H. Torkamaan
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1
Generative AI (GenAI) is rapidly embedding itself in knowledge work, supporting tasks such as writing, analysis, coding, and information synthesis. Although widely promoted as enhancing productivity and learning, concerns persist regarding overreliance, deskilling, and erosion of professional expertise. Current debates typically frame GenAI’s impact on skills in binary terms—upskilling versus deskilling—yet empirical evidence on how workers themselves experience these changes in everyday practice remains limited. This study examines how knowledge workers perceive the impact of GenAI on their professional skills. Semi-structured interviews were conducted with 38 professionals in the Netherlands, including academics (e.g., lecturers and professors) and non-academic professionals (e.g., consultants, analysts, engineers, legal professionals, and public sector employees) with varying levels of experience. Data were analyzed using inductive thematic analysis to identify recurring patterns in participants’ accounts of skill-related change. Four perceived skill outcomes emerged: skill development, skill maintenance, skill erosion, and skill revaluation. Skill development involved acquiring or strengthening competencies through learning from GenAI outputs, expanded information access, and offloading routine tasks to focus on higher-level work. Skill maintenance described situations where participants perceived little or no change, often linked to selective and critical use. Skill erosion referred to diminished ability to perform tasks independently without GenAI support. Skill revaluation captured shifts in perceived skill importance as certain tasks became delegable while others gained prominence. Overall, findings indicate that GenAI’s impact on professional skills is heterogeneous and practice-dependent. The proposed four-outcome framework offers a nuanced account of how workers interpret skill change in everyday GenAI use.
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Generative AI (GenAI) is rapidly embedding itself in knowledge work, supporting tasks such as writing, analysis, coding, and information synthesis. Although widely promoted as enhancing productivity and learning, concerns persist regarding overreliance, deskilling, and erosion of professional expertise. Current debates typically frame GenAI’s impact on skills in binary terms—upskilling versus deskilling—yet empirical evidence on how workers themselves experience these changes in everyday practice remains limited. This study examines how knowledge workers perceive the impact of GenAI on their professional skills. Semi-structured interviews were conducted with 38 professionals in the Netherlands, including academics (e.g., lecturers and professors) and non-academic professionals (e.g., consultants, analysts, engineers, legal professionals, and public sector employees) with varying levels of experience. Data were analyzed using inductive thematic analysis to identify recurring patterns in participants’ accounts of skill-related change. Four perceived skill outcomes emerged: skill development, skill maintenance, skill erosion, and skill revaluation. Skill development involved acquiring or strengthening competencies through learning from GenAI outputs, expanded information access, and offloading routine tasks to focus on higher-level work. Skill maintenance described situations where participants perceived little or no change, often linked to selective and critical use. Skill erosion referred to diminished ability to perform tasks independently without GenAI support. Skill revaluation captured shifts in perceived skill importance as certain tasks became delegable while others gained prominence. Overall, findings indicate that GenAI’s impact on professional skills is heterogeneous and practice-dependent. The proposed four-outcome framework offers a nuanced account of how workers interpret skill change in everyday GenAI use.
SAFELIFT
Safety-Aware Feedback for Ergonomic Lifting & Injury-Free Tasks
Conference paper
(2026)
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Gaetano Dibenedetto, Pasquale Lops, Piero Lovreglio, Marco Polignano, Roberto Ravallese, Helma Torkamaan
Work-related musculoskeletal disorders, often caused by unsafe lifting techniques, remain a persistent threat to worker health and safety. We present SAFELIFT, a safety-aware recommender system that automatically detects risky lifting behaviors and generates corrective feedback. Using monocular video input, SAFELIFT extracts ergonomic parameters to compute the Lifting Index (LI) from the Revised NIOSH Lifting Equation. When the LI exceeds a safety threshold, the system produces both graphical and textual recommendations to promote safer postural strategies. Unlike prior approaches, SAFELIFT requires no wearable sensors or multi-camera setups, enabling scalable and low-cost deployment in workplace environments. To assess its effectiveness, we conducted a two-phase evaluation: (1) domain experts (ergonomists, occupational safety professionals, medical staff) assessed the accuracy and relevance of the recommendations, and (2) lay users evaluated different presentation formats, judging their clarity, helpfulness, and trustworthiness. By integrating ergonomics with recommender system design, SAFELIFT contributes to a new class of context-aware, safety-oriented recommendation technologies for occupational health.
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Work-related musculoskeletal disorders, often caused by unsafe lifting techniques, remain a persistent threat to worker health and safety. We present SAFELIFT, a safety-aware recommender system that automatically detects risky lifting behaviors and generates corrective feedback. Using monocular video input, SAFELIFT extracts ergonomic parameters to compute the Lifting Index (LI) from the Revised NIOSH Lifting Equation. When the LI exceeds a safety threshold, the system produces both graphical and textual recommendations to promote safer postural strategies. Unlike prior approaches, SAFELIFT requires no wearable sensors or multi-camera setups, enabling scalable and low-cost deployment in workplace environments. To assess its effectiveness, we conducted a two-phase evaluation: (1) domain experts (ergonomists, occupational safety professionals, medical staff) assessed the accuracy and relevance of the recommendations, and (2) lay users evaluated different presentation formats, judging their clarity, helpfulness, and trustworthiness. By integrating ergonomics with recommender system design, SAFELIFT contributes to a new class of context-aware, safety-oriented recommendation technologies for occupational health.
