Effects of Hope Intervention on Hope and Quality of Life in Senior People Who Live Alone.

CHAPTER 1

INTRODUCTION

The world now faces an unprecedented time where the senior population will

soon surpass that of children, thus leading to the majority of the population being old or

very old (Zarghami et al., 2018). According to the United Nations, the senior population

is the fastest-growing population in the world (Oliveira et al., 2018). In the United States,

there is a growing number of community-dwelling senior people who present with

increased risks of untoward health effects that come along with aging (Marcus-Varwijk et

al., 2019).

Background and Rationale

There is an increase in the number of seniors living alone (Ng et al., 2015). Living

alone is one of the noticeable factors that affect the well-being of seniors with evidence

that this population most frequently presents with increased risk for reduced hope,

depression, deconditioning, falls, injuries, infections, dehydration, and hypothermia (Yeh

& Lo, 2004). There is also an increased risk for high mortality because of a lack of social

connections and support in this population (Ng et al., 2015). There is an association

between living alone and decreased level of hope and quality of life (QOL) in the senior

population.

Quality of life is the term used to describe an individual’s health and it is an

essential external indicator that reflects the internal being. Quality of life is also perceived

as an essential health research outcome (Hayhurst et al., 2014). Even though QOL is

usually referred to as the reflection of an individual’s health, research findings show that

this indicator includes both health-related factors and non-health-related factors such as

family, friends, jobs, and circumstances of life (All et al., 2017). The QOL in the senior

population is a global issue and a twenty-first-century challenge. Several factors affect

QOL in seniors (Ahmed, 2020), and many research studies have been conducted to

evaluate this vital indicator in this population. Senior people who live alone experience a

higher degree of reduction in their QOL due to isolation, feelings of loneliness, pain,

depression, anxiety, powerlessness, and hopelessness. Loneliness is a multi-dimensional

and complex feeling which has a significant impact on the health and the well-being of

the senior population. Seniors are particularly vulnerable to this feeling because of their

fragility (Rocha-Vieira et al., 2019). Many research studies have been conducted to seek

the impact of loneliness on physical, emotional, and behavioral problems (Rokach, 2007).

Loneliness is a serious threat to seniors’ lives and their QOL (Rocha-Vieira et al., 2019).

Interventions to promote and increase the QOL are essential in reducing or eliminating

the challenges the senior population faces in the twenty-first century.

Hope is an inner source that can enrich life and improve the outlook on life.

Studies conducted to assess the level of hope and its effect on QOL show that there is a

strong connection between the levels of hope and coping with life events. People who

have a higher level of hope adjust to and manage life events effectively with acceptance

and normal ways of living (Chi, 2007). People identify different strategies to foster hope

and cope with life changes.

The literature review indicated that the utilization of non-pharmacological

interventions such as hope and affirmation had positive effects on the QOL Hope is at the

core of human psychology and is a powerful vital force linked to health (Stavarski, 2018;

Leontopoulou, 2020).

Healthcare professionals have an important role in enhancing hopefulness through

hope interventions. Hope can be offered through building relationships with patients and

families, being present, listening actively, giving attention to small improvements in care,

providing comfort, encouraging hope in their religious beliefs, and giving patients what

they needed (Stavarski, 2018).

Hope-fostering interventions and programs aim to promote overall well-being and

reduce psychopathology in a variety of populations and various settings such as

educational organizations, recreational centers, correctional institutions, and therapy and

counseling. Studies indicate that hope-fostering interventions enhance psychosocial

outcomes and reduce depression (Larsen et al., 2015; Leontopoulou, 2020).

Problem Statement

The senior population who lives alone faces many challenges that affect their

general well-being and overall QOL. Seniors who live alone are more vulnerable to

reduced hope and QOL due to a lack of access to the available resources (Haslbeck et al.,

2012). With this fastest-growing population, the world faces an increase in the health-

related challenges that come with this population. Declining hope and QOL in seniors

living alone pose a significant problem both to the healthcare industry and society. While

many pharmacological interventions to improve the level of depression and outlook

towards life have been used in practice, several non-pharmacological interventions that

are available as an alternative to pharmacological treatments have not been effectively

utilized to improve hope levels and QOL in this population.

According to Herth (2001), hope was seen as being vital in strengthening

physiological and psychological defenses (Herth, 2001). In nursing, medicine, and mental

health, an intervention to give or improve hope had been considered to be one of the

interventions. Health care professionals, through non-pharmacological interventions such

as touch, being present, listening, encouraging, and providing education, can reduce the

feeling of hopelessness and increase the level of hope leading to improved QOL (Binaei

et al., 2016). Hope reflected an individual perception of inward potential to overcome a

challenge. Due to this very nature of hope, hope intervention was considered as an

alternative to medication management in positively influencing the QOL (Stavarski,

2018).

Many senior people live alone with no support from their family or society. They

are depressed and without hope. Hope intervention has shown good effects on decreasing

depression and increasing s sense of hope in other populations (Salamanca-Balen et al.,

2021). However, not many studies have been done on the effects of hope intervention on

senior people who live alone. Hence, this study plans to evaluate the effects of Hope

Interventions on senior people who live alone through the Hope Intervention Program

(HIP).

Purpose Statements

The purpose of this project was to investigate the effects of a non-

pharmacological intervention, the hope intervention, on hope and the QOL of senior

people who lived alone.

PICOT Question

How does the implementation of an eight-week hope intervention in seniors 65

years and older who live alone affect their level of hope and quality of life compared to

those levels in seniors 65 years and older who live alone and do not receive the hope

intervention?

P – Seniors 65 years and older who live alone

I – Hope Intervention Program

C – Seniors 65 years and older who live alone and receive hope intervention and

those who do not receive hope intervention

O – Level of hope and QOL

T – After eight weeks of the hope intervention program

CHAPTER 2

LITERATURE REVIEW/CONCEPTUAL DEFINITIONS/

THEORETICAL FRAMEWORK

Literature Review

The purpose of the literature review was to examine previous research on the

effects of hope intervention on the level of hope and QOL and how that may affect senior

people who live alone. The literature review was completed by using the EBSCOhost

health search engine, which included other databases such as the Cumulative Index to

Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, and

ProQuest. Studies were selected based on their relevance to the current study.

Keywords: Seniors, elderly individuals, loneliness, loneliness in older adults,

chronic diseases in older adults, well-being, quality of life in older adults, loneliness and

quality of life, hope, hope interventions, and hope interventions in seniors.

Hope

The word “hope” comes from Old English word “hopa” and translates to having

confidence in the future (Safri, 2016). Very few people are knowledgeable in the science

of hope (Gwinn & Hellman, 2019). Hope is seen as something aspiring, a positive

anticipation of the future, something good to look forward to. It is usually directed

towards the future and reflects personal will power (Safri, 2016; Van Dongen, 1998).

Hope is as old as humanity and vital for the existence of humanity. Hope

influences how a person thinks and acts. Hope not only shapes people’s behavior, but also

motivates them and enables them to keep going during times of distress, discouragement,

and disappointment. Hope creates new possibilities and fills people with strength,

courage, and happiness to move on in life with renewed energy (Safri, 2016). When

individuals have hope, they can change the course of their situation (Van Dongen, 1998).

Hope’s desire can be long-term and even a lifelong endeavor. Hope represents how

individuals perceive their ability to frame their goals, create strategies to meet those

goals, and sustain their motivation to implement those strategies (Chi, 2007; Snyder et

al., 2003). Hope can grow and thrive in the environment where a culture of hope is

valued.

Culture of Hope

Hope is recognized as a bridge between the impossible and the possible. Hope is

sometimes dramatic in harrowing and life-saving events, while at other times, it is seen in

the quiet activities of daily life. Martin Luther King, Jr. embraced hope as the foundation

of creating a hopeful world. He used to love to say that everything that is accomplished in

the world was accomplished by hope (Gwinn & Hellman, 2019). Once people understand

hope, they have the choice to believe in it and then put forth efforts to obtain it. Once

individuals achieve hope, then they can recruit others to develop a community of hope,

thus creating a culture of hope. There is a greater power in the collective culture of hope

than individual hope. Building a culture of hope has shown to support individuals with

purpose, pride, place, and optimism (Gibson & Barr, 2017; Gwinn & Hellman, 2019).

Where the culture of hope is not promoted, outcomes can be less than optimal and

leading to a negative outlook towards life and reduced QOL.

Hope, Illness, and Healing

Illness is a disease condition in which the body or mind is affected. Healing is an

act or process of restoring to health. Hope can play a vital role in healing and coping.

Medical professionals and researchers are increasingly seeing hope as a dynamic,

cognitive, emotional, and motivational process, as well as a way to cope with disease

conditions (Salamanca-Balen et al., 2021). Hope can help individuals cope with serious

and chronic threats to their physical and psychological well-being, leading to enhanced

QOL (Salamanca-Balen et al., 2021). While hopelessness has been linked to depression

and suicide, hope involves having confidence in and expectations of a brighter future in

the face of adverse circumstances (Gupta & Singh, 2020). In psychological and

psychiatric literature, hope is seen as a longing for the betterment of a hopeless outcome,

operationalizing it as a positive goal-related (future-oriented) motivational state and a

dispositional trait that signals a tendency to adopt a positive outlook (Gupta & Singh,

2020). It has further been established that hope can be measured as an essential

restorative factor in the field of healthcare and recovery (Lucas et al., 2019). Hope can

also be fostered through interventions in individuals facing adverse health conditions,

loneliness, and from living alone (Gupta & Singh, 2020).

Quality of Life

Within the arena of health care, QOL is viewed as multidimensional, and

encompassing physical, psychological, emotional, and social well-being. Quality of life is

a multifactor and a value-driven concept (Soósová, 2016). The most important aspects of

QOL in the elderly are autonomy, decision-making power, independence, self-sufficiency,

preservation of health and sensory abilities, absence of pain and illness, economic

security, connectedness with family and friends, and peace and happiness. Quality of life

in the elderly can be negatively influenced by many demanding factors related to the

aging process. The elderly faces a variety of challenges ranging from changes in health

status, identifying with new roles, adjusting to changing social support, and coping with

new restrictions in life posed by the aging process (Soósová, 2016).

Elderly people suffer from economic insecurity and loss of power as they age and

retire from their work, thus leading to reduced QOL. The QOL of the elderly has been a

global concern and is becoming increasingly important for research because of its effect

on public health (Wayadande & Prabhakar, 2020). Quality of life in the elderly is directly

associated with their perceived well-being; hence, it is essential to provide the elderly

with the opportunity to live a long quality life with an understanding that the aging

phenomenon is not only physical, but also a social element that affects how the elderly

feel, live, relate to their life, to other people in their lives, and to their environment (dos

Santos Gomes et al., 2020).

Quality of Life in Senior People Who Live Alone

Quality of life is impacted by living alone. Solitude is worst when people live

alone. They are more vulnerable to poverty, and deprivation becomes more probable the

longer they live alone. Many elderly individuals who live alone express feelings of

isolation and loneliness. Given that dining is primarily a social activity for most

individuals, some elderly persons who live alone do not make complete, well-balanced

meals (Davie-Smith et al., 2017). Eating habits especially tend to change over time

because chronic conditions may overpower the aged and even the strength to prepare a

meal for themselves is a great struggle. The lack of a balanced diet causes their health to

deteriorate. Again, the lifespan of older adults living alone declines each day of their

lives. This could be because of illness due to underlying issues (O'Súilleabháin et al.,

2019).

Factors Affecting the QOL in Senior People

Aging is the progressive phenomenon of change in the physiological,

psychological, and social aspects of an individual. Aging is seen as a holistic concept

since it involves a broad range of issues (Ahmed, 2020). According to the World Health

Organization, those who are entering into their senior life-cycle experience biological,

social, and psychological changes (Bahramnezhad et al., 2017).

In the United States, debilitating conditions such as heart disease, dementias, type

2 diabetes, arthritis, and cancer are the leading drivers of disability, deaths, and healthcare

expenditure. Studies indicate that seniors age 60 and older experience Alzheimer’s

disease and other dementias at a greater rate compared to other age groups, and the risk

for these conditions increases with age. Healthcare and long-term care expenses

associated with these conditions greatly increase the financial burden of healthcare in the

United States to the amount of $290 billion in 2019 (CDC, 2020).

