QuitCoach: A smart mobile application for supporting quitters to manage cigarette cravings

Tobacco smoking is the leading cause of preventable deaths all over the world. Limited access and adherence to effective quitting or relapse interventions might be the potential barriers to quit smoking. These days smartphone apps are being developed as a complement to smoking cessation treatment. Accurate tobacco relapse prediction is of practical importance for recovering tobacco addicts since relapse prediction promotes timely relapse preventions that help addicts stay clean. The aim of this thesis was to report the design and implementation of a smartphone application designed specifically to aid in quit smoking and relapse intervention. From the data collecting over time, QuitCoach should be able to learn the combination of factors like habitual smoking, stress, friends/family and so on through questionnaire presented to the addict, which has a high probability to lead a smoking addict to an episode of smoking and makes use of machine learning algorithms to predict relapse, provide location-based and time-based triggered notifications and provide recommendations to support smoking addicts in their process of smoking cessation.

Furthermore, our application QuitCoach is intended to collect the data, store, predict and visualize the data generated by the addict to help addiction experts to make decisions. Also, our QuitCoach mobile application builds a community of addicts willing to get recovered, that allows users of the application to request recommendations and support/confidence, and share user's relapse related experiences. This mobile application is not only useful for the addicts but also for the addiction therapists to help addicts to recover from the situation, and understand addict’s behavior and also benefits addict with provided support in various stages of relapse.