The Costs of COVID-19: Loneliness, Coping, and ...

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The Costs of COVID-19: Loneliness, Coping, and Psychological The Costs of COVID-19: Loneliness, Coping, and Psychological

Distress in the United States Population Distress in the United States Population

Lauren Hammond Seattle Pacific University

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i

The Costs of COVID-19: Loneliness, Coping, and Psychological Distress in the United States

Population

Lauren Elizabeth Hammond, M.S.

A dissertation submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

In

Clinical Psychology

Seattle Pacific University

School of Psychology, Family, and Community

June 25, 2020

Approved by: Reviewed by:

Amy Mezulis, PhD Amy Mezulis, PhD

Professor of Clinical Psychology Chair, Department of Clinical

Dissertation Chair Psychology

Keyne Law, PhD Katy Tangenberg, PhD

Assistant Professor of Psychology Dean, School of Psychology, Family

Committee Member & Community

Julie Vieselmeyer, PhD

Clinical Psychologist

Committee Member

ii

Acknowledgements

I am grateful for the overwhelming support of my mentor, Dr. Amy Mezulis. Thank you

for taking a chance on me and believing I am more competent than I give myself credit for. I

would also like to thank my other committee members, Drs. Keyne Law and Julie Vieselmeyer for

their input on this project and doing this twice, and Dr. Vieselmeyer for her help cultivating my

love of working with student-athletes. To my female supervisors, thank you for showing me what

it looks like to be a powerful woman in the workforce. I am also grateful for the support of RVT

members Jaclyn Aldrich and Brittany Willey and my lab twin Andrew Fox. Thank you for all the

help and support you’ve provided over the years. I’d like to thank my graduate school cohort for

the joy and laughter they’ve provided during a grueling graduate school process. To my family

and friends, thank you for checking in, offering encouragement, and always believing in me.

Finally, thank you Harley for your unconditional love – you’re the best doggo a girl could hope

for.

As Glennon Doyle says, “If I can sit in the fire of my own feelings, I will keep becoming”

(Untamed, p. 51).

iii

Table of Contents

List of Figures .................................................................................................................................. v

Abstract .......................................................................................................................................... vi

Chapter I: Introduction and Literature Review .............................................................................. 1

Loneliness ................................................................................................................................... 3

Theoretical Foundation ............................................................................................................... 5

Loneliness May Predict Depression and NSSI/SI ...................................................................... 6

Alcohol and Substance Use as Potential Mediators .................................................................... 8

Social Support Seeking as a Potential Moderator ..................................................................... 10

Current Study ............................................................................................................................ 11

Chapter II: Method ....................................................................................................................... 12

Participants ................................................................................................................................ 12

Procedure .................................................................................................................................. 12

Measures ................................................................................................................................... 13

Demographic variables ......................................................................................................... 13

COVID-19 variables ............................................................................................................. 14

Loneliness symptoms ............................................................................................................. 14

Depressive symptoms ............................................................................................................ 14

Alcohol use, non-alcoholic substance use, nonsuicidal self-injury, and suicidality ............. 15

Social support seeking .......................................................................................................... 15

Data Analytic Plan .................................................................................................................... 16

CHAPTER III: Results .................................................................................................................. 20

Data Preparation and Descriptive Analyses .............................................................................. 20

Process Moderated Mediations ................................................................................................. 23

Binary Logistic Regression ....................................................................................................... 28

CHAPTER IV: DISCUSSION ....................................................................................................... 35

Limitations and Future Directions ............................................................................................ 37

References ..................................................................................................................................... 40

iv

List of Tables

Table 1: Descriptive statistics for study variables ........................................................................ 22

Table 2: Correlations between primary variables. ........................................................................ 22

Table 3: Model coefficients for the conditional process model (model 1, alcohol use) ............... 26

Table 4: Model coefficients for the conditional process model (model 1, alcohol use) ............... 26

Table 5: Model coefficients for the conditional process model (model 2, non-alcoholic substance

use) ................................................................................................................................................ 27

Table 6: Model coefficients for the conditional process model (model 2, non-alcoholic substance

use) ................................................................................................................................................ 27

Table 7: Interaction between loneliness, social support seeking, and alcohol use predicting an

increase in NSSI/SI thoughts ........................................................................................................ 31

Table 8: Interaction between loneliness, social support seeking, and non-alcoholic substance use

predicting an increase in NSSI/SI thoughts .................................................................................. 33

v

List of Figures

Figure 1: Proposed moderated mediation between loneliness, social support seeking,

alcohol/non-alcoholic substance use, depression, and nonsuicidal self-injury/suicidal ideation ... 3

Figure 2: Model 1: Proposed moderated mediation of loneliness, social support seeking, alcohol

use, and depression ....................................................................................................................... 18

Figure 3: Model 2: Proposed moderated mediation of loneliness, social support seeking, non-

alcoholic substance use, and depression. ...................................................................................... 19

Figure 4: Model 3: Proposed moderated mediation of loneliness, social support seeking, alcohol

use, and NSSI/SI ........................................................................................................................... 19

Figure 5: Model 4: Proposed moderated mediation of loneliness, social support seeking, non-

alcoholic substance use, and NSSI/SI. .......................................................................................... 20

Figure 6: Model 1: Model coefficients for the conditional process model ................................... 26




Lauren Hammond 437

Abstract

The first reported cases of the 2019 Coronavirus Disease (COVID-19) occurred in late December

2019 in Wuhan, China, with the World Health Organization officially declaring the COVID-19

outbreak a pandemic in March 2020. The COVID-19 pandemic presents a significant public health

challenge, an important part of which is the effects of the outbreak and related efforts to contain

the outbreak (e.g., social distancing) on mental health. As such, the current study sought to better

understand the psychological impact of the outbreak on the United States population. Participants

were 2,284 individuals (78.7% female, 78.7% Caucasian) 18 years of age and older. Four models

assessed the relationships between loneliness, social support seeking, substance use, depression,

nonsuicidal self-injury (NSSI), and suicidal ideation (SI). Results were partially supportive of the

proposed hypotheses. Increased alcohol use mediated the relation between loneliness and

depression (β = 0.004 [0.002], 95% CI [0.00, 0.01]) and level of social support seeking moderated

the relation between loneliness and depression (β = -0.04 [0.02], p < 0.05). However, level of

social support seeking did not moderate the relation between loneliness and alcohol use (F(1,1610)

= 0.03, p = 0.87) and the moderated mediation between loneliness, social support seeking, alcohol

use, and depression was not significant (β = 0.001 [0.001], 95% CI [-0.001 to 0.005]). Moreover,

non-alcoholic substance use did not mediate the relation between loneliness and depression (β =

0.001, 95% CI [-0.001 to 0.005]) and level of social support seeking did not moderate the relation

between loneliness and non-alcoholic substance (F(1, 1613) = 0.13, p = 0.72). The moderated

mediation between loneliness, social support seeking, non-alcoholic substance use, and depression

was not significant (β = -0.0003 [0.001], 95% CI [-0.003 to 0.002]). Neither alcohol (β = -0.26

[0.21], Wald χ2 = 1.51, p = 0.22, OR = 0.77) or non-alcoholic substance use (β = -0.31 [0.30],

Wald χ2 = 1.09, p = 0.30, OR = 0.73) mediated the relationship between loneliness and NSSI/SI.

Lauren Hammond 437

Moreover, level of social support seeking did not moderate the relation between loneliness and

NSSI/SI (β = -0.11 [0.12], Wald χ2 = 0.87, p = 0.35, OR = 0.90). Finally, the moderated mediations

between loneliness, social support seeking, alcoholic (β = -0.10 [0.19], Wald χ2 = 0.30, p = 0.59,

OR = 0.90)/non-alcoholic substance use (β = -0.14 [0.25], Wald χ2 = 0.28, p = 0.60, OR = 0.87),

and NSSI/SI were not significant. Overall, results suggest that substance use may be a maladaptive

coping mechanism and social support seeking an adaptive coping mechanism when experiencing

loneliness. However, methodological concerns about measure construction as well as a highly

biased sample may have limited the current study.

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Chapter I: Introduction and Literature Review

According to the Centers for Disease Control (CDC), a pandemic is “a global outbreak of

disease.” Pandemics emerge when a new virus begins to infect individuals at high rates and

spreads worldwide due too little to no pre-existing immunity. Virus pandemics are often the result

of influenza (flu) viruses. The most recent influenza pandemic was the H1N1 swine flu pandemic

during 2009-2010 which infected approximately 1.4 billion people with a mortality rate of around

0.08%. Recent epidemics that did not reach the scale of pandemics included the Zika virus

outbreak in 2014 and the Ebola outbreak in 2014-2016. In the United States, an opioid epidemic

has been ongoing since 2017 (US Centers for Disease Control). Recent related strains of the

coronavirus have included both SARS (Severe Acute Respiratory Syndrome Coronavirus) and

MERS (Middle East Respiratory Syndrome Coronavirus). Research on the psychological impacts

of both SARS and MERS indicated high symptoms of post-traumatic stress disorder, depression,

and anxiety, as well as greater rates of suicide, somatic complaints, and healthcare profession

burnout (Bonanno et al., 2008; Kim et al., 2018). Additionally, individuals who were quarantined

as a result of SARS exposure were more likely to experience post-traumatic stress symptoms and

report an increase in alcohol use in comparison to individuals who did not experience quarantine

during the outbreak (Wu et al., 2008; 2009).

