How should a virtual agent present psychoeducation?

BACKGROUND AND OBJECTIVE: With the rise of autonomous e-mental health applications, virtual agents can play a major role in improving trustworthiness, therapy outcome and adherence. In these applications, it is important that patients adhere in the sense that they perform the tasks, but also that they adhere to the specific recommendations on how to do them well. One important construct in improving adherence is psychoeducation, information on the why and how of therapeutic interventions. In an e-mental health context, this can be delivered in two different ways: verbally by a (virtual) embodied conversational agent or just via text on the scree

Combination of a six microRNA expression profile with four clinicopathological factors for response prediction of systemic treatment in patients with advanced colorectal cancer

Background First line chemotherapy is effective in 75 to 80% of patients with metastatic colorectal cancer (mCRC). We studied whether microRNA (miR) expression profiles can predict treatment outcome for first line fluoropyrimidine containing systemic therapy in patients with mCRC. Methods MiR expression levels were determined by next generation sequencing from snap frozen tumor samples of 88 patients with mCRC. Predictive miRs were selected with penalized logistic regression and posterior forward selection. The prediction co-efficients of the miRs were re-estimated and validated by real-time quantitative PCR in an independent cohort of 81 patients with mCRC. Results Expression levels of miR-17-5p, miR-20a-5p, miR-30a-5p, miR-92a-3p, miR-92b-3p and miR-98-5p in combination with age, tumor differentiation, adjuvant therapy and type of systemic treatment, were predictive for clinical benefit in the training cohort with an AUC of 0.78. In the validation cohort the addition of the six miR signature to the four clinicopathological factors demonstrated a significant increased AUC for predicting treatment response versus those with stable disease (SD) from 0.79 to 0.90. The increase for predicting treatment response versus progressive disease (PD) and for patients with SD versus those with PD was not significant. in the validation cohort. MiR-17-5p, miR-20a-5p and miR-92a-3p were significantly upregulated in patients with treatment response in both the training and validation cohorts. Conclusion A six miR exp

A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals

WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait

Skewed X-inactivation is common in the general female population

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants

Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior - Data, Analysis Code and Appendix

This repository contains the data, analysis code, and appendix of the paper "Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior" by Nele Albers, Mark A. Neerincx, and Willem-Paul Brinkman, published in Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023). Data The paper is based on data collected during a study on the online crowdsourcing platform Prolific run between 20 May 2021 and 30 June 2021. The Human Research Ethics Committee of Delft University of Technology granted ethical approval for the research (Letter of Approval number: 1523). In this study, smokers who were contemplating or preparing to quit smoking interacted with the text-based virtual coach Sam in up to five conversational sessions. In each session, participants were assigned a new preparatory activity for quitting smoking, such as thinking of and writing down reasons for quitting smoking. Since becoming more physically active may make it easier to quit smoking, half of the activities addressed becoming more physically active. The virtual coach chose from five persuasive strategies to persuade people to do their activity. In the first two sessions, the persuasive strategy was chosen uniformly at random; in the last three sessions, the persuasive strategy was determined by a persuasion algorithm that differed between four conditions. In the next session, participants were asked to indicate the effort they spent on their activity, which served as basis for the reward signal for the persuasion algorithm.  The study was pre-registered in the Open Science Framework (OSF): https://osf.io/k2uac. This pre-registration describes the study design, measures, etc. Note that the data we provide here is only a part of the data collected in the study, namely, the data related to studying the prediction of behavior (i.e., the effort people spent on their activities) based on user states and characteristics. Analysis Code Our analysis can be reproduced using Docker and Jupyter Notebook. We provide instructions for this in the README-files accompanying our analysis code. Appendix We also provide the Appendix of our paper, which contains more information on the virtual coach (including the conversation structure and preparatory activities), persuasion algorithm, data collection, optimal and worst policies computed for research questions Q3 and Q4, and the weighting of samples based on similarity for research question Q6. Regarding the preparatory activities, note that there were two different formulations: one for during the session, and one for the reminder message people received on Prolific.The former asked people to do the activity "after this session" and told people that they would receive the video link in the Prolific reminder message in case the activity involved watching a video; the latter asked people to do the activity "before the next session" in sessions 1-4 and contained the video link in case the activity involved watching a video. All activity formulations can be found together with the virtual coach code: https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/Activities.csv. Custom action code further modifies the reminder message activity formulation for session 5, which is the last session (https://github.com/PerfectFit-project/virtual_coach_rl_persuasion_algorithm/blob/main/actions/actions.py). Further Resources Here are some pointers to further resources: Data on the acceptance of the virtual coach can be found here: https://doi.org/10.4121/19934783.v1. Data on users' needs for a digital smoking cessation application can be found here: https://doi.org/10.4121/20284131.v2. Data on users' action plans for doing the activities (n = 469) and free-text responses to reflective questions about the activities (n = 2026) is available here: https://doi.org/10.4121/21905271.v1. The implementation of the virtual coach Sam is available here: https://doi.org/10.5281/zenodo.6319356.  Journal paper describing the persuasion algorithm and analyzing its effectiveness: https://doi.org/10.1371/journal.pone.0277295. If you have questions about the data, analysis code, or appendix, please contact Nele Albers ([email protected]). </p

