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Digital Wellbeing as a Dynamic Construct



Mobile media support our autonomy by connecting us to persons, contents and services independently of time and place constraints. At the same time, they challenge our autonomy: We face struggles, decisions and pressure in relation to whether, when and where we connect and disconnect. Digital wellbeing is a new concept that refers to the (lack) of balance that we may experience in relation to mobile connectivity. This article develops a theoretical model of digital wellbeing accounting for the dynamic and complex nature of our relationship to mobile connectivity, thereby overcoming conceptual and methodological limitations associated with existing approaches. This model considers digital wellbeing an experiential state of optimal balance between connectivity and disconnectivity that is contingent upon a constellation of person-, device- and context-specific factors. These constellations represent pathways to digital wellbeing that – when repeated –affect wellbeing outcomes. Digital wellbeing interventions are effective when they disrupt these pathways.
Digital Wellbeing as a Dynamic Construct
Mariek M. P. Vanden Abeele
Department of Culture Studies
Tilburg University
Please cite as:
Vanden Abeele, Mariek M. P. (2020). Digital Wellbeing as a Dynamic Construct, Communication
Theory. qtaa024,
Mobile media support our autonomy by connecting us to persons, contents and services
independently of time and place constraints. At the same time, they challenge our autonomy: We
face new struggles, decisions and pressure in relation to whether, when and where we connect
and disconnect. Digital wellbeing is a new concept that refers to the (lack) of balance that we
may experience in relation to mobile connectivity. This article develops a theoretical model of
digital wellbeing that accounts for the dynamic and complex nature of our relationship to mobile
connectivity, thereby overcoming conceptual and methodological limitations associated with
existing approaches. This model considers digital wellbeing an experiential state of optimal
balance between connectivity and disconnectivity that is contingent upon a constellation of
person-, device- and context-specific factors. I argue that these constellations represent pathways
to digital wellbeing that when repeated affect wellbeing outcomes, and that the effectiveness
of digital wellbeing interventions depends on their disruptive impact on these pathways.
Key words: Digital wellbeing, mobile connectivity, mobile media, wellbeing, addiction,
problematic phone use, addictive design, digital wellbeing interventions, digital detox, screen
Over the past 20 years, our work, social and leisure environments have become suffused with
mobile technologies operating on wireless network infrastructures, such as laptops, tablets and
smartphones (ITU, 2017). These mobile technologies afford ubiquitous connectivity: They
connect us to content, contacts and services without time or place constraints (Vanden Abeele,
De Wolf, & Ling, 2018). Operating on a mostly unseen and unknown infrastructure, ever-present
in the background, they form a ‘technological unconsciousness’ (Thrift, 2004). As a result, we
often take ubiquitous connectivity for granted, only noticing it when it is absent - for example
when our phone battery dies, or the wireless network goes down (Ling, 2012). But now that
technologies permit us to be ‘permanently online and permanently connected (POPC; cf.
Vorderer, Krömer, & Schneider, 2016) we face a new challenge: How do we obtain a healthy
balance between connectivity and disconnectivity? In other words: How do we attain digital
Studies show that we hardly disconnect. Smartphones are tapped, swiped and clicked
over 2600 times per day (Dscout, 2016), and people spend close to three hours per day on their
little screens (Deng et al., 2019) - a figure that easily goes up to five hours and more for heavy
users (Deng et al., 2019; Sewall, Bear, Merranko, & Rosen, 2020). While people reap ample
benefits from mobile connectivity, they also struggle with it. Phone use is found, for example, to
interfere with social activities (McDaniel & Drouin, 2019), to distract from work and study
(Duke & Montag, 2017), to lead to procrastination (Schnauber-Stockmann, Meier, & Reinecke,
2018), to cause sleep and health problems (Lanaj, Johnson, & Barnes, 2014), and to induce
negative emotions such as emotional exhaustion and anxiety (Büchi, Festic, & Latzer, 2019). It
should therefore not surprise that three in four young adults (Paul & Talbott, 2017), half of teens,
and one in three parents find that they spend too much time on their screens (Jiang, 2018). Many
also express a desire to reduce screen time, but such attempts often fail (Jiang, 2018). This
suggests that digital wellbeing is difficult to attain.
The ‘quest for digital wellbeing’ (cf. Mason, 2018) thus appears an urgent issue. Wired
Magazine even described it as the “rallying call of our time” (Ardes, 2018). A new industry of
digital wellbeing interventions is developing to respond to this call. These interventions include
digital detox programs, self-help literature, and various digital tools (e.g., the Forest and Moment
apps), all with a shared goal to assist users in ‘re-gaining control’ over their screen time. Tech
behemoths Google and Apple, for example, integrated dedicated digital wellbeing tools into their
operating systems for people to “set limits to” their digital media use (, with the goal
to “keep life, and not the technology in it, front and center” ( To date,
however, research on the effectiveness of digital wellbeing interventions is inconclusive. Digital
detox interventions, for example, appear both positive (e.g., Anrijs et al., 2018) and negative
(e.g., Wilcockson, Osborne, & Ellis, 2019), and while some work suggests that screen time apps
are successful in safeguarding digital wellbeing (e.g., Hiniker, Hong, Kohno, & Kientz, 2016),
other work shows no effect (e.g., Loid, Täht, & Rozgonjuk, 2020). These contradictory findings
suggest that what digital wellbeing is, and how it can be attained, remains ill-understood.
