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. @en

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. @en

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. @en

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.@en

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.@en

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. @en

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. @en

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. @en

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.@en

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.@en

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. @en

In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.@en

In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.@en

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.@en

Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users' feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We defne the feeling of creepiness caused by recommendations and fnd out that it is already known to users of RS. We further fnd out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative infuence on brand and platform attitudes, purchase or consumption intention, user experience, and users' expectations of-and their trust in-RS.@en

Commonly used mood measures are either lengthy or too complicated for repeated use. Mood tracking research is, therefore, associated with challenges such as user dissatisfaction, fatigue, or dropouts from studies. Previous efforts to improve user experience are mostly ambiguous concerning their validity and the extent of improvement they provide (e.g., compared to established measures, such as PANAS). This paper investigates the shortening of a self-reported mood measure using smartphones with four independent samples, and provides a baseline for comparing the usability and accuracy of future measures. It first examines whether user self-assessment of overall positive and negative activations with a two-item measure can capture mood as well as I-PANAS-SF. It next examines user's learning effect in repeated usage of the measure. Finally, it introduces the design of an adaptive mood measure that reduces the number of questions based on its prediction of user mood fluctuations. This adaptive measure can potentially capture specific mood states, as well as overall mood. The paper then explores user satisfaction and compliance with this measure in a longitudinal study. The results of this paper reveal that the investigated two-item measure is a valid and reliable tool for capturing a user's overall mood and mood fluctuations. The negative activation from this measure is associated with stress. Our results suggest that the association between mood and stress generally depends on the measure of mood and its items. We discovered that a non-complex self-explanatory measure is fairly resilient for repeated use with respect to the required effort and the accuracy of the measure in both daily and weekly evaluations. Adaptively reducing the length of a mood measure does not seem to impact user compliance but may slightly improve usability. We also noticed that positive and negative activations have a slightly different pattern of behavior with reference to the preceding mood states.@en

People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find information relevant to one's condition, interpret this in context, and understand the medical terms and relationships. Recommender Systems (RS) already help these systems perform precise information filtering. In this short paper, we look one step ahead and show the progress made towards RS helping users find personalized, complex medical interventions or support them with preventive healthcare measures. We identify key challenges that need to be addressed for RS to offer the kind of decision support needed in high-risk domains like healthcare.@en

A growing number of studies in the computer science and engineering communities are addressing mood, an affective phenomenon related but not equivalent to emotion. While emotion has been investigated intensely in the affective computing domain, the characteristics and applications of mood are relatively unexplored. Through a bottom-up approach, this paper aims to identify in which areas and for what purposes computer scientists and other researchers in the ACM and IEEE communities are studying mood. Based on a literature review of 1,264 peer-reviewed publications, this paper proposes a taxonomy of mood research in affective computing. Despite a wide range of applications and domains, core themes of mood research relate to identifying users' mood, influencing it, or helping users to communicate their mood to others. The conceptualization and definition of mood, however, vary between the studies surveyed and sometimes can fall considerably far from the psychological concept of mood in affect research. In several instances, researchers use the terms mood and emotion interchangeably and do not sufficiently discuss the implications both for their measurements and for the design of affective-computing systems as well. With our study, we aim to contribute a clearer conceptualization of mood research and to provide researchers with a broad overview of the research as well as areas of applications in which mood is addressed.@en

A multi-criteria rating looks for important dimensions to more extensively capture an individual’s opinion about a recommended item. Health Recommender Systems (HRS) is considered to be an emerging domain of recommender systems. In HRS, criteria for a multi-criteria preference elicitation of a recommendation have not yet been fully investigated to the best of our knowledge. In this paper, we investigate the criteria for the rating of a health promotion recommendation using an online survey. Drawing on both the relevant literature and the users’ responses, we came up with a list of 33 criteria that users are considering when they rate a health promotion recommendation. However, these criteria are not equally important to users. We discuss which of these criteria are more important in the users’ opinions. In short, our results show that users consistently consider effectiveness, emotional gain, and giving a good feeling as the most important criteria. Using the criteria derived from the literature, we came up with a model for the importance of the criteria which has three dimensions: effect, effort, and context. This study is the first step toward enhancing our understanding of HRS and the rating of a health promotion recommendation.@en