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Transcending Time and Space with Digital Emotional Support



In the current digital age, emotional support is increasingly received through digital devices. However, virtually all studies assessing the benefits of emotional support have focused on in-person support. Using an experience sampling methodology, we assessed participants’ negative emotions, digital and in-person support for those emotions, and success in regulating them three times per day for 14 days, thus covering a wide range of digital support scenarios (N = 164 participants with 6,530 collective measurement occasions). We also considered whether participants were alone versus with others at the time of their negative emotion and higher versus lower in social avoidance as plausible moderators of when digital support was utilized and effective. We expected more pronounced use and efficacy of digital support when participants were alone and higher in trait social avoidance. However, digital support was used and perceived as effective for regulating negative emotions regardless of these factors and its beneficial effects were on par with those of traditional in-person support. The unique benefits of digital support may not be restricted to socially isolated or socially avoidant users. These findings are timely given the widespread anxiety and isolation under the current COVID-19 pandemic. If transcending time and space with digital emotional support is the new norm, the good news is that it seems to be working.
Transcending Time and Space with Digital Emotional Support
Tyler Colasante*, Lauren Lin*, Kalee De France, and Tom Hollenstein
Author Note
Tyler Colasante, Lauren Lin, and Tom Hollenstein, Department of Psychology, Queen’s
University, Kingston, ON, Canada. Kalee De France, Department of Psychology, Concordia
University, Montreal, QC, Canada.
* These authors contributed equally to this work and should thus be considered co-first
authors despite the presented author order.
This research was supported by a Natural Sciences and Engineering Research Council of
Canada grant (04560-2017) awarded to Tom Hollenstein. The authors declare no conflict(s) of
interest. We thank the participants and the members of the Adolescent Dynamics Lab who helped
with data collection and processing.
Correspondence concerning this article should be addressed to Tom Hollenstein,
Department of Psychology, Humphrey Hall, 62 Arch Street, Queen’s University, Kingston, ON,
Canada K7L 3N6. E-mail:
This is a preprint of the article, “Any time and place? Digital emotional support for digital natives”,
published in American Psychologist, doi:10.1037/amp0000708. Since important changes have been
made for publication, the published version should be consulted and cited in lieu of this version.
In the current digital age, emotional support is increasingly received through digital devices.
However, virtually all studies assessing the benefits of emotional support have focused on in-
person support. Using an experience sampling methodology, we assessed participants’ negative
emotions, digital and in-person support for those emotions, and success in regulating them three
times per day for 14 days, thus covering a wide range of digital support scenarios (N = 164
participants with 6,530 collective measurement occasions). We also considered whether
participants were alone versus with others at the time of their negative emotion and higher versus
lower in social avoidance as plausible moderators of when digital support was utilized and
effective. We expected more pronounced use and efficacy of digital support when participants
were alone and higher in trait social avoidance. However, digital support was used and perceived
as effective for regulating negative emotions regardless of these factors and its beneficial effects
were on par with those of traditional in-person support. The unique benefits of digital support
may not be restricted to socially isolated or socially avoidant users. These findings are timely
given the widespread anxiety and isolation under the current COVID-19 pandemic. If
transcending time and space with digital emotional support is the new norm, the good news is
that it seems to be working.
Keywords: text messaging, computer-mediated communication, emotional support,
emotion regulation, social avoidance, experience sampling method
Transcending Time and Space with Digital Emotional Support
When a negative emotion is experienced, emotional support from others can provide an
empathic connection, a sense of self-worth, and a conduit for emotion regulation (Lougheed,
Hollenstein, Lichtwarck-Aschoff, & Granic, 2015; Marroquín, 2011; Thoits, 2011). Emotional
support has traditionally been received in person, but digital devices have emerged as a
potentially dominant way to navigate emotions with the help of others: Up to 95% of young
adults own a digital device and 45% are “online on a near constant basis” (Pew Research Center,
2018). Digital devices can instantly connect individuals to emotional support regardless of when
they are feeling down and where they are (Leick, 2018; Ellis, 2019). Despite this potential to
transcend time and space, studies of digital device usage have primarily focused on their
potential harms (e.g., Shensa et al., 2020; Twenge, Joiner, Rogers, & Martin, 2018) or the
insufficiencies of digital support relative to in-person support in controlled laboratory settings
(Holtzman, DeClerck, Turcotte, Lisi, & Woodworth, 2017). Thus, we know very little about
when and for whom digital devices are successfully deployed on a daily basis as gateways to on-
demand emotional support.
Emotional Support
A typical (successful) emotional support scenario can be characterized as a process in
which (1) a negative emotion arises, (2) support is received for that emotion, and (3) the emotion
is downregulated (see Lougheed et al., 2015; Marroquín, 2011; Thoits, 2011). However, the
amount of support received typically depends on the intensity of the initial negative emotion.
More intense negative emotions are more difficult to regulate (Sheppes & Gross, 2012) and thus
require more support from others (Luminet IV, Bouts, Delie, Manstead, & Rime, 2000; Nezlek &
Kuppens, 2008). For example, it is relatively easy to reduce a minor annoyance on one’s own,
but anger elicited by a major betrayal is more difficult to regulate without the support of
someone else. Thus, more intense negative emotions are expected to elicit more support from
others. The more that support is perceived to be effective, the more it is thought to result in
successful regulation of the initial negative emotion (Oh, Ozkaya, & LaRose, 2014). For the
present study, we adopted this ‘emotion intensity emotional support emotion regulation
success’ framework to characterize the emotional support process across a variety of naturally
unfolding emotional events.
