oa Intelligent M-Health Technology For Enhanced Smoking Cessation Management

Abstract Smoking-related illnesses are costly to the NHS and a leading cause of morbidity and mortality. Pharmacological treatments including nicotine replacement, some antidepressants, and nicotine receptor partial agonists, as well as individual- and group-based behavioural approaches, can help stop people from smoking. Circa 40% of smokers attempt to quit smoking each year, yet most have rapid relapses. The development of new tools acceptable by a wide range of smokers should be of particular interest. Smartphone interventions such as text messaging have shown some promise in helping people stop smoking. However most of these studies were based on text-messaging interventions with no interactive functionality that can provide better feedback to the smoker. In addition there is increasing evidence that smart mobile phones act as a conduit to behavioural change in other forms of healthcare. A study of currently available iPhone apps for smoking cessation have shown a low level of adherence to key guidelines for smoking cessation; few, if any, recommended or linked the user to proven treatments such as pharmacotherapy, counselling or a “quit line” and smoking cessation program. Hence there is a need for clinical validation of the feasibility of app-based intervention in supporting smoking cessation programmes in community pharmacy settings. The goal of this study is to design and develop an m-health programme platform to support smoking cessation in a community setting. The primary objectives are ascertaining what users require from a mobile app-based smoking cessation system targeting and supporting smokers, and looking into the literature for similar solutions. The study also involves the design and development of an end-to-end smoking cessation management system based on these identified needs; this includes the Patients Hub, Cloud Hub, and Physician/Social Worker Hub, as well as the design and development of a decision support system based on data mining and an artificial intelligent algorithm. Finally, it will implement the system and evaluate it in a community setting.