2.1.1 Behavioural addiction

It has long been posited that use of Information Communication Technologies (ICTs) 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). For more than two decades, the concept of ‘addiction’ has been applied by some researchers to such instances, originally in the form of ‘internet addiction’ (K. S. Young 1998), and more recently ‘cell phone’ and ‘smartphone addiction’ (Chakraborty, Basu, and Vijaya Kumar 2010; Sapacz, Rockman, and Clark 2016), as well as ‘social media’ or ‘Facebook addiction’ (Andreassen et al. 2016; Marino et al. 2018b, 2018a; Ryan et al. 2014; Sleeper et al. 2015). This research tends to focus on a more restricted subset of self-control difficulties than the present thesis, namely struggles with controlling digital device use that are so severe that they result in real negative life impact, where a clinical term may be appropriate. However, because the concept of ‘addiction’ has often been applied somewhat loosely — and because narratives of addiction have had a significant influence on public discourse and users’ personal reflections (Lanette and Mazmanian 2018; Tiku 2018; Orben 2019) — this research space provides a useful point of departure.

‘Internet addiction’ has been defined as ‘time-consuming computer usage that causes distress or impairs functioning in important life domains’ (Chakraborty, Basu, and Vijaya Kumar 2010), mobile phone addiction as ‘repetitive use of mobile phones to engage in behaviour known to be counterproductive to health’ (Lopez-Fernandez et al. 2014), and ‘Facebook addiction’ (also ‘Facebook dependence’ or simply ‘Problematic Facebook Use’) as ‘problematic behaviour characterised by addictive-like symptoms and/or self-regulation difficulties related to Facebook use leading to negative consequences in personal and social life’ (Marino et al. 2018a). Early work on internet addiction found that ‘addicts’ on average spent 8 times as much time online as ‘non-addicts’ and reported problems akin to substance abuse, including negative consequences on educational, work, and personal life, inability to break problematic usage patterns, and withdrawal symptoms when unable to access the internet. `Addicts’ also tended to be heavy users of online chat rooms and multiplayer role playing games, suggesting that addictive use was driven by interactivity and need-provision online rather than “The Internet” as such (cf. more recent work describing social networking sites as “addiction prone technologies,” Tarafdar, Gupta, and Turel (2013); Turel and Serenko (2012); Ryan et al. (2014)). Similarly, research on cell phone addiction has found that users classified as ‘addicts’ experience withdrawal symptoms in absence of their device — feelings of loss, signs of craving, and functional impairment — and a resulting loss of control in managing other activities (Cheever et al. 2014; Lopez-Fernandez et al. 2014; Roberts, Pullig, and Manolis 2015).

However, applying the concept of addiction to ICT use has been enveloped in controversy. Initial work on ‘internet addiction’ modified the American Psychiatric Association’s DSM-IV diagnostic criteria for pathological gambling (K. S. Young 1998), and came to describe it in terms of four components: i) excessive use (often associated with a loss of sense of time or neglect of basic drives), ii) withdrawal symptoms when one’s computer is inaccessible (e.g. anger, tension, or depression), iii) tolerance (e.g. increased hours of use and increased expense on equipment over time), and iv) negative repercussions (e.g. social isolation, fatigue, lying to one’s surroundings about use, poor achievement, etc.) (Tao et al. 2010; Beard and Wolf 2001; Chakraborty, Basu, and Vijaya Kumar 2010). However, the subsequent literature has seen little agreement on how to delineate the boundaries of addictive use, and researchers have often used terms such as ‘addiction,’ ‘abuse,’ and ‘problematic use’ interchangeably (Gutiérrez, Rodriguez de Fonseca, and Rubio 2016). As a result, a wide range of prevalence estimates for addictive use has been given: ‘internet addiction’ has been suggested to affect anywhere between 0.3 and 38% of populations in developed economies (Chakraborty, Basu, and Vijaya Kumar 2010; C. Cheng and Li 2014; Weinstein et al. 2014), ‘smartphone addiction’ similarly between 0 and 38% (Lopez-Fernandez et al. 2014; Pedrero, Rodríguez, and Ruiz 2012), and ‘Problematic Facebook Use’ from 3.1% to 47% (J. Cheng, Burke, and Davis 2019; Jafarkarimi et al. 2016; see also Bányai et al. 2017; Khumsri et al. 2015; Wolniczak et al. 2013). Accordingly, some researchers have pushed back against a perceived pathologisation of everyday patterns of ICT use in this literature, and argued that even frequent, extensive use which distract users from daily life should not be considered “addiction” unless it also leads to functional impairment and psychological distress (Billieux et al. 2015; Kardefelt-Winther et al. 2017; Tran et al. 2019).

