Jing Wu

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Wu
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Jing
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jing.wu@unisg.ch
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+41 71 224 2763

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    FoodCoach: Fully Automated Diet Counseling
    (2025-02-11)
    Jing Wu  
    ;
    Simon Mayer  
    ;
    Simeon Pilz
    ;
    Yasmine S. Antille
    ;
    Jan L. Albert
    Unhealthy dietary habits are a major preventable risk factor for widespread non-communicable diseases (NCD). Diet counseling is effective in managing diet-related NCDs, but constrained by its manual nature and limited (clinical) resources. To address these challenges, we propose a fully automated diet counseling system FoodCoach. It monitors people's food purchases using digital receipts from loyalty cards and provides structured dietary recommendations. We introduce the FoodCoach system's recommender algorithm and architecture, along with evaluation results from a two-arm randomized controlled trial involving 61 participants. The trial results demonstrate the technical feasibility and potential for scalable, fully automated diet counseling, despite not showing a significant change in participants' food purchase healthiness. We further show how others can deploy and extend the FoodCoach system in their own context and provide all relevant component implementations. Our core research contributions are: 1) a novel dietary recommendation algorithm designed and implemented with clinical experts, and 2) a scalable system architecture that employs a knowledge graph for enhanced interoperability and applicability to diverse domains and data sources. From a practical perspective, FoodCoach can augment traditional diet counseling through automatic diet monitoring and evaluation modules. Additionally, it streamlines the counseling process, conserving clinical resources, and ultimately contributing to the reduction of NCD prevalence.
    Type:journal article
    Journal:IEEE Journal of Biomedical and Health Informatics
    DOI:10.1109/JBHI.2025.3540899
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/122042
    Scopus© Citations 10
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    Automatic Classification of High vs. Low Individual Nutrition Literacy Levels from Loyalty Card Data in Switzerland
    (2022-10-24)
    Blöchlinger, Marc  
    ;
    Jing Wu  
    ;
    Mayer, Simon  
    ;
    Fuchs, Klaus
    ;
    Stoll, Melanie
    The increasingly prevalent diet-related non-communicable diseases (NCDs) constitute a modern health pandemic. Higher nutrition literacy (NL) correlates with healthier diets, which in turn has favorable effects on NCDs. Assessing and classifying people's NL is helpful in tailoring the level of education required for disease self-management/empowerment and adequate treatment strategy selection. With recently introduced regulation in the European Union and beyond, it has become easier to leverage loyalty card data and enrich it with nutrition information about bought products. We present a novel system that utilizes such data to classify individuals into high- and low- NL classes, using well-known machine learning (ML) models, thereby permitting for instance better targeting of educational measures to support the population-level management of NCDs. An online survey (n = 779) was conducted to assess individual NL levels and divide participants into high- and low- NL groups. Our results show that there are significant differences in NL between male and female, as well as between overweight and non-overweight individuals. No significant differences were found for other demographic parameters that were investigated. Next, the loyalty card data of participants (n = 11) was collected from two leading Swiss retailers with the consent of participants and a ML system was trained to predict high or low NL for these individuals. Our best ML model, which utilizes the XGBoost algorithm and monthly aggregated baskets, achieved a Macro-F1-score of .89 at classifying NL. We hence show the feasibility of identifying individual NL levels based on household loyalty card data leveraging ML models, however due to the small sample size, the results need to be further verified with a larger sample size.
    Type:journal article
    Journal:MADiMa '22: Proceedings of the 7th International Workshop on Multimedia Assisted Dietary Management
    URL:https://doi.org/10.1145/3552484.3555744
    DOI:10.1145/3552484.3555744
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/108145
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    Item type:Publication,
    Estimating Dietary Intake from Grocery Shopping Data—A Comparative Validation of Relevant Indicators in Switzerland
    (MDPI Open Access Journal, 2021-12-29)
    Jing Wu  
    ;
    Fuchs, Klaus
    ;
    Lian, Jie
    ;
    Haldimann, Mirella Lindsay
    ;
    Schneider, Tanja  
    In light of the globally increasing prevalence of diet-related chronic diseases, new scalable and non-invasive dietary monitoring techniques are urgently needed. Automatically collected digital receipts from loyalty cards hereby promise to serve as an objective and automatically traceable digital marker for individual food choice behavior and do not require users to manually log individual meal items. With the introduction of the General Data Privacy Regulation in the European Union, millions of consumers gained the right to access their shopping data in a machine-readable form, representing a historic chance to leverage shopping data for scalable monitoring of food choices. Multiple quantitative indicators for evaluating the nutritional quality of food shopping have been suggested, but so far, no comparison has validated the potential of these alternative indicators within a comparative setting. This manuscript thus represents the first study to compare the calibration capacity and to validate the discrimination potential of previously suggested food shopping quality indicators for the nutritional quality of shopped groceries, including the Food Standards Agency Nutrient Profiling System Dietary Index (FSA-NPS DI), Grocery Purchase Quality Index-2016 (GPQI), Healthy Eating Index-2015 (HEI-2015), Healthy Trolley Index (HETI) and Healthy Purchase Index (HPI), checking if any of them performs differently from the others. The hypothesis is that some food shopping quality indicators outperform the others in calibrating and discriminating individual actual dietary intake. To assess the indicators’ potentials, 89 eligible participants completed a validated food frequency questionnaire (FFQ) and donated their digital receipts from the loyalty card programs of the two leading Swiss grocery retailers, which represent 70% of the national grocery market. Compared to absolute food and nutrient intake, correlations between density-based relative food and nutrient intake and food shopping data are stronger. The FSA-NPS DI has the best calibration and discrimination performance in classifying participants’ consumption of nutrients and food groups, and seems to be a superior indicator to estimate nutritional quality of a user’s diet based on digital receipts from grocery shopping in Switzerland.
