Examining the Direction of Effects Between COVID-19 Experiences,General Well-Being,Social Media Engagement,and Insomnia Symptoms Among University Students

Abstract

The present longitudinal study assessed the direction of effects between COVID-19 experiences, general well-being, socio-emotional motives for online communication, problematic social media use, and insomnia symptoms, among a sample of emerging adults at university. Participants (N = 619; 64% female; 66% White) completed an online survey 5 months apart during the COVID-19 pandemic. Results from an autoregressive cross-lagged model showed that poorer general well-being at baseline predicted greater endorsement of socio-emotional motives for online communication and higher insomnia symptoms over time. Negative COVID-19 experiences predicted higher social (but not emotional) motives for online communication. Lastly, insomnia symptoms and more problematic social media use predicted more negative COVID-19 experiences 5 months later (but not vice versa). Understanding the mechanisms of these associations will provide a better understanding of the factors that promote positive psychosocial functioning among emerging adults during the current COVID-19 pandemic and may inform psychosocial adjustment during future pandemics.

Keywords

problematic social media use COVID-19 stress sleep quality psychosocial adjustment bidirectional

Examining the Direction of Effects between COVID-19 Experiences, General Well-being, Social Media Engagement, and Insomnia Symptoms among University Students

The Centers for Disease Control and Prevention (CDC) confirmed the first COVID-19 case in the U.S. on January 20, 2020 (Harcourt et al., 2020). On March 11, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic (WHO, 2020). As of June 12, 2021, there have been over 33.2 million confirmed cases and nearly 600,000 deaths due to COVID-19 in the U.S. (CDCa, 2020). Due to the highly contagious nature of COVID-19, governments around the world have adopted restrictive measures on travel and social contact to curb the spread of the virus (CDCb, 2020). These regulations have been shown to be effective for limiting the spread of COVID-19 but have had ripple effects for obstructing daily routines, resulting in increased stress (Park et al., 2020). Consequently, there is a need to explore correlates of psychological well-being in the context of the COVID-19 pandemic among populations that have been shown to be susceptible to mental health challenges (Bono et al., 2020), which includes emerging adults at university/college. Several studies have classified emerging adulthood as a sensitive age period for the onset of mental illness, such as anxiety and depression.

Research investigating well-being among university students has also noted concerns regarding the prevalence of poor sleep (Dowdell & Clayton, 2019). In one study, nearly 30% of students reported having an extremely difficult time falling asleep on three or more days within the past week (Elflein, 2020). Findings from a U.S. study of over 7600 emerging adults across six universities showed that 35% of students reported obtaining less than 7 hours of sleep per night (Becker et al., 2018). One variable that has been consistently linked to both poor mental health and poor sleep is social media use (Rosen et al., 2016). In 2016, results based on a nationally representative sample of U.S. adults showed that 18-to-29-year-olds reported the highest prevalence rates of social media use across various platforms (e.g., Facebook—88% and Instagram—59%) relative to older adults (Greenwood et al., 2016). One of the most appealing features of social media is the “social” element, which allows for the establishment and maintenance of interpersonal relationships (Ellison et al., 2011). Indeed, the maintenance of social ties during emerging adulthood has been deemed an important developmental task of this age period (Roisman et al., 2004) and maintaining positive relationships has been shown to be associated with better psychological well-being, whereas thwarted interpersonal needs predict negative psychological adjustment (Gómez-López et al., 2019). Past research suggests that social and emotional engagement with others are two prevalent motives for social media use among emerging adults (McMillan & Morrison, 2006), but research on the psychosocial correlates of these socio-emotional motives during the COVID-19 pandemic is lacking. Furthermore, the high prevalence of social media use among emerging adults has led to concerns about social media use addiction and its negative implications for mental health (Vannucci et al., 2017).

Psychological well-being, sleep, and social media use have been investigated as important domains of emerging adults’ functioning pre-COVID-19. Thus, it is unsurprising that the burgeoning number of studies examining psychosocial functioning among emerging adults within the context of the COVID-19 pandemic have assessed indices of mental health (e.g., Marroquín et al., 2020), sleep-wake behaviors (e.g., Marelli et al., 2020) and social media use (Pahayahay and Khalili-Mahani, 2020). Very few studies, however, have assessed overlapping associations among these three constructs (Sañudo et al., 2020) within the same study and no studies to date have specifically assessed possible bidirectional associations among these constructs during the COVID-19 pandemic. The goal of the present study, therefore, was to address this important gap in the literature and explore the directionality of associations between emerging adults’ COVID-19 experiences, general well-being, social media use (including socio-emotional motives for electronic communication and problematic social media use) and insomnia symptoms during the COVID-19 pandemic.

Given the heightened stress associated with the pandemic, it is unsurprising that most published studies within the field have focused on assessing stress, anxiety, and depression (Hoyt et al., 2020; Kecojevic et al., 2020) among students. Emerging adults at university may be particularly vulnerable to mental health challenges because the university context may exacerbate existing susceptibilities to mental illness (Pedrelli et al., 2015). In their open-ended reflections on the perceived impact of the pandemic on their personal lives, 36% of university students expressed experiencing some form of psychological distress (Hoyt et al., 2020). When asked to reflect on the specific impact of the pandemic on their mental health, over 70% of students reported that they perceived an increase in their stress and anxiety levels because of the pandemic (Wang et al., 2020). Although past studies have modeled COVID-19 experiences as a predictor of psychological well-being, it is important to examine the role of relevant psychosocial factors as possible predictors of COVID-19 experiences. Therefore, longitudinal studies that specifically examine bidirectional associations between COVID-19 experiences and psychological well-being are warranted.

