On the need for cultural sensitivity in digital wellbeing tools and messages: A UK-China comparison

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020. The excessive and obsessive use of the internet and digital technologies, known as Digital Addiction (DA), is becoming a social issue. Given that it inherently involves the use of technological devices this provides the opportunity to deliver interactive, intelligent prevention and intervention strategies in real-time. However, for any large-scale, multi-national prevention campaign to be optimised cultural differences within the target population must be considered. This study aimed to contribute towards this literature by exploring cultural differences in the acceptance of DA prevention messages in the UK vs China. An initial series of exploratory interviews were conducted with a sample within the UK to determine what strategies may be used to address the overuse of digital devices. These interviews were subjected to content analysis, which was then used as the basis for an online survey that was disseminated throughout the UK and China. A total of 373 useable surveys were returned. There were several statistically significant differences in preferences over how an intervention system should operate. UK participants wished for the system to be easily under their control, whilst behaving largely autonomously when needed, and to also be transparent as to why a message had been triggered. Chinese participants, on the other hand, were less likely to state a preference for such a high degree of control over any such system. Overall, the preferred implementation of such systems does appear to vary between the UK and China, suggesting that any future prevention and intervention strategies take cultural dimensions into consideration

Perceptions and misperceptions of smartphone use: Applying the social norms approach

The social norms approach is an established technique to bring about behaviour change through challenging misperceptions of peer behaviour. This approach is limited by a reliance on self-report and a lack of interactivity with the target population. At the same time, excessive use of digital devices, known as digital addiction, has been recognized as an emergent issue. There is potential to apply the social norms approach to digital addiction and, in doing so, address some of the limitations of the social norms field. In this study, we trialled a social norms intervention with a sample of smartphone users (n = 94) recruited from the users of a commercial app designed to empower individuals to reduce their device usage. Our results indicate that most of the sample overestimated peer use of smartphone apps, demonstrating the existence of misperceptions relating to smartphone use. Such misperceptions are the basis for the social norms approach. We also document the discrepancy between self-report and smartphone usage data as recorded through data collected directly from the device. The potential for the application of the social norms approach and directions for future research are discussed

Digital wellbeing tools through users lens

There is a growing recognition of excessive, compulsive, and hasty use of technology as an emerging form of problematic behavior affecting individuals' emotional, social, and occupational wellbeing. Smartphone overuse, in particular, has been linked to negative effects on users' quality of life, such as anxiety, depression, sleep disturbance and loss in productivity. One strategy to help regulate digital usage and, potentially, increase digital wellbeing is to devise smartphone applications to collect data about usage and increase users' awareness of it and enable them to set limits and alert users accordingly. However, such applications have not been extensively evaluated from the users' perspective and whether they help the basic requirements for digital wellbeing. In this paper, we examine the quality of the emerging family of digital wellbeing smartphone applications from the users' perspective and based on persuasive design and established behavioral change theories. We performed a thematic analysis on the users’ reviews on two popular applications, SPACE Break Phone Addiction and Google Digital Wellbeing (GDW). We report on the factors influencing user acceptance and rejection towards digital wellbeing applications and identify possible challenges and opportunities to improve their design and role in future releases

Problematic internet usage: the impact of objectively Recorded and categorized usage time, emotional intelligence components and subjective happiness about usage

Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI (sociability, emotionality, well-being, self-control), and happiness with phone use can predict PIU and its components (obsession, neglect, and control disorder). A total of 268 participants (Female: 61.6%) reported their demographic and completed a questionnaire that included Problematic Internet Usage Questionnaire short form (PIUQ–SF–6), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), level of happiness with the amount and frequency of smartphone use, and living conditions (whether alone or with others). Their smartphone usage was objectively recorded through a dedicated app. A series of one-way ANOVA revealed no significant difference in PIU for different living conditions and a significant difference in the subjective level of happiness with phone usage (F (3, 264) = 7.55, p < .001), as well as of the frequency of usage where the unhappy group had higher PIU (F (3, 264) = 6.85, p < .001). Multiple linear regression analysis showed that happiness with phone usage (β = −.17), the actual usage of communication (β = .17), social media (β = .19) and gaming apps (β = .13), and trait EI component of self-control (β = −.28) were all significant predictors of PIU. Moreover, gender, age, and happiness with the frequency of phone usage were not significant predictors of PIU. The whole model accounted for the total variance of PIU by 32.5% (Adjusted R2 = .287). Our study contributes to the literature by being among the few to rely on objectively recorded smartphone usage data and utilizing components of trait EI as predictors