Victoria Brügger
Last Name
Brügger
First name
Victoria
Email
victoria.bruegger@unisg.ch
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Item type:Publication, Early health technology assessment of digital diabetes screening in Switzerland: cost-effectiveness and budget impact analyses(2026-02-11); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management(Springer Science and Business Media LLC, 2025-07-17); ; Mia JovanovaElevated postprandial glucose levels present a global epidemic and a major challenge in type-2 diabetes (T2D) management. A key barrier to developing effective dietary interventions for T2D management is the wide inter-individual variation in glycemic and behavioral responses, which limits the impact of one-size-fits-all recommendations. To enable personalized dietary prompts for glycemic control, it is critical to first predict an individual’s susceptibility to elevated postprandial (PPG) levels—or state of momentary vulnerability to PPG excursions. We examined the feasibility of personalized models to predict PPG excursions,and their associated vulnerability states, in the daily lives of 67 Chinese adults with T2D (Mage = 61.39; median = 63.00; 35 women; 2,463 glucose observations). We developed machine learning models trained on past individual observations to predict the next-in-time PPG excursion, using continuous glucose monitoring (CGM) data or CGM data combined with manually-logged meals and glucose-lowering agent intake. On average, personalized models predicted PPG excursions (F1-score: M = 75.88%; median = 78.26%), with substantial variation in predictability across individuals. Notably, no two individuals shared the same dietary and temporal predictors of PPG excursions. This study is the first to predict individual vulnerability states to glucose responses among adults with T2D in China. Findings can help personalize just-in-time adaptive interventions by tailoring dietary prompts based on individuals’ unique vulnerability states to PPG excursions. This approach can inform the development of digital dietary interventions in mHealth apps and clinical decision support tools, thereby helping optimize glycemic control and patient-centered T2D lifestyle management..Type:journal articleJournal:Scientific ReportsVolume:15Issue:1Scopus© Citations 9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wearables and Smartphones for Tracking Modifiable Risk Factors in Metabolic Health: Protocol for a Scoping ReviewBackground Metabolic diseases, such as cardiovascular diseases and diabetes, contribute significantly to global mortality and disability. Wearable devices and smartphones are increasingly used to track and manage modifiable risk factors associated with metabolic diseases. However, no established guidelines exist on how to derive meaningful signals from these devices, often hampering cross-study comparisons. Objective This study aims to systematically overview the current empirical literature on how wearables and smartphones are used to track modifiable (physiological and lifestyle) risk factors associated with metabolic diseases. Methods We will conduct a scoping review to overview how wearable and smartphone-based studies measure modifiable risk factors related to metabolic diseases. We will search 5 databases (Scopus, Web of Science, PubMed, Cochrane Central Register of Controlled Trials, and SPORTDiscus) from 2019 to 2024, with search terms related to wearables, smartphones, and modifiable risk factors associated with metabolic diseases. Eligible studies will use smartphones or wearables (worn on the wrist, finger, arm, hip, and chest) to track physiological or lifestyle factors related to metabolic diseases. We will follow the reporting guideline standards from PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and the JBI (Joanna Briggs Institute) guidance on scoping review methodology. Two reviewers will independently screen articles for inclusion and extract data using a standardized form. The findings will be synthesized and reported qualitatively and quantitatively. Results Data collection is expected to begin in November 2024; data analysis in the first quarter of 2025; and submission to a peer-reviewed journal by the second quarter of 2025. We expect to identify the degree to which wearable and smartphone-based studies track modifiable risk factors collectively (versus in isolation), and the consistency and variation in how modifiable risk factors are measured across existing studies. Conclusions Results are expected to inform more standardized guidelines on wearable and smartphone-based measurements, with the goal of aiding cross-study comparison. The final report is planned for submission to a peer-reviewed, indexed journal. This review is among the first to systematically overview the current landscape on how wearables and smartphones measure modifiable risk factors associated with metabolic diseases. International Registered Report Identifier (IRRID) PRR1-10.2196/59539Type:journal articleJournal:JMIR Research