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BackgroundWearable trackers for monitoring physical activity (PA) and total sleep time (TST) are increasingly popular. These devices are used not only by consumers to monitor their behavior but also by researchers to track the behavior of large samples and by health professionals to implement interventions aimed at health promotion and to remotely monitor patients. However, high costs and accuracy concerns may be barriers to widespread adoption. ObjectiveThis study aimed to investigate the concurrent validity of 6 low-cost activity trackers for measuring steps, moderate-to-vigorous physical activity (MVPA), and TST: Geonaut On Coach, iWown i5 Plus, MyKronoz ZeFit4, Nokia GO, VeryFit 2.0, and Xiaomi MiBand 2. MethodsA free-living protocol was used in which 20 adults engaged in their usual daily activities and sleep. For 3 days and 3 nights, they simultaneously wore a low-cost tracker and a high-cost tracker (Fitbit Charge HR) on the nondominant wrist. Participants wore an ActiGraph GT3X+ accelerometer on the hip at daytime and a BodyMedia SenseWear device on the nondominant upper arm at nighttime. Validity was assessed by comparing each tracker with the ActiGraph GT3X+ and BodyMedia SenseWear using mean absolute percentage error scores, correlations, and Bland-Altman plots in IBM SPSS 24.0. ResultsLarge variations were shown between trackers. Low-cost trackers showed moderate-to-strong correlations (Spearman r=0.53-0.91) and low-to-good agreement (intraclass correlation coefficient [ICC]=0.51-0.90) for measuring steps. Weak-to-moderate correlations (Spearman r=0.24-0.56) and low agreement (ICC=0.18-0.56) were shown for measuring MVPA. For measuring TST, the low-cost trackers showed weak-to-strong correlations (Spearman r=0.04-0.73) and low agreement (ICC=0.05-0.52). The Bland-Altman plot revealed a variation between overcounting and undercounting for measuring steps, MVPA, and TST, depending on the used low-cost tracker. None of the trackers, including Fitbit (a high-cost tracker), showed high validity to measure MVPA. ConclusionsThis study was the first to examine the concurrent validity of low-cost trackers. Validity was strongest for the measurement of steps; there was evidence of validity for measurement of sleep in some trackers, and validity for measurement of MVPA time was weak throughout all devices. Validity ranged between devices, with Xiaomi having the highest validity for measurement of steps and VeryFit performing relatively strong across both sleep and steps domains. Low-cost trackers hold promise for monitoring and measurement of movement and sleep behaviors, both for consumers and researchers.

Sharing, reading and responding to personal narratives on peer-to-peer support websites may provide adolescents with informational and emotional support to feel more confident in coping with stressful events. However, their use may also pose a threat to adolescents' coping self-efficacy. Principles of expressive writing, social sharing of emotions, narrative persuasion and self-effects may provide insight in how these actions may both positively and negatively relate to coping self-efficacy. By using a cross-sectional online survey with 311 Dutch-speaking adolescents between the ages of 14 and 18, this article explores how these actions and social support motives (i.e. information-seeking and emotional support-seeking) are related to adolescents' perceptions about the usefulness of these websites to their coping self-efficacy. The results showed a positive relation between adolescents' social support motives and their belief in the usefulness of these websites to their coping self-efficacy. Therefore, we conclude that it may be an effective coping strategy for many adolescents. There was a negative relation between experience with sharing a personal narrative and coping self-efficacy, meaning that these users did not perceive the website to be helpful to their overall confidence in coping with stressful events. However, this negative relation was reversed when they were motivated to find emotional support with similar others.

