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Graduate Student Journal of Psychology
Spring 2024 - Vol. 22

Copyright 2024 by the Department of Counseling and Clinical Psychology
Teachers College, Columbia University

Loneliness During the COVID-19 Pandemic: 
Implications for Mental Health and Substance Use

 Natural disasters, school and public shootings, 
community violence, domestic terrorism, and most 
recently, the COVID-19 pandemic are all profound 
instances of complex traumatic stressor events. While 
each of these stand alone, the COVID-19 pandemic 
was a global traumatic stressor event that  invoked feel-
ings of fear, uncertainty, and confusion, consequently 
disrupting one’s sense of safety, stability, and securi-
ty worldwide (Taylor, 2022).  The collective trauma 
caused by the COVID-19 pandemic included all the 
devastating societal complications and disruptions 
to daily living that negatively affected individuals, 
families, and communities worldwide (Holman et 
al., 2023). Following the COVID-19 pandemic, the 
collective trauma included the broader psychologi-
cal reactions to the traumatic event itself along with 
details of the event held in the collective memory of 
people (Hirschberger, 2018; Macias et al., 2021). The 
traumatic stressors associated with the COVID-19 
pandemic, such as mandated social distancing, con-
tributed to the risk of  psychological vulnerability to 
psychopathology with detrimental impacts on  mental 
health and well-being (Cordaro et al., 2021). Howev-
er, the effects on mental health from traumatic stress 
associated with the pandemic often overlook lone-
liness and its psychosocial correlates to anxiety and 
depression symptomology and substance use (Cadi-
gan et al., 2023). Understanding these associations 
can mitigate the onset and severity of loneliness and 

psychopathology for future traumatic stressor events 
(Vindegaard & Benros, 2020; Wang et al., 2020). 
 Loneliness is broadly defined as dissatisfaction 
with the quality of one’s social relationships, and 
the perceived absence of connection co-occurs with 
psychological distress (Pinquart & Sorensen, 2001). 
The cognitive dissonance between an individual’s 
desired level of social harmony and their perceived 
experience of lacking connection causes feelings of 
inadequacy, regardless of the objective quality of 
their social network (Burholt et al., 2017). This im-
plies that an absence of social contact does not pre-
dict loneliness. Instead, discontent with the perceived 
intimacy of one’s relationships produces loneliness.
 Outside traumatic stressor events, a broad range 
of risk factors are associated with loneliness, and a 
comprehensive theoretical framework has yet to be 
fully developed (Clark et al., 2021). Previous stud-
ies suggest that loneliness is prevalent throughout all 
demographics and cultures (Hutten et al., 2021; Vic-
tor & Yang, 2012). There is a consistent pattern of 
loneliness across the human lifespan, and individuals 
under 18 and over 65 experience loneliness at high-
er rates than other age groups (Hawkley & Caciop-
po, 2010). Furthermore, other demographic factors, 
such as sex, education level, socioeconomic status, 
ethnicity, and immigration status, are predictors of 
loneliness, with age increasing the likelihood of risk 
(Beutel et al., 2017; Bosma et al., 2015). Some key 

Aim: Traumatic stressor events disrupt the normal daily functioning of individuals and groups, and the consequenc-
es of collective trauma magnify psychopathology and mental health issues. One overlooked mental health implication 
of traumatic stress is loneliness. The current study examines loneliness as a result of traumatic stress and its psycho-
social correlates, including substance abuse and changes in daily health behaviors. Design: Cross-sectional, nation-
wide, online survey. Methods: This study was a cross-sectional, nationwide online survey that included 2,530 adults 
in the United States, 18-83 years old, and examined the associations between loneliness and psychosocial factors and 
substance use during the initial part of the COVID-19 pandemic. Results: Increased loneliness was associated with 
younger age, single marital status, and lower levels of education. Mental health disorders, including major depres-
sion, generalized anxiety, and somatization, were also associated with high levels of loneliness. Further, individuals 
with high levels of loneliness were more likely to report increased substance use, including alcohol and illicit drugs. 
Discussion: The findings of this study indicate that during times of collective traumatic events, high levels of loneli-
ness are a risk factor for mental health and substance use. Further initiatives are warranted to create awareness and insti-
tute routine screenings for symptoms of loneliness to mitigate mental health distress and increases in substance abuse.
 Keywords: loneliness, isolation, mental health, alcohol use, traumatic stress

Millie Cordaro1, Emma Johnson1, Chris Kinstley1, Kelly Haskard-Zolnierek1, Rachel Medina2, Jessica Perrotte1, 
Jeffrey T. Howard3, &  Krista J. Howard1

 1Department of Psychology, Texas State University, 2Seaton Foundation, & 3Department of Public Health, 
University of Texas at San Antonio



23

CORDARO ET AL.

factors influencing loneliness are physical health, men-
tal health, and social support (Hawkley et al., 2008).
 Loneliness combined with depression is asso-
ciated with an increased morbidity risk, including 
major chronic health conditions and poor physi-
cal health (Stek et al., 2005). Specifically for older 
adults, social isolation is associated with a higher 
likelihood of premature mortality. It is related to an 
increased risk for secondary aging, such as cognitive 
decline and various forms of dementia, including 
Alzheimer’s disease (Lara et al., 2019; Wilson et al., 
2007). Social isolation is also associated with higher 
levels of psychological distress (Campagne, 2019).
 Loneliness is a common predictor of well-being, 
and higher levels of loneliness are associated with 
mental health issues and psychopathology (Ernst 
et al., 2022). In addition, chronic isolation can un-
dermine daily functioning and has been extensively 
linked with suicidality and self-harm (Mushtaq et al., 
2014; Stravynski & Boyer, 2001). Several common 
mental health disorders demonstrate comorbidity 
with loneliness, particularly major depressive disor-
der (MDD) and generalized anxiety disorder (GAD; 
Muyan et al., 2016). In fact, social isolation resulted 
in a 25% increase in the global prevalence of depres-
sion and anxiety (World Health Organization, 2022).  
 While the literature is sparse regarding loneliness 
and traumatic stressor events prior to the pandemic 
(Palgi et al., 2012), loneliness during the pandemic 
has been linked to drug and alcohol abuse, and the 
relationship between loneliness and substance use is 
mediated by depression and anxiety (Horigian et al., 
2020). This suggests that as loneliness levels increase, 
substance use also increases, and at least part of this 
relationship operates through poor mental health. It 
has been suggested that substances like alcohol and 
marijuana use temporarily alleviate feelings of social 
isolation (Ingram et al., 2020). As with mental health, 
the pandemic has caused an increase in substance 
use across a wide range of demographics (Fitzke et 
al., 2021). However, quality social support and re-
lational connectedness can buffer the adverse effects 
that loneliness can have on mental health and daily 
functioning. Likewise, resilience and post-traumatic 
growth associated with traumatic stressor events are 
significantly related to social support derived from 
social connection (Hall et al., 2010; Xu & Ou, 2014).