Loneliness and Its Effects on QOL

Loneliness plays a vital part in the physical, psychological, and social

deconditioning of seniors who live alone. Human beings, generally speaking, are social

creatures who, in order to live successfully, need safe and secure social environments to

thrive (Hwang et al., 2020). Social connections that are satisfying are essential for both

mental and physical well-being. Having strained social relationships may contribute to

feelings of loneliness. People who participate in meaningful, valuable activities with

others have a better mood, live longer, and have a greater sense of purpose than those

who do not. According to research, these activities seem to aid in maintaining mental

well-being and may even enhance their cognitive performance. The perception of

loneliness as a worldwide human issue has existed since the beginning of humanity

(Hwang et al., 2020).

Isolation may result in various mental illnesses, including depression, sleep

difficulties, personality disorders, alcoholism, and Alzheimer's disease. There are also

numerous physical illnesses such as type 2 diabetes, autoimmune illnesses, osteoarthritis,

lupus, as well as cardiovascular problems such as heart disease, physiological aging, high

blood pressure (HTN), cancer, obesity, and poor health are all examples of chronic

conditions and impaired hearing as a result of smoking (Hwang et al., 2020). The effects

of loneliness on people’s emotional and physical health, if left untreated, may be very

detrimental. As a result, it is critical to act at the appropriate moment to avoid loneliness

and ensure that patients’ physical and emotional health is preserved.

Loneliness is primarily evident in senior people. Research shows that this could

be due to living alone, detachment from the tradition of origin, or neglect by close family

ties (Hwang et al., 2020). This causes the elderly not to participate fully in society.

Loneliness, especially in seniors, has been linked to an increased risk of anxiety, sadness,

cognitive decline, vascular dementia, and even mortality. Particularly vulnerable are

those who find themselves suddenly alone due to circumstances such as the death of a

spouse or partner, retirement, separation from friends or family, loss of mobility, or lack

of transportation. Physical effects like sleep deprivation, weight gain, weak immune

system, and poor heart health can be evident (Hwang et al., 2020).

Effects of COVID-19 and Isolation on Seniors

During the outbreak of Covid-19, social isolation became mandatory in most

places, especially for the elderly because they were most vulnerable to this virus.

Regardless of whether they were infected or not, the elderly, were restricted to their

homes. It had been suggested that the impacts of COVID solitude might be particularly

acute among older individuals in long-term care (LTC) amenities (Kasar et al., 2020). The

literature review indicates that residents’ feelings of loneliness, despair, abandonment,

and fear and their impact on neurobehavioral health contributed to the rise in the number

of deaths associated with the epidemic. Seniors’ physical and mental health was adversely

impacted by the social distance that occurred throughout the COVID-19 pandemic. As a

result, throughout the confinement period, a multi-component program that included

exercise and psychological techniques were highly suggested for this group of people

(Kasar et al., 2020).

Hope and Quality of Life

Important aspects of QOL include autonomy, decision-making, independence,

self-sufficiency, preservation of health and sensory abilities, absence of pain and illness,

economic security, connectedness with family and friends, and peace and happiness

(Soósová, 2016). Quality of life is one of the most important factors that affect the

disabled and elderly (Zareei Mahmoodabadi et al., 2019). Quality of life in the elderly

can be negatively affected by many demanding factors related to aging. Elderly

individuals face a variety of challenges ranging from changes in health status, adapting to

new roles, adjusting to changing social support, economic insecurity, and coping with

new restrictions posed by advanced age (Soósová, 2016).

Quality of life of the elderly has been a global concern and is becoming

increasingly important for research because of its effect on public health (Wayadande &

Prabhakar, 2020). Quality of life in the elderly is directly associated with their perceived

well-being; therefore, it is essential to provide the elderly with the opportunity to live

long, quality lives and the understanding that aging is not only physical, but also affects

how individuals feel, relate to their life, to other people, and their environments (dos

Santos Gomes et al., 2020). A perceived sense of wellbeing is essential and is strongly

related to improved QOL.

Researchers have long sought to understand the association between hope and

QOL. One study indicates that vitality is one of the important mechanisms accounted in

attaining hope and thus leading to improved QOL. Vitality reflects an individual’s

subjective experience of energy and activity. Vitality is associated with better physical

and psychological health in adults (Lucas et al., 2019). In order to evaluate vitality’s role

in mediating the relationship between dispositional hope and QOL, Lucas et al. (2019)

recruited a sample of 101 adults from a community-based primary care setting in the

United States. The sample included 72 women and 29 men, mostly Caucasian, from 18–

64 years of age. Lucas et al. (2019) assessed hope using the Hope Scale; previous

researchers identified positive relationships between the Hope Scale and personal control

and self-esteem measures, indicating validity for the scale. Lucas et al. (2019) found that

vitality fully mediated between hope and QOL, which included aspects of physical

health, psychological health, social relationships, and environment, confirming the fact

that vitality is a significant mechanism through which hope affected QOL in adults. The

results indicated the need for promoting environments that foster vitality to help activate

hope and promote QOL of adults.

Adult QOL can be negatively impacted by issues related to aging and disease. The

QOL of older adults includes social, psychological, physical, and spiritual factors.

Additional individual-level characteristics that affect well-being include activity,

productivity, income, social status, physical and mental health, longevity, cognitive

efficacy, strong relationships, and satisfaction in life (da Silva & Baptista, 2019). Lack of

purpose in life can lead to reduced QOL and to negative perceptions of life, which can

then lead to reduced hope and meaning in life (Binaei et al., 2016). Focusing on vitality

when caring for adult patients can help facilitate hope and lead to enhanced QOL

outcomes (Lucas et al., 2019). Hope as an inner force has been shown to strengthen and

uplift individuals’ spirits and allow them to look beyond current circumstances (Binaei et

al., 2016).

Researchers have also focused on medically oriented external factors in relation to

hope, such as chronic illnesses, health disparities, health insurance, and how they are

linked to QOL. Chronic illnesses can lead to impaired functional ability (Binaei et al.,

2016). Considering the health disparities related to insurance in the United States,

Wippold and Roncoroni (2020) conducted a study involving structural equation

modelling and 197 adults living with at least one ongoing comorbid ailment. They

examined the relationships between various chronic health issues, the two components of

hope related to agency and pathways, and the health-related QOL among the population.

Pathways and agency are two essential factors in achieving any desired goals. Pathways

are ways people identify that will aid them reach their goals. Agency reflects individuals’

perception of their ability and the drive to achieve their goals. In this study, the hope of

the individuals was tested using the State Hope Scale. They found that multiple

comorbidities, including heart disease, stroke, diabetes, and cancer negatively impacted

the high quality of life of the affected populations. The findings further indicated that

agency mediated the relationship significantly, resulting in positive outcomes (Wippold &

Roncoroni, 2020). Wippold and Roncoroni (2020) emphasized that it is important to

encourage individuals to take charge, have hope, and control their lives in order to

manage chronic health diseases and survive despite the difficulties. Gaining the

motivation to achieve their goals and control their health could enhance hope, even in the

face of long-term diseases and being uninsured or underinsured. An improved attitude of

readiness to attain goals along with hope can help enhance the QOL and health status

(Wippold & Roncoroni, 2020). In an earlier study, Binaei et al. (2016) also found that

severity of heart failure and an uncertain prognosis of this disease impact QOL

negatively; however, hope-promoting strategies were found to be beneficial in improving

this condition. Hope is dynamic and can empower individuals to reach the desired goal

(Chamodraka et al., 2017).

Researchers have also investigated the relationship between hope and QOL in

various types of patients. Alshraifeen et al. (2020) studied the correlation between hope

and QOL among hemodialysis (HD) patients in Jordan. The researchers contended that

hope was important for end-stage renal-disease patients. These individuals received HD

to manage the condition, and hope was thought to be closely related to QOL in this

population. The study involved a cross-sectional design, and 202 convenience patient

samples from six varying dialysis centers were included to evaluate the association

between hope and QOL. The World Health Organization QOL-BREF and the Herth Hope

Index (HHI) were used to measure QOL and hope, respectively. The researchers

identified moderate levels of hope with a viable mean, and participants showed low mean

scores for physical, domain-related QOL. The results revealed that the physical domain

might not be related to hope. However, the findings also revealed that the social and

psychological relationship aspects had improved hope levels related to the QOL. Overall,

study results indicated a positive correlation between the level of hope and QOL in

people receiving HD for end-stage renal disease (Alshraifeen et al., 2020). Patients

receiving HD to manage end-stage renal conditions needed assistance developing hope in

their environments. Alshraifeen et al.’s (2020) findings emphasized that introducing hope

and promoting care while serving HD patients in healthcare facilities could improve their

QOL. These patients often experienced considerable levels of stress since they had to

attend HD sessions for the rest of their lives and they required quality care to improve

their health outcomes. The results showed that understanding the association between

hope and QOL could help healthcare professionals improve the type and level of care

provided to their patients and their families. This information is essential in giving hope

when caring for HD patients in consideration of the expected positive outcomes in their

QOL (Alshraifeen et al., 2020).

Quality of life can also be critical in the decision-making process related to

medical conditions, their management, evaluating outcomes, and future interventions.

Shen et al. (2020) investigated the correlation between hope, self-efficacy, and social

support in 121 triple-negative breast cancer patients as they battled the condition. The

study included a cross-sectional design and patients at the breast cancer treatment center

in Tianjin Medical University Cancer Institute and Hospital, Tianjin, China. The

researchers used convenience sampling to bring participants on board. Participants were

above 18 years old, receiving post-surgery chemotherapy with no prior diagnosis of

cancer, and could communicate in Chinese (Shen et al., 2020). Shen et al. used the HHI

to measure hope, the General Self-Efficacy Scale (GSES) to measure self-efficacy, and

the Functional Assessment of Cancer Therapy Breast Cancer (FACT-B) to measure QOL.

Data were analyzed with independent sample t-tests, one-way Pearson correlation, and

multiple regression analysis (Shen et al., 2020). Shen et al. (2020) found that hope, self-

efficacy, and social support were strongly correlated with the QOL in breast cancer

survivors (P<0.001). Higher-income was shown to contribute to better QOL. The

multiple regression analysis of this study showed that hope, income, cancer stage, self-

efficacy, and social support were significant predictors of the patients’ QOL (P<0.001).

Hope, social support, self-efficacy, and income were observed as positive factors of QOL,

while the cancer stage was the negative predictor of the QOL (Shen et al., 2020). Hope, a

positive predictor of QOL, was an effective strategy that provided adaptive power to help

cancer patients overcome a difficult situation and achieve desired outcomes (Chi, 2007).

Shen et al.’s (2020) study highlights the need for intervention programs to support and

improve income, hope, self-efficacy, and social support for this patient population in

order to improve their QOL.

Understanding the needs of the elderly is crucial to caring for them

(Bahramnezhad et al., 2017). One of the domains of well-being involves the social aspect

of QOL. A supportive environment or a sense of community plays a vital part in

individuals recovering from an injury or assault (Bahramnezhad et al., 2017; Stevens et

al., 2019). Considering the challenges individuals endure in the disease recovery process,

Stevens et l. (2019) conducted a cross-sectional study using a sample of 229 individuals

from three Oxford houses, including democratic, self-running recovery homes, and the

largest network of self-recovery homes in the United States, to identify the role of hope

and community in supporting recovery. Stevens et al.’s (2019) study included a 55% male

and 44.5% female population with the mean age of 38.4 years from multiple ethnic

groups. Stevens et al. used Snyder’s State Hope Scale to measure hope, the Psychological

Sense of Community Scale to measure sense of community, and the World Health

Organization Quality of Life Assessment Brief Version to measure the QOL. Stevens et

al. (2019) assessed whether hope and sense of community predicted the QOL for

populations living in recovery homes, and the findings revealed that both hope and sense

of community were primary predictors of QOL, suggesting they can aid significantly in

the recovery process. However, age, race, and length of stay did not impact the QOL and

hope during recovery (Stevens et al., 2019). There is a strong relationship between all

aspects of social networks and QOL. While hopefulness is often conceptualized as an

individual-level factor, Stevens et al.’s (2019) findings emphasized that hope can

originate from a sense of community, and both factors can lead to improved QOL, thus

aiding in recovery. Stevens et al. (2019) recommended that hope and a sense of

community be encouraged to improve QOL and recovery in patients with challenging

conditions. Another study indicated that creating an environment to promote and improve

relationships among the elderly and other social networks of family, friends, and

neighbors has shown to lead to an improved QOL in the elderly (Bahramnezhad et al.,

2017). Based on the previous researches, the current study may indicate improvement in

the physical, emotional, and psychological aspects of QOL in the elderly through hope-

promoting interventions.

Hope Interventions

Hope intervention is a valued phenomenon that has the power to bring positive

outcomes to the lives of humans during a crisis. Hope is embraced as a protective

experience for people in challenging conditions. Studies indicate that hope-fostering

strategies in the face of difficult situations have been shown to help individuals in

sustaining, continuing, and enduring life events. Several studies have been conducted to

evaluate the effects of hope intervention in various situations (Zareei Mahmoodabadi et

al., 2019).

