Elgar Fleisch

Title

Prof. Dr.

Last Name

Fleisch

First name

Elgar

elgar.fleisch@unisg.ch

ORCID

0000-0002-4842-1117

Phone

+41 71 224 7241

Web Site

http://www.item.unisg.ch

http://www.im.ethz.ch/

Now showing 1 - 10 of 478

Some of the metrics are blocked by your
consent settings
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 160
Some of the metrics are blocked by your
consent settings
Development, Deployment, and Evaluation of DyMand: Wearable AI for In-Situ Detection and Capture of Couples’ Dyadic Interactions in Chronic Disease Management
(Association for Computing Machinery (ACM), 2026-07-24)
Boateng, George
;
Santhanam, Prabhakaran
;
Elgar Fleisch
;
Lüscher, Janina
;
Pauly, Theresa
Dyadic interactions of couples are of interest as they provide insight into relationship quality and chronic disease management. Existing sensing systems primarily infer social structure within groups retrospectively, or capture couples’ interactions at random or scheduled times, which could miss meaningful interpersonal interactions between partners. In this work, we developed a smartwatch-based algorithm that detects interaction moments between partners and enables interaction-aware data capture in everyday settings. It uses the Bluetooth signal strength between two smartwatches, each worn by one partner, and a voice activity detection machine-learning model that we trained, to infer that the partners are interacting and then trigger data collection. We operationalized this sensing approach by developing, deploying, and evaluating DyMand, an open-source smartwatch and smartphone system that integrates multimodal sensor and self-report data collection in situ. We deployed the DyMand system in a 7-day field study and collected and analyzed data about social support and diabetes discussions from 13 (N=26) Swiss-based heterosexual couples managing diabetes mellitus type 2 of one partner. Our system evaluation showed that 77.6% of algorithm-triggered recordings contained partners’ conversation moments compared to 43.8% for scheduled triggers, and that DyMand was easy to use. Preliminary insights into disease management behavior revealed that 5.2% of couples’ conversation moments contained social support, and partners discussed diabetes management 2.4% of the time based on the audio data analyses. In contrast, the self-report data showed that patients received support in 67.7% of couples’ interactions and caregivers provided support in 71.8% of couples’ interactions. DyMand enables insights into dyadic behavior in-situ, and could be used by social, clinical, or health psychology researchers to understand the social dynamics of couples that are managing chronic diseases in everyday life to inform the development and delivery of behavioral interventions.
Type:journal article
Journal:ACM Transactions on Computing for Healthcare
DOI:10.1145/3833380
URI:https://alexandria.unisg.ch/handle/20.500.14171/133092
Some of the metrics are blocked by your
consent settings
“Are You Okay, Honey?”: Recognizing Emotions Among Couples Managing Diabetes in Daily Life Using Multimodal Real-World Smartwatch Data
(MDPI AG, 2026-05-15)
Boateng, George
;
Zhao, Xiangyu
;
Speichert, Malgorzata
;
Elgar Fleisch
;
Lüscher, Janina
Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner’s emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples’ interactions in daily life. In this work, we collected 85 h (1021 5-min samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n = 612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner’s self-reported emotions (valence and arousal). Our results from the best models—balanced accuracies of 63.8% and 78.1% for arousal and valence respectively—are better than the results from (1) chance, (2) prior work that also used data from German-speaking, Swiss-based couples, and (3) partners’ perceptions of each other’s emotions. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being.
Type:journal article
Journal:Sensors
Volume:26
Issue:10
DOI:10.3390/s26103141
URI:https://alexandria.unisg.ch/handle/20.500.14171/126075
Some of the metrics are blocked by your
consent settings
Digital Quality Monitoring for Type 2 Diabetes in Swiss Primary Care: Qualitative Interview Study
(JMIR Publications Inc., 2026-05-12)
Odile-Florence Giger
;
Ackermann, Lola Jo
;
Principe, Marinja
;
Meier, Simon
;
Elgar Fleisch
