Increasing dependency on digital technologies introduces queries related to well-being–when and how digital technology poses a threat, or when it is the most appreciated by users? People have some expectations before using technology, which sometimes may be met, but there might be a mismatch at other times. Nevertheless, the digitalization of services advances and companies modify existing or produce new technologies that do not satisfy users' demands, putting their well-being at risk. Through an empirical inquiry, the present research explores experiences with technologies to shed more light on the main factors that enrich or diminish technology value and influence well-being. Exploiting the circumstances created by the COVID-19 pandemic that fast-forwarded technology use and development, an online inquiry was conducted to assess positive and negative experiences of technologies, focusing on the contexts of work, learning, entertainment, information-seeking, and health. The findings from 578 participants indicate that depending on the role of technologybeing a substitute for certain activities or an opportunity to perform them differently–consequences on well-being can more or less follow expectations. The results are discussed in the context of past research and practical implications for, e.g. technology design or managerial changes that could help overcome users' current challenges. 

Background Older adults are insufficiently physically active, despite its importance for healthy aging. To develop appropriate physical activity interventions, it is necessary to understand their physical activity. This study applies a theoretical perspective, the COM-B model, and a mixed-method design to examine what influences older adults' physical activity levels with three questions: (1) What individual and external factors predict older adults' physical activity levels? (2) What do older adults perceive as influencing their levels of physical activity? (3) To what extent do the quantitative results on older adults' physical activity levels agree and disagree with the qualitative findings on older adults' physical activity levels? Methods A convergent mixed-method design was used with questionnaire (n = 334) and interview (n = 14) data from adults 65 years and older. Regression analyses were used for quantitative measurements: physical activity, age, subjective socioeconomic status, health status, capability, opportunity, motivation, and depression. Content analysis was applied to the qualitative data. The two forms of data were then integrated to provide greater insights than would be obtained by either dataset separately. Results The regression analyses showed that previous physical activity, current motivation, health status, and age significantly predicted older adults' physical activity levels. The content analysis revealed that participants addressed all subcomponents of the COM-B model, indicating its pertinence in understanding how older adults discuss their current physical activity levels. The integrated findings showed convergent and divergent results. Overall results indicated that previous physical activity engagement, present motivation, capability, and opportunity influenced older adults' physical activity levels. Conclusions This study is the first to use this mixed-methods design to examine factors influencing physical activity levels among older adults living in rental apartments with community hosts. The integrated result reveals convergence for findings on motivation and physical capability but divergence on psychological capability, opportunity, and previous physical activity. The findings underscore a complex interplay of factors influencing older adults' physical activity levels and indicate relevance for the COM-B model. The results can guide future research on theoretically informed interventions to promote physical activity and healthy aging. Future research should clarify the role of opportunity for older adults' physical activity.

Consenting to digital services’ privacy policies is standard practice. It often occurs at the early stage of interactions with a given service—during the sign-up process. Still, the most common way of acquiring consent from users is through their acknowledgment of policies by ticking a box. Consequently, users consent, mostly blindly, as they are unlikely to review the full text of policies. The current article presents research investigating factors that may impact user interaction with privacy policies, focusing on the underresearched topic of affective states (valence and arousal). The results of an online experiment (N=88) indicate that privacy policy design can elicit specific affective responses and, when accounting for some characteristics of individuals (e.g., personality traits), it can influence users’ attitudes and behaviors. Particularly, the findings show that privacy awareness and willingness to disclose information might be impacted. Additionally, the analysis of collected data suggests significant associations between some personality traits and affective states, as well as a strong relationship between privacy concerns and willingness to disclose information, contradicting the concept of privacy paradox, often discussed in the privacy literature. Moreover, the results of our qualitative inquiry, where the study respondents had a chance to elaborate on their decisions to agree or disagree with the privacy policy by answering an open-ended question, confirm the quantitative findings, and reveal some of the users needs considering the sign-up process.

In this short paper we describe the implementation of an IoT test-bed in an elementary school. We argue that by adding additional IoT senors to an existing IoT system it is possible to evolve an indoor climate control system into a indoor milieu control system aimed at improving the health and well-being for both pupils and staff who spend their days working in the school environment. Lastly, we call for multidisciplinary action as the domain IoT for health and well-being spans across several different knowledge domains.