The Study
 While there is ample research about general so-
cial isolation and loneliness, less is known about so-
cial isolation and traumatic stressors caused by the 
pandemic, which is considered a historical traumatic 
stressor event (Usher et al., 2020). Given the adverse 
outcomes of loneliness within the general popula-
tion, it is essential to understand the factors related 
to loneliness during the initial part of the COVID-19 
pandemic. Similar studies have examined the relation-
ship between loneliness, mental health, and substance 
use during the pandemic. The present study captures 
these factors while also examining other psychosocial 
factors, including stress, somatization, fatigue, qual-
ity of life, and changes in daily behaviors. Thus, the 
broad aim of this study was to investigate how lone-
liness during a historical traumatic stressor event (i.e., 
pandemic) affected U.S. adults. It was hypothesized 
that high levels of loneliness would correspond with 
poorer mental health and increased substance use. 
 

Method
Study Setting and Sampling
 Between April 14 and April 22, 2020, a total of 
2,530 participants included in this study were recruit-
ed through a nationwide Facebook Sponsored Ads 
campaign, while the recommended stay-at-home ini-
tiatives were in place in the United States. The posted 
online campaign targeted the Facebook newsfeeds of 
76,100 users 18 years of age or older, inviting individ-
uals to participate voluntarily in an anonymous online 
study that evaluated the psychological responses to 
the COVID-19 pandemic. The post provided a link 
directing the participants to a Qualtrics survey. From 
the online post, 4,406 respondents clicked on the link, 
and 2,739 provided informed consent and completed 
the online survey. Participants were informed that they 
could skip any questions they were uncomfortable an-
swering. Only participants who completed the UCLA 
Loneliness Scale portion of the survey are included in 
the current study (N = 2,530). The mean age of partic-
ipants in this study’s sample is 47.7 (SD = 12.9) years, 
ranging between 18 and 83 years, with 89.0% female 
and 91.3% White. Of the participants in this study, 
28.7% (725) were single, 55.1% (1390) were married, 
and 16.1% (407) were divorced, separated, or single. 
Further, the education level breakdown for the partic-
ipants in this study was 5.6% (141) with at most a high 



24

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

contains nine stated problems to which the respon-
dents indicated if they have been bothered by these 
problems in the past two weeks using one of four 
options ranging from “not at all” (0) to “nearly ev-
ery day” (3). An example of a problem is “Feeling 
down, depressed, or hopeless” (Spitzer et al., 1999). 
The PHQ-9 is a psychometrically sound question-
naire commonly used in research and clinical prac-
tices for general populations (Kroenke et al., 2010).
 Generalized Anxiety Disorder. Generalized 
Anxiety Disorder was determined using the Patient 
Health Questionnaire – Generalized Anxiety Disorder 
Subscale (GAD-7; Spitzer et al., 2006). The GAD-7 is 
a brief screening tool commonly used in research and 
clinical practices that provides a provisional diagno-
sis of generalized anxiety disorder (GAD; Lowe et al., 
2008). The GAD-7 includes seven statements in which 
participants indicated if they have been bothered by 
them in the past 2 weeks ranging from “not at all” (0) 
to “nearly every day” (3). This study used the scoring 
algorithm rather than the cut-off scores to determine 
if participants met the criteria for the GAD diagnosis.
 Fatigue. Aspects of Fatigue were assessed using 
the Checklist of Individual Strength (CIS; Vercoulen 
et al., 1999). The CIS includes four domains: general 
fatigue, motivation, physical activity, and concentra-
tion. The composite scale consists of 20 items to which 
the participants respond to prompts such as “Think-
ing requires effort” on a 7-point Likert scale ranging 
from “yes, that is true” (7) to “no, that is not true” 
(0). The overall scale (M = 85.2, SD = 25.4) for this 
study provided good internal consistency (α = .94).
 Quality of Life. Quality of Life was assessed us-
ing the World Health Organization Quality of Life 
BREF Scale (WHOQOL, 1996; WHOQOL-Group., 
1998). The WHOQOL-BREF instrument uses 26 
items to assess four specific domains related to the 
quality of life: physical health, psychological health, 
social relationships, and environment. Examples of 
items from this instrument include, “To what ex-
tent do you feel your life to be meaningful?” and 
“How satisfied are you with the support you get 
from your friends?” Using specific criteria to trans-
form raw scores into standardized scores, the final 
scores range from 0 to 100, with higher values in-
dicating better quality of life (WHOQOL, 1996).
Specific Measures Developed for the Current 
Study 