The first reported cases of the 2019 Coronavirus Disease (COVID-19) occurred in late

December 2019 in Wuhan, China (Wuhan Municipal Health Commission, 2020). By the end of

January 2020, all 34 regions of China reported confirmed cases. Following the rapid spread of the

outbreak in China, cases were soon reported in South Korea, Iran, Japan, and Italy. On March

11th, 2020, the World Health Organization officially declared the COVID-19 outbreak a pandemic.

The United States reported its first confirmed case on January 21st, 2020, in King County,

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Washington, with all states reporting confirmed cases by March 2020. The total number of

confirmed cases in the United States is currently around 2,086,000 with over 115,000 deaths (US

Centers for Disease Control (CDC), Cases & Latest Updates). Individuals diagnosed with

COVID-19 show symptoms such as coughing, shortness of breath, difficulty breathing, and fever,

with severe cases leading to acute respiratory failure, kidney failure, pneumonia, and death (World

Health Organization). Due to the rapidly increasing rates of COVID-19 infections, the CDC and

local, state, and federal governments have recommended social distancing, and in suspected or

confirmed cases, quarantine. Social distancing includes recommendations for individuals to stay

at home as much as possible, avoiding gatherings of more than ten people, only leaving the home

for critical needs (e.g., groceries, medicine, etc.), staying at least six feet apart from other

individuals, and engaging in appropriate hygiene including regular handwashing and avoiding

touching the face. Quarantine includes both the separation and restriction of movement amongst

individuals who have been potentially exposed to COVID-19 in order to reduce the risk of

infecting others (Brooks et al., 2020). While the COVID-19 outbreak continues to spread, data

are being published on its impact on psychological well-being. Studies from China indicated an

increase in stress, and depressive and anxiety symptoms, as well as a reduction in sleep quality in

community samples (Huang & Zhao, 2020; Wang et al., 2020). As social distancing and

quarantine continue to be implemented during the pandemic, face-to-face interaction and overall

human interaction will gradually decline until these orders abate. While prior epidemic and

pandemic studies have focused on psychological outcomes like depression and anxiety, few have

looked at predictors of those outcomes given the unique restrictions in place during a pandemic.

With declining social contact, one such risk factor may be loneliness. Moreover, faced with

increased stress and uncertainty, maladaptive coping strategies such as substance use may increase.

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The current study proposes to examine the relation between loneliness, social support, substance

use, depression, and nonsuicidal self-injury/suicidal ideation (NSSI/SI) symptoms among adults.

I hypothesize that substance use will act as a mediator between loneliness and mental health

outcomes (depression and NSSI/SI), with the strength of the indirect effect of loneliness on

substance use and mental health outcomes contingent upon the level of social support seeking

individuals engage in (see Figure 1).

Figure 1

Proposed moderated mediation between loneliness, social support seeking, alcohol/non-

alcoholic substance use, depression, and nonsuicidal self-injury/suicidal ideation

Loneliness

Loneliness has been defined as an aversive state that is experienced when there is a

discrepancy between the interpersonal relationships one currently has and those that one hopes to

have (Peplau & Perlman, 1982). Loneliness has both emotional and cognitive components; an

unpleasant affective experience and the perception that current relationships are not living up to

Loneliness

Social Support Seeking

Alcohol/ Non-alcoholic

Substance Use

Social Support

Depression NSSI/SI

4

certain expectations. Prior research has consistently shown it is the quality, rather than the

quantity, of relationships one has that influence feelings of loneliness (Asher & Paquette, 2003;

Cutrona, 1982; Jones, 1982; Wheeler et al., 1983). When evaluating social networks, individuals

who have networks composed of both weak and strong ties are less prone to loneliness than

individuals with networks with only strong ties (Van Tilburg, 1990). Furthermore, individuals

with networks consisting only of family/kin ties are more likely to experience loneliness than

individuals with networks consisting of both family/kin and friends/non-kin (Dykstra, 1990;

Silverstein & Chen, 1996). Additionally, research has shown that even though the total amount of

social contact might not vary between nonlonely and lonely people, the type of social contact does.

Nonlonely people tend to engage in more interactions with close family members and friends,

whereas lonely people tend to engage in more interactions with strangers and acquaintances (Jones,

1982). Moreover, loneliness is distinct from simply being socially isolated or alone, as nonlonely

and lonely people show no difference in daily activities or amount of time spent alone (Hawkley

et al., 2003). Loneliness is considered a universal experience, with estimates of 10%-20% of

community samples reporting experiencing loneliness (Beutal et al., 2017) and differences not

observed based on an individual’s age, race, disability status, socioeconomic status, or marital

status (Medora & Woodward, 1986; Neto & Barros, 2000). Gender differences in loneliness have

been reported, with a meta-analysis by Pinquart and Sörensen (2001) showing that women report

higher levels of loneliness than men. However, this difference is more pronounced in studies

where loneliness is measured with only a single item as opposed to multiple items, suggesting a

potential discrepancy based on men’s reluctance to report loneliness when asked directly.

Loneliness can be either short-lived or chronic, with transient feelings of loneliness often

determined by situation specific factors (e.g., type of event, environment, individuals present, etc.)

5

(Asher & Paquette, 2003; Neto & Barros, 2000). Furthermore, loneliness can be classified into

two types, social or emotional loneliness. Social loneliness is due to lack of social relationships

in a group setting, where an individual shares common interests and activities with others.

Emotional loneliness, on the other hand, is due to a lack of a close and intimate relationship or

attachment to another person (Weiss, 1973).

Theoretical Foundation

Several theories have emerged regarding the development and maintenance of loneliness.

The attachment perspective (Weiss, 1974), suggests that there are different types of relationships,

each associated with different social provisions (e.g., sense of self-worth). The absence of specific

types of relationship is therefore associated with loneliness due to the social provision deficits.

Contrasting the deficit approach, the cognitive discrepancy approach (Perlman & Peplau, 1981)

focuses on the subjective evaluation of individuals in their current relationships in comparison to

their relationship standards or goals. Feelings of loneliness are the result of a discrepancy between

reality and the standard or goal as well as the assessment of the time and resources available to

attempt to fix the discrepancy (Dykstra & De Jong Gierveld, 1994; Perlman & Peplau, 1981).

Finally, an evolutionary perspective posits that loneliness functions as a way to motivate an

individual to reinstate contacts within a group (Cacioppo et al., 2006). As the outbreak continues,

increased “stay at home” and “shelter in place” mandates, in addition to social distancing and

quarantine, will lead to a significant reduction in face to face interaction. Many states have seen

“stay at home” and “shelter in place” orders implemented for a month or more, with only essential

business open. These businesses are generally limited to grocery stores and medical offices, with

restaurants open for a takeout only basis. Additionally, parks and nature trails have also seen

increased closures. With individuals working from home, students taking online classes, and

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current orders in place, rates of loneliness are likely to rise significantly amongst the United States

population.

Loneliness May Predict Depression and NSSI/SI

Chronic loneliness has been linked with relational, physical, and mental health concerns.

Loneliness has been associated with increased social withdrawal, shyness, and neuroticisms, as

well as lower frequency of dating and declined participation in religious and extracurricular

activities. Chronic feelings of loneliness may also impact personality and relational schema

development, with individuals experiencing chronic loneliness reporting less intimacy,

understanding, and comfort in relationships while also reporting more distrust, conflict, and

caution. Lonely adolescents have been rated by both teachers and parents as less well-adjusted

and experience higher school dropout rates, declined academic performance, and increased

juvenile delinquency in comparison to nonlonely individuals. Chronic loneliness is also associated

with both low self-esteem and overall life satisfaction (see Heinrich & Gullone, 2006). While not

a disorder in the Diagnostic and Statistical Manual-5 (DSM-5), loneliness has been associated with

several personality disorders, including avoidant personality disorder, borderline personality

disorder, and dependent personality disorder. A study by Snir and colleagues (2015) found that

individuals with avoidant and borderline personality disorders reported feelings of rejection and

isolation before thinking about or engaging in NSSI.

Additionally, loneliness has been associated with depression in both adolescents and adults.