Herth (2001) conducted a study to develop and evaluate the Hope Intervention

Program (HIP) based on Hope Process Framework. The study was conducted on a

convenience sample of 38 adults with the first recurrence of cancer who were going

through cancer treatment. The main research variable was to assess the helpfulness of

HIP components in maintaining hope. The HIP was composed of eight sessions to

address four attributes of hope derived from the Hope Process Framework: the

experiential process, relational process, spiritual or transcendent process, and rational

thought process. The HIP was administered over the course of eight weeks, where

participants met as a group for a 2-hour session each week. The entire study lasted for

more than 18 months. Study participants were evaluated at the end of the study, and then

at three, six, and nine-month intervals after the last session. Study results indicated that

HIP had a positive impact on participants in rebuilding and maintaining a hopeful outlook

towards life (Herth, 2001).

The elderly phase is a period that can greatly and adversely impact the QOL

because the aging process leads to physical and mental changes that can result in

diminished self-confidence and uncertainty, thus necessitating effective hope

interventions. Zareei Mahmoodabadi et al. (2019) conducted a study using a pre-test and

post-test design to assess the effectiveness of a hope therapy program. The sample

included 24 elderly women in daily care centers with participants divided into two

groups: the experimental group (n=12) and the awaiting group (n=12). The hope therapy

program consisted of eight sessions for the experimental group, and they used the Quality

of Life Scale to measure the impact of the therapy on the experimental group (Zareei

Mahmoodabadi et al., 2019). The researchers compared the hope levels between the

experimental and awaiting groups and found a significant difference between the

populations. The multivariate analysis of variance (MANOVA) analysis used by the

researchers indicated that the depression, anxiety, physical function, mental performance,

and satisfaction in life of those in the experimental group improved significantly after

participating in the hope therapy program. Zareei Mahmoodabadi et al.’s (2019) results

indicated that hope therapy can be an effective intervention for improving the QOL in

elderly populations. The study also showed that elderly individuals can benefit from hope

therapy. Daily care centers should consider instituting hope therapy routinely to improve

QOL outcomes for elderly populations (Zareei Mahmoodabadi et al., 2019).

Hernandez and Overholser (2021) observed that there were limited studies on

interventions targeting hopelessness/hope and that most studies targeted associated

constructs like sociality and depression and entail secondary measures of

hopelessness/hope. They conducted a systematic review of hope/hopefulness

interventions for older adults by reviewing existing literature to evaluate the effectiveness

of these interventions. They evaluated 36 studies and concluded that psychological

interventions dependent on life review effectively enhanced hope in various samples

including grieving, depressed, or medically ill patients. Findings also revealed limited

support for exercise programs, education interventions for the ill, exercise for the elderly,

and dignity therapy for patients. Life review-based interventions included self-directed

expression exercises or therapeutic discussions to assist individuals envision their life

from a longitudinal aspect in order to promote hopeful and purposeful meaning in life

(Hernandez & Overholser, 2021).

Knowing that a terminal or serious disease diagnosis can negatively impact the

affected populations significantly, Salamanca-Balen et al. (2021) conducted a systematic

review of the literature on the effectiveness of hope-fostering interventions in palliative

care. Salamanca-Balen et al. (2021) used the Cochrane criteria to assess for bias in the

studies they reviewed. Their review of the literature showed that hope interventions

resulted in increasing hope levels, decreased depression levels, and improved outcomes

in the psycho-spiritual well-being in palliative care patients affected by chronic illnesses.

However, hope did not enhance QOL for palliative care patients (Salamanca-Balen et al.,

2021). While Salamanca-Balenet et al.’s (2021) study highlighted the impact of hope on

improved outcomes in palliative care patients, further research is needed to confirm the

results, especially in elderly populations.

Hope and the Elderly

Hope has also been linked to resilience, which can help elderly individuals cope

with the loneliness of living alone. Gupta and Singh (2020) conducted a correlational

study involving purposive sampling to examine the relationship between resilience and

hope in the elderly. The sample included 151 elderly individuals aged 60 to 80.

Researchers used the Connor-Davidson Resilience Scale and Adult Hope Scale to

measure levels of resilience and hope, respectively. The data were analyzed by T-test,

ANOVA, and the Pearson correlation coefficient (Gupta & Singh, 2020). These

researchers found a moderate positive correlation (r= 0.741) between hope and resilience

in the elderly, suggesting that hope can assist older people to meet adverse challenges.

This study also revealed that loneliness played a significant part and was found to be

positively correlated with depression and anxiety and negatively correlated with self-

efficacy, resilience, and psychological and physical health. High resilience, a way of

adapting well to adverse and uncertain circumstances, was strongly associated with

positive outcomes including lower depression levels and increased longevity. The

researchers also found that age, marital status, living standards, the number of family

members and their occupations, among other basic factors, were associated with

resilience and hope; however, gender did not impact resilience and hope. Older people

living alone should be moved to environments where they can interact or have

opportunities to interact with others to help enhance hope and resilience. The study

showed that hope and resilience were related in the elderly population, and resilience

could lead to improved QOL (Gupta & Singh, 2020).

To examine the subjective well-being and hope in the elderly, Gupta and Singh

(2019) conducted a correlational study comprised of 151 elderly individuals 60–80 years

of age. The study was designed to compare the well-being and hope levels of

institutionalized and non-institutionalized elderly individuals. The population was chosen

for the study iterate to a mean age of 70.83. Random sampling led to 79 participants from

two nursing homes in Kanpur City, and 72 participants were randomly sampled from

older people living with their families (Gupta & Singh, 2019). Gupta and Singh used the

subjective well-being tool (1992) to measure participants’ well-being or ill-being and they

used the Adult Hope Scale to measure hope levels. Study results revealed that

institutionalized elderly had better overall subjective well-being and scored high for hope

status compared to non-institutionalized individuals (Gupta & Singh, 2019). The results

showed that there was a significant difference in the well-being status between these two

populations. The study also revealed that the mean value of ill-being for non-

institutionalized individuals was higher than that of institutionalized individuals. The

non-institutionalized individuals had limited support from their family members, unlike

institutionalized elderly populations who were scheduled for therapy sessions in their

centers. The researchers concluded that the limited opportunities for interactions for

elderly individuals could result in psychological problems, loss of hope, and induced ill-

being that deters well-being. The institutionalized elderly showed a significant difference

in their levels of hope outcomes, indicating that their environment encouraged wellness-

promoting hope and general well-being. These results indicated the need for family

members to support and comfort the elderly to enhance their hope and improve their

well-being. Aged populations need care and attention for positive psychological

settlement that eventually results in their well-being (Gupta & Singh, 2019).

Effects of Hope-Fostering Strategies in Seniors Who Live Alone

There are several methods for fostering hope in seniors such as leaving a legacy,

accomplishing short-term objectives, supporting relatives and friends, turning off your

mind, signs of hope, bright ideas, truthful information from healthcare providers, and

symptom management, among others (Salamanca-Balen et al., 2021). Older individuals

may need different ways of sustaining and nurturing optimism than younger ones. In

addition, it is easy for hope to have a long-lasting impact on the health and well-being of

people throughout their lives. It is a critical psychological resource that assists people

through a range of challenging situations in their lives.

Furthermore, hope has been recognized as a critical component in developing a

feeling of meaning and purpose in one’s life and the improvement of overall QOL in

older people (Salamanca-Balen et al., 2021). As people get older, they often face

cognitive and emotional difficulties and losses in their lives. These difficulties and

failures resulted in their inclusion in assisted care facilities for some of these adults. As a

consequence of moving into long-term care, older people may experience additional

losses, increasing their susceptibility to despair, sickness, and loss of hope. As a result,

finding methods to instill hope in the hearts of senior people is critical in maintaining and

ensuring health and wellness (Salamanca-Balen et al., 2021).

Summary

The literature review for this research revealed a notable association between

hope and QOL. The QOL of the elderly has become a subject of increased research due to

the rising risk of becoming public health threat. Studies indicate that QOL in the elderly

was intertwined with their perceived level of well-being; hence, it was important to

provide them with all the resources needed to live a long, quality life. Hope is a vital

force that affects QOL in the elderly and plays an important part in coping and healing.

Several studies reveal the importance of hope-promoting strategies in improving QOL.

The literature review indicates a gap in the knowledge surrounding the effects of hope on

QOL in the senior population. The current study was designed to examine whether there

was improvement in the physical, emotional, and psychological aspects of QOL in the

elderly with a mixed-gender sample after hope therapy.

Conceptual Definitions

Senior People

The senior population is defined as people aged 65 and over (Organization for

Economic Co-operation and Development, 2021; Wayadande & Prabhakar, 2020). For

this research, senior people were defined as those who are 65 years of age and older.

Living Alone

For the purpose of the current study, living alone as seniors who lived in their own

residences, predominantly by themselves.

Quality of Life

Nursing in gerontology and geriatrics ranks QOL as one of the most essential

indicators (Soósová, 2016). The WHO defined QOL as “an individual's perception of

their position in life in the context of the culture and value systems in which they live and

in relation to their goals, expectations, standards and concerns” (Whoqol Group, 1994, p.

11). WHO defined QOL in a broader sense that included physical, mental health, social

relationship, level of independence, and personal beliefs (Wayadande & Prabhakar,

2020). Quality of life is the degree to which an individual is healthy and independent and

can participate actively in life events. The current study administered the Older People’s

Quality of Life Questionnaire (OPQOL) scale before and after the interventions to assess

and measure QOL in study participants.

Hope

According to Webster’s Dictionary, hope is a concept of the future: “To desire

with expectation of obtaining fulfillment” (Merriam-Webster, n.d.a). The definition of

hope, according to the Oxford English Dictionary, is “grounds for believing that

something good may happen.” (Gwinn & Hellman, 2019, p. 8.). In current study, hope

was measured by the Herth Hope Index (HHI).

Hope Intervention

According to Webster’s Dictionary, intervention is defined as an action taken to

affect an outcome or improve a situation (Merriam-Webster, n.d.b). Hope intervention is

described as hope-seeking and hope-inspiring strategies that foster hope in individuals

(Herth, 2001). This study utilized the eight-week Hope Intervention Program (HIP)

developed by Dr. K. Herth to enhance hope and QOL in seniors who lived alone. After

implementation of the eight-week program, the hope and the QOL levels were measured

using the HHI scale.

Theoretical Framework

The theoretical framework helps understand the association between interventions

and their effect on people’s overall well-being. Theoretical assertions describe the

relationships that exist among the main concepts of the theoretical model. They aid in

developing the appropriate plans and interventions for practice and they also assist in

evaluating the effectiveness of the interventions for further applications (Butts & Rich,

2015). Betty Neuman’s systems model (1972) was used as the theoretical framework for

this project (Alligood, 2018).

Betty Neuman’s Systems Model

Betty Neuman, a nursing theorist, unfolded Neuman’s systems model (1972).

Systems theories such as Betty Neuman’s systems model (1972) are grounded on the

premise that individuals consist of systems that are connected to and influenced by one

another. Two assumptions of this theory are that energy is required to maintain the

harmony in an organizational state, and any dysfunction in one system has an influence

on other systems (Butts & Rich, 2015). The wholistic nature of the model allows for a

wide range of creativity and has been used in a wide variety of healthcare settings.

Neuman’s systems model’s approach to understanding the nature of system

stability and how to prevent system damage made this model an appropriate framework

for the current project. Because of its premises, Betty Neuman’s systems model (1972)

was chosen to guide this study.

Betty Neuman proposed that the person should be treated as a whole system.

Neuman’s systems model consists of four metaparadigm concepts: person, health,

environment, and nursing. This model focuses on the human needs for protection or relief

from stress (de Almeida et al., 2018).

Person

Neuman’s model views clients as a whole person with many parts that are in

dynamic interaction with one another (Figure 1). The systems model indicated that five

variables—physiological, spiritual, psychological, socio- cultural, and developmental—

all simultaneously affect the client system (Alligood, 2018; Butts & Rich, 2015).

Figure 1. Neuman’s systems model: Nursing metaparadigm concepts.

The physiological aspect of care pertains to the physical body structure, the socio-

cultural reflects the influence of social and cultural components, the psychological refers

to mental interaction with the environment, the spiritual factor pertains to the influence of

spiritual beliefs, and the developmental component refers to age-related processes and

activities (Ahmadi & Sadeghi, 2017). Because this model focuses on retraining wellness

and ensuring optimal wellness, Neuman classified her systems model as a wellness model

(Alligood, 2018; Butts & Rich 2015).