For the purposes of the present thesis, this literature suggests severe self-control difficulties in relation to digital device use among some smaller subset of users. Moreover, the high prevalence estimates of ‘addictive’ use in studies that apply less strict inclusion criteria suggests that milder struggles with self-control are widespread.

2.1.2 Common self-control struggles

The current surge of public discussion around self-control struggles and unwanted distraction — and the recent related initiatives by some of the tech giants (Apple 2018; Gonzalez 2018; Google 2018) — have a broader focus, namely daily self-control struggles assumed to be experienced by many, if not most, users (cf. Centers 2018; Wolwerton 2018). Thus, a wealth of articles and opinion pieces have in recent years appeared on the topic in major news outlets, viral blog posts, and popular science books (e.g., Alter 2017; Eyal 2019; Foer 2016; Harris 2016; Knapp 2013; Popescu 2018; Wu 2016). Empirical research suggests that this does not merely reflect another ‘moral panic’ over new technology in society (cf. Lanette and Mazmanian 2018; Orben 2019): whereas it is unclear how often habits of digital device use meet clinical definitions of addiction, there is wide support for the claim that a high proportion of people regularly experience difficulties with self-control (Hiniker et al. 2016; Ko et al. 2016, 2015; Lundquist, Lefebvre, and Garramone 2014; Tran et al. 2019; Whittaker et al. 2016).

Initial work on intentional ‘non-use’ looked into why some users quit, or take breaks from Facebook (Baumer and Adams 2013), Twitter (Schoenebeck 2014), or other social networking sites. This work found that a major motivation for disengaging was struggles with distraction and self-control, with, e.g., users reporting deactivating Facebook because they found it ‘too interesting,’ or ‘superficial yet addictive’ and felt a need to delete or deactivate their accounts to concentrate on work or break out of a perceived addiction (Baumer and Adams 2013).

Most recent research has focused on smartphone use, where a number of studies have found broad frustration among ordinary users with their usage habits, particularly in relation to self-control struggles with high-reward, low-demand experiences such as games and browsing social media (Lukoff et al. 2018; Tran et al. 2019; Ames 2013; U. Lee et al. 2014; Shin and Dey 2013). For example, in a survey by Ko et al. (2015) posted in large online communities, 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’ (see also Ko et al. 2016; Oulasvirta et al. 2012). 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 (Ko et al. 2015; cf. Elhai et al. 2016; Hofmann, Vohs, and Baumeister 2012; Lim et al. 2017).

In a survey of American smartphone users recruited on MTurk, 58% said they wanted to spend less time using their phones (Hiniker et al. 2016). Elaborating on their responses, many said they wanted to make context-dependent changes to their use, such as use their phones less before bedtime, or restructure time spent so that, e.g., their proportion of time in work-related activities were higher during commutes (Hiniker et al. 2016; see also Przybylski and Weinstein 2017).

In a recent study by Tran et al. (2019), which interviewed American participants from three subpopulations (high school students, college students, and post-graduation adults), participants reported filling every moment of downtime with habitual smartphone checking, which they engaged in with minimal awareness. They expressed frustration with their checking habits, particularly in relation to apps that drove them into compulsive use without adding enduring value to their lives, and most had tried regulating their use by deleting such apps after a sense of frustration built up.