According to the DSM-5, depressive symptoms include depressed or anhedonic mood, physical

changes including changes in appetite, weight, sleep, energy, and psychomotor agitation or

retardation, and cognitive changes including decreased concentration, and increased guilt,

worthlessness, and suicidal ideation. While sharing similar causes, such as shyness, poor social

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skills, and maladaptive attribution styles, loneliness involves appraisals specifically in regard to

the social domain of an individual’s life, whereas depression is more global and involves appraisals

across multiple domains (Boivin et al., 1995). Furthermore, loneliness may play a casual role in

both the development of and maintenance of depression (Green et al., 1992). In a recent study,

Beutel and colleagues (2017) found loneliness was associated with depression, with over 50% of

lonely participants endorsing depressive symptoms in comparison to the 5% endorsing symptoms

in the nonlonely group.

When assessing the relation between loneliness and nonsuicidal self-injury (NSSI) and

suicidal ideation (SI), loneliness has been found to be a risk factor for both. NSSI is defined as

the intentional and direct damage of one’s own body tissue without suicidal intent (Nock &

Favazza, 2009), whereas SI refers to thoughts of wanting to kill oneself. NSSI engagement is

related to an increased risk for and co-occurrence with suicide attempts, with previous researchers

finding that as many as 70% of young adults currently engaging in NSSI have made at least one

prior suicide attempt, and 55% of young adults currently engaging in NSSI reported multiple past

attempts (Jacobson & Gould, 2007; Klonsky et al., 2013; Nock et al., 2006). Individuals engaging

in NSSI report greater loneliness than individuals not engaging in NSSI (Turner et al., 2017), and

NSSI may be implemented as an affect regulation strategy and/or an attempt to gain social support

or resources. When assessing the relation between loneliness and suicidal ideation, the most recent

theoretical suicide models all highlight social connection as a key component in regard to both

suicide attempts and death by suicide. The Interpersonal Theory of Suicide (Joiner, 2005; Van

Orden et al., 2010) suggests the interaction between a sense of thwarted belongingness and

perceived burdensomeness to others interact to increase risk for death by suicide. The Three Step

Theory of Suicide (3ST; Klonsky & May 2014; 2015) suggests that the interaction between pain,

8

hopelessness, and low and/or lack of social connectedness interact to increase risk for death by

suicide (Klonsky & May 2014; 2015). Finally, the Integrated Motivational-Volitional Model of

Suicide (IMV; O’Conner, 2011; O’Conner & Kirtley, 2018) suggests that background factors

(stress, environment, and life events) and the development of defeat/humiliation appraisals and a

sense of entrapment with social conflicts, social support, and thwarted belongingness being key

contributors to the forming of these appraisals, interact to increase risk for death by suicide. When

assessing motivation behind suicide attempts, feelings of loneliness have often been listed as

prompting factors contributing to both suicide attempts and death by suicide (see Heinrich &

Gullone, 2006). Additionally, population-based surveys have found that lonely individuals report

higher rates of NSSI/SI than nonlonely individuals (Stravynski & Boyer, 2001). Moreover, Beutal

and colleagues (2017) found that 42% of lonely individuals endorsed suicidal ideation in

comparison to 6% of nonlonely individuals. However, little research has been conducted on both

depression and thoughts of NSSI/SI during a pandemic and their interactions with feelings of

loneliness.

Alcohol and Substance Use as Potential Mediators

When assessing the relation between loneliness and alcohol use, evidence is mixed, with

some studies finding lonely individuals are more likely to abuse alcohol than nonlonely individuals

while others have found no difference or a decline in alcohol consumption between lonely and

nonlonely individuals (Cacioppo et al., 2000; Page & Cole, 1991; Sadava & Pak, 1994). A study

by Wilson and Moulton (2010) found that 63% of adults aged 45 years and older who had received

diagnoses of either alcohol or drug abuse reported being lonely. Moreover, when assessing older

adults in day treatment programs for alcohol abuse, feelings of loneliness preceded the first drink

during a typical day (Schonfeld & Dupree, 1991). When assessing the relation between loneliness

9

and non-alcoholic substance use, lonely individuals have higher rates of recreational substance use

than nonlonely individuals (Cacioppo et al., 2000). Furthermore, non-alcoholic substance use is

often associated with an inability to engage in meaningful interpersonal relationships as well as

increased social isolation (Chou et al., 2011). As with alcohol consumption, research indicates

that individuals in treatment for substance abuse endorse higher levels of loneliness than the

general population (Orzeck & Rokach, 2004). Further research is needed to determine the relation

between loneliness, alcoholic and non-alcoholic substance use, and psychological distress during

a pandemic.

In community samples, alcohol and non-alcoholic substance use are consistently related to

increased rates of both depression and NSSI/SI. The relation between alcohol use and depression

is well-established (Boschloo et al., 2011; Schuckit, 2006) with data from the National

Comorbidity Survey (1992) of the United States public estimating that among individuals

diagnosed with alcohol dependence, almost 25% of men and 50% of women were diagnosed with

co-morbid major depression. Additionally, significant associations in community samples

between non-alcoholic substance use and major depression have also been found (Connor et al.,

2008; Regier et al., 1990; Robins et al., 1988; Weissman & Meyers, 1980). Moreover, in a meta-

analysis by Moller and colleagues (2013), results from several community samples indicated both

alcohol and non-alcoholic substance use were strongly associated with self-harm. Finally,

according to the CDC, “problematic substance use” was identified in almost 30% of individuals

who died by suicide in 2015. Among those tested for alcohol (approximately 54% of decedents),

40% tested positive for alcohol. Of those tested for non-alcoholic substances (approximately 40%

of decedents), 26% tested positive for opioids, 22% for marijuana, 9% for amphetamines, and 6%

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for cocaine (Jack et al., 2018). Further research is needed to examine these associations during a

pandemic.

Social Support Seeking as a Potential Moderator

Social support has been defined as “the resources provided by other persons” (Cohen &

Syme, 1985). These resources may include emotional support, such as feeling cared for by others,

and instrumental support, such as financial or physical assistance from others. While related,

social support and loneliness are distinct social conditions. Specifically, loneliness is the

subjective discrepancy between desired and actual social contact whereas social support is a

similarly subjective interpretation in which individuals perceive they are part of a social network

(Tomaka et al., 2006). As such, individuals may feel lonely even with strong social support if they

perceive a discrepancy between the amount of social contact they are currently engaging in and

the amount of social contact they want to be engaging in (e.g., an individual may feel he/she has

strong social support from the friends he/she has but desires more friends).

Social support has been found to be negatively related to both alcoholic and non-alcoholic

substance use (Barrera et al., 1993; Lindenberg et al., 1993; Maton & Zimmerman, 1992).

Moreover, studies have also found higher general social support to be positively related to

abstinence among individuals seeking treatment for substance use, with larger and more supportive

networks leading to greater abstinence (Billings & Moos, 1983; Havassay et al., 1991; Longabaugh

et al., 1993; Rumpf et al., 2002; Zywiak et al., 2002). Additionally, individuals engaging in

substance use have reported lower levels of social support in community samples (Galea et al.,

2004; Peirce et al., 2000; Simpson & Miller, 2002; Stice et al., 2004). As loneliness is associated

with both increased alcohol and substance use, as well as lower social support, higher levels of

social support may serve as a buffer between loneliness and increased alcohol and substance use.

11

Furthermore, social support may also serve as a buffer against depression, NSSI, and SI.

In a meta-analysis by Gariépy and colleagues (2016) reviewing one-hundred studies on the relation

between social support and depression, social support was found to be an important protective

factor against depressive symptoms. For adults, both spousal and family support were protective

factors, with mixed evidence for friend support. Additionally, both receiving and giving social

support to a spouse were related to lower levels of depressive symptoms (Gariépy et al., 2016).

Further, a meta-analysis by Wang and colleagues (2018) looking at longitudinal studies found less

social support at baseline predicted greater depressive symptom severity and poorer recovery and

remission, even after controlling for baseline depression. When assessing the relation between

social support, NSSI, and SI, research indicates individuals are more likely to engage in NSSI

following conflicts within their social network (Turner et al., 2017). Additionally, higher levels

of social support have been linked with both decreased suicidal ideation and suicide attempts

(Chioqueta & Stiles, 2007; Kleiman & Liu, 2013). Little research to date has examined the

interactions between loneliness, social support seeking, alcohol and substance use, depression, and

NSSI/SI during a pandemic.

Current Study

The current study aimed to address gaps within the pandemic literature, particularly by

understanding how loneliness elicited by social distancing may affect substance use, depression,

and NSSI/SI, and how these effects may vary depending on the person’s level of social support

seeking. Given previous research, I hypothesized the following:

Hypothesis 1. Individuals with higher loneliness symptoms as a result of the pandemic will be

more likely to have higher depression and NSSI/SI symptoms than individuals with lower

loneliness symptoms as a result of the pandemic.