Digital wellbeing has the potential to become a key concept in research on digital media
use and wellbeing, with ample practical relevance. The concept can inform users, health
practitioners, designers and developers in the industry as well as policy makers about people’s
struggles with ubiquitous connectivity, and what can be done to help people foster healthier
mobile media habits, with or without the use of digital wellbeing interventions. To date,
however, we have only a limited theoretical vocabulary to describe what digital wellbeing is to
guide empirical inquiry. Conceptual boundaries are needed to avoid that digital wellbeing
becomes a bandwagon concept for related constructs such as smartphone addiction, or is used as
a proxy to refer to every negative relationship between screen time and wellbeing outcomes. In
this manuscript, I propose a working definition of digital wellbeing, present a conceptual model
for its study, and explore issues and challenges associated with the proposed approach in an
attempt to advance our understanding of the paradoxical relationship we have with ubiquitous
connectivity in our everyday life.
The Mobile Connectivity Paradox
Mobile technology substantially increases autonomy in everyday life (Castells, Fernandez-
Ardevol, Qiu, & Sey, 2009; Vanden Abeele et al., 2018): People can perform their social roles,
manage their social networks and access personalized information and services anywhere,
anytime. Moreover, they can easily and instantaneously respond to information by flexibly
adjusting the situation or their actions. When their train is delayed, for example, people can use
their laptop to catch up on work, use a mobile messaging app to inform their partner and stream
music to their phone to relax.
But there is a mobile connectivity paradox: while ubiquitous connectivity can support
autonomy, it can also challenge that very experience. Autonomy is challenged when mobile
technologies exert direct control over thoughts and behaviors by directing attention away from
people’s primary activities. Developed against the background of an attention economy, mobile
technology is designed to lure attention (Eyal, 2014; Williams, 2018). As a result, people may
unintentionally abandon their work, social and leisure activities to engage in unintended screen
time. While this screen time may be pleasurable in itself, one can experience it as excessive,
inappropriate and sometimes even problematic, for example, when it hampers responsiveness to
children (Vanden Abeele, Abels, & Hendrickson, 2020), reduces productivity (Duke & Montag,
2017), invokes negative feelings (Aalbers, McNally, Heeren, de Wit, & Fried, 2019), leads to
dangerous behaviors such as texting-while-driving (Bayer & Campbell, 2012), or is simply
experienced as meaningless or as a waste of time (Hiniker et al., 2016; Lukoff, Yu, Kientz, &
Hiniker, 2018).
Mobile technologies also challenge autonomy by controlling thoughts and behaviors in a
more indirect way. The SIM card functions as a ‘mobile address’ that makes individuals track-
and-traceable (Thrift, 2004). While this infrastructure of individual addressability gives the
freedom to instantly communicate, act and respond, it has also contributed to a global culture of
ubiquitous connectivity, fraught with expectations about immediate availability and
accountability (Licoppe & Smoreda, 2005; Ling, 2017; Vanden Abeele et al., 2018; Vorderer et
al., 2016). These expectations constrain the freedom to refrain from connectivity: People may
experience control in the form of real or perceived pressure to check, act and respond, and they
face new responsibilities for negotiating their availability and accountability (Vanden Abeele et
al., 2018).
The mobile Connectivity Paradox refers to this experience of being caught between
autonomy and a loss of control, which becomes visible in people’s ambivalence towards mobile
connectivity in their everyday lives. While a majority recognizes the importance of mobile
connectivity for self-governed living, many report that they are simultaneously concerned about
the time they spend on screens and the pressure they experience to connect. People struggle with
decisions on whether, when and where to connect and perhaps more importantly disconnect
(e.g., Aagaard, 2020; Lyngs et al., 2020). This paradoxical experience, that is oftentimes
mentioned to in both public (e.g., Ardes, 2018) and scholarly discussions (e.g., Hiniker et al.,
2016), lies at the core of the quest for digital wellbeing: How can we optimally embed mobile
connectivity in our life so that it supports individual autonomy without experiencing a loss of
control? To properly answer this question, we require a definition of digital wellbeing.
Towards a Definition of Digital Wellbeing
Digital wellbeing is often implicitly defined by juxtaposing it against undesirable phone
habits (i.e., drawing a parallel between phone use and unhealthy eating habits; see also Sutton,
2017) or against afflictions that represent digital ill-being, such as technology addictions (Lee,
Lee, & Park, 2019; Roffarello & De Russis, 2019). This is surprising, as the concept of general
wellbeing is generally not understood as the absence of an undesirable state, but rather as a state
of “optimal psychological experience and functioning” (Deci & Ryan, 2006, p. 1). Drawing
arguments from ongoing debates between scholars in the field of behavioral addictions research
and the definitional work on the conceptualization of general wellbeing, I argue that a more valid
conceptualization of digital wellbeing is attained if we differentiate digital wellbeing and
addiction and acknowledge that ubiquitous connectivity brings both value and discomfort to our
lives. To that end, four considerations are important.
Consideration 1: Avoiding medicalization
A simple way to conceptualize digital wellbeing is to consider it the opposite of digital
media addiction. A lack of ‘addiction symptoms’, then, should equate with digital wellbeing.
This conceptualization of digital wellbeing falls short, however. It assumes that problems with
digital media use are symptomatic of an underlying pathology or mental health disorder: a digital
media addiction (Andreassen, 2015; Griffiths, 2019). Such a dependence is diagnosed by
gauging the individual’s behavior against widely recognized symptoms, such as suffering from
withdrawal symptoms when technology is removed, requiring more usage to attain the same
effect (‘tolerance) and being mentally preoccupied with the technology or its use (cf. Pontes,
Kuss, & Griffiths, 2015).