Reliability and validity analyses for the coding of information entered into the Ehealth4MDD database

This dataset contains all data and analysis scripts pertaining to the research conducted for the CyberPsychology23 conference paper: “EHealth4MDD: a database of e-health systems for the prevention and treatment of depressive disorders.” In the scope of the research conducted and described in this paper, we have developed a relational database to systematically describe e-mental health systems for the prevention and treatment of Major Depressive Disorder (MDD). For the purpose of creating this database, literature had to be retrieved from PubMed, Scopus, and Web of Knowledge and filtered for inclusion and exclusion based on title, abstract, and full-paper. Samples of records at each stage were double coded. Once the final body of literature was identified, information from the papers had to be extracted (coded) and entered into the database. Four of the database attributes were selected to be double coded again on samples. Furthermore, a set of scales was developed of which we assessed concurrent validity. We here deliver the version of the database used for the analyses as well as all files and documents required for potential replication

Negotiation performance video tests

The study was set up as a between-subjects experiment which includes two groups: negotiation knowledge informed group and non-informed group. The dataset includes the scores the participants from the two groups got after they completed the video test and the sample rating/coding results from two coders. 128 participants were recruited by using Crowd Sourcing sites: Mechanical Turk. They were instructed to finish the negotiation knowledge video test in Qualtrics

Data and analysis underlying two user studies on acceptance and effect of computer-based perspective broadening support for appraisal training

Data files, statistical analysis scripts, and output files underlying the analyses presented in the paper by Ursula M. Beer, Mark A. Neerincx, Nexhmedin Morina, Willem-Paul Brinkman, "Computer-based Perspective Broadening Support for Appraisal Training: Acceptance and Effects", International Journal of Technology and Human Interaction (IJTHI). In this paper, two studies are reported. The first analysis (study 1) is carried out on data collected in workshops on perspective broadening incorporating technological support among two populations: Dutch soldiers (n=58) and Dutch firefighters (n=17). Data focuses on acceptance and response to a support application. The second analysis (study 2) is carried out on data collected in a lab experiment with a group of university students (n=60). The focus of this experiment was to compare the effect of the support tool to peer-based training condition

Data from a study of the effect of robot pedagogical interaction style on children's learning in a scientific task.

Data from a study of the effect of robot pedagogical interaction style on children's learning in a scientific task.  Includes data of learning gain (pre- and post-test scores), learning approach (duration of play, number of trials, number of controled variable trials) and perceived robot style. Includes data from 73 children (aged 8-11, M/F) recruited from after-school care and primary schools in the Netherlands. Written informed consent was obtained form legal guardians. Verbal consent from the child was obtained prior to the experiment. The research was approved by the Human Research Ethics Committee of Delft University of Technology. Resource will be filled after publicaton of the article containing a full description of method as well. </p