HealthIUI
Workshop on Intelligent and Interactive Health User Interfaces
As Artificial Intelligence (AI) continues to transform health and care, the integration of Intelligent User Interfaces (IUI) in health and wellness applications presents both significant opportunities and challenges. This workshop aims to bring together researchers and practitioners from HCI, AI, healthcare, and related fields to explore how IUIs can impact long-term user engagement, personalization, and trust in health-oriented interactive systems. We focus on interdisciplinary approaches to design systems that are technically advanced but also responsive to user needs, demands of context of use, values and ethical requirements, and privacy. Through presentations, discussions, and collaborative sessions, participants will identify key challenges, share emerging solutions, and outline pathways for responsible and impactful innovation in health IUI.
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As Artificial Intelligence (AI) continues to transform health and care, the integration of Intelligent User Interfaces (IUI) in health and wellness applications presents both significant opportunities and challenges. This workshop aims to bring together researchers and practitioners from HCI, AI, healthcare, and related fields to explore how IUIs can impact long-term user engagement, personalization, and trust in health-oriented interactive systems. We focus on interdisciplinary approaches to design systems that are technically advanced but also responsive to user needs, demands of context of use, values and ethical requirements, and privacy. Through presentations, discussions, and collaborative sessions, participants will identify key challenges, share emerging solutions, and outline pathways for responsible and impactful innovation in health IUI.
Victim blaming in safety analysis occurs when attention is directed toward individual behavior rather than the systemic factors shaping safety risks, weakening prevention efforts. As Large Language Models (LLMs) begin assisting in safety–critical decision-making, it is important to examine whether they reproduce such tendencies. This study tested 144 road-crash scenarios using ChatGPT-4o and DeepSeek-V3 under a structured interaction protocol to assess how they assign responsibility. Scenarios varied by risk behavior, injury severity, demographics, national context, and driving purpose. Each scenario was analyzed through three sequential prompts examining prevention strategies, primary responsibility attribution, and AcciMap-based ratings across six system levels. When asked about prevention, nearly 90% of model recommendations targeted systemic interventions, including policy, infrastructure, and organizational measures, indicating systems-oriented reasoning under this prompting condition. When asked to assign responsibility, however, both models shifted toward narrower, context-driven attribution. Private driving scenarios produced full driver responsibility, whereas work-related scenarios assigned primary responsibility to employers in 69% of cases. These patterns suggest that the core challenge observed here is not demographic bias but prompt-sensitive analytical inconsistency. Both models shifted between systemic and individual analytical orientations depending on question structure rather than consistently applying systems safety principles across prompts. These findings highlight analytical consistency as a key requirement for the responsible use of generative AI in safety–critical contexts such as transportation safety.
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Victim blaming in safety analysis occurs when attention is directed toward individual behavior rather than the systemic factors shaping safety risks, weakening prevention efforts. As Large Language Models (LLMs) begin assisting in safety–critical decision-making, it is important to examine whether they reproduce such tendencies. This study tested 144 road-crash scenarios using ChatGPT-4o and DeepSeek-V3 under a structured interaction protocol to assess how they assign responsibility. Scenarios varied by risk behavior, injury severity, demographics, national context, and driving purpose. Each scenario was analyzed through three sequential prompts examining prevention strategies, primary responsibility attribution, and AcciMap-based ratings across six system levels. When asked about prevention, nearly 90% of model recommendations targeted systemic interventions, including policy, infrastructure, and organizational measures, indicating systems-oriented reasoning under this prompting condition. When asked to assign responsibility, however, both models shifted toward narrower, context-driven attribution. Private driving scenarios produced full driver responsibility, whereas work-related scenarios assigned primary responsibility to employers in 69% of cases. These patterns suggest that the core challenge observed here is not demographic bias but prompt-sensitive analytical inconsistency. Both models shifted between systemic and individual analytical orientations depending on question structure rather than consistently applying systems safety principles across prompts. These findings highlight analytical consistency as a key requirement for the responsible use of generative AI in safety–critical contexts such as transportation safety.