The Potential Benefits of Digital Devices for Emotional Support
The use of cell phones and related digital devices has recently evolved at a precipitous
rate (Pew Research Center, 2018). In the wake of this increase, popular opinion and researchers
alike have primarily focused on the harms of digital devices (e.g., anxiety, depression; O’Keeffe
& Clarke-Pearson, 2011; Shensa et al., 2020). However, empirical support for these negative
effects is limited and mixed (Booker, Kelly, & Sacker, 2018; Heffer, Good, Daly, MacDonell, &
Willoughby, 2019; Jelenchick, Eickhoff, & Moreno, 2013; Kross et al., 2013; Twenge &
Campbell, 2018; Twenge et al., 2018). For example, cross-sectional studies have found that
heightened digital activity is associated with lower well-being (Twenge & Campbell, 2018;
Twenge et al., 2018), whereas longitudinal evidence—which can clarify the direction of effects
over time—suggests that lower well-being predicts subsequent increases in digital activity (not
vice versa; Heffer et al., 2019). While researchers continue to debate the harms (or lack thereof)
of digital devices, much less attention has been paid to the benefits of such devices (Ellis, 2019).
An overlooked and potentially advantageous function of digital devices may be their
ability to connect users to others, anytime and anywhere (Leick, 2018; Ellis, 2019). Digital
devices were initially designed for interpersonal communication and are thus intimately tied to
how we navigate our emotions and relate to others (Kardos, Unoka, Pléh, & Soltész, 2018; Wei
& Lo, 2006). Indeed, participants in one study said they would miss interpersonal contact and
social support the most if they lost their cell phone for five days and those who were more likely
to regulate their negative emotions digitally were also more likely to miss their device (Hoffner
& Lee, 2015). The near-constant social connection afforded by digital devices likely plays an
important role in managing the impact of daily negative emotions.
Some studies have experimentally compared the benefits of digital versus in-person
interactions and emotional support. Sherman, Michikyan, and Greenfield (2013) found that self-
reported bonding was highest when participants engaged in a face-to-face interaction, followed
by video, audio, and instant messaging interactions. In a similar study, participants who engaged
in an in-person conversation reported higher levels of positive mood and basic needs satisfaction
in comparison to those who instant messaged (Sacco & Ismail, 2014). In the most direct existing
assessment of digital emotional support, Holtzman et al. (2017) found that participants who
received digital emotional support via text messaging after a stressor induced in the lab were less
likely to feel better than those who received in-person emotional support. Furthermore, a survey-
based study found that having more friends on social media to confide in was associated with
greater odds of depression, whereas a surplus of in-person supports was linked to lower odds of
depression (Shensa et al., 2020). While these results suggest that emotional support received
through digital means might be less effective than support received in person and perhaps even
harmful to one’s mental health, they present with significant limitations. A single, experimentally
induced emotional state and corresponding text-based support—as in Holtzman et al. (2017)—
may not reflect the range of negative emotions and digital supports experienced on a day-to-day
basis. With the exception of direct messaging, social media—the focus of Shensa et al. (2020)—
can be relatively shallow and socially diffuse: Reaching out to one’s broader social network may
not be the best way to ensure quality emotional support for specific emotions in the heat of the
moment (Frison & Eggermont, 2015; Nesi, Choukas-Bradley, & Prinstein, 2018). Therefore, it is
likely that existing studies have not tapped into the full breadth and potential of digital emotional
support. Naturalistic, daily measurement may provide a window into understanding when and by
whom digital devices are being used to navigate common emotional experiences. Focusing on
real life may further explain, at least in part, why purportedly harmful digital devices are being
used so frequently.
Contextual and Individual Difference Moderators
Few, if any, studies have considered moderators of digital emotional support
effectiveness. The plethora of null and mixed results from harm-focused studies suggests that the
link between digital activity and general well-being is nuanced. In other words, digital activity
may be harmful in some situations and for some people, but less harmful or even helpful in other
situations and for other people (see Heffer et al., 2019; Orben, 2020). Similarly, the use and
efficacy of digital emotional support may depend on contextual factors and individual
differences in traits that render digital emotional support more or less appropriate. For the present
study, we considered two possible factors underlying the use and success of digital support: the
availability of in-person support and trait-level tendencies of social avoidance.
The presence of others. Negative emotions are characterized by a lack of control and/or
comfort (Berkowitz, 1989; Oatley & Duncan, 1992; Raghunathan, Pham, & Corfman, 2006).
Digital emotional support may satisfy immediate control more than it satisfies comfort. Relative
to in-person support, digital support is available on demand (Leick, 2018; Ellis, 2019). This
immediacy may be helpful for alleviating urgent threat and high arousal, especially when the
recipient is alone and in-person support is not immediately available. In contrast, digital support
may be less effective for comforting feelings of loss. Unlike in-person support, digital support
lacks physical touch and it often lacks the dynamic and interactive facial and bodily cues that can
facilitate comfort (White & Dorman, 2001). Thus, when an individual is with others and in-
person support is available, digital support may be less likely and possibly perceived as a less
effective route.
Trait social avoidance. The strategic use of digital versus in-person emotional support
based on whether the individual is alone or not is likely a normative behavior. However,
individuals who demonstrate a stronger preference for social avoidance may opt for digital
support regardless of whether they are alone or with others. Individuals high in social avoidance
tend to feel uncomfortable in the presence of others and avoid social interactions (Elliot, Gable,
& Mapes, 2006). This does not always mean that such individuals prefer to be alone and do not
need emotional support from others; they may lack the confidence or motivation to approach
others for in-person support (Barry, Nelson, & Christofferson, 2013; Coplan & Rubin, 2010),
particularly while experiencing negative emotions. Thus, digital devices may provide individuals
high in social avoidance with a safe haven from which they can seek emotional support, even
when in-person support is available.
The Present Study
The current study employed an intensive experience sampling approach to assess
participants’ digital and in-person emotional support scenarios as they naturally unfolded on a
daily basis (14 days, 3 prompts per day, up to 42 instances of emotional support per participant).
We modelled emotional support at each prompt as a process in which (1) a negative emotion was
experienced, (2) support was received for that emotion, and (3) the emotion was regulated.