Finally, related work on multitasking and media use has found that people often perceive their use of internet services to be in conflict with other important goals (Rosen, Mark Carrier, and Cheever 2013; S. Xu, Wang, and David 2016; Reinecke et al. 2018). For example, an experience sampling study of adults in the US questioned participants at random times during the day about whether and how they were currently using media (Reinecke and Hofmann 2016). In 51% of sampled episodes, participants were engaged in media use, with internet use the most common form (55% of instances; the most frequent content was social media, including Facebook and Twitter, video streaming sites, including YouTube, and online news sites). Importantly, participants reported that their use conflicted with other important goals on more than half of all use occurrences (61%) suggesting a high prevalence of self-control struggles with digital media use.

Some recent work on associations between device use and measures of well-being has called for careful examination of when self-reported frustration over, e.g., habitual smartphone use reflect users’ own lived experience, and when it reflects internalised narratives of, e.g., distracted addicts neglecting a morally ‘superior’ external world (Harmon and Mazmanian 2013; Orben 2019). This is an important point, and the study mentioned above by Tran et al. (2019) was specifically conducted as such a deeper examination among a random sample of smartphone users. Tran et al. (2019) found that, in addition to reporting general frustration with aspects of smartphone use they experienced as compulsive, participants also critically reflected on what use was meaningful and what was not, and described steps they had taken to minimise specific usage that did not provide them value. Whereas the influence on participant self-report of larger social narratives in relation to addictive use should be kept in mind, this research evidence suggests that reports of self-control struggles do reflect real aspects of users’ lived experience.

2.1.3 Challenges from constant connectivity and the attention economy

It is to be expected that tools which provide instant and permanent access to endless information, entertainment, and social connection come with self-control challenges. Thus, psychologists studying self-control have long known that relying on conscious willpower as self-control strategy in environments where distractions and temptations are readily available is unreliable: in general, people who are better at self-control instead reduce their exposure to temptation in the first place, and/or develop habits that make their intended behaviour more reliant on automatic processes than conscious control (Galla and Duckworth 2015; Angela L. Duckworth et al. 2016; Hofmann et al. 2012; Ent, Baumeister, and Tice 2015). Therefore, we should predict that digital devices, which make a wide range of behaviours constantly available with minimal effort, would come with significant self-control challenges and a corresponding need to translate people’s ordinary strategies for managing temptation into the digital realm.

This challenge is compounded by the business models of many tech companies, which incentive design that nudges people into using services frequently and extensively, a dynamic often referred to as the ‘attention economy’ (Davenport and Beck 2001; Einstein 2016; Wu 2016): in 1971, Herbert Simon noted that when information is overabundant, human attention becomes a scarce resource, which must be allocated efficiently among the information sources that may consume it (Simon 1971). Simon made this point in the context of system design, where he argued that digital systems should filter information to ensure the user is not distracted or overwhelmed by less relevant details. More recently, however, Simon’s insight has been applied to analyse how the explosion of readily available information via ICTs has led to a breakneck competition among technology and media companies over capturing and holding users’ attention (Harris 2016; Leslie 2016; Wu 2016). This competition has been intensified by the rise of ‘free’ as a dominant business model: if users do not pay for the product, such as in services provided by Facebook, Google, and a large majority of mobile apps (“Distribution of Free and Paid Android Apps 2019,” n.d.), companies usually monetise products through advertising, collection of user data (cf. ‘surveillance capitalism,’ Zuboff (2015)), or in-app purchases. This in turn creates a financial incentive to design in ways that keep users coming back to their digital devices and services and make them spend as much time as possible. (Perhaps unsurprisingly, the American author Nir Eyal’s book Hooked: How to Build Habit-Forming Products (2014), became an international best-seller.)

The incentives of the attention economy create a potential misalignment between how the end-user wishes to use a digital service (e.g., using Facebook for specific tasks such as event coordination or messaging) and how the product designer wishes the end-user to behave (e.g., spending as much time as possible on Facebook to optimise advertising revenue). Advertising agencies competing for attention is, of course, not a new development (Wu 2016). However, what is new — and why the challenge at this moment in history is particularly important to tackle — is that portable digital devices extend the battle for attention to any time of day and any situation, from the bathroom to the bedroom; can influence users in multi-modal and interactive formats; and can precisely target audiences using previously collected user data, at scale (Boyd and Crawford 2012; Fogg 2003).