10th International Workshop on Mental Health and Well-being
From Research to Practice in Mental Healthcare
Conference paper
(2025)
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Hyeokhyen Kwon, Talayeh Aledavood, Daniel A. Adler, Xuhai Xu, Asif Salekin, Varun Mishra, Sang Won Bae, Akane Sano, Helma Torkamaan, More Authors
Ubiquitous computing technologies (UbiComp) are emerging as crucial tools for collecting behavioral, physiological, social, and environmental data to enable early symptom detection, deliver preventative interventions, and support ongoing symptom management. With decades of success in demonstrating the feasibility of using UbiComp technologies to support well-being and mental health in general populations, researchers are exploring the use of these technologies for clinical populations living with mental illness, such as schizophrenia. However, designing, implementing, and validating these technologies in a clinical setting is complex and faces multiple challenges, including ensuring clinical relevance, developing novel analytics systems, integration into existing care systems, user engagement, ethical considerations, and long-term feasibility. This workshop aims to bring together researchers, service providers, practitioners, and industry professionals to collaboratively explore these challenges and discuss strategies for evaluating and validating these technologies in real-world clinical settings. We are calling for papers that inspire new research directions, including co-designing systems with multiple healthcare stakeholders. Building on nine years of success, we continue to support the UbiComp community in advancing reliable, responsible, and effective mental health technologies that can potentially extend UbiComp technologies to support improving patient outcomes in clinical settings at scale.
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Ubiquitous computing technologies (UbiComp) are emerging as crucial tools for collecting behavioral, physiological, social, and environmental data to enable early symptom detection, deliver preventative interventions, and support ongoing symptom management. With decades of success in demonstrating the feasibility of using UbiComp technologies to support well-being and mental health in general populations, researchers are exploring the use of these technologies for clinical populations living with mental illness, such as schizophrenia. However, designing, implementing, and validating these technologies in a clinical setting is complex and faces multiple challenges, including ensuring clinical relevance, developing novel analytics systems, integration into existing care systems, user engagement, ethical considerations, and long-term feasibility. This workshop aims to bring together researchers, service providers, practitioners, and industry professionals to collaboratively explore these challenges and discuss strategies for evaluating and validating these technologies in real-world clinical settings. We are calling for papers that inspire new research directions, including co-designing systems with multiple healthcare stakeholders. Building on nine years of success, we continue to support the UbiComp community in advancing reliable, responsible, and effective mental health technologies that can potentially extend UbiComp technologies to support improving patient outcomes in clinical settings at scale.
Designing Health Recommender Systems to Promote Health Equity
A Socioecological Perspective
Journal article
(2025)
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C.A. Figueroa, H. Torkamaan, Ananya Bhattacharjee, Hanna Hauptmann, Kathleen Guan, Gayane Sedrakyan
Health recommender systems (HRS) have the capability to improve human-centered care and prevention by personalizing content, such as health interventions or health information. HRS, an emerging and developing field, can play a unique role in the digital health field as they can offer relevant recommendations, not only based on what users themselves prefer and may be receptive to, but also using data about wider spheres of influence over human behavior, including peers, families, communities, and societies. We identify and discuss how HRS could play a unique role in decreasing health inequities. We use the socioecological model, which provides representations of how multiple, nested levels of influence (eg, community, institutional, and policy factors) interact to shape individual health. This perspective helps illustrate how HRS could address not just individual health factors but also the structural barriers—such as access to health care, social support, and access to healthy food—that shape health outcomes at various levels. Based on this analysis, we then discuss the challenges and future research priorities. We find that despite the potential for targeting more complex systemic challenges to obtaining good health, current HRS are still focused on individual health behaviors, often do not integrate the lived experiences of users in the design, and have had limited reach and effectiveness for individuals from low socioeconomic status and racial or ethnic minoritized backgrounds. In this viewpoint, we argue that a new design paradigm is necessary in which HRS focus on incorporating structural barriers to good health in addition to user preferences. HRS should be designed with an emphasis on health systems, which also includes incorporating decolonial perspectives of well-being that challenge prevailing medical models. Furthermore, potential lies in evaluating the health equity effects of HRS and leveraging collected data to influence policy. With changes in practices and with an intentional equity focus, HRS could play a crucial role in health promotion and decreasing health inequities.
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Health recommender systems (HRS) have the capability to improve human-centered care and prevention by personalizing content, such as health interventions or health information. HRS, an emerging and developing field, can play a unique role in the digital health field as they can offer relevant recommendations, not only based on what users themselves prefer and may be receptive to, but also using data about wider spheres of influence over human behavior, including peers, families, communities, and societies. We identify and discuss how HRS could play a unique role in decreasing health inequities. We use the socioecological model, which provides representations of how multiple, nested levels of influence (eg, community, institutional, and policy factors) interact to shape individual health. This perspective helps illustrate how HRS could address not just individual health factors but also the structural barriers—such as access to health care, social support, and access to healthy food—that shape health outcomes at various levels. Based on this analysis, we then discuss the challenges and future research priorities. We find that despite the potential for targeting more complex systemic challenges to obtaining good health, current HRS are still focused on individual health behaviors, often do not integrate the lived experiences of users in the design, and have had limited reach and effectiveness for individuals from low socioeconomic status and racial or ethnic minoritized backgrounds. In this viewpoint, we argue that a new design paradigm is necessary in which HRS focus on incorporating structural barriers to good health in addition to user preferences. HRS should be designed with an emphasis on health systems, which also includes incorporating decolonial perspectives of well-being that challenge prevailing medical models. Furthermore, potential lies in evaluating the health equity effects of HRS and leveraging collected data to influence policy. With changes in practices and with an intentional equity focus, HRS could play a crucial role in health promotion and decreasing health inequities.