We considered plausible moderators to explain when and for whom digital emotional
support was more or less utilized and effective. Specifically, we assessed the use and efficacy of
digital versus in-person emotional support when participants were alone versus with others at
each prompt. We mirrored this contextual moderator at the trait level by assessing the use and
efficacy of digital versus in-person support for participants higher versus lower in social
avoidance. Our core hypotheses were as follows:
Digital hypothesis 1. When participants were alone (as opposed to with others), more
intense negative emotions would be associated with more digital support and, in turn, more
digital support would be associated with more emotion regulation success.
Digital hypothesis 2. For participants who were higher in social avoidance, more intense
negative emotions would be associated with more digital support and, in turn, more digital
support would be associated with more emotion regulation success.
To ensure that our core findings were unique to digital support, we controlled for in-
person support in all analyses and made the following complementary hypotheses:
In-person hypothesis 1. When participants were with others (as opposed to alone), more
intense negative emotions would be associated with more in-person support and, in turn, more
in-person support would be associated with more emotion regulation success.
In-person hypothesis 2. For participants who were lower in social avoidance, more
intense negative emotions would be associated with more in-person support and, in turn, more
in-person support would be associated with more emotion regulation success.
Participants (N = 188 participants with up to 7,770 prompts; 86% female; Mage = 19.10
years, SD = 2.95; 57% Caucasian, 29% Asian, 4% Black, 1% Latin American, and 9%
multiethnic/other) were recruited from an undergraduate introductory psychology course at a
medium-sized university in Canada. They participated in exchange for course credit. For details
on reduced sample used in analyses, see data reduction section below.
Initial visit. Participants registered online and were e-mailed a consent form. After
providing consent, they attended an in-person session at the university library in which they
completed a questionnaire on their demographics and social and emotional functioning,
including the social avoidance scale under investigation as a moderator in the present study. They
then downloaded the MetricWire experience sampling smartphone app (MetricWire, Kitchener,
ON) on their personal digital device with the help of a research assistant who also instructed
them on the experience sampling portion of the study. To prepare participants for reporting the
intensity (rather than presence versus absence) of daily negative emotions, they were provided
with examples of negative emotions along a continuum from 1 (not intense at all, I barely
noticed it) to 10 (the most intense).
Experience sampling. Starting the day after their initial visit, participants were prompted
by the MetricWire app to answer a brief set of questions regarding their negative emotions,
digital/in-person support received for those emotions, and perceived success in regulating them
every day at 11:00am, 4:00pm, and 8:30pm for 14 days (42 prompts). We elected for three
(rather than more) prompts per day to align with an earlier study we conducted with an early
adolescent sample that was in school at the time. Nonetheless, having three prompts ensured
sufficient time to actually experience a notable negative emotion between prompts. Each prompt
took approximately 1–2 minutes to complete and participants were given a 90-minute completion
window to accommodate their varying schedules. Research assistants monitored the fidelity of
prompt completion every evening and sent reminder e-mails to those who missed any prompts.
At the end of the 14-day period, participants were e-mailed a debriefing letter and allotted their
course credit.
Emotion intensity. At the beginning of each prompt, participants were asked to report the
intensity of the strongest negative emotion experienced since their last prompt on a 10-point
scale ranging from 1 (not intense at all, I barely noticed it) to 10 (the most intense).
Emotional support. Participants then reported how much digital support they received for
their negative emotion from people who were not physically with them (e.g., through text,
calling) and, separately, how much in-person emotional support they received from people who
were physically with them. They indicated both on a 10-point scale ranging from 1 (no support
at all) to 10 (a lot of support).
Emotion regulation success. Participants reported how successful they were in
regulating their negative emotion on a 10-point scale ranging from 1 (not at all) to 10 (very).
Alone/with others. For each prompt, we asked participants who was around them when
they experienced their negative emotion (parents/guardians, siblings, teacher, students at school,
staff at school, friends, boyfriend/girlfriend, boss, coworkers, strangers, other, I was alone). We
then binary coded responses as 0 = alone or 1 = with others.
Social avoidance. Participants completed the 8-item Behavioral Social Avoidance
subscale of the Cognitive–Behavioral Avoidance Scale (Ottenbreit & Dobson, 2004). They rated
each item (e.g., “I tend to make up excuses to get out of social activities”, “I tend to remain to
myself during social gatherings or activities”) on a 5-point scale ranging from 1 (not at all true
for me) to 5 (extremely true for me). The scale was reliable ( = .87).
Data Reduction
Four participants (aged 29, 29, 36, and 48 years) were excluded because they were
significantly older than the rest of the sample (17–22 years). To ensure data integrity, we
excluded participants who responded to less than 50% of all prompts (n = 17). We then excluded
individual prompts for which participants responded to less than 50% of questions (n = 1,260).
We also excluded prompts for which participants reported the lowest possible intensity score for
their negative emotion (i.e., ‘1 – not intense at all, I barely noticed it’) because such scores
implied no need for regulation and thus no need for emotional support (n = 359). We decided not
to impute or estimate missing data because the questions we asked were specific to each
prompt/situation. Our final sample size for analyses was 164 (N = 6,530 prompts; 88% female).
This sample size provided ~75% power to detect at least a medium-sized effect in a multilevel
analysis framework with our estimated intra-class correlation (ICC; see results section below;
Kleiman, 2020).
