline meeting as possible. At this follow-up meeting,

CGs were asked to complete the following questionnaires independently: the CMAI-SF

(Appendix J), and the DRS (Appendix K). At the same time, an interviewer administered

the following questionnaires to the PWD in a separate room: DQoL affect subscales

(Appendix G), the QoL-AD (Appendix H), the SAS (Appendix I), and the DRS

56

(Appendix K). This marked the termination of the study for participants in the

experimental group.

Control Group – Follow-Up Meeting

If the participant was assigned to the control group, they were given the option to

personalize the music on their iPod® to their taste. If they chose to fill the iPod® with

their favorite music, a researcher erased the unfamiliar music previously contained on the

iPod Shuffle® and helped the participants add their favorite music to the now blank

device in the exact same manner as the experimental group at Time 1. All participants

were allowed to keep the equipment (e.g., gift cards, mp3 players, headphones, chargers,

headphones splitters, etc.) that they were given for the intervention.

57

CHATPER III

RESULTS

3.1. Statistical Analyses and Data Treatment

SPSS Statistical Software (Version 24.0, IBM Statistics) was used to enter and

analyze data. Data were entered manually by the primary researcher. Reverse coded

items were reversed in the paper interview so as to avoid confusion during data entry.

Following data entry, descriptive statistics and frequency analyses were run for all items.

By examining range, any data points that exceeded the parameters of the scale were

identified, the hard copy of the interview was consulted, and the appropriate value was

filled in. All missing data were coded as ‘99’ and missing values were defined as ‘99’ for

all numerical variables in SPSS.

The “Analyze Patterns” function in SPSS was used to assess missing data for all

items composing outcome scales. In sum, 1.71% of all items were missing, with 98.29%

of values being present. Of the 30 cases, 16 had at least one missing value. Of the 144

variables, 33 (22.92%) had at least one missing value. For listening time, 3 participants

had missing data (i.e., participants failed to complete their weekly listening logbooks).

Each of these three dyads indicated to the primary researcher that they had listened to

music regularly over the month-long study, but failed to record their listening time.

58

Given the small percentage of missing data across cases, a single imputation

approach was used to handle missing data. Person-mean imputation was used to replace

missing values for all items within the outcome scales. Person-mean imputation is a

technique for substituting missing data using the mean of non-missing values within a

given Likert scale (Downey & King, 1998; Bono et al., 2007). If, for example, a

participant scores (4, 3, 4, 4, 99) on a 5-item Likert scale where 99 is missing, the value

would be replaced with the mean of (4, 3, 4, 4). The new data would be (4, 3, 4, 4, 3.75).

Regarding listening time, three participants had missing data. For this single

“dosage” variable, which was used as a covariate in the final analyses, item-mean

imputation was used to impute a value for the three missing data points. The item-mean

for this imputation was generated from the mean of the overall sample (as opposed to a

separate mean for the control/experimental group subsamples). Evidence suggests that

item-mean imputation for covariates produces unbiased estimates of the treatment effect

and maintains type 1 error rate accuracy in randomized-controlled trials (Kahan, 2014;

White & Thompson, 2005).

The dosage covariate was used in the final analyses was developed from the

PWD’s number of listening sessions as indicated in the weekly logbooks. Among three

dosage variables (total number of listening sessions, total listening time, and average time

per session), the number of listening sessions was the only dosage variable that was

significantly correlated with any of the DVs. Specifically, the number of listening

sessions was significantly correlated with Time 2 quality of life score (r = .37, p < .05),

and was trending toward a significant correlation with baseline quality of life score (r =

.31, p = .10). Regarding this dosage variable, it is possible that the total amount of time

59

spent listening was not as important as the number of listening sessions during the month-

long intervention. For example, suppose that PWD “A” listens for 45 minutes twice per

week and PWD “B” listens for 15 minutes six times per week. Both participants will

have listened for 1 hour and 30 minutes over the course of the week, but PWD “A” will

have gone 5 days of the week without listening compared to PWD “B’s” one day.

Typical approaches to analyzing intervention efficacy require a dosage of at least

80% of the prescribed intervention (Armijo-Olivo et al., 2009). However, given the

exploratory nature of this study, participants were not be excluded from analyses for

failing to meet 80% of their prescribed dosage. Rather, using ‘number of listening

sessions’ as a covariate in final analyses permitted insights about how dosage level

impacted the effectiveness of the intervention.

Multiple regression analyses were used to test each hypothesis. Each model was

structured using the following predictors: a centered baseline assessment of the DV being

tested, a dichotomized group assignment variable, MMSE score, and number of listening

sessions (dosage). The key variable in each model was the dichotomized group

assignment variable. The regression coefficient of this variable was assessed using a t-

test with n – k – 1 degrees of freedom, where n = sample size, and k = the number of

independent variables in the model. A significant regression coefficient for the group

assignment variable represented a significant difference in scores on the dependent

variable for PWDs in the personalized music listening group relative to the unfamiliar

music group (i.e., a significant regression coefficient for this variable indicates that group

assignment accounts for unique variance in the DV after accounting for the baseline score

on the outcome measure, MMSE score, and number of listening sessions).

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The use of a regression framework for hypothesis testing in the current study was

chosen over an ANCOVA framework for its ability to maximize power in a small

sample. To illustrate, separate post-hoc power analyses were conducted using G*Power

(Faul et al., 2007). First, an analysis was conducted of a single regression coefficient

(two-tailed) in a linear fixed regression model, with a medium effect size of f2

= .25, 30

participants, 4 predictors, and an alpha level of .05. This yields an achieved power level

(1 – β error probability) of .75. The same analysis within an ANCOVA framework

(including main effects and interactions) yields an achieved power level of .16. The

multiple regression technique for assessing group differences has been used in similar

intervention studies in dementia (Judge et al., 2012; Bass et al., 2013).

3.2. Efficacy Testing

Results of each regression analysis are reported in the corresponding subsections

below. For each regression analysis, covariates included the number of listening sessions

(dosage), MMSE score of the PWD, and the centered baseline version of the dependent

variable. The unstandardized regression coefficient for group assignment (B) is reported

for each regression analysis.

Due to the existing differences at baseline from this small sample, analyses also

were conducted with the inclusion of the PWD’s age, income, relationship to care partner

(spouse/non-spouse), and the level of education. Across all analyses, none of these

demographic covariates added significant variance to the model. However, despite being

non-significant effects, the addition of these three variables led to reduced significance of

the key variable (group assignment) in the analyses for both relationship strain and

61

anxiety. It is likely that the drop in significance in this respect was due to high

multicollinearity between group assignment and income (r = -.59, p = .001), relationship

to the caregiver (r = -.41, p = .03), and age (r = .33, p = .08).

An additional concern regarding the inclusion of these non-significant predictors

in the final statistical model is over-fitting. It has been suggested that a minimum

number of subjects per variable in linear regression analysis be between 10 and 15

(Harrell, 2001; Green 1991). Thus, the required minimum sample size for a regression

model with the aforementioned demographic variables would be in the range of 70 to 105

dyads. For these reasons, demographic variables were not included in the final analyses

reported below. This issue is discussed in greater detail in the limitations section of this

study.

3.2.1. Mood

It was hypothesized (H1) that listening to personalized music over a four week

period would improve the overall mood of PWDs relative to participants in the control

group. Separate regression analyses were conducted for the positive and negative affect

subscales of the DQOL rather than a combined score. This decision was made due to

existing literature indicating that positive and negative affect are not opposite ends of a

single spectrum, but two separate constructs altogether (Russell & Carroll, 1999). That

is, a person can score high or low on both positive affect and negative affect.

In the regression model for negative mood, group assignment was non-significant,

B = .28, t(25) = 1.47, p = .16. Additionally, MMSE and session count were both non-

significant (p = .53 and p = .25, respectively). See Figure 1.

Figure 1:

62

In the regression model for positive mood, group assignment was a non-

significant predictor of positive mood at T2, B = .35, t(25) = 1.79, p = .09. MMSE score

was a significant covariate in the model for positive mood, B = .04, t(25) = -2.77, p = .01.

Session count remained non-significant, p = .38. See Figure 2.

Figure 2:

3.2.2. Anxiety

3

3.2

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Unfamiliar Music Personalized Music

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It was hypothesized (H2) that listening to personalized music over a four week

period would lead to reduced anxiety for PWDs relative to participants in the control

group. In the regression model for anxiety (SAS score), group assignment was a

significant predictor of anxiety, B = -.17, t(25) = -2.15, p = .04. PWDs in the personalized

music group demonstrated greater reductions in anxiety relative to the unfamiliar music

group. MMSE and session count were both non-significant predictors of anxiety (p = .71

and p = .95, respectively). See Figure 3.

Figure 3:

3.2.3. Behavioral Expressions of Dementia

It was hypothesized (H3) that listening to personalized music over a four week

period would lead to fewer behavioral expressions of dementia for PWDs relative to

participants in the control group. In the regression model for behavioral expressions of

dementia (CMAI score), group assignment was non-significant, p > .05. PWDs in the

personalized music listening group did not demonstrate significantly fewer behavioral

expressions of dementia relative to the control group. MMSE and session count were

0

0.1

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0.4

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T1 T2

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Unfamiliar Music Personalized Music

64

both non-significant predictors of behavioral expressions of dementia at T2 (p = .70 and p

= .19, respectively). See Figure 4.

Figure 4:

3.2.4. Relationship Strain

It was hypothesized (H4) that listening to personalized music over a four week

period would improve the dyadic relationship between the PWD and the CG relative to

dyads in the control group. In the regression model for relationship strain (DRS, full

scale), group assignment was a significant predictor of relationship strain at T2, B = -.33,

t(25) = -2.61, p = .02. PWDs in the personalized music group demonstrated greater

reductions in relationship strain relative to the unfamiliar music group. MMSE and

session count were both non-significant predictors of relationship strain (p = .15 and p =

.59, respectively). See Figure 5.

Figure 5:

1

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Unfamiliar Music Personalized Music

65

Separate identical regression analyses were conducted separately for the Positive

Interaction and Role Strain subscales of the DRS (see “Section 3.3: Psychometric

Testing” for a rationale). For the Positive Interaction subscale, group assignment

remained a significant predictor of positive interaction at T2, B = -.35, t(25) = -2.12, p =

.04. PWDs in the personalized music listening group showed greater improvement in

positive interaction at T2 relative to the unfamiliar music group (see Figure 5). MMSE

score and session count remained non-significant (p = .46 and p = .32, respectively). See

Figure 6.

Figure 6:

1

1.2

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T1 T2

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Unfamiliar Music Personalized Music

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*Note: Lower scores represent more positive interaction (less strain).

The Role Strain subscale of the DRS demonstrated similar results. Group assignment

remained a significant predictor of role strain at T2, B = -.42, t(25) = -2.21, p = .04. PWDs

in the personalized music listening group showed greater reductions in role strain at T2

relative to the unfamiliar music group (see Figure 6). MMSE score and session count

remained non-significant (p = .10 and p = .90, respectively). See Figure 7.

Figure 7:

1

1.2

1.4

1.6

1.8

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2.2

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T1 T2

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3.2.5. Quality of Life

It was hypothesized (H5) that listening to personalized music over a four week

period would lead to improved quality of life for PWDs relative to participants in the

control group. In the regression model for quality of life (QOL score), group assignment

was non-significant, p = .15. MMSE score and session count were both non-significant

predictors of behavioral expressions of dementia at T2, although MMSE was nearing

significance (p = .06 and p = .12, respectively). See Figure 8.

Figure 8:

3.3. Acceptability and Feasibility

Questions designed to assess acceptability and feasibility were included in the

weekly logbooks. Given the potentially confusing nature of new technology, participants

were asked each week if they experienced any technology problems. At week one, one

participant reported a technology problem—the block charger in the kit wasn’t working.

2.5

2.6

2.7

2.8

2.9

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3.1

3.2

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Unfamiliar Music Personalized Music

68

Another block charger was provided. At week two, two participants reported technology

problems—one participant couldn’t find the charging cable, but located it following a call

to the primary researcher. The second participant found that the iPod would not charge.

After contacting the primary researcher, it was discovered that the charging cable was not

plugged in all the way to the device. At week three, there were no technology problems

reported. At week four, one participant reported a technology problem—they were

having trouble adding additional music from iTunes.

Following the fourth week of recording listening times in the logbook, CGs were

asked to fill out a survey about their experience with the study. Items in this questionnaire

included “My loved one seemed to enjoy listening to personalized music,” and “I will

continue to play personalized music for my loved one in the future,” for example.

Overall, participants were very favorable toward the personalized music intervention.

For a full account of items and response means, see Table 2.

Table 2.

Acceptability and Feasibility Results for Participants in the Experimental Group (n = 12).

Item M SD

1. My loved one seemed to enjoy listening to personalized music 3.92 .29

2. I would recommend listening to personlized music to others in my situation 3.83 .39

3. I would recommend this study to others in my situation 3.83 .39

4. Playing personalized music for my loved one required too much effort on

my part 1.17 .58

5. The technology aspect of this study was too complicated to make it worth

my time. 1.33 .49

6. The researchers provided enough resources for me to be effective at

providing personalized music 3.75 .45

7. I will continue to play personalized music for my loved one in the future 3.92 .29

Note: Values are the mean of reported scores on a 4-point Likert scale (1 = strongly disagree, 4

= strongly agree).

M = mean, SD = standard deviation

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CHAPTER IV

DISCUSSION

This study examined the efficacy of a personalized music listening intervention

on PWDs’ mood, behavioral expressions of dementia, anxiety, quality of life, and

relationship strain between the CG and PWD. Significant positive outcomes were found

for anxiety and relationship strain. Further, results indicated high levels of acceptability

and feasibility (discussed below).

