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The respective benefits and drawbacks of manual food journaling and automated dietary monitoring (ADM) suggest the value of semi-automated journaling systems combining the approaches. However, the current understanding of how people anticipate strategies for implementing semi-automated food journaling systems is limited. We therefore conduct a speculative survey study with 600 responses, examining how people anticipate approaches to automatic capture and prompting for details. Participants feel the location and detection capability of ADM sensors influences anticipated physical, social, and privacy burdens. People more positively anticipate prompts which contain information relevant to their journaling goals, help them recall what they ate, and are quick to respond to. Our work suggests a tradeoff between ADM systems' detection performance and anticipated acceptability, with sensors on facial areas having higher performance but lower acceptability than sensors in other areas and more usable prompting methods like those containing specific foods being more challenging to produce than manual reminders. We suggest opportunities to improve higher-acceptability, lower-accuracy ADM sensors, select approaches based on individual and practitioner journaling needs, and better describe capabilities to potential users. 

Dietary intake, eating behaviors, and context are important in chronic disease development, yet our ability to accurately assess these in research settings can be limited by biased traditional self-reporting tools. Objective measurement tools, specifically, wearable sensors, present the opportunity to minimize the major limitations of self-reported eating measures by generating supplementary sensor data that can improve the validity of self-report data in naturalistic settings. This scoping review summarizes the current use of wearable devices/sensors that automatically detect eating-related activity in naturalistic research settings. Five databases were searched in December 2019, and 618 records were retrieved from the literature search. This scoping review includedN = 40 studies (from 33 articles) that reported on one or more wearable sensors used to automatically detect eating activity in the field. The majority of studies (N = 26, 65%) used multi-sensor systems (incorporating > 1 wearable sensors), and accelerometers were the most commonly utilized sensor (N = 25, 62.5%). All studies (N = 40, 100.0%) used either self-report or objective ground-truth methods to validate the inferred eating activity detected by the sensor(s). The most frequently reported evaluation metrics were Accuracy (N = 12) and F1-score (N = 10). This scoping review highlights the current state of wearable sensors’ ability to improve upon traditional eating assessment methods by passively detecting eating activity in naturalistic settings, over long periods of time, and with minimal user interaction. A key challenge in this field, wide variation in eating outcome measures and evaluation metrics, demonstrates the need for the development of a standardized form of comparability among sensors/multi-sensor systems and multidisciplinary collaboration.