The feasibility of a context sensing smoking cessation smartphone application (Q Sense): a mixed methods study

This is the final version of the article. It first appeared from JMIR Publications via http://mhealth.jmir.org/2016/3/e106/${\bf Background:}$ A major cause of lapse and relapse to smoking during a quit attempt is craving triggered by cues from a smoker's immediate environment. To help smokers address these cue-induced cravings when attempting to quit, we have developed a context aware smoking cessation app (Q Sense) which uses a smoking episode reporting system combined with location sensing and geofencing to tailor support content and trigger support delivery in real time. ${\bf Objectives:}$ We sought to: 1) assess smokers’ compliance with reporting their smoking in real time and identify reasons for non-compliance, 2) assess the app's accuracy in identifying user-specific high risk locations for smoking, 3) explore the feasibility and user-perspective of geofence-triggered support and 4) identify any technological issues or privacy concerns. ${\bf Methods:}$ An explanatory sequential mixed methods design was used where data collected by the app informed semi-structured interviews. Participants were smokers who owned an Android smartphone and were willing to set a quit date within one month (N=15). App data included smoking reports with context information and geolocation, End of Day (EoD) surveys of smoking beliefs and behaviour, support message ratings and app interaction data. Interviews were undertaken and analysed thematically (n=13). Quantitative and qualitative data were analysed separately and findings presented sequentially. ${\bf Results:}$ Three participants (20%) discontinued use of the app prematurely. Pre-quit date, the mean number of smoking reports received was 38 (SD 21) per participant or 2 (SD 2) per day per participant. EoD surveys indicated that participants underreported smoking on at least 56% of days. Geolocation was collected in 97% of smoking reports with a mean accuracy of 32 (SD 17) meters. Five out of nine (56%) eligible participants received geofence-triggered support. Interaction data indicated that 50% of geofence-triggered message notifications were tapped within 30 minutes of being generated, resulting in delivery of a support message, and 79% of delivered messages were rated by participants. Qualitative findings identified multiple reasons for non-compliance in reporting smoking, most notably due to environmental constraints and forgetting. Participants verified the app’s identification of their smoking locations, were largely positive about the value of geofence-triggered support and had no privacy concerns about the data collected by the app. ${\bf Conclusions:}$ User-initiated self-report is feasible for training a cessation app about an individual’s smoking behaviour although underreporting is likely. Geofencing was a reliable and accurate method of identifying smoking locations, and geofence-triggered support was regarded positively by participants.Medical Research Council (Grant ID: RG73592