Today, social media play an important role in people’s daily lives. Many people use social media to satisfy their personal and social needs, such as enhancing self-image, acquiring self-esteem, and gaining popularity. However, when social media are used obsessively and excessively, behavioural addiction symptoms can occur, leading to negative impacts on one’s life, which is defined as a problematic attachment to social media. Research suggests that tools can be provided to assist the change of problematic attachment behaviour, but it remains unclear how such tools should be designed and personalised to meet individual needs and profiles. This study makes the first attempt to tackle this problem by developing five behavioural archetypes, characterising how social media users differ in their problematic attachments to them. The archetypes are meant to facilitate effective ideation, creativity, and communication during the design process and helping the elicitation and customisation of the variability in the requirements and design of behaviour change tools for combatting problematic usage of social media. This was achieved by using a four-phase qualitative study where the diary study method was considered at the initial stage, and also the refinement and confirmation stage, to enhance ecological validity.

Procrastination on social networking sites (SNS) can impact academic performance and user's well-being. SNSs embed features that encourage users to be always connected and updated, e.g., the notification features. Such persuasive features can exploit peer pressure as well and lead users to believe they are expected to interact immediately, especially for those who may have less impulse control and seek for relatedness and popularity. We argue that SNS can be built to host countermeasures for such behavior and help people regulate their usage and preoccupation about it better. In this paper, we presented a mixed-method study including a qualitative (i.e., focus groups, diary, interviews, and co-design) and a quantitative phase (i.e., a survey) with 334 participants. Through the qualitative phase, we identified: (1) features of an SNS seen by participants as facilitators for procrastination, e.g., notification, immersive design, and surveillance of presence, and (2) countermeasures, such as reminders, chat timer, and goal setting, can be facilitated via SNS design to combat procrastination, and (3) a pairing between the features and the countermeasures. We then (4) confirmed these results and the pairing through the survey phase. Our study showed that countermeasures could be implemented to be universal across all SNS on one or even more device.

People's relationship with social media and their contacts on them can be problematic. People may engage in social media in a compulsive and hasty style to increase their popularity, reputation and enhance their self-esteem. However, this problematic attachment to social media may result in side effects on people's well-being. Therefore, people may need assistance to reform their relationship with social media in a way that it maintains different aspects of their online interaction, such as empathy with others and maintaining their popularity and relatedness. In order to provide the tools and methods to support people in reforming their relationship with social media, towards a healthier usage style, we need to understand the experience of people who suffer a problematic relationship with them. Most studies on the topic are based on methods which would lack ecological validity, e.g. using surveys and interviews, and do not capture or imitate such a digital experience as lived. In an attempt to better explore how people experience problematic attachment and relationship with social media, and their associated emotions, we conducted a multistage qualitative method study including a diary study to gather lived experience. We aim to inform both users and designers towards a managed and tool-supported reform of their problematic relationship with social media and, ultimately, having a healthier online interaction.

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users’ sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. The mental health apps were classified into two categories: guidance-based and tracking-based apps. The results include the increased number of users of mental health apps during the COVID-19 period as compared to pre-COVID-19. Adults (aged 24 and above), compared to emerging adults (aged 15–24 years), were found to have a higher usage of overall mental health apps and guidance-based mental health apps. Furthermore, during the COVID-19 pandemic, males were found to be more likely to launch overall mental health apps and guidance-based mental health apps compared to females. The findings from this paper suggest that despite the increased usage of mental health apps amongst males and adults, user engagement with mental health apps remained minimal. This suggests the need for these apps to work towards improved user engagement and retention.

Many people worldwide rely on social media to satisfy their social needs for relatedness, learning and enhancing selfesteem. However, over-reliance on social media often results in problematic attachment, which risks personal, social and financial wellbeing. From a design perspective, we argue that social media can be improved with tools to manage such problematic attachment and help users reform their interaction style, social expectations and online identity to restore a healthy reliance. Designing such behaviour change tools can be challenging due to the characteristics of people with problematic behaviours, e.g. denial, relapse and cognitive dissonance. This paper explores the role of social media in such attachment and reveals associated psychological states. Our method provides an ecologically valid exploration through employing diary studies as a data collection method, aiming to introduce countermeasures for problematic attachment in future social media design.

Social technology, in particular, social media has increasingly become part of individual's life. People rely on social media interaction to relate with other people and satisfy their leisure or behavioural support purposes. Features such as those presented in the Honeycomb ecology, e.g. sharing, profiling and reputation, are used as means to that end. Recent designs of technology embed social features in them, enabling people to talk to the machine and interact with it as a sentient being, e.g. chatbot and robots, or use it as a mediator for social interaction, e.g. Social Internet of Things (SIoT). However, we argue that over-reliance on social media may result in a problematic attachment to it leading to a negative impact on users' wellbeing. We conduct a multi-stages study to explore the negative psychological states that social media features can trigger. We mainly employ diary studies trying to increase the ecological validity of our data. We also discuss the enrichment of social media with tools to aid people in staying in control of their attachment and the risks such tools can introduce to user experience as well as their behaviour change process.

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Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.