ProtocolsVolume:13DOI:10.2196/59539Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using Commercial Wearables for Lifestyle and Metabolic Phenotyping : A Study Protocol of the GLOW UP Study(2025-06-13); ; Jovanova, MiaThe GLOW UP (GLucose Observation and Wearable Utilisation Project) study assesses the predictive value of wearable-based lifestyle factors (e.g., physical activity, sleep, diet) for preventing type 2 diabetes (T2D). The study examines prediabetes primarily based on HbA1c and other glucose metabolism indicators, such as fasting plasma glucose and insulin resistance. By leveraging real-world data from commercially available wearables, the study explores whether lifestyle actors monitored in free-living conditions can predict early glucose metabolism abnormalities. The study also aims to assess the relative importance of each lifestyle factor, offering insights into personalized glucose dynamics and lifestyle patterns, potentially advancing personalized T2D prevention. This prospective case-control study involves 12 weeks of continuous lifestyle monitoring using wearables. Participants are adults aged 45 and above from Switzerland, selected based on specific criteria. Lifestyle behaviors, including physical activity, sleep, diet, stress, and substance use, are tracked via smartwatches and smartphone applications. Blinded continuous glucose monitoring is used to capture glucose profiles. Baseline and follow-up measurements include HbA1c, fasting glucose, insulin levels, and other metabolic markers. Predictive modeling, incorporating machine learning techniques like LASSO and time series models, will assess the relationship between lifestyle factors and glucose metrics. Additional clustering and regression analyses will examine the association between glucose profiles and metabolic characteristics. The study will evaluate the performance of wearable-derived lifestyle data in predicting HbA1c classifications as the primary outcome. Secondary outcomes include the predictive performance of wearable data on other metabolic markers (e.g., fasting plasma glucose, insulin resistance) and the feasibility of wearable-based individualized glucose profiling. Further analysis will explore lifestyle patterns across metabolic profiles, associations between lifestyle factors and glucose levels, and feature importance across metabolic subgroups. Findings are expected to provide detailed insights into how real-time lifestyle data can inform T2D risk at the individual level. The GLOW UP study has the potential to contribute to behavioral nutrition and physical activity fields by introducing a data-driven approach to T2D prevention through digital biomarkers. Anticipated findings may support the development of personalized, data-driven strategies for early T2D risk assessment and preventive healthcare in Switzerland and beyond.Type:conference poster - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wearables and smartphones for tracking modifiable risk factors in metabolic health: a scoping review(2025-06-13); ; Jovanova, MiaMetabolic diseases, such as cardiovascular diseases and diabetes, are leading causes of death worldwide. Wearable devices and smartphones are increasingly used to monitor modifiable risk factors, including lifestyle behaviors such as nutrition, physical activity, stress, sleep, and substance use, as well as physiological markers, which can improve the management of metabolic diseases. This review will systematically scope the current literature to identify which modifiable (lifestyle and physiological) risk factors are most frequently studied in wearable and smartphone-based metabolic health research and to what extent measures of these risk factors are consistent across studies, particularly regarding measurement methods. A scoping review will be conducted to overview how wearable and smartphone-based studies measure modifiable risk factors related to metabolic diseases. Five databases (Scopus, Web of Science, PubMed, Cochrane Central Register of Controlled Trials, and SPORTDiscus) from 2019 to 2024, with search terms related to wearables, smartphones, and modifiable risk factors associated with metabolic diseases. Eligible studies will use smartphones and/or wearables (worn on the wrist, finger, arm, hip, and chest) to track physiological and/or lifestyle factors related to metabolic diseases. The review will follow reporting standards from PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and JBI guidance on scoping review methodology. Two reviewers will independently screen articles for inclusion and extract data using a standardized form. Data collection is expected to begin in November; data analysis in the first quarter of 2025, and submission to a peer-reviewed journal by the second quarter in 2025. We expect to identify the degree to which wearable and smartphone-based studies track modifiable risk factors collectively (versus in isolation). Additionally, we will scope the consistency and variation in how modifiable risk factors are measured