1 Related Work

1.1 Evidence on Self-Control Challenges

It has long been known that digital device use for some subset of users can become associated with severe breakdowns of self-regulation, causing distress or impaired functioning in important life domains (Chakraborty, Basu, and Vijaya Kumar 2010). Thus, for more than two decades, the concept of ‘addiction’ has been applied by some researchers to such instances, originally in terms of ‘internet addiction’ (Young 1998), and more recently ‘social media’ and ‘Facebook addiction’ (Sleeper et al. 2015; Marino et al. 2018b, 2018a; Ryan et al. 2014; Andreassen et al. 2016), as well as ‘cell phone’ and ‘smartphone addiction’ (Chakraborty, Basu, and Vijaya Kumar 2010; Sapacz, Rockman, and Clark 2016).

The current surge of public discussion around self-control struggles and unwanted distraction - as well as related initiatives by some of the tech giants (Google 2018; Gonzalez 2018; Apple 2018) - has a broader focus, namely daily self-control struggles experienced by most users (cf. Wolwerton 2018; Centers 2018). Here, an accumulating body of evidence suggests that a majority of people experience difficulties with self-control over device use (Lundquist, Lefebvre, and Garramone 2014; Ko et al. 2015, 2016; U. Lee et al. 2014; Hiniker et al. 2016; Whittaker et al. 2016). For example, in a survey by Ko et al. (Ko et al. 2015), a majority of smartphone users felt they were overusing their devices (64%) and wanted to change their usage habits (60%). The patterns respondents wished to change clustered around two themes: too frequent short usage, where incoming notifications or urges to e.g. check the news derailed focus from tasks they wished to complete; and excessive long usage, where e.g. habitually checking devices before bedtime ‘sucked them in’. Most users also reported that their strategies for changing this behaviour most often failed, especially when relying on ‘willpower’, because good intentions to limit use tended to be overridden by momentary impulses . In addition to a growing amount of evidence from such surveys (Ko et al. 2016; Hiniker et al. 2016), the wealth of articles and opinion pieces that in recent years have appeared on the topic in major news outlets, viral blog posts, and popular science books (e.g. Alter 2017; Wu 2016; Harris 2016; Knapp 2013; Foer 2016; Popescu 2018), as well as the fact that Apple, Google, and Facebook perceive a sufficiently large demand for features supporting user self-control to respond (Apple 2018; Google 2018; Ranadive and Ginsberg 2018), suggest that day-to-day struggles with self-regulation over device use are very common.

1.2 Theory Applied in Related Research

1.2.1 Digital Self-Control Tools

To understand the theory use in prior relevant HCI work, we identified 17 HCI papers which have either built novel design intervention or evaluated existing interventions to support self-control over digital device use – see Table 1.1. For each of these, we examined which self-regulation theories were applied to guide tool development (if designing new interventions) and/or to evaluate effects on user behaviour and perceptions. These papers were a subset of a large body of literature on self-control in relation to digital device use, spanning psychology, neuroscience, behavioural economics, philosophy, and HCI, continuously collected over a period of two years (500+ papers altogether). While the literature included here aimed at comprehensively summarising current work in this area of HCI, we note that they were not collected through a formal systematic review process.

In 7 out of 17 papers, no underlying self-regulation theory was specified, with tool development and/or assessment informed only by user-centered design methods such as surveys and interviews with target users and/or design experts. The 10 remaining papers all referred to distinct theoretical models from psychology (Social Cognitive Theory, classical conditioning, strength model of self-control), cognitive neuroscience (attentional resource theory, cognitive load theory, multitasking and inhibitory brain function), (behavioural) economics (nudge theory, framing effects, rational choice), behaviour change, and addiction research. For the purposes of the present paper, we note that none of the papers reviewed relied on dual systems models of self-regulation (cf. Pinder et al. 2018; Cox et al. 2016).

Table 1.1: Theories applied in existing work on digital self-control tools.

