Elgar Fleisch
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Fleisch
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Elgar
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elgar.fleisch@unisg.ch
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+41 71 224 7241
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  • Publication
    Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data
    (Assoc. of Information Systems, )
    Chanson, Mathieu
    ;
    Bogner, Andreas
    ;
    Bilgeri, Dominik
    ;
    Fleisch, Elgar
    ;
    Wortmann, Felix
    A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.
    Type: journal article
    Journal: Journal of the Association for Information Systems
    Volume: Vol. 20, Issue 9
    Issue: Article 10
    DOI: 10.17705/1jais.00567
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/116717
    Scopus© Citations 103
  • Publication
    Proactive behavior in voice assistants: A systematic review and conceptual model
    ( 2024)
    Caterina Bérubé
    ;
    Marcia Nissen orcid-logo
    ;
    Rasita Vinay
    ;
    Alexa Geiger
    ;
    Tobias Budig
    ;
    Aashish Bhandari
    ;
    Catherine Rachel Pe Benito
    ;
    Nathan Ibarcena
    ;
    Olivia Pistolese
    ;
    Pan Li
    ;
    Abdullah Bin Sawad
    ;
    Elgar Fleisch
    ;
    Christoph Stettler
    ;
    Bronwyn Hemsley
    ;
    Shlomo Berkovsky
    ;
    Tobias Kowatsch orcid-logo
    ;
    A. Baki Kocaballi
    Voice assistants (VAs) are increasingly integrated into everyday activities and tasks, raising novel challenges for users and researchers. One emergent research direction concerns proactive VAs, who can initiate interaction without direct user input, offering unique benefits including efficiency and natural interaction. Yet, there is a lack of review studies synthesizing the current knowledge on how proactive behavior has been implemented in VAs and under what conditions proactivity has been found more or less suitable. To this end, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. We searched for articles in the ACM Digital Library, IEEExplore, and PubMed, and included primary research studies reporting user evaluations of proactive VAs, resulting in 21 studies included for analysis. First, to characterize proactive behavior in VAs we developed a novel conceptual model encompassing context, initiation, and action components: Activity/status emerged as the primary contextual element, direct initiation was more common than indirect initiation, and suggestions were the primary action observed. Second, proactive behavior in VAs was predominantly explored in domestic and in-vehicle contexts, with only safety-critical and emergency situations demonstrating clear benefits for proactivity, compared to mixed findings for other scenarios. The paper concludes with a summary of the prevailing knowledge gaps and potential research avenues.
    Type: journal article
    Journal: Computers in Human Behavior Reports
    URL: https://www.sciencedirect.com/science/article/pii/S2451958824000447
    DOI: 10.1016/j.chbr.2024.100411
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/119758
  • Publication
    Smartwatches for non‐invasive hypoglycaemia detection during cognitive and psychomotor stress
    ( 2024)
    Martin Maritsch
    ;
    Simon Föll
    ;
    Vera Lehmann
    ;
    Naïma Styger
    ;
    Caterina Bérubé
    ;
    Mathias Kraus
    ;
    Stefan Feuerriegel
    ;
    Tobias Kowatsch orcid-logo
    ;
    Thomas Züger
    ;
    Elgar Fleisch
    ;
    Felix Wortmann
    ;
    Christoph Stettler
    Type: journal article
    Journal: Diabetes, Obesity and Metabolism
    Volume: 26 (3)
    DOI: 10.1111/dom.15402
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118950
  • Publication
    Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars
    ( 2024)
    Vera Lehmann
    ;
    Thomas Zueger
    ;
    Martin Maritsch
    ;
    Michael Notter
    ;
    Simon Schallmoser
    ;
    Caterina Bérubé
    ;
    Caroline Albrecht
    ;
    Mathias Kraus
    ;
    Stefan Feuerriegel
    ;
    Elgar Fleisch
    ;
    Tobias Kowatsch orcid-logo
    ;
    Sophie Lagger
    ;
    Markus Laimer
    ;
    Felix Wortmann
    ;
    Christoph Stettler