Beyond concerns about the impact of the pandemic on well-being, researchers have also investigated the consequences of the pandemic for electronic communication, including social media use. Public health guidelines on limiting in-person social contact (CDCa, 2020) led to an increased reliance on technology-mediated communication. Many studies have reported increased time spent on social media and various digital communication and entertainment platforms (e.g., Netflix, Zoom) since the pandemic (Pahayahay and Khalili-Mahani, 2020). Studies have linked time spent on social media and other digital communication platforms to worse psychological outcomes and poor sleep among emerging adults before the pandemic (Scott et al., 2017; Vannucci et al., 2019). During the pandemic, mass media coverage of the pandemic has been associated with higher stress (Park et al., 2020), and higher COVID-19-related social media use was associated with worse mental health (Drouin et al., 2020; Kecojevic et al., 2020; Zhao & Zhou, 2020).

Despite the evidence linking social media use and poor psychological functioning, some reports have challenged the dominant narrative that social media use contributes to poor mental health. For example, recent findings based on three nationally representative adolescent samples from the U.S. and the U.K. showed that the relationship between time spent on social media use and depressive symptoms weakened over time (Vuorre et al., 2021). Importantly, several studies have shown that there may be interpersonal and psychological benefits of social media use (Coyne et al., 2013). During the pandemic, social media and other digital communication platforms have provided opportunities to connect with friends and family, amidst the stay-at-home orders and social distancing regulations (Watson et al., 2020). According to the uses and gratification theory, understanding the role of media use in psychosocial adjustment requires researchers to move beyond a mere assessment of time spent on various media platforms and instead assess specific motives that drive individuals’ media use (Katz et al., 1974). Within the context of the pandemic, individuals reported informational, emotional, and peer support motives for using various online and social media platforms (Zhong et al., 2021). In one study, findings indicated that 34% of participants reported using online communication with friends and family as a coping strategy to reduce COVID-19 stress (Park et al., 2020). While socio-emotional motives for social media use may be well-intentioned, it is unclear whether these motives facilitate specific online behaviors that effectively promote well-being (Jarman et al., 2021). Overall, findings from studies examining the link between motives for social media use and psychological well-being have been mixed (Perugini & Solano, 2021; Schivinski et al., 2020). Within the field, there is a lack of theoretical models and empirical evidence on the mechanisms that explain dynamic relationships between motives for social media use and psychological well-being. Overall, it appears that socio-emotional motives may develop from two oppositional factors—either from thwarted interpersonal needs (Ryan & Deci, 2000) suggesting a negative association between socio-emotional motives and well-being or from high social competence, as proposed by the rich get richer hypothesis (Dredge & Schreurs, 2020), which would suggest a positive association between socio-emotional motives and well-being. Combined, these two competing hypotheses could result in an overall null association between socio-emotional motives for social media use and psychological well-being.

One area of concern relating to emerging adults’ social media use is the potential for developing an addiction (Shensa et al., 2017). According to the habit-developed-cognitive-affective-behavioral theory of social media dependence, a normative behavior (e.g., social media use) may lead to dependence (i.e., a form of “soft addiction”) that ultimately results in addictive behavior through maladaptive cognitions and distorted affect (Wang et al., 2015). Furthermore, results from an empirical test of their theoretical model indicated that deficiencies in self-regulation was a significant positive predictor of psychological dependence—albeit concurrently (Wang et al., 2015). Thus, in addition to the unidirectional predictive effect of problematic social media use on poor psychological functioning (e.g., Boer et al., 2021), there is also theoretical support for poor psychological functioning as an antecedent to problematic social media use (Davis, 2001), creating the possibility of a vicious cycle. Importantly, however, the direction of effects between problematic social media use and psychological well-being has not been investigated within the context of the COVID-19 pandemic.

Past research on the relationship between social media use and psychosocial functioning has often included a discussion of sleep problems as a possible mechanism, owning to the vast body of research linking poor sleep to negative emotional adjustment (Keles et al., 2020). According to the displacement hypothesis, social media use reduces the window of opportunity for adequate sleep (Woods & Scott, 2016), especially among emerging adults at university who already struggle to balance competing academic, extra-curricular, personal, and relational demands. In one U.S. study based on a nationally representative sample of young adults (ages 19–32 years old), results showed that both volume and frequency of social media use were significant risk factors for reporting high sleep disturbance (Levenson et al., 2016). Increased physiological and cognitive arousal due to emotional content on social media, increased emotional investment in social media, as well as exposure to blue light, which reduces melatonin, are possible mediators of the link between social media use and poor sleep (Riemann et al., 2010; Scott & Woods, 2018, 2019). Notably, emerging research highlights the need to investigate bidirectional associations between social media use and sleep, given the evidence that poor sleep may precede social media use because insomnia symptoms may drive individuals to engage in online activity as a way to cope with disturbed sleep (Scott & Woods, 2019; Tavernier & Willoughby, 2014). However, no studies to date have specifically assessed the direction of effects between specific motives for social media use, problematic social media use, and sleep quality within the context of the pandemic.

In terms of the relationship between sleep and psychological well-being, experimental evidence supports the causal role of adequate sleep for positive emotional functioning (Gujar et al., 2011; Short & Louca, 2015). Fewer experimental studies have investigated the alternative path to examine whether poor emotional functioning influences sleep duration, although many researchers theorize about significant bidirectional associations between psychological well-being and sleep behaviors (Kahn et al., 2013). The sleep to forget and sleep to remember (SFSR) model of emotional memory processing proposes that adequate sleep facilitates the memory encoding of highly charged affective experiences, while simultaneously reducing the emotional reactivity of that experience, which serves as a protective factor against the development of mood disorders (Walker & van der Helm, 2009). Empirically tested theoretical models provide support for bidirectional associations between sleep and emotional functioning (Kahn et al., 2013). According to the neurocognitive model of insomnia, both cognitive-behavioral (e.g., rumination) and neurobiological (e.g., diurnal cortisol) factors negatively impact psychological and physiological states that promote sleep, resulting in impaired sleep processes and ultimately poor psychological adjustment, including depression, addiction, and anxiety (Riemann et al., 2010). Negative psychological functioning is also theorized to predict increased stress, leading to a state of “conditioned arousal,” which acts as a key mechanism that perpetuates a vicious cycle between emotion regulation and insomnia (Riemann et al., 2010).