Background: General practitioners (GPs) manage most type 2 diabetes (T2D) cases worldwide but face increasing workloads. To support guideline-based T2D care, the Swiss Society of Endocrinology and Diabetology (SSED) developed a quality monitoring tool for GPs to track and document patient clinical outcomes, such as glycated hemoglobin testing, blood pressure measurement, and diabetes-related complications in primary care. By aggregating these elements, the SSED score provides a structured way to assess adherence to recommended standards of diabetes care. Although this approach shows promise for improving health outcomes, the score is currently available in paper format and creates an additional administrative burden for physicians. Objective: This study examined challenges in T2D management in Swiss primary care, assessed limitations of the current SSED quality score, and identified strategies to enable the potential digital implementation and wider adoption of a digital SSED quality score in routine practice. Methods: A total of 38 qualitative interviews were conducted with 39 participants, representing 4 stakeholder groups relevant to digital T2D care: health care professionals (n=12), individuals with T2D (n=12), software developers (n=10), and health insurer representatives (n=5). Data were analyzed using thematic analysis. Results: Participants highlighted persistent challenges in T2D management, including time pressure for health care professionals, fragmented care, and a lack of personalized support for individuals with T2D. Barriers to digital quality monitoring included poor integration with GP systems, misaligned incentives, and limited relevance to multimorbidity. Suggested facilitators included embedding digital tools, such as a digital SSED score, into existing workflows, optimizing task shifting for GPs, integrating patient-centered features, and improving data sharing. Conclusions: A digital SSED score could support GPs by integrating patient-entered data, generating automated alerts, and enabling pharmacy-based follow-up. Successful adoption is likely to depend on GP workflow integration, system interoperability, and aligned incentives. Involving stakeholders early is essential to ensure that new digital tools meet the needs of both individuals with T2D and GPs.
Type:journal article
Journal:Journal of Medical Internet Research
Volume:28
DOI:10.2196/82960
URI:https://alexandria.unisg.ch/handle/20.500.14171/126074
Some of the metrics are blocked by your
consent settings
Feasibility of the “LvL UP” trial: a pilot sequential multiple assignment randomised trial of an adaptive, holistic mHealth lifestyle coaching intervention
(Springer Science and Business Media LLC, 2026-01-03)
Shenglin Zheng
;
Oscar Castro
;
Jacqueline Louise Mair
;
Ahmad I. Jabir
;
Sarah Yi Xuan Tan
Background Mobile Health (mHealth) interventions are promising for addressing the growing burden of noncommunicable diseases and common mental disorders but often focus on single domains and lack adaptability. LvL UP (“Level Up”) is a holistic mHealth lifestyle coaching intervention that integrates physical activity, diet, and emotional regulation. It provides blended coaching support through an app-based conversational agent with adaptive human support. This pilot trial assessed the feasibility of delivering the LvL UP intervention and implementing its adaptive procedures using a sequential multiple assignment randomised trial (SMART) design. Methods This eight-week pilot trial was conducted from 29 March to 1 August 2024. We recruited adults in Singapore aged 21–59 at risk of chronic conditions. Participants were randomised 2:1 to the intervention (LvL UP app with a peer supporter–LvL UP Buddy) or comparison (control app with educational resources). After four weeks, non-responders (defined as completing < 6 digital coaching sessions or rated session usefulness < 4/5) were re-randomised 1:1 to continue or receive three additional motivational interviewing (MI)-informed sessions with a human coach; responders remained on their original allocation. Primary outcomes included feasibility indicators: recruitment, LvL UP Buddy enrolment, non-responder rate, trial retention, data completion rate, and intervention engagement. Secondary outcomes measured changes from baseline to eight weeks in mental well-being, psychological distress, physical activity, sleep duration, and fruit and vegetable intake. Six progression criteria were prespecified to guide advancement to a definitive trial. Results Of the 458 individuals screened, 394 were eligible, and 123 were enrolled (82 interventions; 41 controls). Most intervention participants (95.1%) were paired with a LvL UP Buddy. Thirty-eight participants (46.3%) were non-responders; of those assigned to MI-informed sessions, 52.6% (10/19) completed all three. Eight-week retention was high (91.5% intervention; 92.7% control), with 12.2% missing data. Positive trends were observed in mental well-being (2.12, 95% CI [-0.58, 4.82]), psychological distress (-0.94 [-2.08, 0.20]), and sleep duration (0.49 h/week [0.17, 0.82]). The study met five of six prespecified progression criteria: recruiting ≥ 60 participants within six weeks, achieving ≥ 75% retention, maintaining ≤ 20% missing data, obtaining a 40–60% non-responder rate, and showing a positive change in ≥ 1 health-related outcome. The digital coaching session adherence fell below the target (39.5% vs. 70%). Conclusions LvL UP was feasible to deliver and evaluate using a SMART design. The results provide strong operational guidance and a solid foundation for the refinement and implementation of a fully powered trial. Trial registration ClinicalTrials.gov, TRN: NCT06360029, Registration date: 7 April 2024.