Paper	Summary	Self-regulation theory
Lottridge et al. 2012 (Lottridge et al. 2012)	Firefox extension which classifies URLs as work or non-work, then makes non-work tabs less prominent and displays time spent	Multitasking, inhibitory brain function (Gazzaley et al. 2008)
Löchtefeld et al. 2013 (Löchtefeld, Böhmer, and Ganev 2013)	AppDetox, an Android app which let users voluntarily create rules intended to keep them from certain apps	None
Collins et al. 2014 (Collins et al. 2014)	RescueTime, a commercial Windows/Mac application which provides visualisations of how much time is spent in different applications	Cognitive Load theory (Block, Hancock, and Zakay 2010)
Lee et al. 2014 (H. Lee et al. 2014)	SAMS, an Android app for tracking smartphone usage and setting time limits for app use	Relapse prevention model (Witkiewitz and Marlatt 2004, Gustafson2011), clinical guidelines for internet addiction (Young 1999)
Ko et al. 2015 (Ko et al. 2015)	NUGU, a smartphone app which let users set goals for limiting usage, then share performance with friends and receive encouragement	Social Cognitive Theory (Bandura 1991)
Andone et al. 2016 (Andone et al. 2016)	Menthal, a smartphone app displaying the ‘MScore’, a single number summarising overall phone usage, as well as a series of main usage measures	None
Hiniker et al. 2016 (Hiniker et al. 2016)	MyTime, an Android app showing time spent in apps (and whether a daily limit was hit) plus a daily prompt asking what the user wished to achieve	None
Kim et al. 2016 (Kim et al. 2016)	TimeAware, an ambient Windows and Mac widget which presents time spent in ‘distracting’ or ‘productive’ applications	Framing effects (Marteau 1989)
Ko et al. 2016 (Ko et al. 2016)	Lock n’ LoL, a smartphone app which lets users as a group set their phones in a lock mode in which notifications are muted and usage restricted	None
Ruan et al. 2016 (Ruan et al. 2016)	PreventDark, an Android app which detects phone use in the dark and notifies the user that they should put it away	None
Whittaker et al. 2016 (Whittaker et al. 2016)	MeTime, a computer application providing a floating visualisation of time spent in different applications within the last 30 mins	None
Kim et al. 2017 (I. Kim et al. 2017)	Let’s FOCUS, an Android and iOS app letting users enter a ‘virtual room’ where notifications and apps are blocked; links to location or time	None
Kim et al. 2017 (J. Kim, Cho, and Lee 2017)	PomodoLock, a PC and Android application plus Chrome extension which blocks distracting apps and websites during 25 minute focus sessions	Strength model of self-control (Baumeister, Vohs, and Tice 2007)
Marotta et al. 2017 (Marotta and Acquisti 2017)	Freedom, a commercial Windows/Mac/Android/iOS app which blocks access to distracting parts of the web or the internet altogether	Rational choice, ‘self-commitment’ (Bryan, Karlan, and Nelson 2010)
Kovacs et al. 2018 (Kovacs, Wu, and Bernstein 2018)	HabitLab, a Chrome extension in which the user sets time limit goals for specific sites, then tries a range of interventions to reach the goal	Numerous, including goal setting theory (Locke and Latham 2002), operant conditioning (Baron, Perone, and Galizio 1991), and self-consistency theory (Sherman 1980)
Mark et al. 2018 (Mark, Czerwinski, and Iqbal 2018)	Freedom, described above	Attentional resources (Wickens 1980), Big 5 (McCrae and Costa, Jr. 1999)
Okeke et al. 2018 (Okeke et al. 2018)	Android app nudging users to close Facebook when a usage limit has been hit, using pulsing vibrations that stop when the user leaves the site	Nudge theory (Sunstein and Thaler 2008), negative reinforcement (Iwata 1987)

1.2.2 Digital Behaviour Change Interventions

A large body of HCI work exists on how digital tools can assist behaviour change in general (Consolvo, McDonald, and Landay 2009; Li, Dey, and Forlizzi 2010; Schueller, Muñoz, and Mohr 2013; Kersten-van Dijk et al. 2017; Yardley et al. 2016; Epstein et al. 2015). A main focus within such research on ‘Digital Behaviour Change Interventions’ (DBCIs, (Pinder et al. 2018)) is health, for example in relation to how digital interventions may help users exercise more (Consolvo, McDonald, and Landay 2009), quit smoking (Abroms et al. 2011; Heffner et al. 2015), eat more healthily (Coughlin et al. 2015; Okumus, Bilgihan, and Ozturk 2016), cope with stress (Konrad et al. 2015; Gimpel, Regal, and Schmidt 2015), or manage chronic conditions (Wang et al. 2014).

Since digital self-control tools can be seen as a subset of DBCIs, understanding how self-regulation theory has been applied within this research area is relevant for the present paper. One recent review of 85 DBCI studies (Orji and Moffatt 2018) found the Transtheoretical Model (or Stages of Change) (Prochaska, DiClemente, and Norcross 1993) to be the most commonly referenced (13/85 papers), followed by Goal Setting Theory (Locke and Latham 2002) (5/85) and Social Conformity Theory (Asch 1951; Epley and Gilovich 1999) (3/85). 60% (51/85) did not specify any theoretical basis (cf. Wiafe and Nakata 2012; Schueller, Muñoz, and Mohr 2013), and none specified dual systems theory (cf. Pinder et al. 2018). The review also found that among studies which did specify underlying theories, most only mentioned them without explaining how the theoretical constructs informed the design and/or evaluation of actual intervention components (Orji and Moffatt 2018).

Another recent comprehensive review (Pinder et al. 2018) noted that most theories applied in DBCI studies assume a rational, deliberative process as a key determinant of behaviour (e.g. the Transtheoretical Model (Prochaska, DiClemente, and Norcross 1993) or the Theory of Planned Behaviour (Ajzen 1991)). The authors further argued (after extensive review and discussion) that dual systems theory could be well placed to guide DBCIs research focusing on long-term behaviour change through breaking and forming habitual behaviour .

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