The pandemic may have provided more opportunities for sleep time because of public health regulations that limited travel and work (Sañudo et al., 2020), but the increased COVID-19-related stress, and isolation may have negatively impacted the quality of individuals’ sleep (Cellini et al., 2020). Among a U.S. sample of emerging adults, depressive symptoms, anxiety symptoms, and higher COVID-19-related worry were significant concurrent predictors of poor sleep quality (Hyun et al., 2021). Given that most studies on sleep and well-being during the pandemic has been based on cross-sectional studies, it remains to be determined whether sleep problems at baseline may predispose individuals to more negative COVID-19-related experiences and poorer general well-being or whether the relationship is reversed.

The Present Study

One limitation of the available research is the overreliance on cross-sectional data used to make assumptions about the direction of effects regarding COVID-19 experiences and various indices of psychosocial adjustment. The COVID-19 pandemic has led to rapid changes in the lives of emerging adults over a relatively short period of time. It is, therefore, necessary to move beyond cross-sectional studies that provide a single snapshot of adjustment in time, to longitudinal research that can help ascertain changes in psychosocial functioning as emerging adults navigate the ever-changing context of this pandemic (Wade et al., 2020). The goal of the present study was to explore the direction of effects between negative COVID-19 experiences, general well-being, socio-emotional motives for electronic communication, problematic social media use, and insomnia symptoms, among a sample of emerging adults at university. To help clarify the nature of these associations, we explored possible bidirectional associations over 5 months during the pandemic. Specific directional hypotheses supported by relevant theory and empirical research reviewed above are outlined below:

General well-being and social media engagement: We predicted a significant negative unidirectional association from general well-being to socio-emotional motives for electronic communication. We predicted a significant negative bidirectional association between general well-being and problematic social media use.

Social media engagement and insomnia symptoms: We predicted a null association between socio-emotional motives for electronic communication and insomnia symptoms. We predicted a significant unidirectional effect from problematic social media use to insomnia symptoms.

General well-being and insomnia symptoms: We predicted a significant negative bidirectional association between general well-being and insomnia symptoms.

Negative COVID-19-related experiences in relation to general well-being, socio-emotional motives for electronic communication, problematic social media use, and insomnia symptoms: Generally, we expected that more negative COVID-19-related experiences would be associated with more negative psychosocial functioning, but we did not specify any directional hypotheses for these relationships, given the unprecedented nature of the pandemic, which overlapped with unique stressors related to racism and political movements in the U.S. Although the trajectory of confirmed cases and deaths is on an overall downward trend in the U.S. since May 2021, many emerging adults in the U.S. and around the world are still living through the ripple effects of the pandemic. Findings from the present study, therefore, have important theoretical and empirical implications for understating psychosocial adjustment both within the context of the current pandemic and may also inform preparations for future pandemics.

Method

Participants and Procedures

Participants were 619 emerging adults, aged 18–29 years old (Mean = 21.8, SD = 2.2), who were enrolled at various universities and colleges across the U.S. Approximately two-thirds of participants identified as female (64%); 75% identified as heterosexual; and the majority (89%) identified as non-Hispanic, from diverse racial backgrounds (66% White, 17% Asian, 8% Black, 9% Other). Only 12% of participants were first-generation students. Table 1 shows descriptive statistics for the following participant characteristics: gender, race, ethnicity, level of study, living arrangement, first-generation status, family income, educational aspiration, and employment status.

Table 1.

Participant Background Characteristics at Baseline.

Variable	Frequency (%)
Gender
Male	208 (33.6)
Female	395 (63.8)
Other	16 (2.6)
Race
Asian	108 (17.4)
Black or African American	47 (8.7)
Caucasian	410 (66.2)
Other	54 (8.7)
Ethnicity
Hispanic, Latino, or Spanish	70 (11.3)
Non-Hispanic, Latino, or Spanish origin	549 (88.7)
Level of study
Freshman	91 (14.7)
Sophomore	124 (21.6)
Junior	161 (26.0)
Senior	233 (37.6)
Living arrangement (as of May 1, 2020)
Single	60 (9.7)
Shared	559 (90.3)
First-generation status
First-generation	77 (12.4)
Non-first generation	532 (85.9)
Unknown status	10 (1.6)
Family income
Unknown	90 (14.5)
Less than $20,000	28 (4.5)
$20,000–$44,999	86 (13.9)
$45,000–139,999	217 (35.1)
$140,000–$149,999	30 (4.8)
$150,000–$199,999	59 (9.5)
$200,000 or more	109 (17.6)
Educational aspiration
Graduate from a 4-year college/university	173 (27.9)
Complete a master’s degree or other equivalent	221 (35.7)
Complete a Ph.D., M.D., J.D., or other equivalent professional degree	218 (35.2)
Other	7 (1.1)
Employment status
No, but actively searching for employment	225 (36.3)
Confirmed position but have not begun working	82 (13.2)
Yes, currently working	142 (22.9)
Unemployed (not actively searching for employment)	170 (27.5)

N = 619.

Participants were recruited through online social media posts and an online participant panel through Mechanical Turk (MTurk). Participants were invited to participate in a 3-wave longitudinal research study on COVID-19 experiences, sleep, and psychosocial adjustment. The present study is based on the first two assessments. Participants completed a 20-minute online survey through Qualtrics. Time 1 data collection took place between May 27 and June 17, 2020 and Time 2 data collection took place between September 22 and October 29, 2020. Participants who were recruited through social media posts were entered for a chance to win 1 of 25 gift cards valued at $10.00 at Time 1 and $20.00 at Time 2. Participants who were recruited through MTurk received $2.50 at Time 1 and $3.00 at Time 2. At Time 1, 904 individuals consented to participate in the study. Participants were excluded because of failed attention checks at baseline or Time 2 (n = 44; 5%), incomplete surveys (n = 134; 15%), were not undergraduate students (n = 72; 8%) and were older than 30 years old (n = 35; 4%), which resulted in a sample size of 619 participants. Of these participants, 344 (55.6%) completed the survey at Time 2. The study’s procedures were approved by the Institutional Review Board at Wesleyan University.