But this technology-addiction-as-a-disease approach (cf. Van der Linden, 2015) is under
debate: It medicalizes people’s problematic relationship with digital media as a clinical
condition, while some scholars even question whether smartphone addiction is a ‘real’ concept
(Harris, Regan, Schueler, & Fields, 2020). Of late, steadily more scholars argue against the
medicalization implied by the label of media addiction, because it easily misclassifies users
who occasionally experience some problems with digital media as individuals suffering from a
disorder (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015; Kardefelt‐Winther et al.,
2017; Starcevic, Billieux, & Schimmenti, 2018). Such misclassification leads to an
overpathologization of everyday behaviors and experiences
(Billieux, Schimmenti et al., 2015).
Rather than medicalizing the condition of these false positives as a clinical disorder, it might
therefore be more valid to consider the experience of sometimes, having some struggles' as one
of a lack of digital wellbeing (Cecchinato et al., 2019), rather than as a pathological condition
that is so severe that it needs clinical help (Van Rooij & Kardefelt-Winther, 2017).
Consideration 2: Acknowledging Hedonic and Eudemonic Experiences
Which criteria need to be met, then, to identify (a lack of) digital wellbeing? Although
there is debate among behavioral addictions researchers, the broad consensus is that technology
use becomes excessive and problematic when individuals (1) lose control over it, and (2)
For example, often inspired by anecdotal observation (Billieux et al., 2015), ordinary behaviors such as ‘dancing’,
or ‘selfie taking’ are transformed into a pathology by developing a diagnostic screening tool and using it in a large
population to confirm their incidence (e.g., Balakrishnan & Griffiths, 2018; Maraz, Urbán, Griffiths, &
Demetrovics, 2015). The screening tools, however, sometimes screen for harmless if not positive aspects of the
behavior. This procedure become more than a fad when scholars plea for formal inclusion of these assessments in
psychiatric diagnostic manuals (e.g., Bragazzi & Del Puente, 2014). A recent systematic review of problematic
smartphone use scales by Harris et al. (2020) does an excellent job of identifying the many issues associated with
current measurement instruments.
subsequently experience a significant functional impairment in their everyday lives (Kardefelt‐
Winther et al., 2017; Pies, 2009). While some scholars operationalize these criteria into
symptoms that are either present or absent (e.g., Griffiths, 2005), others advocate conceiving of
them as continua, ranging from an absence of loss of control and functional impairment to a
severe experience of these criteria (Van Rooij & Kardefelt-Winther, 2017).
While this brings nuance to the debate, it still assumes our relation to technology as a
unipolar phenomenon that, at best, is ‘not problematic’. Such an approach ignores that people
might also develop a positive relationship with digital technologies through hedonic and
eudemonic experiences, which are known to contribute to wellbeing (Henderson & Knight,
2012; Huta, 2016; Ryan & Deci, 2001). Hedonic experiences occur when we derive pleasure
from using digital media, such as when we enjoy entertaining content on our phones (Reinecke
& Hofmann, 2016). In fact, it is the hedonic responses that people experience when using digital
media that make it so difficult to resist using them (Van Koningsbruggen, Hartmann, Eden, &
Veling, 2017). When these pleasurable experiences are under control, however, these ‘controlled
pleasures’ may lead to positive experiences (e.g., Bauer, Loy, Masur, & Schneider, 2017).
Eudemonic experiences occur when digital media use adds meaning to life, for example because
it supports us to achieve personal goals (Lukoff et al., 2018). Such functional support may occur,
for example, when digital connectivity aids to master complex logistical arrangements, such as
the microcoordination of a group event (Ling & Lai, 2016).
Hedonic and eudemonic experiences form synergetic pathways to wellbeing (cf.
Henderson & Knight, 2012). It is conceivable that when people reap hedonic and eudemonic
benefits from digital connectivity, their digital wellbeing increases. A definition of digital
wellbeing thus needs to consider such benefits by focusing on experiences of controlled pleasure
and functional support in addition to experiences of loss of control and functional impairment.
Consideration 3: Acknowledging Temporal Variability and Person-Specificity
A third consideration is whether our relationship to digital connectivity remains stable
over time and whether this relationship manifests itself similarly across individuals.
Technology addiction is generally assumed to be a temporally stable and structurally invariant
condition that can thus be diagnosed with a ‘one-size-fits-all screening instrument (e.g., Huang,
2010; Yu & Shek, 2013). Recent literature questions the validity of this assumption. Temporal
stability appears an unwarranted assumption, as research shows that excessive media use is
sometimes only a temporary and potentially functional coping response to a stressful life
event (Kardefelt-Winther, 2014; Kardefelt‐Winther, 2017; Li, Zhang, Li, Zhen, & Wang, 2010).
Structural invariance also appears an unwarranted assumption, as studies show that problematic
use can take on different forms, in relation to the pathways leading to it (Billieux, 2012).
Moreover, general screening instruments have difficulty differentiating passionate and
enthusiastic media users from pathological users (e.g., Charlton & Danforth, 2007).
The wellbeing literature can help out here. General definitions of wellbeing emphasize
that wellbeing is a subjective experience that can fluctuate over time (Diener, Suh, Lucas, &
Smith, 1999; Headey & Wearing, 1989), By not defining a priori criteria for what counts as
‘being well’ but by rather approaching wellbeing as an experiential state, these definitions
accommodate temporal variability in, and person-specific manifestations of, wellbeing. In a
similar vein, digital wellbeing can be understood as an experiential state. As with
conceptualizations of general wellbeing, this subjective experience of digital wellbeing is
assumed to comprise affective states and cognitive appraisals (cf. Diener, 1994; Shmotkin, 2005)
that are dynamic: They fluctuate over time as they interact with various internal and external-
contextual influences (cf. Cummins, Eckersley, Pallant, Van Vugt, & Misajon, 2003; Headey &
Wearing, 1989). In the case of digital wellbeing, however, these emotional and cognitive
appraisals reflect one’s evaluation of digital connectivity rather than the evaluation of one’s life.