2.1.4 The rise of digital self-control tools

In parallel with the massive investment of resources in keeping users ‘hooked’ on digital systems, online stores have seen the rise of a counter-movement in the form of a niche for ‘digital self-control tools’ (DSCTs). Thus, hundreds upon hundreds of apps and browser extensions now cater to people struggling with self-control over device use, and claim to provide means of resistance in the battle over their attention (Lyngs 2019d; Lyngs et al. 2019). As we will review in Chapter 3, these tools provide a wide range of interventions which may, e.g., block or remove digital distractions, track and visualise device use, remind users of their goals, or provide rewards and punishments for how devices are used. For example, they may restrict the amount of functionality available on devices’ home screen (such as the Android app LessPhone Launcher, Mohan (2019)) gamify self-control by tying device use to the wellbeing of virtual creatures (such as the smartphone app and browser extension Forest, Seekrtech (2018)), or hide or change content on distracting websites (such as the browser extension Newsfeed Eradicator, JDev (2019)). Some of these tools now have millions of users (Google Play (2017), cf. Chapter 4).

In 2018, the perceived demand for such tools, as well as public debate and pressure on tech platforms fanned by organisations including the Center for Humane Technology (formerly know as ‘Time Well Spent’), led Mark Zuckerberg to announce “making sure that time spent on Facebook is time well spent” as his annual personal challenge (Z. Mark 2018), and his company to announce tools for ‘managing your time on Facebook and Instagram’ (Ranadive and Ginsberg 2018); and Apple and Google to similarly implement features for visualising and limiting use on their operating systems (Apple 2018; Google 2018; Apple 2019). In 2019, public discussion has continued unabated, with a recent US senate hearing on the perceived threats of ‘Persuasive Technology’ (“Optimizing for Engagement: Understanding the Use of Persuasive Technology on Internet Platforms” 2019), and a call from a UK All-Party Parliamentary Group for a duty of care to be established on social media companies (All Party Parliamentary Group on Social Media and Young People’s Mental Health and Wellbeing 2019). Following the turning of the tide, Nir Eyal published a new book, this time called indistractable, focused on tools for managing attention and self-control when digital technologies are designed to hook their users in (Eyal 2019).

2.2.1 User behaviour vs ‘true intentions’ in HCI

In human-computer interaction research, the question of how to bridge the potential gap between what users actually do and what they ‘really wanted’ to do has a relatively long history. In the 1960’s, Warren Teitelman’s ‘Do What I Mean’ (DWIM) philosophy argued that systems should not just execute whatever potentially erroneous instructions users put into a terminal (Teitelman 1966). Instead, they should try to interpret users’ true intentions and correct their errors (the implication being DWIM, Now What I Say (or Do)). In practice, however, Teitelman’s error-correction systems were critiqued as merely reflecting what their designer would have meant (‘do what Teitelman means,’ Steele and Gabriel (1996)).

The issue has cropped up in more fundamental ways in the domains of decision support and recommender systems, where the gap is not just between what the user typed and what they really intended, but between recorded interaction behaviour and what can be inferred about the user’s wants and needs. In recent years, behaviourism has been the dominant paradigm for recommendation, expressed in trends such as measuring user activity in A/B tests instead of capturing user experiences in surveys and ethnographic analyses, and ignoring people’s explicit preferences when it disagrees with their behaviour (Ekstrand and Willemsen (2016); industry case studies include Facebook’s introduction of the newsfeed which involved striking contrast between users’ sentiment and their behaviour, Fisher (2018)). However, much recent critique of social media platforms has revolved around the problems associated with equating users’ true needs and desires with simple behavioural measures of engagement (cf. current debates about whether removing ‘like’ counts on social media platforms would benefit user well-being Martineau (2019)). Whereas people’s behaviour obviously provides some valuable information about their goals — on the basis of which an extensive knowledge base has been generated about how recommendations can effectively lead users to action — relying purely on behaviour may render recommender systems unable to distinguish between addiction and the user deriving value from the system (Ekstrand and Willemsen 2016). Thus, Ekstrand and Willemsen (2016) argued that in order to know whether users are satisfied with their choices both short-term and long-term; to know what keeps them from aligning their actions and desires if they are dissatisfied; and to know whether the recommender system is helping or hindering the user in achieving the goals they have for their life, explicit input from the user is necessary.