Analytic Approach
Given the nested structure of our data (i.e., up to 42 prompts belonging to the same
participant), we used multilevel modelling in Mplus 8 (Muthén & Muthén, 1998–2017) to
account for nonindependence in our data (Kenny, Kashy, & Bolger, 1998; Raudenbush & Bryk,
2002). Our within-level, repeated measures were emotion intensity, digital and in-person
emotional support, emotion regulation success, and whether participants were alone/with others
when they experienced their negative emotion. Our measures at the between level (i.e., fixed
within participants but differing between them) were social avoidance and gender (0 = female, 1
= male). Although gender was not focal to this study, we controlled for it because previous
studies have documented gender differences in emotion regulation (e.g., Nolen-Hoeksema &
Aldao, 2010). We did not consider moderation by gender because our sample was predominantly
female, leaving our male group underpowered. We followed established centering procedures for
multilevel models (Raudenbush & Bryk, 2002). Specifically, we group-mean centered emotion
intensity, digital emotional support, and in-person emotional support at the within level to
account for within-person effects (e.g., “Does receiving more digital/in-person emotional support
than usual [for that participant] coincide with greater emotion regulation success?”). We grand-
mean centered social avoidance at the between level to account for between-person effects (e.g.,
“Do participants with higher social avoidance than other participants tend to experience less
emotion regulation success?”). Alone/with others and gender were not centered because they
already had meaningful zero points.
We built our model in two steps. As depicted in Figure 1, we first conducted a multiple
indirect effects model for each prompt at the within level linking emotion intensity to digital and
in-person support (paths a1 and a2, respectively) and, in turn, linking digital and in-person
support to emotion regulation success (paths b1 and b2, respectively). We controlled for the
direct effect of emotion intensity on emotion regulation success (path c’) and the between-level
effect of gender on emotion regulation success. The indirect effects calculated from this model
(i.e., a1 x b1 and b1 x b2) tested our emotional support process in which more intense negative
emotions resulted in more digital and in-person support, in turn resulting in greater emotion
regulation success.
Our core hypotheses, however, pertained to the moderation of digital support paths. At
the second step, we added alone/with others as a within-level moderator of paths a1, a2, b1, and
b2 (see middle of Figure 1). Moderation of the a1 path tested if more intense negative emotions
were associated with more digital support for prompts when participants were alone versus with
others. Moderation of the b1 path tested if more digital support was associated with more
emotion regulation success when participants were alone versus with others (collectively testing
digital hypothesis 1). At the same step, we added social avoidance as a between-level moderator
of the same paths. This allowed us to test if more intense negative emotions were associated with
more digital support for participants who were higher versus lower in social avoidance, as well
as if more digital support was associated with more emotion regulation success for participants
who were higher versus lower in social avoidance (collectively testing digital hypothesis 2).
Accounting for paths a2, b2, and their moderation (i.e., testing in-person hypotheses 1 and 2)
allowed us to compare digital and in-person support effects in the same model and determine the
extent to which our hypothesized moderation effects were exclusive to digital support. Finally,
we calculated indices of moderated mediation to determine if moderated paths further disrupted
indirect effects (see Hayes, 2015).
Figure 1. Multilevel model predicting emotion regulation success.
Zero-order correlations and descriptive statistics are reported in Table 1. A preliminary
null model for emotion regulation success revealed an estimated ICC of .40. In other words, 40%
of the variance in emotion regulation success could be explained by differences between
participants; thus, by extension, up to 60% of the variance was within individuals, indicating that
emotion regulation success varied across prompts. This aligns with other intensive self-reported
longitudinal studies, which typically yield ICCs in the .20–.40 range (Bolger & Laurenceau,
At step 1, paths a1 and a2 were significant, indicating that prompts with more intense
negative emotions had more digital and in-person emotional support (estimates = .18, .13, SEs
= .02, .03, ps < .001, 95% CIs [.13, .22], [.08, .18], respectively). Paths b1 and b2 were also
significant, indicating that prompts with more digital and in-person emotional support were
characterized by greater emotion regulation success (estimates = .06, .12, SEs = .02, .02, ps < .
002, 95% CIs [.02, .09], [.08, .16], respectively). A significant path c’ indicated that prompts with
more intense negative emotions were characterized by lower emotion regulation success
(estimate = −.29, SE = .03, p < .001, 95% CI [−.34, −.24]). Both indirect effects (a1 x b1 and b1
x b2) were also significant (estimates = .01, .02, SEs = .003, .004, ps < .002, 95% CIs [.004, .02],
[.009, .02], respectively), suggesting that prompts with more intense negative emotions were
more likely to be characterized by emotion regulation success through more proximal digital and
in-person support. In terms of random effects, the intercepts of emotion regulation success,
digital emotional support, and in-person emotional support varied significantly between
participants (estimates = 2.85, 3.70, 3.54, SEs = .33, .43, .41, ps < .001, 95% CIs [2.27, 3.57],
[2.95, 4.64], [2.81, 4.45], respectively). Paths a1, a2, b1, b2, and c’ (Figure 1) also varied
significantly between participants (estimates = .04, .06, .02, .03, .08, SEs = .009, .01, .005, .006, .
01, ps < .003, 95% CIs [.03, .06], [.04, .08], [.008, .03], [.02, .05], [.06, .11], respectively). These
latter random effects indicated significant between-participant variability in the interrelations or
slopes of study constructs. Importantly, there was significant variability in the way participants’
negative emotions related to their digital/in-person support, as well as in the way their digital/in-
person support related to their perceived emotion regulation success. We thus proceeded to the
next step of our model: predicting the significant variability in these relations with plausible
Results after including moderators at step 2 are reported in Table 2. Against our digital
hypotheses 1 and 2, none of the paths leading to or from digital emotional support—including
indirect effects—were significantly moderated by alone/with others or social avoidance. In other
words, the indirect effect from more intense negative emotions to more digital support and, in
turn, greater emotion regulation success held regardless of whether or not participants were alone
versus with others at each prompt or higher versus lower in social avoidance. However, path a2
from emotion intensity to in-person emotional support was significantly moderated by alone/with
others. Consistent with our in-person hypothesis 1, more intense negative emotions were
associated with more in-person emotional support when participants were with others (estimate =
.17, SE = .03, p < .001, 95% CI [.12, .22]), but not when they were alone (estimate = .04, SE = .