Measures

Demographics and Background Variables

Several questions assessed socio-demographic information and background characteristics (e.g., age, gender, race, ethnicity, family income, employment status, and living situation). A “sample” variable was created to identity participants who were recruited via social media posts and participants recruited through MTurk. Based on past research (Bono et al., 2020; Hoyt et al., 2020; Perz et al., 2020), we accounted for shared method variance between several socio-demographic factors (gender, sexual orientation, race-ethnicity, first-generation status, and living arrangement) and the main study variables at baseline. Furthermore, sleep duration and time spent on social media use were included as time-varying covariates in the main autoregressive cross-lagged model.

Negative COVID-19 Experiences

The Epidemic Pandemic Impacts Inventory (EPII; Grasso et al., 2020) is a 92-item scale which was developed to measure perceived negative implications of the COVID-19 pandemic across various domains of one’s personal and family life. 19 items across 5 subscales of the EPII were selected to assess negative experiences of the COVID-19 pandemic: social (3 items; e.g., “Unable to physically attend classes and interact face-to-face with classmates”); emotional (5 items; e.g., “Not satisfied with mental health treatment or therapy); physical (2 items; e.g., “Overeating or eating more unhealthy food”); quarantine (3 items; e.g., “Isolated or quarantined due to possible exposure to and/or symptoms of COVID-19”); and infection (6 items; e.g., “Currently have symptoms of the disease but have not been tested”). Response options were adapted from the original scale to reflect the experiences of family members who either lived with or lived away from the individual and were coded as follows: Yes (Me) = 1, Yes (person in home) = 1, Yes (immediate family member, not physically living with me) = 1, and No or N/A = 0. Responses were, therefore, based on an event-type scale approach, where a score of “1” indicated the presence of a specific negative experience—either for that individual and/or for a family member. A score of “0” indicated the absence of that negative experience. Scores were summed across all items and coded such that higher scores indicated more negative COVID-19 experiences. Traditional psychometric properties of reliability (e.g., Cronbach’s alpha) are inappropriate for event-type scales such as the EPII and were, therefore, not computed (Felix et al., 2019). Recent studies demonstrating significant associations between EPII scores and various indices of poor psychological health (e.g., PTSD, anxiety, depression, and perceived stress) provide evidence of construct validity for the EPII (see Grasso et al., in press).

General Well-being

The General Well-being Schedule (GWB; Fazio, 1977) was used to assess well-being, based on 12 items across four domains: anxiety (4 items; e.g., “Have you been anxious, worried, or upset?”), self-control (3 items; e.g., “Have you been in firm control of your behavior, thoughts, emotions, or feelings?”), depression (3 items; e.g., “Have you felt down hearted and blue?”), and general health (2 items; e.g., “How concerned or worried about your health have you been?”). Nine items were based on a 6-point Likert scale (e.g., 0 = Not at all, 1= Most of the time, 2 = A good bit of the time, 3 = Some of the time, 4 = A little of the time, and 5 = None of the time) and three items were based on an 11-point rating scale (e.g., 0 = Very concerned to 10 = Not at all concerned). Scores were summed across all items and coded such that higher scores indicate better well-being (i.e., lower anxiety, higher self-control, lower depression, and better general health). Cronbach’s α for both Time 1 and Time 2 was .896.

Problematic Social Media Use

The 9-item Social Media Disorder Scale (SMDS; Van den Eijnden et al., 2016) was used to assess problematic social media use. Participants responded to nine Yes/No questions (e.g., “Think about your patterns of social media use since COVID-19. During the past MONTH, have you… regularly found that you can’t think of anything else but the moment that you will be able to use social media again?”). Scores were summed with higher scores indicating greater problematic social media use. Cronbach’s α at Time 1 and Time 2 were .782 and .770, respectively.

Motives for Online Communication

Five items from the Motivations for Electronic Interaction Scale (Nesi & Prinstein, 2015) were used to assess social and emotional motives for online communication. Participants were instructed to think about the reasons why they engaged in online/electronic communication across all online platforms and rate the extent to which each item reflected their motives. Items were adapted to the COVID-19 pandemic context. The social motive subscale included 2 items (e.g., “…to make social plans with my friends, and/or romantic partner”) and the emotional motive subscale included three items (e.g., “…to try to get support when I am upset about COVID-19). Responses ranged from 1 = Not at all true or N/A to 5 = Extremely true. Scores were averaged and coded such that higher scores indicated a greater endorsement of social and emotional motives for online communication. Cronbach’s α for the social and emotional subscales at Time 1 were .683 and .778, respectively. Cronbach’s α for the social and emotional subscales at Time 2 were .511 and .591, respectively.

Time Spent Using Social Media

Participants responded to the single item, “On an average day, how much time do you spend on social media? Think about all the different accounts you use and provide your best guess.” Response options ranged from 0 to 13, where 0 = I do not use social media, 1 = Less than 30 minutes, 2 = 30 minutes to 1 hour, 3 = 1 hour to 1.5 hours, 4 = 1.5 hours–2 hours through …11= 5 hours–5.5 hours, 12 = 5.5 hours–6 hours, and 13 = over 6 hours.

Insomnia Symptoms

The 7-item Insomnia Severity Index (ISI; Morin et al., 2011) was used to assess insomnia symptoms (e.g., “Difficulty falling asleep”). Responses ranged from 1 = None to 5 = Very severe. Scores were summed across all items and coded such that higher scores indicated greater insomnia symptoms or poorer sleep quality. Cronbach’s α for Time 1 and Time 2 were .860 and .860, respectively.

Sleep Duration

Sleep duration was assessed separately for the week (“During the week, how many actual hours of sleep do you think you typically get per night?”) and weekend (“During the weekend, how many actual hours of sleep do you think you typically get per night?”). Response options ranged from 4 = 4 hours or less to 10 = 10 hours or more, with 30-minute increments between each scale anchor within this range. A composite sleep duration score was created by averaging participants’ sleep duration across the week and weekend.

