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This left 367 tools in the final dataset.įigure 3.2: Functionality of digital self-control tools (N = 367)Ī summary of the prevalence of features is shown in Figure 3.2. 6ĭuring the coding process, we excluded a further 13 tools - 3 duplicates, e.g. where ‘pro’ and ‘lite’ versions had no difference in described functionality, 7 that had become inaccessible after the initial search, and 3 that lacked sufficiently well-described functionality to be coded. In addition to the granular feature coding, we noted which main feature cluster(s) represented a tool’s ‘core’ design, according to the guideline that 25% or more of the tool’s functionality related to that cluster (a single tool could belong to multiple clusters). Using this coding sheet, two authors independently reviewed 60 additional apps and browser extensions each and a third author these 120 tools, as well as all remaining.Īfter comparing and discussing the results, a final codebook was developed, on the basis of which the first author revisited and recoded the features in all tools. Initially, three of the authors independently reviewed and classified features in 10 apps and 10 browser extensions (for a total of 30 unique apps and 30 unique browser extensions) before comparing and discussing the feature categories identified and create the first iteration of the coding sheet. ‘reward/punishment’ (drawing on our previous work in this area (Lyngs 2018 a)). 2013 Orji and Moffatt 2018), with the prior expectation that the relevantįeatures would be usefully classified as subcategories of the mainįeature clusters ‘block/removal’, ‘self-tracking’, ‘goal advancement’ and Iteratively developed a coding sheet of feature categories (cf. Stawarz, Cox, and Blandford 2014, 2015 Stawarz et al. 4.3 Using the model directly to guide intervention research and designįollowing similar reviews, we coded functionality based on theĭescriptions, screenshots, and videos available on a tool’s store page.4.2 Gaps identified by the dual systems model.4.1 Research opportunities prompted by widely used or theoretically interesting design features.3.2.4 Mapping identified tool features to theory.
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3.1.3 Identifying Apps and Extensions to Analyse.3.1.2 Identifying Potentially Relevant Apps and Extensions.3.1.1 Initial Keyword Search and Data Clean Up.3 A review and analysis of current digital self-control tools.2.5 Self-control limitations and the Expected Value of Control.2 An Integrative Dual Systems Approach to Digital Self-Control.1.2.2 Digital Behaviour Change Interventions.1.1 Evidence on Self-Control Challenges.