JMIR Publications

ABSTRACT

Background:

Smoking and overeating are repetitive hand-to-mouth behaviors that contribute to highly prevalent yet preventable diseases. Most existing wearable systems have not been validated in free-living conditions to detect these behaviors in real-time.

Objective:

Leveraging shared behavioral patterns of eating and smoking, we developed HabitSense, a wearable system that integrates thermal sensors, a privacy conscious camera, and on-device algorithms, to detect smoking and eating events in real time and trigger a paired smartwatch to collect contextual data using ecological momentary assessment (EMA). We evaluated the detection accuracy of HabitSense in a free-living user study.

Methods:

Seventeen participants (9 in the smoking cohort and 8 in the eating cohort) were instructed to wear HabitSense, a custom necklace paired with a smartwatch, during waking hours for 7 consecutive days. Two separate machine-learned algorithms processed data from the thermal sensor array and camera on-device. When HabitSense predicted a smoking or eating event, the smartwatch prompted a micro- Ecological Momentary Assessment (micro-EMA) asking the participant to confirm or refute the prediction (“Are you smoking?” yes/no; “Are you eating?” yes/no). Additionally, an integrated camera recorded video to enable visual confirmation of each predicted smoking and eating event.

Results:

In total, 780.6 hours of sensor data were collected, capturing 217 smoking episodes and 87 eating episodes. The necklace generated 229 smoking-event predictions, of which 209 (91%) were true positives and 20 (9%) were false positives. 8 undetected smoking episodes were identified through manual review of the video footage (3% of total episodes). Participants responded to 212 EMA smoking-event prompts (92.6%); of these responses, 206 (97.2%) were correct (i.e., participants responded “yes” during actual smoking events and vice-versa). The necklace also generated 84 eating-event predictions, of which 67 (79.8%) were true positives and 17 (20.2%) were false positives. 20 undetected meals were identified in video footage (23% of total meals).

Conclusions:

The findings suggest that the proposed system is feasible for automated and objective monitoring of contextual triggers associated with smoking relapse. HabitSense demonstrated high accuracy in smoking detection and strong response rates to smoking-triggered EMAs, supporting its potential for real-time behavioral assessment in free-living settings. For eating detection, the variability and complexity of food-related behaviors indicate that more advanced machine-learning approaches may be required, particularly for deployment on highly resource-constrained wearable devices. Future work will expand EMA queries to capture contextual factors surrounding smoking and eating episodes, leverage these data to develop just-in-time smartwatch-based interventions. Ultimately, this work aims to enable a personalized, adaptive intervention system that accounts for individual differences in behavior, a dimension often insufficiently addressed in current smoking cessation strategies.