Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

The Use of Augmented Reality in Retail: A Review of Literature

Novel digital technologies are affording ways to superimpose perceptual information (be it auditory, visual, haptic or olfactory) onto our reality, e.g. in retail environments. These technologies that aim to enhance reality are generally called Augmented Reality (AR) technologies. Today, the field of research focused on AR retail has evolved to mature enough state that an overview of the state-of-the-art, results and ways in which AR has been employed in research is needed. Therefore, in this study we conduct a systematic literature review of the academic corpus focused on AR retail. We report on how and where AR is employed in retail, what technological characteristics of AR are commonly analyzed as well as what potential psychological and behavioral outcomes AR is capable of evoking. Overall, AR is a technology with high potential for in-store and remote (online) shopping in terms of evoking both utilitarian and hedonic experiences

Using augmented reality for shopping : a framework for AR induced consumer behavior, literature review and future agenda

Purpose A current technological trend, which has gained even more traction recently due to the COVID-19 pandemic, is the use of augmented reality (AR) in shopping environments. AR is addressing contemporary challenges rooted in online shopping (e.g. in terms of experientiality and try-on) and is fundamentally reshaping consumers' experiences. The purpose of this study is to provide a synthesized and structured overview of the state-of-the-art research focused on AR shopping. Design/methodology/approach The authors conduct a systematic literature review of the empirical academic corpus focused on shopping via AR technology. Findings The review reveals the diverse psychological (cognitive, affective, and social) as well as behavioral outcomes related to the use of AR in the shopping context. The authors integrate the results into a framework for AR induced consumer behavior in shopping, thereby providing an important overview of the dynamics in AR-related shopping and the factors influencing the adoption of the technology by consumers. Specifically, the authors encountered that the technological abilities of AR (e.g. in terms of interactivity, vividness, informativeness, etc.) are a source for enhanced utilitarian and hedonic shopping experiences that can support intentions to purchase a product, reuse an AR app, or recommend it to others. Importantly, our review reveals the demand for several avenues for future research. Originality/value The authors provide an overview and synthesis of how and where AR is employed in shopping contexts, what theories and technological characteristics of AR are commonly analyzed, and what psychological and behavioral outcomes AR has been found to evoke. Based on our findings, the authors derive a framework that illustrates the dynamics in AR shopping and give an in-depth discourse on 13 future research agenda points related to thematic, theoretical, methodological, and technological matters.©2022 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis.

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis

High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations.

The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data and RNA sequencing from a common set of 1220 cancer cases, we report hundreds of genes for which the presence within 100 kb of an SV breakpoint associates with altered expression. For the majority of these genes, expression increases rather than decreases with corresponding breakpoint events. Up-regulated cancer-associated genes impacted by this phenomenon include TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. TERT-associated breakpoints involve ~3% of cases, most frequently in liver biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involve ~1% of non-amplified cases. For many genes, SVs are significantly associated with increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the promoter is often increased with nearby SV breakpoint, which may involve inactivation of repressor elements

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Triggers of Collaborative Innovation in Online User Communities

This study contributes to the understanding of collaborative innovation in online user communities. Aside from providing evidence for the existence of these communities, prior research focused on users&#8217; motivations, backgrounds, and roles at the micro level but largely neglected to examine the effects of individual user activities on joint activities at the community level. By applying a netnographic research design, which is followed by a content analysis step and logistic regression analysis, we explore to what degree different user activities trigger collaborative innovation inside a community. We find two factors inherent to the initial post of a thread, problem complexity and collaboration intention, which explain the probability of collaborative innovation. The likelihood of joint activities is raised significantly if the contribution of a user ranks high on both dimensions. By quantifying collaborative user innovation, we hope to encourage the inclusion of user activities in future policy considerations. Moreover, understanding the effects of individual user activities at the community level may help companies to understand users of technologies better and to identify opportunities for collaboration

A Taxonomy of Virtual Reality Applications for the Treatment of Anxiety Disorders

Anxiety disorders are one of the most common psychiatric disorders. Exposure therapy has proven to be a suitable way of treatment for anxiety symptoms. Currently, there are ambitions to digitalise exposure therapy based on Virtual Reality (VR). While a variety of studies verify the effectiveness of Virtual Reality Exposure Therapy (VRET), less research focus on the application of VRET in practice. The objective of this paper is to identify and compare VRET application scenarios from a theoretical and empirical perspective to develop a taxonomy of VRET applications. The findings suggest that companies should focus on holistic VR environments, concentrate their applications on specific age groups and integrate features for the treatment of mild symptoms. Research opportunities occur with regards to the examination of the effectiveness of VRET for mild symptoms and VRET as a new type of stand-alone therapy

Towards Virtual 3D Asset Price Prediction Based on Machine Learning

Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators