A Context-Sensing Mobile Phone App (Q Sense) for Smoking Cessation: A Mixed-Methods Study

Area of Science:

• Digital Health
• Behavioral Science
• Mobile Health Interventions

Background:

• Cues in the environment trigger smoking cravings, leading to relapse.
• A context-aware smoking cessation app, Q Sense, was developed to address cue-induced cravings.
• The app uses real-time reporting, location sensing, and geofencing to deliver tailored support.

Purpose of the Study:

• Assess compliance with real-time smoking reports and reasons for noncompliance.
• Evaluate the app's accuracy in identifying high-risk smoking locations.
• Explore the feasibility and user perception of geofence-triggered support.
• Identify technological and privacy concerns.

Main Methods:

• Explanatory sequential mixed-methods design combining app data and semistructured interviews.
• 15 participants (Android users, planning to quit within one month) provided app data.
• Data included smoking reports, geolocation, end-of-day surveys, support ratings, and app interactions.
• 13 participants were interviewed, and data were analyzed separately.

Main Results:

• 20% of participants discontinued app use; underreporting occurred on 56.2% of days.
• Geolocation accuracy was 31.6 meters; 56% of eligible participants received geofence-triggered support.
• 50% of triggered notifications were acted upon; 78.2% of delivered messages were rated.
• Participants found the app identified smoking locations accurately and viewed geofence support positively, with no privacy concerns.

Conclusions:

• User-initiated self-reporting is feasible for training cessation apps, despite likely underreporting.
• Geofencing accurately identifies smoking locations and enables effective, positively received support delivery.
• The Q Sense app demonstrates potential for real-time, personalized smoking cessation support.