HealthIUI
Workshop on Intelligent and Interactive Health User Interfaces
Conference paper
(2025)
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Peter Brusilovsky, Denis Parra, Behnam Rahdari, Shriti Raj, Helma Torkamaan
The HealthIUI workshop explores the integration of intelligent user interfaces in health and care, focusing on AI-driven solutions that enhance user engagement, support clinical decision-making, and improve health information access. The workshop brings together experts from human-computer interaction, AI, and healthcare to address challenges such as transparency, usability, and ethical considerations in AI-assisted health applications. Topics covered include generative AI for patient and caregiver support, AI-powered clinical decision support, adaptive visualization for consumer health information, and explainable AI in nursing care. Through paper presentations and discussions, the workshop fosters interdisciplinary collaboration to advance intelligent health interfaces that balance technical innovation with user-centric design principles.
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The HealthIUI workshop explores the integration of intelligent user interfaces in health and care, focusing on AI-driven solutions that enhance user engagement, support clinical decision-making, and improve health information access. The workshop brings together experts from human-computer interaction, AI, and healthcare to address challenges such as transparency, usability, and ethical considerations in AI-assisted health applications. Topics covered include generative AI for patient and caregiver support, AI-powered clinical decision support, adaptive visualization for consumer health information, and explainable AI in nursing care. Through paper presentations and discussions, the workshop fosters interdisciplinary collaboration to advance intelligent health interfaces that balance technical innovation with user-centric design principles.
Lift It Up Right
A Recommender System for Safer Lifting Postures
Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety. This paper presents a health recommender system designed to prevent injury by assessing and correcting posture for lifting techniques. Leveraging monocular video input, our method estimates key ergonomic parameters to compute the Lifting Index based on the Revised NIOSH Lifting Equation. When the computed Lifting Index exceeds a predefined safety threshold, the system automatically generates graphical and textual recommendations to guide the worker towards safer postural strategies. This safety-aware recommender system provides interpretable and actionable feedback without requiring wearable sensors or multi-camera setups, making it suitable for deployment in real-world workplace environments. By integrating ergonomics with recommender system design, we contribute to a new class of context-aware, safety-oriented recommendation technologies tailored for occupational health.
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Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety. This paper presents a health recommender system designed to prevent injury by assessing and correcting posture for lifting techniques. Leveraging monocular video input, our method estimates key ergonomic parameters to compute the Lifting Index based on the Revised NIOSH Lifting Equation. When the computed Lifting Index exceeds a predefined safety threshold, the system automatically generates graphical and textual recommendations to guide the worker towards safer postural strategies. This safety-aware recommender system provides interpretable and actionable feedback without requiring wearable sensors or multi-camera setups, making it suitable for deployment in real-world workplace environments. By integrating ergonomics with recommender system design, we contribute to a new class of context-aware, safety-oriented recommendation technologies tailored for occupational health.
Launched in 2016, the Health Recommender Systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors. Despite the unforeseen pause due to the COVID-19 pandemic and other challenges, the workshop’s influence persisted through its vibrant community and publications. Our aim with the 6th HealthRecSys is to reignite these conversations and provide a forward-thinking platform that revisits the foundational elements that have contributed to the field’s growth. However, the workshop aspires to do more by infusing new perspectives and tackling the most pressing global challenges and technological innovations head-on with contemporary themes such as the impact of global health crises, generative AI models, personalized and self-managed care, and the increasing focus on health equity. HealthRecSys is dedicated to strengthening the network of researchers working on health recommender systems, drawing participants from an array of health and care domains. Through our combined interactive and paper based workshop format, we aim at cultivating a cross-disciplinary community that promotes collaboration among recommender systems specialists, healthcare professionals, ethicists, and policymakers, among others.
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Launched in 2016, the Health Recommender Systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors. Despite the unforeseen pause due to the COVID-19 pandemic and other challenges, the workshop’s influence persisted through its vibrant community and publications. Our aim with the 6th HealthRecSys is to reignite these conversations and provide a forward-thinking platform that revisits the foundational elements that have contributed to the field’s growth. However, the workshop aspires to do more by infusing new perspectives and tackling the most pressing global challenges and technological innovations head-on with contemporary themes such as the impact of global health crises, generative AI models, personalized and self-managed care, and the increasing focus on health equity. HealthRecSys is dedicated to strengthening the network of researchers working on health recommender systems, drawing participants from an array of health and care domains. Through our combined interactive and paper based workshop format, we aim at cultivating a cross-disciplinary community that promotes collaboration among recommender systems specialists, healthcare professionals, ethicists, and policymakers, among others.