03, p = .16, 95% CI [−.02, .10]).1 The index of moderated mediation for this effect was also
significant (estimate = .01, SE = .005, p = .02, 95% CI [.002, .02]), indicating that the prompt-
level ‘emotion intensity in-person emotional support emotion regulation success’ indirect
1 To rule out competing effects of interaction terms and/or model overcrowding, we also ran separate models for
each term. The results were virtually identical to those when testing all terms simultaneously.
effect was significant when participants were with others (estimate = .02, SE = .007, p < .004,
95% CI [.007, .03]), but not when they were alone (estimate = .006, SE = .006, p = .31, 95% CI
[−.006, .02]).
Against our in-person hypothesis 2 and consistent with corresponding findings for digital
support, direct and indirect effects involving in-person emotional support were not moderated by
social avoidance. Path c’ remained significant and negative at this step and, overall, higher levels
of social avoidance were associated with lower levels of emotion regulation success.
This intensive experience sampling study aimed to elucidate real-life, day-to-day
processes of digital emotional support. We tracked participants’ negative emotions, receipt of
digital and in-person support for those emotions, and ultimate success in regulating them three
times per day for 14 days. We expected the use and efficacy of digital support to be more
pronounced when participants were alone versus with others (and vice versa for in-person
support). Extending this contextual analysis, we hypothesized that individuals higher in trait
social avoidance would rely more on digital support and find it more effective for reducing
intense negative emotions. Interestingly, we found that digital support was utilized in response to
intense negative emotions and perceived as effective regardless of these factors.
Digital Emotional Support in the Presence of Others
One of the potential disadvantages of digital (relative to in-person) emotional support is
that it lacks touch and is often text based without physical cues. The facial expressions and
bodily cues of others can signal empathic concern and acceptance (White & Dorman, 2001), and
touch triggers a neurochemical process that can lower stress physiology and negative
emotionality in the recipient (Ellingsen, Leknes, Løseth, Wessberg, & Olausson, 2016;
Lougheed, Koval, & Hollenstein, 2016). Tactile communication is deeply entrenched in our day-
to-day emotional lives (Chang, 2008) and evolution—even nonhuman primates spend a
significant portion of their lives grooming one another beyond what would be expected if such
behavior was purely hygienic (Dunbar, 1991). Because digital support lacks physical touch and
—with the exception of video calling—bodily cues that can facilitate comfort (White & Dorman,
2001), we did not expect it to be utilized as much when physical support was available.
However, our results did not reflect this digital disadvantage. Even when participants were with
others and in-person support was presumably available, they still sought digital support for
intense negative emotions and perceived that support to be effective.
Warmth (e.g., sincerity) and competence (e.g., intelligence, efficacy) are considered
universal dimensions by which individuals select and evaluate who they relate with (Fiske,
Cuddy, Glick, 2007). For the present analysis, we could not delineate participants’ closeness to or
desirability of those they were with. Nonetheless, it may be that participants more often preferred
digital competence over in-person warmth. Getting quality support from the right, competent
person—even through digital means—may be more important than receiving warmth from a less
desirable supporter in a face-to-face interaction. Also, the significant use and efficacy of digital
emotional support in the presence of others may have been driven by instances when participants
were with someone who they did not feel as comfortable confiding in and instead reached out
digitally to someone they trusted more. If this was the case, our results suggest that ‘texting the
one you love’ is a particularly effective way to manage your negative emotions when ‘you don’t
love the one you’re with’. More generally, participants may have found it easier to discuss
difficult content surrounding negative emotions through digital means—much like adolescents
and parents tend to initiate uncomfortable discussions through digital devices (Barrie,
Bartkowski, & Haverda, 2019).
Digital Emotional Support when Alone
One of the potential advantages of digital (relative to in-person) emotional support is its
on-demand availability (Leick, 2018; Ellis, 2019). Before the invention of digital devices,
individuals who were alone and experiencing negative emotions had the option of reaching out to
others through landline telephone, which had specific constraints (e.g., the intended target had to
be near their phone) and customs (e.g., not to call during evening hours). This lack of immediate
emotional support was essentially solved by real-time instant messaging. Customs of availability
have also eroded such that people are available for longer stretches of the day—and night—
through their digital devices, which they tend to keep close by (Cheever, Rosen, Carrier, &
Chavez, 2014; Steeves, 2014). We expected the on-demand benefits of digital support to be more
apparent when participants were alone—addressing lapses in the availability of in-person
support. However, our results suggest that digital users are taking advantage of the immediacy
and ubiquitous availability of digital emotional support regardless of their social surroundings.
Digital Emotional Support and Social Avoidance
We also hypothesized that the use and efficacy of digital emotional support would vary as
a function of participants’ trait levels of social avoidance. Even when in-person support is
available, some individuals experience barriers to realizing its benefits. Those high in social
avoidance may experience anxiety around social encounters (Elliot et al., 2006) despite their
desire for social connection (Barry et al., 2013; Coplan & Rubin, 2010). For such individuals,
digital devices may represent a safe option to receive support from others without the pressures
of in-person contact. We thus expected individuals higher in social avoidance to be more likely to
receive and benefit from digital emotional support. However, the use and efficacy of digital
support held regardless of participants’ trait social avoidance. It is still possible that socially
avoidant participants in our sample were opting for digital support because they perceived it to
be safer and more effective. However, the same digital habits were apparent in less socially
avoidant participants. This suggests that the widespread use of digital devices for emotional
support—regardless of time, place, and circumstance—is not restricted to individuals with
socially avoidant tendencies; instead, it may actually be normative.
In-Person Emotional Support
In line with our hypotheses, in-person emotional support was utilized less in response to
intense negative emotions when participants were alone. This suggests that participants did not
tend to seek out in-person support when it was not immediately available at the time of their
negative emotion. Although largely expected, this context-sensitive use of in-person support
confirms that our contextual moderator (i.e., whether participants were alone or not) was
meaningfully reflected in participants’ responding. This lends further credence to our context-
insensitive digital findings by suggesting that participants were still considering their social
surroundings when they counterintuitively sought digital support in the presence of others.