Type:journal article
Journal:International Journal of Behavioral Nutrition and Physical Activity
DOI:10.1186/s12966-025-01869-7
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124865
Scopus© Citations 1
Some of the metrics are blocked by your
consent settings
Structured Large Language Model Workflows for Motivational Interviewing in Health Behavior Change: Proof-of-Concept Study
(JMIR Publications, 2026-07-06)
Shenoi, Akshaye
;
Li, Tianze
;
Jabir, Ahmad Ishqi
;
Pitkethly, Amanda
;
Elgar Fleisch
Background Motivational interviewing (MI) is an effective approach for supporting health behaviorchange, but face-to-face delivery is resource-intensive and difficult to scale. Rule-based conversational agents (CAs) can improve access; however, their scripted interactions and limited language flexibility constrain MI delivery. While large language models (LLMs) are increasingly being used for MI coaching, their conversational fidelity and quality compared with human coaches and rule-based CAs remain understudied. Objective This study aimed to describe the development of an LLM-based CA, Artificially Intelligent Motivational Interviewing (Aimi), orchestrated with structured workflows, and to evaluate its feasibility, conversational fidelity, and user perceptions during MI coaching interactions. Methods We developed Aimi using structured LLM workflows designed to enhance MI fidelity. We conducted a within-participants study, where 18 adults interacted with (1) Aimi, (2) a novice MI-trained human coach, and (3) a rule-based CA during live text-based role-play coaching sessions. Transcripts were independently evaluated by an MI expert using the Motivational Interviewing Skill Code, Version 2.0 (MISC-2), to assess MI competency and fidelity. Participants completed a user experience questionnaire to provide general feedback and to assess session alliance, dialogue relevance, empathy, engagement, linguistic quality, and perceived motivation to change. Feedback from users was thematically summarized and categorized under strengths and weaknesses for each approach. Results Aimi achieved fidelity scores comparable to those of the novice human coach and higher than those of the rule-based CA on summary metrics, including higher reflection-to-question ratios (median 0.84, IQR 0.62-0.92 vs 0.62, IQR 0.42-0.74 vs 0.25, IQR 0.17-0.38), more complex reflections (median 66.67%, IQR 46.97%-76.92% vs 50%, IQR 34.38%-61.88% vs 0.00%, IQR 0%-50%), and greater elicitation of client change talk (median 90.83%, IQR 85.89%-100% vs 73.21%, IQR 63.10%-83.19% vs 66.67%, IQR 57.86%-81.94%). User experience ratings showed no significant differences across conditions. User feedback revealed distinct strengths and limitations across the coaching interactions. Participants described Aimi’s interactions as personalized, fluid, and adaptive, though sometimes overly reflective and lengthy. The novice human coach was viewed as empathetic and supportive but slow to respond, whereas the rule-based coach was viewed as efficient and structured yet limited in depth and personalization. Conclusions This study demonstrates the technical feasibility of structured LLM-workflows for MI coaching and their capacity to maintain conversational fidelity comparable to that of a novice MI-trained human coach. Given the role-play paradigm, single-rater coding, and small convenience sample, these comparative findings should be interpreted as exploratory. Our findings serve as a foundational baseline for the development of scalable behavior change interventions in clinical settings.
Type:journal article
Journal:JMIR Formative Research
Volume:10
DOI:10.2196/94036
URI:https://alexandria.unisg.ch/handle/20.500.14171/131145
Some of the metrics are blocked by your
consent settings
Early health technology assessment of digital diabetes screening in Switzerland: cost-effectiveness and budget impact analyses
(2026-02-11)
Wasu Mekniran
;
Victoria Brügger
;
Magdalena Fuchs
;
Qiuhan Jin
;
Benjamin Wirth