Introduction: The study aimed to explore characteristics of responders to a sleep robot intervention for adults with insomnia, and the likelihood that participants responded to the intervention. Methods: Data from the intervention and the control group in a randomized waitlist-controlled trial (n = 44) were pooled together after both had undergone the intervention. A repeated measures ANOVA and Friedman tests were used to explore changes over time. Differences in baseline characteristics between responders (n = 13), defined as a reduction of -5 on the Insomnia Severity Index from pre- to post-intervention, and non-responders (n = 31) were analyzed with t-tests and chi-square tests. Finally, logistic regression models were estimated.Results: Baseline anxiety was the only statistically significant difference between responders and non-responders (p = 0.03). A logistic regression model with anxiety and sleep quality as predictors was statistically significant, correctly classifying 83.3% of cases. Discussion: The results imply that people with lower anxiety and higher sleep quality at baseline are more likely to report clinically significant improvements in insomnia from the sleep robot intervention.

Background: Digital health interventions, such as personalized SMS coaching, are considered affordable and scalable methods to support healthy lifestyle changes. SMS, or texting, is a readily available service to most people in Sweden, and personalizedSMS coaching has shown great promise in supporting behavior changes.

Objective: This study aims to explore the effectiveness of highly personalized SMS coaching for behavior change according to the Capability, Opportunity, Motivation–Behavior (COM-B) model on a sample of physically inactive adults in a nonprofit fitness organization in Sweden.

Methods: The study used a mixed methods design in which clients acted as their own controls. The participants were clients(n=28) and fitness consultants (n=12). Three types of data were collected: (1) quantitative data at baseline and after the SMS intervention and the waitlist from the clients, (2) qualitative data from semistructured interviews with the fitness consultants, and(3) pseudonymized texting conversations between the fitness consultants and clients.

Results: Overall, the results showed that personalized SMS coaching was effective in supporting the clients’ behavior changes. The quantitative analysis showed how the clients’ capabilities (Cohen d=0.50), opportunities (Cohen d=0.43), and relationship with the fitness consultants (Cohen d=0.51) improved during the SMS intervention in comparison with baseline. Furthermore, the qualitative analysis revealed how personalized texts added value to existing work methods (eg, increasing continuity and flexibility) and how the relationship between the clients and fitness consultants changed during the intervention, which helped motivate the clients.

Conclusions: Personalized SMS coaching is an effective method for supporting healthy behavior changes. The human connection that emerged in this study needs to be further explored to fully understand the effectiveness of a digital health intervention.

The popularity of health and well-being applications is on the rise. These applications often collect and process personal and sensitive information about individuals for different purposes. Nonetheless, users’ freedom of choice around protection and sharing may be compromised, for example, by forwarding such information to third parties without user awareness or control, especially with current developments in AI-based complex data processing technologies. Despite the popularity of health and well-being applications, the purpose of sharing and its interplay with trust, privacy concerns, and their antecedents is seldom explored. The present article addresses this gap by conducting an online study (N = 315), which shows that, generally, people’s sharing preferences depend on their trust and privacy concerns. However, the findings indicate potential differences between male and female participants considering sharing information for the greater good, such as scientific research. Our findings are discussed in light of sociocultural and social role theories of prosocial behavior and previous research. Considering the growing interest in building usable infrastructures for data sharing and user empowerment, practical implications for designers and policymakers are highlighted. 

In a well-functioning digital economy, consumers should be able to make autonomous and informed choices, and companies compete fairly. One of the barriers preventing such well-functioning is dark patterns—designs that mislead users into making specific purchase-related choices. In this research, through a qualitative inquiry (expert interviews), we classify dark patterns based on the harmful ways such designs affect the digital market. Moreover, we analyze data using the behavior change framework and illustrate ways to prevent dark patterns and grant consumers greater protection and autonomy. Our exploratory results outline potential solutions policymakers might apply to improve digital market well-functioning.

For new services to be successful, users (customers, patients, citizens, etc.) must embrace and sometimes engage in behaviors that are both new and difficult for them. As research has put more and more emphasis on the importance of being user-centric, it is hard to understand why the behavior change that many times are required for the user has not been problematized more. In this chapter we present five evidence-based ways that describe and explain how behavior change can enable important services such as sustainability, education, and health—services make the world a better place. 

We propose a tentative research plan to increase students’ mental health

in elementary schools by implementing Internet of Things (IoT) technology. The

research plan should answer how to support students’ mental health using IoT

solutions and the critical factors influencing testbeds for IoT solutions with the

previously mentioned purpose. Our intended research method is Design Science,

which we plan to use stepwise.