Numerous ways to explicitly elicit users’ preferences have been explored in the literature, including user ratings and example-critiquing in recommender systems (Pommeranz et al. 2012; Pu and Chen 2009), and pairwise comparison in decision support systems (Aloysius et al. 2006; L. Chen and Pu 2004). Moreover, pioneering work in this space explored ways to let users inspect and tweak a system’s model of them (Cook and Kay 1994; J. Kay 1997, 1995; Pu and Chen 2009). However, explicit methods have their own challenges. Foremost, the elicitation process itself influences what users say they want. For example, users may prefer different options based on whether they are framed as losses or gains (Pommeranz et al. 2012), or depending on the moment in time in which they are asked. What point in time reflects what the user ‘really’ wants - the most recent, a weighted average over the last day / week / year, or something else (Kahneman and Riis 2005; Redelmeier and Kahneman 1996)? Jameson et al. (2014) suggest that decision-support systems should optimise for outcomes that “the chooser is (or would be) satisfied with in retrospect, after having acquired the most relevant knowledge and experience,” but add that “Admittedly, this assumption is subject to debate…” (2014, 35).

Another point of tension is between broad or narrow construals of the user’s context. One of the foundational tenets of user-centred design, as articulated by Ritter, Baxter, and Churchill (2014), is to consider the user context more broadly. That is, to move beyond the immediate, task-related issues pertaining to a specific product, where the user’s goals can be more easily approximated, and instead view applications of technology as “the development of permanent support systems and not one-off products that are complete once implemented and deployed” (Ritter, Baxter, and Churchill 2014, 44). In other words, the designer should consider longer-term effects of systems on people’s lives, which in turn requires deeper insight in order to align systems with users’ more general goals, values, and life situation (cf. Peters, Calvo, and Ryan 2018). However, this runs the risk of not being actionable, partly because it involves a potentially boundless number of concerns in relation to a product’s impact, and partly because it leaves a much bigger role for normative disagreements over values and visions of the ‘good life’ and the role digital devices ‘ought’ to play, which are not easily resolved (cf. Orben 2019).

Summing up, Pommeranz et al. (2012) noted that “More research is needed to design preference elicitation interfaces that elicit correct preference information from the user” (2012, 361), and called for explicit consideration in HCI research of the normative aspects that might be required to determine such ‘correctness’ (cf. Lyngs et al. 2018).

2.2.2 The view from self-control research: Aligning device use with valued, longer term goals in the face of conflicting impulses

Whereas HCI research has run up against the challenge of distinguishing between what people do and what they ‘really want’ in relation to systems design, fundamental research on self-control within psychology, neuroscience, and behavioural economics have tackled this problem space for decades, with an aim to understand the mechanisms involved (Baumeister, Vohs, and Tice 2007; Hagger et al. 2010; Inzlicht, Schmeichel, and Macrae 2014; Kotabe and Hofmann 2015; Shea et al. 2014).

In contrast to notions of ‘rational actors’ with consistent preferences, this research has highlighted that people frequently experience internal conflict in which short-term desires (e.g., checking one’s Instagram feed) conflict with longer term goals that they, upon reflection, deem to be more valuable (e.g., doing well on a school test, Angela L. Duckworth and Steinberg (2015)). Often, people fail to act in accordance with their stated longer term goals (Consolvo, McDonald, and Landay 2009), which behavioural economists have described as ‘time-inconsistent preferences’ (Hoch and Loewenstein 1991; Ariely and Wertenbroch 2002) and explained through decision biases such as a tendency to value potential rewards less the further away they are in time (‘future discounting,’ Ainslie (2010); Critchfield and Kollins (2001)). For example, even though we may value getting a good night’s sleep and know that tomorrow we will appreciate having gone to bed at a reasonable hour, the short-term gratification of, e.g., watching another YouTube video often looms larger in the moment (see Ariely and Wertenbroch 2002).