The motto of the conspiracist, "Do your own research," may seem ludicrous to scientists. Indeed, it is often dismissed as a mere rhetorical device that conspiracists use to give themselves the semblance of science. In this perspective paper, we explore the information-seeking activities ("research") that conspiracists do engage in. Drawing on the experimental psychology of aha experiences, we explain how these activities, as well as the epistemic experiences that precede (curiosity) or follow (insight or "aha" experiences) them, may play a crucial role in the appeal and development of conspiracy beliefs. Aha moments have properties that can be exploited by conspiracy theories, such as the potential for false but seemingly grounded conclusions. Finally, we hypothesize that the need for autonomous epistemic agency and discovery is universal but increases as people experience more uncertainty and/or feel epistemically excluded in society, hence linking it to existing literature on explaining conspiracy theories. Recent events have made it painfully clear that conspiracy beliefs can tear deep rifts in society and that we still have not found an adequate, de-escalating response to this. To understand the appeal of conspiracy theories and find new, humanizing ways to talk about them, we propose in this perspective paper to start from the universal human need to autonomously make discoveries through personal knowledge-generating actions. Indeed, psychological research shows that the aha experiences that accompany subjective discoveries create confidence in and perceived ownership of ideas that may be exploited by conspiracy theories. We hypothesize that people experiencing more uncertainty and/or epistemic exclusion in society will especially feel the need to re-establish autonomous epistemic agency and discovery. While this explanation starts from shared human experiences and practices, it also illustrates the potential of those processes to lead to a narrowed world and ossified cognition. [ABSTRACT FROM AUTHOR]

Online hate speech (OHS) is a prevalent issue on social media with harmful consequences for individuals and society. Counteracting this issue requires a better understanding of the factors that drive behaviors such as posting hate speech or making hateful comments. The present study contributes to this need by investigating the personal, social, and digital determinants for OHS perpetration. By conducting a two‐round Delphi study, data was collected from an interdisciplinary sample of experts. Practitioners were included in this sample to bridge gaps between research and fieldwork on OHS. The first survey collected information through open‐ended questions, which was then organized in different themes and lists of determinants in a thematic analysis. These determinants were rated for relevance in a follow‐up survey. Data analysis of the second survey used nonparametric statistics. From a total list of 129 determinants, the participants reached a consensus on 27 determinants as most relevant (median > 4) for facilitating the production and dissemination of OHS. An ecological perspective was applied to interpret these findings. As a result, we propose an ecological model of personal, social, and digital determinants for online hate perpetration to illustrate their influence on individual behavior as well as their possible connections. The model provides a comprehensive overview of factors facilitating OHS perpetration, which can help to identify points for intervention. [ABSTRACT FROM AUTHOR]

(131w) As news media developed native advertising to attract advertisers, they depend on an increasing cost (time and energy) of news users to engage with it. Thus, they have to take news users' needs into account as well. Following stakeholder marketing and service-dominant logic, this paper contends that the news ecosystem is a service ecosystem with value exchanged and co-created between stakeholders. Through in-depth interviews with users from a popular free news app, this study explores how news users perceive the value exchange of native advertising on news sites and the perceived value of individual native advertisements. The perceived value is determined by news users' goal to consume newsworthy and informative content. To cater for this goal, native advertising needs to properly align content, advertiser type and ad position in the newsfeed. [ABSTRACT FROM AUTHOR]

Communicating about new or unknown health risks is challenging because it requires audiences to engage with and process novel and often complex health information. This study examines how texts can convey awareness and increase knowledge about health risks people are unaware of. The focus is on how text genre (narrative, expository, and mixed-genre) affects relevant emotional (arousal, transportation) and cognitive outcomes (knowledge and risk severity), measured using both online (electrodermal activity) and offline self-report measures. Mixed-effects model analyses revealed that narrative texts exhibit the highest self-reported arousal, transportation, and risk severity. Additionally, transportation mediates the relationship between text genre and risk severity. Ultimately, mixed-genre texts produced significantly higher arousal peaks and confidence ratings on knowledge posttests compared to expository texts. Taken together, the findings suggest that narrative texts perform better at raising awareness, whereas mixed-genre texts seem more effective in learning. The implications for health risk communication are discussed. [ABSTRACT FROM AUTHOR]

Digital disconnection has emerged as a concept describing the actions people take to limit their digital connectivity to enhance their well-being. To date, evidence on its effectiveness is mixed, leading to calls for greater consideration of why, how, when, and for whom digital disconnection works. This article responds to these calls, presenting a framework that differentiates four key harms that contribute to experiences of digital ill-being (time displacement, interference, role blurring, and exposure effects). Using these four harms as a starting point, the framework explains: (1) why people are motivated to digitally disconnect; (2) how specific disconnection strategies (i.e. placing limits on time, access, channels, and contents, interactions and features) may help them; and for whom (3) and under which conditions (when) these strategies can be effective. [ABSTRACT FROM AUTHOR]

Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally. [ABSTRACT FROM AUTHOR]