Plan of Analysis

The main analyses were based on an autoregressive cross-lagged model in AMOS (Arbuckle, 2020; Version 27), which explored possible bidirectional associations between the six main study variables (Negative COVID-19 experiences, general well-being, social motive for online communication, emotional motive for online communication, problematic social media use, and insomnia symptoms), controlling for possible bidirectional associations with two time-varying covariates, sleep duration and time spent using social media. Autoregressive paths were specified from Time 1 to Time 2 for all variables. Cross-lagged paths were specified between the six key variables and the two time-varying covariates. To account for shared method variance, correlations were specified among all variables at baseline, including Time 1 demographic variables (gender, race-ethnicity, sexual orientation, living arrangement, first-generation status, and sample). Correlations were also specified among the error terms for all variables measured at Time 2. Missing data was estimated using full information maximization likelihood (FIML) in AMOS (Enders & Bandalos, 2001). To evaluate model fit, the comparative fit index (CFI ≥.95), the root mean square error approximation (RMSEA ≤.05), and the global chi-square (non-significant) were examined.

Missing Data

There were no significant differences between participants who had complete data (n = 344) and those who had baseline data only (n = 275) on general well-being, negative COVID-19 experiences, motives for electronic communication, or insomnia symptoms at baseline. Further, no differences were found for gender, race, ethnicity, first-generation status, living arrangement, sexual orientation, sleep duration, or time spent on social media at baseline. Those with incomplete data were more likely to have been recruited via social media posts, compared to the MTurk sample, χ² (1, N = 619) = 5.70, p = .017, and more likely to be seniors compared to continuing students, χ ² (1, N = 619) = 5.07, p = .024. Missing data was handled using FIML, which is one of the most valid recommended approaches for handling missing data, relative to listwise or pairwise deletion (Enders, 2013; Enders & Bandalos, 2001).

Results

Preliminary Analyses

Results of independent samples t-tests comparing mean differences on the main study variables between various groups of individuals based on the socio-demographic factors at baseline are presented in a supplementary Table S1. Descriptives and bivariate correlations for the main study variables at Time 1 and Time 2 are presented in Table 2. More negative COVID-19 experiences were correlated with higher social and emotional motives for electronic communication, more problematic social media use, more insomnia symptoms, and lower general well-being. A greater endorsement of the emotional (but not the social) motives for electronic communication was significantly correlated with lower well-being and poorer sleep quality. In addition, higher emotional motives for electronic use were correlated with greater social motives for electronic use. Further, problematic social media use was positively correlated with social and emotional motives for electronic communication, time spent on social media use, and insomnia symptoms. Finally, insomnia symptoms and problematic social media use were negatively correlated with general well-being.

Table 2.

Bivariate Correlations and Descriptives for Study Variables at Time 1 and Time 2.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1. GWB₁	—
2. COVID-NEG₁	−.31***	—
3. MEC-Soc₁	−.07	.20***	—
4. MEC-Emo₁	−.39***	.23***	.44***	—
5. ISI₁	−.54***	.26***	.07	.30***	—
6. SDwkwd₁	.19***	−.08*	.04	−.08*	−.32***	—
7. SMU-Time ₁	−.16***	.12**	.15***	.29***	−.15***	−.03	—
8. SMUD₁	−.33***	.29***	.20***	.48***	.29***	−.06	.48***	—
9. GWB₂	.61***	−.23***	−.08	−.18***	−.32***	.10	−.11*	−24***	—
10. COVID-Neg₂	−.21***	.56***	.13*	.18**	.24***	−.11*	.06	.29***	−.33***	—
11. MEC-Soc₂	−.28***	.24***	.36	.50***	.16**	.04	.24***	.31***	−.35***	.25***	—
12. MEC-Emo₂	−.31***	.23***	.40***	.47***	.19***	−.03	.21***	.33***	−.36***	.22***	.76***	—
13. ISI₂	−.43***	.15**	.03	.17**	.56***	−.23***	.10	.24***	−.55***	.23***	.27***	.23***	—
14. SDwkwd₂	.16**	−.06	−.04	−.09	−.14*	.54***	−.02	−.10	.18**	−.08	.12*	−.10	−.36***	—
15. SMU-Time₂	−.03	.03	.16**	.19**	.15**	.02	.61***	.33***	−.10	.10	.26***	.24***	.10	−.06	—
16. SMUD₂	−.28***	.20***	.17**	.35***	.24***	−.10	.38***	.64***	−.35***	.29***	.34***	.41***	.33***	−.14**	.39***	—
Mean (SD)	50.3 (14.0)	9.9 (4.9)	3.2 (1.1)	2.6 (1.0)	16.9 (5.9)	7.6 (1.3)	6.5 (3.5)	2.4 (2.1)	49.5 (14.0)	8.7 (5.0)	2.9 (1.0)	2.7 (0.9)	16.1 (5.4)	7.4 (1.2)	5.5 (3.2)	2.3 (2.1)
Range	12–84	0–51	1–5	1–5	7–35	4–10	0–13	0–9	12–84	0–51	1–5	1–5	7–35	4–10	0–13	0–9

Note. GWB = General Well-being; COVID-Neg = Negative COVID-19 experiences; MEC-Soc = Social motives for electronic communication; MEC-Emo = Emotional motives for electronic communication; ISI = Insomnia severity index; SDwkwd = Sleep duration (averaged across the week and weekend); SMU-Time = Time spent on social media use; SMUD = Social media use disorder.

Subscripts 1 and 2 indicate Time 1 and Time 2 assessments, respectively.

*p < .05; **p < .01; ***p < .001.

Primary Analyses: Assessing Bidirectional Associations

Results of model fit indices indicated good fit: (CFI = .985; RMSEA = .039 [.027–.050]; χ² (48) = 92.23, p < .001). Results of all autoregressive and cross-lagged paths are presented in Table 3. All stability paths were significant, although there was noticeable variability in the strength of the autoregressive paths, which ranged from β = .15, p =. 004 for social motive for electronic communication to β = .61, p < .001, for general well-being.

Table 3.

Autoregressive and Cross-Lagged Paths for Main Variables.