Consideration 4: Acknowledging Ambivalence
Finally, a definition of digital wellbeing needs to consider the joint occurrence of positive
and negative experiences. All too often, restricting screen time is proposed as a simple solution
to attain digital wellbeing (e.g.,Twenge, 2017). Interventions such as digital detox programs and
screen time apps (e.g., Apple Screen Time) build on this assumption. But by attempting to
eliminate the negative outcomes of connectivity, we risk sacrificing its positive outcomes
(Hiniker et al., 2016, p. 4746). In other words, straightforward constraints on connectivity can
deprive users of positively valued aspects of technology use. This could explain why
interventions such as smartphone abstinence are often ineffective (e.g., Wilcockson et al., 2019).
This brings us to the Mobile Connectivity Paradox: The problems we experience with ubiquitous
connectivity are an inherent, and therefore inescapable downside of the benefits it provides us
with. Because we cannot have one without the other, digital wellbeing is a matter of optimizing
the ambivalence, of carefully adjusting our connectivity so that it provides us with controlled
pleasure and maximally supports us to achieve our goals, while causing a minimal degree of
functional impairment and loss of control.
This understanding of digital wellbeing echoes scholars conception of general wellbeing
as a ‘dynamic equilibrium’ between personality factors, life events and subjective experiences
(Headey & Wearing, 1989). Similarly, digital wellbeing is the outcome of a dynamic equilibrium
between the individual benefits and drawbacks that accrue from mobile connectivity.
A Definition of Digital Welbeing
Taking into consideration the above, I propose a definition of digital wellbeing that does
not medicalize people’s relationship with technology, assumes that connectivity brings both
problems and benefits, acknowledges the subjective and dynamic nature of our experiences with
technology, and recognizes the ambivalence of our relationship to technology:
Digital wellbeing is a subjective individual experience of optimal balance between the
benefits and drawbacks obtained from mobile connectivity. This experiential state is comprised
of affective and cognitive appraisals of the integration of digital connectivity into ordinary life.
People achieve digital wellbeing when experiencing maximal controlled pleasure and functional
support, together with minimal loss of control and functional impairment.
Based on this definition, we can now work towards a model of digital wellbeing that
allows intra- and interpersonal variability in the balance of benefits and drawbacks. To that end,
we must avoid straightforward cause-and-effect thinking, and rather model digital wellbeing as a
dynamic system that is influenced by not only person-, but also by device- and context-specific
Towards a Dynamic System Model of Digital Wellbeing
Cause-and-effect thinking dominates current research, with several studies
straightforwardly linking screen time measures to wellbeing. Twenge, for example, identifies
screen time as a direct predictor of mental health problems such as depression (e.g., Twenge,
Joiner, Rogers, & Martin, 2018) and even suicidal ideation (e.g., Twenge et al., 2018). Several
scholars warn for the ‘conceptual and methodological mayhem’ (cf. Kaye, Orben, Ellis, Hunter,
& Houghton, 2020) associated with this approach. For example, re-analyzing Twenge et al.’s
(2018) data, Orben and Przybylski (2019) and Ophir, Lipshits-Braziler, and Rosenberg (2019),
found negligible associations between digital media use and wellbeing, that were highly
contingent on methodological choices, such as item selection procedures, resulting in misleading
interpretations. These observations have fueled a call for greater methodological and analytical
rigor in this field (e.g., Davidson & Ellis, 2019; Kaye et al., 2020).
While the debate on digital harm- and how to best estimate it - rages on (see, e.g.
Twenge, Haidt, Joiner, and Campbell’s (2020) commentary and Orben and Przybylski’s (2020)
response), recent evidence shows that screen time in itself appears not as straightforwardly
harmful as commonly assumed: If a relationship between screen time and wellbeing exists, it is
likely a nuanced, moderate and reciprocal association (Orben et al., 2019). To examine this
association, we have to build conceptual models and use empirical methodologies that
disentangle the “many nuanced factors, contexts, situational circumstances, temporal effects, and
interactions” (Whitlock & Masur, 2019, p. E2).
A conceptual model of digital wellbeing as a dynamic system can move the debate
forward by reducing the risk of making faulty or over-simplified cause-effect judgments. By
assuming that experiences of digital wellbeing are not only temporary and idiosyncratic, but also
contingent upon a complex constellation of potentially interrelated factors, digital wellbeing is
not reduced to a problem of psychologically predisposed individuals who use digital media
excessively, but rather recognizes that we live in a deeply mediatized world in which digital
devices such as the smartphone have a double-sided nature, “as object, or an instance of material
culture” (Miller, 2014, p. 214). As such, our experiences with these interactive, dialogical media
(cf. Gergen, 2002) are not only of our own making, but also shaped by devices in their material
form, and by normative expectations, behaviors and rituals that pertain to specific social and
situational contexts. To answer the question how individuals can attain digital wellbeing, we thus
need to understand how persons, devices and contexts interact, and be open to the idea that
screen time might not necessarily be the culprit
. To that end, we can approach associations
between person- device- and context-specific factors as a constellation of pathways in a system
that help or hamper specific individual in their quest for digital wellbeing (see Figure 1).