    BACKGROUND Hypoglycemia, one of the most dangerous acute complications of diabetes, poses a substantial risk for vehicle accidents. To date, both reliable detection and warning of hypoglycemia while driving remain unmet needs, as current sensing approaches are restricted by diagnostic delay, invasiveness, low availability, and high costs. This research aimed to develop and evaluate a machine learning (ML) approach for the detection of hypoglycemia during driving through data collected on driving characteristics and gaze/head motion. METHODS We collected driving and gaze/head motion data (47,998 observations) during controlled euglycemia and hypoglycemia from 30 individuals with type 1 diabetes (24 male participants; mean ±SD age, 40.1±10.3 years; mean glycated hemoglobin value, 6.9±0.7% [51.9±8.0 mmol/mol]) while participants drove a real car. ML models were built and evaluated to detect hypoglycemia solely on the basis of data regarding driving characteristics and gaze/head motion. RESULTS The ML approach detected hypoglycemia with high accuracy (area under the receiver-operating characteristic curve [AUROC], 0.80±0.11). When restricted to either driving characteristics or gaze/head motion data only, the detection performance remained high (AUROC, 0.73±0.07 and 0.70±0.16, respectively). CONCLUSIONS Hypoglycemia could be detected noninvasively during real car driving with an ML approach that used only data on driving characteristics and gaze/head motion, thus improving driving safety and self-management for people with diabetes. Interpretable ML also provided novel insights into behavioral changes in people driving while hypoglycemic. (Funded by the Swiss National Science Foundation and others; ClinicalTrials.gov numbers, NCT04569630 and NCT05308095.)
    Type: journal article
    Journal: NEJM AI
    URL: https://ai.nejm.org/doi/10.1056/AIoa2300013
    DOI: 10.1056/aioa2300013
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/119328
  • Publication
    Can digital health researchers make a difference during the pandemic? Results of the single-arm chatbot-led Elena+: Care for COVID-19 interventional study
    ( 2023)
    Joseph Ollier
    ;
    Pavani Suryapalli
    ;
    Elgar Fleisch
    ;
    Florian von Wangenheim
    ;
    Jacqueline Louise Mair
    ;
    Alicia Salamanca-sanabria
    ;
    Tobias Kowatsch orcid-logo
    Background: The current paper details findings from Elena+: Care for COVID-19, an app developed to tackle the collateral damage of lockdowns and social distancing, by offering pandemic lifestyle coaching across seven health areas: anxiety, loneliness, mental resources, sleep, diet and nutrition, physical activity, and COVID-19 information. Methods: The Elena+ app functions as a single-arm interventional study, with participants recruited predominantly via social media. We used paired samples T-tests and within subjects ANOVA to examine changes in health outcome assessments and user experience evaluations over time. To investigate the mediating role of behavioral activation (i.e., users setting behavioral intentions and reporting actual behaviors) we use mixed-effect regression models. Free-text entries were analyzed qualitatively. Results: Results show strong demand for publicly available lifestyle coaching during the pandemic, with total downloads (N = 7′135) and 55.8% of downloaders opening the app (n = 3,928) with 9.8% completing at least one subtopic (n = 698). Greatest areas of health vulnerability as assessed with screening measures were physical activity with 62% (n = 1,000) and anxiety with 46.5% (n = 760). The app was effective in the treatment of mental health; with a significant decrease in depression between first (14 days), second (28 days), and third (42 days) assessments: F2,38 = 7.01, p = 0.003, with a large effect size (η2G = 0.14), and anxiety between first and second assessments: t54 = 3.7, p = <0.001 with a medium effect size (Cohen d = 0.499). Those that followed the coaching program increased in net promoter score between the first and second assessment: t36 = 2.08, p = 0.045 with a small to medium effect size (Cohen d = 0.342). Mediation analyses showed that while increasing number of subtopics completed increased behavioral activation (i.e., match between behavioral intentions and self-reported actual behaviors), behavioral activation did not mediate the relationship to improvements in health outcome assessments. Conclusions: Findings show that: (i) there is public demand for chatbot led digital coaching, (ii) such tools can be effective in delivering treatment success, and (iii) they are highly valued by their long-term user base. As the current intervention was developed at rapid speed to meet the emergency pandemic context, the future looks bright for other public health focused chatbot-led digital health interventions.
