The commercial smartphone applications have been subjected to a series of content analyses, with the aims of providing an overview of what types of applications are available and identifying any underlying issues and inaccuracies in existing applications. The first content analysis of alcohol-related smartphone applications was undertaken by Weaver ER et al. (2013) [4], who evaluated 384 applications from Apple iTunes and Google Play store. The applications were identified using the search term “alcohol”, and only the top 250 applications on each store were included. A total of 50% of the sampled applications were entertainment applications, and only a minority (11%) were applications that provided information about health or methods to reduce drinking. Their assessment of the applications for blood alcohol concentration, which used data acquired from the Patron Offending and Intoxication in Night-time Entertainment Districts (POINTED) study, highlighted inaccurate estimates of blood alcohol concentrations levels when compared to the actual data acquired from breathalyser test [4].

Two years later, a review by Crane D et al. (2015) [5] of alcohol-related applications available in the United Kingdom sought to identify applications which used behavioural change techniques. As above, the applications were identified from both Apple iTunes and Google Play store using the keyword, “alcohol”, but only the initial 200 results were included in the study. A total of four separate searches between April to May 2014 were conducted, with 800 search results considered. A total of 662 unique applications were identified and, as Weaver ER. (2013) [4] had found, the minority (13.7%) of these applications were helping in alcohol reduction, with the majority (53.9%) being entertainment applications [5]. Crane et al. (2015) [5] found that the most commonly utilised behavioural change techniques identified were enabling individuals to self-record their behaviours (54.1%), followed by the provision of information of excessive usage of alcohol (42.6%), feedback on performance (41.0%), and options for alternative and subsequent support (24.6%) [5]. In 2017, but relating to applications identified in 2015, Hoeppner BB et al. (2017) published a third content analysis [2]. Using the keywords, “drinking”, “drink”, “alcohol”, “alcoholism”, and “sobriety”, 266 applications were identified in the Android store. Their content analysis sought to determine if alcohol applications provided any form of tailored messages as well as well as describing their standard functionalities [2]. They found almost two-thirds of applications (60%) provided some mechanism for tailoring of feedback. The most common form of tailored messages was based on drinking, demographics, and time [2], but only a minority provided tailored feedback based on triggers (1.9%) or pre-set goals (0.8%). Once again, the most common tools available were blood alcohol concentration calculators, applications that provided information, tracking calendars, and motivational applications. In summary, all three content analyses found that the alcohol applications available in stores were for either blood alcohol concentration calculation or entertainment. A similar focus on information or entertainment has also been observed in reviews of applications for cannabis use disorder; Ramo DE et al. (2015), using the search terminologies “cannabis” and “marijuana”, found 27.0% of identified applications to focus on the provision of information and 20.3% on entertainment [6]. Table 1 provides an overview of characteristics and key findings of each of the reviews.

Often, content analyses are limited to identification of functionalities in selected applications, but two of the three reviews of alcohol applications discussed above have included greater detail, with Crane D et al. (2015) identifying behavioral change techniques and Hoeppner BB et al. (2017) identifying applications that provided tailoring and customization of messages. This change in focus is seen in the recent content analyses of other types of smartphone applications. Morrissey EC et al. (2016) [7] reviewed existing medication adherence applications on the application stores and coded the identified applications based on the taxonomy of behavioural change. They found that behavioural change techniques, namely that of action planning, prompting, self-monitoring, and feedback on behaviour, to be commonly incorporated. Similarly, Ubhi HK et al. (2016) [8] also managed to identify common behavioural change techniques that have been incorporated in applications helping with smoking cessation. Some of the techniques include that of supporting identity change, rewarding abstinence, provision of advice on changing routines, and coping with cravings.

The change in the trends of content analyses since 2015 is pertinent. It is commonly known that interventions grounded in theory are complex, and thus, this would render them difficult to implement in research settings, especially on a smartphone device (Michie S et al., 2013) [9]. Very often, only certain elements of the complex theory are incorporated into an application. Behavioural change techniques have been defined as the “smallest, observable, replicable components with the potential to bring about change in behaviour” [5]. Thus, by identifying the behavioural change technique that has been included in an application, it helps to identify the complex underlying theory that the application is grounded in and helps to prove that the application is based on evidence. A taxonomy of 93 common behavioural change techniques has been identified previously by Michie S et al. (2013) [9]. These 93 behavioural techniques are clustered into 16 categories, which includes scheduled consequences, reward and threat, repetition and substitution, associations, covert learning, natural consequences, health consequences, feedback and monitoring, goals and planning, social support, comparison of behaviour, self-belief, comparison of outcomes, identity, shaping knowledge, and regulation.