school diploma, 30.4% (766) with some college, 31.5% 
(796) with a 4-year degree, and 32.5% (820) with a grad-
uate or professional degree. This study was approved 
by the University Institutional Review Board (#7221).
Demographics
 Participants were asked to provide information about 
their age, gender identity, race and ethnicity, marital sta-
tus, education level, and if they had children under 18.
Instruments 
Psychosocial Measures
 Loneliness. Loneliness was assessed using the 
20-item UCLA Loneliness Scale (UCLA; Russell et 
al., 1978). This measure assesses feelings of loneliness 
and social isolation using a 4-point Likert scale with 
responses ranging from “I never feel this way” (1) to 
“I often feel this way” (4), and total scores range be-
tween 20 to 80, with higher scores indicating greater 
loneliness. Examples of items from this scale include 
“How often do you feel left out?” and “How of-
ten do you feel isolated from others?” This is a re-
liable and validated scale (Russell, 1996; Russell et 
al., 1980). For this study, the overall scale (M = 44.9, 
SD = 11.7) provided excellent internal consisten-
cy (α = .94). The cut-offs for loneliness severity on 
the UCLA scale were adapted from Cacioppo and 
Patrick (2008), which included: Total score < 28 = 
No/Low Loneliness, Total score 28 - 43 = Moderate 
Loneliness, and Total score > 43 = High Loneliness.
 Perceived Stress. Stress was measured using the 
Perceived Stress Scale (PSS; Cohen et al., 1983). The 
PSS assesses general life stressors using ten items with-
in a 1-month time frame. Participants were asked to 
respond to each item using a 4-point Likert scale 
ranging from “never” (1) to “very often” (4), with 
total scores ranging between 10 and 40, with high-
er scores indicating greater stress. An example of an 
item on this scale is, “How often have you felt ner-
vous and stressed?” The PSS is commonly used in 
both research and clinical practices and is shown to 
be a valid and reliable scale (Cohen et al., 1983; Lee, 
2012). The overall scale (M = 19.3, SD = 7.4) for this 
study provided good internal consistency (α = .90).
 Major Depressive Disorder. Major Depres-
sive Disorder (MDD) was assessed using the Patient 
Health Questionnaire – Major Depression Subscale 
(PHQ-9; Spitzer et al., 2006). Using a scoring algo-
rithm to determine if the participants meet the crite-
ria for a provisional diagnosis of MDD, the PHQ-9 



25

CORDARO ET AL.

n = 945), and High Loneliness (> 43; HL; n = 1,421).
 The comparisons of the demographic variables 
are shown in Table 1. There was a significant differ-
ence between the comparison groups when evaluat-
ing age, such that the participants in the NLL group 
were older than those in the ML and HL groups (p   
= .014). No significant differences were identified 
between the participants in the comparison groups 
when evaluating gender, race/ethnicity, living arrange-
ments, or children under the age of 18 (all ps > .05). 
When comparing participants on marital status, there 
was a significant difference in proportions of partici-
pants in each of the comparison groups, such that 
there was a higher proportion of participants in the 
HL group were either Single or Divorced/Widowed 
and a higher proportion of participants in the NLL 
group who were Married (p = .011). Further differ-
ences were identified between the comparison groups 
based on education levels. A higher proportion of par-
ticipants identified as having Some College (possibly 
being college students) were in the HL group. In con-
trast, many participants in the NLL group indicated 
having Graduate or Professional degrees (p = .025).
 Age, marital status, and education level were in-
cluded as covariates for the comparisons of psycho-
social variables (see Table 2). There was a significant 
difference in levels of perceived stress between the 
participants in the three comparison groups. Those in 
the HL group had the highest mean score, followed by 
those in the ML group, and those in the NLL group 
had the lowest level of perceived stress (p < .001). 
When comparing rates of psychopathology, partici-
pants in the HL group had the highest proportion of 
individuals who met the criteria for major depressive 
disorder (MDD, 33.8%), generalized anxiety disor-
der (GAD, 25.3%), and somatization disorder (SD, 
20.1%), compared to those in both the ML (MDD, 
9.4%; GAD, 9.4%, SD, 9.8%) and NLL (MDD, 1.7%; 
GAD, 3.9%, SD, 1.6%) groups (all ps < .01). When 
comparing the four domains of the CIS, the partici-
pants in the HL group had significantly higher scores 
in all subscales (fatigue, concentration, motivation, 
and physical activity; all ps ≤ .001) compared to the par-
ticipants in the NLL group. Further differences were 
identified when comparing the quality-of-life indices 
between the participants in the comparison groups 
on the four subscales of the WHOQOL-BREF. The 
participants in the NLL group reported significant-

 Changes in Daily Behaviors. Changes in Daily 
Behaviors were assessed through specific items created 
by the researchers for this study. The participants were 
asked to indicate whether certain general daily health be-
haviors, including sleep, food consumption, and tech-
nology use, had stayed the same, increased, decreased, 
or were not applicable since before the pandemic began.
 Changes in Substance Use Behaviors. Chang-
es in Substance Use Behaviors were assessed through 
specific items created by the researchers for this study. 
The participants were asked to indicate whether cer-
tain substance use behaviors had stayed the same, 
increased, decreased, or were not applicable since be-
fore the pandemic began. The specific substance 
use behaviors assessed included: alcohol use, ciga-
rette use, marijuana use, opioid drug use, illicit drug 
use, anti-anxiety medication use, and sleep aid use.
Data Collection and Data Analysis
 The data collected for this study were weighted to 
the total U.S. population based on the 2018 U.S. Cen-
sus Bureau population estimates by age, sex, and race/
ethnicity (U.S. Census Bureau, 2020). Four age strata, 
two sex strata, and four race/ethnicity strata were used 
for the weighting procedure. To account for geographic 
clustering, a variable was also created based on the first 
two digits of the U.S. zip codes requested during data 
collection. Complex Sample Designs were used to con-
duct chi-square tests of independence for categorical 
comparisons and one-way ANOVAs for continuous 
variables, controlling for age, marital status, and edu-
cation level and adjusting for population weights, stra-
ta, and clustering. Post-hoc comparisons are reported 
in the tables for variables with significant differences. 
The significance level was set at α = .05, and pairwise 
deletion was used for any missing data points. A post-
hoc power analysis using a one-way ANOVA, with N 
= 2,530, α = .05, and moderate effect size, indicated ad-
equate power (1-β) = 1.00. All analyses were conduct-
ed using SPSS version 27 (IBM, Inc., Chicago, IL).

Results
 A total of 2,530 participants were included in the 
study and were placed into one of three comparison 
groups based on their total scores on the UCLA Lone-
liness Scale. The cut-off scores for loneliness severity 
on the UCLA scale were adapted from Cacioppo and 
Patrick (2008) and included: No/Low Loneliness (< 
28; NLL; n = 164), Moderate Loneliness (28-43; ML; 



26

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

.003). Of the other substances compared, no signifi-
cant differences in increases between the comparison 
groups were identified based on cigarette use, an-
ti-anxiety medication use, or sleep aid use (all p > .05).