The purpose and the contents of the HIP in the current study was to provide

interventions, keeping in mind the wholistic nature of an individual. The project manager

implemented interventions through HIP that included social, relational, spiritual, and

rational processes to address any impact of living alone on hope levels and QOL in order

to promote wellness in these areas.

Environment

The Neuman (1972) systems model holds that human beings are open systems

that interact with environmental stressors (see Figure1; Alligood, 2018). The environment

is defined as external, created, or internal factors that can affect the system. The internal

environments are found within the system; created environments are developed

unconsciously, while external environments are developed outside the system (Alligood,

2018; Neuman & Fawcett, 2012). The clients are in a relationship with their environment

where they interact with their environment by adjusting to the environment or by

adjusting the environment to themselves (Braga et al., 2018; Cunha et al., 2019).

All factors, whether they are possible or actual responses to stressors, affect

individuals. Stress can be termed as augmenting, inescapable, and painful; it is generally

considered an intuitive state of mind that can be uncontrolled. Neuman’s model focuses

on individuals’ relationship with stress, how to handle it, and its reconstitution (de

Almeida et al., 2018). The major challenge is seeking a solution to eliminate or minimize

the effects of stressors in the system. Interventions should be aimed at maintaining

stability between the environment and the variables of the clients (Alligood, 2018).

Neuman described that adjustment is a process by which individuals meet and satisfy

their needs. Since many needs are present that can affect stability and balance, the

adjustment process is dynamic and continuous.

Aging poses greater challenges and living alone can contribute to the exacerbation

of underlying issues affecting the level of hope, outlook towards life, and the QOL in

seniors. Stigmatization and isolation are external factors that trigger stressors in senior

people. These stressors can further lead to created and internalized environments of

loneliness, which can then affect their level of hope and QOL and pose serious health

risks. This study proposed to examine the effects of HIP that can assist in this process of

adjustment. The HIP interventions to establish a caring and supportive environment and

build a sense of community through interventions addressed some of these challenges of

improving the level of hope and QOL in senior population.

Health

Neuman considered her system theory as a wellness model. She defined health as

energy that yields the highest quality system stability at any point. Neuman perceived

health as a continuum or scale of wellness to sickness, which is dynamic and always

subject to change. According to the model, there is optimal wellness (negentropy) when

the system’s needs are met; when they are not met, there is sickness, and when there is

insufficient energy to support life, then death results (entropy; Alligood, 2018).

Every participant in the study had a unique system. If adaptation to the changes

and challenges that came as a result of living alone were not stable, they could experience

a breach in the continuum of the system leading to less-than-desired levels of wellness.

Through HIP, the project manager promoted the individual’s system stabilization through

acquirement, retention, and maintenance of optimal wholeness and wellness in the

participants (de Almeida et al., 2018). Through HIP, the sense of hope level and QOL

which pertain to the health and wellness spectrum may be improved and maintained in

addition to stabilization of the wellness system.

Nursing

According to Neuman’s philosophy, nursing is a unique profession that is

concerned with all the factors affecting the individual and thus believes in the importance

of providing the wholistic approach (Akhlaghi et al., 2021). In this model, the nurse is

considered a key participant with the client; the nurse is interested in assessing how the

clients are affected by environmental stressors such as living alone, as in this study. The

seniors adjust to living alone by accepting their state of decreased mobility and loss of a

loved one, among other factors. The nursing interventions link four main concepts of this

model: people, environment, nursing, and health (Braga et al., 2018; Cunha et al., 2019;

see Figure 1). Nurses play an important role in creating a relationship among individuals,

health, and the environment to prepare individuals to adapt to the life changes in order to

improve and maintain the QOL (Akhlaghi et al., 2021).

Lines of Defense

In her system-based model, Betty Neuman described other components: stress,

systemic feedback loop, and lines of defenses. Neuman also emphasized that the lines of

defense are protective mechanisms that reflect how the individual or system adapts after

adjusting to the stressors. These lines of defense include the flexible line of defense,

normal line of defense, and lines of resistance (Alligood, 2018).

Flexible Line of Defense

The flexible line of defense is the outer layer, and it is the protective mechanism

that surrounds and protects the normal line of defense from invasion by various stressors.

It is dynamic and can be quickly changed in response to these stressors. The relationship

of the physiological, spiritual, psychological, socio-cultural, and developmental variables

can affect an individual’s response to the stressors. It is important that the flexible line of

defense be strengthened to prevent possible future damage (Alligood, 2018; McEwen &

Wills, 2014).

The Normal Line of Defense

This is the model’s middle layer. It reflects a stable state that the individual or

system adapts to after the adjustment to stressors. This stability is the result of an

individual’s lifestyle, coping behaviors, and developmental stage. It becomes a standard

for the wellness-deviance determination of clients in the system (Alligood, 2018;

McEwen & Wills, 2014).

Lines of Resistance

This is the innermost layer that represents protective mechanisms that are

activated when there is a penetration of the normal line of defense due to stressors in the

system such as the immune system response to the stressors (Alligood, 2018). According

to Neuman (as cited in Alligood), this state symbolizes a movement to an illness on a

wellness-illness continuum. However, when there is sufficient energy, the system can be

reclaimed by restoring the regular defense line below, at, or above its initial level

(Alligood, 2018).

Neuman emphasized that in order to deal with these stressors, different

interventions at different levels, such as primary, secondary, and tertiary interventions, are

needed. The primary intervention occurs before the system is invaded, the secondary

intervention occurs after the system has been invaded, while the tertiary intervention

occurs during the reclamation process (de Almeida et al., 2018).

The stressors that are mainly found in senior people could range between fear,

worry, anxiety, and frustration. In addition, a variation in the organic system could also be

a source of intrapersonal stressors. Interpersonal stressors also occur between individuals

due to various reasons causing relationships strain (de Almeida et al., 2018). According to

Neuman, extra personal stressors occur due to external environmental factors which are

often found outside the client’s boundaries at the proximal range, such as living alone in

this study (Alligood, 2018).

Nurses are active contributors in prevention strategy since it is considered one of

the most important interventions in protecting the individual while providing care. In this

study, primary, secondary, and tertiary interventions through HIP were aimed at

equipping the seniors with strategies to tackle the stressors that attempted to attack their

lines of defense. Nurses play a pivotal role in taking care of the senior people in society,

especially those who are isolated and do not have family or social support. It is important,

therefore, for nurses to determine how stressors affect the systems of seniors, ultimately

affecting their QOL. The Newman systems model has a broad application in current and

future nursing practice. The use of this model by nurses and practitioners can provide

wholistic, unified, and goal-oriented client care (Gale, 2020).

CHAPTER 3

METHODOLOGY

This research was a two-group experimental study conducted with pre- and post-

test interventions. The study participants included the vulnerable population; therefore,

every precaution was taken to keep the participants safe during the study.

PICOT Question

The literature review shows the adverse effects of living alone on senior peoples’

overall well-being. Studies indicate that these detrimental effects can be eliminated or

minimized through strategies that promote a sense of well-being in this population. Hope-

fostering interventions and programs aim to promote overall well-being in a variety of

populations and also various settings such as educational centers, therapy, counseling,

recreational organizations, and correctional facilities. Studies reveal that hope-fostering

interventions enhance psychosocial outcomes and reduce depression. These interventions

are aimed at addressing participants’ strengths and the strengths they hope to develop

more (Larsen et al., 2015; Leontopoulou, 2020). Based on the previous literature

findings, the current study PICOT question was developed.

The guiding PICOT question for this study was as follows: In seniors 65 years

and older who live alone, how does the implementation of eight-weeks hope intervention

compared to seniors 65 years and older who live alone and do not receive the hope

intervention affect the level of hope and quality of life?

Hope Intervention Program

The HIP that the current study implemented was developed by Dr. K. Herth in

2001. The HIP is composed of eight sessions to address four attributes of hope derived

from the Hope Process Framework: experiential process, relational process, rational

thought process, and spiritual or transcendent process. The HIP was administered over the

course of eight weeks where the participants met as a group for a 2-hour session each

week (Herth, 2001; see Appendix A). The literature suggests that the length of time must

be sufficient (e.g., 7–10 weeks) for the study to be effective and bring changes in the

participants which have been shown to continue for a longer time afterward (Herth,

2001). The current study administered this program just as it was administered, over an

eight-week period, and evaluated the effects of HIP on the level of hope and QOL in

seniors who lived alone.

Project Design

In the current scholarly project for HIP and its effect on the level of hope and

QOL in senior people who live alone, a two-group experimental study design was

applied. An experimental design was applicable because the participants were randomly

assigned to experimental and control groups (Bordens & Abbott, 2008). All qualified

participants (see Appendix B) completed Herth Hope Index (HHI) and OPQOL scale

both at the inception and again at the end of the eight-week HIP, which was the

independent variable applied in order to assess the measure the of level of hope and QOL,

the dependent variables. The HIP was then implemented in the form of in-person group

therapy for those who were in the experimental group; during this time, they participated

in weekly hope-fostering activities through discussion and hope-engendering exercises.

The final phase of the study constituted a post-test to all participants to examine the

participants’ level of hope and a post-test to re-examine their QOL at the conclusion of

the interventions.

Recruitment

For this project, participants were recruited from the local community. Flyers for

the project were distributed in local community senior centers, health and wellness

clinics, churches, grocery stores, restaurants, and Public Health Department. Prior to the

participants’ consent, the project manager conducted an informational meeting to

introduce the project with the invitation to join the study.

Population and Sample

Population

The population of interest corresponded to elderly participants who lived alone.

The sample consisted of elderly participants selected from the general community in

Southwest Michigan.

Inclusion Criteria Included

a. Participants 65 years or older

b. Participants who lived alone in their residences

c. Participants who spoke English and were able to read and write English

d. Participants who scored 0-2 on Eastern Clinical Oncology Group (ECOG)

performance status screening (see Appendix C)

Exclusion Criteria Included

a. Participants who were bedbound

b. Participants who had any mental illness

Sample/Power Analysis

The study design was developed through consultation with a statistician. A power

analysis was conducted in G*Power 3.1.9.7 to determine the minimum sample size

requirement (Faul et al., 2014). By applying the use of a mixed model ANOVA and

utilizing a medium effect size (f = 0.25), a two-group, a pre-test-post-test comparison, a

significance level of .05, and a power of .80, it was determined that a minimum of 34

participants would be sufficient for the data collection (approximately 17 participants in

each group), as seen in Figure 2.

Figure 2. Power analysis for mixed model ANOVA.

Instruments, Interventions, Procedure, and Data Collection

Instruments

The current study utilized two instruments: The HHI and the OPQOL to measure

hope and QOL in the selected population.

Herth Hope Index

The HHI (see Appendix D) was developed to assess hope in adults in clinical

settings (Herth, 1992). The HHI consists of 12 survey items using a four-point Likert

scale ranging from (1 = Strongly Disagree to 4 = Strongly Agree). Permission was

granted to administer the HHI from the original author, Dr. Kaye Herth (see Appendix E).

The HHI has been translated into a variety of languages. Construct validity has been

established with the HHI through the use of an exploratory factor analysis (EFA) with a

varimax rotation. The results of the EFA identified a three-factor solution with

approximately 61% of the variance being explained by the factors (Herth, 1992). The

three factors correspond to an individual’s perception of present and future, inner

optimistic readiness, and interconnectedness with self and others. Reliability of the scales

was established with the Cronbach’s alpha test of internal consistency. Cronbach’s alpha

coefficients for the three scales ranged from .78 to .86. For the purpose of this research,

the overall scale for hope was utilized. Scoring consisted of adding the points for the total

scale. Hope was a measurable, continuous variable with possible scores ranging from 12-

48 points. The higher score indicates a higher level of hope (Herth, 1992).

Older People’s Quality of Life

Questionnaire Scale

The OPQOL (see Appendix F) is a 35-item survey designed to measure older

populations’ QOL. The OPQOL utilizes a five-point Likert scale ranging from 1 (strongly

disagree) to 5 (strongly agree). The survey has established both acceptable reliability and

validity. Cronbach’s alpha test of internal consistency met the acceptable threshold,

ranging between .70 and .90 for multiple samples (Bowling, 2009). Convergent validity

was established with significant correlations between age, marital status, and QOL. A

principal component analysis (PCA) revealed approximately 60.58% of the variance in

QOL could be explained by a nine-factor structure (Bowling, 2009). The QOL variable

was computed through a composite score of the respective items. Quality of life was a

measurable, continuous variable with possible scores ranging from 35-175 points. The

higher score reveals higher level of QOL.