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Hypothesis 2. Individuals with increased alcoholic/non-alcoholic substance use as a result of the

pandemic will be more likely to have higher depression and NSSI/SI symptoms than individuals

without increased alcoholic/non-alcoholic substance use as a result of the pandemic.

Hypothesis 3. Alcoholic/non-alcoholic substance use will mediate the relation between loneliness

and depression and NSSI/SI symptoms such that individuals with higher loneliness symptoms will

be more likely to report increased alcoholic/non-alcoholic substance use as a result of the pandemic

and, will in turn, be more likely to have higher depression and NSSI/SI symptoms.

Hypothesis 4. Social support seeking will moderate the relation between loneliness and increased

alcoholic/non-alcoholic substance use such that individuals with higher loneliness symptoms and

lower social support seeking will be more likely to report increased alcoholic/non-alcoholic

substance use than individuals with higher loneliness symptoms and higher social support seeking.

Hypothesis 5. Social support seeking will moderate the relation between loneliness and both

depression and NSSI/SI symptoms such that individuals with higher loneliness symptoms and

lower social support seeking will have higher depression and NSSI/SI symptoms than individuals

with higher loneliness symptoms and higher social support seeking.

Chapter II: Method

Participants

The current study utilized a participant pool from an ongoing larger IRB approved study

which evaluates the psychological well-being of adults in the United States following the outbreak

of COVID-19. Participants were recruited through social media and were eligible to participate if

they were at least 18 years old, currently resided in the United States, and provided study consent.

Procedure

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Participants completed an online baseline questionnaire, administered via the Qualtrics

platform, which assessed participants’ loneliness symptoms, depressive symptoms, and change in

maladaptive coping skill use (e.g. substance use, NSSI/SI) over the past month in response to the

COVID-19 outbreak in the United States. Participant baseline data was collected from late March

until mid April. Other questionnaires were included in the baseline questionnaire that participants

completed as part of the larger study and are unrelated to the current research question.

Additionally, participants had the option to complete six brief, follow-up questionnaires upon

completion of the baseline questionnaire. The follow-up questionnaires are also part of the larger

study and unrelated to the current research question. There was very little risk to participants in

this study. Participants were able to skip any questions they did not feel comfortable answering

and could stop participating in the study at any time. There were no direct benefits for participating

in the study.

Measures

Demographic variables

General demographic information was collected with author-constructed text entry and

multiple-choice questions at the beginning of the questionnaire. Information collected included

biological sex, race, age, and household size. Of the participants that provided information on

their gender (n = 2284), 78.7% identified as female, 11.9% identified as male, 1.2% identified as

other (e.g., genderqueer, nonbinary), and 0.2% identified as transgender. Participants (n = 2283),

were 78.7% white/Caucasian, 4.6% biracial or multiracial, 4.1% Asian American, 2.6%

black/African American, 1.2% “other”, 0.7% Native American/American Indian, and 0.1% Pacific

Islander. Of the participants who provided information about their age (n = 2281), 42.4% were

25-34 years old, 18.6% 35-44 years old, 11.6% 18-24 years old, 11.0% 45-54 years old, 5.7% 60

14

years or older, and 2.5% 55-59 years old. When assessing household size (n = 2266), 50.8% of

participants lived in 2-3 person households, 23.8% 4-5 person households, 13.2% single person

households, and 3.5% in 6+ persons households.

COVID-19 variables

COVID-19 specific information was collected with author-constructed text entry and

multiple choice questions at the beginning of the questionnaire. Health information collected

included three questions on self-health vulnerability status (e.g., “Do you consider yourself part

of a vulnerable population?” Examples include being immunocompromised, pregnant, have an

underlying medical condition, etc.), symptom status (e.g., “Have you experienced COVID-19

symptoms?”), and compliance with social distancing. The majority of participants (63.4%)

reported they were not part of a vulnerable population (n = 2189), had not experienced COVID-

19 symptoms (78.5%; n = 2189), and were compliant with social distancing guidelines most if

not all of the time (78.2%, n = 2128).

Loneliness symptoms

Loneliness was assessed using the UCLA Loneliness Scale Short Form-8 (ULS-8; Hays &

DiMatteo, 1987). The ULS-8 is an 8-item measure, on which participants rated how often they

experienced loneliness (e.g., “I lack companionship,” “There is no one I can turn to”) over the past

month on a scale from 1 (never) to 4 (always). Higher scores indicated a greater degree of

loneliness. The ULS-8 has demonstrated high levels of both discriminant and convergent validity,

as well as reliability, with internal consistencies in previous studies ranging from α = 0.72 to 0.84

(Doğan et al., 2011; Hays & DiMatteo, 1987; Wu & Yao, 2008). The internal consistency for the

current study was α = 0.84.

Depressive symptoms

15

Depressive symptoms were assessed using the Center for Epidemiologic Studies

Depression Scale-10 (CESD-10; Anderson et al., 1994). The CESD-10 is a 10-item measure, on

which participants rated how often they had experienced depression symptoms (e.g., “I felt

depressed,” “my sleep was restless”) over the past month on a scale from 0 (rarely or none of the

time; < 1 day) to 3 (most or all of the time; 5–7 days). Higher scores indicated a greater level of

depressive symptomatology, with a score of 10 or higher indicative of clinically significant

distress. In previous studies, the CESD-10 has demonstrated internal consistencies ranging from

α = 0.69 to 0.89, and evidence of concurrent and discriminant validity with other depression

symptom checklists (Baron et al., 2017). The internal consistency for the current study was α =

0.86.

Alcohol use, non-alcoholic substance use, nonsuicidal self-injury, and suicidality

Author-constructed multiple choice questions asked about changes in alcohol

consumption, non-alcoholic substance use, and thoughts about engaging in nonsuicidal self-

injury/suicidal ideation (e.g., “As a result of the COVID-19 pandemic, has your alcohol

consumption changed over the past month?). Response options were “decreased”, “stayed the

same,” and “increased.” When assessing participant responses, 18.1% reported an increase in

alcohol consumption (n = 1718), 7.1% an increase in non-alcoholic substance use (n = 1721), and

5.2% an increase in thoughts of engaging in nonsuicidal self-injury or suicidal ideation (n = 1721).

Social support seeking

Social support seeking was assessed using the social support subscales of the Brief COPE

(Carver, 1997). The social support scales of the Brief COPE consist of 4 items, on which

participants rated how they responded to a stressful event (e.g., “I try to get advice from someone

about what to do,” “I discuss my feelings with someone”) over the past month on a scale from 1

16

(I usually don’t do this at all) to 4 (I usually do this a lot). Higher scores indicated a higher degree

of social support seeking. In previous studies, the social support scales of the Brief COPE have

demonstrated strong construct validity and internal consistencies ranging from α = 0.80 to 0.87

(Snell et al., 2011). The internal consistency for the current study was α = 0.80.

Data Analytic Plan

Data were analyzed in SPSS 26.0 with moderated mediation binary logistic regression

using Process 3.0 (model 8; Hayes, 2017). To address Hypotheses 1-5, a total of four separate

moderated mediation analyses were run, with loneliness symptoms entered as the independent

variable, social support seeking entered as a moderator of both the a and c’ pathways, depression

and NSSI/SI symptoms as the dependent variables, and alcohol use and non-alcoholic substance

use entered as the mediator variables to determine whether loneliness, social support seeking,

alcohol/non-alcoholic substance use and/or the interaction between loneliness, social support

seeking, and alcohol/non-alcoholic substance use predicted depression and/or NSSI/SI symptoms

as a result of the outbreak. For all analyses, alcohol use and non-alcoholic substance use changes

were coded as -1 for “decreased use,” 0 for “use stayed the same,” and 1 for “increased use.”

Additionally, due to the overall low prevalence of NSSI and SI within the sample (~5%), NSSI/SI

engagement was pooled and coded as 0 to represent “no increase in thoughts of engagement” and

1 for “increase in thoughts of engagement” for thoughts of engaging in NSSI, SI, or both. As

previous research has found associations between gender, age, and loneliness, these variables were

included as covariates in all analyses. Due to the low percentage of transgender/”other” gender

(~1%) participants in the sample, gender was recoded as 0 for “male” and 1 for “female” for

analyses, with transgender/”other” gender coded as missing. Additionally, as household size may

serve as either a buffer or risk factor for both loneliness and social support, it was also included as

17

a covariate. Finally, COVID-19 specific health risk factors of health vulnerability, having

symptoms, and compliance with social distancing were included as covariates in analyses, as each

may impact human-to-human interaction and, in turn, loneliness. For all analyses, self-

identification as a member of a vulnerable population and having symptoms were coded as 0 for

“no” and 1 for “yes.”