[insert Figure 1 about here]
Person-specific Factors: A Unique User
Research identified several stable personality traits, such as impulsivity (Billieux, Van
der Linden, & Rochat, 2008) or a fear-of-missing-out (Franchina, Vanden Abeele, Van Rooij, Lo
Coco, & De Marez, 2018; see Table 1 for more examples) that increase one’s susceptibility to
develop problems with digital media use. A dynamic system model of wellbeing, however,
should also include intra-individually variable factors, such as affective and cognitive states that
interact with experiences of digital wellbeing, both in direct and indirect ways (see Table 1).
Mood, for example, has been found to associate with momentary experiences of media
enjoyment (Reinecke & Hofmann, 2016). Another example is state boredom. At work, state
boredom is contingent on the momentary context (e.g., time of day, work activity), which may
drive people to seek distraction online (Mark, Iqbal, Czerwinski, & Johns, 2014), which can lead
to feelings of reduced productivity (Mark, Iqbal, Czerwinski, & Johns, 2015).
Recent studies identified some states directly related to digital wellbeing experiences, in
the form of affective and cognitive appraisals resulting from digital connectivity (see Table 1).
These may be associated with experiences of controlled pleasure, loss of control and of
functional support/impairment. For instance, Reinecke et al. (2018) mention a cognitive state
On the contrary, in a society where media use is integrated deeply into every social domain, the physical world
may even cast a shadow on pleasurable or meaningful experiences with technology.
‘state online vigilance’, a state of mental preoccupation with, readiness to respond to and
constant monitoring of online content and communication.
For a dynamic system approach to digital wellbeing, it is important not to consider these
states in isolation, but to understand that devices and contexts can play a crucial role in
producing them. With respect to the device, for example, recent research found that the mere
visibility of one’s smartphone suffices to trigger online vigilance (Johannes, Veling,
Verwijmeren, & Buijzen, 2018). This warrants further investigation of device-specific factors.
Device-specific Factors: The Danger of the Device
Our experience of digital wellbeing cannot be dissociated from our digital media devices.
In constant competition over consumer attention, technology developers design devices with
operating systems, applications and interfaces that keep users ‘hooked’ (Williams, 2018; Eyal,
2014). Such addictive design’ (cf. Yousafzai, Hussain, & Griffiths, 2014) capitalizes on the fact
that humans are evolutionary hardwired to constantly scan the environment for new information,
including of a social nature (Eyal, 2014). Smartphones in particular embed such a reward
, turning people into “lab rats constantly pressing levers to get tiny pellets of social
or intellectual nourishment” (Carr, 2010, p. 117). It is precisely because digital media are so hard
to resist to, that people seek ways to manage their ‘distractive potential’ (Hiniker et al., 2016)
and reduce the ‘toll of overconnection’ (Baym, Wagman, & Persaud, 2020).
Digital media such as smartphones operate on an underlying technological infrastructure
that is built on the premise of portability, availability, locatability, and multimediality (Schrock,
2015). Although the choice for a particular device, app or app settings is often personally
motivated, such choices may have a durable impact on experiences of digital wellbeing. For
Note that a recent study by Johannes, Dora, and Rusz (2019) supports the notion that social media apps are
perceived as high in reward, but refutes the idea that these rewards capture attention.
instance, the choice for a ‘dumb phone’ might self-protect individuals against the (feared) impact
of overconnection (Morrison & Gomez, 2014; see Table 1 for more factors).
Not all our device interactions are the straightforward result of choices. System features
such as notification systems, for instance, depend on external parties that ‘notify’. Notifications
embody mobile technologies’ interactive and dialogical nature (cf. Gergen, 2002). They alert the
user of potentially rewarding, dynamically updated, information (Oulasvirta, Rattenbury, Ma, &
Raita, 2011), such as that others attempt to engage with them (Bayer, Campbell, & Ling, 2016).
This dynamic element may affect digital wellbeing experiences, for instance by activating a state
of vigilance in the user (Johannes et al., 2018).
Devices-specific factors may also influence digital wellbeing via their contribution to
distinct behavioral patterns, such as fragmentation and habituation (Bayer, Campbell, & Ling,
2016; Deng et al., 2019). These are associated with dynamic content applications and system
features such as haptic feedback features (Bayer et al., 2016; Oulasvirta et al., 2011). Similarly,
notifications (Bayer et al., 2016; Schnauber-Stockmann et al., 2018), post-play functions and
algorithmic curation (Horeck, Jenner, & Kendall, 2018) can become gateways to lengthier usage
sessions and binge behaviors - sometimes referred to as ‘going down the rabbit hole’ (Collier,
2016). Such events can affect digital wellbeing, for example by inducing feelings of guilt or
shame over one’s procrastination (cf. Du, van Koningsbruggen, & Kerkhof, 2018; Reinecke &
Hofmann, 2016).
We do not interact with their devices in a vacuum, however: The interactive and
dialogical nature of digital media implies that our use of them cannot be considered in separation
from our social context.
Context-specific Factors: A Culture of Connectivity
We live in a context of ubiquitous connectivity, now that persons - and increasingly also
objects have become individually addressable. As a result, we must negotiate how to respond
to the demands and expectations stemming from this addressability (Vanden Abeele et al., 2018).
Some contexts come with time and/or place constraints on connectivity that can be anticipated,
and are therefore relatively stable: During flights or in movie theatres, connectivity is constrained
and sometimes even prohibited. In other contexts, such as a formal board meeting, rules may be
more implicit but nonetheless expected. When contexts set clear boundaries for connectivity,
they may impact our experienced digital wellbeing: Forced (dis-)connectivity may be enjoyed or
missed, and meaningful or meaningless.
In other contexts, bounds to connectivity may be less clear, requiring a more active
negotiation. There may be solitary contexts in which digital connectivity needs to be negotiated
because it competes with personal goals and obligations (Hofmann, Reinecke, & Meier, 2016),
for instance, when using digital media while studying. Facing such goal conflicts, people have to
weigh (often short-term) rewards from media use against more remote goals such as obtaining a
degree or acquiring a new skill.