Objectives Digital biomarkers offer scalable screening for type 2 diabetes, yet adoption is stalled by uncertainty regarding economic viability. This study evaluates the cost-effectiveness and budget impact of digital screening compared to opportunistic screening from a Swiss payer perspective. Methods A probabilistic Markov cohort model was developed to simulate at-risk Swiss adults (age ≥45, BMI ≥25 kg/m²) over a 40-year horizon. The model incorporates a digital attrition parameter, inputs derived from Swiss-specific sources (e.g., the CoLaus study and FSO life tables), and statutory tariffs. Costs and outcomes were discounted at 3.0%. Results In the deterministic base-case, digital screening yielded an incremental cost-effectiveness ratio of CHF 2,912 per quality-adjusted life-year gained. Probabilistic sensitivity analysis indicated a 93.2% probability of cost-effectiveness at the CHF 50,000 threshold. The budget impact analysis estimated a Year 1 gross investment budget of CHF 27 million to identify prevalent cases, followed by long-term savings from averted complications. Conclusions Digital screening can be highly cost-effective in Switzerland. While the required Year 1 gross investment poses a liquidity challenge, reimbursement via pathway-oriented models under the Swiss tariff could align incentives with long-term complication avoidance.
Type:journal article
Journal:MedRxiv
URL:https://www.medrxiv.org/content/10.64898/2026.02.10.26345992v1
DOI:10.64898/2026.02.10.26345992
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/125108
Some of the metrics are blocked by your
consent settings
Digital health technologies and stakeholder incentives in type-2 diabetes prevention
(Sage, 2026-03-12)
Wasu Mekniran
;
Wilma Diethelm
;
Victoire Stalder
;
Elgar Fleisch
;
Tobias Kowatsch
Background Type-2 diabetes (T2D) is largely preventable through sustained lifestyle change, yet healthcare systems face challenges in implementing and sustaining lifestyle interventions at scale. Digital health technologies (DHTs), offering personalized risk assessments, remote monitoring, and behavior change support, may support T2D prevention. However, the systemic role of DHTs within the T2D prevention ecosystem remains poorly understood. This study examines (RQ1) What stakeholder incentives are associated with prevention engagement among payers, providers, and individuals? (RQ2) What incentive patterns are associated with DHT adoption in T2D prevention? (RQ3) How is DHT adoption associated with value exchange among stakeholders in the T2D prevention ecosystem? Methods We conducted a systematic literature review to identify existing incentives in preventive care (RQ1). Business model data from leading DHT companies in T2D prevention (via PitchBook and Crunchbase) were analyzed to examine emerging incentive patterns (RQ2). We conducted expert interviews (N = 26) and synthesized findings using the e3-value framework to map stakeholder relationships (RQ3). Results Our findings show that financial and non-financial incentives for prevention are often temporally misaligned. Engagement in lifestyle-based prevention is linked to short-term rewards, health, and convenience benefits for individuals and long-term cost savings for payers. DHT adoption for T2D prevention is associated with three key patterns: enhancing personalization and convenience for individuals, supporting value-based payment models for payers, and improving workflow efficiency for providers. Conclusions DHTs may help align stakeholder incentives by promoting (1) sustained engagement in lifestyle prevention programs (i.e., continuous glucose monitoring with real-time dietary or activity feedback) and providing individuals with micro-rewards (i.e., for behavior change and improved clinical outcomes). These coordinated feedback loops could be embedded within (2) outcome-based reimbursements for payers and linked to (3) automated workflows to improve provider efficiency (i.e., risk stratification). Realizing this potential requires updated reimbursement models, integrated stakeholder coordination, and supportive policy frameworks.
Type:journal article
Journal:DIGITAL HEALTH
Volume:12
URL:https://journals.sagepub.com/doi/10.1177/20552076261425402
DOI:10.1177/20552076261425402
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/125276
Some of the metrics are blocked by your
consent settings
Nutrition Info and Other Front-of-Package Labels and Simulated Food and Beverage Purchases
(American Medical Association (AMA), 2025-10-17)
Anna H. Grummon
;
Kevin O’Sullivan
;
Joshua Petimar
;
Cristina J. Y. Lee
;
Amanda B. Zeitlin
