Technology-driven dyadic interaction in caregiving.

BACKGROUND: Due to the ongoing pandemic and the resulting community lockdowns, people with dementia and their family might be at risk of social deprivation and increased relationship strains. Technological means have the potential to engage participants in meaningful positive interactions. The tablet-based activation system I-CARE offers social activities specifically designed for people with dementia and their caregivers, by offering user-specific contents adapted to their needs and sensitivities. Little is known about the impact of Covid-19 on social health for this population. The ongoing study, presented as a part of the Marie-Curie Innovative-Training-Network action, H2020-MSCA-ITN, grant agreement 813196, assesses how COVID-19 has impacted community-dwelling dementia caregiving dyads. Contextual factors of technology use and motivations for inviting technology into social interactions is explored. METHOD: As a part an ongoing pre-post mixed-methods feasibility study, baseline assessments through semi-structured interviews were conducted and subjected to inductive thematic statement analysis by two independent researchers. RESULT: Participants differed in how COVID-19 restrictions impacted their lives and how they coped with dementia, revealing different motivations for inviting technology into their lives. Dyads who were socially active pre-COVID-19, and who managed use technology to maintain social participation during COVID-19, reported to have been less negatively impacted by COVID-19 restrictions. Four subthemes within "Social technology during COVID-19" were identified. CONCLUSION: During and beyond this pandemic, social technology is a valuable tool to promote social participation in this population. Successful uptake of technology is dependent on customizing to individuals' needs and conditions.

I-CARE-An Interaction System for the Individual Activation of People with Dementia.

I-CARE is a hand-held activation system that allows professional and informal caregivers to cognitively and socially activate people with dementia in joint activation sessions without special training or expertise. I-CARE consists of an easy-to-use tablet application that presents activation content and a server-based backend system that securely manages the contents and events of activation sessions. It tracks various sources of explicit and implicit feedback from user interactions and different sensors to estimate which content is successful in activating individual users. Over the course of use, I-CARE's recommendation system learns about the individual needs and resources of its users and automatically personalizes the activation content. In addition, information about past sessions can be retrieved such that activations seamlessly build on previous sessions while eligible stakeholders are informed about the current state of care and daily form of their protegees. In addition, caregivers can connect with supervisors and professionals through the I-CARE remote calling feature, to get activation sessions tracked in real time via audio and video support. In this way, I-CARE provides technical support for a decentralized and spontaneous formation of ad hoc activation groups and fosters tight engagement of the social network and caring community. By these means, I-CARE promotes new care infrastructures in the community and the neighborhood as well as relieves professional and informal caregivers.

Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research.

The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions, and in naturalistic choice settings. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as a starting point for further analyses. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

Predicting altcoin returns using social media.

Cryptocurrencies have recently received large media interest. Especially the great fluctuations in price have attracted such attention. Behavioral sciences and related scientific literature provide evidence that there is a close relationship between social media and price fluctuations of cryptocurrencies. This particularly applies to smaller currencies, which can be substantially influenced by references on Twitter. Although these so-called "altcoins" often have smaller trading volumes they sometimes attract large attention on social media. Here, we show that fluctuations in altcoins can be predicted from social media. In order to do this, we collected a dataset containing prices and the social media activity of 181 altcoins in the form of 426,520 tweets over a timeframe of 71 days. The containing public mood was then estimated using sentiment analysis. To predict altcoin returns, we carried out linear regression analyses based on 45 days of data. We showed that short-term returns can be predicted from activity and sentiments on Twitter.