Interventions

Group intervention for the experimental group consisted of an 8-session hope

intervention protocol adapted from the HIP by Dr. Kaye Herth. The program took place

over an eight-week period. Each session was conducted over a 2-hour period. Each

session content, activities, and exercises were as follows (Herth, 2001; see Appendix A

for more details):

• Session 1: (a) Overview of sessions and (b) getting acquainted exercises.

• Session 2: (a) Discuss the meaning of hope and hope as active waiting, (b)

discuss the dialectic relationship between hope and hopelessness, and (c) discuss threats

to hope.

• Session 3: (a) Develop a hope mantel and (b) begin a hope journal, tape, or

drawing to chronicle one’s hope journey.

• Session 4: (a) Discuss the reciprocal/interdependent nature of hope, (b)

discuss the role of family and friends in the hope journey, (c) discuss community

resources, (d) develop a list of one’s hopelets, and (e) develop a hope energy-savers

basket.

• Session 5: (a) Discuss and implement value clarification exercises (focus on

values thought important and usual sources of strength), (b) discuss and share possible

spiritual resources, (c) implement life awareness activities, (d) develop a joy collage, and

(e) discuss photos/pictures that represent hope.

• Session 6: (a) Discuss reality surveillance and goal setting as it impacts hope,

(b) discuss success mapping, (c) develop a hope kit, (d) discuss and practice a variety of

cognitive reframing strategies, and (e) discuss the role of past memories on hope.

• Session 7: (a) Begin a hope memories book, (b) discuss the role of nature in

hope, and (c) discuss the value of lightheartedness and how to engender more in one’s

life (e.g., funny bone exercises).

• Session 8: (a) Share current and potential future use of the hope mantel and

the hope journal, tape, or drawing; (b) discuss a hope engendering and maintenance plan;

program evaluation tool.

Procedure and Data Collection

The procedure for the study was as follows. After the successful proposal defense,

Andrews University Institutional Review Board (IRB) approval was obtained (see

Appendix G). Invitation flyers (see Appendix H) for the study were placed in the local

community senior centers, wellness-clinics, grocery stores, restaurants, and Public Health

Department for 4 weeks prior to the beginning of the study. The purpose of the flyer was

to advertise the opportunity of participating in the research study and briefly introduce

the study focus. The invitation flyers consisted of project manager’s contact information

including mobile phone number. Interested participants were asked to contact the project

manager by calling the phone number provided on the flyer.

Two weeks before the implementation of the program, a face-to-face interview

with participants was conducted at the local wellness clinic. These local clinics were

chosen because of their proximity to the center of town. Strict clinic guidelines according

to the Health Department were followed during each meeting.

During the initial contact, informed consent (See Appendix I) was obtained after

explaining the study and answering participants’ questions. The consent form outlined the

purpose of the study, inclusion and exclusion criteria, and the estimated time frame for

completion of the study. The participants provided consent to continue with the survey

process.

Upon providing consent, the participants were directed to the screening for the

inclusions criteria of the study. The participants who did not meet the inclusion criteria

were removed from the study. Qualifying participants were then asked to respond to a

demographic survey (See Appendix J). Following the demographic survey, the HHI

survey and OPQOL were administered as pre-tests for the baseline. Assistance was

provided as needed to answer and appropriately complete the survey if the participants

did not understand the questions.

A random identifier was assigned to each participant. Using the Excel command,

RANDBETWEEN, the participants were then randomly assigned to experimental (coded

1) and control groups (coded 0). After the last session of the hope intervention in Week 8,

the project manager administered the HHI and OPQOL surveys as post-tests to both

experimental and random groups via pen and paper. The post-test responses were

matched to the pre-test through the random identifier assigned to each participant.

Finally, a token of a $25.00 gift card was given to each participant who completed the

study.

Data Analysis Plan

The data were uploaded into the SPSS version 27.0 for Windows. Participants

who did not respond to the full HHI and OPQOL questionnaire were removed from

further analysis. Frequencies and percentages were used to summarize the demographic

variables such as gender and ethnicity. A series of chi-square tests of independence were

used to assess for differences in the demographic distribution based on random

assignment to treatment and control groups. A chi-square analysis is used when the

strength of the relationship between two nominal-level variables are tested (Pallant,

2020). In addition, percentages and frequencies were examined for the most and least

helpful intervention activities. Means and standard deviations were used to examine the

trends of the continuous-level variables such as the HHI and OPQOL scales. Cronbach’s

alpha test of internal consistency and reliability were reevaluated for the hope and QOL

scales. The strength of the alpha values was interpreted through the use of George and

Mallery’s (2020) guidelines, in which α < .5 Unacceptable, α > .5 Poor, α > .6

Questionable, α > .7 Acceptable, α > .8 Good, and α > .9 Excellent.

Inferential analyses were used for the research. In order to address the PICOT

question, two independent sample t-tests were first conducted to analyze for baseline

differences in level of hope and QOL between the experimental and control groups. Two

additional independent sample t-tests were conducted to analyze for post-test differences

in level of hope and QOL between the experimental and control groups. Two mixed

model ANOVAs were conducted to assess for differences in hope and QOL before and

after the intervention and between the experimental and control groups. A mixed model

ANOVA is appropriate when testing for differences in a continuous variable over time

and between groups (Pallant, 2020). Before analyzing the data, the assumption of

normality was tested with Shapiro-Wilk tests. Homogeneity of variance was tested with

Levene’s test. The non-parametric Wilcoxon-Signed Rank tests were intended to be

conducted to test for differences in hope and QOL between pre-test and post-test if the

assumption of normality was not met.

The mixed model ANOVA examined three effects: the within-effect, the

between-effect, and the interaction effect. The within-effect tested for differences

between pre-test and post-test. The between-effect tested for differences between

experimental and control groups. The interaction effect tested for differences over time

and between groups, simultaneously. Statistical significance was evaluated at the

generally accepted level, α = .05.

Ancillary Analysis

A Pearson correlation was conducted to assess the strength of the relationship

between hope and QOL. The Pearson correlation was an ancillary test because the

analysis was a separate examination from the PICOT question and the mixed model

ANOVA. A Pearson correlation is appropriate when testing the strength of the association

between two continuous-level variables (Pallant, 2020). Prior to analysis, the assumptions

of normality and linearity were tested on the data. Linearity was visually assessed with a

scatterplot between hope and QOL. If the assumptions were not supported, a non-

parametric Spearman correlation was planned to be conducted as an alternative.

The Pearson or Spearman correlation coefficients can range from 0 (no

relationship) to +1 (perfect positive linear relationship) or -1 (perfect negative linear

relationship). Positive correlation coefficients reveal a direct association, meaning that as

one variable increases, the other variable also shows improvement. Negative correlation

coefficients identify an inverse relationship: as one variable increases, the other variable

decreases. Cohen’s standard (Cohen, 1988) was used to evaluate the correlation

coefficient to determine the strength of the relationship where coefficients above .50

represent a large association; coefficients between .30 and .49 represent a medium

association; and .10 and .29 represent a small association.

Confidentiality

Confidentiality was ensured by randomly identifying the participants without

using their personal identifying information. The study assigned a random identifier to

each participant through the use of the Excel command, RANDBETWEEN, to administer

the surveys and collect the data. All the information was placed in a secure place

accessible only with a secure password by the project manager. The data will be kept for

5 years and then will be destroyed from both computer and external hard drives.

Project Timeline

Table 1 below outlines the timeline of the project from its commencement to its

end.

Table 1

Project Timeline

Timeline Dates

Events

April 4th-April 21st, 2022

Recruiting process. Demographic, ECOG surveys/Pre-

tests (HHI & OPQOL) data collection.

April 25th-April 28th, 2022

Session 1: Building a sense of community. Introduction

to the program, getting acquainted with HIP exercises.

May 3rd-May 4th, 2022

Session 2: Searching for hope/experimental process.

Discuss meaning of hope and identify areas of hope in

life.

May 9th-May 11th, 2022

Session 3: Searching for hope/discuss threats to hope.

Develop a hope mantel and begin a hope journal (see

Appendix A).

May 16th-May 18th, 2022

Session 4: Connecting with others/relational process.

Discuss the role of family & friends. Discuss community

resources.

May 23rd-May 25th, 2022

Session 5: Expanding the boundaries.

Spiritual/transcendent process. Discuss value

clarification exercises. Share possible spiritual resources.

Develop joy collage.

May 30th-June 1st, 2022

Session 6: Building the hopeful veneer/rational thought

process. Discuss cognitive reframing strategies, role of

hope in past memories. Develop hope kit.

June 7th-June 8th, 2022

Session 7: Building the hopeful veneer. Discuss the role

of nature and lightheartedness

June 13th-June 15th, 2022

Session 8: Reflecting and evaluating. Post-test data

collection. Project evaluation. Gift-card given with

thanks.

August-November, 2022

Analysis and write up

CHAPTER 4

RESULTS

The purpose of this project was to evaluate the effects of a non-pharmacological

intervention, in this case, the utilization of hope interventions on hope and on the QOL in

senior people who live alone. In this chapter, the findings of the data analyses are

presented. Frequencies and percentages were used to describe trends in the demographic

data. Cronbach’s alpha test of reliability was used to evaluate the internal consistency of

the measures. To address the PICOT question, two mixed model ANOVAs were

conducted to evaluate for differences in level of hope and QOL following the

intervention. Ancillary analyses were conducted to test for the relationship between level

of hope and QOL.

Data Collection

After receiving approval from Andrews University IRB, a total of 200 project

invitation flyers (see Appendix H) for the study were sent out and placed in the local

community senior centers, wellness-clinics, grocery stores, restaurants, and Public Health

Department for 4 weeks prior to the beginning of the study. A total of 43 individuals from

the general community in Southwest Michigan approached the project manager to learn

more about the research project and to participate in the study. Nine individuals declined

to take part in the study after learning the length of the program. Thirty-four participants

decided to enroll in the project and signed the informed consent.

During the initial screening, three participants were excluded from the study. Of

those three participants, two individuals did not meet the criteria for living alone. One

participant was 64 years old, which was lower than the inclusion criteria for being at least

65 years old. A total of 31 participants completed the demographic and pre-test HHI and

OPQOL surveys using the pen and paper. The Excel command, RANDBETWEEN, was

used to randomly assign participants to the experimental or control groups. The

RANDBETWEEN function works by randomly selecting an integer between two

numbers. Using this command, study adults were randomly assigned to experimental

(coded 1) and control groups (coded 0). After pre-test data collection, participants in the

control group were asked to return after 8 weeks to complete the post-test. I recorded the

pre-test survey scores electronically on the Excel spreadsheet.

The experimental group consisted of 16 participants and the control group

consisted of 15. However, three individuals from the experimental group did not

complete the intervention in its entirety and only attended part of the program. Two

additional participants dropped out of the intervention program due to illness. Thus, these

participants were excluded from the final data analysis. Three participants from the

control group did not return to complete the post-test, so they were excluded from the

final data analysis (see Figure 3).

Figure 3. Participant flow chart.

Data Analysis

Frequencies and percentages were used for the nominal variables. Chi-square tests

were conducted to examine the strength of the relationships of the nominal-level

variables by experimental and control groups. There were no statistically significant

differences in the demographic variables between the two groups. The age variable was

not listed because the participants were recruited based on whether participants’ age was

Individuals Approached n=43

Excluded

n=9 declined to participate

n-2 did not live alone n=1 64 years old

Randomized n=31

Control group

n=15 randomized

Excluded n=3 no post-survey

Analyzed n=12

Experimental group

n=16 randomized

Excluded n=5

(2 dropped out & 3 did not attend the

HIP 100%)

Analyzed n=11

65 or higher or not. The finding of the chi-square test between ECOG and group was

statistically significant, χ2(2) = 7.11, p = .029. Table 2 presents the findings of the cross-

tabulations. Figure 4 provides details of participants in both groups for ECOG.

Table 2

Cross-Tabulation between Nominal Variables and Group

Group

Variable

Experimental

(n = 11)

Control

(n = 12)

χ2

Gender

0.96

.328

Male

0 (0.00%)

1 (8.33%)

Female

11 (100.00%)

11 (91.67%)

Ethnicity

0.01 .949

Black or African American

1 (9.09%)

1 (8.33%)

White

10 (90.91%)

11 (91.67%)

Marital Status

1.29 .524

Single

1 (9.09%)

0 (0.00%)

Divorced

5 (45.45%)

5 (41.67%)

Widowed

5 (45.45%)

7 (58.33%)

Educational Level

1.05 .592

High school graduate

6 (54.55%)

5 (41.67%)

College graduate

4 (36.36%)

4 (33.33%)

Graduate

1 (9.09%)

3 (25.00%)

ECOG

7.11

.029

(0) Fully active

3 (27.27%)

9 (75.00%)

(1) Restricted in physical

strenuous activity

4 (36.36%) 3 (25.00%)

(2) Able to ambulate &

self-care but unable to

perform work activities

4 (36.36%) 0 (0.00%)

Figure 4. Number of participants in both groups for ECOG Scores 0-2.