Hence, this literature has defined ‘self-controlled behaviour’ as “actions aligned with valued, longer term goals in the face of conflicting impulses to seek immediate gratification” (Angela L. Duckworth and Steinberg 2015, 32). Sometimes, self-controlled behaviour involves inhibiting an undesired impulse (e.g., suppressing an urge to check one’s phone in a social situation) whereas it at other times involves strengthening a desired action (e.g., spending time on a MOOC course rather than on Facebook, cf. Fujita (2011); Angela L. Duckworth and Steinberg (2015)).

Research in this space has also investigated the many “tricks” by which people try to out outmanoeuvre their myopic present self, from distracting themselves from temptations in the heat of the moment, to ‘pre-committing’ to a particular course of action, or simply avoiding exposure to temptation (Angela L. Duckworth et al. 2016; Ariely and Wertenbroch 2002). Ultimately, all such strategies attempt to decrease the strength of momentarily rewarding, but ultimately less desired, impulses and/or increase the strength of enduringly beneficial, but relatively less immediately gratifying ones (Angela L. Duckworth et al. 2016).

Self-control has been studied under various names, including effortful control, will power, ego-resilience, and cognitive control and the literature on the topic is vast (see Angela L. Duckworth and Steinberg 2015; Inzlicht, Bartholow, and Hirsh 2015). For our purposes, the main take-away is that across research in this space, the notion of ‘success’ — what people ‘really want’ — is defined as being able to act in accordance with one’s enduringly valued goals in the face of conflicting urges that may be more potent in the moment (Angela L. Duckworth and Steinberg 2015). In chapter 3, we expand on one particular approach drawn from this literature, dual systems theory, which we then adapt to the context of self-control struggles in relation to digital device use, and apply throughout the rest of this thesis.

2.3.1 Tools/design patterns investigated

The existing studies have investigated a wide range of design patterns: Overall, some focus on visualisations of device use (e.g., Whittaker et al. (2016)’s MeTime displayed a floating window of relative time spent in computer applications over the past 30 minutes); others on blocking distractions (e.g., Marotta and Acquisti (2017) studied effects of using the commercial tool Freedom to block distracting websites among crowdworkers on Amazon’s platform Mechanical Turk); and yet others on goal setting (e.g., Ko et al. (2015)’s NUGU let users set goals for limiting smartphone use and share goals, contexts, and performance with friends).

Whereas earlier work mainly focused on one specific implementation of a potential design pattern, more recent investigations have moved towards more informative studies of (i) how varying key implementation parameters influences effectiveness, (ii) how interventions can be made more useful by being sensitive to context, and (iii) how cross-device use can be taken into account.
In relation to varying parameters, recent studies have varied amount of friction for accessing distraction instead of simply studying binary blocking: J. Kim, Jung, et al. (2019) compared effects of blocking target apps at three levels of intensity, and J. Kim, Park, et al. (2019) compared effects of requiring the user to type in digit combinations of varying lengths before target apps could be accessed.
In relation to context sensitivity, Park et al. (2017) investigated use of built-in smartphone sensors to detect socially appropriate moments to deliver notifications, and Tseng et al. (2019) studied how distraction blocking in work contexts might be made more useful by being automatically triggered at break-to-work transitions.
In relation to cross-device use, J. Kim, Cho, and Lee (2017) investigated benefits of coordinated distraction blocking on personal computers and smartphones, and Kovacs et al. (2019) evaluated potential ‘spillover’ effects from one device (or one distraction) to another, when applying interventions to reduce use of target apps or websites on smartphones and computers.

2.3.2 Evaluation approaches

In evaluating the effectiveness of such design patterns, most studies (20/29) have taken an experimental approach and compared user behaviour and perceptions when applying a particular intervention to its absence and/or some alternative intervention. Four studies have been purely observational and studied use and perceptions when, e.g., releasing a tool in online stores or deploying it as part of a student well-being campaign. Four studies have focused on (or included) reviews of available tools in online stores and described their features and/or created typologies of functionality. Two studies evaluated only whether the technical aspects of a proposed intervention worked as intended, but not how it influenced users’ behaviour or perceptions.

