Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being

Morshed, Mehrab Bin; Kulkarni, Samruddhi Shreeram; Saha, Koustuv; Li, Richard; Roper, Leah Galante; Nachman, Lama; Lu, Hong; Mirabella, Lucia; Srivastava, Sanjeev; Barbaro, Kaya de; Choudhury, Munmun De; Plötz, Thomas; Abowd, Gregory D.

doi:10.1145/3533390

Cited by 10 publications

(3 citation statements)

References 110 publications

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“…Studying diets through digital traces has been an active area of research ( 10 , 46 , 47 ). Large-scale passively sensed signals have been harnessed in university campus environments to measure factors of well-being ( 29 ) and performance ( 48 ), pointing toward the feasibility of leveraging behavioral traces for campus-centric applications ( 49 – 51 ).…”

Section: Discussionmentioning

confidence: 99%

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action

Gligorić,

Zbinden,

Chiolero

et al. 2024

Front. Nutr.

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Although diets influence health and the environment, measuring and changing nutrition is challenging. Traditional measurement methods face challenges, and designing and conducting behavior-changing interventions is conceptually and logistically complicated. Situated local communities such as university campuses offer unique opportunities to shape the nutritional environment and promote health and sustainability. The present study investigates how passively sensed food purchase logs typically collected as part of regular business operations can be used to monitor and measure on-campus food consumption and understand food choice determinants. First, based on 38 million sales logs collected on a large university campus over eight years, we perform statistical analyses to quantify spatio-temporal determinants of food choice and characterize harmful patterns in dietary behaviors, in a case study of food purchasing at EPFL campus. We identify spatial proximity, food item pairing, and academic schedules (yearly and daily) as important determinants driving the on-campus food choice. The case studies demonstrate the potential of food sales logs for measuring nutrition and highlight the breadth and depth of future possibilities to study individual food-choice determinants. We describe how these insights provide an opportunity for stakeholders, such as campus offices responsible for managing food services, to shape the nutritional environment and improve health and sustainability by designing policies and behavioral interventions. Finally, based on the insights derived through the case study of food purchases at EPFL campus, we identify five future opportunities and offer a call to action for the nutrition research community to contribute to ensuring the health and sustainability of on-campus populations—the very communities to which many researchers belong.

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Section: Discussionmentioning

confidence: 99%

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action

Gligorić,

Zbinden,

Chiolero

et al. 2024

Front. Nutr.

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“…Facial expressions recognition and eating monitoring technologies can detect stress levels and emotional triggers that lead to unhealthy eating behaviors [5]. Wearables offer a convenient solution to detect potential health issues such as binge eating, overeating (stressrelated), or anorexia [6].…”

Section: Introductionmentioning

confidence: 99%

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Bello

Zhou

Lukowicz

2020

Sensors

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Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).

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“…Studying diets through digital traces has been an active area of research (10,46,47). Large-scale passively sensed signals have been harnessed in university campus environments to measure factors of well-being (29) and performance (48), pointing toward the feasibility of leveraging behavioral traces for campus-centric applications (49)(50)(51).…”

Section: Further Background and Scopementioning

confidence: 99%

Data_Sheet_1.pdf

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Food, Mood, Context: Examining College Students’ Eating Context and Mental Well-being

Cited by 10 publications

References 110 publications

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action

Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones

Data_Sheet_1.pdf

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