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Health behaviour is crucial for influencing health, making it a key component in health promotion. However, changing behaviours is complex, as many factors interact to determine health behaviours. Information, awareness, and knowledge are important but not enough. It is essential to move beyond focusing solely on individual psychological and cognitive factors to an understanding of the complex processes involved in health behaviour change. Social-ecological models account for these complex processes but risk being overly broad and all-encompassing.

This qualitative grounded theory study explores how individual, interpersonal, and environmental factors interplay to influence health behaviour, and examines how social-ecological models in health promotion can be tailored to address different ecological needs. Participants were recruited from a community-based cardiovascular disease-prevention program in Northern Sweden. Data was collected through in-depth interviews about health and health behaviours throughout the life course among middle-aged men and women.

The results illustrate how factors obstructing or enabling health behaviours vary in patterned ways for individuals with different health identities. Social-ecological interventions could be more effective if adapted to the specific needs of people with different health identities. In addition to screening for various risk factors, screening for health identities could be helpful in designing social-ecological health-promoting interventions.

Introduction: The increased focus on the ethical aspects of artificial intelligence (AI) follows the increased use in society of data-driven analyses of personal information collected in the use of digital applications for various purposes that the individual is often not aware of. The purpose of this study is to investigate how values and norms are transformed into design choices in a participatory design process of an AI-based digital coaching application for promoting health and to prevent cardiovascular diseases, where a variety of expertise and perspectives are represented.

Method: A participatory design process was conducted engaging domain professionals and potential users in co-design workshops, interviews and observations of prototype use. The design process and outcome was analyzed from a responsible design of AI systems perspective.

Results: The results include deepened understanding of the values and norms underlying health coaching applications and how an AI-based intervention could provide person-tailored support in managing conflicting norms. Further, the study contributes to increased awareness of the value of participatory design in achieving value-based design of AI systems aimed at promoting health through behavior change, and the inclusion of social norms as a design material in the process.

Conclusion: It was concluded that the relationship between the anticipated future users and the organization(s) or enterprises developing and implementing the health-promoting application is directing which values are manifested in the application.

Introduction: Human-centric artificial intelligence (HCAI) focuses on systems that support and collaborate with humans to achieve their goals. To better understand how collaboration develops in human-AI teaming, further exploration grounded in a theoretical model is needed. Tuckman's model describes how team development among humans evolves by transitioning through the stages of forming, storming, norming, performing, and adjourning. The purpose of this pilot study was to explore transitions between the first three stages in a collaborative task involving a human and a human-centric agent.

Method: The collaborative task was selected based on commonly performed tasks in a therapeutic healthcare context. It involved planning activities for the upcoming week to achieve health-related goals. A calendar application served as a tool for this task. This application embedded a collaborative agent designed to interact with humans following Tuckman's stages of team development. Eight participants completed the collaborative calendar planning task, followed by a semi-structured interview. Interviews were transcribed and analyzed using inductive content analysis.

Results: The results revealed that the participants initiated the storming stage in most cases (n = 7/8) and that the agent initiated the norming stage in most cases (n = 5/8). Additionally, three main categories emerged from the content analyses of the interviews related to participants' transition through team development stages: (i) participants' experiences of Tuckman's first three stages of team development; (ii) their reactions to the agent's behavior in the three stages; and (iii) factors important to the participants to team up with a collaborative agent.

Conclusion: Results suggest ways to further personalize the agent to contribute to human-agent teamwork. In addition, this study revealed the need to further examine the integration of explicit conflict management into human-agent collaboration for human-agent teamwork.

Metaphors have for long time been used for describing and explaining the roles of technology in human activity. Such descriptions are embedding an increasing level of ambience, where technology is expected to sense, interpret, and adapt to an individual's needs and wishes, while at the same time, the demands for transparency and accountability is making way for new regulations and guidelines for systems based on artificial intelligence (AI).The purpose of this research is to explore social roles of humans and AI systems, and to identify open research questions and challenges when designing for transparency and sense of control. 