4.2. Efficacy

Gerdner’s Mid-Range Theory was the conceptual model upon which the

hypotheses H1, H2, and H3 were based. Specifically, it was hypothesized that

personalized music listening would result in improved mood (H1), anxiety (H2), and

reduced behavioral expressions of dementia (H3). Following the Mid-Range Theory,

behavioral expressions of dementia should benefit most directly from personalized music

listening. The present study did not support this hypothesis. It is possible that behavioral

expressions of dementia are a proximal outcome of personalized music listening. That is,

behavioral symptoms of dementia may be reduced during listening and/or immediately

following listening (as articulated by the Mid-Range Theory), but when examined as a

distal outcome, as they were in this study, no effects were detected. Additionally, the

70

CG’s report of behavioral expressions of dementia was provided for the entire month in

retrospect during the follow-up interview. It is possible that if a different method of

tracking behavioral expression were utilized (such as a daily log or observational

approach, for example), a more precise measure of the number of behavioral expressions

of dementia could be determined. This may be a consideration for future studies.

Another possibility regarding the lack of change in behavioral expressions of dementia is

that the population of PWDs in the present study was in the mild-to-moderate stages of

dementia. In a population of PWDs in the moderate-to-late stages of dementia, when

behavioral expressions of dementia are more prevalent, perhaps a personalized music

intervention for PWDs in the community would be more effective in this regard.

This intervention demonstrated improvements in the domains of anxiety and

relationship strain, with a trend toward improvement in mood. This study attempted to

isolate the effects of personalized music listening on the PWD by utilizing a control

group that received an unfamiliar playlist of music. As such, this is one of the first

applied research studies designed to examine the benefits of the personalized music

listening relative to general music listening (as opposed to a care-as-usual comparison

group), in a population of PWDs (Gerdner, 2000). Moreover, this is the first study to

examine this research question in a population of community-dwelling PWDs. The

findings from this study provide support for the beneficial nature of personalized music

listening relative to unfamiliar music listening. These findings are consistent with the

existing literature indicating that personalized music listening for PWDs may be

especially engaging due to the well-preserved musical memory ability of PWDs

(Jacobsen, et al., 2015; Gerdner, 1997).

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The effect of personalized music listening on positive mood was approaching

significance in the present study. Similar to behavioral expressions of dementia, it may

be that mood is most improved proximally (during and immediately after listening).

Despite the lack of significance in this domain in the present study, it is recommended

that mood be considered as an outcome variable in future research. It may be that the

sample size in this pilot study was not sufficient to detect a significant effect of mood, or

that the one-month duration of the study was not long enough to promote a change in

overall mood.

Anxiety was significantly reduced in the personalized music group relative to the

unfamiliar music group over the one month intervention period. This is not the first study

to report anxiety-related benefits of personalized music listening for PWDs. Sung and

colleagues (2010) found similar outcomes for anxiety in an institutionalized population of

PWDs that were part of a personalized music listening group relative to a control (no

music) group after 6 weeks. Additionally, this finding is consistent with the Mid-Range

Theory supposition that personalized music provides an enjoyable and meaningful

stimulus for PWDs to focus their attention. This meaningful stimulus may occupy the

PWD’s attention in place of other potentially distressing and/or confusing stimuli that are

anxiety provoking. Moreover, in comparison to behavioral expressions of dementia and

mood, anxiety is relatively stable. Thus, it follows that anxiety may be a more

appropriate distal outcome measure of the effects of personalized music on well-being of

PWDs.

In the present study, the PWD’s quality of life did not demonstrate a significant

increase as a result of personalized music listening. There are several possibilities as to

72

why improvements were not seen in this domain. First, it is possible that there is a small

effect of personalized music listening on quality of life and this study did not have the

power to detect it. Indeed, the pattern of data for quality of life was similar to the pattern

of data for mood. Alternatively, it may be that the one-month duration of the present

intervention was not long enough to elicit an effect of quality of life. In contrast to mood,

quality of life is relatively stable over time (Atkinson, 1982). With an increased duration

of listening, it is possible that the slight trend visible from the present results would

continue over time and reach statistical significance. Another possibility is that

personalized music listening does not have a substantial impact on quality of life.

Questions from the quality of life measure used in the present study centered on highly

impactful domains of life such as finances, functional ability, marriage, hobbies, etc.

Personalized music, while enjoyable and engaging, may not be powerful enough to

impact many of these domains.

Findings from this study indicated that personalized music has a positive impact

on the relationship between the PWD and the CG—both in domains of role strain and

positive interaction. This finding may be unique to dyads living in the community relative

to PWDs living in long-term care receiving care from a professional CG. First,

personalized music may draw on shared memories between a PWD and an informal CG.

The use of personalized music by a CG to engage a PWD may be a source of positive

interaction between the two members of the dyad. Personalized music use in long-term

care facilities may provide more of an opportunity to engage residents while staff

members attend to other tasks. As such, interaction during listening sessions may be

more limited in this population.

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During the in-person training with the CG on the iPod® and the general use of

personalized music, it was suggested that personalized music be used during the time of

the day when behavioral expressions of dementia are more common. For many PWDs,

sun-downing, or the prevalence of behavioral expressions of dementia later in the day, is

common. CGs were instructed to pre-empt these episodes if possible by engaging the

PWD in listening prior to these times of the day in order to increase mood and reduce the

likelihood of sun-downing. It is possible that this practice may have accounted for a

perceived improvement in relationship between the PWD and the CG. However, if this

were the case, it is unclear why improvements in behavioral expressions of dementia

were not detected in this study. Moreover, the instrument that was used for relationship

strain in this study demonstrated marginal reliability. Several of the items in this scale

had high rates of missing responses by participants. Despite this, the Dyadic

Relationship Scale is currently the only instrument aimed at assessing relationship strain

in this population. Future studies may consider modifying the items in this scale that had

high rates of missing data. Further, the reliability tests from the present study indicate

that the DRS may be most reliable when separated into its subscales.

4.2. Acceptability and Feasibility

Several factors were examined in the present study to assess the acceptability and

feasibility of this pilot intervention. Specifically, acceptability and feasibility were

assessed by examining attrition rates, adverse events, adherence to the study protocol,

and results from the acceptability questionnaire.

Of the 34 dyads that met all screening requirements and were randomly assigned

to the treatment or comparison group, 4 dyads ended up withdrawing from the study; an

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11.8% attrition rate. Three of these participants were originally assigned to the

experimental group and one was assigned to the control group. In comparison to

previously published studies examining the benefits of personalized music for persons

with dementia, the present study demonstrated a slightly higher attrition rate. Sung and

colleagues (2006) reported a 10% attrition rate. In a subsequent study, Sung and

colleagues (2011) reported an attrition rate of 8.3%. The higher attrition rate in the

present study may be attributable to the fact that participants in this study were

community dwelling rather than institutionalized. In this particular intervention design,

the administration of the music listening was largely dependent on the initiative of an

informal CG in the home rather than a formal CG or a researcher in an institutional

setting. The added responsibility of an informal CG to engage the PWD in this study

may help to explain the higher attrition rate.

None of the non-completers in the present study reported withdrawing due to an

adverse event. One participant discontinued participation due to lack of interest, and

three participants withdrew because they felt that their schedules were too busy (or had

become too busy due to unforeseen circumstances) to continue participating. Among the

participants who completed the study, one adverse event was reported by a caregiver

during a weekly follow-up call. This PWD was in the personalized music group. The

CG reported that the PWD became upset by a song on the playlist that brought back

memories of the PWD’s father. During the follow-up call, the CG discussed the situation

with the primary researcher. It was decided that the best course of action was to remove

the song from the playlist. During the next week’s follow-up call, the CG reported that

the PWD experienced no adverse events during that week’s listening. Based on the

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feedback of caregivers to the acceptability questionnaire, participants reported an

overwhelmingly positive experience participating in the study. Participants’ primary

recommendation for improving the study was to increase the number of songs on the

PWD’s playlist.

None of the participants in the control group reported familiarity with any of the

songs on the unfamiliar music playlist.

Regarding adherence to the treatment procedure, 27 participants of 30 completed

their weekly logbooks for the month. The three dyads that did not complete their

logbooks indicated that they did the listening, but failed to record their listening times.

All participants were given a goal listening dosage of 20 minutes per day for 5 days per

week for each week of the study. There were 20 participants during week 1 (66.7%) that

met this goal, 20 participants at week 2, 20 participants at week 3, and 14 participants at

week 4 (46.7%). There was quite a bit of variability in the number of sessions and the

duration of the listening sessions in which participants engaged. Some participants

preferred more frequent but shorter listening sessions while some participants preferred

fewer but longer listening sessions.

Given the lack of similar interventions for community-dwelling PWDs, it is

difficult to gauge whether this adherence rate is high or low. There are several factors

that may have influenced the adherence rate in this particular study. First, this

intervention protocol was extremely flexible in nature. Compared to other intervention

protocols wherein the number of sessions, the timing of sessions, and the content of each

session is rigidly controlled, this intervention relied on a much looser approach, allowing

CGs and PWDs to listen when they felt it was convenient, and for a duration that they

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desired. While all participants were given a goal dosage, there was no penalty for failing

to meet this dosage. Another factor which may have contributed to the adherence rates of

participants was the equipment that participants were given to keep following their

completion of the program. Though there was no direct monetary incentive for

participating in the study, the ability to keep the iPod Shuffle® may have been an

incentive for some to participate, and to continue participating through the end of the

month-long participation window. Many CGs who were not previously familiar with

Apple® products were interested in learning how to use iTunes® in-depth so that they

could add, remove, and continue changing the PWD’s playlist after the study ended.

Finally, CGs may have been encouraged to use and continue using music as a means of

engaging PWDs after the baseline meeting with the primary researcher. During this

meeting, the primary researcher demonstrated how to use the iPod® and the CG had an

opportunity to witness the PWD using the iPod for the first time. Several CGs, especially

those in the personalized music group, indicated surprise during this meeting about how

positively the PWD responded to the use of the iPod® and to the music in general.

Overall, the uniqueness of the present protocol relative to other music-based

interventions designed for PWDs make firm conclusions about the treatment fidelity

difficult. The rate of completion, high acceptability as indicated by the acceptability

questionnaire, and the lack of adverse events support the feasibility of this intervention.

Regarding intervention dosage, a more rigid intervention protocol may increase the

number of PWDs who met the required dosage. However, a more structured approach to

either administering or monitoring the listening of the PWD comes with a potential

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tradeoff of a time and resource-intensive experimental design and/or greater perceived

intrusion in the lives of the participants.

4.3. Clinical Implications

The present study supports the use of personalized music as a means of improving

several domains of psychosocial well-being for PWDs living in the community.

Personalized music interventions have predominantly been conducted in institutionalized

populations of PWDs such as those living in nursing homes or assisted living facilities.

This study takes a first step to extend these findings to individuals in the community.

Currently, it is estimated that 70% of persons with Alzheimer’s disease and related

dementias live in the community (Alzheimer’s Association, 2018). Among these

individuals, 74% live with an informal caregiver (Alzheimer’s Association, 2018). Thus,

community-dwelling individuals, as included in the current study, represent the largest

population of PWDs in the United States in terms of living arrangement. Although

accessibility to this population and implementation in the community are definite

challenges regarding the use of personalized music for these individuals, the present

study suggests that personalized music may nevertheless be a relatively low-cost means

of engaging these PWDs in a beneficial way.

The results from this study provide additional evidence to support the efficacy of

non-pharmacological approaches to intervention for PWDs. A primary benefit of this

type of intervention for PWDs is that engagement in personalized music, development of

a playlist, and implementation of the program does not require a high degree of

intervention expertise. Familiarity with the software and hardware used in the present

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study is common, especially among the computer-savvy younger generations. In fact, the

Music and MemoryTM

organization relies heavily on high school age volunteers in

nursing homes and assisted living facilities to help PWDs setup and use their

personalized music devices.

The psychometric results from the PWD self-report data on the instruments used

in the present study adds further evidence to an existing body of literature suggesting that

PWDs are able to reliably self-report on subjective information (see Section 1.2). The

present sample included PWDs with an average MMSE score of 20 (SD = 6.1), indicating

a moderate level of impairment on average. This body of literature is important to ensure

that the true perspective of the PWD (as opposed to a potentially inaccurate proxy-report)

is obtained in dementia research.

4.4. Limitations

A primary limitation of the present study is the presence of group differences in

several key demographic areas. Participants assigned to the experimental group had, on

average, a lower level of education, lower income, lower MMSE score, were less likely

to have a spousal CG, and were older than those in the control group. In a study with a

larger sample size, the process of randomization likely would have been sufficient to

ensure that these variables matched across groups. While possible to control for these

demographic variables statistically in the regression analyses, the sample size and the

high multicollinearity between demographic factors and group assignment made the

inclusion of these variables statistically problematic (as discussed in the Efficacy Testing

subsection of the Results section). A “catch-22,” the increased N-size that would be

needed to include these predictors in the regression models would likely have been

79

sufficient to overcome the group differences merely through the process of random

assignment—hence eliminating the necessity for statistical control of these demographic

variables in the regression models altogether. Nevertheless, future studies should be sure

to consider these demographic factors when interpreting results.

A limitation is that the majority of participants in this study were recruited from

caregiver support groups via the Alzheimer’s Association. It is possible that this

recruitment method has some degree of inherent bias. Participants already engaged in

community-based resources aimed at helping PWDs and CGs may be more receptive and

engaged in the program and less likely to withdraw. With a sample of individuals

recruited via other means, it is possible that adherence would not have been as high as it

was in the present study. This potential source of bias presents a challenge for future

research in this area. Locating and recruiting PWDs and CGs from the community

without using existing community-based dementia services to access this population is

difficult. Potential sources of recruitment for future studies include hospitals, physicians’

offices, senior apartments, home healthcare organizations, and public advertisements.