    Type: journal article
    Journal: Frontiers in Public Health - Sec. Digital Public Health
    Volume: 11
    URL: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1185702/full
    DOI: 10.3389/fpubh.2023.1185702
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117763
    Scopus© Citations 1
  • Publication
    Machine learning for non-invasive sensing of hypoglycaemia while driving in people with diabetes
    (Wiley Online Library, 2023-02-15)
    Lehmann, Vera
    ;
    Zueger, Thomas
    ;
    Maritsch, Martin
    ;
    Kraus, Mathias
    ;
    Albrecht, Caroline
    ;
    Bérubé, Caterina
    ;
    Feuerriegel, Stefan
    ;
    Wortmann, Felix
    ;
    Kowatsch, Tobias orcid-logo
    ;
    Styger, Naïma
    ;
    Lagger, Sophie
    ;
    Laimer, Markus
    ;
    Fleisch, Elgar
    ;
    Stettler, Christoph
    Type: journal article
    Journal: Diabetes, Obesity and Metabolism
    Volume: 26
    DOI: https://doi.org/10.1111/dom.15021
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107754
  • Publication
    A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development
    (JMIR, 2022-05-25)
    Föll, Simon
    ;
    Lison, Adrian
    ;
    Maritsch, Martin
    ;
    Klingberg, Karsten
    ;
    Lehmann, Vera
    ;
    Züger, Thomas
    ;
    Srivastava, David
    ;
    Jegerlehner, Sabrina
    ;
    Feuerriegel, Stefan
    ;
    Fleisch, Elgar
    ;
    Exadaktylos, Aristomenis
    ;
    Wortmann, Felix
    Type: journal article
    Volume: 6
    Issue: 6
    DOI: https://doi.org/10.2196/35717
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108692
  • Publication
    Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review
    (JMIR Publications, 2022-05-25)
    Jakob, Robert
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    Harperink, Samira
    ;
    Rudolf, Aaron Maria
    ;
    Fleisch, Elgar
    ;
    Haug, Severin
    ;
    Mair, Jacqueline
    ;
    Salamanca, Alicia
    ;
    Kowatsch, Tobias orcid-logo
    Background: Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions. Objective: This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks. Methods: A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings. Results: The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%). Conclusions: This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app’s intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.
    Type: journal article
    Journal: Journal of Medical Internet Research
    Volume: 24
    URL: https://www.jmir.org/2022/5/e35371
    DOI: 10.2196/35371
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108693
    Scopus© Citations 69
  • Publication
    TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning
    (IEEE, 2022-06)
    Jokic, Stefan
    ;
    Cleres, David
    ;
    Rassouli, Frank
    ;
    Steurer-Stey, Claudia
    ;
    Puhan, Milo A.
    ;
    Brutsche, Martin
    ;
    Fleisch, Elgar
    ;
    Barata, Filipe
    Type: journal article
    Journal: IEEE Journal of Biomedical and Health Informatics
    Volume: 26
    Issue: 6
    DOI: https://doi.org/10.1109/jbhi.2022.3152944
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108671
  • Publication
    Type: journal article
    Journal: Research Technology Management
    Volume: 65
    Issue: 2
    URL: https://doi.org/10.1080/08956308.2022.2015951
    DOI: 10.1080/08956308.2022.2015951
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108995
    Scopus© Citations 3