Journal article
(2024)
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H. Torkamaan, S. Steinert, Martijn Warnier, F.M. Brazier, O. Oviedo-Trespalacios, M.S. Pera, O. Kudina, S. Kernan Freire, H. Verma, Sage Kelly, M.T. Sekwenz, J. Yang, K.L.L. van Nunen
Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.
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Large Language Models (LLMs) are expected to significantly impact various socio-technical systems, offering transformative possibilities for improved interaction between humans and technology. However, their integration poses complex challenges due to the intricate interplay between societal structures, human behaviour, and technological innovation. This research explores these multifaceted challenges, emphasising the need for a human-centered approach in integrating LLMs to ensure that technological advancements are aligned with ethical standards and societal needs. Utilizing a structured methodology comprising a workshop, literature analysis, and expert collaborations, the study uses a multi-dimensional human-centered AI framework to guide the responsible integration of LLMs. Key insights include the importance of inclusive data, considering unintended consequences, maintaining privacy, and respecting intellectual property rights. The paper identifies and advocates for principles like human-in-the-loop, continuous longitudinal studies, proactive awareness campaigns, and regular audits to develop LLMs that are ethically sound, adaptable, and effectively integrated into various socio-technical systems, thus addressing user needs and broader societal impacts. The paper also underlines the importance of collaboration among academia, industry, and policymakers to develop LLMs that are ethically aligned, socially beneficial, and adaptable to future societal needs. The findings offer valuable insights into the strategic integration of LLMs, advocating for a broader research perspective beyond industrial motivations to fully understand and leverage LLMs in socio-technical landscapes.
Book chapter
(2024)
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H. Torkamaan, S.N.R. Buijsman, Mohammad Tahaei, Ziang Xiao, Daricia Wilkinson, Bart P. Knijnenburg
This chapter explores the principles and frameworks of human-centered artificial intelligence (AI), specifically focusing on user modeling, adaptation, and personalization. It introduces a four-dimensional framework comprising paradigms, actors, values, and levels of realization that should be considered in the design of human-centered AI systems. This framework highlights a perspective-taking approach with four lenses of technology-centric, user-centric, human-centric, and future-centric perspectives. Ethical considerations, transparency, fairness, and accountability, among other aspects, are highlighted as values when developing and deploying AI systems. The chapter further discusses the corresponding human values for each of these concepts. Opportunities and challenges in human-centered AI are examined, including the need for interdisciplinary collaboration and the complexity of addressing diverse perspectives. Human-centered AI provides valuable insights for designing AI systems that prioritize human needs, values, and experiences while considering ethical and societal implications.
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This chapter explores the principles and frameworks of human-centered artificial intelligence (AI), specifically focusing on user modeling, adaptation, and personalization. It introduces a four-dimensional framework comprising paradigms, actors, values, and levels of realization that should be considered in the design of human-centered AI systems. This framework highlights a perspective-taking approach with four lenses of technology-centric, user-centric, human-centric, and future-centric perspectives. Ethical considerations, transparency, fairness, and accountability, among other aspects, are highlighted as values when developing and deploying AI systems. The chapter further discusses the corresponding human values for each of these concepts. Opportunities and challenges in human-centered AI are examined, including the need for interdisciplinary collaboration and the complexity of addressing diverse perspectives. Human-centered AI provides valuable insights for designing AI systems that prioritize human needs, values, and experiences while considering ethical and societal implications.
Mood Measurement on Smartphones
Which Measure, Which Design?
Mood, often studied using smartphones, influences human perception, judgment, thought, and behavior. Mood measurements on smartphones face challenges concerning the selection of a proper mood measure and its transfer, or translation, into a digital application (app) that is user-engaging. Addressing these challenges, researchers sometimes end up developing a new interaction design and modifying the classic mood measure for an app. However, the extent to which such design alterations can impact user compliance, user experience, and the accuracy of mood measurements throughout a mood self-tracking study is unclear. In this paper, we explore and investigate how the selection of a mood measure (from two widely used measures) and its design alteration (from three options of classic, chatbot, and interactive designs) impact the (i) validity, (ii) user compliance, and (iii) user experience of mood measurement apps. For this purpose, we conducted a hybrid study with a mixed design in three parts. The first part suggests that a measure's validity can be susceptible to design modifications and introduces the concept of measure's resilience which can be essential when modifying the interaction design of a measurement tool. The second part discovers that both the type and design of the chosen measure can impact user compliance. This part also portrays a more complete picture of user compliance by demonstrating the use of several variables to investigate compliance. This investigation reveals that user compliance is not just about the response duration or length of a measurement tool. The final part finds that a measure or its design does not significantly influence the user experience for a well-designed app. In this part, we also discover which user experience criteria are more impactful for improving user compliance when designing mood tracking (or mood self-tracking) tools. Our results further suggest that, for a resilient measure, the interactive design is more likely to attract users and have higher user compliance and satisfaction as a whole. Ultimately, choosing a measure or design alternative would be a three-way trade-off between the measure's validity (or accuracy), user compliance, and user satisfaction, which researchers have to prioritize. A successful mood measurement with a smartphone needs to balance both concepts of app quality and assessment quality.
