BACKGROUND
The Sense2Quit study introduces innovative advancements in smoking cessation technology by developing a comprehensive mobile application (app) that integrates with smartwatches to provide real-time interventions for people living with HIV (PWH) who are attempting to quit smoking
OBJECTIVE
To develop an accurate smoking cessation app that utilizes everyday smartwatches and an AI model to enhance the recognition of smoking gestures by effectively addressing confounding hand gestures that mimic smoking, thereby reducing false positives. The app ensures seamless usability across Android and iOS platforms, with optimized communication and synchronization between devices for real-time monitoring.
METHODS
The Sense2Quit system utilizes a Convolutional Neural Network (CNN) model specifically trained to distinguish smoking gestures from similar hand-to-mouth activities. By incorporating confounding gestures into the model's training process, the system achieves high accuracy while maintaining efficiency on mobile devices. To validate the model, 30 participants, all PWH who smoked cigarettes, were recruited. Participants wore smartwatches on their wrists and performed various hand-to-mouth activities, including smoking and other gestures like eating and drinking. Each participant spent 15 to 30 minutes completing the tasks, with each gesture lasting 5 seconds. The app was developed using the Flutter framework to ensure seamless functionality across Android and iOS platforms, with robust synchronization between the smartwatch and smartphone for real-time monitoring.
RESULTS
The CNN model achieved an F1 score of 97.52% in detecting smoking gestures, effectively differentiating between smoking and 15 other daily hand-to-mouth activities, such as eating, drinking, and yawning. The cross-platform app, developed using Flutter, demonstrated consistent performance across Android and iOS devices, with only a 0.02-point difference in user experience ratings between the platforms (iOS: 4.52, Android: 4.5). The app’s continuous synchronization ensures accurate, real-time tracking of smoking behaviors, enhancing the system’s overall utility for smoking cessation.
CONCLUSIONS
Sense2Quit represents a significant advancement in smoking cessation technology. It delivers timely, just-in-time interventions through innovations in cross-platform communication optimization and the effective recognition of confounding hand gestures. These improvements enhance the accuracy and accessibility of real-time smoking detection, making Sense2Quit a valuable tool for supporting long-term cessation efforts among PWH trying to quit smoking.
INTERNATIONAL REGISTERED REPORT
RR2-10.2196/49558