Contradictions with Previous Findings and Limitations
Overall, the present findings run contrary to Holtzman et al.’s (2017) findings linking
digital emotional support via text messaging to less successful stress reduction in comparison to
in-person emotional support. However, the Holtzman et al. (2017) study had small sample sizes
in each condition, a between-subjects design, one kind of stressor (public speaking), and a degree
of experimental control that limited ecological validity. The authors acknowledged that in-person
emotional support may not outperform digital emotional support in daily life when individuals
experience a range of negative emotions for different reasons in different contexts and have
greater opportunities and options for seeking and receiving emotional support. Our findings
suggest that, across a broad range of emotional stressors and situations, digital emotional support
is actually just as effective as in-person emotional support and, unlike in-person support, it is not
restricted to being in the presence of others.
Our findings also contradict Shensa et al.’s (2020) study demonstrating 20% greater odds
of depression per 1-unit increase in social media-based emotional support. However, closer
inspection of their conceptual and methodological approaches offers some potential explanations
for this discrepancy. Shensa and colleagues’ (2020) social media support scale largely tapped into
the availability of supportive others on social media and did not account for how close the
participants were to their social media supporters or whether other, more effective supports were
available. In contrast, we assessed a range of digital support scenarios while controlling for in-
person support. We did not delineate specific types of digital support, but it is likely that
participants in our study were primarily reaching out to close contacts via instant messaging (the
preferred form of digital contact among young adults; Skierkowski & Wood, 2012). Social media
can be relatively shallow and socially diffuse, which does not necessarily lend itself to quality
emotional support for specific emotions in the heat of the moment (Frison & Eggermont, 2015;
Nesi et al., 2018). By focusing solely on social media, Shensa and colleagues (2020) may have
tapped into these limitations, which may explain the positive link between social media-based
emotional support and depression in their study. Indeed, people with high levels of depression
might rely on social media for support because they lack a more immediate, higher quality
support network (Lakey & Cronin, 2008).
Several limitations of the present study and avenues for further inquiry should be noted
and addressed. This study was the first to consider the relative efficacy of digital emotional
support when participants were alone versus with others. A logical next step would be to consider
the source and quality of in-person support (e.g., emotionally close friends and family versus
acquaintances and strangers) to determine if seeking digital emotional support is less likely in the
presence of close others and more likely in the presence of less close others. Similarly, we did
not delineate the format of digital emotional support; future studies may wish to consider the
relative efficacy of digital support through texting, calling, video calling, social media, and other
platforms. Moreover, we focused on a digitally inclined sample of young adults; preference for
digital support likely varies based on the age of participants and their comfort level with
technology. We also relied entirely on self-reports, which was a trade-off of our intensive
longitudinal design. Future multimethod studies could assess physiology as a biological correlate
of emotionality and regulatory success. Finally, more frequent daily assessments may provide an
even more fine-grained account of digital support in daily life.
Conclusions and Relevance to the COVID-19 Pandemic
In sum, we adopted a naturalistic approach to assess the use and efficacy of digital versus
in-person emotional support. While we expected the use and efficacy of digital support to vary as
a function of participants’ social surroundings and trait levels of social avoidance, we found that
it was employed and effective regardless of these factors. Thus, while we designed our study
open to the potential benefits and limitations of digital emotional support, our results painted a
more beneficial picture than we anticipated. These findings are nonetheless welcome given the
overwhelming focus on harms in the extant digital device literature.
At the time of writing this report, a significant portion of the world is under self-isolation
from a coronavirus pandemic. As a result of COVID-19, people are experiencing heightened
anxiety and, due to social distancing, must rely more on their digital devices for emotional
support. A recent survey found that only 27% of individuals think that digital interactions can be
just as effective as in-person interactions (Pew Research Center, 2020). Our results suggest that
digital emotional support may be more effective than what the public currently believes.
Moreover, prevailing biases against digital interactions may be depriving some people of an
accessible and successful regulatory tool in a time of crisis. When we are isolated, emotional
support from the ones we love is just a text or video call away and the benefits of digital support
may be just as potent as ‘the real thing’. The unique benefits of digital emotional support also
applied to less isolated and socially avoidant users in our study. From this perspective, digital
emotional support may be an effective regulatory tool both during and beyond the COVID-19
pandemic. More benefit-focused studies are needed for a well-rounded understanding of when
and for whom digital devices are most helpful. In turn, digital users can capitalize on this
information as they transcend time and space in the digital age.
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Table 1
Zero-Order Correlations and Descriptive Statistics
Variable 1 2 3 4 5 6 7 M (SD)
1. Emotion regulation success 1 6.01(2.61)
2. Digital emotional support .10*** 1 3.22(2.88)
3. In-person emotional support .17*** .44*** 1 3.40(2.97)
4. Emotion intensity .31*** .22*** .19*** 1 4.24(2.48)
5. Alone/with others .08*** .09*** .36** .03** 1 .60(.49)
6. Social avoidance −.15 1 2.00(.82)
7. Gender .18* — −.14 1
Note. Significant effects bolded. Digital/in-person emotional support, emotion intensity, and alone/with
others = within correlations. Social avoidance and gender = between correlations. Alone/with others (0 =
alone, 1 = with others). Gender (0 = female, 1 = male). ***p < .001. **p < .01. *p < .05.