To test the relation between loneliness, social support seeking, alcohol and non-alcoholic

substance use, and depression, the moderated mediation analyses progressed in the piecemeal

approach recommended by Hayes (2017). All continuous variables were transformed into z-scores

prior to conducting the regression analyses. First, using a simple mediation model, the effects of

loneliness on depression were assessed both directly and indirectly through both alcohol and non-

alcoholic substance use. Next, using a simple moderation model, the effect of loneliness on both

alcohol and non-alcoholic substance use as moderated by social support seeking were assessed.

Third, using a simple moderation model, the effect of loneliness on depression was assessed as

moderated by social support seeking, with alcohol/non-alcoholic substance use entered as a

covariate. Finally, the mediation and moderation models were combined to estimate the

conditional indirect effect of loneliness on depression through alcohol and non-alcoholic substance

use as moderated by social support seeking on the a and c’ paths. Bias-corrected confidence

intervals (CI 95%) were constructed using 10,000 bootstrap samples to evaluate the statistical

significance of the direct and indirect effects.

To test the relation between loneliness, social support seeking, alcohol and non-alcoholic

substance use, and NSSI/SI, moderated mediation analyses were conducted using binary logistic

regression. For the linear logistic regressions, age, gender, household number, and symptom status

were entered as covariates in Block 1. Loneliness, social support seeking, and alcohol and non-

18

alcoholic substance use were entered in Block 2 to assess the main effects of these variables on

NSSI/SI. The interaction terms between loneliness and social support seeking, loneliness and

alcohol and non-alcoholic substance use, and social support seeking and alcohol and non-alcoholic

substance use were entered in Block 3. Finally, the three-way interaction term between loneliness,

social support seeking, and alcohol and non-alcoholic substance use were entered in Block 4. The

chi-square value of the omnibus test of model coefficients was used to assess block statistical

significance. Wald’s chi-square test, odds ratios, and 95% confidence intervals were used to assess

main and interaction effects.

Figure 2

Model 1: Proposed moderated mediation of loneliness, social support seeking, alcohol use, and

depression

Loneliness


Alcohol Use

Social Support

Depression

19

Figure 3

Model 2: Proposed moderated mediation of loneliness, social support seeking, non-alcoholic

substance use, and depression

Figure 4

Model 3: Proposed moderated mediation of loneliness, social support seeking, alcohol use, and

NSSI/SI

Loneliness


Non-alcoholic Substance Use

Social Support

Depression

Loneliness


Alcohol Use

Social Support

NSSI/SI

20

Figure 5

Model 4: Proposed moderated mediation of loneliness, social support seeking, non-alcoholic

substance use, and NSSI/SI

CHAPTER III: Results

Data Preparation and Descriptive Analyses

Missing data for the ULS-8 and COPE were handled with multiple imputation in SPSS

26.0. Approximately 28% of participants (n = 687) had some missing data. Following

recommendations by Olinsky and colleagues (2003), ULS-8 and COPE data were imputed for

participants with less than 24% missing item level data for the ULS-8 and COPE, with age and

gender included in the imputation as predictors. No participants were excluded from the

imputation due to missing data greater than 24% of items. Due to the nature of the variables, the

covariates and alcohol use and non-alcoholic substance use were not imputed. Additionally, the

outcome variables, depression, and NSSI/SI, were not imputed.

Following data preparation, data were examined for normality using skewness, kurtosis

(see Table 1), and Shapiro-Wilk tests of normality. Skewness was within the normal range for

loneliness, depression, social support seeking, alcohol use, and non-alcoholic substance use, and

Loneliness



Social Support

NSSI/SI

21

kurtosis was within normal range for loneliness, depression, social support seeking, and alcohol

use. The Shapiro-Wilk test of normality indicated that loneliness (SW = 0.99, p < 0.01), depression

(SW = 0.99, p <0.01), and social support seeking (SW = 0.97, p < 0.01) were non-normal. Due

to the low base rate of NSSI/SI in the general population, NSSI/SI data were unsurprisingly non-

normal, with greater percentage of participants not endorsing symptoms of NSSI or SI (SW = 0.29,

p < 0.01). However, based on the central limit theorem, the sampling distribution tends to be

normal in large samples, regardless of the shape of the data, and data shape should not impact

analyses (Field, 2009).

Correlations between all variables are reported in Table 2. Of note, age was negatively

correlated with loneliness, social support seeking, non-alcoholic substance use, depression, and

NSSI/SI. Female gender was positively correlated with loneliness, social support seeking, alcohol

use, and depression. Number of people in the household was negatively correlated with loneliness,

social support seeking, depression, and NSSI/SI. Loneliness was positively correlated with

depression and NSSI/SI, alcohol use was positively correlated with non-alcoholic substance use

and depression, non-alcoholic substance use was positively correlated with depression and

NSSI/SI, and depression was positively correlated with NSSI/SI.

22

Table 1

Descriptive statistics for study variables

Min Max M SD Skewness Kurtosis

ULS-8 8.00 32.00 17.94 5.11 0.96 -0.70

CESD-10 0.00 30.00 14.17 6.57 0.00 -0.76

COPE 4.00 16.00 11.32 2.99 -0.23 -0.63

Alcohol -1.00 1.00 0.17 0.57 0.00 -0.17

Substance -1.00 1.00 0.08 0.34 1.47 4.60

NSSI/SI 0.00 1.00 64.2* 5.2* 3.24 8.49 Note. ULS-8 = UCLA Loneliness Scale-8, CESD = Center for Epidemiological Studies Depression Scale-10. For NSSI/SI, M is the %

of participants endorsing an increase in thoughts of engagement while SD is the % of participants not endorsing an increase in

thoughts of engagement.

Table 2

Correlations between primary variables

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

1. Age

2. Gender -.11**

3. VulSelf .31** -.01

4. Symp -.06** .06* .05*

5. House# -.04 -.08** -.09** -.01

6. Dist -.16** .00 -.07** .01 -.01

7. ULS-8 -.21** .08** -.02 .07** -.09** -.01

8. COPE -.11** .17** -.01 .05* -.06** -.03 -.03

9. Alc -.01 .07** -.03 -.01 .02 .01 .10 .04

10. Sub -.06* .01 .03 -.01 .02 .06* .16 .03 .17**

11. CESD -.23** .19** .04 .17** -.09** -.02 .54** .14** .31** .08**

12. N/SI -.16** .02 -.02 .03 -.06* .05 .29** .77 .00 .05* .31** Note. ULS-8 = UCLA Loneliness Scale-8, CESD = Center for Epidemiological Studies Depression Scale-10, *p <0.05, **p < .01.

23

Process Moderated Mediations

For model 1, the moderated mediation analysis was conducted in piecemeal fashion.

Model coefficients, standard errors, p values, and model summary information can be found in

Table 3 and Figure 6, and the conditional direct and indirect effects can be found in Table 4. In

the first step, the simple mediation between loneliness, alcohol use, and depression suggested a

statistically significant direct effect for the loneliness to alcohol (β = 0.03 [0.01], p = 0.05), alcohol

to depression (b = 0.14 [0.03], p < 0.01), and loneliness to depression (β = 0.53 [0.02], p < 0.01)

pathways. When assessing the covariates, female gender was associated with increased alcohol

use (b = 0.11 [0.04], p < 0.01). Additionally, younger age (b = -0.09 [0.02], p <0.01), female

gender (b = 0.31 [0.06], p <0.01), identifying as part of a vulnerable population (b = 0.17 [0.05],

p < 0.01), and having symptoms of COVID19 (b = 0.29 [0.06], p < 0.01) were related to increased

depressive symptoms. Moreover, the indirect effect of loneliness through alcohol use to

depression was significant (β = 0.004 [0.002], 95% CI [0.00, 0.01]). Results suggested that

participants higher in loneliness were more likely to endorse increased alcohol use, and, in turn,

increased depression. In the second step, the conditional effects of loneliness to alcohol use were

assessed as a function of the proposed moderator, social support seeking. The interaction between

loneliness and social support seeking was not significant (β = 0.004 [0.01], p = 0.76). In the third

step, the moderating effect of social support seeking on loneliness in the prediction of depression

was assessed, with alcohol use added as a covariate. The interaction between loneliness and social

support was significant (β = -0.04 [0.02], p < 0.05). Results of the Johnson-Neyman analysis

indicated that the conditional effect of loneliness on depression was statistically significant at all

levels of social support seeking. As such, the effect of loneliness on depression was significantly

positive for any value of social support seeking in the data (see Hayes, 2017). Taken together,

24

inconsistent with hypotheses, the indirect effect of loneliness on depression through alcohol use

was not conditional on levels of social support seeking (β = 0.0003 [0.002], 95% CI [-0.003,

0.004]) and social support seeking did not moderate the relation between loneliness and alcohol

use (F(1,1610) = 0.03, p = 0.87). However, consistent with hypotheses, alcohol use significantly

mediated the relationship between loneliness and depression (β = 0.004 [0.002], 95% CI [0.0001,

0.009]). Partially consistent with hypotheses social support seeking significantly moderated the

direct path from loneliness to depression (F(1, 1609) = 5.56, p < 0.05). However, participants with

higher loneliness symptoms reported higher depression at any level of social support seeking.