Other situations that may require a negotiation over connectivity may stem from our
membership to social groups and institutional contexts. People perform various social roles in
such groups and institutions. Because mobile connectivity affords them to activate these social
roles irrespective of space and time, roles may blur. Thus, individuals have to negotiate their
connectivity in accordance to the momentary goals and obligations pertaining to each role
(Vanden Abeele et al., 2018). A parent must negotiate, for example, whether a work email is
urgent enough to give it priority over playing with their child.
In the same vain, people may experience pressure from normative expectations
concerning availability and reciprocity in their groups and institutions (Hall & Baym, 2012;
Laursen, 2005; Licoppe & Smoreda, 2005; Quan-Haase & Collins, 2008; Taylor & Harper,
2003). These expectations are often tacit, but in institutional contexts these may be formalized as
rules and policies such as those concerning telework or email-after-work-hours (e.g., Piszczek,
2017). Digital wellbeing may depend on the demands that these expectations place on one’s (dis-
)connectivity. Especially when demands from one’s social groups and institutional contexts
conflict, digital wellbeing may suffer. Expectations, rules and polices surrounding connectivity
can reproduce underlying power hierarchies (e.g., Licoppe & Smoreda, 2005), so that, for
exanple, employees perceive normative pressure to respond to their employer’s emails after work
hours, resulting in the experience ‘availability stress’ (cf. Steele, Hall, & Christofferson, 2020) in
response to email notifications. They may keep responding to these emails nonetheless, out of
fear for a negative evaluation.
Finally, distinct from the above solitary, group and institutional contexts mentioned
above and in Table 1, we may also consider the impact of broader socio-cultural transformations
on digital wellbeing. Addictive design is indicative of an increasing commodification of our
attention by ‘invisible virtual employers’ who often - without our explicit consent or even
awareness - blur our roles as consumer and worker (Van Dijck, 2014; Vanden Abeele et al.,
2018; Williams, 2018). We may also look at processes of acceleration (Rosa, 2013; Wajcman,
2015) and individual responsibilization (Vanden Abeele et al., 2018) as broader contexts that
shape digital wellbeing experiences.
Digital Wellbeing Interventions: Disrupting the System?
According to Thrift (2004), repetitive patterns in our way of doing things often reveal
invisible performative infrastructures that characterize the ‘track-and-trace’ model of
contemporary society (Thrift, 2004). Representing digital wellbeing as a dynamic system makes
such performative infrastructures visible in the form of pathways between person-, device- and
context-specific factors that interact to produce experiences of digital wellbeing. Digital
wellbeing interventions, then, can be understood as potential disruptors of the system via their
effects on these pathways. Recent work of Baym et al. (2020), for example shows how a period
of facebook abstinence led to greater mindful scrolling which solved some (but not all) issues
with overconnection.
Recent scholarly work within the HCI community is of value here. Scholars have
classified various relevant features in these interventions (e.g., Roffarello & De Russis, 2019),
identified mechanisms explaining why features ‘work’ - or not (e.g., Lyngs et al., 2019), and
developed agendas for researching the design and development of digital wellbeing interventions
(e.g., Cecchinato et al., 2019; Hiniker et al., 2016). These efforts align with the adoption of a
dynamic systems approach when they acknowledge the complex and person-specific nature of
digital wellbeing, and its contingency on personal characteristics and preferences, contexts of use
and design choices embedded in technology (e.g., Hiniker et al., 2016; Lyngs et al., 2019; Lyngs
et al., 2020). Future research in this area will benefit from an additional focus on within-person
fluctuations, and the potential idiosyncracy of these mechanisms. This can also help to
differentiate the various levels at which interventions may be addressed, such as the level of the
technology (e.g., a digital tool that limits connectivity), the individual (e.g., in the shape of self-
imposed restrictions on connectivity), the group (e.g., household screen use rules) and the
institution (e.g., workplace policies). Research might identify that disruption occurs in multiple
pathways simultaneously, thereby potentially amplifying or dampening an intervention’s total
effect. Digital detoxes, for example, may reduce availability stress, but simultaneously activate
users’ fear of missing out, leading to a zero sum effect on a user’s appreciation of connectivity.
Researching Digital Wellbeing: Methodological Implications
A dynamic system approach to digital wellbeing can foster discussion on digital media
use effects. In such a dynamic system approach, antecedents and outcomes still matter. Dynamic
and stable factors may influence individual system components and repeated experiences of (a
lack of) digital wellbeing may have longer-term consequences for an individual’s wellbeing.
However, by assuming intra-individual variability rather than a one-size-fits-all pattern, and by
accounting for the ambivalence that individuals may experience in relation to ubiquitous
connectivity grateful in one moment, and frustrated the next - it overcomes limitations of
extant research approaches.
A dynamic system approach to digital wellbeing has empirical implications. It requires
innovative data collection techniques and research methodologies that can expose repetitions in
our way of doing things, so that we can lift the veil on the technological unconsciousness (cf.
Thrift, 2004). This implies that methods relying on self-reports of media behavior are not an
optimal choice: They are notoriously inaccurate as the frequent, fragmented and habitual nature
of media behaviors makes it difficult to retrieve them from memory (Vanden Abeele, Beullens,
& Roe, 2013). Moreover, inaccuracies in self-reported media use also correlate with psycho-
social wellbeing (Sewall et al., 2020), casting doubt on the validity of self-reported associations
between screen time and wellbeing.