	B	SE	β	P
General well-being _T1 → general well-being _T2	0.61	0.05	.61	<.001
General well-being _T1 → COVID-19-neg exp. _T2	0.02	0.02	.07	.217
General well-being _T1 → MEI-Social _T2	−0.01	0.00	-.12	.035
General well-being _T1 → MEI-Emotional _T2	−0.01	0.00	-.16	.007
General well-being _T1 → insomnia symptoms _T2	−0.07	0.02	-.19	<.001
General well-being _T1 → social media use disorder _T2	−0.01	0.01	-.07	.194
COVID-19-Neg exp. _T1 → COVID-19-neg exp. _T2	0.52	0.05	.51	<.001
COVID-19-Neg exp. _T1 → general well-being _T2	−0.07	0.13	-.03	.587
COVID-19-Neg exp. _T1 → MEI-Social _T2	0.02	0.01	.10	.044
COVID-19-Neg exp. _T1 → MEI-Emotional _T2	0.01	0.01	.06	.245
COVID-19-Neg exp. _T1 → insomnia symptoms _T2	−0.02	0.05	-.02	.720
COVID-19-Neg exp. _T1 → social media use disorder _T2	0.00	0.02	.00	.929
MEI-social _T1 → MEI-Social _T2	0.13	0.05	.15	.004
MEI-social _T1 → COVID-19-neg exp. _T2	0.10	0.22	.02	.660
MEI-social _T1 → general well-being _T2	−0.55	0.61	-.04	.368
MEI-social _T1 → MEI-Emotional _T2	0.20	0.04	.25	<.001
MEI-social _T1 → insomnia symptoms _T2	0.04	0.24	.01	.862
MEI-social _T1 → social media use disorder _T2	0.03	0.09	.02	.697
MEI-emotional _T1 → MEI-Emotional _T2	0.21	0.05	.24	<.001
MEI-emotional _T1 → COVID-19-neg exp. _T2	−0.02	0.27	.00	.952
MEI-emotional _T1 → general well-being _T2	1.26	0.75	.09	.094
MEI-emotional _T1 → MEI-Social _T2	0.32	0.06	.34	<.001
MEI-emotional _T1 → insomnia symptoms _T2	−0.24	0.29	-.05	.416
MEI-emotional _T1 → social media use disorder _T2	0.09	0.11	.04	.409
Insomnia symptoms _T1 → insomnia _T2	0.41	0.05	.44	<.001
Insomnia symptoms _T1 → COVID-19-neg exp. _T2	0.10	0.05	.12	.025
Insomnia symptoms _T1 → general well-being _T2	−0.04	0.13	-.02	.771
Insomnia symptoms _T1 → MEI-Social _T2	0.00	0.01	-.02	.720
Insomnia symptoms_T1 → MEI-Emotional _T2	0.00	0.01	-.01	.911
Insomnia symptoms_T1 → social media use disorder _T2	0.00	.02	.01	.812
Social media use disorder _T1 → social media use disorder _T2	0.54	0.05	.54	<.001
Social media use disorder _T1 → general well-being _T2	−0.40	0.36	-.06	.265
Social media use disorder _T1 → COVID-19-neg exp. _T2	0.41	0.13	.18	.001
Social media use disorder _T1 → MEI-Social _T2	0.02	0.03	.05	.435
Social media use disorder _T1 → MEI-Emotional _T2	0.05	0.02	.11	.050
Social media use disorder _T1 → insomnia _T2	0.23	0.14	.09	.109

Note: B = Unstandardized regression coefficient; SE = Standard error; β = Standardized regression coefficient; COVID-19-Neg Exp. = Negative COVID-19 experiences; MEI-Social = Social motive for electronic communication; MEI-Emotional = Emotional motive for electronic communication; T1 = Time 1; T2 = Time 2. Not shown are autoregressive and cross-lagged paths for the covariates, sleep duration and time spent on social media use.

Table S1.

Relationship between Covariates and Key Study Variables at Baseline.

	COVID-19 Experiences	General Well-being	MEI-Social	MEI-Emotional	Insomnia Symptoms
Gender
Female	13.45 (5.47)	48.92 (13.73)	3.34 (1.10)	2.81 (1.04)	17.06 (5.82)
Male	11.55 (5.79)	53.65 (13.71)	2.82 (1.07)	2.30 (0.98)	16.42 (5.81)
t (607)	−4.02***	4.04***	−5.64***	−5.95***	−1.29
Race-ethnicity
Non-Hispanic white	13.35 (5.84)	50.14 (14.23)	3.22 (1.12)	2.61 (1.02)	16.53 (5.58)
Other	12.11 (5.46)	50.49 (13.77)	3.06 (1.11)	2.68 (1.08)	17.52 (6.25)
t (617)	−2.66**	0.30	−1.78	0.81	2.05*
Sexual orientation
Non-heterosexual	13.95 (6.23)	43.39 (13.00)	3.11 (1.16)	2.88 (1.01)	18.85 (5.79)
Heterosexual	12.49 (5.50)	52.52 (13.65)	3.17 (1.10)	2.56 (1.04)	16.31 (5.78)
t (616)	2.75**	−7.24***	−0.67	3.25**	4.70***
Living arrangement
Live alone	10.80 (5.62)	52.25 (13.05)	3.12 (1.13)	2.49 (0.95)	17.25 (6.05)
Shared living	13.07 (5.68)	50.07 (14.13)	3.16 (1.11)	2.65 (1.05)	16.90 (5.86)
t (617)	−2.94**	1.14	−0.27	−1.11	0.44
First-generation status
Non-first generation	13.07 (5.66)	50.67 (14.19)	3.20 (1.10)	2.64 (1.05)	16.68 (5.75)
First-generation	12.06 (5.94)	47.55 (12.75)	2.96 (1.19)	2.66 (1.02)	18.47 (6.37)
t (607)	1.44	1.83	1.74	−0.16	−2.52*
Sample
Local	14.60 (6.02)	49.58 (13.58)	3.53 (1.01)	2.75 (1.06)	16.42 (5.62)
Qualtrics panel	11.74 (5.22)	50.73 (14.32)	2.91 (1.12)	2.56 (1.02)	17.26 (6.02)
t (617)	−6.255***	0.996	−6.961***	−2.249*	1.725