Table 2
Multilevel Model Predicting Emotion Regulation Success
Outcome Predictor Estimat
SE p95% CI
Within level
Emotion regulation success intercept Intercept 5.84 .15 <.00
5.55, 6.13
Digital emotional support (path b1) .08 .02 .001 .03, .13
In-person emotional support (path b2) .10 .04 .004 .03, .17
Emotion intensity (path c’) .30 .03 <.00
.35, .24
Alone/with others .12 .07 .084 −.02, .26
Digital Emotional Support x Alone/with others −.03 .03 .229 −.09, .02
In-Person Emotional Support x Alone/with others .01 .04 .772 −.06, .08
Digital emotional support intercept Emotion intensity (path a1) .19 .03 <.00
.13, .25
Alone/with others .06 .06 .326 −.06, .17
Emotion Intensity x Alone/with others −.02 .03 .625 −.08, .05
In-person emotional support intercept Emotion intensity (path a2) .04 .03 .160 −.02, .10
Alone/with others 1.62 .06 <.00
1.50, 1.74
Emotion Intensity x Alone/with others .13 .03 <.00
.06, .19
Between level
Within-level emotion regulation success intercept Social avoidance .38 .16 .018 .70, .07
Gender .62 .41 .132 −.19, 1.42
Emotion intensity–digital emotional support slope (path a1) Social avoidance .03 .03 .388 −.03, .08
Emotion intensity–in-person emotional support slope (path a2) Social avoidance .02 .03 .541 −.04, .08
Digital emotional support–emotion regulation slope (path b1) Social avoidance .03 .02 .252 −.02, .07
In-person emotional support–emotion regulation slope (path b2) Social avoidance .004 .03 .888 −.05, .05
Note. Significant effects bolded. Alone/with others (0 = alone, 1 = with others). Gender (0 = female, 1 = male).
... These digital natives found support received digitally (e.g., through texting, video calling) to be just as effective as support received in person for managing intense negative emotions. Moreover, their perceived efficacy of digitally mediated support did not change as a function of whether they were alone versus with others at the time of their negative emotion or higher versus lower on trait social avoidance (Colasante, Lin, DeFrance, & Hollenstein, 2020). Thus, in contrast with adults who largely regard digital interactions as insufficient relative to "the real thing," youth perceptions appear to be relatively positive as they routinely engage in supportive digital interactions. ...
... Social Baseline Theory posits that we have evolved brain and behavioral mechanisms that assume proximity to important others and that these reduce regulatory effort through risk distribution (e.g., safety in numbers) and load sharing (such that burdens are not taken on alone). From this perspective, it may be worthwhile to differentiate the benefits of digital networks and interconnectedness (e.g., those demonstrated by our previously reported findings on digital emotional support; Colasante et al., 2020) from the problems that arise from digital isolation and the sense that one's emotional load is not shared (perhaps through passive browsing and comparisons on social media; Primack et al., 2017). ...
In addition to being largely atheoretical, empirical work on youth digital experiences has been notably adevelopmental, dominated by researchers and authors with little to no training in developmental science. Moreover, studies focusing on novel digital issues within the developmental field itself have been surprisingly sparse. Thus, our hope is that this issue of Psychological Inquiry not only promotes greater interest in development in the digital age, but serves as a call to action for the entire developmental field to advance our understanding of this rapidly evolving generational shift in the way youth live. As a discipline, developmentalists have a responsibility to take a leadership role in youth digital research, apply their expertise, collaborate, and guide multidisciplinary inquiries into youth digital experiences with time-tested models of developmental processes. To this end, our commentary will elaborate on and extend the implications of the identity development model put forth in the target article by focusing on several key aspects of socioemotional development. First, we share the functionalist perspective as an important lens through which to comprehend moment-by-moment digital experiences. Apps, platforms, and hardware come and go, but the fundamental properties and drivers of human behavior, cognition, and emotion have not suddenly evolved. Second, we build on the agency and communion framework of the target article by accounting for both individual- and family-level socioemotional processes in digital contexts. Finally, we conclude with broad recommendations for moving digital developmental research forward by building on extant theory, conducting robust science, and embracing the complexity of both developmental processes and the affordances of digital devices.
... Restrictions imposed to curb the spread of the virus have included limitations in movement, mandatory remote work for large parts of the population, and limited access to friends and family. Several studies have investigated the farreaching impact of the pandemic and these restrictions on people's physical and mental states, e.g., by looking at correlations between trust in authorities and personality traits and perceived stress (Yamada et al., 2021), the importance of mental health support systems (Clomén et al., 2020;Russo, Hanel, Altnickel, & van Berkel, 2021), and the importance of digital support for emotional well-being (Colasante, Lin, DeFrance, & Hollenstein, 2020). In particular, the disruption of mental health services has further aggravated the problem (World Health Organization, 2020). ...
Technology plays an increasingly prominent role in emotional lives. Researchers have begun to study how people use devices to cope with and shape emotions: a phenomenon that has been called Digital Emotion Regulation. We report a study of the impact of the COVID-19 pandemic upon young people's digital habits and emotion regulation behaviors. We conducted a two-wave longitudinal survey, collecting data from 154 university students both before and during the COVID-19 pandemic. During the pandemic, participants were subject to increased emotional distress as well as restrictions on movement and social interaction. We present evidence that participants' emotion regulation strategies changed and became more homogeneous during the pandemic, with participants resorting to digital tools when offline strategies were less available, while also becoming more emotionally dependent upon their devices. This study underscores the growing significance of the digital for contemporary emotional experience, and contributes to understanding the potential role for technology in supporting well-being during high-impact events.
Coronavirus disease 2019 (COVID‐19) continues to ravage communities across the world. Despite its primary effect on the respiratory system, the virus does not solely impact those with underlying lung conditions as initially predicted. Indeed, prognosis is worsened (often fatal) in patients with pre‐existing hyperinflammatory responses (e.g., hypertension, obesity, diabetes), yet the mechanisms by which this occurs are unknown. A number of psychological conditions are associated with inflammation, suggesting that these may also be significant risk factors for negative outcomes of COVID‐19. In this review, we evaluate preclinical and clinical literature suggesting that chronic stress‐induced hyperinflammation interacts synergistically with COVID‐19‐related inflammation, contributing to a potentially fatal cytokine storm syndrome. In particular, we hypothesize that both chronic stress and COVID‐19‐related hyperinflammation are a product of glucocorticoid insufficiency. We discuss the devastating effects of SARS‐CoV‐2 on structural and functional aspects of the biological stress response and how these induce exaggerated inflammatory responses, particularly interleukin (IL)‐6 hypersecretion. We postulate that chronic stress should be considered a significant risk factor for adverse COVID‐19‐related health outcomes, given overlapping peripheral and central immune dysregulation in both conditions. We conclude by discussing how people with a history of chronic stress could mitigate their risk for COVID‐19 complications, identifying specific strategies that can be implemented during self‐isolation. This article is protected by copyright. All rights reserved.