Discussion
 The purpose of the study was to identify and pro-
file individuals in the U.S. population who reported 
high levels of loneliness during the initial pandemic 
lockdown, and to examine how loneliness relates with 
mental health, pandemic concerns, and substance use. 
We found an effect of age, with the youngest partici-
pants expressing higher rates of loneliness. This is con-
sistent with previous research on age as a risk factor 
for loneliness in studies that used the same measure, 
such as the UCLA-3 Loneliness Scale (von Soest et al., 
2020). Younger populations tend to emphasize social 
group size and closeness of relationships as more criti-
cal facilitators of social support (Green et al., 2001). In 
the current study, younger individuals may have been 
more impacted by the pandemic as a result of loneliness 
since they were isolated from broader networks of social 
support.  This finding adds to the literature that tends 
to focus solely on older adult populations traditionally 
identified as at risk for loneliness (Chawla et al., 2021).
  Married individuals made up a large majority of 
the group with no/low loneliness (76.9%), while sin-
gle and divorced/widowed participants showed the 
largest percentage in the high loneliness category. Re-
search indicates that having a spouse increases overall 
well-being and reduces feelings of loneliness. It is likely 
that having a committed partner in residence during 
the COVID-19 lockdown provided built-in social 
connection and social support, reducing many mar-
ried couples’ levels of loneliness (Stack & Eschleman, 
1998). Within education levels, individuals with a 
graduate or professional degree made up over half of 
the no/low loneliness group. This confirms previous 
findings, as education is associated with numerous fac-
tors that may be protective against loneliness, includ-
ing increased well-being, broader social circles, greater 
job satisfaction, better health, and lower divorce rates 
(Oreopoulos & Salvanes, 2011). Conversely, adults 
without a college degree experienced more financial 
hardships and disproportionately lost their jobs during 
the lockdown; both are risk factors for loneliness 
and may help explain this result (Parker et al., 2021).
 As expected, high loneliness scores were associat-

ly better quality of life in the physical health (p = 
.013), psychological (p <.001), social relationships (p  
<.001), and environmental (p <.001) subscales com-
pared to the participants in the HL comparison group.
 The comparisons of the changes in general daily 
behaviors from before the onset of the pandemic also 
controlled for age, marital status, and education level 
and are shown in Table 3. There was a significant dif-
ference between comparison groups on the change in 
sleep, such that a higher proportion of the HL group 
indicated a decrease in sleep (p = .006). A question was 
asked about changes in eating behaviors. While no sig-
nificant differences were identified between the groups 
based on junk food consumed, there was a difference 
in total food consumed. A greater proportion of par-
ticipants in the HL group indicated an increase or a 
decrease in food consumption, while a greater propor-
tion of participants in the NLL and ML groups report-
ed no changes in food consumption (p = .011). Ques-
tions about changes in physical activity showed that 
a greater proportion of participants in the HL group 
indicated a decrease in outdoor physical activity (both 
p = .005). Changes in technology use also differed sig-
nificantly among the comparison groups. A higher 
proportion of participants in the ML and HL groups 
reported increased news coverage (watched or read; p 
= .038). A higher proportion of the HL group report-
ed a significant increase in TV watching (p = .015).
 The last set of comparisons is focused on in-
creases in substance use behaviors during the initial 
COVID-19 Pandemic (see Table 4). For these com-
parisons, only those individuals who indicated that 
they use those substances are included. There is a 
significant difference in the increased rates of alcohol 
use since the pandemic began for the participants in 
the ML (36.5%) and HL (39.5%) groups compared to 
the participants in the NLL group (18.4%; p = .002). 
Likewise, the HL group had higher rates of increased 
marijuana use (42.5%) as compared to the NLL group 
(30.9%) and ML group (37.7%; p = .002). Increases in 
opioid use were also significantly higher for those in 
the HL group compared to the NLL or ML groups 
(p = .006). While the overall number of participants 
who use illicit drugs is small, there was a significant 
increase noted in illicit drug use between the partici-
pants in the comparison groups, with the highest pro-
portion (38.4%) in the HL group, followed by 20.5% 
in the NLL group, and 14.9% in the ML group (p = 



27

CORDARO ET AL.

swing in psychological distress, psychopathology, and 
maladaptive coping via alcohol, marijuana, opioids, 
and illicit drug use. In addition, individuals experi-
encing higher levels of loneliness were sleeping less, 
eating more or less food compared to pre-pandemic 
functioning, and were less likely to be physically active. 
Last, participants reporting higher levels of loneliness 
showed increased smartphone use, internet use, and 
increased news consumption and TV watching. Col-
lectively, these findings suggest that the use of alcohol, 
marijuana, opioids, illicit drugs, food consumption, 
and news consumption were behaviors that could tem-
porarily alleviate or numb depression and/or anxiety 
symptoms and perceived loneliness. These findings on 
daily health behaviors are striking in that these changes 
are not typically associated with loneliness and can eas-
ily go unrecognized. This creates cause for concern in 
that individuals suffering from loneliness likely are not 
being screened for changes in general health behaviors. 
Therefore, these individuals with higher levels of lone-
liness are likely to elude screening, detection, and ulti-
mately intervention and treatment (Gordy et al., 2021).
 Finally, results showed that higher levels of loneli-
ness were associated with increased alcohol consump-
tion, marijuana, opioid, and illicit drug use. However, 
there were no significant associations between loneli-
ness and the increased use of other substances such as 
cigarettes, anti-anxiety medications, or sleep aids. Pre-
vious studies showed a link between loneliness and sub-
stance use (Horigian et al., 2020) and between loneli-
ness and regular alcohol use (Bragard et al., 2022), and 
are consistent with the findings in the current study.
Limitations
 While the findings of this study have several prac-
tical implications for therapeutic interventions and 
bring awareness to the potential negative effects as-
sociated with high levels of loneliness, several factors 
in this cross-sectional study limited the interpreta-
tion of the data collected. Participants were recruited 
randomly via Facebook Ads, and respondents (N = 
2,530) were 47.7 (SD = 12.9) years old, ranging be-
tween 18 to 83 years, with 89.0% being female and 
91.3% white. Since this sample is not representative 
of the population, results must be interpreted ac-
cordingly. Secondly, this study was cross-sectional 
and used self-report measures, which may increase 
self-presentation bias. Further, since the participants 
were recruited through Facebook, it is plausible that 