An additional limitation to the present study was the restriction to an Apple®

framework and the limited resources (specifically, the number of songs) that could be

provided to each participant. Several alternative options exist which have the potential to

deliver a larger playlist to PWDs. An organization in Scotland called Playlist for Life®

has developed an app in recent years designed to engage PWDs with personalized music.

This app is installed on a smartphone or tablet, and requires a monthly fee to access

unlimited music via Spotify® streaming service. The app has a built in feature that helps

PWDs and CGs find their favorite music. This type of personalized music delivery has

80

the fantastic benefit of unlimited music, but requires a reliable internet connection, a

smartphone or tablet (rather than an iPod Shuffle®, which is very simplistic and easy to

use), and may be more costly in the long-term due to the monthly subscription. The

benefit of this app is that CGs would not have to learn to use iTunes® on a separate

computer in order to add or remove songs from a PWD’s playlist, nor would it be

necessary to learn to interface the iPod® and iTunes®. Moreover, a streaming service

does not require the storage of music files on a local computer or hard drive; music is

streamed in real time via the internet.

In the United States, internet access may be difficult to obtain or costly; especially

for low-SES families and families living in rural areas where there are few internet

service provider options. For these individuals, using an iPod® may be more cost

effective. Individuals can access iTunes® at a Wi-FiTM

hotspot such as a library or coffee

shop to add music to their playlist, and use the iPod® to engage PWDs at home in a

location without internet access. For institutions such as nursing homes, assisted living

facilities, and adult day care centers, the Apple® framework is cost effective.

Organizations can invest in a single computer with iTunes® to use as a hub for loading

multiple iPod Shuffles®, which are relatively inexpensive. As of yet, there is no one-

size-fits-all option for introducing personalized music to PWDs living in the community.

Future studies may consider a flexible approach to choosing a music service for this

population.

In the present study, participants in the control group were given a single playlist

of 24 songs (see Appendix N). Over the course of the one-month of listening, it is

possible that participants became familiar with the songs on the unfamiliar playlist. That

81

is, over time this music may have become familiar to participants, potentially posing a

similar benefit in the later weeks of the study as the personalized music provided to

PWDs in the personalized music group. In future studies, it may be beneficial to alter the

comparison group playlist during each listening session. This would be very challenging

and costly to accomplish with the iPod® and iTunes® approach, but would be reasonably

easy to implement if a streaming-based approach to music listening were used, such as

the Playlist for Life®, discussed above.

An additional limitation to the present study is that the primary researcher (who

conducted all interviews, training, and weekly follow-up calls), was not blinded to group

assignment, nor were the participants. Several efforts were made to minimize the impact

of experimenter bias in this regard. First, participants were randomly assigned to a group

after the completion of the baseline interview. Second, during the follow-up interview,

the experimenter was blinded to the baseline scores of the PWD. In future research, it

would be ideal to design a study in which the interviewer is blinded to group assignment

and a separate experimenter conducts the training and playlist development aspects of the

study. This would help to reduce potential sources of bias in the study design. As a final

limitation, participants in the personalized music listening group may have been primed

by the information packet they were given about the benefits of personalized music.

4.5. Future Directions for Research

It should be emphasized that the outcomes from this pilot study represent

preliminary results that should be used to guide future intervention development. The

most obvious future direction for this research is an increased sample size. Due to the

82

lack of existing data, the hypotheses presented and tested in this study require replication

and expansion to larger samples.

While this intervention was dyadic in nature, CG-derived benefits of personalized

music were not explored in the present study. Few studies have examined the impact of

personalized music on informal CGs. Given that personalized music may be a useful tool

for informal CGs regarding the engagement of persons with dementia, a potential

opportunity for CG respite, and (although not supported by the data from this study) a

potential means of reducing difficult behavioral expressions of dementia, CG outcomes

should be considered for inclusion in future research.

Due to the self-report nature of the PWD interviews at baseline and follow-up,

PWDs in the later stages of dementia were not included in the present study. Ultimately,

individuals in the later stages of dementia may stand to benefit the most from an

intervention of this nature. As cognitive and functional abilities decline later in the

course of the disease, the ability of personalized music to capitalize on a remaining

strength of PWDs may translate into a greater derived benefit for these individuals. A

notable difficulty of extending this type of protocol to PWDs in the later stages is

determining a way of measuring outcomes reliably from a population of individuals that

may have trouble communicating their own perspectives. In the present study, the

MMSE (particularly, a cutoff score of 10) was used to determine if the PWD would be

able to participate in the self-report process. While the MMSE is a widely used measure

of cognitive status for PWDs, it is an assessment of global cognitive functioning. Several

objective domains assessed by the MMSE such as orientation to time and place,

quantitative reasoning, and short-term memory for new information may not be relevant

83

to the PWD’s ability to participate in self-report research. An instrument aimed

specifically at assessing the subjective reporting capacity of PWDs (ability to

comprehend questions and communicate internal states, values and preferences, for

example) would be useful to extend this intervention to able PWDs who may have scored

below the MMSE cutoff used in the present study.

While several basic research studies in this field support the finding that musical

memory is well-preserved relatively late into the course of Alzheimer’s disease (Jacobsen

et al., 2015; King et al., 2018), no effort was made in the present study to confirm a

diagnosis of Alzheimer’s disease. All participants in the present study had a confirmed

or suspected diagnosis of dementia, and performed within a range on the MMSE which

indicated mild-to-moderate levels of impairment. If the preservation of musical memory

is a phenomenon that is unique to Alzheimer’s disease relative to other forms of

dementia, it is possible that the impact of a personalized music intervention would be

more robust in an Alzheimer’s-specific sample of PWDs. Alternatively, basic research in

neuroscience may benefit from examining preservation of the musical memory system in

PWDs with non-Alzheimer’s pathologies.

An increased intervention duration may be worthwhile to investigate the benefits

of personalized music listening over the long-term. This would allow for a better

understanding of how personalized music impacts relatively stable psychosocial

constructs such as quality of life or depression, for example. Additionally, a longer-term

intervention, or long-term follow-up after completion of the intervention would provide

insights into listening adherence over time. A replication of this research design with an

extended intervention period may pose some challenges, however. In this study, dyads

84

assigned to the control group were aware that the playlists they were given would be

personalized after the one-month intervention period. It may be less likely that dyads

would be receptive to listening to an unfamiliar playlist for an extended period of time

such as 3 months or 6 months. Furthermore, it is possible that PWDs in an unfamiliar

music listening group would come to learn and remember the music on a single playlist

after repeated exposure during an extended intervention period. This would seem to

necessitate a changing comparison group playlist, as discussed in the limitations sections

above.

One of the challenges associated with personalized music interventions is

determining which music is most personally meaningful to the PWD. For individuals in

the moderate-to-later stages of dementia, remembering specific songs, artists, and

composers poses a distinct challenge. Recommendations made by the CG and the use of

song sampling were used in the present study to overcome this challenge. While these

playlists appeared to be more beneficial than unfamiliar music in domains of anxiety and

relationship strain, it is difficult to know if there are personally meaningful songs missing

from the PWD’s playlist that would be even more effective. It would be particularly

interesting to see if playlists created prior to or immediately following a dementia

diagnosis (when the PWD is able to compile his or her ideal playlist) are more beneficial

in the moderate and later stages of the disease relative to playlists created by PWDs in

these stages with the help of a CG.

Additionally, most people have a preference for music that spans several genres

and moods. Future studies might examine the benefits of listening to a variety of such

genres and moods. For example, perhaps listening to a favorite relaxing orchestral piece

85

is best in the evening while listening to upbeat swing is best in the daytime. Future

assessments of personalized music should attempt to capture this detail of a PWD’s

preferred playlist. Questions such as “What type of music do you prefer to listen to when

you are sad/happy/angry?” might be a good starting point for creating separate favorite

playlists for different moods. While this study has highlighted some important overall

effects of personalized music, there is a great deal of specificity regarding the artists,

connections to the music, moods, genres, associated memories, etc. that have yet to be

explored in the personalized music literature.

In larger longitudinal studies, the benefits of a personalized music intervention

could be examined in several additional domains. For example, it would be particularly

interesting to know if the use of a personalized music intervention might delay the

progression of cognitive decline over time, if it would help to delay institutionalization of

PWDs living in the community, reduce caregiver burden over time, and/or to delay the

advancement of behavioral expressions of dementia. The Stress-Process Model, both for

individuals with dementia and for caregivers, may be a useful conceptual model to inform

the selection of outcome variables for PWDs and CGs in future studies.

86

REFERENCES

Afram, B., Stephan, A., Verbeek, H., Bleijlevens, M. H., Suhonen, R., Sutcliffe, C., ... &

Meyer, G. (2014). Reasons for institutionalization of people with dementia:

informal caregiver reports from 8 European countries. Journal of the American

Medical Directors Association, 15(2), 108-116.

Aguirre, E., Woods, R. T., Spector, A., & Orrell, M. (2013). Cognitive stimulation for

dementia: a systematic review of the evidence of effectiveness from randomised

controlled trials. Ageing research reviews,12(1), 253-262.

Allain, P., Chaudet, H., Nicoleau, S., Etcharry-Bouyx, F., Barré, J., Dubas, F., ... & Le

Gall, D. (2007). Étude de la planification de l’action au moyen du test du plan du

zoo dans la maladie d’Alzheimer. Revue Neurologique,163(2), 222-230.

Alzheimer's Association. (2018). 2018 Alzheimer's disease facts and figures. Alzheimer's

& Dementia, 12(4), 459-509.

American Music Therapy Association (AMTA) (2006) Music Therapy and Alzheimer’s

Disease. PDF Available at: http://www.musictherapy.org/

assets/1/7/MT_Alzheimers_2006.pdf.

Aminzadeh, F., Byszewski, A., Molnar, F. J., & Eisner, M. (2007). Emotional impact of

dementia diagnosis: exploring persons with dementia and caregivers’

perspectives. Aging and Mental Health, 11(3), 281-290.

Aneshensel, C. S., Pearlin, L. I., Mullan, J. T., Zarit, S. H., & Whitlatch, C. J. (1995).

Profiles in caregiving: The unexpected career. San Diego, CA US: Academic

Press.

87

Apóstolo, J. L. A., Cardoso, D. F. B., Rosa, A. I., & Paúl, C. (2014). The effect of

cognitive stimulation on nursing home elders: A randomized controlled

trial. Journal of Nursing Scholarship, 46(3), 157-166.

Arlt, S., Lindner, R., Rösler, A., & von Renteln-Kruse, W. (2008). Adherence to

medication in patients with dementia. Drugs & aging, 25(12), 1033-1047.

Armijo-Olivo, S., Warren, S., & Magee, D. (2009). Intention to treat analysis,

compliance, drop-outs and how to deal with missing data in clinical research: a

review. Physical therapy reviews, 14(1), 36-49.

Atkinson, T. (1982). The stability and validity of quality of life measures. Social

Indicators Research, 10(2), 113-132.

Ayalon, L., Gum, A. M., Feliciano, L., & Areán, P. A. (2006). Effectiveness of

nonpharmacological interventions for the management of neuropsychiatric

symptoms in patients with dementia: a systematic review. Archives of internal

medicine, 166(20), 2182-2188.

Bahar-Fuchs, A., Clare, L., & Woods, B. (2013). Cognitive training and cognitive

rehabilitation for mild to moderate Alzheimer’s disease and vascular

dementia. Cochrane Database Syst Rev, 6.

Baker, F. A., Grocke, D., & Pachana, N. A. (2012). Connecting through music: A study

of a spousal caregiver-directed music intervention designed to prolong fulfilling

relationships in couples where one person has dementia. Australian Journal of

Music Therapy, 23.

88

Ballard, C., Day, S., Sharp, S., Wing, G., & Sorensen, S. (2008). Neuropsychiatric

symptoms in dementia: importance and treatment considerations. International

Review of Psychiatry, 20(4), 396-404.

Ballard, C., Lowery, K., Powell, I., O'Brien, J., & James, I. (2000). Impact of behavioral

and psychological symptoms of dementia on caregivers. International

Psychogeriatrics, 12(S1), 93-105.

Bamford, C., & Bruce, E. (2000). Defining the outcomes of community care: the

perspectives of older people with dementia and their carers. Ageing and

Society, 20(05), 543-570.

Banerjee, S., Smith, S. C., Lamping, D. L., Harwood, R. H., Foley, B., Smith, P., …

Knapp, M. (2006). Quality of life in dementia: more than just cognition. An

analysis of associations with quality of life in dementia. Journal of Neurology,

Neurosurgery, and Psychiatry, 77(2), 146–148.

http://doi.org/10.1136/jnnp.2005.072983

Bass, D. M., Judge, K. S., Lynn Snow, A., Wilson, N. L., Morgan, R., Looman, W. J., ...

& Garcia‐Maldonado, M. (2013). Caregiver outcomes of partners in dementia

care: Effect of a care coordination program for veterans with dementia and their

family members and friends. Journal of the American Geriatrics Society, 61(8),

1377-1386.

Baumgartner, H. (1992). Remembrance of things past: Music, autobiographical memory,

and emotion. NA-Advances in Consumer Research Volume 19.

89

Beck, C., Heithoff, K., Baldwin, B., Cuffel, B., O'Sullivan, P., & Chumbler, N. (1997).

Assessing disruptive behavior in older adults: The Disruptive Behavior

Scale. Aging & Mental Health, 1(1), 71-80.

Benzinger, T. L., Blazey, T., Jack, C. R., Koeppe, R. A., Su, Y., Xiong, C., ... & Cairns,

N. J. (2013). Regional variability of imaging biomarkers in autosomal dominant

Alzheimer’s disease. Proceedings of the National Academy of Sciences, 110(47),

E4502-E4509.

