Communication Theory ISSN 1050-3293
ORIGINAL ARTICLE
Digital We 11 being as a Dynamic Construct
Mariek M. P. Vanden Abeele ©
Tilburg Center for Cognition and Communication, Department of Communication and Cognition, Department of Culture Studies, Tilburg University, the Netherlands
Mobile media support our autonomy by connecting us to persons, content 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.
Keywords: Digital Wellbeing, Mobile Connectivity, Mobile Media, Wellbeing, Addiction, Problematic Phone Use, Addictive Design, Digital Wellbeing Interventions, Digital Detox, Screen Time
doi:10.1093/ct/qtaa024
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, Kromer, & Schneider, 2016) we face a new challenge:
Corresponding author: e-mail: mariekvandenabeele@gmail.com
932 Communication Theory 31 (2021) 932-955 ©The Author(s) 2020. Published by Oxford University Press on behalf
of International Communication Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.0rg/licensesA3y/4.O/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
M. M. P. Vanden Abeele
Digital Wellbeing as a Dynamic Construct
How do we obtain a healthy balance between connectivity and disconnectivity? In other words: How do we attain digital wellbeing?
Studies show that we hardly disconnect. Smartphones are tapped, swiped and clicked over 2,600 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 (Biichi, Festic, & Latzer, 2019). It should therefore not be a 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 (Apple.com), with the goal to "keep life, and not the technology in it, front and center" (wellbeing.google). 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, Táht, & 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 policymakers 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
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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 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
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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, 2008, 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 well-being. 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, &
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Schimmenti, 2018). Such misclassification leads to an overpathologization of everyday behaviors and experiences1 (Billieux 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: (a) lose control over it, and (b) 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
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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, 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 well-being, 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, Osborne and Ellis, 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
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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 wellbeing
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 factors.
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).
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While the debate on "digital harm"—and how to best estimate it—rages on (see, e.g. Twenge, Haidt, Joiner, & 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 and Przybylski, 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.2 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).
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).
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Time 1 —     K ■ ■■
Person-specific
Time 1 -
Time I - i, \, k. . . Time \        K ...
Figure 1 A dynamic system of digital wellbeing.
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 "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" (Eyal, 2014; Williams, 2018). Such "addictive design" (cf. Yousafzai, Hussain, & Griffiths, 2014) capitalizes on the fact that humans are evolutionarily hardwired to constantly scan the environment for new information, including of a social nature (Eyal, 2014). Smartphones in particular embed such a reward infrastructure,3 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
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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, and Christofferson (2020)	
Digital stress	Steele, Hall, and Christofferson (2020)	□ o
Social approval anxiety	Steele, Hall, and Christofferson (2020)	S
Digital stress	Steele, Hall, and Christofferson (2020)	o £U
Media enjoyment	Reinecke and Hofmann (2016)	O. CD Q.
Screen time guilt/shame	Du, van Koningsbruggen, and Kerkhof (2018);	—h —1 o
	Reinecke and Hofmann, (2016)	3 3-
Stable traits		■a w
Impulsivity	Billieux, Van der Linden, and Rochat (2008)	n?
Trait anxiety	Elhai, Levine, Dvorak, and Hall (2016)	a ai Q.
Self-control	Reinecke and Hofmann (2016)	CD 3
Trait fear-of-missing-out	Franchina et al. (2018)	o b
Momentary affective and cognitive states		c ■a
Mood	Reinecke and Hofmann (2016)	com/ct
Stress	Aalbers, McNally, Heeren, de Wit, and Fried	
	(2019)	m
Exhaustion	Reinecke and Hofmann (2016)	o' CD
State boredom	Mark, Iqbal, Czerwinski, and Johns (2014)	W
Mindfulness	Baym, Wagman, and Persaud (2020); Bauer,	to
	Loy, Masur, and Schneider (2017)	W M
State fear-of-missing-out	Elhai, Rozgonjuk, Liu, and Yang (2020)	CJl CO M
Device-specific factors		O)
Stable characteristics		CJl O"
Longer-term abstinence	Baym, Wagman, and Persaud (2020)	>< ca
Smartphone resistance	Ribak and Rosenthal (2015)	c CD cn
Operating systems and embedded digi-	Lyngs et al. (2019); Specker Sullivan and	o D
tal wellbeing functionalities	Reiner (2019)	00
App installed, including digital well-	Hiniker et al. (2016); Lyngs et al. (2019);	Ui CD
being apps	Specker Sullivan and Reiner (2019)	"O CD
App settings/features	Lyngs et al. (2020); Fitz et al. (2019)	3 CT
Momentary characteristics		CD -! M
Short-term abstinence	Eijnden, Doornwaard, and Bogt (2017)	O M W
Device mere presence	Przybylski and Weinstein (2012); Johannes, Veling, Verwijmeren, and Buijzen (2018)	
Notifications	Johannes, Veling, Verwijmeren, and Buijzen (2018)	
(Continued)
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Table 1 (continued) Person-specific factors
Algorithmic curation Post-play function Device-induced behaviors Media repertoires
Habitual checking routines Binge behaviors
Context-specific factors Stable characteristics
Times and places with clear boundaries
Momentary characteristics
Competing goals & obligations, potentially from competing social roles
Real and perceived pressure to (dis-) connect
Availability and reciprocity norms
Formal and informal rules, expectations, policies, punishments, and rewards Socio-cultural transformations of society
Commodincation of attention
Acceleration
(Control) Responsibilization
M. M. P. Vanden Abeele
Horeck, Jenner, and Kendall (2018) Horeck, Jenner, and Kendall (2018)
Stragier, Hendrickson, Vanden Abeele and De
Marez (2019) Bayer, Campbell and Ling (2016) Flayelle, Maurage, Vögele, Karila, and Billieux
(2019)
Baron and af Segerstad (2010)
Hofmann, Reinecke, and Meier (2016); Chesley (2005)
Licoppe and Smoreda (2005); Quan-Haase and
Collins (2008) Hall and Baym (2012); Laursen (2005); Taylor
and Harper (2003) Piszczek (2017)
Specker Sullivan and Reiner (2019); Williams (2018)
Rosa (2013); Wajcman (2008, 2015) Vanden Abeele, de Wolf and Ling (2018)
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 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).
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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.
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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 disrupters 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 Human-Computer Interaction (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
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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
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(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, etc. (Kaufmann & Peil, 2019) to help reveal what digital wellbeing means to individual users, and how digital wellbeing experiences intersect with broader aspects of culture.
Conclusion
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.
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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.
Notes
1 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, Urban, 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.
2 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.
3 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.
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