Tobias Kowatsch
Title
Prof. Dr.
Last Name
Kowatsch
First name
Tobias
Email
tobias.kowatsch@unisg.ch
ORCID
Phone
+41 71 224 7244
Web Site
382 results
Now showing 1 - 10 of 382
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Item type:Publication, A care quality dashboard for general practitioners managing patients with diabetes mellitus type 2: user-centered design and prototype evaluation(Springer Science and Business Media LLC, 2026-05-09); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep phenotyping of suicidal ideation after discharge from psychiatric inpatient care: study protocol for an interdisciplinary, multicentre prospective observational study in Psychiatric University Hospitals(BMJ, 2026-02) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, “Are You Okay, Honey?”: Recognizing Emotions Among Couples Managing Diabetes in Daily Life Using Multimodal Real-World Smartwatch Data(MDPI AG, 2026-05-15); Lüscher, JaninaCouples 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 articleJournal:SensorsVolume:26Issue:10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Perceived Value of Virtual Reality Simulations to Gain Insights into Delirium: A Pilot Study(European Delirium Association, 2026-05-04) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reminder Strategies to Improve Meal-Logging Adherence: Protocol for a Microrandomized Trial(JMIR Publications Inc., 2026-10-05); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Same Breath, Different Device: Comparing VR and Smartphone Biofeedback Breathing TrainingSlow breathing holds promise in strengthening mental resilience and reducing acute stress. Prior work presented distinct prototypes of smartphone and VR mobile biofeedback breathing trainings (MBBTs) but lacked direct comparisons. This study aimed to evaluate whether a VR MBBT offers advantages over smartphone MBBT in perceived (relaxation) effectiveness and intention to use, while maintaining non-inferiority in perceived breathing detection accuracy. A within-subject laboratory study with 67 participants was conducted. No evidence was found that the VR MBBT was perceived as more effective. The breathing detection algorithm, originally optimized for smartphones, was perceived as non-inferior in VR MBBT, indicating potential for cross-platform scalability. We found no evidence that intention to use was higher for VR, suggesting that practical considerations such as accessibility and cost may outweigh technology-based preferences. Although these findings are promising, future research must assess their applicability in the real world and ensure accessibility to VR for equitable use.Type:journal articleJournal:Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Impact of a Reward-and-Progression Loop on Engagement with a Mobile Breathing Training: Preliminary Findings from a 15-day Field Study(ACM, 2026-04-13); Engagement with digital health interventions (DHIs) often declines over time, with many users discontinuing use after initial adoption. Gamification has been proposed to address this challenge, yet evidence on its effectiveness remains mixed. This exploratory study examines whether a reward-and-progression loop (RPL), linking performance to daily-constrained collectible coins and unlockable visual features, is associated with differences in engagement in a mobile biofeedback breathing training (MBBT). Twenty participants were randomly assigned to a control or an RPL condition and used the application over 15 days. Engagement was operationalized using two metrics: the number of active days and median session duration per user. Participants in the RPL condition showed more active days than those in the control condition (median = 7 vs. 2). Session duration was comparable across conditions, though more variable in the RPL condition. These preliminary findings suggest that RPLs may support more frequent engagement in DHIs and warrant further investigation.Type:journal articleJournal:Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluating Cross-Study Generalization of Receptivity Models for Just-in-Time Adaptive Interventions(Association for Computing Machinery (ACM), 2026-09-30)Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Beyond the Binary: Operationalizing Receptivity to Digital Health Interventions as a Time-to-Event Spectrum(Association for Computing Machinery (ACM), 2026-09-30)Scopus© Citations 1