Berger, G., Bernhardt, T., Schramm, U., Müller, R., Landsiedel‐Anders, S., Peters, J., ...

& Frolich, L. (2004). No effects of a combination of caregivers support group and

memory training/music therapy in dementia patients from a memory clinic

population. International Journal of Geriatric Psychiatry,19(3), 223-231.

Berkman, L. F., & Syme, S. L. (1979). Social networks, host resistance, and mortality: a

nine-year follow-up study of Alameda County residents. American journal of

Epidemiology, 109(2), 186-204.

Blackburn, R., & Bradshaw, T. (2014). Music therapy for service users with dementia: a

critical review of the literature. Journal of psychiatric and mental health

nursing, 21(10), 879-888.

Blood, A. J., Zatorre, R. J., Bermudez, P., & Evans, A. C. (1999). Emotional responses to

pleasant and unpleasant music correlate with activity in paralimbic brain

regions. Nature neuroscience, 2(4), 382-387.

Bookwala, J., & Schulz, R. (1996). Spousal similarity in subjective well-being: the

Cardiovascular Health Study. Psychology and aging, 11(4), 582.

90

Bourgeois, M. S. (2002). “Where is my wife and when am I going home?” The challenge

of communicating with persons with dementia. Alzheimer's Care Today, 3(2),

132-144.

Bradford, A., Brenes, G. A., Robinson, R. A., Wilson, N., Snow, A. L., Kunik, M. E., ...

& Amspoker, A. B. (2013). Concordance of self-and proxy-rated worry and

anxiety symptoms in older adults with dementia. Journal of anxiety

disorders, 27(1), 125-130.

Brod, M., Stewart, A. L., Sands, L., & Walton, P. (1999). Conceptualization and

measurement of quality of life in dementia: The dementia quality of life

instrument (DQoL). The Gerontologist, 39(1), 25-36.

Brodaty, H., & Donkin, M. (2009). Family caregivers of people with dementia.

Dialogues Clin Neurosci, 11(2), 217-228.

Brody, E. M. (1981). “Women in the middle” and family help to older people. The

Gerontologist, 21(5), 471-480.

Brotons, M., & Marti, P. (2003). Music therapy with Alzheimer's patients and their

family caregivers: a pilot project. Journal of Music Therapy, 40(2), 138-150.

Camic, P. M., Williams, C. M., & Meeten, F. (2013). Does a ‘Singing Together Group’

improve the quality of life of people with a dementia and their carers? A pilot

evaluation study. Dementia, 12(2), 157-176.

Camp, C. J. (2017). Flexibility in persons with dementia In J. D. Sinnott (Ed.), Identity

flexibility during adulthood. (pp. 99-114). Cham, Switzerland: Springer

International Publishing AG.

91

Cantor, M. H. (1983). Strain among caregivers: A study of experience in the United

States. The Gerontologist.

Carpenter, B. D., Kissel, E. C., & Lee, M. M. (2007). Preferences and life evaluations of

older adults with and without dementia: reliability, stability, and proxy

knowledge. Psychology and aging, 22(3), 650.

Caspi, E. (2013). Time for change: Persons with dementia and “behavioral expressions,”

not “behavior symptoms.” Journal of the American Medical Directors

Association, 14(10), 768–769. http://doi.org/10.1016/j.jamda.2013.05.019

Chapman, S. B., Weiner, M. F., Rackley, A., Hynan, L. S., & Zientz, J. (2004). Effects of

cognitive-communication stimulation for Alzheimer's disease patients treated with

donepezil. Journal of Speech, Language, and Hearing Research, 47(5), 1149-

1163.

Cipriani, G., Lucetti, C., Nuti, A., & Danti, S. (2014). Wandering and dementia.

Psychogeriatrics, 14(2), 135-142.

Clair, A. A. (1996). The effect of singing on alert responses in persons with late stage

dementia. Journal of Music Therapy, 33(4), 234-247.

Clair, A. A., & Bernstein, B. (1990a). A comparison of singing, vibrotactile and

nonvibrotactile instrumental playing responses in severely regressed persons with

dementia of the Alzheimer's type. Journal of Music Therapy, 27(3), 119-125.

Clair, A. A., & Bernstein, B. (1990b). A preliminary study of music therapy

programming for severely regressed persons with Alzheimer's-type dementia.

Journal of Applied Gerontology, 9(3), 299-311.

92

Clare, L. (2003). Managing threats to self: awareness in early stage Alzheimer's

disease. Social science & medicine, 57(6), 1017-1029.

Clare, L., & Woods, R. T. (2004). Cognitive training and cognitive rehabilitation for

people with early-stage Alzheimer's disease: A review.Neuropsychological

rehabilitation, 14(4), 385-401.

Cohen‐Mansfield, J. (1986). Agitated behaviors in the elderly: II. Preliminary results in

the cognitively deteriorated. Journal of the American Geriatrics Society, 34(10),

722-727.

Cohen-Mansfield, J. (1997). Conceptualization of agitation: results based on the Cohen-

Mansfield agitation inventory and the agitation behavior mapping

instrument. International Psychogeriatrics, 8(S3), 309-315.

Cohen-Mansfield, J. (2000). Theoretical Frameworks for Behavioral Problems in

Dementia. Alzheimer's Care Today, 1(4), 8-21.

Cohen-Mansfield, J. (2001). Nonpharmacologic interventions for inappropriate behaviors

in dementia: a review, summary, and critique. The American Journal of Geriatric

Psychiatry: Official Journal of the American Association for Geriatric Psychiatry,

9(4), 361–381. http://doi.org/10.1176/appi.ajgp.9.4.361

Cohen-Mansfield, J. (2008). Agitated behavior in persons with dementia: the relationship

between type of behavior, its frequency, and its disruptiveness. Journal of

psychiatric research, 43(1), 64-69.

Cohen-Mansfield, J. (2015). Non-pharmacological interventions for agitation in

dementia: various strategies demonstrate effectiveness for care home residents;

further research in home settings is needed. Evidence Based Nursing.

93

Cohen-Mansfield, J., Dakheel-Ali, M., & Marx, M. S. (2009). Engagement in persons

with dementia: the concept and its measurement. The American journal of

geriatric psychiatry, 17(4), 299-307.

Cohen-Mansfield, J., Dakheel-Ali, M., Marx, M. S., Thein, K., & Regier, N. G. (2015).

Which unmet needs contribute to behavior problems in persons with advanced

dementia? Psychiatry research, 228(1), 59-64.

Cohen-Mansfield, J., & Jensen, B. (2008). Assessment and treatment approaches for

behavioral disturbances associated with dementia in the nursing home: Self-

reports of physicians' practices. Journal of the American Medical Directors

Association, 9(6), 406-413.

Cohen-Mansfield, J., Marx, M. S., & Rosenthal, A. S. (1989). A description of agitation

in a nursing home. Journal of Gerontology, 44(3), M77-M84.

Cohen-Mansfield, J., Marx, M. S., & Werner, P. (1992). Agitation in elderly persons: an

integrative report of findings in a nursing home. International

Psychogeriatrics, 4(04), 221-240.

Cohen-Mansfield, J., & Mintzer, J. E. (2005). Time for change: the role of

nonpharmacological interventions in treating behavior problems in nursing home

residents with dementia. Alzheimer Disease & Associated Disorders,19(1), 37-40.

Cohen-Mansfield, J., & Werner, P. (1995). Environmental influences on agitation: An

integrative summary of an observational study. American Journal of Alzheimer's

Disease and Other Dementias, 10(1), 32-39.

Cooke, M. L., Moyle, W., Shum, D. H., Harrison, S. D., & Murfield, J. E. (2010a). A

randomized controlled trial exploring the effect of music on agitated behaviours

94

and anxiety in older people with dementia. Aging and Mental Health, 14(8), 905-

916.

Cooke, M., Moyle, W., Shum, D., Harrison, S., & Murfield, J. (2010b). A randomized

controlled trial exploring the effect of music on quality of life and depression in

older people with dementia. Journal of Health Psychology,15(5), 765-776.

Cooper, C., Mukadam, N., Katona, C., Lyketsos, C. G., Ames, D., Rabins, P., ... &

Brodaty, H. (2012). Systematic review of the effectiveness of non-

pharmacological interventions to improve quality of life of people with

dementia. International Psychogeriatrics, 24(06), 856-870.

Cotrell, V., & Schulz, R. (1993). The perspective of the patient with Alzheimer’s disease:

a neglected dimension of dementia research. The Gerontologist, 33(2), 205–211.

http://doi.org/10.1093/geront/33.2.205

Clark, P. A., Tucke, S. S., & Whitlatch, C. J. (2008). Consistency of information from

persons with dementia An analysis of differences by question

type. Dementia, 7(3), 341-358.

Crystal, H. A., Grober, E., & Masur, D. A. V. I. D. (1989). Preservation of musical

memory in Alzheimer's disease. Journal of Neurology, Neurosurgery &

Psychiatry, 52(12), 1415-1416.

Dawson, N. T., Powers, S. M., Krestar, M., Yarry, S. J., & Judge, K. S. (2013). Predictors

of self-reported psychosocial outcomes in individuals with dementia. The

Gerontologist, 53(5), 748-759.

95

De Boer, M. E., Hertogh, C. M., Dröes, R. M., Riphagen, I. I., Jonker, C., & Eefsting, J.

A. (2007). Suffering from dementia–the patient's perspective: A review of the

literature. International Psychogeriatrics, 19(06), 1021-1039.

Dileo, C. & Bradt, J. (2005). Medical music therapy: A Meta-analysis of the literature

and an agenda for future research. Cherry Hill, NJ: Jeffrey Books.

Eshkoor, S. A., Hamid, T. A., Nudin, S. S. H., & Mun, C. Y. (2014). Does substance

abuse contribute to further risk of falls in dementia. Aging, Neuropsychology, and

Cognition, 21(3), 317-324.

Everard, K. M., Lach, H. W., Fisher, E. B., & Baum, M. C. (2000). Relationship of

activity and social support to the functional health of older adults. The Journals of

Gerontology. Series B, Psychological Sciences and Social Sciences, 55(4), S208–

S212. http://doi.org/10.1093/geronb/55.4.S208

Ettema, T. P., Dröes, R. M., de Lange, J., Ooms, M. E., Mellenbergh, G. J., & Ribbe, M.

W. (2005). The concept of quality of life in dementia in the different stages of the

disease. International Psychogeriatrics, 17(03), 353-370.

Farzana, F., Sreekanth, V., Mohiuddin, M. K., Mohan, V., Balakrishna, N., & Ahuja, Y.

R. (2015). Can individual home‐based cognitive stimulation therapy benefit

Parkinson's patients with mild to moderate cognitive impairment? International

journal of geriatric psychiatry, 30(4), 433-435.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses

using G* Power 3.1: Tests for correlation and regression analyses. Behavior

research methods, 41(4), 1149-1160.

96

Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible

statistical power analysis program for the social, behavioral, and biomedical

sciences. Behavior research methods, 39(2), 175-191.

Fauth, E. B., & Gibbons, A. (2014). Which behavioral and psychological symptoms of

dementia are the most problematic? Variability by prevalence, intensity, distress

ratings, and associations with caregiver depressive symptoms. International

journal of geriatric psychiatry, 29(3), 263-271.

Feil, N. (2014). Validation therapy with late-onset dementia populations. Caregiving in

dementia: Research and applications, 199-218.

Feinberg, L. F., & Whitlatch, J. (2002). Decision-making for persons with cognitive

impairment and their family caregivers. American journal of Alzheimer's disease

and other dementias, 17(4), 237-244.

Feinstein, J. S., Duff, M. C., & Tranel, D. (2010). Sustained experience of emotion after

loss of memory in patients with amnesia. Proceedings of the National Academy of

Sciences, 107(17), 7674-7679.

Ferretti, L., McCurry, S. M., Logsdon, R., Gibbons, L., & Teri, L. (2001). Anxiety and

Alzheimer's disease. Journal of Geriatric Psychiatry and Neurology, 14(1), 52-

58.

Finkel, S. (2000). Introduction to behavioural and psychological symptoms of dementia

(BPSD). International journal of geriatric psychiatry.

Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: a practical

method for grading the cognitive state of patients for the clinician.Journal of

psychiatric research, 12(3), 189-198.

97

Friedman, E. M., Shih, R. A., Langa, K. M., & Hurd, M. D. (2015). US prevalence and

predictors of informal caregiving for dementia. Health Affairs,34(10), 1637-1641.

Frisoni, G. B., Pievani, M., Testa, C., Sabattoli, F., Bresciani, L., Bonetti, M., ... &

Thompson, P. M. (2007). The topography of grey matter involvement in early and

late onset Alzheimer's disease. Brain, 130(3), 720-730.

Garland, K., Beer, E., Eppingstall, B., & O'Connor, D. W. (2007). A comparison of two

treatments of agitated behavior in nursing home residents with dementia:

simulated family presence and preferred music. The American journal of geriatric

psychiatry, 15(6), 514-521.

Gerdner, L. A. (1992). The effects of individualized music on elderly clients who are

confused and agitated. Unpublished Masters thesis, University of Iowa.

Gerdner, L. A. (1997). An individualized music intervention for agitation. Journal of the

American Psychiatric Nurses Association, 3(6), 177-184.

Gerdner, L. A. (2000). Effects of individualized versus classical “relaxation” music on

the frequency of agitation in elderly persons with Alzheimer's disease and related

disorders. International Psychogeriatrics, 12(01), 49-65.

Gerdner, L. A. (2005). Use of individualized music by trained staff and family:

translating research into practice. Journal of gerontological nursing, 31(6), 22-30.