ed with stress, depression, anxiety, and somatization 
symptomology. Previous research has documented 
loneliness as a predictor of mental health issues and 
disorders, whereby perceived loneliness acts as a risk 
mechanism for the onset and a sustaining factor for 
maintaining psychopathology symptoms (Law et al., 
2023). In addition, participants in the ML and HL 
groups had increased rates of alcohol use, marijuana 
use, opioid use, and illicit drug use, demonstrating 
higher levels of loneliness as a risk factor for maladap-
tive coping. However, in the current study, cigarette 
use, anti-anxiety medication use, and sleep aid use 
were not significantly different among loneliness com-
parison groups. Previous research findings on loneli-
ness and substance abuse are mixed and inconsistent 
in the literature, depending on the type of substance 
being used and measured (Cadigan et al., 2023).
 In addition, those reporting higher loneliness lev-
els had more difficulty with sleep, focus, motivation, 
and physical activity than the ML or NLL groups. 
All four items from the CIS used in this study share 
symptoms of loneliness, anxiety, and depression. Re-
cent research shows that perceived stress levels during 
the beginning of the pandemic increased globally, and 
women, students, and young adults were the most 
at-risk (Gamonal-Limacoco et al., 2022). Similarly, 
prevalence rates of GAD, which is related to loneliness 
and stress (Cordaro et al., 2021), were higher at the 
beginning of the pandemic than before the outbreak. 
Health and physical safety concerns and uncertain-
ty about the future are examples of uncontrollable, 
pandemic-related worries that can help explain the 
increase in GAD symptoms. Notably, participants 
with HL reported depressive symptoms at a higher 
rate than perceived stress, GAD, or somatization dis-
order. Not surprisingly, the NLL group had the high-
est quality of life and best scores on physical health, 
psychological, social relationships, and environmen-
tal measures. This stable pattern of the results of the 
psychosocial comparisons confirms previous find-
ings about the symptomology of loneliness and the 
broad domains of life that social isolation influences 
(Hawkley & Cacioppo, 2010; Jaremka et al., 2013).
 Loneliness was a major mechanism of COVID-19 
pandemic traumatic stress through the disruption and 
upending of general daily behaviors established prior 
to the onset of this traumatic stressor event. Perceived 
loneliness due to social isolation contributed to an up-



28

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

connections play in buffering loneliness, quality social 
support as a protective measure should be incorporat-
ed into prevention and early prevention strategies as 
well. For example, including early health promotion 
and treatment strategies for boosting opportunities 
for social interactions, improving social skills and so-
cial cognition, and enhancing social support (Masi et 
al., 2011) in the initial stages of collective trauma can 
help mitigate the unwelcomed experience of loneliness.

References
Beutel, M. E., Klein, E. M., Brahler, E., Reiner, I., 

Junger, C., Michal, M., Wiltink, J., Wild, P., Mun-
zel, T., Lackner, K. J., & Tibubos, A. N. (2017). 
Loneliness in the general population: Prevalence, 
determinants, and relations to mental health. 
BMC Psychiatry, 17(1). https://doi.org/10.1186/
s12888-017-1262-x   

Bosma, H., Jansen, M., Schefman, S., Hajema, K., 
& Feron, F. (2015). Lonely at the bottom: A 
cross-sectional study on being ill, poor, and lone-
ly. Public Health, 129(2), 185-187. https://doi.
org/10.1016/j.puhe.2014.11.016   

Bragard, E., Giorgi, S., Juneau, P., & Curtis, B. L. 
(2022). Daily diary study of loneliness, alcohol, 
and drug use during the COVID-19 pandem-
ic. Alcohol, Clinical and Experimental Research, 
46(8), 1539–1551. https://doi.org/10.1111/
acer.14889 

Burholt, V., Windle, G., Morgan, D. J., & Team., C. W. 
(2017). A social model of loneliness: The roles of 
disability, social resources, and cognitive impair-
ment. Gerontologist, 57(6), 1020–1030. https://
doi.org/10.1093/geront/gnw125  

Cacioppo, J. T., & Patrick, W. (2008). Loneliness: Hu-
man nature and the need for social connection (1st 
ed.). W.W. Norton & Co. 

Cadigan, J. M., Calhoun, B. H., Rhew, I. C., & Lee, 
C. M. (2023). Trajectories of loneliness during 
COVID‐19 pandemic and associations with men-
tal health and substance use. Journal of Research 
on Adolescence, 33(3), 816–-827. https://doi.
org/10.1111/jora.12841

Campagne, D. M. (2019). Stress and perceived isola-
tion (loneliness). Archives of Gerontology & Geri-
atrics, 82, 192–199. https://doi.org/10.1016/j.
archger.2019.02.007  

Chawla, K., Kunonga, T. P., Stow, D., Barker, R., 

they may report greater levels of loneliness compared 
to individuals who do not frequent social media. That 
said, while loneliness has been connected to social 
media use, research suggests that lonely individuals 
often benefit from social media interactions (Song 
et al., 2014). While the UCLA Loneliness Scale is 
widely regarded as a reliable measure, confounding 
factors such as daily mood, environmental stressors, 
and potential distractions, which were further impact-
ed by the pandemic, make the current study novel.
Future Directions
 The present study’s sample focused on individu-
als in the general population. However, more research 
on marginalized or at-risk groups experiencing lone-
liness during traumatic stressor events is warranted, 
including aging populations and those with other less 
typical mental health issues and disabilities. For exam-
ple, individuals with intellectual and developmental 
disabilities are more likely to experience loneliness 
(Gilmore & Cuskelly, 2014; Perese & Wolf, 2009). 
There is also evidence that the COVID-19 pandemic 
disproportionately impacted these individuals due to 
issues such as a temporary lack of caregivers, restric-
tion of visitors to group homes or long-term care 
settings, and the closing of day habilitations and vo-
cational programs (Constantino et al., 2020). Further 
research is needed to assess loneliness trends due to 
the pandemic in these underrepresented populations. 
Conclusion
 In preparation for future traumatic stressor events, 
the consequences of loneliness can present challenges, 
yet preventative and early intervention strategies can 
be used to protect those most at risk. This study pro-
vides a nuanced depiction of loneliness during a time 
of traumatic stress with implications for mental health 
and coping behaviors. Our findings suggest that indi-
viduals with greater perceived loneliness, particularly 
young single adults, may experience increased levels 
of psychopathology (i.e., MDD and GAD) and mal-
adaptive coping via increased alcohol and substance 
use. Therefore, in addition to screening for MDD and 
GAD, the use of screening tools for loneliness is rec-
ommended. Further, these individuals with higher lev-
els of perceived loneliness were at increased risk for fa-
tigue, lack of focus and motivation, and lethargy. Thus, 
it is important to screen and detect somatic complaints 
that can accompany feelings of loneliness and psycho-
pathology. Given the important role that close social 