A socio-technical relationship framework was developed for assessing the social roles of AI systems, and for designing for change in roles and relationships. The framework was developed based on activity theory, metaphors for human-technology interaction, and emergent research on human-AI collaboration. By focusing on meaningful shared activity, the situations when technology is socially and personally relevant can be distinguished from the situations where technology is functionally relevant.The identified roles are partly overlapping and fluent depending on the situation, which increases the need for transparency and accountability, and consequently, AI techniques that allows explainability, negotiation and adaptation of the enacted roles.The framework is exemplified in two case studies to elicit role transformations in a work and a home environment respectively, where an individual's changing need for supporting development of capabilities and autonomy through AI-based technology are addressed.

We identify a number of open research questions and propose to apply the framework to capture and design for developing capability in humans and AI systems, collaborative capabilities in human-AI teaming, and for eliciting the ethical and moral consequences of AI systems operating within a person’s zone of development.

Background: Artificial intelligence (AI) technology is constantly and rapidly evolving and has the potential to benefit occupational therapy (OT) and OT clients. However, AI developments also pose risks and challenges, for example in relation to the ethical principles of OT. One way to support future AI technology aligned with OT ethical principles may be through human-centered AI (HCAI), an emerging branch within AI research and developments with a notable overlap of OT values and beliefs.

Objective: To explore the risks and challenges of AI technology, and how the combined expertise, skills, and knowledge of OT and HCAI can contribute to harnessing its potential and shaping its future, from the perspective of OT’s ethical values and beliefs.

Results: Opportunities for OT and HCAI collaboration related to future AI technology include ensuring a focus on 1) occupational performance and participation, while taking client-centeredness into account; 2) occupational justice and respect for diversity, and 3) transparency and respect for the privacy of occupational performance and participation data.

Conclusion and Significance: There is need for OTs to engage and ensure that AI is applied in a way that serves OT and OT clients in a meaningful and ethical way through the use of HCAI.

Health-promoting digital agents, taking on the role of an assistant, coach or companion, are expected to have knowledge about a person's medical and health aspects, yet they typically lack knowledge about the person's activities. These activities may vary daily or weekly and are contextually situated, posing challenges for the human-Agent interaction. This pilot study aimed to explore the experiences and behaviors of older adults when interacting with an initially unknowledgeable digital agent that queries them about an activity that they are simultaneously engaged in. Five older adults participated in a scenario involving preparing coffee followed by having coffee with a guest. While performing these activities, participants educated the smartwatch-embedded agent, named Virtual Occupational Therapist (VOT), about their activity performance by answering a set of activity-ontology based questions posed by the VOT. Participants' interactions with the VOT were observed, followed by a semi-structured interview focusing on their experience with the VOT. Collected data were analyzed using an activity-Theoretical framework. Results revealed participants exhibited agency and autonomy, deciding whether to adapt to the VOT's actions in three phases: Adjustment to the VOT, partial adjustment, and the exercise of agency by putting the VOT to sleep after the social conditions and activity changed. Results imply that the VOT should incorporate the ability to distinguish when humans collaborate as expected by the VOT and when they choose not to comply and instead act according to their own agenda. Future research focuses on how collaboration evolves and how the VOT needs to adapt in the process.

This article presents an empirical requirement elicitation study for an argumentation-based digital companion for supporting behavior change, whose ultimate goal is the promotion and facilitation of healthy behavior. The study was conducted with non-expert users as well as with health experts and was in part supported by the development of prototypes. It focuses on human-centric aspects, in particular user motivations, as well as on expectations and perceptions regarding the role and interaction behavior of a digital companion. Based on the results of the study, a framework for person tailoring the agent's roles and behaviors, and argumentation schemes are proposed. The results indicate that the extent to which a digital companion argumentatively challenges or supports a user's attitudes and chosen behavior and how assertive and provocative the companion is may have a substantial and individualized effect on user acceptance, as well as on the effects of interacting with the digital companion. More broadly, the results shed some initial light on the perception of users and domain experts of “soft,” meta-level aspects of argumentative dialogue, indicating potential for future research.