Gerdner, L. A. (2010). Individualized Music for Elders with Dementia. Journal of

Gerontological Nursing, 36(6), 7–15. http://doi.org/10.3928/00989134-20100504-

01

Gerdner, L. A., Hartsock, J., & Buckwalter, K. C. (2000). Assessment of Personal Music

Preference (family version). The University of Iowa Gerontological Nursing

98

Interventions Research Center: Research Development and Dissemination Core,

Iowa City, Iowa.

Giebel, C. M., Sutcliffe, C., Stolt, M., Karlsson, S., Renom-Guiteras, A., Soto, M., …

Challis, D. (2014). Deterioration of basic activities of daily living and their impact

on quality of life across different cognitive stages of dementia: a European study.

International Psychogeriatrics, 26(08), 1283–1293.

http://doi.org/10.1017/S1041610214000775

Giebel, C. M., Sutcliffe, C., & Challis, D. (2015). Activities of daily living and quality of

life across different stages of dementia: a UK study. Aging & mental health,

19(1), 63-71.

Gill, S. S., Bronskill, S. E., Normand, S. L. T., Anderson, G. M., Sykora, K., Lam, K., ...

& Gurwitz, J. H. (2007). Antipsychotic drug use and mortality in older adults with

dementia. Annals of internal medicine, 146(11), 775-786.

Gill, S. S., Rochon, P. A., Herrmann, N., Lee, P. E., Sykora, K., Gunraj, N., ... &

Mamdani, M. (2005). Atypical antipsychotic drugs and risk of ischaemic stroke:

population based retrospective cohort study. Bmj, 330(7489), 445.

Gilley, D. W., Whalen, M. E., Wilson, R. S., & Bennett, D. A. (1991). Hallucinations and

associated factors in Alzheimer's disease. The Journal of neuropsychiatry and

clinical neurosciences.

Gómez-Gallego, M., Gomez-García, J., & Ato-García, M. (2014). Confirmatory factor

analysis of the quality of life in Alzheimer’s disease scale in patients with

Alzheimer’s disease. Experimental aging research,40(3), 266-279.

99

Green, S. B. (1991). How many subjects does it take to do a regression analysis.

Multivariate behavioral research, 26(3), 499-510.

Groussard, M., Viader, F., Hubert, V., Landeau, B., Abbas, A., Desgranges, B., ... &

Platel, H. (2010). Musical and verbal semantic memory: two distinct neural

networks?. Neuroimage, 49(3), 2764-2773.

Guetin, S., Portet, F., Picot, M. C., Pommié, C., Messaoudi, M., Djabelkir, L., ... &

Touchon, J. (2009). Effect of music therapy on anxiety and depression in patients

with Alzheimer’s type dementia: randomised, controlled study. Dementia and

geriatric cognitive disorders, 28(1), 36-46.

Guzmán-Vélez, E., Feinstein, J. S., & Tranel, D. (2014). Feelings without memory in

Alzheimer disease. Cognitive and Behavioral Neurology, 27(3), 117-129.

El Haj, M., Fasotti, L., & Allain, P. (2012). The involuntary nature of music-evoked

autobiographical memories in Alzheimer’s disease. Consciousness and

Cognition, 21(1), 238-246.

Harman, G., & Clare, L. (2006). Illness representations and lived experience in early-

stage dementia. Qualitative health research, 16(4), 484-502.

Harrell, F. E. (2001) Regression Modeling Strategies, with Applications to Linear

Models, Survival Analysis and Logistic Regression. New York, NY: Springer.

Hawkins, N. A., Ditto, P. H., Danks, J. H., & Smucker, W. D. (2005). Micromanaging

death: process preferences, values, and goals in end-of-life medical decision

making. The Gerontologist, 45(1), 107-117.

Herrmann, N., Mamdani, M., & Lanctot, K. L. (2004). Atypical antipsychotics and risk

of cerebrovascular accidents. American Journal of Psychiatry,161(6), 1113-1115.

100

Hien Le, T., Cumming, R. G., Cameron, I. D., Chen, J. S., Lord, S. R., March, L. M., ...

& Sambrook, P. N. (2005). Atypical antipsychotic medications and risk of falls in

residents of aged care facilities. Journal of the American Geriatrics

Society, 53(8), 1290-1295.

Hsu, M. H., Flowerdew, R., Parker, M., Fachner, J., & Odell-Miller, H. (2015).

Individual music therapy for managing neuropsychiatric symptoms for people

with dementia and their carers: a cluster randomised controlled feasibility

study. BMC geriatrics, 15(1), 1.

Hwang, J. P., Tsai, S. J., Yang, C. H., Liu, K. M., & Lirng, J. F. (2000). Repetitive

phenomena in dementia. The International Journal of Psychiatry in

Medicine, 30(2), 165-171.

Husebo, B. S., Ballard, C., & Aarsland, D. (2011). Pain treatment of agitation in patients

with dementia: a systematic review. International journal of geriatric

psychiatry, 26(10), 1012-1018.

Huybrechts, K. F., Gerhard, T., Crystal, S., Olfson, M., Avorn, J., Levin, R., ... &

Schneeweiss, S. (2012). Differential risk of death in older residents in nursing

homes prescribed specific antipsychotic drugs: population based cohort

study. Bmj, 344, e977.

Imbimbo, B. P., & Giardina, G. A. M. (2011). γ-secretase inhibitors and modulators for

the treatment of Alzheimer's disease: disappointments and hopes. Current topics

in medicinal chemistry, 11(12), 1555-1570.

Iveson, C. (2002). Solution-focused brief therapy. Advances in Psychiatric

Treatment, 8(2), 149-156.

101

Jacobsen, J. H., Stelzer, J., Fritz, T. H., Chételat, G., La Joie, R., & Turner, R. (2015).

Why musical memory can be preserved in advanced Alzheimer’s

disease. Brain, 138(8), 2438-2450.

Jack, C. R., & Holtzman, D. M. (2013). Biomarker modeling of Alzheimer’s disease.

Neuron, 80(6), 1347-1358.

Janata, P. (2009). The neural architecture of music-evoked autobiographical

memories. Cerebral Cortex, bhp008.

Janata, P. (2012). Effects of widespread and frequent personalized music programming

on agitation and depression in assisted living facility residents with Alzheimer-

type dementia. Music and Medicine, 4(1), 8-15.

Janata, P., Birk, J. L., Van Horn, J. D., Leman, M., Tillmann, B., & Bharucha, J. J.

(2002). The cortical topography of tonal structures underlying Western

music. science, 298(5601), 2167-2170.

Jäncke, L. (2008). Music, memory and emotion. Journal of biology, 7(6), 1.

Johanson, G. A., & Brooks, G. P. (2009). Initial scale development: sample size for pilot

studies. Educational and Psychological Measurement.

Judge, K. S., Menne, H. L., & Whitlatch, C. J. (2010). Stress process model for

individuals with dementia. The Gerontologist, 50(3), 294–302.

http://doi.org/10.1093/geront/gnp162

Judge, K. S., Yarry, S. J., & Orsulic-Jeras, S. (2010). Acceptability and feasibility results

of a strength-based skills training program for dementia caregiving dyads. The

Gerontologist, 50(3), 408-417.

102

Julious, S. A. (2005). Sample size of 12 per group rule of thumb for a pilot study. Pharm

Stat, 4(4), 287-291.

Kaiser, N. C., Liang, L. J., Melrose, R. J., Wilkins, S. S., Sultzer, D. L., & Mendez, M. F.

(2014). Differences in anxiety among patients with early-versus late-onset

Alzheimer’s disease. The Journal of neuropsychiatry and clinical

neurosciences, 26(1), 73-80.

Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L., ...

& Zaslavsky, A. M. (2002). Short screening scales to monitor population

prevalences and trends in non-specific psychological distress. Psychological

medicine, 32(06), 959-976.

King, J. B., Jones, K. G., Goldberg, E., Rollins, M., MacNamee, K., Moffit, C., ... &

Breitenbach, K. R. (2018). Increased Functional Connectivity After Listening to

Favored Music in Adults With Alzheimer Dementia. The Journal of Prevention of

Alzheimer's Disease, 1-7.

Koger, S. M., Chapin, K., & Brotons, M. (1999). Is music therapy an effective

intervention for dementia? A meta-analytic review of literature.Journal of Music

Therapy, 36(1), 2-15.

Kogan, A. C., Wilber, K., & Mosqueda, L. (2016). Person‐Centered Care for Older

Adults with Chronic Conditions and Functional Impairment: A Systematic

Literature Review. Journal of the American Geriatrics Society, 64(1), e1-e7.

Krestar, M. L., Looman, W., Powers, S., Dawson, N., & Judge, K. S. (2012). Including

individuals with memory impairment in the research process: the importance of

103

scales and response categories used in surveys. Journal of Empirical Research on

Human Research Ethics, 7(2), 70-79.

Kuske, B., Hanns, S., Luck, T., Angermeyer, M. C., Behrens, J., & Riedel-Heller, S. G.

(2007). Nursing home staff training in dementia care: a systematic review of

evaluated programs. International Psychogeriatrics,19(05), 818-841.

Lai, C. (2016). Person‐Centered Dementia Care. The Encyclopedia of Adulthood and

Aging.

Lewis, V., Bauer, M., Winbolt, M., Chenco, C., & Hanley, F. (2015). A study of the

effectiveness of MP3 players to support family carers of people living with

dementia at home. International Psychogeriatrics, 27(03), 471-479.

Livingston, G., Kelly, L., Lewis-Holmes, E., Baio, G., Morris, S., Patel, N., ... & Cooper,

C. (2014a). A systematic review of the clinical effectiveness and cost-

effectiveness of sensory, psychological and behavioural interventions for

managing agitation in older adults with dementia.

Livingston, G., Kelly, L., Lewis-Holmes, E., Baio, G., Morris, S., Patel, N., ... & Cooper,

C. (2014b). Non-pharmacological interventions for agitation in dementia:

systematic review of randomised controlled trials. The British Journal of

Psychiatry, 205(6), 436-442.

Logsdon, R. G., Gibbons, L. E., McCurry, S. M., & Teri, L. (1999). Quality of life in

Alzheimer's disease: Patient and reports. Journal of Mental Health and Aging, 5,

21–32.

104

Logsdon, R. G., Gibbons, L. E., McCurry, S. M., & Teri, L. (2002). Assessing quality of

life in older adults with cognitive impairment. Psychosomatic medicine, 64(3),

510-519.

Logsdon, R. G., McCurry, S. M., & Teri, L. (2007). Evidence-based psychological

treatments for disruptive behaviors in individuals with dementia. Psychology and

aging, 22(1), 28.

Lyketsos, C. G., Lopez, O., Jones, B., Fitzpatrick, A. L., Breitner, J., & DeKosky, S.

(2002). Prevalence of neuropsychiatric symptoms in dementia and mild cognitive

impairment: results from the cardiovascular health study. Jama, 288(12), 1475-

1483.

Maslow, K. (2013). Person centered care for people with dementia: Opportunities and

challenges. Generations, 37(3), 8-15.

Maslow, K., Fazio, S., Ortigara, A., Kuhn, D., & Zeisel, J. (2013). From Concept to

Practice: Training in Person-Centered Care for People with

Dementia. Generations, 37(3), 100-107.

Matsunaga, S., Kishi, T., & Iwata, N. (2015). Memantine monotherapy for Alzheimer’s

disease: a systematic review and meta-analysis. PloS one, 10(4), e0123289.

Mendez, M. F., & Shapira, J. S. (2013). Hypersexual behavior in frontotemporal

dementia: a comparison with early-onset Alzheimer’s disease. Archives of sexual

behavior, 42(3), 501-509.

Montgomery, R. J., & Kosloski, K. (1994). A longitudinal analysis of nursing home

placement for dependent elders cared for by spouses vs adult children. Journal of

Gerontology, 49(2), S62-S74.

105

Morris, L. W., Morris, R. G., & Britton, P. G. (1988). The relationship between marital

intimacy, perceived strain and depression in spouse caregivers of dementia

sufferers. British Journal of Medical Psychology, 61(3), 231-236.

Moyle, W., Venturto, L., Griffiths, S., Grimbeek, P., McAllister, M., Oxlade, D., &

Murfield, J. (2011). Factors influencing quality of life for people with dementia:

A qualitative perspective. Aging & Mental Health, 15(8), 970–977.

http://doi.org/10.1080/13607863.2011.583620

Moyle, W., Murfield, J. E., Griffiths, S. G., & Venturato, L. (2012). Assessing quality of

life of older people with dementia: A comparison of quantitative self‐report and

proxy accounts. Journal of Advanced Nursing, 68(10), 2237-2246.

Mozley, C. G., Huxley, P., Sutcliffe, C., Bagley, H., Burns, A., Challis, D., &

Cordingley, L. (1999). ‘Not knowing where I am doesn't mean I don't know what

I like’: cognitive impairment and quality of life responses in elderly people.

International journal of geriatric psychiatry, 14(9), 776-783.

National Dementia Initiative (2013) Dementia care: The quality chasm. Retrieved from:

https://www.leadingage.org/uploadedFiles/Content/Members/Nursing_Homes/Qu

ality/DementiaCareTheQualityChasm.pdf

Neal, M., & Briggs, M. (2003). Validation therapy for dementia. Cochrane Database Syst

Rev, 3.

Nichols, L. O., Martindale-Adams, J., Burns, R., Graney, M. J., & Zuber, J. (2011).

Translation of a dementia caregiver support program in a health care system—

REACH VA. Archives of Internal Medicine, 171(4), 353-359.

106

Noguchi, D., Kawano, Y., & Yamanaka, K. (2013). Care staff training in residential

homes for managing behavioural and psychological symptoms of dementia based

on differential reinforcement procedures of applied behaviour analysis: A process

research. Psychogeriatrics, 13(2), 108-117.

