Introduction

Note: This is the author’s version of the work. The definitive Version of Record was published in CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK, doi.org/10.1145/3290605.3300361.

Smartphones and laptops give their users access to an astonishing range of tasks anywhere, anytime. While this provides innumerable benefits, a growing amount of public discussion and research attention focuses on a perhaps unexpected downside (Baumer and Adams 2013; McDaniel and Coyne 2014; Lundquist, Lefebvre, and Garramone 2014; Wu 2016; Alter 2017; Leslie 2016; Foer 2016; Tiku 2018): Having immense amounts of functionality available instantly and permanently often makes it difficult for users to focus on their current task and avoid being overly distracted by notifications or habitual check-ins (Marotta and Acquisti 2017; Thomas, Azmitia, and Whittaker 2016; Stothart, Mitchum, and Yehnert 2015; Dabbish, Mark, and González 2011). This challenge is compounded by the business models of many tech companies, which incentivise design that nudges people into using services frequently and extensively in order to optimise advertising revenue (cf. the ‘attention economy’ (Davenport and Beck 2001; Wu 2016; Harris 2016; Einstein 2016)).

In response, a recent movement in HCI has called for more research into intentional ‘non-use’ of information and communication technologies (ICTs) (Baumer and Adams 2013; Satchell and Dourish 2009; Hiniker et al. 2016). Initial work looked into why some users quit, or take breaks from Facebook (Baumer and Adams 2013), Twitter (Schoenebeck 2014), or other social networking sites, and a substantial body of related research has now established that a majority of users feel conflicted about the time they spend with internet-connected digital technologies and struggle with effective self-control (Ames 2013; Foot 2014; Portwood-Stacer 2012; U. Lee et al. 2014; Sleeper et al. 2015; Ko et al. 2015, 2016; Lundquist, Lefebvre, and Garramone 2014).

Researchers within this space have started to design, implement and test novel tools for supporting self-control over device use, using interventions such as gamification and social sharing of total time spent on one’s smartphone (with rewards for reducing use) (Ko et al. 2015) or visualisation of laptop use (Whittaker et al. 2016). Meanwhile, an entire market niche has appeared on the Android and Apple app stores, as well as on browser extension ‘web stores’, wherein hundreds upon hundreds of apps and extensions cater to people struggling with self-control over device use, and provide interventions claimed to help users. Some of these ‘digital self-control tools’ — such as Forest (Seekrtech 2018), which gamifies self-control through growing of virtual trees – have gathered millions of users (Google Play 2017).

Yet, while the challenge of supporting self-control over use of always-connected digital devices has become widely discussed, our understanding of how best to approach it remains limited (cf. Whittaker et al. 2016; Mark, Iqbal, and Czerwinski 2017; Mark, Czerwinski, and Iqbal 2018; Cox et al. 2016). While a growing number of studies have developed and evaluated novel design interventions, no systematic reviews have mapped design features in the hundreds of digital self-control tools that currently exist on the app and web stores (cf. Velthoven, Powell, and Powell 2018). Moreover, new design interventions developed by HCI researchers have mostly been informed by user interviews and intuitions of interface designers (Löchtefeld, Böhmer, and Ganev 2013; Hiniker et al. 2016; Whittaker et al. 2016), or by theories including cognitive load theory (Collins et al. 2014), Social Cognitive Theory (Ko et al. 2015) and nudge theory (Okeke et al. 2018; Kim et al. 2016). Meanwhile, the dual systems and value-based models prominent in current cognitive neuroscience research on self-regulation1 (Botvinick and Braver 2015; Vohs and Baumeister 2016; Shenhav et al. 2017; Berkman et al. 2017; Shea et al. 2014; Wood and Rünger 2016) have yet to be applied (cf. Cox et al. 2016; Pinder et al. 2018).

In this paper, we review 367 apps and browser extensions for digital self-control from the Google Play, Chrome Web, and Apple App stores, and identify common design features and strategies. As a theoretical lens to organise and evaluate these tools and provide a deeper understanding of the self-control challenges they seek to address, we adapt and apply an integrative dual systems model drawn from established work within the cognitive neuroscience of self-regulation. Extending recent attempts at applying dual systems theory to digital behaviour change interventions (Pinder et al. 2018), our formulation of the model incorporates recent research on the ‘expected value of control’ (Shenhav, Botvinick, and Cohen 2013) as mediator of the strength of conscious self-control. This, we argue, demystifies the concept of self-control and helps clarify how specific design features may work to scaffold successful self-control.

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  1. In cognitive psychology, Self-regulation is commonly used as an umbrella term for all regulatory processes in the service of goal-directed behaviour, including automatic habits, and self-control more restrictively about conscious, deliberate attempts at overriding immediate impulses that conflict with one’s goals (Hagger et al. 2010; Baumeister, Vohs, and Tice 2007; Milyavskaya and Inzlicht 2018; Duckworth, Gendler, and Gross 2014).↩