29

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loneliness in people with intellectual disability: 
An explanatory model. Journal of Policy and Prac-
tice in Intellectual Disabilities, 11(3), 192–199. 
https://doi.org/10.1111/jppi.12089  

Gordy, A., Luo, H. H. W., Sidline, M., & Brownlee, 
K. (2021). The missing measure of loneliness: A 
case for including neededness in loneliness scales. 
International Journal of Environmental Re-
search and Public Health, 19(1), 429. https://doi.
org/10.3390/irerph19010429  

Green, L. R., Richardson, D. S., Lago, T., & Schat-
ten-Jones, E. C. (2001). Network correlates 
of social and emotional loneliness in young 
and older adults. Personality and Social Psy-
chology Bulletin, 27(3), 281–288. https://doi.
org/10.1177/0146167201273002  

Hall, B. J., Hobfoll, S. E., Canetti, D., Johnson, R. 
J., Palmieri, P. A., & Galea, S. (2010). Exploring 
the associating between posttraumatic growth 
and PTSD. Journal of Nervous and Mental Dis-
ease, 198(3), 180–186. https://doi.org/10.1097/
nmd.0b013e3181d1411b  

Hawkley, L. C., & Cacioppo, J. T. (2010). Loneli-
ness matters: A theoretical and empirical review 
of consequences and mechanisms. Annals of Be-
havioral Medicine, 40(2), 218–227. https://doi.
org/10.1007/s12160-010-9210-8    

Hawkley, L. C., Hughes, M. E., Waite, L. J., Masi, 
C. M., Thisted, R. A., & Cacioppo, J. T. (2008). 
From social structural factors to perceptions of 
relationship quality and loneliness: The Chicago 
Health, Aging, and Social Relations Study. The 
Journals of Gerontology: Series B, 63(6), S375–
S384. https://doi.org/10.1093/geronb/63.6.s375  

Hirschberger, G. (2018). Collective trauma and the 
social construction of meaning. Frontiers in 
Psychology, 9, 1441. https://doi.org/10.3389/
fpsyg.2018.01441  

Holman, E. A., Jones, N. M., Garfin, D. R., & Sil-
ver, R. C. (2023). Distortions in time perception 
during collective trauma: Insights from a national 
longitudinal study during the COVID-19 pan-
demic. Psychological Trauma: Theory, Research, 
Practice, and Policy, 15(5), 800–807. https://doi.
org/10.1037/tra0001326 

Horigian, V. E., Schmidt, R. D., & Feaster, D. J. 
(2020). Loneliness, mental health, and substance 
use among US young adults during COVID-19. 

Craig, D., & Hanratty, B. (2021). Prevalence of 
loneliness amongst older people in high-income 
countries: A systematic review and meta-anal-
ysis. PloS One, 16(7), e0255088. https://doi.
org/10.1371/journal.pone.0255088  

Clark, M., Bonnici, J., & Azzopardi, A. (2021). Lone-
liness in Malta: Findings from the first national 
prevalence study. Journal of Social and Personal 
Relationships, 38(9), 2751–2771. https://doi.
org/10.1177/02654075211020120  

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). 
A global measure of perceived stress. Journal of 
Health and Social Behavior, 24(4), 386–396. 
https://doi.org/10.2307/2136404  

Constantino, J. N., Sahin, M., Piven, J., Rodgers, R., & 
Tschida, J. (2020). The impact of COVID-19 on 
individuals with intellectual and developmental dis-
abilities: Clinical and scientific priorities. The Amer-
ican Journal of Psychiatry, 177(11), 1091–1093. 
https://doi.org/10.1176/appi.ajp.2020.20060780  

Cordaro, M., Grigsby, T. J., Howard, J. T., Deason, R. 
G., Haskard-Zolnierek, K., & Howard, K. (2021). 
Pandemic-specific factors related to generalized 
anxiety disorder during the initial COVID-19 
protocols in the United States. Issues in Mental 
Health Nursing, 42(8), 747–757. https://doi.org
/10.1080/01612840.2020.1867675    

Ernst, M., Niederer, D., Werner, A. M., Czaja, S. J., 
Mikton, C., Ong, A. D., Rosen, T., Brahler, E., 
& Beutel, M. E. (2022). Loneliness before and 
during the COVID-19 pandemic: A systematic 
review with meta-analysis. American Psycholo-
gist, 77(5), 660–677. https://doi.org/10.1037/
amp0001005  

Fitzke, R. E., Wang, J., Davis, J. P., & Pedersen, E. R. 
(2021). Substance use, depression, and loneliness 
among American veterans during the COVID-19 
pandemic. The American Journal on Addic-
tions, 30(6), 552–559. https://doi.org/10.1111/
ajad.13211  

Gamonal-Limacoco, S., Montero-Mateos, E., Loza-
no-Lopez, M. T., Macia-Casas, A., Matias-Fernan-
dez, J., & Roncero, C. (2022). Perceived stress in 
different countries at the beginning of the corona-
virus pandemic. International Journal of Psychi-
atry and Medicine, 57(4), 309–322. https://doi.
org/10.1177/00912174211033710  

Gilmore, L., & Cuskelly, M. (2014). Vulnerability to 



30

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

(2021). Despues de la Tormenta: Collective trau-
ma following Hurrican Maria in a northeastern 
Puerto Rican community in the United States. 
Journal of Community Psychology, 49(1), 118–
132. https://doi.org/10.1002/jcop.22442  