    Type:journal article
    Journal:nutrients
    Volume:14 (1)
    Issue:159
    URL:https://www.mdpi.com/2072-6643/14/1/159
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/109608
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    The Effect of a Future-Self Avatar mHealth Intervention on Physical Activity and Food Purchases: The FutureMe Randomized Controlled Trial
    (2021-07)
    Mönninghoff, Annette  
    ;
    Fuchs, Klaus
    ;
    Jing Wu  
    ;
    Albert, Jan
    ;
    Mayer, Simon  
    Background: Insufficient physical activity and unhealthy diets are contributing to the rise in non-communicable diseases. Preventative mobile health (mHealth) interventions may enable reversing this trend, but present bias might reduce their effectiveness. Future-self avatar interventions have resulted in behavior change in related fields, yet evidence whether such interventions can change health behavior is lacking. Objective: Our primary objectives are to investigate the impact of a future-self avatar mHealth intervention on physical activity and food purchasing behavior, and to examine the feasibility of a novel automated nutrition tracking system. We also aim to understand how this intervention impacts related attitudinal and motivational constructs. Methods: We conducted a 12-week parallel randomized-controlled trial (RCT), followed by semi-structured interviews. German-speaking smartphone users aged ≥18 years living in Switzerland, and using at least one of the two leading Swiss grocery loyalty cards, were recruited for the trial. Data were collected from November 2020 to April 2021. The intervention group received the FutureMe intervention—a physical activity and food purchase tracking mobile phone application that uses a future-self avatar as the primary interface and provides participants with personalized food basket analysis and shopping tips. The control group received a conventional, text- and graphic-based primary interface intervention. We pioneered a novel system to track nutrition leveraging digital receipts from loyalty card data analyzing food purchases in a fully automated way. Data were consolidated in 4-week intervals and non-parametric tests were conducted to test for within- and between-group differences. Results: We recruited 167 participants; 95 eligible participants were randomized into either the intervention (n=42) or control group (n=53). The median age was 44.00 years (IQR 19.00), and the gender ratio was balanced (female 52/95, 55%). Attrition was unexpectedly high with only 30 participants completing the intervention, negatively impacting the statistical power of our study. The FutureMe intervention led to directional, small increases in physical activity (median +242 steps/day) and to directional improvements in the nutritional quality of food purchases (median –1.28 British Food Standards Agency Nutrient Profiling System Dietary Index points) at the end of the intervention. Intrinsic motivation significantly increased (P=.03) in the FutureMe group, but decreased in the control group. Outcome expectancy directionally increased for the FutureMe group, but decreased for the control group. Leveraging loyalty card data to track the nutritional quality of food purchases was found to be a feasible and an accepted fully automated nutrition tracking system. Conclusions: Preventative future-self avatar mHealth interventions promise to encourage improvements in physical activity and food purchasing behavior in healthy population groups. A full-powered RCT is needed to confirm this preliminary evidence and to investigate how future-self avatars might be modified to reduce attrition, overcome present bias, and promote sustainable behavior change.
    Type:journal article
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/110264
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    ShoppingCoach: Using Diminished Reality to Prevent Unhealthy Food Choices in an Offline Supermarket Scenario
    (ACM, 2024-05-11)
    Jannis Rene Strecker  
    ;
    Jing Wu  
    ;
    Kenan Bektas  
    ;
    Conrad Vaslin  
    ;
    Simon Mayer  
    Non-communicable diseases, such as obesity and diabetes, have a significant global impact on health outcomes. While governments worldwide focus on promoting healthy eating, individuals still struggle to follow dietary recommendations. Augmented Reality (AR) might be a useful tool to emphasize specific food products at the point of purchase. However, AR may also add visual clutter to an already complex supermarket environment. Instead, reducing the visual prevalence of unhealthy food products through Diminished Reality (DR) could be a viable alternative: We present Shopping-Coach, a DR prototype that identifies supermarket food products and visually diminishes them dependent on the deviation of the target product’s composition from dietary recommendations. In a study with 12 participants, we found that ShoppingCoach increased compliance with dietary recommendations from 75% to 100% and reduced decision time by 41%. These results demonstrate the promising potential of DR in promoting healthier food choices and thus enhancing public health.
    Type:conference contribution
    DOI:10.1145/3613905.3650795
    URI:https://www.alexandria.unisg.ch/handle/20.500.14171/119766
    Scopus© Citations 13
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