Cronbach’s Alpha Test of Internal Consistency and Reliability

Composite scores were calculated for hope and QOL through use of the HHI and

OPQOL, respectively. Cronbach’s alpha test of reliability and internal consistency were

evaluated for the two scales. The strength of the alpha values was interpreted through the

guidelines developed by George and Mallery (2020), in which α < .5 Unacceptable,

α > .5 Poor, α > .6 Questionable, α > .7 Acceptable, α > .8 Good, and α > .9 Excellent.

Hope and QOL met the acceptable level of internal consistency (α > .70) for both pre-test

and post-test measurements. This indicated that the participants answered the survey

questions in a consistent manner. Table 3 presents the findings of Cronbach’s alpha test

of reliability.

Table 3

Cronbach’s Alpha Test of Reliability

Variable

Number of items

Hope

Pre-test

.91

Post-test

.83

QOL

Pre-test

.95

Post-test

.92

PICOT Question

How does the implementation of an eight-week hope intervention in seniors 65

years and older who live alone affect their level of hope and quality of life compared to

those levels in seniors 65 years and older who live alone and do not receive the hope

intervention?

In order to address the PICOT question, two independent sample t-tests were first

conducted to analyze for pre-test differences in the level of hope and QOL between the

experimental and control groups. Two additional independent sample t-tests were

conducted to analyze for post-test differences in the level of hope and QOL between the

experimental and control groups. Two mixed model ANOVAs were conducted to assess

for differences in hope and QOL before and after the intervention and between the

experimental and control groups. A mixed model ANOVA is appropriate when analyzing

for differences in a continuous level variable over time and by group (Tabachnick &

Fidell, 2019). Prior to analysis, the assumptions of normality and homogeneity of

variances were tested on the data.

Normality Assumption: Shapiro-Wilk Tests

A series of Shapiro-Wilk tests was used to assess the normality assumption. The

normality of assumption reveals that the data falls in a normal distribution, which is an

important factor in using parametric analysis (Mishra et al., 2019). The findings of the

Shapiro-Wilk tests were not statistically significant for hope at post-test (p =.512), and

QOL at pre-test (p = .075) and post-test (p = .822); therefore, the assumption of

normality was supported for these variables. This finding indicates that these variables

approximately follow a bell-shaped distribution, which is one of the assumptions for

parametric analysis. The finding of the Shapiro-Wilk test was statistically significant for

hope at pre-test (p = .017), indicating that the assumption of normality was not supported

for this variable. This finding indicates that the distribution of scores for hope at pre-test

did not resemble a bell-shaped curve. Table 4 presents the findings of the Shapiro-Wilk

tests.

Table 4

Shapiro-Wilk Tests on Hope and QOL (n = 23)

Variable

Shapiro-Wilk Tests

Test Statistic

Hope

Pre-test

0.89

.017

Post-test

0.96

.512

QOL

Pre-test

0.92

.075

Post-test

0.98

.822

Homogeneity of Variance Assumption:

Levene’s Tests

A series of Levene’s tests was used to assess the homogeneity of variance

assumption, which is one of the assumptions for parametric analysis. Homogeneity of

variances is an assumption in which the population variances of two or more samples are

considered equal. The findings of the Levene’s tests were not statistically significant for

hope at pre-test (p = .499) and post-test (p = .839), and QOL at post-test (p = .513);

therefore, the assumption of homogeneity of variance was supported for these variables.

The finding of the Levene’s test was statistically significant for QOL at pre-test (p =

.042), indicating that the assumption of homogeneity of variance was not supported for

this variable. Due to the assumption of normality not being supported for hope at pre-test

and homogeneity of variance not being supported for QOL at pre-test, a series of non-

parametric Wilcoxon-Signed Rank tests were used as a follow-up to the mixed model

ANOVAs. Table 5 presents the findings of the Levene’s tests.

Table 5

Levene’s Tests on Hope and QOL (n = 23)

Variable

Levene’s Tests

Test Statistic

Hope

Pre-test

0.47

.499

Post-test

0.04

.839

QOL

Pre-test

4.69

.042

Post-test

0.44

.513

Independent Sample t-tests: Pre-test

Independent sample t-tests were used to examine for pre-test differences between

experimental and control groups. The independent sample t-tests for hope (t = -1.60, p =

.125) and QOL (t = -1.72, p = .100) were not statistically significant, indicating that at

pre-test, there were no significant differences in scores between experimental and control

groups. Table 6 presents the findings of the independent sample t-tests for pre-test hope

and QOL scores.

Table 6

Independent Sample t-tests for Hope and QOL at Pre-test

Variable

Experimental

Control

t(21)

Hope pre-test

37.45

7.24

41.50

4.76

-1.60

.125

QOL pre-test

114.91

12.17

126.25

18.49

-1.72

.100

Independent Sample t-tests: Post-test

Independent sample t-tests were used to examine for post-test differences between

experimental and control groups. The independent sample t-tests for hope (t = -0.51, p =

.618) and QOL (t = -1.34, p = .194) were not statistically significant, indicating that at

post-test there were no significant differences in scores between experimental and control

groups. Table 7 presents the findings of the t-tests for post-test hope and QOL scores.

Table 7

Independent Sample t-tests for Hope and QOL at Post-test

Variable

Experimental

Control

t(21)

Hope post-test

40.91

4.39

41.83

4.37

-0.51

.618

QOL post-test

134.09

15.70

142.42

14.04

-1.34

.194

Mixed Model ANOVA: Hope

The findings of the within-subjects effect, time (pre-test vs. post-test), were not

statistically significant, F(1, 21) = 2.50, p = .129, ηp2 = .106, indicating that there were no

significant differences in hope before and after the intervention. Within-subjects is the

pre-test/post-test comparison, so both experimental and control groups were merged for

this comparison. Approximately 10.6% of the variance in hope scores can be explained

by the time factor. For the overall sample (n = 23), mean hope scores before and after the

intervention were 39.57 and 41.39, respectively.

The findings of the between-subjects effect, group membership (experimental vs.

control), were not statistically significant, F(1, 21) = 1.79, p = .195, ηp2 = .079, indicating

that there were no significant differences in hope between experimental and control

groups. Between-subjects looked at group comparison, so all pre-test and post-test were

merged for this comparison. Approximately 7.9% of the variance in hope scores can be

explained by the group factor. The mean hope scores for the experimental and control

groups were 39.18 and 41.67, respectively.

The findings of the interaction effect (time*group) were not statistically

significant, F(1, 21) = 1.70, p = .207, ηp2 = .075, indicating that there were no significant

differences in hope by the combination of the time and group. The interaction effect

analyzed pre-test experimental, pre-test control, post-test experimental, and post-test

control. For the experimental group, the mean hope scores before and after the

intervention were 37.45 and 40.91, respectively. Approximately 7.5% of the variance in

hope scores can be explained by the interaction effect, time*group. For the control group,

the mean hope scores before and after the intervention were 41.50 and 41.83,

respectively.

Table 8 presents the results of each effect for the mixed model ANOVA. The

means and standard deviations for hope scores over time and by group are presented in

Table 9. Figure 5 presents a line plot for hope scores over time and by group.

Table 8

Mixed Model ANOVA for Hope Scores by Time and Group

Source

F(1, 21)

ηp2

Within-subjects effect (Time: Pre-test vs. Post-test)

2.50

.129

.106

Between-subjects effect (Group: Experimental vs. Control)

1.79

.195

.079

Interaction effect (Time*Group)

1.70

.207

.075

Table 9

Means and Standard Deviations for Hope Scores by Time and Group

Source

Experimental

Control

Total

Hope (Pre-test)

37.45

7.24

41.50

4.76

39.57

6.28

Hope (Post-test)

40.91

4.39

41.83

4.37

41.39

4.30

Total

39.18

5.82

41.67

4.57

Figure 5. Line plot for hope scores over time and by group.

Mixed Model ANOVA: QOL

The findings of the within-subjects effect, time (pre-test vs. post-test), were

statistically significant, F(1, 21) = 105.51, p < .001, partial ηp2 = .834, indicating that

there were significant differences in QOL before and after the intervention.

Approximately 83.4% of the variance in QOL scores can be explained by the time factor.

This finding indicates that for the combined sample (experimental and control group),

there was a significant improvement in QOL. For the overall sample (n = 23), mean QOL

scores before and after the intervention were 120.83 and 138.43, respectively.

The findings of the between-subjects effect, group membership (experimental vs.

control), were not statistically significant, F(1, 21) = 2.55, p = .126, ηp2 = .108, indicating

that there were no significant differences in QOL between experimental and control

groups. Approximately 10.8% of the variance in QOL scores can be explained by the

group factor. The mean QOL scores for the experimental and control groups were 124.50

and 134.34, respectively.

The findings of the interaction effect (time*group) were not statistically

significant, F(1, 21) = 0.77, p = .391, ηp2 = .035, indicating that there were no significant

differences in QOL by the combination of the time and group. For the experimental

group, the mean QOL scores before and after the intervention were 114.91 and 134.09,

respectively. Approximately 3.5% of the variance in QOL scores can be explained by the

interaction effect, time*group. For the control group, the mean QOL scores before and

after the intervention were 126.25 and 142.42, QOL scores over time and by group are

presented in Tables 10 and 11. Figure 6 presents a line plot for QOL scores over time and

by group.

Table 10

Mixed Model ANOVA for QOL Scores by Time and Group

Source

F(1, 21)

ηp2

Within-subjects effect (Time: Pre-test vs. Post-test)

105.51

<.001

.834

Between-subjects effect (Group: Experimental vs. Control)

2.55

.126

.108

Interaction effect (Time*Group)

0.77

.391

.035

Table 11

Means and Standard Deviations for QOL Scores by Time and Group

Source

Experimental

Control

Total

QOL (Pre-test)

114.91

12.17

126.25

18.49

120.83

16.49

QOL (Post-test)

134.09

15.70

142.42

14.04

138.43

15.12

Total

124.50

13.94

134.34

16.27

Figure 6. Line plot for QOL scores over time and by group.

Wilcoxon-Signed Rank Tests for

Hope and QOL

A series of Wilcoxon-Signed Rank tests was conducted as non-parametric follow-

ups because the assumption of normality and homogeneity of variance assumptions were

not supported, which were essential for parametric analysis. Instead of using the mean to

calculate for the pre- and post-hope level and QOL, a series of Wilcoxon-Signed Rank

Tests used the median to calculate pre-test and post-test hope and QOL. For the

experimental groups, there was a significant improvement in hope (p = .034) and QOL (p

= .003) following the intervention. The control group also demonstrated a significant

increase in QOL. Table 12 presents the findings of the Wilcoxon-Signed rank tests.

Table 12

Wilcoxon-Signed Rank Tests for Hope and QOL Before and After Intervention

Source

Pre-test

Post-

test

Median

Wilcoxon-Signed Rank Test Statistic

Hope

Experimental

38.00

40.00

-2.12

.034

Control

42.00

-0.28

.783

QOL

Experimental

113.00

135.00

-2.94

.003

Control

126.50

141.50

-2.99

.003

Ancillary Analysis

Spearman correlations were conducted to analyze the strength of the relationship

between hope and QOL. Spearman correlations were conducted instead of Pearson

correlations because the normality assumption was not supported for hope scores at pre-

test. In addition, homogeneity of variance was not supported for QOL scores at pre-test.

Scatterplots for the relationships at pre-test and post-test are presented in Figures 7 and 8.

Both scatterplots appeared to depict positive associations between hope and QOL.

The finding of the Spearman correlation between hope and QOL at pre-test was

statistically significant (rs = .64, p < .001). The finding of the Spearman correlation

between hope and QOL at post-test was also statistically significant (rs = .74, p < .001).

The correlation coefficients were positive and represented a large effect (r > .50). Table

13 presents the findings of the Spearman correlations.

Figure 7. Scatterplot between hope and QOL at pre-test.

Figure 8. Scatterplot between hope and QOL at post-test.

Table 13

Spearman Correlations between QOL and Hope

Variable

QOL (pre-test)

QOL (post-test)

Hope (pre-test)

.64*

Hope (post-test)

.74*

*Denotes correlation is significant at .001 level.