Norberg, A., Melin, E., & Asplund, K. (1986). Reactions to music, touch and object

presentation in the final stage of dementia. An exploratory study.International

Journal of Nursing Studies, 23(4), 315-323.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

O’Bryant, S. E., Humphreys, J. D., Smith, G. E., Ivnik, R. J., Graff-Radford, N. R.,

Petersen, R. C., & Lucas, J. A. (2008). Detecting dementia with the mini-mental

state examination in highly educated individuals. Archives of neurology, 65(7),

963-967.

Olderog-Millard, K. A., & Smith, J. M. (1989). The influence of group singing therapy

on the behavior of Alzheimer's disease patients. Journal of Music Therapy, 26(2),

58-70.

Orrell, M., Aguirre, E., Spector, A., Hoare, Z., Woods, R. T., Streater, A., ... & Russell, I.

(2014). Maintenance cognitive stimulation therapy for dementia: single-blind,

multicentre, pragmatic randomised controlled trial. The British Journal of

Psychiatry, 204(6), 454-461.

O'Rourke, H. M., Duggleby, W., Fraser, K. D., & Jerke, L. (2015). Factors that affect

quality of life from the perspective of people with dementia: a

metasynthesis. Journal of the American Geriatrics Society, 63(1), 24-38.

107

Orsulic-Jeras, S., Shepherd, J. B., & Britton, P. J. (2003). Counseling older adults with

HIV/AIDS: A strength-based model of treatment. Journal of Mental Health

Counseling, 25(3), 233.

Osborne, H., Simpson, J., & Stokes, G. (2010). The relationship between pre-morbid

personality and challenging behaviour in people with dementia: A systematic

review. Aging & mental health, 14(5), 503-515.

Ostwald, S. K., Duggleby, W., & Hepburn, K. W. (2002). The stress of dementia: view

from the inside. American journal of Alzheimer's disease and other

dementias, 17(5), 303-312.

Pai, M. C., & Jacobs, W. J. (2004). Topographical disorientation in community‐residing

patients with Alzheimer's disease. International journal of geriatric

psychiatry, 19(3), 250-255.

Panisset, M., Roudier, M., Saxton, J., & Boiler, F. (1994). Severe impairment battery: a

neuropsychological test for severely demented patients. Archives of

neurology, 51(1), 41-45.

Park, H., & Pringle Specht, J. K. (2009). Effect of individualized music on agitation in

individuals with dementia who live at home. Journal of gerontological

nursing, 35(8), 47-55.

Pearlin, L. I., Mullan, J. T., Semple, S. J., & Skaff, M. M. (1990). Caregiving and the

stress process: An overview of concepts and their measures. The

gerontologist, 30(5), 583-594.

Pereira, C. S., Teixeira, J., Figueiredo, P., Xavier, J., Castro, S. L., & Brattico, E. (2011).

Music and emotions in the brain: familiarity matters.PloS one, 6(11), e27241.

108

Pinquart, M., & Sörensen, S. (2011). Spouses, adult children, and children-in-law as

caregivers of older adults: a meta-analytic comparison. Psychology and

aging, 26(1), 1.

Piquard, A., Derouesné, C., Lacomblez, L., & Siéroff, É. (2004). Planification et activités

de la vie quotidienne dans la maladie d’Alzheimer et les dégénérescences

frontotemporales. Psychologie & NeuroPsychiatrie du vieillissement, 2(2), 147-

156.

Plailly, J., Tillmann, B., & Royet, J. P. (2007). The feeling of familiarity of music and

odors: the same neural signature?. Cerebral cortex, 17(11), 2650-2658.

Pollack, N. J., & Namazi, K. H. (1992). The effect of music participation on the social

behavior of Alzheimer's disease patients. Journal of Music Therapy, 29(1), 54-67.

Porter, V. R., Buxton, W. G., Fairbanks, L. A., Strickland, T., O'Connor, S. M.,

Rosenberg-Thompson, S., & Cummings, J. L. (2003). Frequency and

characteristics of anxiety among patients with Alzheimer's disease and related

dementias. The Journal of neuropsychiatry and clinical neurosciences, 15(2),

180-186.

Raglio, A., Bellandi, D., Baiardi, P., Gianotti, M., Ubezio, M. C., & Granieri, E. (2013).

Listening to music and active music therapy in behavioral disturbances in

dementia: a crossover study. Journal of the American Geriatrics Society, 61(4),

645-647.

Raina, P., Santaguida, P., Ismaila, A., Patterson, C., Cowan, D., Levine, M., ... &

Oremus, M. (2008). Effectiveness of cholinesterase inhibitors and memantine for

109

treating dementia: evidence review for a clinical practice guideline. Annals of

internal medicine, 148(5), 379-397.

Ready, R. E., Carvalho, J. O., Green, R. C., Gavett, B. E., & Stern, R. A. (2011). The

structure and validity of self-reported affect in mild cognitive impairment and

mild Alzheimer's disease. International Psychogeriatrics,23(06), 887-898.

Reamy, A. M., Kim, K., Zarit, S. H., & Whitlatch, C. J. (2011). Understanding

discrepancy in perceptions of values: individuals with mild to moderate dementia

and their family caregivers. The Gerontologist, 51(4), 473-483.

Reamy, A. M., Kim, K., Zarit, S. H., & Whitlatch, C. J. (2013). Values and preferences

of individuals with dementia: perceptions of family caregivers over time. The

Gerontologist, 53(2), 293-302.

Rosen, W. G., Mohs, R. C., & Davis, K. L. (1984). A new rating scale for Alzheimer's

disease. The American journal of psychiatry.

Rowe, J. W., & Kahn, R. L. (1997). Successful aging. The gerontologist,37(4), 433-440.

Russell, J. A., & Carroll, J. M. (1999). On the bipolarity of positive and negative affect.

Psychological bulletin, 125(1), 3.

Ryan, E. B., Bannister, K. A., & Anas, A. P. (2009). The dementia narrative: Writing to

reclaim social identity. Journal of Aging Studies, 23(3), 145-157.

Sabol, V. K., Resnick, B., Galik, E., Gruber-Baldini, A. L., Morton, P. G., & Hicks, G. E.

(2011). Exploring the factors that influence functional performance among

nursing home residents. Journal of aging and health,23(1), 112-134.

110

Schneider, L. S., Dagerman, K. S., & Insel, P. (2005). Risk of death with atypical

antipsychotic drug treatment for dementia: meta-analysis of randomized placebo-

controlled trials. Jama, 294(15), 1934-1943.

Schulz, R., & Martire, L. M. (2004). Family caregiving of persons with dementia:

prevalence, health effects, and support strategies. The American journal of

geriatric psychiatry, 12(3), 240-249.

Sebern, M. D., & Whitlatch, C. J. (2007). Dyadic relationship scale: A measure of the

impact of the provision and receipt of family care. The Gerontologist, 47(6), 741-

751.

Seignourel, P. J., Kunik, M. E., Snow, L., Wilson, N., & Stanley, M. (2008). Anxiety in

dementia: A critical review. Clinical Psychology Review, 28, 1071–1082.

doi:10.1016/j.cpr.2008.02.008

Seitz, D. P., Brisbin, S., Herrmann, N., Rapoport, M. J., Wilson, K., Gill, S. S., ... &

Conn, D. (2012). Efficacy and feasibility of nonpharmacological interventions for

neuropsychiatric symptoms of dementia in long term care: a systematic

review. Journal of the American Medical Directors Association,13(6), 503-506.

Seitz, D. P., Gill, S. S., Herrmann, N., Brisbin, S., Rapoport, M. J., Rines, J., ... & Conn,

D. K. (2013). Pharmacological treatments for neuropsychiatric symptoms of

dementia in long-term care: a systematic review. International

Psychogeriatrics, 25(02), 185-203.

Shelton, E. G. (2014). Stage of change discrepancies among individuals with dementia

and caregivers. Master’s Thesis. Cleveland State University.

111

Shelton, E. G., Orsulic-Jeras, S., Whitlatch, C. J., & Szabo, S. M. (2017). Does it Matter

if We Disagree? The Impact of Incongruent Care Preferences on Persons with

Dementia and Their Care Partners. The Gerontologist, 58(3), 556-566.

Simmons-Stern, N. R., Budson, A. E., & Ally, B. A. (2010). Music as a memory

enhancer in patients with Alzheimer's disease. Neuropsychologia, 48(10), 3164-

3167.

Sink, K. M., Holden, K. F., & Yaffe, K. (2005). Pharmacological treatment of

neuropsychiatric symptoms of dementia: a review of the evidence. Jama, 293(5),

596-608.

Sitzer, D. I., Twamley, E. W., & Jeste, D. (2006). Cognitive training in Alzheimer's

disease: a meta‐analysis of the literature. Acta Psychiatrica Scandinavica, 114(2),

75-90.

Snow, A. L., Kunik, M. E., Molinari, V. A., Orengo, C. A., Doody, R., Graham, D. P., &

Norris, M. P. (2005). Accuracy of self-reported depression in persons with

dementia. Journal of the American Geriatrics Society, 53, 389–396.

doi:10.1111/j.1532-5415.2005.53154.x

Solai, L., Schultz, S. K., & Kunik, M. E. (2015). Non-Pharmacological Management of

Behavioral Disturbance in Dementia. The American Journal of Geriatric

Psychiatry, 23(3), S22-S23.

Spector, A., Orrell, M., & Goyder, J. (2013). A systematic review of staff training

interventions to reduce the behavioural and psychological symptoms of dementia.

Ageing research reviews, 12(1), 354-364.

112

Spector, A., Orrell, M., & Woods, B. (2010). Cognitive Stimulation Therapy (CST):

effects on different areas of cognitive function for people with

dementia. International journal of geriatric psychiatry, 25(12), 1253-1258.

Spector, A., Throgrimsen, L., Woods, B., Orrell, M., & Hirsch, C. H. (2004). Cognitive

stimulation therapy improved cognition and quality of life in dementia. Evidence-

based medicine, 9(2), 49.

Sung, H. C., Chang, A. M., & Abbey, J. (2006). The effects of preferred music on

agitation of older people with dementia in Taiwan. International journal of

geriatric psychiatry, 21(10), 999.

Sung, H. C., Chang, A. M., & Lee, W. L. (2010). A preferred music listening intervention

to reduce anxiety in older adults with dementia in nursing homes. Journal of

clinical nursing, 19(7‐8), 1056-1064.

Suzuki, M., Kanamori, M., Nagasawa, S., Tokiko, I., & Takayuki, S. (2007). Music

therapy‐induced changes in behavioral evaluations, and saliva chromogranin A

and immunoglobulin A concentrations in elderly patients with senile

dementia. Geriatrics & gerontology international, 7(1), 61-71.

Svansdottir, H. B., & Snaedal, J. (2006). Music therapy in moderate and severe dementia

of Alzheimer's type: a case–control study. International psychogeriatrics, 18(04),

613-621.

Swanberg, M. M., Tractenberg, R. E., Mohs, R., Thal, L. J., & Cummings, J. L. (2004).

Executive dysfunction in Alzheimer disease. Archives of Neurology, 61(4), 556-

560.

113

Testa, M. A., & Simonson, D. C. (1996). Assessment of quality-of-life outcomes. New

England journal of medicine, 334(13), 835-840.

Teri, L., Ferretti, L. E., Gibbons, L. E., Logsdon, R. G., McCurry, S. M., Kukull, W. A.,

... & Larson, E. B. (1999). Anxiety in Alzheimer's disease: Prevalence and

comorbidity. The Journals of Gerontology Series A: Biological Sciences and

Medical Sciences, 54(7), M348-M352.

Thorgrimsen, L., Selwood, A., Spector, A., Royan, L., de Madariaga Lopez, M., Woods,

R. T., & Orrell, M. (2003). Whose quality of life is it anyway?: The validity and

reliability of the Quality of Life-Alzheimer's Disease (QoL-AD) scale. Alzheimer

Disease & Associated Disorders, 17(4), 201-208.

Tower, R. B., & Kasl, S. V. (1996). Depressive symptoms across older spouses:

longitudinal influences. Psychology and aging, 11(4), 683.

Uchino, B. N., Cacioppo, J. T., & Kiecolt-Glaser, J. K. (1996). The relationship between

social support and physiological processes: a review with emphasis on underlying

mechanisms and implications for health.Psychological bulletin, 119(3), 488.

Van Breukelen, G. J. (2006). ANCOVA versus change from baseline had more power in

randomized studies and more bias in nonrandomized studies. Journal of clinical

epidemiology, 59(9), 920-925.

van der Lee, J., Bakker, T. J., Duivenvoorden, H. J., & Dröes, R. M. (2014). Multivariate

models of subjective caregiver burden in dementia: a systematic review. Ageing

research reviews, 15, 76-93.

Vanstone, A. D., & Cuddy, L. L. (2009). Musical memory in Alzheimer disease. Aging,

Neuropsychology, and Cognition, 17(1), 108-128.

114

Vanstone, A. D., Cuddy, L. L., Duffin, J. M., & Alexander, E. (2009). Exceptional

preservation of memory for tunes and lyrics. Annals of the New York Academy of

Sciences, 1169(1), 291-294.

Vanstone, A. D., Sikka, R., Tangness, L., Sham, R., Garcia, A., & Cuddy, L. L. (2012).

Episodic and semantic memory for melodies in Alzheimer's disease. Music

Perception: An Interdisciplinary Journal, 29(5), 501-507.

Wang, P. S., Schneeweiss, S., Avorn, J., Fischer, M. A., Mogun, H., Solomon, D. H., &

Brookhart, M. A. (2005). Risk of death in elderly users of conventional vs.

atypical antipsychotic medications. New England Journal of Medicine, 353(22),

2335-2341.

Warchol, K. (2006). Facilitating Functional and Quality‐of‐Life Potential: Strength‐based

Assessment and Treatment for All Stages of Dementia. Topics in Geriatric

Rehabilitation, 22(3), 213-227.