When assessing the impact of the covariates in the final model, younger age (b = -0.08 [0.02], p <

0.01), female gender (b = 0.11 [0.06], p < 0.01), being part of a vulnerable population (b = 0.17

[0.04], p < 0.01), and having COVID19 symptoms (b = 0.27 [0.06], p < 0.01) were related to

increased depressive symptoms.

For model 2, the moderated mediation analysis occurred in piecemeal fashion similar to

model 1. In the first step, the simple mediation between loneliness, non-alcoholic substance use,

and depression suggested a statistically significant direct effect for both the substance use to

depression (b = 0.15 [0.06], p < 0.01) and loneliness to depression (β = 0.53 [0.02], p < 0.01)

pathways. When assessing the covariates, younger age (b = -0.02 [0.01], p < 0.01) was associated

with increased non-alcoholic substance use. Additionally, younger age (b = -0.09 [0.02], p <0.01),

female gender (b = 0.33 [0.06], p <0.01), identifying as part of a vulnerable population (b = 0.16

[0.05], p < 0.01), and having symptoms of COVID19 (b = 0.29 [0.06], p < 0.01), were all

significantly associated with increased depression symptoms. However, the indirect effect of

loneliness through non-alcoholic substance use to depression was not significant (β = 0.001, 95%

CI [-0.001 to 0.005]). In the second step, the conditional effects of loneliness to non-alcoholic

25

substance use were assessed as a function of the proposed moderator, social support seeking. The

interaction between loneliness and social support seeking was not significant (β = -0.0004 [0.01],

p = 0.96). In the third step, the moderating effect of social support seeking on loneliness in the

prediction of depressive symptoms was assessed, with non-alcoholic substance use added as a

covariate. The interaction between loneliness and social support seeking was significant (β = -

0.04 [0.02], p < 0.05). As in model 1, the Johnson-Neyman analysis indicated that at all levels of

social support seeking, the conditional effect of loneliness on depression was statistically

significant. As such, the effect of loneliness on depression was significantly positive for any value

of social support seeking in the data (see Hayes, 2017). The integrated moderated mediation

reflected the results in the piecemeal approach that preceded it. Model coefficients, standard

errors, p values, and model summary information can be found in Tables 5 and Figure 7. Taken

together, inconsistent with hypotheses, the indirect effect of loneliness on depression through non-

alcoholic substance use was not conditional on levels of social support seeking (β = -0.0003

[0.001], 95% CI [-0.003 to 0.002]). Additionally, non-alcoholic substance use did not mediate the

relationship between loneliness and depression (β = 0.001 [0.001], 95% CI [-0.001 to 0.005]) and

social support seeking did not moderate the relation between loneliness and non-alcoholic

substance use (F(1, 1613) = 0.13, p = 0.72). However, partially consistent with hypotheses, the

direct path from loneliness to depression was moderated by any level of social support seeking

(F(1,1612) = 5.30, p < 0.05). When assessing the impact of the covariates in the final model,

younger age (b = -0.07 [0.02], p < 0.01), female gender (b = 0.24 [0.06], p < 0.01), being part of a

vulnerable population (b = 0.16 [0.05], p < 0.01), and having COVID19 symptoms (b = 0.26 [0.06],

p < 0.01) were related to increased depressive symptoms. These conditional direct and indirect

effects can be found in Table 6.

26

Table 3

Model coefficients for the conditional process model (model 1, alcohol use)

M (Alcohol) Y (Depression)

/b SE p /b SE p

X (Loneliness) 0.03 0.01 0.04 0.54 0.02 0.00**

M (Alcohol) -- -- -- 0.14 0.03 0.00**

W (Social

Support)

0.02 0.01 0.21 0.15 0.02 0.00**

X x W 0.00 0.01 0.87 -0.04 0.02 0.02*

Constant -0.01 0.09 0.95 -0.06 0.12 0.62

R2 = 0.01, F(9,1610) = 1.57, p = 0.12 R2 = 0.43, F(10,1609) = 120.55, p = 0.00**

Note. X is the independent variable, Y is the dependent variables, M is the mediator, W is the moderator, and X x W is the

interaction between the independent variable and the moderator. *p <0.05, **p < .01.

Table 4

Model coefficients for the indirect and direct effects of the conditional process model (model 1, alcohol use)

Indirect Effect Direct Effect

W Effect 95% CI Effect SE p

-1.04 0.00 -0.002, 0.01 0.58 0.03 0.00**

-0.01 0.00 -0.0001, 0.01* 0.55 0.02 0.00**

1.37 0.00 -0.002, 0.01 0.49 0.03 0.00**

Note. W is the moderator. *p <0.05, **p < .01.

Figure 6

Model 1. Model coefficients for the conditional process model

Note. *p <0.05, **p < .01

Loneliness


Alcohol Use

ort

Depression0.55**

27

Table 5

Model coefficients for the conditional process model (model 2, non-alcoholic substance use)

M (Substance) Y (Depression)

Coeff. SE p Coeff. SE p

X (Loneliness) 0.01 0.01 0.25 0.55 0.02 0.00**

M (Substance) -- -- -- 0.13 0.06 0.02*

W (Social

Support)

0.01 0.01 0.17 0.15 0.02 0.00**

X x W 0.00 0.01 0.72 -0.04 0.02 0.02*

Constant 0.09 0.05 0.11 -0.08 0.12 0.51 R2 = 0.01, F(9,1613) = 2.01, p = 0.03 R2 = 0.42, F(10,1612) = 118.57, p = 0.00**

Note. X is the independent variable, Y is the dependent variables, M is the mediator, W is the moderator, and X x W is the

interaction between the independent variable and the moderator. *p <0.05, **p < .01.

Table 6

Model coefficients for the indirect and direct effects of the conditional process model (model 2, substance use)

Indirect Effect Direct Effect

W Effect 95% CI Effect SE p

-1.04 0.00 -0.001, 0.01 0.59 0.03 0.00**

-0.01 0.00 -0.001, 0.01* 0.55 0.02 0.00**

1.37 0.00 -0.003, 0.01 0.50 0.03 0.00**

Note. W is the moderator. *p <0.05, **p < .01.

Figure 7

Model 2. Model coefficients for the conditional process model.

Note: *p <0.05, **p < .01

Loneliness



Social Support

Depression0.55**

28

Binary Logistic Regression

For model 3, direct and indirect effects were assessed using a model-building approach.

For each block, model significance was assessed using the omnibus test of model coefficients to

ensure each block demonstrated significantly better fit. In block 1, the effect of model covariates

on increase in NSSI/SI thoughts were assessed. For each category increase in age (b = -0.74 [0.13],

Wald χ2 = 33.08, p = 0.00, OR = 0.48), the odds of having NSSI/SI thoughts decreased by 52%,

and for each additional person in the household, the odds of having NSSI/SI thoughts decreased

by 23% (b = -0.33 [0.15], Wald χ2 = 4.95, p = 0.03, OR = 0.77). In block two, the main effects of

loneliness, social support seeking, and alcohol use on thoughts of NSSI/SI were assessed, with

loneliness significantly predicting increases in NSSI/SI thoughts (β = 1.15 [0.13], Wald χ2 = 80.91,

p = 0.00, OR = 3.14), but not social support seeking (β = 0.02 [0.10], Wald χ2 = 0.06, p = 0.81,

OR = 1.03) or alcohol use (b = -0.05 [0.17], Wald χ2 = 0.09, p = 0.00, OR = 0.77). As loneliness

increased by 1 standard deviation, the probability of an individual having an increase in NSSI/SI

thoughts was 3.14 times greater than the probability of not having an increase in NSSI/SI thoughts.

In block three, the mediating and moderating interactions were assessed. Inconsistent with

hypotheses, the interactions between loneliness and alcohol (β = -0.26 [0.21], Wald χ2 = 1.51, p =

0.22, OR = 0.77), loneliness and social support seeking (β = -0.11 [0.12], Wald χ2 = 0.87, p =

0.35, OR = 0.90), and social support seeking and alcohol use (β = -0.15 [0.17], Wald χ2 = 0.82, p

= 0.37, OR = 0.86) were not significant. Finally, in block four, the three-way interaction between

loneliness, social support seeking, and alcohol was assessed. Inconsistent with hypotheses, this

interaction did not predict an increase in NSSI/SI thoughts (β = -0.10 [0.19], Wald χ2 = 0.30, p =

0.59, OR = 0.90). Results of these analyses can be found in Table 7 and Figure 8.