across existing studies. Results are expected to inform standardized guidelines on wearable and smartphone-based measurements, with the goal to aid cross-study comparison. The final report is planned for submission to a peer-reviewed journal. This review is among the first to systematically overview how wearables and smartphones measure modifiable risk factors associated with metabolic diseases and gaps in the measurement of these factors in digital metabolic health research.Type:conference poster - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Diabetes Screening in Switzerland: A cost-effectiveness analysis(2025-11-14); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reminder Strategies to Improve Meal-Logging Adherence: Micro-Randomized Trial Protocol (Preprint)(JMIR Publications Inc., 2026-05-04); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, GLOW UP Study: Protocol for an Observational Digital Biomarker Study for Prediabetes Screening and Digital Phenotyping(medRxiv, 2026-03-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Personalizing dietary interventions by predicting individual vulnerability to glucose excursions(2024); ; Mia JovanovaAbstract Elevated postprandial glucose levels pose a global epidemic and are crucial in cardiometabolic disease management and prevention. A major challenge is inter-individual variability, which limits the effectiveness of population-wide dietary interventions. To develop personalized interventions, it is critical to first predict a person’s vulnerability to postprandial glucose excursions—or elevated post-meal glucose relative to a personal baseline—with minimal burden. We examined the feasibility of personalized models to predict future glucose excursions in the daily lives of 69 Chinese adults with type-2 diabetes (Mage=61.5; 50% women; 2’595 glucose observations). We developed machine learning models, trained on past individual context and meal-based observations, employing low-burden (continuous glucose monitoring) or additional high-burden (manual meal tracking) approaches. Personalized models predicted glucose excursions (F1-score:M=74%; median=78%), with some individuals being more predictable than others. The low burden-models performed better for those with consistent meal patterns and healthier glycemic profiles. Notably, no two individuals shared the same meal and context-based vulnerability predictors. This study is the first to predict individual vulnerability to glucose excursions among a sample of Chinese adults with type-2 diabetes. Findings can help personalize just-in-time-adaptive dietary interventions to unique vulnerability to glucose excursions in daily live, thereby helping improve diabetes management.Type:working paperJournal:medRxiv - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wearables and smartphones for modifiable risk factors in metabolic health: a scoping review protocol(2024); ; Mia JovanovaBackground Metabolic diseases, such as cardiovascular diseases and diabetes, contribute significantly to global mortality and disability. Wearable devices and smartphones increasingly track physiological and lifestyle risk factors and can improve the management of metabolic diseases. However, the absence of clear guidelines for deriving meaningful signals from these devices often hampers cross-study comparisons. Objective Thus, this scoping review protocol aims to systematically overview the current empirical literature on how wearables and smartphones are used to measure modifiable risk factors associated with metabolic diseases. Methods We will conduct a scoping review to overview how wearables and smartphones measure modifiable risk factors related to metabolic diseases. We will search six databases (Scopus, Web of Science, ScienceDirect, PubMed, ACM Digital Library, and IEEE Xplore) from 2019 to 2024, with search terms related to wearables, smartphones, and modifiable risk factors associated with metabolic diseases. We will apply the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Arksey and O’Malley’s scoping review methodology. Eligible studies will use smartphones and/or wearables (worn on the wrist, finger, arm, hip, and chest) to track physiological and/or lifestyle factors related to metabolic diseases. Two reviewers will independently screen articles for inclusion. Data will be extracted using a standardized form, and the findings will be synthesized and reported qualitatively and quantitatively. Results The study is expected to identify potential gaps in measuring modifiable risk factors in current digital metabolic health research. Results are expected to inform more standardized guidelines on wearable and smartphone-based measurements to aid cross-study comparison. The final report is planned for submission to an indexed journal. Conclusions This review is among the first to systematically overview the current landscape on how wearables and smartphones are used to measure modifiable risk factors associated with metabolic diseases.Type:working paperJournal:medRxiv