Werner, P., Koroknay, V., Braun, J., & Cohen‐Mansfield, J. (1994). Individualized care

alternatives used in the process of removing physical restraints in the nursing

home. Journal of the American Geriatrics Society,42(3), 321-325.

White, M., & Epston, D. (1990). Narrative means to therapeutic ends. WW Norton &

Company.

Whitlatch, C. J., Feinberg, L. F., & Tucke, S. (2005). Accuracy and consistency of

responses from persons with cognitive impairment.Dementia, 4(2), 171-183.

Woods, B., Aguirre, E., Spector, A. E., & Orrell, M. (2012). Cognitive stimulation to

improve cognitive functioning in people with dementia.Cochrane Database Syst

Rev, 2.

115

Yarry, S. J., Judge, K. S., & Orsulic-Jeras, S. (2010). Applying a strength-based

intervention for dyads with mild to moderate memory loss: Two case

examples. Dementia, 1471301210384315.

Yiannopoulou, K. G., & Papageorgiou, S. G. (2013). Current and future treatments for

Alzheimer’s disease. Therapeutic advances in neurological disorders, 6(1), 19-

33.

Zahodne, L. B., Ornstein, K., Cosentino, S., Devanand, D. P., & Stern, Y. (2015).

Longitudinal relationships between Alzheimer disease progression and psychosis,

depressed mood, and agitation/aggression. The American Journal of Geriatric

Psychiatry, 23(2), 130-140.

Zarit, S. H., Femia, E. E., Kim, K., & Whitlatch, C. J. (2010). The structure of risk factors

and outcomes for family caregivers: implications for assessment and

treatment. Aging & mental health, 14(2), 220-231.

Zung, W. W. (1971). A rating instrument for anxiety disorders.Psychosomatics, 12(6),

371-379.

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APPENDIX

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Appendix A: Demographic Information

Demographic Item CR Response CG Response

Can you please tell me your first and last name?

How would you identify your gender?

Male Female Male Female

What is your marital status? Married Single Divorced Widowed

Married Single Divorced Widowed

What is your relationship to your family member with memory and thinking challenges/ your loved one/ your family member with dementia?

Spouse Child Other relative Non-relative Parent

Spouse Child Other relative Non-relative Parent

Are you Hispanic or Latino? yes no yes no

How would you identify your race?

White non-hispanic White hispanic American Indian/Alaska

Native Asian Black or African-American Native Hawaiian/other

Pacific Islander Other race not specified Persons reporting 2 or more

races

White non-hispanic White hispanic American Indian/Alaska

Native Asian Black or African-American Native Hawaiian/other

Pacific Islander Other race not specified Persons reporting 2 or more

races

Are you a Veteran? yes no yes no

What is your education level? Less than a high school diploma

High school diploma or GED diploma

Some College Bachelor’s degree Master’s degree

Less than a high school diploma

High school diploma or GED diploma

Some College Bachelor’s degree Master’s degree

118

Appendix B: Mini-Mental State Examination (Folstein, Folstein, & McHugh, 1975)

Now I’d like to ask you a few questions and give you some problems to solve. Please try to answer as best as you can. 1. What

WRITE DOWN RESPONSE Incorrect Correct

year is this? 0 1

season is this? 0 1

month of the year is this? 0 1

is today’s date? 0 1

day of the week is this? 0 1

2. What

WRITE DOWN RESPONSE Incorrect Correct

county are we in? 0 1

state are we in? 0 1

city/town are we in? 0 1

is the name of this place/room? 0 1

floor of this place are we on? 0 1

3. I am going to name three objects. After I have said all three objects, I want you to repeat

them. Remember what they are because I am going to ask you to name them again in a few minutes.

“ball” “car” “man” Please repeat the three items for me.

RECORD EXACT RESONSE

0 1 2 3

NUMBER OF TRIALS

SCORE ONE POINT FOR EACH CORRECT ANSWER. IF RESP DOES NOT REPEAT ALL THREE, REPEAT UNTIL THEY HAVE LEARNED ALL THREE, OR UP TO A MAXIMUM OF 5 TIMES. ALLOW 20 SECONDS FOR REPLY. SCORE FIRST RESPONSE ONLY.

4. Spell the word WORLD. Now would you please spell WORLD backwards? (ALLOW 30

SECONDS)

D L R O W RECORD EXACT RESPONSE

0 1 2 3 4 5

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SCORE ONE POINT FOR EACH LETTER IN THE CORRECT ORDER.

5. Now what were the names of the three objects that I asked you to remember?

RECORD EXACT RESONSE

0 1 2 3

ONE POINT FOR EACH CORRECT ANSWER: BALL CAR MAN. ALLOW 10 SECONDS

6. Could you please tell me what this is (POINT TO PENCIL) and what this is (POINT TO

WATCH).

RECORD EXACT RESPONSE 0 1 2

HAVE THE IWD NAME THEM AS YOU POINT. ONE POINT FOR EACH CORRECT ANSWER. ALLOW 10 SECONDS EACH.

7. I’d like you to repeat the following phrase after me: “No ifs, ands, or buts.” (ALLOW 10

SECONDS)

RECORD EXACT RESPONSE 0 1

8. Please read this and do what it tells you to do: [USE CHOICE CARD A]

CLOSE YOUR EYES 0 1

IF IWD JUST READS AND DOES NOT THEN CLOSE EYES, THEN PROBE: “PLEASE READ THE WORDS ON THIS PAGE AND DO WHAT IT TELLS YOU TO DO”. ALLOW 10 SECONDS.

REMINDER: TRY TO HAVE RESP. FINISH QUESTIONS 9-11. IF RESP. IS PHYSICALLY UNABLE TO USE HAND(S), NOTE THIS AND SCORE MMSE ACCORDINGLY (I.E., _____/25). SKIP TO NEXT SECTION.

9. Could you please tell me if you are left or right handed? (Alternate left/right in statement,

e.g., if RESP is right handed, ask them to take paper in left hand.) Now I’d like you to

take this paper in your right/left hand

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fold the paper in half 0 1 2 3

and put the paper on the table

SCORE ONE POINT FOR EACH CORRECTLY PERFORMED COMMAND. ALLOW 30 SECONDS.

10. Would you please write any complete sentence on this piece of paper.

SENTENCE CORRECT? 0

NO 1

YES

SENTENCE MUST CONTAIN A SUBJECT, VERB, AND MAKE SENSE. IGNORE SPELLING ERRORS. IF RESPONDENT CANNOT WRITE, ASK HIM OR HER TO SAY A SENTENCE. ALLOW 30 SECONDS.

11. Now I’d like you to copy this design please. [USE CHOICE CARD B]

DESIGN CORRECT? 0

NO 1

YES

ALL 10 ANGLES MUST BE PRESERVED, AND THE 2 MUST INTERSECT TO FORM A 4-SIDED FIGURE. ALLOW 60 SECONDS.

WHILE IWD IS COPYING DESIGN, USE THE TIME TO GO BACK AND CALCULATE TOTAL MMSE SCORE.

Total number correct out of 30 ___ ___ 88

Total number correct out of 25 ___ ___ 88

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Choice Card A [remove text before administering]

CLOSE YOUR EYES.

______________________________________________________________________________

Choice Card B [remove text before administering]

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Appendix C: The Assessment of Personal Music Preference (Patient Version) (Gerdner,

Hartsock, & Buckwalter, 2000)

Music is often a very important part of people’s lives. Please complete the following

based on your personal music preference.

Before illness, how important a role did music play in your life?

_____ 1. Very Important

_____ 2. Moderately Important

_____ 3. Slightly Important

_____ 4. Not Important

Do/did you play a musical instrument?

If yes, please specify (examples: piano, guitar).

Do/did you enjoy singing?

If yes, please specify (examples: around-the house, church choir).

Do/did you enjoy dancing?

If yes, please specify (examples: attended dance lessons, participated in dance contests)

The following is a list of different types of music. Please indicate your three (3) most

favorite types with 1 being the most favorite, 2 the next, and 3 the third favorite.

_____ 1. Country and Western

_____ 2. Classical

_____ 3. Spiritual/Religious

_____ 4. Big Band/Swing

_____ 5. Folk

_____ 6. Blues

_____ 7. Jazz

_____ 8. Rock and Roll

_____ 9. Easy Listening

_____ 10. Cultural or Ethnic Specific (examples: Czech polkas, Ravi Shankar Indian

sitar)

_____ 11. Other: _____________________________________________

What form does your favorite music take?

_____ 1. Vocal

_____ 2. Instrumental

_____ 3. Both

Please identify specific songs/selections which make you feel happy.

Please identify specific artist(s)/performers(s) that you enjoy listening to the most.

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Please identify specific albums, audio-cassette tapes, or compact discs contained in your

personal music library.

124

Appendix D: The Assessment of Personal Music Preference (Family Version) (Gerdner,

Hartsock, & Buckwalter, 2000)

Music is often a very important part of people’s lives. Please complete the questionnaire

based on your knowledge of your family member’s music preference.

Before illness, how important a role did music play in his/her life?

_____ 1. Very Important

_____ 2. Moderately Important

_____ 3. Slightly Important

_____ 4. Not Important

Does/did he/she play a musical instrument?

If yes, please specify (examples: piano, guitar).

Does/did he/she enjoy singing?

If yes, please specify (examples: around-the house, church choir).

Does/did he/she enjoy dancing?

If yes, please specify (examples: attended dance lessons, participated in dance contests)

The following is a list of different types of music. Please indicate the individual’s three

(3) most favorite types with 1 being the most favorite, 2 the next, and 3 the third favorite.

_____ 1. Country and Western

_____ 2. Classical

_____ 3. Spiritual/Religious

_____ 4. Big Band/Swing

_____ 5. Folk

_____ 6. Blues

_____ 7. Jazz

_____ 8. Rock and Roll

_____ 9. Easy Listening

_____ 10. Cultural or Ethnic Specific (examples: Czech polkas, Ravi Shankar Indian

sitar)

_____ 11. Other: _____________________________________________

Please put a check ( ) beside the most correct choice to the following questions.

What form does the individual’s favorite music take?

_____ 1. Vocal

_____ 2. Instrumental

_____ 3. Both

Please identify specific songs/selections that make your family member feel happy.

Please identify specific artist(s)/performers(s) that the individual enjoyed/enjoys listening

to the most.

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Please identify specific albums, audio-cassette tapes, or compact discs contained in your

family member’s personal music library.

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Appendix E: Diary-Based Fidelity Assessment Form (Weeks 1-3)

Music Tracking Logbook

Please complete this assessment once per week over the course of the study.

Name _______________________ Date_________________________

1. Please use this form to keep track of music listening for the week:

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

How many minutes did your loved one spend listening to music?

2. Have you had any technology problems that have prevented or deterred you or your

loved one from listening to personalized music?

____Yes

____No

Please describe:

________________________________________________________________________

________________________________________________________________________

3. Have you listened to music with your loved one during the past week?

____Yes

____No

4. Has listening to music helped to resolve any behavioral problems associated with

your loved one’s memory loss in the past week? (e.g., agitation, unwillingness to eat,

repetitive question asking, etc.)

____Yes

____No

Please describe:

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

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Appendix F: Diary-Based Fidelity Assessment Form (Week 4)

Music Tracking Logbook

Please complete this assessment once per week over the course of the study.

Name _______________________ Date_________________________

1. Please use this form to keep track of music listening for the week:

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

How many minutes did your loved one spend listening to music?

2. Have you had any technology problems that have prevented or deterred you or your

loved one from listening to personalized music?

____Yes

____No

Please specify:

________________________________________________________________________

________________________________________________________________________

3. Have you listened to music with your loved one during the past week?

____Yes

____No

4. Has listening to music helped to resolve any behavioral problems associated with

your loved one’s memory loss in the past week? (e.g., agitation, unwillingness to eat,

repetitive question asking, etc.)

____Yes

____No

If yes, please specify:

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

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Please respond to the following questions on a scale of 1. STRONGLY DISAGREE, 2.

DISAGREE, 3. AGREE, and 4. STRONGLY AGREE (circle that which applies)

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

________________________________________________________________________

1. My loved one seemed

to enjoy listening to

personalized music

Strongly Disagree Disagree Agree Strongly Agree

2. I would recommend

listening to personalized

music to others in my

situation

Strongly Disagree Disagree Agree Strongly Agree

3. I would recommend

this study to others in my

situation

Strongly Disagree Disagree Agree Strongly Agree

4. Playing personalized

music for my loved one

required too much effort

on my part

Strongly Disagree Disagree Agree Strongly Agree

5. The technology aspect

of this study was too

complicated to make it

worth my time.

Strongly Disagree Disagree Agree Strongly Agree

6. The researchers

provided enough

resources for me to be

effective at providing

personalized music

Strongly Disagree Disagree Agree Strongly Agree

7. I will continue to play

personalized music for

my loved one in the

future

Strongly Disagree Disagree Agree Strongly Agree

8. Any other comments

about this study that you

would like to share?

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Appendix G: Dementia Quality of Life Instrument – Positive and Negative Affect

Subscales (Brod, Stewart, Sands, & Walton, 1999)

Now, I’m going to ask you about some feelings that you might have felt in the past week. Using this card [USE CHOICE CARD G], I’d like you to tell me how often you felt that way in the past week?

In the past week, how often have you felt_____?