N = 619; *p < .05; **p< .01; ***p < .001

For cross-lagged paths (see Figure 1), poorer general well-being at baseline predicted greater endorsement of both social (β = −.12, p = .035) and emotional motives (β = −.16, p = .007) for electronic communication at Time 2. Higher well-being at baseline also predicted fewer insomnia symptoms at Time 2 (β = −.19, p < .001). More negative COVID-19 experiences at baseline predicted higher endorsement of the social (but not emotional) motives for electronic communication at Time 2, (β = .10, p = .044). Insomnia symptoms and problematic social media use at baseline predicted more negative COVID-19 experiences at Time 2, (β = .12, p = .025 and β = .18, p = .001, respectively), but not vice versa. Lastly, there was only one significant bidirectional relationship, such that higher social motive for electronic communication at baseline predicted higher emotional motive for electronic communication at Time 2, (β = .25, p < .001), and, in turn, higher emotional motive for electronic communication at baseline predicted higher social motive for electronic communication at Time 2, (β = .24, p < .001).

Figure 1.

Significant Paths among Key Study Variables. Note. Values are standardized beta coefficients; values in parentheses are standard errors; Superscripts 1 and 2 indicate Time 1 and Time 2, respectively. *p < .05; **p < .01; ***p < .001.

Discussion

The goal of the present study was to explore the direction of effects between negative COVID-19 experiences, general well-being, social and emotional motives for electronic communication, problematic social media use, and insomnia symptoms among a sample of emerging adults at university. Findings showed that poorer general well-being at baseline predicted greater endorsement of both social and emotional motives for electronic communication over time. Emerging adults who were less well-adjusted psychologically may have a greater need to seek interpersonal support to cope with their psychological and health challenges. Social distancing regulations may have channeled this need for interpersonal support to the online context (Pahayahay and Khalili-Mahani, 2020). In one study of adults, higher anxiety was significantly associated with increased use of technology to connect with others to cope with the pandemic (Drouin et al., 2020). For emerging adults who face interpersonal and psychological challenges, the ease and accessibility that online communication provides may be an appealing feature when seeking emotional support during periods of isolation (Thomas et al., 2020). Some online platforms also provide opportunities to participate in specific causes that might facilitate a sense of belonging among individuals who might otherwise feel disconnected from their physical social networks. For example, the Black Lives Matter protests of the summer of 2020 provided opportunities for Black and minority youth to congregate in online spaces to seek emotional support resources (Miller et al., 2020). Furthermore, Asians and Asian-Americans—who have been the target of racial discrimination during the pandemic—have actively engaged with social media to cope with COVID-19-related racism (Yang et al., 2020).

Emerging adults who were better psychologically adjusted at baseline reported improved sleep quality 5 months later during the pandemic. Self-efficacy might be one mediating factor in the link between well-being and insomnia symptoms (Gipson et al., 2020). Another possibility is that higher well-being might promote psychological resources such as grit and resilience that facilitate effective coping during the pandemic (Paredes et al., 2021), which may, in turn, reduce insomnia symptoms. Although many researchers have theorized about the bidirectional association between sleep and emotion regulation (Kahn et al., 2013), most empirical studies focus on sleep characteristics as predictors of psychological functioning (Harvey, 2011), while less attention has been devoted to exploring psychological functioning as a predictor of sleep behaviors. Interestingly, findings from the current study support a unidirectional association between well-being and sleep, where higher general well-being at baseline predicted reduced insomnia symptoms over time, but poor sleep at baseline did not predict changes in well-being over time, which is consistent with findings from Tavernier et al. (2019). Within the context of the COVID-19 pandemic, lower well-being might be associated with changes in emerging adults’ living arrangements (e.g., moving back home with parents), which may have direct implications for sleeping environments (e.g., crowded living and work spaces) that negatively affect sleep quality.

Interestingly, results showed that negative COVID-19 experiences at baseline did not predict sleep quality over time, but poorer sleep quality at baseline predicted more negative COVID-19 experiences over time. Insomnia symptoms may be more prevalent among emerging adults with greater financial worries, who may have had to seek employment (Kecojevic et al., 2020), putting them at a greater risk for negative COVID-19 experiences. Furthermore, given the link between insomnia and physical health it is possible that those who already struggled with insomnia symptoms at the baseline may have had poorer lifestyle habits that may have exacerbated their risk for negative COVID-19 experiences. Good sleep quality may serve as an important physiological and psychological resource that protects individuals from the negative implications of COVID-19 (Morin et al., 2020), which is in line with the sleep to forget, sleep to remember (SFSR) hypothesis (Walker & van der Helm, 2009). Negative COVID-19 experiences at baseline did not predict insomnia symptoms over time. One reason for this null effect may be related to the nature of the items that were used to assess the negative COVID-19 experiences. The items covered a relatively wide range of consequences from social and emotional well-being to implications for physical health. Specific domains of adjustment (e.g., mental health) might have stronger implications for sleep problems, relative to other domains (e.g., missing out on social activities). In fact, some of the negative implications of the pandemic, such as having limited social contact with others, might promote better sleep. For example, stay-at-home orders reduced opportunities for alcohol consumption and socializing with friends—a common university activity that has been found to negatively impair sleep quality. Thus, the different domains of adjustment assessed as part of the negative COVID-19 experiences measure could have had both positive and negative implications for subsequent sleep quality, resulting in an overall null effect.