Masi, C. M., Chen, H.-Y., Hawkley, L. C., & Ca-
cioppo, J. T. (2011). A meta-analysis of interven-
tions to reduce loneliness. Personality and Social 
Psychology Review, 15(3), 219–266. https://doi.
org/10.1177/1088868310377394  

Mushtaq, R., Shoib, S., Shah, T., & Mushtaq, S. 
(2014). Relationship between loneliness, psychi-
atric disorders and physical health? A review on 
the psychological aspects of loneliness. Journal 
of Clinical and Diagnostic Research, 8(9), 1–4. 
https://doi.org/10.7860/jcdr/2014/10077.4828  

Muyan, M., Chang, E. C., Jilani, Z., Yu, T., Lin, J., & 
Hirsch, J. K. (2016). Loneliness and negative af-
fective conditions in adults: Is there any room for 
hope in predicting anxiety and depressive symp-
toms? The Journal of Psychology, 150(3), 333–341. 
https://doi.org/10.1080/00223980.2015.1039474  

Oreopoulos, P., & Salvanes, K. G. (2011). Priceless: 
the nonpecuniary benefits of schooling. Journal 
of Economic Perspectives, 25(1), 159–184. https://
doi.org/jep.25.1.159  

Palgi, Y., Shrira, A., Ben-Ezra, M., Shiovitz-Ezra, S., & 
Ayalon, L. (2012). Self- and other- oriented poten-
tial lifetime traumatic events as predictors of lone-
liness in the second half of life. Aging & Mental 
Health, 16(4), 423–430. https://doi.org/10.1080
/13607863.2011.638903  

Parker, K., Minkin, R., & Bennett, J. (2020, Septem-
ber 24). Economic fallout from COVID-19 con-
tinues to hit lower-income Americans the hardest. 
Pew Research Center. https://www.pewresearch.
org/social-trends/2020/09/24/economic-fallout-
from-covid-19-continues-to-hit-lower-income-
americans-the-hardest/ 

Perese, E., & Wolf, M. (2009). Combatting loneli-
ness among persons with severe mental illness: 
Social network interventions’ characteristics, ef-
fectiveness, and applicability. Issues in Mental 
Health Nursing, 26(6), 591–609. https://doi.
org/10.1080/01612840590959425  

Pinquart, M., & Sorensen, S. (2001). Influences on 
loneliness in older adults: A meta-analysis. Basic 
and Applied Social Psychology, 23(4), 245–266. 

Journal of Psychoactive Drugs, 53(1), 1–9. https://
doi.org/10.1080/02791072.2020.1836435  

Hutten, E., Jongen, E. M. M., Hajema, K., Ruiter, 
R. A. C., Hamers, F., & Bos, A. E. R. (2021). 
Risk factors of loneliness across the life span. 
Journal of Social and Personal Relationships, 
39(5), 1482–1570. https://doi.org/https://doi.
org/10.1177/02654075211059193  

Ingram, I., Kelly, P. J., Deane, F. P., Baker, A. L., 
Goh, M. C. W., Raftery, D. K., & Dingle, G. A. 
(2020). Loneliness among people with substance 
use problems: A narrative systematic review. Drug 
and Alcohol Dependence, 39(5), 447–483. https://
doi.org/10.1111/dar.13064 

Jaremka, L. M., Fagundes, C. P., Peng, J., Bennett, J. 
M., Glaser, R., Malarkey, W. B., & Kiecolt-Glaser, 
J. K. (2013). Loneliness promotes inflammation 
during acute stress. Psychological Science, 24(7), 
1089–1097. https://doi.org/10.1177/095679761
2464059     

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. 
(2010). The patient health questionnaire somat-
ic, anxiety, and depressive symptom scales: A 
systematic review. General Hospital Psychiatry, 
32(4), 345–359. https://doi.org/10.1016/j.gen-
hosppsych.2010.03.006  

Lara, E., Caballero, F. F., Rico-Uribe, L. A., Olaya, 
B., Haro, J. M., Ayuso-Mateos, J. L., & Miret, M. 
(2019). Are loneliness and social isolation associ-
ated with cognitive decline? International Jour-
nal of Geriatric Psychiatry, 34(11), 1613–1622. 
https://doi.org/10.1002/gps.5174  

Law, C., Belli, G. M., & Boisseau, C. L. (2023). Char-
acteristics associated with loneliness and coping 
skills utilization during the COVID-19 pandemic. 
Psychological Reports, 126(6), 2690–2706. https://
doi.org/10.1177/00332941221093744

Lee, E. (2012). Review of the psychometric evidence 
of the Perceived Stress Scale. Asian Nursing Re-
search, 6(4), 121–127. https://doi.org/10.1016/j.
anr.2012.08.004 

Lowe, B., Decker, O., Muller, S., Brahler, E., Schellberg, 
D., Herzog, W., & Herzberg, P. Y. (2008). Valida-
tion and standardization of the Generalized Anxi-
ety Disorder Screener (GAD-7) in the general pop-
ulation. Medical Care, 46(3), 266–274. https://
doi.org/10.1097/MLR.0b013e318160d093  

Macias, R. L., LeBron, A., Taylor, K., & Silva, M. 



31

CORDARO ET AL.

sy-072720-020131  
U.S. Census Bureau. (2020). Annual estimates of 

the resident population by sex, age, race, and His-
panic origin: April 1, 2010 to July 1, 2018, 2020. 
https://www.census.gov/data/tables/time-series/
demo/popest/2010s-national-detail.html 

Usher, K., Bhullar, N., & Jackson, D. (2020). Life in 
the pandemic: Social isolation and mental health. 
Journal of Clinical Nursing, 29(15-16), 2756–
2757. https://doi.org/10.1111/jocn.15290 

Vercoulen, J. H. M. M., Alberts, M., & Blejenberg, 
G. (1999). The checklist of individual strength 
(CIS). Gedragstherapie, 32, 131–136. 