NEVER SELDOM SOME-

TIMES OFTEN

VERY

OFTEN

a. Happy 0 1 2 3 4

b. Afraid 4 3 2 1 0

c. Anxious 4 3 2 1 0

d. Cheerful 0 1 2 3 4

e. Lonely 4 3 2 1 0

f. Irritated 4 3 2 1 0

g. Content 0 1 2 3 4

h. Frustrated 4 3 2 1 0

i. Hopeful 0 1 2 3 4

j. Embarrassed 4 3 2 1 0

k. Found something that made you laugh

0 1 2 3 4

l. Worried 4 3 2 1 0

m. Joked and laughed with others 0 1 2 3 4

n. Nervous 4 3 2 1 0

o. Sad 4 3 2 1 0

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Appendix H: Quality of Life in Alzheimer’s Disease (QoL-AD) (Logsdon, Gibbons,

McCurry, & Teri, 1999)

Quality of Life-AD

Instructions for Interviewers

The QOL-AD is administered in interview format to individuals with dementia, following the

instructions below. Hand the form to the participant, so that he or she may look at it as you give

the following instructions (instructions should closely follow the wording given in bold type):

I want to ask you some questions about your quality of life and have you rate different

aspects of your life using one of four words: poor, fair, good, or excellent.

Point to each word (poor, fair, good, and excellent) on the form as you say it.

When you think about your life, there are different aspects, like your physical health,

energy, family, money, and others. I’m going to ask you to rate each of these areas. We

want to find out how you feel about your current situation in each area.

If you’re not sure about what a question means, you can ask me about it. If you have

difficulty rating any item, just give it your best guess.

It is usually apparent whether an individual understands the questions, and most individuals who

are able to communicate and respond to simple questions can understand the measure. If the

participant answers all questions the same, or says something that indicates a lack of

understanding, the interviewer is encouraged to clarify the question. However, under no

circumstances should the interviewer suggest a specific response. Each of the four possible

responses should be presented, and the participant should pick one of the four.

If a participant is unable to choose a response to a particular item or items, this should be noted in

the comments. If the participant is unable to comprehend and/or respond to two or more items,

the testing may be discontinued, and this should be noted in the comments.

As you read the items listed below, ask the participant to circle her/his response. If the participant

has difficulty circling the word, you may ask her/him to point to the word or say the word, and

you may circle it for him or her. You should let the participant hold his or her own copy of the

measure, and follow along as you read each item.

1. First of all, how do you feel about your physical health? Would you say it’s poor, fair,

good, or excellent? Circle whichever word you think best describes your physical health

right now.

2. How do you feel about your energy level? Do you think it is poor, fair, good, or excellent?

If the participant says that some days are better than others, ask him or her to rate how she/he has

been feeling most of the time lately.

3. How has your mood been lately? Have your spirits been good, or have you been feeling

down? Would you rate your mood as poor, fair, good, or excellent?

4. How about your living situation? How do you feel about the place you live now?

Would you say it’s poor, fair, good, or excellent?

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5. How about your memory? Would you say it is poor, fair, good, or excellent?

6. How about your family and your relationship with family members? Would you describe

it as poor, fair, good, or excellent? If the respondent says they have no family, ask about

brothers, sisters, children, nieces, nephews.

7. How do you feel about your marriage? How is your relationship with (spouse’s name). Do

you feel it’s poor, fair, good, or excellent? Some participants will be single, widowed, or

divorced. When this is the case, ask how they feel about the person with whom they have the

closest relationship, whether it’s a family member or friend. If there is a family caregiver, ask

about their relationship with this person. It there is no one appropriate, or the participant is

unsure, score the item as missing. If the participant's rating is of their relationship with someone

other than their spouse, note this and record the relationship in the comments section.

8. How would you describe your current relationship with your friends? Would you say it’s

poor, fair, good, or excellent? If the respondent answers that they have no friends, or all their

friends have died, probe further. Do you have anyone you enjoy being with besides your

family? Would you call that person a friend? If the respondent still says they have no friends,

ask how do you feel about having no friends—poor, fair, good, or excellent?

9. How do you feel about yourself—when you think of your whole self, and all the different

things about you, would you say it’s poor, fair, good, or excellent?

10. How do you feel about your ability to do things like chores around the house or other

things you need to do? Would you say it’s poor, fair, good, or excellent?

11. How about your ability to do things for fun, that you enjoy? Would you say it’s poor,

fair, good, or excellent?

12. How do you feel about your current situation with money, your financial situation? Do

you feel it’s poor, fair, good, or excellent? If the respondent hesitates, explain that you don’t

want to know what their situation is (as in amount of money), just how they feel about it.

13. How would you describe your life as a whole. When you think about your life as a whole,

everything together, how do you feel about your life? Would you say it’s poor, fair, good, or

excellent?

SCORING INSTRUCTIONS FOR THE QOL:

Points are assigned to each item as follows: poor=1, fair=2, good=3, excellent=4.

The total score is the sum of all 13 items.

132

Appendix I: Zung Self-Rating Anxiety Scale (SAS) (Zung, 1974)

133

Appendix J: Cohen Mansfield Agitation Inventory – Short Form (Werner, Cohen-

Mansfield, Koroknay, & Braun, 1994)

134

135

Appendix K: Dyadic Relationship Strain (Sebern & Whitlatch, 2007)

Now, I’m going to read some statements. Then, I’d like you to tell me how much you agree or disagree with each statement. [USE CHOICE CARD D]. Use this card to help you with your responses.

In the past month: Strongly

Agree Agree

Disagree Strongly

Disagree

a. I felt closer to her/him than I have in awhile 0 1 2 3

b. I have learned some good things about my [CG]

0 1 2 3

c. I felt angry toward her/him 3 2 1 0

d. I felt depressed because of my relationship with her/him

3 2 1 0

e. I felt resentful toward her/him 3 2 1 0

f. I have had more patience than I have had in the past

0 1 2 3

g. I have learned some good things about myself

0 1 2 3

h. I felt that my relationship with her/him was strained

3 2 1 0

i. I have learned some nice things about other people in my life

0 1 2 3

j. Communication between my [CG] and me has improved

0 1 2 3

136

Appendix L: Telephone Screener

Music and MemorySM

Telephone Recruitment Script

Hello, may I speak with __________?

Hello! My name is ________________ [Insert Name] from __________________ [Insert

organization]. I am calling to talk with you about the Music and Memory program that we are

offering in conjunction with Benjamin Rose and the Ohio Department on Aging. If you have

about 15 minutes to talk, I’d like to tell you about the program. If you’re interested in you and

your loved one participating, I’d also like to ask you some questions. Is now an okay time to

talk?

Great! Before we begin, I would like to make sure you are in a safe place and can talk freely

with me.

Thank you!

Benjamin Rose Institute on Aging is working with the Ohio Department on Aging to implement a

program called Music and Memory. The goal of this program is to help you connect with the

music you know and love. Research shows that listening to music can help people with a wide

range of challenges. Many people find renewed joy in life through music, especially when the

music is personally meaningful. The family members of people who participate in Music and

Memory also frequently benefit from the program. The program itself is being offered to you at

no cost, with the exception of any equipment and/or music you wish to purchase in the future.

As part of your participation in the Music and Memory program, a student from Cleveland State

University will set up a time to meet with you in person to gather some basic information about

you and your loved one. With the information you provide, we will share with you with the

equipment and information you’ll need to get started with Music and Memory. This may include

sending you equipment, such as an iPod Shuffle and headphones for listening to personalized

music at home, as well as helping to start a music library on your home computer. We will

provide some forms for you to fill out and send back to us over the course of one month which

will tell us about how things are going with the music. After one month of using the Music and

Memory program, a student from Cleveland State University will contact you for a final time to

137

ask you similar questions again in order to monitor how the program is going. The information

you provide will be very useful in helping us learn whether the program is working and also how

to make the program available to others who could benefit.

Does this sound like something you’d be interested in?

IF NO: That’s okay. If you change your mind or if you have any further questions, please call the

Music and Memory staff at Benjamin Rose at (216) 373-1615 -or- email at

musicandmemory@benrose.org. Thank you for your time.

IF YES, THEN CONTINUE: Great! I’d like to ask you a few questions to make sure that you are

eligible for the program, before I go on. The reason is that the program is designed for people in

a particular situation.

I. Eligibility *Required questions

Screener Item CG Response

1) *Does the person you provide care for

have trouble with his/her memory? yes no (If no, STOP)

2) What year was CR’s memory

problem first noticed?

3) What are the CR’s memory-related

symptoms?

4) Has CR talked to a doctor about memory

concerns? yes no

5) Does CR have a diagnosis for memory

problems? yes no (If no, go to #7)

6) If diagnosed, what is diagnosis? Alzheimer’s Disease

Other Dementia

Vascular Dementia

Other Diagnosis:

7) What year was CR diagnosed?

8) Is CR taking medications for memory

problems? yes no (If no, go to #10)

9) What medications is CR taking for memory

problems?

Aricept (Donepezil)

Reminyl (Galantimine)

Excelon (Rivastigmine)

Namenda (Memantine)

Other:

10) *Does CR receive assistance from a CG as

a result of having memory challenges? yes no

11) When did CG begin assisting CR due to

memory?

12) How many days out of the week does the

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Not Eligible Script: I’m very sorry, but unfortunately, you are not eligible for this program. This program requires that you (STATE THE REASON THAT THE PERSON IS NOT ELIGIBLE). I thank you for your time and wish you the best of luck. Please feel free to contact Benjamin Rose for other programs that you may be qualifies for. END CALL Eligible Script: Thank you. Based on your responses, you are eligible to participate in Music and Memory. Would it be all right to set up a meeting with you and your loved-one in-person to get started with the program? We are able to meet at the Benjamin Rose Institute on Aging in Cleveland (11890 Fairhill Rd, Cleveland, OH 44120), at your own home, or at another location if you prefer.

If YES, “Great, where would you prefer to meet?” ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

“When would be a good day and time to meet with you?

_____________________________________________________________________________________________________________________________________________________________________________________________________________

CG help the CR with care tasks? (If less than 5, STOP)

13) Does the CR still drive? yes (If yes, go to #13) no

14) If CR does not drive, why?

15) Does person have primary responsibility

for CR? yes no

16) Where does the CR reside? Alone in community

With CG

With another family member

Non-community

Other:

(If in Nursing Home, STOP)

17) *Do you have a computer with internet

access?

18) *If yes, is this computer a portable laptop,

or a desktop workstation?

yes no

19) *Are you familiar with iPods, iTunes, or

other Apple products?

yes no

(If YES, go to #19)

20) *Do you own an iPhone or iPod? yes no

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“Excellent. I will plan on seeing you and your loved one at _______(PLACE) on the_______(DAY) at ________(TIME). In the meantime, in case I need to contact you, is this phone number the best place to reach you?

If NO, “For this project, it would be a requirement to meet in person to set up the Music and Memory program. I can give you a phone number to call and set up a meeting if you change your mind. Does this sound okay?

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Appendix M: Personalized Music Assessment

Personalized Music Assessment (Experimental group T1, Control group T2)

Participant ID: ____________

1. Did CG/PWD/family create a playlist before interview? YES NO

2. What style(s) of music does the PWD prefer?

3. What specific artists does the PWD prefer?

4. What specific songs does the PWD prefer?

If possible, the interviewer should sample identified songs for the PWD before purchasing them

(in the iTunes store, play song preview). Respond to the following questions regarding this

sampling process.

5. Was the interviewer able to sample songs for the PWD to listen to? YES NO

i. If not, why? _____________________________________________________

6. Did the PWD exhibit a physical response to hearing the music samples? (e.g., foot tapping,

swaying, head bobbing, smiling). YES NO

While sampling each song, ask the PWD: “Do you like this song?”

7. Was PWD able to respond yes/no? YES NO

While sampling each song, ask the PWD: “Would you like me to add this song to your playlist?”

8. Was PWD able to respond yes/no? YES NO

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Appendix N. Control Group Playlist

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Appendix O: CONSORT Diagram

Excluded (n= 8)

Not meeting inclusion criteria (n= 1)

Declined to participate (n= 2)

Other reasons (n= 5)

Non-completers (n= 1)

Completers (n= 15)

Allocated to control (n= 16)

Non-completers (n= 3)

Completers (n= 15)

Allocated to intervention (n= 18)

Allocation

Follow-Up

PWD given MMSE (n= 38)

Expressed interest (n= 71)

Completed phone screener

(n= 46)

Excluded

Did not meet MMSE requirements

(n= 4)

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Appendix P: Commonly Selected Genres and Preferred Artists of Personalized Music

Group Participants

Genre Number of Participants Favorite Artists

Gospel 6

Luther Vandross, Mahalia Jackson, Sam Cooke, Mississippi Mass Choir, Shirley Caesar, Chicago Mass Choir, Marvin Sapp, Yolanda Adams, Heziah Walker, James Cleveland

Rock/Classic Rock 5

Billy Joel, P!nk, Supertramp, Journey, The Rolling Stones, The Who, Jethro Tull, John Fogerty, The Eagles, Pete Seger, Bruce Springsteen, Fats Domino, J Giles Band, Chuck Berry, Elvis Presley

Golden Oldies 4 Nat King Cole, Dean Martin, Frank Sinatra, The Temptations, The Beatles, The Beach Boys

Big Band/Swing/Jazz 4 Billie Holiday, Nat King Cole, The Rat Pack, Sinatra, Ella Fitzgerald, Louis Armstrong, Glenn Miller

Blues 3 B.B. King, Muddy Waters, John Lee Hooker

RnB 3 Mariah Carey, Aretha Franklin, Diana Ross

Classical 3 Beethoven, flute music, piano concertos, misc artists

Pop/Soft Rock 3 Barry Manilow, Josh Groban, Perry Como, Beach Boys

Country/Folk 3 Alan Jackson, Garth Brooks, Jimmy Rodgers, Hank Williams, Johnny Cash

Bluegrass 2 Bill Monroe, Jimmy Martin

Musicals 2 Irving Berlin, Rogers and Hammerstein, Jerome Kern

Polka/Waltz/Accordion 1 Frankie Yankovic, misc artists