29

For model 4, direct and indirect effects were assessed using a model building approach

similar to model 3, with model significance assessed for each block using the omnibus test of

model coefficients to ensure each block was a statistically better fit. In block 1, the interactions

between model covariates and increase in NSSI/SI thoughts were assessed. For each category

increase in age (b = -0.74 [0.13], Wald χ2 = 33.06, p = 0.00, OR = 0.45) the odds NSSI/SI thoughts

decreased by 55%, and for each additional person in the household, the odds of NSSI/SI thoughts

decreased by 28% (b = -0.32 [0.15], Wald χ2 = 4.86, p = 0.03, OR = 0.72). In block two, the main

effects of loneliness, social support seeking, and non-alcoholic substance use on thoughts of

NSSI/SI engagement were assessed, with loneliness (β = 1.15 [0.13], Wald χ2 = 81.25, p = 0.00,

OR = 3.15) and non-alcoholic substance use (b = 0.53 [0.27], Wald χ2 = 3.87, p = 0.05, OR =

1.69) significantly predicting an increase in NSSI/SI thoughts. As loneliness increased by 1

standard deviation, the probability of an individual having an increase in NSSI/SI thoughts was

1.15 times greater than the probability of not having an increase in NSSI/SI thoughts. As non-

alcoholic substance use increased, the probability of an individual having an increase in NSSI/SI

thoughts was 1.69 times greater than the probability of not having an increase in NSSI/SI thoughts.

In block three, the mediating and moderating interactions were assessed. Inconsistent with

hypotheses, the interactions between loneliness and non-alcoholic substance use (β = -0.31 [0.30],

Wald χ2 = 1.09, p = 0.30, OR = 0.73), loneliness and social support seeking (β = -0.08 [0.12],

Wald χ2 = 0.50, p = 0.48, OR = 0.92), and social support seeking and non-alcoholic substance use

(β = 0.04 [0.26], Wald χ2 = 2.57, p = 0.11, OR = 1.51) were not significant. Finally, in block four,

the three-way interaction between loneliness, social support seeking, and non-alcoholic substance

use was assessed. Inconsistent with hypotheses, this interaction did not predict an increase in

30

NSSI/SI thoughts (β = -0.14 [0.25], Wald χ2 = 0.28, p = 0.60, OR = 0.87). Results of these analyses

can be found in Table 8 and Figure 9.

31

Table 7

Interaction between loneliness, social support seeking, and alcohol use predicting an increase in NSSI/SI thoughts

Nagelkerke R2 β SE Wald χ2 OR p

Block 1 0.09**

Constant 0.05 0.69 0.01 1.05 0.94

Gender 0.32 0.38 0.70 1.38 0.40

Age -0.74 0.13 33.08 0.48 0.00**

Household # -0.33 0.15 4.95 0.72 0.03*

VulSelf 0.02 0.23 0.01 1.02 0.93

Symptoms 0.12 0.30 0.17 1.13 0.68

Distance 0.09 0.13 0.48 1.09 0.49

Block 2 0.24**

Constant -1.90 0.77 6.02 0.15 0.01

Gender 0.30 0.41 0.54 1.36 0.46

Age -0.53 0.13 15.96 0.59 0.00**

Household # -0.07 0.15 0.25 0.93 0.62

VulSelf -0.24 0.25 0.91 0.79 0.34

Symptoms -0.46 0.32 2.11 0.63 0.15

Distance 0.21 0.14 2.27 1.23 0.13

ULS-8 1.15 0.13 80.91 3.14 0.00**

COPE 0.02 0.10 0.06 1.03 0.81

Alc -0.06 0.17 0.09 0.95 0.77

Block 3 0.24**

Constant -1.93 0.78 6.11 0.15 0.01

Gender 0.32 0.42 0.57 1.37 0.45

Age -0.54 0.14 16.29 0.58 0.00**

Household # -0.08 0.15 0.32 0.92 0.57

VulSelf -0.15 0.32 0.21 0.87 0.64

Symptoms -0.45 0.32 2.03 0.15 0.64

Distance 0.20 0.14 2.11 1.23 0.15

ULS-8 1.21 0.14 74.89 3.35 0.00**

COPE 0.17 0.17 1.05 1.18 0.31

Alc 0.23 0.28 0.68 1.26 0.41

ULS-8 x COPE -0.11 0.12 0.87 0.90 0.35

ULS-8 x Alc -0.26 0.21 1.51 0.77 0.22

COPE x Alc -0.15 0.17 0.82 0.86 0.37

Block 4 0.24**

Constant -1.92 0.78 6.02 0.15 0.01

Gender 0.32 0.42 0.57 1.37 0.45

Age -0.54 0.14 16.25 0.58 0.00**

Household # -0.08 0.15 0.37 0.92 0.56

VulSelf -0.23 0.25 0.84 0.80 0.36

Symptoms -0.14 0.32 0.20 0.87 0.65

Distance 0.20 0.14 2.13 1.23 0.15

ULS-8 1.21 0.14 74.94 3.34 0.00**

COPE 0.14 0.18 0.61 1.15 0.44

Alc 0.19 0.29 0.44 1.21 0.51

ULS-8 x COPE -0.25 0.21 1.36 0.78 0.24

ULS-8 x Alc -0.25 0.21 1.39 0.78 0.24

COPE x Alc -0.02 0.29 0.01 0.98 0.94

ULS8 x COPE

x Alc

-0.10 0.19 0.30 0.90 0.59

Note. ULS-8 = UCLA Loneliness Scale-8, CESD = Center for Epidemiological Studies Depression Scale-10. *p <0.05, **p < .01.

32

Figure 8


Note. *p <0.05, **p < .01

Loneliness


Alcohol Use

Social Support

NSSI/SI1.21**

33

Table 8

Interaction between loneliness, social support seeking, and non-alcoholic substance use predicting an increase in NSSI/SI

thoughts

Nagelkerke R2 β SE Wald χ2 OR p

Block 1 0.09**

Constant 0.05 0.69 0.01 1.05 0.95

Gender 0.32 0.38 0.69 1.38 0.41

Age -0.74 0.13 33.06 0.45 0.00**

Household # -0.32 0.15 4.88 0.72 0.02*

VulSelf 0.03 0.23 0.01 1.02 0.91

Symptoms 0.13 0.30 0.18 1.13 0.67

Distance 0.09 0.13 0.48 1.09 0.49

Block 2 0.24**

Constant -1.98 0.78 6.46 0.14 0.01

Gender 0.29 0.41 0.50 1.34 0.48

Age -0.53 0.13 15.51 0.59 0.00**

Household # -0.07 0.15 0.26 0.93 0.61

VulSelf -0.27 0.25 1.16 0.77 0.28

Symptoms -0.08 0.31 0.06 0.93 0.81

Distance 0.21 0.14 2.20 1.23 0.14

ULS-8 1.15 0.13 81.25 3.15 0.00**

COPE 0.02 0.10 0.04 1.02 0.83

Sub 0.53 0.27 3.87 1.69 0.05*

Block 3 0.25**

Constant -2.13 0.79 7.16 0.12 0.01

Gender 0.36 0.42 0.73 1.43 0.40

Age -0.53 0.14 15.24 0.59 0.00**

Household # -0.06 0.15 0.17 0.94 0.68

VulSelf -0.25 0.25 0.98 0.78 0.32

Symptoms -0.14 0.32 0.20 0.87 0.66

Distance 0.21 0.14 2.16 1.23 0.14

ULS-8 1.20 0.14 75.04 3.33 0.00**

COPE 0.04 0.17 0.05 1.04 0.82

Sub 0.73 0.39 4.48 2.07 0.06

ULS-8 x COPE -0.08 0.12 0.50 0.91 0.48

ULS-8 x Sub -0.31 0.30 1.09 0.73 0.30

COPE x Sub 0.41 0.26 2.57 1.51 0.11

Block 4 0.25**

Constant -2.11 0.79 7.06 0.12 0.01

Gender 0.35 0.42 0.70 1.42 0.40

Age -0.53 0.14 15.32 0.59 0.00**

Household # -0.06 0.15 0.18 0.94 0.67

VulSelf -0.25 0.25 0.99 0.78 0.32

Symptoms -0.14 0.32 0.19 0.87 0.67

Distance 0.20 0.14 2.12 1.23 0.15

ULS-8 1.21 0.14 75.82 3.35 0.00**

COPE 0.01 0.18 0.00 1.01 0.95

Sub 0.66 0.41 2.53 1.93 0.11

ULS-8 x COPE -0.06 0.12 0.21 0.95 0.65

ULS-8 x Sub -0.28 0.30 0.83 0.76 0.36

COPE x Sub 0.55 0.36 2.31 1.73 0.13

ULS8 x COPE

x Sub

-0.14 0.25 0.28 0.87 0.60

Note. ULS-8 = UCLA Loneliness Scale-8, CESD = Center for Epidemiological Studies Depression Scale-10. *p <0.05, **p < .01.