2.3.3 Findings

2.3.4 From one-size-fits-all to bespoke interventions

As more studies have been conducted, the findings from, especially, later studies have begun to unravel more specific questions related to when and for whom particular interventions are beneficial (cf. Klasnja et al. 2017). We have nowhere near enough evidence to answer such questions conclusively, but the existing findings do allow us to put together a preliminary picture of some of the considerations for design patterns that relate to (i) level of friction/user flexibility (where a fine balance needs to be struck), (ii) emotional state (where the usefulness of the same tool for the same user may vary over time), and (iii) individual differences (where, in particular, distraction blocking seems to be more useful for people who struggle more with distractions at the outset).

In relation to level of friction/user flexibility, the evolving evidence suggests that more intrusive strategies such as blocking need to strike a delicate balance. Some studies have found that when users need to self-initiate blocking of distractions, they simply do not engage with it and fail to benefit. For example, Marotta and Acquisti (2017) found that enforced blocking of Facebook and YouTube during working hours improved MTurk crowdworkers’ productivity and earnings, but when participants actively had to choose which websites to block and for how long, there was no effect. A version of J. Kim, Jung, et al. (2019)’s GoalKeeper, however, which strictly locked out users from target apps for the rest of the day after the daily limit was reached, seemed to be too restrictive and was experienced very negatively by many users, eliciting stress and anxiety, partly because it did not accommodate “out of routine” incidents where participants needed to use a particular app after being locked out. By contrast, Tseng et al. (2019)’s UpTime was viewed very positively by users –— and reduced self-reported stress –— despite applying automatic blocking of distractions, perhaps because users here could negotiate an override of the blocking via a chatbot. There may not be a simple answer to how blocking tools can strike the right design balance —– whereas participants in J. Kim, Jung, et al. (2019)’s study of GoalKeeper preferred a medium level of friction where they still retained some control, some also said they would find the strong-lockout version highly useful in specific situations where a particularly high level of focus is required, such as during exam periods.

In relation to emotional state, the evolving evidence suggests that this affects strategies’ usefulness, in somewhat complex ways. Thus, some participants using PomodoLock (J. Kim, Cho, and Lee 2017) to block distractions said they used the tool when they were ‘in a relaxed state without time pressure’ — when they were pressed for time, the tool was not necessary because the perceived external pressure already forced them into a state of concentration high enough to avoid self-interruptions (perhaps somewhat in contradiction to those of J. Kim, Jung, et al. (2019)’s participants who wanted strong-lockout during stressed exam periods). Similarly, J. Kim, Park, et al. (2019)’s study of varying levels of effort before target apps could be opened found that the effect of discouraged use was particularly strong when users were tired. More evidence needs to be collected, but findings here may usefully be compared to research from the self-control literature on how emotional states influence the ability to avoid temptation (e.g., Facebook users find it more difficult to self-regulate use of the network when in negative mood, Ryan et al. (2014); cf. Tice, Bratslavsky, and Baumeister (2001)).

Finally, recent studies have begun to investigate how design patterns’ effectiveness depends on individual difference. Thus, exploratory findings from studies of distraction blocking suggest the biggest benefits accrue to users who struggle the most with handling distractions at the outset: Mark et al. (2018) reported that those participants who experienced the greatest increase in focus with distractions blocked were those who self-reported as being more susceptible to social media distractions. Similarly, Kim et al. (2017) reported that the biggest productivity increase from using PomodoLock accrued to those who performed worst at baseline. Though an alternative interpretation in these two studies is a ‘ceiling’ effect — if one already scores high on some measure, there may be less room for improvement when an intervention is applied — J. Kim, Park, et al. (2019) found complementary evidence: whereas requiring participants to type in a greater number of digits before being allowed to used an app led to the greatest decline in use for most participants, there was a subgroup for whom a simple pause-only intervention achieved the same effect size. Thus, there may be some spectrum of individual differences that predict how intrusive design patterns should be to be effective.