Summary

The demographic analysis indicated that there were no statistically significant

differences in the demographic variables between the two groups except for ECOG.

Results showed that there was, however, a statistically significant difference between

ECOG and group (p = .029). Independent sample t-tests conducted to examine for pre-

test differences between experimental and control groups showed no statistical

significance for hope (t = -1.60, p = .125) and QOL (t = -1.72, p = .100). Independent

sample t-tests performed to examine for post-test differences between experimental and

control groups indicated no significant differences for hope (t = -0.51, p = .618) and

QOL (t = -1.34, p = .194). Mixed model ANOVA for hope showed no significant

findings. Mean hope scores for the experimental group before and after the interventions

were 37.45 and 40.91, respectively, indicating that there was some increase in hope level

in the experimental group before and after the intervention, but it was not statistically

significant. Mixed model ANOVA for QOL indicated that there were significant

differences in both groups before and after the intervention F(1, 21) = 105.51, p < .001.

Wilcoxon-Signed Rank tests conducted as back-up for the parametric revealed that there

was a significant improvement in hope (p = .034) and QOL (p = .003) following the

intervention. Spearman correlations conducted to analyze the strength of the relationship

between hope and QOL depicted positive associations between hope and QOL.

CHAPTER 5

DISCUSSION

There is now an increasing senior population across the world. This will soon

surpass that of children, thus leading to a majority that is old or very old (Zarghami et al.,

2018). Studies indicate that the number of elderly people living alone continues to grow

(Ng et al., 2015). Living alone has also shown to affect the wellbeing of the elderly

population, impacting their hope and QOL (Gupta & Singh, 2020; Hwang et al., 2020;

Yeh & Lo, 2004).

The main objective of this project was to determine whether an 8-week Hope

Intervention Program would improve the levels of hope and QOL in elderly people who

lived alone. This chapter includes discussion of the major findings of the study and their

relationship to the objectives and the theoretical framework. In addition, the chapter

includes discussions of the project’s significance and its impact on future nursing practice

and nursing research. Furthermore, study strengths, limitations, and plans for

dissemination are described, as well as how the project relates to the Doctor of Nursing

Practice (DNP) Essentials as defined by the American Association of Colleges of

Nursing ([AACN], 2006).

Summary of Study Findings

The purpose of this project was to evaluate the effects of a non-pharmacological

intervention, in this case, the use of HIP on hope and on the QOL in senior individuals

who live alone. To the project manager’s knowledge, this study was the first which

utilized HIP to explore the levels of hope and QOL in the elderly population.

Participants’ Demographics

The population for this project was drawn from the general community in

Southwest Michigan. Thirty-one individuals from Southwest Michigan originally signed

the informed consent to participate in the study. A final total of 23 participants completed

the study—11 participants in the experimental group and 12 participants in the control

group.

There were no statistically significant differences pertaining to demographic

variables between the experimental and control groups except for ECOG scores. It was

observed that the majority participants in the study were white. There was one black

participant in the experimental group and one in the control group, and no Hispanics

represented in the study. There was an under-representation of male participants in the

study—they were all female except for one male participant. These findings are

consistent with Berrien County demographics. According to the U.S. Census Bureau

(2021), Berrien County Demographics Summary showed that the largest Berrien County

racial/ethnic groups are White 79.9% followed by Black 14.4% and Hispanic 6.1%. U.S.

Census Bureau also indicated that the Berrien County, Michigan Gender Ratio was

50.7% females to 49.3% male. There was no statistically significant difference in the

marital status or education level between both groups. The study recruited participants

based on whether their age was 65 or higher. Therefore, no age range data was available.

The ECOG score was statistically significant between the two groups. It showed

that more participants in the experimental group reported restricted activity levels

compared to the control group. A total of four (36.36%) participants in the experimental

group reported that they were able to ambulate and do self-care, but were unable to carry

out work activity, while none in the control group reported that they were restricted with

work activities. While three (27.27%) participants in the experimental group said they

were fully active, nine (75%) participants in the control group reported that they were

fully active. These factors may have influenced the final results since individuals who

remain active tend to have a positive outlook towards life (Lucas et al., 2019).

Hope Level Analysis

The HHI survey was utilized to assess the baseline for the level of hope for both

the experimental and control groups. The survey was completed by both groups at the

end of the eight weeks. Independent sample t-tests conducted to examine for pre-test

differences between the experimental and control groups showed no statistical

significance for pre-test hope (t = -1.60, p = .125). The results also indicated that the

independent sample t-tests for post-test hope (t = -0.51, p = .618) was not statistically

significant. This showed that there was no significant difference on the level of hope after

the HIP intervention between the experimental and control groups.

Zareei Mahmoodabadi et al. (2019) indicated that the hope therapy program

conducted on 24 elderly women showed a statistically significant difference in the level

of hope before and after the intervention in the study group. According to Herth (2001),

HIP positively influenced in rebuilding and maintaining hopeful veneer in 38 participants

with first-time recurrent cancer who were receiving cancer treatment. Hence, it was

expected that hope interventions would reveal a similar effect in the current study.

The findings in the current research led me to identify that other variables may

have had an impact on the results such as sample size, ECOG scores, and social

desirability bias. The project manager noted sample size and how it may have played a

role influencing the results as one of the variables. The assumption of normality was not

supported for the hope variable at pre-test as indicated by the Shapiro-Wilk test, which

showed a statistical significance for hope at pre-test (p = .017), meaning the collected

data for hope at pre-test did not resemble a normal distribution, which was essential for

parametric analysis. The literature indicates that as the sample size decreases, sufficient

power is not guaranteed to affect the results. To confirm the power in the normality test,

it is essential to obtain an adequate sample (Kim & Park 2019). Howell (2013) revealed

that when the sample size is greater than 50 cases, violations of normality are not

problematic. Thus, 50 cases is the target where normality violations are not problematic.

The sample size was a limitation for all analyses. This situation may play a role in

influencing the post-hope level as not being significant between experimental and control

groups despite the HIP intervention in the experimental group.

The project manager observed another variable that may have influenced the

findings—ECOG scores between both groups. The current study finding of the chi-square

test between ECOG and group was statistically significant, χ2(2) = 7.11, p = .029. The

current study experimental group had a greater level of impairment reflected by ECOG

scores, compared to the control group (see Table 2), indicating that the participants in the

control group were more active with better ability to care for themselves than the

participants in the experimental group. The significant difference on ECOG between

groups may contribute to the insignificance in the pre- and post-hope level.

The project manager also noted that social desirability bias in the self-reported

surveys could have influenced the results. Some participants in the experimental group

said during some of the sessions that they may have reported high scores for the pre-tests

because they did not want to be seen as less hopeful in life, thereby answering in ways

that put them in a more positive light, rather than answering honestly. Social desirability

bias refers to the research participants’ tendency to select responses that they believe are

more socially desirable or acceptable rather than selecting responses that reflect true

feelings and thoughts (Krumpal, 2013). The literature shows that selecting appropriate

data collection strategies that can reduce respondents’ uneasiness when answering a

sensitive question may minimize this bias and generate more valid data (Krumpal, 2013).

Strategies such as techniques to introduce the study, asking questions, establishing

rapport, collecting data in an environment that allows for privacy, and, when possible,

collecting data through mail surveys rather than face-to-face or telephone interviews have

shown to reduce the social desirability bias (Bergen & Labonté, 2020). The current

participants voiced social desirability bias may have influenced the results.

In addition, current study findings showed that the mean hope score at pre-test for

the experimental group was lower (37.45) compared to the hope scores at pre-test for the

control group (41.50), indicating that the difference in hope already existed between the

two groups. Therefore, the significant differences on hope level before the intervention

indicated that the hope level was not equally distributed between two groups. This

unequal distribution on pretest hope may contribute to the insignificant results after the

intervention for the participants between two groups. In addition, participants in the

experimental group received 8 weeks HIP, and participants in the control group might

also have gone to the wellness clinic and senior center for individual visits. Therefore, the

hope level may also have improved in the control group after 8 weeks, resulting in the

insignificance of posttest hope between the two groups.

QOL Level Analysis

The current study results also revealed that the independent sample t-tests for

QOL were not statistically significant at pre-test (t = -1.72, p = .100) and post-test (t = -

1.34, p = .194), indicating no significant differences at post-test in scores between

experimental and control groups. In the previous literature, improved perception and

understanding of hope through hope intervention has shown to increase the QOL among

the elderly (Zareei Mahmoodabadi et al., 2019). Another study conducted by Binaei et al.

(2016) showed that hope-promoting strategies were beneficial in improving the QOL in

people with chronic conditions. The current study results did not reflect the previous

study findings. As noted with hope level results analysis, other variables such as smaller

sample size, ECOG scores, and social desirability bias may have contributed to the result

of no statistically significant differences with QOL before and after the interventions.

The finding of the Levene’s test was statistically significant for QOL at pre-test (p

= .042), indicating that the assumption of homogeneity of variance was not supported for

this variable plays a role influencing the post QOL result. This may contribute to the

QOL as not significant in post-test. Homogeneity of variance, which is essential for both

t-tests and F tests analysis, refers to the assumption in which the population variances of

two or more samples are considered equal. The literature shows that the smaller sample

size greatly influences the values of individual samples on variance. As the sample size

increases, this variability becomes stable (Kim & Park 2019; Uttley, 2019)

Even though the independent sample t-tests for QOL were not statistically

significant, a mixed model ANOVA for QOL within-subjects effect showed statistically

significant differences, indicating that there were significant differences in QOL before

and after the intervention for all participants in both the experimental and control groups.

Therefore, some of the reasons may be because participants in the experimental group

received 8 weeks HIP and participants in control group might also have gone to the

wellness clinic and senior center for individual visits. Therefore, the QOL were all

improved in all participants due to socialization and human interactions.

Hope and QOL

Even though the independent sample t-tests for hope were not statistically

significant, the mixed model ANOVA showed that the mean hope scores in the

experimental group was increased from 37.45 (pre-hope) to 40.91 (post-hope) in the

experimental group as compared to the control group—41.50 (pre-hope) to 41.83 (post-

hope; see Table 9). The line plot for hope scores revealed a positive impact on the

experimental group as noted in Figure 5. Instead of using the mean to calculate for the

pre- and post-hope level, a series of Wilcoxon-Signed Rank Tests used the median to

calculate pre-test and post-test hope. The findings indicated that there was a significant

improvement in hope (p=0.034) after the intervention (see Table 12). This result showed

that HIP had a positive impact on hope perception in participants in the experimental

group.

Even though the independent t-tests did not indicate significant improvement in

QOL, the mixed model ANOVA for the pre- and post-QOL score of within-subject for all

participants was significantly different. In other words, the QOL scores were improved

for all participants in both the experimental and control group. The QOL score increased

from 114.91 to 134.09 in the experimental group. The QOL score also increased from

126.25 to 142.42 in the control group (see Table 11). In the line plot, improvement was

indicated in both groups (see Figure 6). A series of Wilcoxon-Signed Rank Tests used the

median instead of the mean to calculate pre-test and post-test QOL. It indicated that there

was a significant improvement in QOL in both experimental (p=0.003) and control

(p=0.003) groups (see Table 12). This result showed that HIP had a positive impact on

QOL perception in participants in the experimental group. However, for the participants

in the control group, their improvement in QOL may be because of the frequent visits to

the wellness clinic for their own purpose or people interactions in the wellness clinic, as

well. Still, for the participants in the experimental group, HIP may have had a positive

influence on the QOL outcome.

The study results showed positive associations between hope and QOL as noted in

Figures 7 and 8. Hope is an essential factor that strengthens both physiological and

psychological defenses leading to an improved level of QOL (Herth, 2001). Studies

indicated that there is a positive relationship between the level of hope and QOL

(Alshraifeen et al., 2020; Shen et al., 2020).

In the United States, there is an increasing number of senior individuals who live

alone and present adverse health effects associated with aging (Marcus-Varwijk et al.,

2019). Sickness is a disease condition that can affect body, mind, and spirit. Hope plays a

crucial part with coping and healing. Studies indicate that hope interventions can benefit

individuals by rebuilding and maintaining a hopeful outlook towards life (Chi, 2007;

Herth, 2001; Salamanca-Balen et al., 2021). The results of the current study are

consistent with the literature findings that there are positive associations between hope

and QOL.

Implications for Practice

Evidence-based approaches are essential in nursing practice to provide quality

care through improved education and treatment modalities. This was designed to examine

whether elderly people who live alone could benefit from the Hope Intervention Program.