Social isolation and loneliness have become everyday concerns forpopulations all over the world as these factors are affecting both physical and mentalhealth in a negative way. Feelings of isolation and loneliness are increasinglyacknowledged as a health risk among older persons. ICTs have been recognized aseffective tools to combat social isolation among older people. The aim of this studywas to explore factors of significance when introducing a tablet-based systemproviding digital social activities for older persons with home care. Participants were17 persons, age 70 and older, who lived alone and had assistance from home care.This exploratory study used cross-sectional qualitative data analyzed throughthematic analysis. Three themes were generated: 1) lacking vocabulary related tothe context, 2) intuitive user interface may replace extensive instructions and 3)unwillingness to commit to a pre-defined measure of performance.

Background and Objective: When agents (e.g. a person and a social robot) perform a joint activity to achieve a joint goal, they require sharing a relevant group intention, which has been defined as a We-intention. In forming We-intentions, breakdown situations due to conflicts between internal and “external” intentions are unavoidable, particularly in healthcare scenarios. To study such We-intention formation and “reparation” of conflicts, this paper has a two-fold objective: introduce a general computational mechanism allowing We-intention formation and reparation in interactions between a social robot and a person; and exemplify how the formal framework can be applied to facilitate interaction between a person and a social robot for healthcare scenarios.

Method: The formal computational framework for managing We-intentions was defined in terms of Answer set programming and a Belief-Desire-Intention control loop. We exemplify the formal framework based on earlier theory-based user studies consisting of human-robot dialogue scenarios conducted in a Wizard of Oz setup, video-recorded and evaluated with 20 participants. Data was collected through semi-structured interviews, which were analyzed qualitatively using thematic analysis. N=20 participants (women n=12, men=8, age range 23-72) were part of the study. Two age groups were established for the analysis: younger participants (ages 23-40) and older participants (ages 41-72).

Results: We proved four theoretical propositions, which are well-desired characteristics of any rational social robot. In our study, most participants suggested that people were the cause of breakdown situations. Over half of the young participants perceived the social robot's avoidant behavior in the scenarios.

Conclusions: This work covered in depth the challenge of aligning the intentions of two agents (for example, in a person-robot interaction) when they try to achieve a joint goal. Our framework provides a novel formalization of the We-intentions theory from social science. The framework is supported by formal properties proving that our computational mechanism generates consistent potential plans. At the same time, the agent can handle incomplete and inconsistent intentions shared by another agent (for example, a person). Finally, our qualitative results suggested that this approach could provide an acceptable level of action/intention agreement generation and reparation from a person-centric perspective.

Functional Status Information (FSI) describes physical and mental wellness at the whole-person level. It includes information on activity performance, social role participation, and environmental and personal factors that affect the well-being and quality of life. Collecting and analyzing this information is critical to address the needs for caring for an aging global population, and to provide effective care for individuals with chronic conditions, multi-morbidity, and disability. Personal knowledge graphs (PKGs) represent a suitable way for meaning in a complete and structured way all information related to people’s FSI and reasoning over them to build tailored coaching solutions supporting them in daily life for conducting a healthy living. In this paper, we present the development process related to the creation of a PKG by starting from the HeLiS ontology in order to enable the design of an AI-enabled system with the aim of increasing, within people, the self-awareness of their own functional status. In particular, we focus on the three modules extending the HeLiS ontology aiming to represent (i) enablers and (ii) barriers playing potential roles in improving (or deteriorating) own functional status and (iii) arguments driving the FSI collection process. Finally, we show how these modules have been instantiated into real-world scenarios.