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As social media use becomes increasingly widespread among adolescents, research in this area has accumulated rapidly. Researchers have shown a growing interest in the impact of social media on adolescents’ peer experiences, including the ways that the social media context shapes a variety of peer relations constructs. This paper represents Part 2 of a two-part theoretical review. In this review, we offer a new model for understanding the transformative role of social media in adolescents’ peer experiences, with the goal of stimulating future empirical work that is grounded in theory. The transformation framework suggests that the features of the social media context transform adolescents’ peer experiences by changing their frequency or immediacy, amplifying demands, altering their qualitative nature, and/or offering new opportunities for compensatory or novel behaviors. In the current paper, we consider the ways that social media may transform peer relations constructs that often occur at the group level. Our review focuses on three key constructs: peer victimization, peer status, and peer influence. We selectively review and highlight existing evidence for the transformation of these domains through social media. In addition, we discuss methodological considerations and key conceptual principles for future work. The current framework offers a new theoretical perspective through which peer relations researchers may consider adolescent social media use.
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Background: Adolescents are among the highest consumers of social media while research has shown that their well-being decreases with age. The temporal relationship between social media interaction and well-being is not well established. The aim of this study was to examine whether the changes in social media interaction and two well-being measures are related across ages using parallel growth models. Methods: Data come from five waves of the youth questionnaire, 10-15 years, of the Understanding Society, the UK Household Longitudinal Study (pooled n = 9859). Social media interaction was assessed through daily frequency of chatting on social websites. Well-being was measured by happiness with six domains of life and the Strengths and Difficulties Questionnaire. Results: Findings suggest gender differences in the relationship between interacting on social media and well-being. There were significant correlations between interacting on social media and well-being intercepts and between social media interaction and well-being slopes among females. Additionally higher social media interaction at age 10 was associated with declines in well-being thereafter for females, but not for males. Results were similar for both measures of well-being. Conclusions: High levels of social media interaction in early adolescence have implications for well-being in later adolescence, particularly for females. The lack of an association among males suggests other factors might be associated with their reduction in well-being with age. These findings contribute to the debate on causality and may inform future policy and interventions.
Background: Emotional support is highly protective against poor mental health. Though several measures of emotional support exist, none specifically addresses social media (SM) as a source of emotional support. Therefore, the objectives of this study were to determine if SM-based emotional support is an extension of or distinct construct from face-to-face (FTF) emotional support and to assess the independent associations between each domain of emotional support and depression risk among U.S. young adults. Methods: In March 2018, we surveyed 2408 18-30 year olds. We assessed perceived FTF emotional support with the brief PROMIS emotional support scale and perceived SM-based emotional support using a new four-item measure. Depression risk was assessed using the PHQ-9. We performed factor analysis (FA) to determine the underlying factor structure of all items and to develop composite scales. Multivariable logistic regression was used to examine the independent association between each resulting emotional support scale and depression risk. Results: FA revealed two distinct constructs. FTF emotional support was associated with 43% lower odds of depression per 1-unit increase on the 5-point scale (AOR = 0.57, 95% CI = 0.52-0.63). However, SM-based emotional support was significantly associated with 20% greater odds of depression per 1-unit increase on the 5-point scale (AOR = 1.20, 95% CI = 1.09-1.32). Limitations: This study utilized a cross-sectional design and self-report data. Conclusions: While FTF emotional support was associated with slightly lower odds of depression, SM-based emotional support was associated with slightly greater odds of depression. It may be valuable for clinicians treating individuals with depression to ask about sources of emotional support.
Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. To date, research within psychological science often frames new technology as problematic with overwhelmingly negative consequences. However, this paper argues that the latest generation of psychometric tools, which aim to assess smartphone usage, are unable to capture technology related experiences or behaviors. As a result, many conclusions concerning the psychological impact of technology use remain unsound. Current assessments have also failed to keep pace with new methodological developments and these data-intensive approaches challenge the notion that smartphones and related technologies are inherently problematic. The field should now consider how it might re-position itself conceptually and methodologically given that many ‘addictive’ technologies have long since become intertwined with daily life.
Research by Twenge, Joiner, Rogers, and Martin has indicated that there may be an association between social-media use and depressive symptoms among adolescents. However, because of the cross-sectional nature of this work, the relationship among these variables over time remains unclear. Thus, in this longitudinal study we examined the associations between social-media use and depressive symptoms over time using two samples: 594 adolescents (Mage = 12.21) who were surveyed annually for 2 years, and 1,132 undergraduate students (Mage = 19.06) who were surveyed annually for 6 years. Results indicate that among both samples, social-media use did not predict depressive symptoms over time for males or females. However, greater depressive symptoms predicted more frequent social-media use only among adolescent girls. Thus, while it is often assumed that social-media use may lead to depressive symptoms, our results indicate that this assumption may be unwarranted.
This book analyses articles that appeared in popular periodicals from the 1920s to the present, each revealing the panic that parents and adults have expressed about media including radio, television, video games and the Internet for the last century. Karen Leick argues that parents have continuously shown an intense anxiety about new media, while expressing a romanticized nostalgia for their own youth. Recurring tropes describe concerns about each "addictive" new media: children do not play outside anymore, lack imagination, and may imitate violent or other inappropriate content that they encounter.