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FoodCoach: Societal Implications of Digital Receipt-based Diet Monitoring & Interventions enabled by Graph Analytics Techniques
Type
fundamental research project
Start Date
March 1, 2020
End Date
February 28, 2024
Acronym
FoodCoach
Status
ongoing
Description
Diet-related non-communicable diseases are the leading cause of mortality worldwide, accounting for more deaths than all other, non-diet-related mortality causes combined (Forouzanfar et al., 2015). Previous research on developing effective strategies to improve dietary consumption primarily focus on the individual level (Schneider, 2019), on monitoring dietary intake and behavioral interventions influencing dietary habits. Unfortunately, until today, such strategies that are enabled by digital dietary self-tracking remain limited by resource- and personal constraints (Vignerová et al. 2011), are discontinued due to the manual logging involved (Fuchs et al. 2018), are not tailored to the individual (Brug et al. 2003), or suffer under self-selection of healthy individuals (König et al. 2018).
Aiming to overcome these drawbacks, this interdisciplinary Swiss-Korean research collaboration entitled ‘FoodCoach’ proposes a novel, scalable and tailored approach towards diet monitoring and interventions. Therefore, we plan to apply artificial intelligence to process customers’ automatically collected digital receipts from grocery purchases in order to i) estimate households’ and individual dietary behavior, and ii) tailor adaptive interventions to participants, based on their purchase behavior and estimated nutritional context, in order to support healthier food choices.
Thereby, ‘FoodCoach’ aims to overcome the contemporary limitations of modern diet-related mobile health applications (mHealth). Simultaneously, the proposed project identifies potential adoption drivers (e.g. convenience) and barriers (e.g. privacy concerns) by exploring people’s lived experience of digital receipt-based dietary tracking based on survey and focus group research. This affords the opportunity for users’ responses (but also non-users’ responses) to co-shape the set-up of the study, the design of the interfaces, as well as the kinds of information and hence interventions provided. The project aims to compile the largest digital receipt-based diet panel globally and assess this novel approach’s accuracy, scalability, efficacy and ability to reach previously uninvolved users, an important prerequisite in the mitigation of diet-related diseases.
Aiming to overcome these drawbacks, this interdisciplinary Swiss-Korean research collaboration entitled ‘FoodCoach’ proposes a novel, scalable and tailored approach towards diet monitoring and interventions. Therefore, we plan to apply artificial intelligence to process customers’ automatically collected digital receipts from grocery purchases in order to i) estimate households’ and individual dietary behavior, and ii) tailor adaptive interventions to participants, based on their purchase behavior and estimated nutritional context, in order to support healthier food choices.
Thereby, ‘FoodCoach’ aims to overcome the contemporary limitations of modern diet-related mobile health applications (mHealth). Simultaneously, the proposed project identifies potential adoption drivers (e.g. convenience) and barriers (e.g. privacy concerns) by exploring people’s lived experience of digital receipt-based dietary tracking based on survey and focus group research. This affords the opportunity for users’ responses (but also non-users’ responses) to co-shape the set-up of the study, the design of the interfaces, as well as the kinds of information and hence interventions provided. The project aims to compile the largest digital receipt-based diet panel globally and assess this novel approach’s accuracy, scalability, efficacy and ability to reach previously uninvolved users, an important prerequisite in the mitigation of diet-related diseases.
Leader contributor(s)
Partner(s)
Prof. Jaewook Byun, Halla University, South Korea
Funder
Range
HSG + other universities
Range (De)
HSG + andere
Division(s)
Eprints ID
247859
results