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Mood, often studied using smartphones, influences human perception, judgment, thought, and behavior. Mood measurements on smartphones face challenges concerning the selection of a proper mood measure and its transfer, or translation, into a digital application (app) that is user-engaging. Addressing these challenges, researchers sometimes end up developing a new interaction design and modifying the classic mood measure for an app. However, the extent to which such design alterations can impact user compliance, user experience, and the accuracy of mood measurements throughout a mood self-tracking study is unclear. In this paper, we explore and investigate how the selection of a mood measure (from two widely used measures) and its design alteration (from three options of classic, chatbot, and interactive designs) impact the (i) validity, (ii) user compliance, and (iii) user experience of mood measurement apps. For this purpose, we conducted a hybrid study with a mixed design in three parts. The first part suggests that a measure's validity can be susceptible to design modifications and introduces the concept of measure's resilience which can be essential when modifying the interaction design of a measurement tool. The second part discovers that both the type and design of the chosen measure can impact user compliance. This part also portrays a more complete picture of user compliance by demonstrating the use of several variables to investigate compliance. This investigation reveals that user compliance is not just about the response duration or length of a measurement tool. The final part finds that a measure or its design does not significantly influence the user experience for a well-designed app. In this part, we also discover which user experience criteria are more impactful for improving user compliance when designing mood tracking (or mood self-tracking) tools. Our results further suggest that, for a resilient measure, the interactive design is more likely to attract users and have higher user compliance and satisfaction as a whole. Ultimately, choosing a measure or design alternative would be a three-way trade-off between the measure's validity (or accuracy), user compliance, and user satisfaction, which researchers have to prioritize. A successful mood measurement with a smartphone needs to balance both concepts of app quality and assessment quality.
Journal article
(2023)
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Oscar Oviedo-Trespalacios, Amy E. Peden, Timothy Gallagher, Steffen Steinert, Ashleigh J. Filtness, Genserik Reniers, Thomas Cole-Hunter, Arianna Costantini, Milad Haghani, J. E. Rod, Sage Kelly, Helma Torkamaan, Amina Tariq, James David Albert Newton
ChatGPT is a highly advanced AI language model that has gained widespread popularity. It is trained to understand and generate human language and is used in various applications, including automated customer service, chatbots, and content generation. While it has the potential to offer many benefits, there are also concerns about its potential for misuse, particularly in relation to providing inappropriate or harmful safety-related information. To explore ChatGPT's (specifically version 3.5) capabilities in providing safety-related advice, a multidisciplinary consortium of experts was formed to analyse nine cases across different safety domains: using mobile phones while driving, supervising children around water, crowd management guidelines, precautions to prevent falls in older people, air pollution when exercising, intervening when a colleague is distressed, managing job demands to prevent burnout, protecting personal data in fitness apps, and fatigue when operating heavy machinery. The experts concluded that there is potential for significant risks when using ChatGPT as a source of information and advice for safety-related issues. ChatGPT provided incorrect or potentially harmful statements and emphasised individual responsibility, potentially leading to ecological fallacy. The study highlights the need for caution when using ChatGPT for safety-related information and expert verification, as well as the need for ethical considerations and safeguards to ensure users understand the limitations and receive appropriate advice, especially in low- and middle-income countries. The results of this investigation serve as a reminder that while AI technology continues to advance, caution must be exercised to ensure that its applications do not pose a threat to public safety.
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ChatGPT is a highly advanced AI language model that has gained widespread popularity. It is trained to understand and generate human language and is used in various applications, including automated customer service, chatbots, and content generation. While it has the potential to offer many benefits, there are also concerns about its potential for misuse, particularly in relation to providing inappropriate or harmful safety-related information. To explore ChatGPT's (specifically version 3.5) capabilities in providing safety-related advice, a multidisciplinary consortium of experts was formed to analyse nine cases across different safety domains: using mobile phones while driving, supervising children around water, crowd management guidelines, precautions to prevent falls in older people, air pollution when exercising, intervening when a colleague is distressed, managing job demands to prevent burnout, protecting personal data in fitness apps, and fatigue when operating heavy machinery. The experts concluded that there is potential for significant risks when using ChatGPT as a source of information and advice for safety-related issues. ChatGPT provided incorrect or potentially harmful statements and emphasised individual responsibility, potentially leading to ecological fallacy. The study highlights the need for caution when using ChatGPT for safety-related information and expert verification, as well as the need for ethical considerations and safeguards to ensure users understand the limitations and receive appropriate advice, especially in low- and middle-income countries. The results of this investigation serve as a reminder that while AI technology continues to advance, caution must be exercised to ensure that its applications do not pose a threat to public safety.