Victor, C. R., & Yang, K. (2012). The prevalene of 
loneliness among adults: A case study of the 
United Kingdom. The Journal of Psychology, 
146(1-2), 85–104. https://doi.org/10.1080/00
223980.2011.613875 

Vindegaard, N., & Benros, M. E. (2020). COVID-19 
pandemic and mental health consequences: Sys-
tematic review of the current evidence. Brain, 
Behavior, and Immunity, 89, 531–542. https://
doi.org/10.1016/j.bbi.2020.05.048  

von Soest, T., Luhmann, M., & Gerstorf, D. (2020). 
The development of loneliness through ado-
lescence and young adulthood: Its nature, cor-
relates, and midlife outcomes. Developmental 
Psychology, 56(10), 1919–1934. https://doi.
org/10.1037/dev0001102   

Wang, C., Pan, R., Wan, X., Tan, Y., Xu, L., Ho, 
C. S., & Ho, R. C. (2020). Immediate psycho-
logical responses and associated factors during 
the initial stage of the 2019 coronavirus dis-
ease (COVID-19) epidemic among the general 
population in China. International Journal of 
Environmental Research and Public Health, 
17(5), 1729. https://www.mdpi.com/1660-
4601/17/5/1729 

WHOQOL, I. (1996). WHOQOL-BREF Intro-
duction, Administration, Scoring and Generic 
Version of the Assessment: Field Trial Version. 
https://www.who.int/mental_health/media/
en/76.pdf?ua=1  

WHOQOL-Group. (1998). Development of the 
World Health Organization WHOQOL-BREF 
quality of life assessment. Psychological Medi-
cine, 28(3), 551–558. 

Wilson, R. S., Krueger, K. R., Arnold, S. E., Schnei-

https://doi.org/10.1207/s15324834basp2304_2  
Russell, D. W. (1996). UCLA Loneliness Scale (Ver-

sion 3): Reliability, validity, and factor structure. 
Journal of Personality Assessment, 66(1), 20–40. 
https://doi.org/10.1207/s15327752jpa6601_2 

Russell, D. W., Peplau, L. A., & Cutrona, C. E. (1980). 
The Revised UCLA Loneliness Scale: Concur-
rent and discriminate validity evidence. Journal of 
Personality and Social Psychology, 39(3), 472–480. 
https://doi.org/10.1037//0022-3514.39.3.472 

Russell, D. W., Peplau, L. A., & Ferguson, M. L. 
(1978). Developing a measure of loneliness. Jour-
nal of Personality Assessment, 42(3), 290–294. 
https://doi.org/10.1207/s15327752jpa4203_11 

Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., 
Victor, A., Omori, K., & Allen, M. (2014). Does 
Facebook make you lonely? A meta analysis. Com-
puters in Human Behavior, 36, 446–452. https://
doi.org/10.1016/j.chb.2014.04.011  

Spitzer, R. L., Kroenke, K., & Williams, J. B. W. 
(1999). Validation and unity of a self-report ver-
sion of PRIME-MD: The PHQ primary care 
study. JAMA: The Journal of the American Med-
ical Association, 282(18), 1737–1744. https://doi.
org/10.1001/jama.282.18.1737  

Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Lowe, 
B. (2006). A brief measure for assessing general-
ized anxiety disorder: the MDD-7. Archives of 
Internal Medicine, 166(10), 1092–1097. https://
doi.org/10.1001/archinte.166.10.1092 

Stack, S., & Eschleman, J. R. (1998). Marital status 
and happiness: A 17-nation study. Journal of 
Marriage and Family, 60(2), 527–536. https://
doi.org/10.2307/353867  

Stek, M. L., Vinkers, D. J., Gussekloo, J., Beekman, 
A. T., van der Mast, R. C., & Westendorp, R. G. 
(2005). Is depression in old age fatal only when 
people feel lonely? American Journal of Psychia-
try, 162(1), 178–180. https://doi.org/10.1176/
appi.ajp.162.1.178  

Stravynski, A., & Boyer, R. (2001). Loneliness in rela-
tion to suicide ideation and parasuicide: A pop-
ulation-wide study. Suicide and Life-Threatening 
Behavior, 31(1), 32–40. https://doi.org/10.1521/
suli.31.1.32.21312  

Taylor, S. (2022). The psychology of pandemics. 
Annual Review of Clinical Psychology, 18, 581–
609. https://doi.org/10.1146/annurev-clinp-



32

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

der, J. A., Kelly, J. F., Barnes, L. L., Tang, Y., & 
Bennett, D. A. (2007). Loneliness and risk of 
Alzheimer’s disease. Archives of General Psychi-
atry, 64(2), 234. https://doi.org/10.1001/arch-
psyc.64.2.234    

World Health Organization. (2022). COVID-19 pan-
demic triggers 25% increase in prevalence of anxiety 
and depression worldwide. https://www.who.int/
news/item/02-03-2022-covid-19-pandemic-trig-
gers-25-increase-in-prevalence-of-anxiety-and-de-
pression-worldwide 

Xu, J., & Ou, L. (2014). Resilience and quality 
of life among Wenchuan earthquake survi-
vors: The mediating role of social support. 
Public Health, 128(5), 430–437. https://doi.
org/10.1016/j.puhe.2014.03.002  



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Table 1

Demographic Comparisons (N = 2,530)

Note. Values shown are Means or Column Percentages (Standard Errors).
Post-hoc comparisons use alphabetical superscripts to denote significant group differences 
with  a = No/Low Loneliness; b = Moderate Loneliness; and c = High Loneliness.



34

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

Table 2

Psychosocial Comparisons (Controlling for Age, Marital Status, and Education Level)

Note. Values shown are Means or Column Percentages (Standard Errors).
Post-hoc comparisons use alphabetical superscripts to denote significant group differences with a = No/Low 
Loneliness; b = Moderate Loneliness; and c = High Loneliness.



35

CORDARO ET AL.

Table 3Table 3

Changes in Daily Behaviors (Controlling for Age, Marital Status, and Education Level)

Note. Values shown are Means (Standard Errors).
Post-hoc comparisons use alphabetical superscripts to denote significant group differences with
a = No/Low Loneliness; b = Moderate Loneliness; and c = High Loneliness.



36

LONELINESS, MENTAL HEALTH, AND SUBSTANCE ABUSE

Table 4

Substance Use Increases During Initial COVID-19 Pandemic (Based on Current Users; 

Controlling for Age, Marital Status, and Education Level)

Note. Values shown are Column Percentages (Standard Errors).
Post-hoc comparisons use alphabetical superscripts to denote significant group differences with
a = No/Low Loneliness; b = Moderate Loneliness; and c = High Loneliness.


