Adherence and engagement with a cognitive behavioral therapy-based conversational agent (Wysa for Chronic Pain) among adults with chronic pain: Survival analysis

BACKGROUND: Digital applications are commonly used to support mental health and well-being. However, successfully retaining and engaging users to complete digital interventions is challenging, and comorbidities such as chronic pain further reduce user engagement. Digital conversational agents (CAs) may improve user engagement by applying engagement principles that have been implemented within in-person care settings. OBJECTIVE: To evaluate user retention and engagement with an artificial intelligence-led digital mental health app (Wysa for Chronic Pain) that is customized for individuals managing mental health symptoms and coexisting chronic pain. METHODS: In this ancillary survival analysis of a clinical trial, participants included 51 adults who presented to a tertiary care center for chronic musculoskeletal pain, who endorsed coexisting symptoms of depression or anxiety (Patient-Reported Outcomes Measurement Information System score of ≥55 for depression or anxiety), and initiated onboarding to an 8-week subscription of Wysa for Chronic Pain. The study outcomes were user retention, defined as revisiting the app each week and on the last day of engagement, and user engagement, defined by the number of sessions the user completed. RESULTS: Users engaged in a cumulative mean of 33.3 sessions during the 8-week study period. The survival analysis depicted a median user retention period (i.e., time to complete disengagement) of 51 days, with the usage of a morning check-in feature having a significant relationship with a longer retention period (P=.001). CONCLUSIONS: Our findings suggest that user retention and engagement with a CBT-based CA built for users with chronic pain is higher than standard industry metrics. These results have clear implications for addressing issues of suboptimal engagement of digital health interventions and improving access to care for chronic pain. Future work should use these findings to inform the design of evidence-based interventions for individuals with chronic pain and to enhance user retention and engagement of digital health interventions more broadly. TRIAL REGISTRATION: ClinicalTrials.gov NCT04640090; https://clinicaltrials.gov/ct2/show/NCT04640090

Understanding the impact of an AI-enabled conversational agent mobile app on users’ mental health and wellbeing with a self-reported maternal event: a mixed method real-world data mHealth study

BackgroundMaternal mental health care is variable and with limited accessibility. Artificial intelligence (AI) conversational agents (CAs) could potentially play an important role in supporting maternal mental health and wellbeing. Our study examined data from real-world users who self-reported a maternal event while engaging with a digital mental health and wellbeing AI-enabled CA app (Wysa) for emotional support. The study evaluated app effectiveness by comparing changes in self-reported depressive symptoms between a higher engaged group of users and a lower engaged group of users and derived qualitative insights into the behaviors exhibited among higher engaged maternal event users based on their conversations with the AI CA.MethodsReal-world anonymised data from users who reported going through a maternal event during their conversation with the app was analyzed. For the first objective, users who completed two PHQ-9 self-reported assessments (n = 51) were grouped as either higher engaged users (n = 28) or lower engaged users (n = 23) based on their number of active session-days with the CA between two screenings. A non-parametric Mann–Whitney test (M–W) and non-parametric Common Language effect size was used to evaluate group differences in self-reported depressive symptoms. For the second objective, a Braun and Clarke thematic analysis was used to identify engagement behavior with the CA for the top quartile of higher engaged users (n = 10 of 51). Feedback on the app and demographic information was also explored.ResultsResults revealed a significant reduction in self-reported depressive symptoms among the higher engaged user group compared to lower engaged user group (M–W p = .004) with a high effect size (CL = 0.736). Furthermore, the top themes that emerged from the qualitative analysis revealed users expressed concerns, hopes, need for support, reframing their thoughts and expressing their victories and gratitude.ConclusionThese findings provide preliminary evidence of the effectiveness and engagement and comfort of using this AI-based emotionally intelligent mobile app to support mental health and wellbeing across a range of maternal events and experiences

Understanding the impact of an AI-enabled conversational agent mobile app on users' mental health and wellbeing with a self-reported maternal event: a mixed method real-world data mHealth study.

Peer reviewed: TrueBACKGROUND: Maternal mental health care is variable and with limited accessibility. Artificial intelligence (AI) conversational agents (CAs) could potentially play an important role in supporting maternal mental health and wellbeing. Our study examined data from real-world users who self-reported a maternal event while engaging with a digital mental health and wellbeing AI-enabled CA app (Wysa) for emotional support. The study evaluated app effectiveness by comparing changes in self-reported depressive symptoms between a higher engaged group of users and a lower engaged group of users and derived qualitative insights into the behaviors exhibited among higher engaged maternal event users based on their conversations with the AI CA. METHODS: Real-world anonymised data from users who reported going through a maternal event during their conversation with the app was analyzed. For the first objective, users who completed two PHQ-9 self-reported assessments (n = 51) were grouped as either higher engaged users (n = 28) or lower engaged users (n = 23) based on their number of active session-days with the CA between two screenings. A non-parametric Mann-Whitney test (M-W) and non-parametric Common Language effect size was used to evaluate group differences in self-reported depressive symptoms. For the second objective, a Braun and Clarke thematic analysis was used to identify engagement behavior with the CA for the top quartile of higher engaged users (n = 10 of 51). Feedback on the app and demographic information was also explored. RESULTS: Results revealed a significant reduction in self-reported depressive symptoms among the higher engaged user group compared to lower engaged user group (M-W p = .004) with a high effect size (CL = 0.736). Furthermore, the top themes that emerged from the qualitative analysis revealed users expressed concerns, hopes, need for support, reframing their thoughts and expressing their victories and gratitude. CONCLUSION: These findings provide preliminary evidence of the effectiveness and engagement and comfort of using this AI-based emotionally intelligent mobile app to support mental health and wellbeing across a range of maternal events and experiences

