Current Project Highlight
NutriBonAR helps consumers to improve diets by simply looking at their receipts.
We 
1 
2 !
1: Retail, 2: Health
Current Project Highlight
1 2 !1: Retail, 2: Health
We leverage retail data assets, such as product ingredients & digital receipts, to overcome current limitations of mobile health apps.
Our focus areas are increasing nutrition literacy, tailoring of health interventions & scalable, automatic monitoring of diet intake.
Our work on scalable & inclusive health apps aims to contribute to mitigating diet-related diseases such as obesity, diabetes & hypertension.
Although thousands of mobile health (mHealth) apps exist, they suffer under low retention & acceptance. We review dieta mHealth's retention behavior & best practice.
Learn more!
Retail data can be integrated in mHealth & induce applicable nutrition literacy, especially relevant due to the growing influence of processed & convenience food.
Learn more!
Different to printed static traffic labels, mHealth apps can leverage product ingredient data to tailor interventions to each consumer based on individual needs.
Learn more!
Monitoring diets is hard due to required manual logging of meals. We use machine learning to infer diets via automatically recorded purchase data from loyalty cards.
Learn more!
Nowadays, users can choose from 2'814 diet mHealth apps1. With only little regulation by authorities2, users & dieticians suffer under a myriad of apps to choose from.
Diet apps suffer under self-selection of healthy users, short-lived retention & low adherence 3, such that at least 50% of patients with diet-related diseases do not actively use them 4.
Barriers of usage among today's diet apps lie in the extreme effort involved in manual diet logging, underreporting of diet, non-personalized & abstract recommendations, non-pleasant user experience.
We recommend mHealth designers to include automated data collection methods, just-in-time-adaptive interventions, gamification, & state-of-art implementations of features, design, behavior-change-technologies!
1: 42matters.com (2019), 2: FDA.gov (2019), 3: Nutritiontest (2018), 4: Langford (2019), JMIR mHealth
Literacy is a necessary prerequesite for healthy diets. Still, 50% of consumers lack important applicable skills1, required to mitigate today's challenge of identifying healthy alternatives among processed food items in convenience retail.
You can only improve, what you know. As most consumers are unaware about their own exposure to consumption-related risks , new tools are needed to self-assess individual risk potential.
With the EU1169 regulation, retailers & brands published their product data, including image and ingredients, which can can help to train literacy & assess risk exposure in mHealth-mediated empirical studies.
With our Skinprotect app, users become able to scan their household inventory of beauty and cleaning products to assess their exposure to toxicologic substances. The study demonstrated that retail data can support the collection of population-specific exposure factors for complex exposure models.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
1: Nutritiontest (2018)
Behavior > Link > Behavior Interventions ,* Behavior Interventions at Purchase Time , * Aggregating data instead of single products , * New interfaces
Do not know their intake levels or exposure. Do not know outcome expectancy of their actions.
Retail data, image and ingredients can help to train and assess exposure in empirical studies.
Together with SGE and Buero
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We use OCR to leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
1: Author (Year), Outlet
* high effort, low acceptance & retention, Do not know their intake levels or exposure. Do not know outcome expectancy of their actions.
Autonomous Passive ,* Scale > 4 M Households , * Relevant Data , * GDPR / Anonymous ,
Retail data, image and ingredients can help to train and assess exposure in empirical studies.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
We leverage existing retail data assets, such as product ingredients & digital receipts, to overcome the limitations of state-of-the-art mobile health solutions.
1: Author (Year), Outlet
2: Author (Year), Outlet
3: Author (Year), Outlet
Our work is conducted together with our partners from academia and industry, in order to ensure rigor and relevance of our work.
Research
Auto-ID Labs ETH/HSG is a joint research institute at ETH Zurich and University St. Gallen with a focus on the internet-of-things in retail.
Swiss Society for Nutrition SGE-SSN disseminates information and ongoing research on nutrition and health within Switzerland.
The Center for Digital Health Interventions designs digital health interventions based on digital biomarker and digital coaching research.
Financial support
GS1 is the world's largest standardization organization and supports research on digitization in retail.
CSS is Switzerland's largest health insurance and supports digital health initiatives.
Emmi is Switzerland's largest dairy produce and wants to support consumers to follow a balanced diet as basis for an active lifestyle.
Data
Migros is Switzerland's largest retailer and published its ingredient data as part of their Open Data strategy.
Trustbox is Switzerland's largest manufacturer-mandated product composition database.
Openfood is the world's largest open-source product composition database and maintained by thousands of contributers.
Codecheck is Switzerland's largest product composition database and also features a lot of beauty and healthcare products.
FoodRepo is a product composition database maintained by EPFL.
Bitsaboutme is a fully encrypted GDPR-based service that allows consumers to retrieve and process their personal data themselves.
Holo-One is a Swiss startup that engages in augmented and mixed Reality applications.
Adnexo is a Swiss startup that works in the internet of things in the agriculture sector.
We leverage existing data such as product composition data, product images, printed & digital receipts.
Our apps are designed with experts such as Swiss Society for Nutrition, & tested in realistic field-studies.
We frequently publish in relevant scientific outlets of digital epidemiology, mobile health, information systems, to share our findings with the public & scientific community.
| Automation of Data Collection in diet-related mHealth: a Review of Publicly Available and Well-Adopted Apps Fuchs, K., Vuckovac, D., Ilic, A., 2018, in ICTC 2018 Proceedings, IEEE, Jeju, South Korea, DOI: 10.13140/RG.2.2.23332.48001 |
| Assessing exposure factors in the smartphone generation: Design and Evaluation of a smartphone app that collects use patterns of cosmetics and household chemicals Von Goetz, N., Garcia-Hidalgo, E., Balachandran, C., Fuchs, K. , Frey, R., Ilic, A., 2018, in Journal Of Food and Chemical Toxicology, Elsevier, DOI: 10.1016/j.fct.2018.05.060 |
| Universal Food Allergy Number Frey, R., Ryder, B., Fuchs, K., and Ilic, A. 2016, in IOT 2016 Proceedings (Best Poster Award), Stuttgart, Germany, DOI: 10.1145/2991561.2998462 |
| Swiss FoodQuiz: Inducing Nutritional Knowledge via a Visual Learning based Serious Game Fuchs, K., Huonder, V., Vuckovac, D., and Ilic, A. 2016, in ECIS 2016 Proceedings, Istanbul, Turkey, DOI: 10.13140/RG.2.2.23332.48001 |
| Swiss FoodQuiz: Poster Fuchs, K., Huonder, V., Vuckovac, D., and Ilic, A. 2016, in Prototype Poster, ECIS 2016 |
| Proposing A New Standard For Food Allergies Frey, R., Ryder, B., Fuchs, K., and Ilic, A. 2016, in GS1 S+I Conference 2016, Brussels, Belgium |
| Leveraging Food Databases for Nutritional Behavior Change Fuchs, K., Vuckovac, D., and Ilic, A. 2016, in GS1 S+I Conference 2016, Brussels, Belgium |
Collaboration: If you want to work together with us, please check our current projects and positions and get in touch
new friends!If you are interested in our work, we look forward to hearing from you: team@autoidlabs.ch