Recommendations as Challenges
Estimating Required Effort and User Ability for Health Behavior Change Recommendations
Conference paper
(2022)
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Helma Torkamaan, Jürgen Ziegler
Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item is a task or behavior change activity, several variables, such as the difficulty of the task and users' ability to achieve it, in addition to user preferences and needs, determine the suitability of the recommendations. This paper focuses on how user ability and task difficulty concepts can be integrated into the recommendation process to personalize health activity recommendations. To this end, we compare five approaches, some borrowed from the sports and gaming world, and explore their application, advantages, and drawbacks. Through a study of two weeks, we obtained a suitable dataset to investigate how these algorithms can be used for a health recommender system (HRS) and which one is the most appropriate choice for an online HRS in terms of characteristics and flexibility required for behavior change related tailoring. We compared this choice with a baseline algorithm as part of a fully functional HRS to assess the feasibility and impact of integrating the user ability and required effort concepts on the user engagement with the recommendations in an online longitudinal study of two weeks. The results overall suggest that such integration is effective, and in addition to realizing health behavior change requirements, it improves user engagement with the recommendations.
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Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item is a task or behavior change activity, several variables, such as the difficulty of the task and users' ability to achieve it, in addition to user preferences and needs, determine the suitability of the recommendations. This paper focuses on how user ability and task difficulty concepts can be integrated into the recommendation process to personalize health activity recommendations. To this end, we compare five approaches, some borrowed from the sports and gaming world, and explore their application, advantages, and drawbacks. Through a study of two weeks, we obtained a suitable dataset to investigate how these algorithms can be used for a health recommender system (HRS) and which one is the most appropriate choice for an online HRS in terms of characteristics and flexibility required for behavior change related tailoring. We compared this choice with a baseline algorithm as part of a fully functional HRS to assess the feasibility and impact of integrating the user ability and required effort concepts on the user engagement with the recommendations in an online longitudinal study of two weeks. The results overall suggest that such integration is effective, and in addition to realizing health behavior change requirements, it improves user engagement with the recommendations.
Conference paper
(2021)
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Katja Herrmanny, Helma Torkamaan
Supporting personal health with Decision Support Systems (DSS) and, specifically, recommender systems (RS) is a promising and growing area of research. Integrating the user in the loop is vital in such health systems due to the complexity of recommendations, gravity of the decisions and the reliance on user autonomy. However, for such a purpose, to the best of our knowledge there exists no profound or comprehensive framework nor model to guide system designers, to exploit the full potential of integrating users in the system's reasoning process by design. In this paper, we present a multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components, has been derived from an iterative mixed-methods development and evaluation procedure, including expert workshops and extensive multidisciplinary literature reviews. Users are accordingly integrated into the whole process from system reasoning until decision making through the following actionable design strategies: (1) Empower: Enabling them to understand the result generation and implications, (2) Encourage: encouraging them to question and reflect system outcomes and to get involved in the generation process and (3) Engage: enabling them to take an active role by facilitating and providing opportunities for user control. The framework offers support to designers of personal health-related DSS and RS in properly integrating users into their systems.
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Supporting personal health with Decision Support Systems (DSS) and, specifically, recommender systems (RS) is a promising and growing area of research. Integrating the user in the loop is vital in such health systems due to the complexity of recommendations, gravity of the decisions and the reliance on user autonomy. However, for such a purpose, to the best of our knowledge there exists no profound or comprehensive framework nor model to guide system designers, to exploit the full potential of integrating users in the system's reasoning process by design. In this paper, we present a multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components, has been derived from an iterative mixed-methods development and evaluation procedure, including expert workshops and extensive multidisciplinary literature reviews. Users are accordingly integrated into the whole process from system reasoning until decision making through the following actionable design strategies: (1) Empower: Enabling them to understand the result generation and implications, (2) Encourage: encouraging them to question and reflect system outcomes and to get involved in the generation process and (3) Engage: enabling them to take an active role by facilitating and providing opportunities for user control. The framework offers support to designers of personal health-related DSS and RS in properly integrating users into their systems.
Conference paper
(2021)
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Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan
Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system. This paper extends the prior works and goes beyond the algorithmic fairness in recommender systems by highlighting the non-algorithmic viewpoint on the fairness in these systems. The paper proposes an evaluation methodology that can be used to assess the fairness of a recommender system perceived by its users. We have adopted a well-known model and re-formulated it to suit the particular characteristics of the recommender systems, and accordingly, their corresponding users. Our proposed methodology can be used in order to elicit the feedback of the users, along with three important dimensions, i.e., Engagement, Representation, and Action & Expression. We have formed a set of survey questions that address the aforementioned dimensions, as a set of examples to assess the fairness in a recommender system.