Understanding People With Chronic Pain Who Use a Cognitive Behavioral Therapy–Based Artificial Intelligence Mental Health App (Wysa): Mixed Methods Retrospective Observational Study

BackgroundDigital health interventions can bridge barriers in access to treatment among individuals with chronic pain. ObjectiveThis study aimed to evaluate the perceived needs, engagement, and effectiveness of the mental health app Wysa with regard to mental health outcomes among real-world users who reported chronic pain and engaged with the app for support. MethodsReal-world data from users (N=2194) who reported chronic pain and associated health conditions in their conversations with the mental health app were examined using a mixed methods retrospective observational study. An inductive thematic analysis was used to analyze the conversational data of users with chronic pain to assess perceived needs, along with comparative macro-analyses of conversational flows to capture engagement within the app. Additionally, the scores from a subset of users who completed a set of pre-post assessment questionnaires, namely Patient Health Questionnaire-9 (PHQ-9) (n=69) and Generalized Anxiety Disorder Assessment-7 (GAD-7) (n=57), were examined to evaluate the effectiveness of Wysa in providing support for mental health concerns among those managing chronic pain. ResultsThe themes emerging from the conversations of users with chronic pain included health concerns, socioeconomic concerns, and pain management concerns. Findings from the quantitative analysis indicated that users with chronic pain showed significantly greater app engagement (P<.001) than users without chronic pain, with a large effect size (Vargha and Delaney A=0.76-0.80). Furthermore, users with pre-post assessments during the study period were found to have significant improvements in group means for both PHQ-9 and GAD-7 symptom scores, with a medium effect size (Cohen d=0.60-0.61). ConclusionsThe findings indicate that users look for tools that can help them address their concerns related to mental health, pain management, and sleep issues. The study findings also indicate the breadth of the needs of users with chronic pain and the lack of support structures, and suggest that Wysa can provide effective support to bridge the gap

The ModERN Resource: Genome-Wide Binding Profiles for Hundreds of Drosophila and Caenorhabditis elegans Transcription Factors.

To develop a catalog of regulatory sites in two major model organisms, Drosophila melanogaster and Caenorhabditis elegans, the modERN (model organism Encyclopedia of Regulatory Networks) consortium has systematically assayed the binding sites of transcription factors (TFs). Combined with data produced by our predecessor, modENCODE (Model Organism ENCyclopedia Of DNA Elements), we now have data for 262 TFs identifying 1.23 M sites in the fly genome and 217 TFs identifying 0.67 M sites in the worm genome. Because sites from different TFs are often overlapping and tightly clustered, they fall into 91,011 and 59,150 regions in the fly and worm, respectively, and these binding sites span as little as 8.7 and 5.8 Mb in the two organisms. Clusters with large numbers of sites (so-called high occupancy target, or HOT regions) predominantly associate with broadly expressed genes, whereas clusters containing sites from just a few factors are associated with genes expressed in tissue-specific patterns. All of the strains expressing GFP-tagged TFs are available at the stock centers, and the chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center and also through a simple interface (http://epic.gs.washington.edu/modERN/) that facilitates rapid accessibility of processed data sets. These data will facilitate a vast number of scientific inquiries into the function of individual TFs in key developmental, metabolic, and defense and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks and globally across the life spans of these two key model organisms

The ModERN Resource: Genome-Wide Binding Profiles for Hundreds of Drosophila and Caenorhabditis elegans Transcription Factors.

To develop a catalog of regulatory sites in two major model organisms, Drosophila melanogaster and Caenorhabditis elegans, the modERN (model organism Encyclopedia of Regulatory Networks) consortium has systematically assayed the binding sites of transcription factors (TFs). Combined with data produced by our predecessor, modENCODE (Model Organism ENCyclopedia Of DNA Elements), we now have data for 262 TFs identifying 1.23 M sites in the fly genome and 217 TFs identifying 0.67 M sites in the worm genome. Because sites from different TFs are often overlapping and tightly clustered, they fall into 91,011 and 59,150 regions in the fly and worm, respectively, and these binding sites span as little as 8.7 and 5.8 Mb in the two organisms. Clusters with large numbers of sites (so-called high occupancy target, or HOT regions) predominantly associate with broadly expressed genes, whereas clusters containing sites from just a few factors are associated with genes expressed in tissue-specific patterns. All of the strains expressing GFP-tagged TFs are available at the stock centers, and the chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center and also through a simple interface (http://epic.gs.washington.edu/modERN/) that facilitates rapid accessibility of processed data sets. These data will facilitate a vast number of scientific inquiries into the function of individual TFs in key developmental, metabolic, and defense and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks and globally across the life spans of these two key model organisms

Perspectives on ENCODE

The Encylopedia of DNA Elements (ENCODE) Project launched in 2003 with the long-term goal of developing a comprehensive map of functional elements in the human genome. These included genes, biochemical regions associated with gene regulation (for example, transcription factor binding sites, open chromatin, and histone marks) and transcript isoforms. The marks serve as sites for candidate cis-regulatory elements (cCREs) that may serve functional roles in regulating gene expression1. The project has been extended to model organisms, particularly the mouse. In the third phase of ENCODE, nearly a million and more than 300,000 cCRE annotations have been generated for human and mouse, respectively, and these have provided a valuable resource for the scientific community.11Nsciescopu

Expanded encyclopaedias of DNA elements in the human and mouse genomes

AbstractThe human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.11Nsciescopu