Betty Neuman’s systems model which holds that individuals should be treated

holistically, provided the theoretical underpinning for this study.

Neuman viewed the person as an open system with physiological, developmental,

sociocultural, psychological, and spiritual variables. The system is protected by a series

of concentric rings. Stressors, which may be interpersonal or environmental, have the

potential of penetrating the normal line of defense when it offers inadequate protection

and disturb the wellness of the client (Alligood, 2018). Interventions should focus on the

relief of and protection from stressors (Butts & Rich, 2015; de Almeida et al., 2018). The

Hope Intervention Program consisted of interventions that incorporated education and

activities to address the four attributes of hope to meet and improve the social, emotional,

spiritual, and rational needs of patients, ideally leading to an increased level of hope and

improved QOL (Herth, 2001).

Current findings showed that individuals who participated in the program showed

some improvement in their levels of hope and QOL according to A Line Plot (Figures 5

and 6) and Wilcoxon-Signed Rank tests (Table 12), even though they were not

statistically significant according to the independent t-tests (Tables 6 and 7). This points

to the observation that HIP led to some positive trending in improving hope and QOL

levels. Because hope and QOL are multidimensional (Soósová, 2016), nurses may need

to collaborate with other healthcare professionals to treat the individual as a whole

person. Approaches that focus on empathy, encouragement, interactions with family and

friends, appreciation for nature, involvement in the community activities, and gratitude,

for example, are shown to be effective in improving hope and QOL in seniors living

alone (Herth, 2001).

The HIP could be incorporated in the curriculum for health professionals as one

of the interventions to increase hope and QOL in elderly people. School systems could

work with local senior centers and local governmental agencies to connect students with

seniors in local communities and develop a buddy system where students can interact

with seniors regularly to improve their outlook toward life and have a positive impact on

the community.

Project Strengths

There were not many studies done on seniors living alone regarding this topic and

HIP. This project sought to fill the needs of the growing elderly population who live

alone in order to improve the levels of hope and QOL by utilizing the HIP. In addition,

the study utilized an in-person hope intervention in a very special period which was

during the COVID pandemic. During that time, seniors who lived alone needed to have

support to improve their hope and QOL not only for the loneliness but also for the

pandemic “isolation” on human interaction. Therefore, the project timing was a strength.

For the HIP evaluation, participants in the experimental group were asked to

evaluate the HIP by responding to 16 questions. They were asked to rate the HIP sessions

and the exercise activities included in the HIP on a scale of 1 (Most helpful) to 5 (Not

helpful at all) (see Appendix K). All the participants in the experimental group completed

the evaluation. The participants’ overall evaluation of the HIP was positive. The key

findings were that 45.5% rated HIP as “most helpful” and 54.5% rated it as “helpful.” No

one rated HIP as not helpful. One participant commented that the “sessions truly blessed

me. Include me in the next program.” Another participant commented that the “classes

were wonderful. I wish there was another class.”

Project Limitations

Project limitations included sample size, accessibility of the program, length of

the program, and self-reported data. The power analysis indicated that a minimum of 17

participants was required in each group. In the current study, even though the

experimental group started with 16 participants at the beginning of the HIP intervention,

it ended with 11 participants for the final analysis. Even though the data showed a

statistical insignificance for hope after the HIP, the HIP interventions had some positive

effect on the hope and QOL levels as indicated by the non-parametric tests. Mixed model

ANOVA within-subject effects indicated statistical significance for both groups for QOL.

Therefore, a larger sample size may be needed to increase study power and statistical

certainty.

The project manager also identified the fact that the length of the program was a

limitation. During the recruitment, many individuals said that if the program had been for

4-6 weeks, they would have participated in the study. Some individuals also said that if

the HIP was offered through online sessions or even as one-on-one sessions, they would

have felt comfortable participating. Future studies can be conducted to assess the HIP

effects on these variables.

Implications

Hope intervention has the potential of enhancing hope and QOL in elderly people.

The data suggested that people experienced an increase in their hope levels, thus

positively impacting QOL. The findings indicate that the current study positively

influenced the participants who received interventions. The current study findings may

add to professionals’ knowledge about hope and its effect on hope and QOL in seniors,

especially those who live alone.

The HIP instructions were safe, easy to follow, and were an inexpensive way to

enhance hope and QOL in participants. Professionals may consider all or some of the

aspects of the HIP as a part of their care plan to help others live a hopeful and quality life.

Approaches that focus on empathy, encouragement, appreciation for nature, and gratitude

which were part of the HIP were shown to be effective in improving hope and QOL in

seniors living alone. Nurses should be aware of these intervention approaches to improve

hope and QOL in seniors living alone. Study findings may increase interest in other

professionals to conduct future studies including multiple sites for recruitment to obtain

larger sample size that would allow for more data points, increase the power of the study.

Future studies may compare the effects of HIP between individuals who have been cared

for either an assisted living facility or at home.

Plan for Dissemination

This study will be sent to ProQuest for publication following the approval of the

associated departments of Andrews University in order to expand the distribution of the

study findings. A PowerPoint presentation of the study results will be shared at the local

home health agency and senior centers. The purpose is to encourage and educate nurses

caring for the elderly to adapt the HIP and perhaps seek other approaches for their

sessions as they meet with clients. The project findings will also be presented via poster

presentation or oral presentations at nursing conferences or interdisciplinary conferences.

A manuscript will be prepared for publication.

Recommendations

According to the study findings, five recommendations are suggested:

• A similar study can be replicated using a larger sample size to increase

statistical power.

• A similar study can be conducted to utilize online hope intervention.

• A study could be conducted to investigate the effectiveness of hope

intervention between group therapy and one-on-one therapy.

• A study could be conducted to investigate the effectiveness of hope

intervention between in-person and on-line therapy.

• This project can be applied to elderly individuals living in assisted living

settings.

Use of the DNP Essentials

The doctor of nursing practice (DNP) degree was developed as a response to the

complex demands and changes in health care. The project was guided by Essentials for

Doctoral Education for Advanced Nursing practice, which are the foundational

competencies for an advance practice role (AACN, 2006). Seven of the eight Essentials

were utilized (I, II, III, IV, VI, VII, and VIII).

Essential I: Scientific Underpinnings for Practice

Essential I: Scientific Underpinnings for Practice addresses the scientific

foundation of nursing and the importance of integrating the science of nursing with

mastery from human physiology, psychology, as well as ethics and organizational

sciences in order to offer the most advanced practices in nursing (Chism, 2109). The

project manager recognized the importance of wholeness of health in individuals and

their continued interaction with their surroundings. Working among the elderly

population, the project manager identified the factors that were affecting their wellbeing.

Feelings of loneliness, pain, depression, anxiety, powerlessness, and hopelessness were

some of the identified components affecting the QOL in this population. This recognition

along with literature review on QOL and interventions to improve QOL led me to the

study topic. This project utilized a systems model theory by Betty Neuman, along with

education and hope intervention to seniors living alone to alleviate the feelings of

loneliness and improve their hope and QOL levels. The project manager was mindful of

the ethics concepts and obtained IRB approval and followed IRB protocol.

Essential II: Organizational and Systems Leadership

for Quality Improvement and Systems Thinking

Essential II involves recognizing and assessing the needs of people, communities,

and institutions. Conceptualizing new practice models that are founded on nursing

science to address the present and future demands of the people is a distinguished aspect

of DNP graduates (AACN, 2006). The current study was developed and implemented

during COVID 19 challenging times. Essential II assisted the project manager to be

keenly aware of the dynamics between organizational processes and their impact on

health care providers’ policy changes and the potential effects on those who are receiving

care. As an agent of change, it allowed me to research and explore alternative ways, in

this case, the HIP, in order to reach the identified population to improve hope and QOL in

this population. The project manager plans to share the findings with managers and other

professionals in order to implement HIP as one of the quality improvement measures for

improving QOL in the elderly population.

Essential III: Clinical Scholarship and Analytical

Methods for Evidence-Based Practice

Essential III: Clinical Scholarly and Analytical Methods for Evidence-Based

Practice (EBP) focuses on DNP’s role of combining clinical experience with scientific

research and then translating it into practice, thus enforcing the relationship between

science and practice (AACN, 2006). Evidence-based practice that is strengthened by

solid research and clinical skill, is central to modern health care. The DNP is poised to

play a critical role in its development. As a clinical manager, working in the Home

Health industry, the project manager noted that COVID isolation worsened the overall

health of those individuals who lived alone in the communities, as well as those who

lived in assisted living facilities and yet felt lonely and depressed due to confinement to

their rooms. This phenomenon led to reduced healthcare services and a decline in their

physical, emotional, and social conditions. This project sought to improve hope and QOL

for seniors who lived alone, as well as to evaluate the HIP for uses for future purposes in

this identified population in order to deliver evidence-based quality care within the

assisted living settings, organizations, and communities.

Essential IV: Information Systems-Technology and Patient-

Care Technology for the Improvement and

Transformation of Healthcare

Understanding the importance of Essential IV assisted me to communicate

effectively with the participants and deliver the interventions during this project though

the utilization of information systems/technology. The literature is clear that proficiency

in information technology is essential in promoting the use of evidence-based practices

(Chism, 2019). The project manager investigated and accessed the instruments (HIP and

other screening tools), implemented and evaluated the project through these systems in

order to address identified issues and improve quality care (Zaccagnini & White, 2017).

The project manager utilized text messaging to communicate with the participants as

needed and delivered some of the interventions via PowerPoint presentation, making the

sessions more meaningful and productive. Statistical analysis was conducted through

SPSS. The project manager was successful in completing this study effectively with the

help of information systems/technology.

Essential VI: Interprofessional Collaboration for Improving

Patient and Population Health Outcomes

Essential VI focuses on facilitating and team building aspects of the DNP’s role.

Interprofessional collaboration has become an integral part of decision-making with

healthcare delivery (Chism, 2109). Working in collaboration with other committee

members, the project manager was able to plan, implement, and evaluate this project

successfully. Team members consisted of the project chair, a chiropractor, and a

statistician. The team members assisted with needed guidance to complete the project.

The project manager developed the recruitment flyer in collaboration with the chair and

another committee member and distributed it in the local wellness clinics, communities,

churches, and senior centers. The project manager visited the local senior centers and

contacted seniors at the wellness clinics to recruit for the study. The project manager

communicated the study details in person and via phone with potential participants in

order to build interest in participating in the study. The project manager was successful in

generating interest in the local wellness clinics and elderly population in order to

understand the factors affecting the QOL in the elderly and the strategies for improving

QOL in the elderly who live alone.

Essential VII: Clinical Prevention and Population

Health for Improving the Nation’s Health

This Essential expects the DNP graduates to identify gaps in the healthcare

system and develop programs for health promotion and risk-reduction in order to impact

the health status of people in multiple settings (AACN, 2006). The project manager

achieved these goals by identifying the gap in the literature regarding elderly people

living alone and by implementing interventions through HIP to increase their awareness

of hope and QOL to promote their wellbeing. Many participants in the experimental

group indicated that they were grateful for the project and that the HIP enhanced their

knowledge of hope and the resources available to improve and maintain their health and

well-being.

Essential VIII: Advanced Nursing Practice

One of the goals of an advanced nurse is to contribute positively to optimal

patient care through evidence-based therapeutic interventions. Advanced nurses with a

DNP degree are paving the way and influencing patient care each and every day (Chism,

2019). The project manager was able to demonstrate advanced thinking in identifying and

incorporating theory, best practice, and in implementing an intervention with the

identified population based on the literature findings. Health professionals should

continue to evaluate the outcomes of this intervention in order to add to the knowledge,

bridge the gap, and promote hope and QOL in the senior population.

Conclusion

This project was conducted during the COVID-19 pandemic in order to bridge the

gap in the knowledge of the effects of the Hope Intervention Program on hope and QOL.

COVID-19 affected many, especially those that were elderly and lived alone. They were

isolated and experienced greater levels of feelings of loneliness, fear, depression, despair,

hopelessness, and abandonment (Kasar et al., 2020).

The project addressed the essential needs of the growing elderly population in

order to improve the levels of hope and QOL by utilizing the HIP during these

challenging times. Even though the data demonstrated no statistical insignificance in

improving hope and QOL after the intervention, many seniors still wanted to get out of

the house and be supported by others. This theme came out repeatedly during the

sessions, positively affecting others who were unsure of the in-person sessions. The

participants were provided with resources that are available in the community to assist

seniors. This study gave an opportunity for the elderly in the community to enjoy life

again through coming together, sharing, discussion, learning, connecting with others, and

building new relationships.

Effects of Hope Intervention on Hope and Quality of Life in Senior People Who Live Alone.

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