Surprisingly, more negative COVID-19 experiences at baseline predicted higher social, but not emotional motives for electronic communication at Time 2. The EPII encompassed different subscales of experiences such as infection, quarantine, and experiences related to mental and physical health challenges. Perhaps those with higher EPII scores had more experiences specific to being apart from family and friends (e.g., quarantine, change in living arrangements) and therefore were more likely to use electronic interactions to connect with other than someone who did not experience as much separation (e.g., already living off-campus). The null finding for COVID-19 experiences and emotional motives could indicate that the EPII items were not capturing a diverse number of COVID-19 stressors that could explain differences in emotional motives for electronic use. In addition, we were limited to examining emotional motives during the pandemic only, rather than examining change in emotional motives from pre-pandemic to pandemic levels. Perhaps those who were already motivated by emotional needs pre-pandemic did not change those behaviors due to pandemic-specific stressors.

In addition to assessing socio-emotional motives for online communication, the present study also examined the role of problematic social media use as an additional dimension of social media engagement. Results indicated that more problematic social media use at baseline predicted more negative COVID-19 experiences over time. Problematic social media use encompasses elements of addictive behaviors (Van den Eijnden et al., 2016), which may promote ineffective coping strategies, that might put individuals at a higher risk for negative COVID-19 experiences. Moreover, the highly charged socio-political context of the summer of 2020 (Weine et al., 2020), which overlapped with the pandemic, may have contributed to individuals’ social media engagement in a way that exposed them to COVID-19 conspiracy theories, leading to reduced compliance with public health regulations and subsequently, more negative COVID-19-related consequences (Banai et al., 2021).

Interestingly, there was only one significant bidirectional relationship, such that higher social motives for electronic communication at baseline predicted higher emotional motives for electronic communication 5 months later, and vice versa. During stressful events, adults tend to increase relationship-seeking behaviors (Kaniasty, 2012). Previous studies have linked social media use to less loneliness and greater interpersonal social support (Wright, 2012). Our findings suggest that these different motives may provide a positive feedback loop for emerging adults as they navigate their interpersonal needs.

Limitations and Future Research

Findings from the current study should be interpreted within the context of the study’s limitations. First, findings cannot be generalized to emerging adults outside of the current sample. Emerging adults at university represent a unique subgroup with experiences that differ from emerging adults who do not attend university. Furthermore, the COVID-19 pandemic—although a global phenomenon—has manifested differently in different parts of the world, owning to differences in governmental resources and regulations. Thus, the results from the present study may differ across emerging adults from different communities. Second, the lack of a racially diverse sample prohibited the investigation of race-ethnicity as a possible moderator of the associations in this study. This is a necessary avenue for future research, given the fact that racial-ethnic minorities have been disproportionately impacted by the pandemic (Laster Pirtle, 2020). Third, the correlational design of the study prohibits causal claims regarding the significant associations reported. Nevertheless, the value of longitudinal research to understand how the implications of the COVID-19 pandemic manifests psychosocially among diverse groups of emerging adults should not be discounted (Wade et al., 2020). Fourth, although we controlled for time spent on social media, we did not assess the content of electronic communication. It will be important for future research to determine whether social and emotional coping motives overlap with information-seeking motives for electronic communication, given the increased emotional toll of information overload and misinformation (Zhong et al., 2021). Fifth, there was a relatively high attrition rate. Notably, in addition to testing for systematic bias within the data, the handling of missing data was based on the use of FIML, which is one of the most valid approaches to missing data analysis because of its ability to account for bias (Enders, 2013).

Despite these limitations, the study makes an important contribution to the literature by providing a test of the direction of effects between relevant indices of psychosocial adjustment among emerging adults during the COVID-19 pandemic. Findings have implications for emerging adults’ understanding of the factors that may promote sleep and psychological well-being. Results of the present study highlight the need for policy makers and university administration to provide opportunities for positive electronic communication to support emerging adults who are at risk for poor psychological functioning. The fact that poor sleep quality was associated with an increased risk of negative consequences of COVID-19 suggests that emerging adults may benefit from adopting good sleep hygiene that promote healthy sleep.

Conclusion

The COVID-19 pandemic remains a major global stressor for many individuals across various developmental age groups and socio-demographic contexts. Emerging adults, who are navigating the challenges and opportunities of the university experience may be especially susceptible to mental health challenges as they balance competing demands across different domains—academics, interpersonal relationships, autonomy, and financial independence—while developing their sense of identity. Our results showed that a greater sense of well-being was associated with a reduced need to seek social and emotional support online. Higher well-being also had positive implications for reducing insomnia symptoms. Importantly, better sleep quality appeared to protect individuals from experiencing negative consequences of COVID-19, but negative COVID-19 experiences did not subsequently predict sleep quality. Negative COVID-19 experiences promoted the need to seek social support online. Problematic social media use was a significant risk factor for experiencing more negative COVID-19-related consequences. Lastly, social and emotional coping motives for online communication appear to reinforce each other over time. These findings provide intriguing opportunities for future research to examine specific mechanisms of these associations to further clarify the factors that promote optimal sleep and positive psychosocial adjustment among emerging adults during the current COVID-19 pandemic and beyond.

Footnotes

Acknowledgments

The authors would like to express their gratitude to all the study participants, who graciously shared their personal experiences and gave their time and effort during this pandemic. We also thank Ruby Rechler for assistance with recruitment and for providing feedback on the study’s measures and design. Finally, the first author extends her deepest gratitude to her in-laws Ann and Kenneth Dubar, for consistent and reliable childcare during the COVID-19 pandemic, which allowed for the necessary writing and data analysis for this manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by a small grants program (Grants in Support of Scholarship) from Wesleyan University, which was awarded to the first author.

ORCID iDs

Royette T. Dubar:

Nicole K. Watkins:

Author Biographies

Royette T. Dubar is currently an Assistant Professor in the Department of Psychology and the Director of the Sleep and Psychosocial Adjustment Lab at Wesleyan University. Her research program examines the nature of the associations (including bidirectionality, mechanisms, and moderators) between sleep characteristics and various indices of psychosocial functioning with a focus on adolescents and emerging adults.

Nicole K. Watkins currently a Post-Doctoral Fellow in the Psychology Department at Wesleyan University. Her research interests focus on exploring associations between family transitions and family functioning with adolescent and emerging adult health and psychosocial well-being, and examining the role of romantic relationships during emerging adulthood.

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