Device logging and mobile experience sampling are promising alternatives. These data
collection techniques can capture ‘in situ’ experiences, and can assess idiosyncratic
manifestations of digital wellbeing: Device logging can document patterns in digital media use
behaviors, identifying bursts of activity as well as repetitive behaviors occurring daily, weekly,
and over longer durations (Stragier, Hendrickson, Vanden Abeele & De Marez, 2019).
Additionally, relevant dynamic device- and context specific factors, such as the amount of
incoming notifications and the spatio-temporal context of device use, can be logged. Mobile
experience sampling, a systematic data collection technique based on the diary method
(Csikszentmihalyi & Larson, 2014), can inform about individuals’ momentary experiences in a
low-threshold and non-time-consuming way (Karnowski, 2013). Data about their momentary
cognitive/affective states and situational contexts can be used to build models that explain how
processes take place within an individual (i.e. are idiosyncratic), how processes are linked over
different time scales, and to what extent processes differ across individuals (Keijsers & van
Roekel, 2018).
Both smartphone logging and mobile experience sampling are promising tools to unearth
temporal, non-linear, and reciprocal relationships (Whitlock & Masur, 2019). The implication for
media effects researchers is that they will have to embrace the computational turn in media
effects studies by, for instance, adopting machine learning techniques to extract ‘patterned
behavior’ from device logs, network modelling techniques to examine the dynamic nature of
digital wellbeing systems, and advanced time series modelling techniques to examine whether
repeated failures in experiencing digital wellbeing predict short-, but also longer-term wellbeing
outcomes such as burnout and depression.
Similarly, for interpretive-critical scholarship these data collection techniques imply that
researchers must embrace the developing digital ethnographic turn in culture studies, using novel
approaches such as ‘appnography’ or log/experience sampling data as cultural probes.
Appnography approaches apps as intermediaries of culture: An analysis of such hybrid offline-
online digital spaces can reveal how users, app features and contexts work together in (re-
)producing ideologies and power structures (Cousineau, Oakes, & Johnson, 2019). To gain
greater insight of the ‘in situ’ experiences of individuals, device logs represent ‘snapshots’ that
can probe users to reflect on prior digital wellbeing experiences (Kaufmann, 2018). Additionally,
researchers can embrace qualitative alternatives to experience sampling, such as asking
individuals to document momentary experiences via mobile messaging, using words, pictures,
video, emoji, hashtags, etcetera (Kaufmann & Peil, 2019) to help reveal what digital wellbeing
means to individual users, and how digital wellbeing experiences intersect with broader aspects
of culture.
When building representations of reality, scholars need to consider how to conceptually
and empirically approach the phenomenon of interest. Current research on the relationship
between digital media use and wellbeing is in an impasse, because conceptual models appear
inadequate to capture the complexity of the relationships that individuals have with digital
media, and empirical approaches lead to inconsistent findings and are criticized for lacking
methodological rigor. I argue that we can overcome this impasse by building a new theory of
digital wellbeing that focuses on momentary experiences of balance between connectivity and
disconnectivity. These experiences arise out of interactions between persons, devices and
contexts that can be modelled and empirically investigated as pathways in a dynamic system of
wellbeing. A dynamic system approach to digital wellbeing can bring new insight into the
mechanisms that lead people to experience problems with digital media use. Moreover, it can
help understand under which circumstances digital wellbeing interventions such as digital detox
programs or screen time tools are more or less successful.
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Table 1
Examples of stable and dynamic person-, device- and context-specific factors associated
experiences of digital wellbeing
Person-specific factors
Affective and cognitive appraisals of digital connectivity
online vigilance
Reinecke et al., 2018
cognitive overload
Steele, Hall, & Christofferson, 2020
digital stress
Steele, Hall, & Christofferson, 2020
social approval anxiety
Steele, Hall, & Christofferson, 2020
digital stress
Steele, Hall, & Christofferson, 2020
media enjoyment
Reinecke & Hofmann, 2016
screen time guilt/shame
Du, van Koningsbruggen, & Kerkhof, 2018; Reinecke
& Hofmann, 2016
Stable traits
Billieux, Van der Linden, & Rochat, 2008
trait anxiety
Elhai, Levine, Dvorak, & Hall, 2016
Reinecke & Hofmann, 2016
trait fear-of-missing-out
Franchina et al., 2018
Momentary affective and cognitive states
Reinecke & Hofmann, 2016
Aalbers, McNally, Heeren, de Wit, & Fried, 2019
Reinecke & Hofmann, 2016
state boredom
Mark, Iqbal, Czerwinski, & Johns, 2014
Baym, Wagman, & Persaud, 2020; Bauer, Loy, Masur,
& Schneider, 2017
state fear-of-missing-out
Elhai, Rozgonjuk, Liu, & Yang, 2020
Device-specific factors
Stable characteristics
longer-term abstinence
Baym, Wagman, & Persaud, 2020
smartphone resistance
Ribak & Rosenthal, 2015
operating systems and embedded
digital wellbeing functionalities
Lyngs et al., 2019; Specker Sullivan & Reiner, 2019
app installed, including digital
wellbeing apps
Hiniker et al., 2016; Lyngs et al., 2019; Specker
Sullivan & Reiner, 2019
app settings/features
Lyngs et al., 2020; Fitz et al., 2019
Momentary characteristics
short-term abstinence
Eijnden, Doornwaard, & Bogt, 2017
device mere presence
Przybylski & Weinstein, 2012; Johannes, Veling,
Verwijmeren, & Buijzen, 2018
Johannes, Veling, Verwijmeren, & Buijzen, 2018
algorithmic curation
Horeck et al., 2018
post-play function
Horeck et al., 2018
Device-induced behaviors
media repertoires
Stragier, Hendrickson, Vanden Abeele & De Marez,
habitual checking routines
Bayer, Campbell & Ling, 2016
binge behaviors
Flayelle, Maurage, Vögele, Karila, & Billieux, 2019
Context-specific factors
Stable characteristics
times and places with clear boundaries
Baron & af Segerstad, 2010
Momentary characteristics
competing goals & obligations,
potentially from competing social roles
Hofmann, Reinecke, & Meier, 2016; Chesley, 2005
real and perceived pressure to (dis-
Licoppe & Smoreda, 2005; Quan-Haase & Collins,
availability and reciprocity norms
Hall & Baym, 2012; Laursen, 2005; Taylor & Harper,
formal and informal rules,
expectations, policies, punishments
and rewards
Piszczek, 2017
Socio-cultural transformations of society
commodification of attention
Specker Sullivan & Reiner, 2019; Williams, 2018
Rosa, 2013; Wajcman, 2008, 2015
(control) responsibilization
Vanden Abeele, de Wolf & Ling, 2018
Figure 1:
A dynamic system of digital wellbeing
... Recently, the field of digital effects research is shifting toward a more idiographic approach to understanding the associations between digital technology use and psychological distress (see Beyens et al., 2020;Valkenburg et al., 2021;vanden Abeele, 2020). This approach is signified by a shift away from general measures of digital technology use (e.g., overall screen time duration) toward specific aspects of use (e.g., content, affordances, etc.), and focusing on for whom the digital technology use-psychological distress effects are most salient rather than on aggregate effects. ...