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Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system. This paper extends the prior works and goes beyond the algorithmic fairness in recommender systems by highlighting the non-algorithmic viewpoint on the fairness in these systems. The paper proposes an evaluation methodology that can be used to assess the fairness of a recommender system perceived by its users. We have adopted a well-known model and re-formulated it to suit the particular characteristics of the recommender systems, and accordingly, their corresponding users. Our proposed methodology can be used in order to elicit the feedback of the users, along with three important dimensions, i.e., Engagement, Representation, and Action & Expression. We have formed a set of survey questions that address the aforementioned dimensions, as a set of examples to assess the fairness in a recommender system.
Conference paper
(2021)
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Helma Torkamaan, Jurgen Ziegler
Behavior change for health promotion is a complex process that requires a high level of personalization, which health recommender systems, as an emerging area, have been trying to address. Despite the advantages of behavior change theories in explaining individuals' behavior and standardizing the behavior change program overall, these theoretical models are either overlooked or unreported for the most part in health promotion systems, a small share of them being related to mental well-being. For a health recommender system to personalize interventions, the interventions should be properly designed, and the behavior change aspects should be adequately integrated into the recommendation process. This paper demonstrates an implementation guideline derived from a practical approach in integrating behavior change theories and persuasive design principles into an example mobile-based health recommender system for mental health promotion. This implementation maps a set of relevant theories for designing the health recommender system into a set of requirements using a functional framework. By realizing these requirements, one can assure that the behavior change theories are at the very least considered. This effort serves as a guideline for future implementations and highlights elements that could perhaps be used for other health or recommendation domains and, particularly, user integration purposes.
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Behavior change for health promotion is a complex process that requires a high level of personalization, which health recommender systems, as an emerging area, have been trying to address. Despite the advantages of behavior change theories in explaining individuals' behavior and standardizing the behavior change program overall, these theoretical models are either overlooked or unreported for the most part in health promotion systems, a small share of them being related to mental well-being. For a health recommender system to personalize interventions, the interventions should be properly designed, and the behavior change aspects should be adequately integrated into the recommendation process. This paper demonstrates an implementation guideline derived from a practical approach in integrating behavior change theories and persuasive design principles into an example mobile-based health recommender system for mental health promotion. This implementation maps a set of relevant theories for designing the health recommender system into a set of requirements using a functional framework. By realizing these requirements, one can assure that the behavior change theories are at the very least considered. This effort serves as a guideline for future implementations and highlights elements that could perhaps be used for other health or recommendation domains and, particularly, user integration purposes.
STRETCH
Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data
Conference paper
(2021)
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Chunpai Wang, Shaghayegh Sahebi, Helma Torkamaan
Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement behaviors, interest in activities, and their stress levels. In this paper, we propose a novel multi-view tensor decomposition method for stress and user behavior modeling with heterogeneous data, which could provide personalized stress tracking and plausible user behavior modeling across time. To the best of our knowledge, it is the first method that could model user stress and behavior at the same time with multiple resources of data, such as stress measurement, activity rating, and engagement. Our experiments show that leveraging multiple resources of data could not only improve predictions with sparse data, but also results in discovering the underlying stress-activity patterns. We demonstrate the effectiveness of our proposed model on the dataset collected via a self-contained stress management mobile application.
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Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement behaviors, interest in activities, and their stress levels. In this paper, we propose a novel multi-view tensor decomposition method for stress and user behavior modeling with heterogeneous data, which could provide personalized stress tracking and plausible user behavior modeling across time. To the best of our knowledge, it is the first method that could model user stress and behavior at the same time with multiple resources of data, such as stress measurement, activity rating, and engagement. Our experiments show that leveraging multiple resources of data could not only improve predictions with sparse data, but also results in discovering the underlying stress-activity patterns. We demonstrate the effectiveness of our proposed model on the dataset collected via a self-contained stress management mobile application.
Exploring chatbot user interfaces for mood measurement
A study of validity and user experience
Conference paper
(2020)
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Helma Torkamaan, Jürgen Ziegler
With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user's mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools.
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With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user's mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools.
Conference paper
(2020)
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Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner
HealthRecSys 2020 was the 5th International Workshop on Health Recommender Systems held in conjunction with the 14th ACM Conference on Recommender Systems. This workshop followed the previous workshop in 2019 [4] and focused on the application and potentials of recommender systems on health promotion, health care, and health-related topics. By engaging in the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure. This year, in particular, COVID-19-related contributions were discussed.
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HealthRecSys 2020 was the 5th International Workshop on Health Recommender Systems held in conjunction with the 14th ACM Conference on Recommender Systems. This workshop followed the previous workshop in 2019 [4] and focused on the application and potentials of recommender systems on health promotion, health care, and health-related topics. By engaging in the discussion and representation of health domains into recommender systems, this workshop facilitated the cross-domain collaborations and exchange of knowledge and infrastructure. This year, in particular, COVID-19-related contributions were discussed.