2.3.5 Guiding theory

In this section, we make a few remarks about use of theory in HCI before providing an overview of the theory existing studies have applied to guide development and/or evaluation of interventions, as well as of theory applied in HCI research on behaviour change more generally. To pre-empt the findings, around half of the existing studies did not specify a guiding framework. Among those that do, a wide range of conceptual frameworks have been applied, and specific constructs are often presented as being instrumental to the development of an intervention. However, conceptual frameworks that explicitly address internal struggle between longer-term goals and conflicting impulses and habits — such as dual systems theories — have rarely been applied. More application of such frameworks has been called for by HCI researchers studying digital self-control as well as behaviour change more widely.

2.4.1 (Theoretical) Exploring the usefulness of dual systems theory

As noted, frameworks which explicitly address interaction between conflicting goals, impulses, and habits have rarely been applied in existing studies. This is somewhat surprising, as such interactions are at the very core of self-control struggles. Among currently used frameworks, only J. Kim, Jung, et al. (2019)’s use of dual systems theory explicitly focuses on such interactions. Existing theoretical work on digital self-control (Cox et al. 2016) and digital behaviour change interventions (Pinder et al. 2018) have called for wider application of dual systems theory.

Chapter 3, 4, 5, and 6 of this thesis explore this research opportunity, by summarising a modern version of dual systems theory and applying it as a lens for organising, evaluating, and generating hypotheses in relation to design patterns for digital self-control.

2.4.2 (Empirical) Broad assessment of design patterns and implementations

Ideally, we want in hand clear evidence of the effects of representative examples of all plausible strategies and implementations across the entire design space of digital self-control interventions (J. Kim, Jung, et al. 2019; G. Mark, Czerwinski, and Iqbal 2018). The existing studies have made a good start, but some intervention ideas that are prominent among digital self-control tools in online stores (e.g., associating device use with the well-being of a virtual creature, as illustrated by Forest which has over 10 million users on Android alone, Seekrtech (2018)) have yet to be evaluted in HCI research. More generally, whereas controlled studies are appropriate and feasible for evaluating and comparing a small number of strategies, they are difficult to scale to broadly assess large numbers of strategies and implementations.

One opportunity for a complementary approach, which might help scope the range of strategies and implementations to explore in controlled studies, is to investigate the landscape of digital self-control tools on app and web stores. Here, widely available tools potentially represent hundreds of thousands of natural ‘micro-experiments’ (Daskalova 2018; J. Lee et al. 2017) in which individuals self-experiment with apps that represent not only one or more intervention strategies, but particular designs of those strategies. Other areas of HCI research have fruitfully taken this approach (e.g. research on mental health, Bakker et al. (2016); Lui, Marcus, and Barry (2017)), but it has so far been under-explored in relation to digital self-control. Chapters 3 and 4 explore this approach by analysing design features, user numbers, ratings, and reviews for a large sample of digital self-control tools on the Google Play, Chrome Web, and Apple App stores. Chapter 6 further explores how a broad sample of existing interventions can be used to elicit personal needs for such interventions in a workshop setting.

2.4.3 (Empirical) Understanding individual differences

Another research opportunity relates to understanding individual differences, where open questions include, e.g., whether some types of interventions are only effective for some people, and how widely people’s definitions of ‘distraction’ in relation to digital device use vary (G. Mark, Czerwinski, and Iqbal 2018; Cecchinato et al. 2019; Lukoff 2019). Existing research does not yet allow us to confidently answer such questions, and so additional collection of evidence is required to substantiate exploratory findings from intervention studies (e.g., G. Mark, Czerwinski, and Iqbal 2018; Hiniker et al. 2016; J. Kim, Cho, and Lee 2017) and ethnographic work (e.g., Lukoff et al. 2018; Tran et al. 2019).

Chapters 4, 5 and 6 contribute to this task: In Chapter 4, thematic analysis of user reviews for DSCTs in online stores contributes evidence on how people vary in the types of design patterns they seek; in Chapter 5, we draw out some of the individual variation emerging from, in particular, the semi-structured interviews with participants in a study of UI interventions for Facebook; and in Chapter 6, we present findings from workshops in which we investigated personal digital self-control struggles and used a card sorting task to elicit individual differences in preferences among potential interventions.

2.4.4 (Methodological) Improving evaluation to generate usable evidence