Importance In January 2025, the US Food and Drug Administration (FDA) released a proposed rule to require front-of-package nutrition information (Nutrition Info) labels that would signal when packaged foods contain low, medium, or high levels of saturated fat, sodium, or added sugars (ie, nutrients of concern). However, it is unknown whether this labeling system would encourage healthier food and beverage purchases. Objectives To test whether Nutrition Info labels similar to those proposed by the FDA could lead to healthier food and beverage purchases compared with other existing or proposed labeling systems. Design, Setting, and Participants This randomized clinical trial was conducted online from October 31 to November 21, 2024. Participants included a national sample of US adults who reported being their household’s primary shopper. Data were analyzed from November 26, 2024, to August 27, 2025. Intervention Participants were randomized to exposure to 1 of 6 front-of-package labeling systems: positive labels (ie, labels that communicate only the positive attributes of a food) only, Nutrition Info labels (similar to the FDA’s proposal), positive plus Nutrition Info labels, “High In” labels (similar to designs the FDA tested to signal when products contain high levels of nutrients of concern), positive plus High In labels, or spectrum labels (similar to designs used internationally rating products from least to most healthy). Main Outcomes and Measures Participants shopped for foods and beverages in a large, simulated online grocery store. The primary outcome was healthfulness of participants’ food and beverage selections, assessed using the United Kingdom’s Ofcom Nutrient Profiling Model scores (ranging from 0 to 100, with higher scores indicating healthier choices). Scores were compared between groups using the average differential effect (ADE). Results A total of 5636 participants completed the trial (3400 [60%] women; mean [SD] age, 40.3 [12.6] years). The spectrum labels led to healthier purchases compared with both the positive labels only (ADE, 2.42; 95% CI, 1.66-3.17; P &lt; .001) and all other labeling systems (range of ADEs, −1.87 [95% CI, −2.63 to −1.11] to −2.45 [95% CI, −3.21 to −1.69]; P &lt; .001). By contrast, the Nutrition Info, positive plus Nutrition Info, High In, and positive plus High In labels did not lead to healthier purchases compared with the positive labels only (range of ADEs, −0.04 [95% CI, −0.80 to 0.72; P = .92] to 0.54 [95% CI, −0.22 to 1.30; P = .16]). Label effects did not differ by nutrition literacy, household income, or educational attainment. Conclusions and Relevance In this randomized clinical trial of food labeling systems, spectrum labels that rate foods from least to most healthy led to healthier purchases than positive labels and Nutrition Info labels similar to those proposed by the FDA. These findings suggest that spectrum labels may be more promising than both existing positive labels and the FDA’s proposed labels for promoting healthier food purchases. Trial Registration ClinicalTrials.gov Identifier: NCT06516627
Type:journal article
Journal:JAMA Network Open
Volume:8
Issue:10
DOI:10.1001/jamanetworkopen.2025.37389
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124698
Scopus© Citations 10
Some of the metrics are blocked by your
consent settings
Feasibility of the LvL UP digital lifestyle coaching intervention designed to prevent non-communicable diseases and common mental disorders
(Springer Science and Business Media LLC, 2025-12-22)
Jacqueline L. Mair
;
Ahmad I. Jabir
;
Alicia Salamanca-Sanabria
;
Oscar Castro
;
Shenglin Zheng
LvL UP is a smartphone-based lifestyle coaching intervention aimed at improving health behaviours, mental well-being, and preventing noncommunicable diseases and common mental disorders. It features a ‘talk and tools’ approach, combining automated health literacy coaching via conversational agent with digital tools such as journaling, life hacks, and slow-paced breathing exercises. An ‘in-the-wild’ mixed-methods study was conducted in Singapore to evaluate LvL UP’s feasibility and acceptability to inform a future definitive trial. The app was available on iOS and Android from March to August 2023 and was promoted through online and offline strategies. Data collection included in-app surveys, usage metrics, and interviews, summarised using descriptive statistics and template analysis. The app was downloaded 307 times. Data from 99 active users were analysed. Most users were female and aged 21–35 years with mild to moderate vulnerabilities in physical activity, diet, and depressive symptoms. Engagement was highest during the first eight days, with 9% remaining engaged for up to 50 days. Users rated technology acceptance highly, finding the app enjoyable, easy to use, and informative. Suggested improvements included streamlined onboarding, fixing bugs, shortening dialogues, and adding rewards. The findings support LvL UP’s feasibility and have informed enhancements for future trials.
Type:journal article
Journal:Scientific Reports
DOI:10.1038/s41598-025-30960-z
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/124784
Scopus© Citations 3

Elgar Fleisch

Filters

Settings

Sort By

Results per page