34

Figure 9


Note. *p <0.05, **p < .01

Loneliness



Social Support

NSSI/SI1.21**

35

CHAPTER IV: DISCUSSION

The COVID-19 outbreak presents substantial concerns about the impact of a pandemic on

mental health and psychological well-being. Significant disruptions to daily life due to the

pandemic have occurred during the past few months, with continued disruptions expected in the

next few months. “Stay at home” directives and social distancing guidelines have had a major

impact on socialization, limiting face-to-face interaction. As such, loneliness in the general public

is expected to rise. Prior research indicates both the lack of social provisions (Weiss, 1974) and a

discrepancy in desired versus actual relationships contribute to the development and maintenance

of loneliness (Dykstra & De Jong Gierveld, 1994; Perlman & Peplau, 1981). Higher levels of

loneliness have been associated with increased depression (Beutel et al., 2017; Green et al., 1992),

NSSI engagement (Turner et al., 2017), and suicide attempts and death by suicide (Beutal et al.,

2017; Heinrich & Gullone, 2006; Stravynski & Boyer, 2001). Moreover, higher levels of

loneliness have been linked with both increased alcohol and non-alcoholic substance use

(Cacioppo et al., 2000; Chou et al., 2011; Orzeck & Rokach, 2004; Schonfeld & Dupree, 1991;

Wilson & Moulton, 2010).

The current study had three purposes. The first purpose was to examine the interaction

between loneliness and depression and loneliness and NSSI/SI during the COVID-19 pandemic.

The second purpose of the study was to examine potential mechanisms through which loneliness

impacted depression and NSSI/SI, particularly through increased alcohol and non-alcoholic

substance use. The third purpose was to examine whether level of social support seeking impacted

the relationships between loneliness, alcohol/non-alcoholic substance use, depression, and

NSSI/SI. I hypothesized that increased loneliness would predict increased depression and

NSSI/SI. Additionally, I hypothesized higher levels of loneliness would predict increased

36

depression and NSSI/SI through increased alcohol/non-alcoholic substance use. Finally, I

hypothesized that increased loneliness leading to increased substance use and in turn, increased

depression and NSSI/SI, would depend on how much social support seeking individuals engaged

in.

Results were partially supportive of the proposed hypotheses. As indicated in models 1

and 2, higher levels of loneliness were related to increased depression, consistent with prior

research showing loneliness plays a casual role in the development and maintenance of depression

(Beutel et al., 2017; Green et al., 1992) and suggest these same interactions happen during

unexpected large scale stressors, like the COVID-19 pandemic. Loneliness was also related to

increased alcohol use but not non-alcoholic substance use, which is partially consistent with prior

research in which increased loneliness is related to increased substance use (Cacioppo et al., 2000;

Orzeck & Rokach, 2004). Both increased alcohol and non-alcoholic substance use were related to

increased depression, but only increased alcohol use served as a mechanism by which higher

loneliness led to increased depression. In regard to the mixed prior research assessing the

relationship between loneliness and alcohol use (Cacioppo et al., 2000; Page & Cole, 1991; Sadava

& Pak, 1994), these findings provide evidence for an interaction between increased loneliness

symptoms and increased alcohol use, as well as providing a means through which increased

loneliness leads to increased depression symptoms. In regard to social support seeking, any level

of social support seeking heightened the relation between loneliness and depression. However,

level of social support seeking did not impact the relation between loneliness and substance use or

the relation between loneliness, substance use, and depression, which is inconsistent with prior

research that highlights the protectiveness of social support on both substance use and depression

symptoms (Barrera et al., 1993; Lindenberg et al., 1993; Maton & Zimmerman, 1992).

37

In models 3 and 4, only loneliness was related to increased thoughts of NSSI/SI, which is

consistent with prior research showing increased loneliness is a risk factor for NSSI/SI (Heinrich

& Gullone, 2006; Stravynski & Boyer, 2001; Turner et al., 2017). Increased non-alcoholic

substance use was related to increased NSSI/SI, but not increased alcohol use, which is partially

consistent with prior research (Jack et al., 2018; Moller et al., 2013). Substance use was not a

mechanism through which increased loneliness led to increased thoughts of NSSI/SI. Moreover,

level of social support seeking did not impact the relation between loneliness and substance use,

the relation between loneliness and NSSI/SI, or the interaction between loneliness, substance use,

and NSSI/SI. Similar to models 1 and 2, these results are contrary to prior research highlighting

social support as a protective factor against NSSI/SI (Chioqueta & Stiles, 2007; Kleiman & Liu,

2013; Turner et al., 2017).

Limitations and Future Directions

Methodological concerns may have contributed to the nonsignificant results concerning

alcoholic substance use and NSSI/SI. As prior research indicates strong relations between

loneliness, social support, and alcoholic substance use, as well as social support, alcohol/non-

alcoholic substance use, and NSSI/SI, the way data was collected is likely one of the major

contributors to nonsignificant results. Alcoholic/non-alcoholic substance use were measured

based on comparison to prior substance use before COVID-19 and do not reflect consumption

frequency or amount. As such, individuals may be engaging in clinically significant substance

use, but consider this use within “normal” limits, thereby preventing the most accurate reflection

of problematic substance use in the sample. Moreover, social support seeking, and not subjective

feelings of social support or the quality of social support, was assessed in the larger study. As

such, it makes sense that individuals who were experiencing high levels of loneliness would have

38

engaged in higher levels of social support seeking. As the measure only assessed reaching out to

one’s social network and did not assess the quantity or the quality of the help provided, it follows

that higher levels of loneliness were related to higher levels of depression at any level of social

support seeking. Quantity and/or quality of social support received, versus simply the act of

reaching out for help, may better functionally serve as a buffer between loneliness and depression.

Furthermore, NSSI/SI increases only occurred in a small percentage of the sample (~5%), which

likely contributed to underpowered analyses and biased results. Similar to the substance use

questions, only increase in NSSI/SI thoughts during COVID-19 in comparison to pre-COVID

NSSI/SI thoughts was assessed, likely preventing the most accurate reflection of NSSI/SI in the

sample. NSSI/SI thought frequency or amount was not assessed. Previous research with

community samples suggests NSSI/SI in adult community samples can range anywhere from 13%

to 24% of the general population (Gratz et al., 2002; Whitlock et al., 2006), which is not reflected

in the current study sample. Additionally, the study sample was highly biased (78.7% female,

78.7% white/Caucasian) and may not be reflective of the general population.

Study results have several clinical implications. With a vaccine not expected until early

2021, the pandemic will continue to significantly impact daily life for the near future. As such,

the psychological impacts of social distancing and quarantine will remain prevalent issues. With

telehealth options more readily available, individuals may seek out therapy at higher rates. The

current study highlights the importance of addressing loneliness during the pandemic, as higher

levels of loneliness were related to higher depression symptoms and thoughts of engaging in

NSSI/SI. Moreover, providing more adaptive coping skills (e.g., relaxation, mindfulness,

problem-solving skills, etc.) to clients, instead of maladaptive skills like alcohol or non-alcoholic

substance use, may help reduce symptoms of distress. As lonely individuals are reaching out to

39

social networks, helping clients cultivate healthy, supportive, and fulfilling relationships is of great

importance. Assisting clients in finding ways to engage with their social networks and address

physical distancing through available media platforms, particularly platforms like Zoom or

Facetime that mirror “face-to-face” interactions, as opposed to engaging with social networks via

social media sites like Facebook or Twitter, may significantly improve mood and reduce

psychological distress. Finally, this study identifies several at risk populations that may be more

likely to experience psychological distress during the outbreak. Individuals who were female,

younger in age, identified as part of a vulnerable population, and had COVID-19 symptoms had

higher depression symptoms. Additionally, individuals who were younger in age and lived in

smaller households had increased thoughts of NSSI/SI. In addition to standard risk factors, this

study offers clinicians several COVID-19 specific risk factors to screen for in clients. Moving

forward, research aimed at designing measures specific to epidemic, pandemic, and/or other large

scale health traumas may help more accurately capture psychological distress. Measures have

been created for psychological responses to natural disasters and other traumas (like sexual

assault), but no such measures exist for health disasters. Additionally, measure development

focusing on capturing loneliness specifically related to social distancing and quarantine would help

improve understanding on how loneliness experienced under these conditions may be similar to or

different from loneliness experienced in non-pandemic times. With a vaccine for COVID-19 not

expected until the beginning of 2021 at the earliest, the need for continued research on the

psychological impact of the pandemic in order to inform interventions to effectively help

individuals cope with the pandemic and its effects is of utmost importance.

40

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