Despite a plethora of research, the link between digital-technology use and psychological distress among young adults remains inconclusive. Findings in this area are typically undermined by methodological limitations related to measurement, study design, and statistical analysis. Addressing these limitations, we examined the prospective, within-persons associations between three aspects of objectively measured digital-technology use (duration and frequency of smartphone use, duration of social-media use) and three aspects of psychological distress (depression, anxiety, and social isolation) among a sample of young adults ( N = 384). Across 81 different model specifications, we found that most within-persons prospective effects between digital-technology use and psychological distress were statistically nonsignificant, and all were very small—even the largest effects were unlikely to register a meaningful impact on a person’s psychological distress. In post hoc subgroup analyses, we found scant evidence for the claim that digital-technology use is more harmful for women and/or younger people.
... This conceptualization departs from earlier definitions by emphasizing the dynamic and fluid nature of digital well-being, which is achieved specifically when digital users derive measured pleasure (hedonic well-being) and functional support (eudemonic well-being) from engagement with digital devices in specific contexts. Thus, digital well-being is not an immutable, trait-like occurrence, but rather an ambivalent state that results from the interplay of person-, digital device-, and context-specific factors (Vanden Abeele, 2020). This definition of well-being is particularly useful in the context of the current study due to the seemingly juxtaposed experiences of nature/the outdoors and our digital presence. ...
Research on our connection to nature (CTN) and sense of well-being has gained increased attention in recent years. It is often argued that CTN is an important, yet diminishing, human need, especially when juxtaposed with our increased time spent with screens and social media. Yet, little is known about the potential for social media, CTN, and well-being to form positive relationships. From the self-presentation framework, this study sought to better understand these relationships by examining the connection between three forms of nature posts (self, friends', and celebrities'), CTN, and two forms of well-being (vitality and body appreciation). Results from a cross-sectional survey demonstrated significant positive links between nature posts and CTN. Regression analysis showed active posting (self-nature posts) but not passive exposure (friends', celebrities' posts) was significantly linked to CTN, when also accounting for time spent outdoors and age. There was also a significant relationship between nature posts and well-being. Regression analyses revealed active posts but not passive exposure to be linked to vitality, while only celebrities’ posts were positively linked to body appreciation. Finally, CTN served as a significant mediator between nature posts and well-being. The findings are discussed in the context of nature-related self-presentation online and attention restoration, and in contrast to the often-deterministic view of the impact of technology use on CTN.
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This introductory chapter reviews Cyberpsychology research considering the effect of digital technologies on psychological wellbeing. Whilst there are clear benefits that come from being digitally connected, our reliance on them brings its own problems. This chapter explores the downsides of habitual involvement in terms of stress, depression, anxiety and dependency. Current explanations for these negative effects focus on the displacement of healthy behaviour as being the root cause but here an alternative approach is proposed that focuses on the displacement of attention. The attentional challenge of digital technologies is explored by looking at our innate attentional capacities and the demands placed upon them by the expectations of computational interfaces, multitasking and deliberate exploitation by digital designers.KeywordsMindfulnessDigital interactionTechnostressDisplacement effectDigital dependencyDigital wellbeingDigital addictionAttentionDisplacement of attentionMultitaskingAttention economyMindlessnessDigital mediaSocial media
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The aim of the article is to perform a detailed literature review of the most significant research in the con-text of well-being and business in order to identify the main elements and dimensions in the business environment, offering it as a potentially effective management tool for the company’s productivity. The main research method is the literature review using the comparative research method. There are identified emerging new concepts like technological and digital well-being at work, as the most current factors influencing productivity. Further research of well-being will include more highlight on digitalization and distance work circumstances.
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Debates concerning the impacts of screen-time are widespread. Existing research presents mixed findings, and lacks longitudinal evidence for any causal or long-term effects. We present a critical account of the current shortcomings of the screen-time literature. These include: poor conceptualisation, the use of non-standardised measures that are predominantly self-report, and the issues with measuring screen-time over time and context. Based on these issues, we make a series of recommendations as a basis for furthering academic and public debate.
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