scholarly journals An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study

10.2196/12106 ◽  
2018 ◽  
Vol 6 (11) ◽  
pp. e12106 ◽  
Author(s):  
Becky Inkster ◽  
Shubhankar Sarda ◽  
Vinod Subramanian
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Real World ◽  
Well Being ◽  
Mixed Methods Study ◽  
Data Evaluation ◽  
Real World Data ◽  
World Data ◽  
Mental Well Being

scholarly journals An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study (Preprint)

2018 ◽  
Author(s):  
Becky Inkster ◽  
Shubhankar Sarda ◽  
Vinod Subramanian
Keyword(s):  
Mental Health ◽  
Real World ◽  
Mental Health Problems ◽  
Well Being ◽  
Data Evaluation ◽  
Real World Data ◽  
World Data ◽  
Symptoms Of Depression ◽  
Average Improvement ◽  
Mental Well Being

BACKGROUND A World Health Organization 2017 report stated that major depression affects almost 5% of the human population. Major depression is associated with impaired psychosocial functioning and reduced quality of life. Challenges such as shortage of mental health personnel, long waiting times, perceived stigma, and lower government spends pose barriers to the alleviation of mental health problems. Face-to-face psychotherapy alone provides only point-in-time support and cannot scale quickly enough to address this growing global public health challenge. Artificial intelligence (AI)-enabled, empathetic, and evidence-driven conversational mobile app technologies could play an active role in filling this gap by increasing adoption and enabling reach. Although such a technology can help manage these barriers, they should never replace time with a health care professional for more severe mental health problems. However, app technologies could act as a supplementary or intermediate support system. Mobile mental well-being apps need to uphold privacy and foster both short- and long-term positive outcomes. OBJECTIVE This study aimed to present a preliminary real-world data evaluation of the effectiveness and engagement levels of an AI-enabled, empathetic, text-based conversational mobile mental well-being app, Wysa, on users with self-reported symptoms of depression. METHODS In the study, a group of anonymous global users were observed who voluntarily installed the Wysa app, engaged in text-based messaging, and self-reported symptoms of depression using the Patient Health Questionnaire-9. On the basis of the extent of app usage on and between 2 consecutive screening time points, 2 distinct groups of users (high users and low users) emerged. The study used mixed-methods approach to evaluate the impact and engagement levels among these users. The quantitative analysis measured the app impact by comparing the average improvement in symptoms of depression between high and low users. The qualitative analysis measured the app engagement and experience by analyzing in-app user feedback and evaluated the performance of a machine learning classifier to detect user objections during conversations. RESULTS The average mood improvement (ie, difference in pre- and post-self-reported depression scores) between the groups (ie, high vs low users; n=108 and n=21, respectively) revealed that the high users group had significantly higher average improvement (mean 5.84 [SD 6.66]) compared with the low users group (mean 3.52 [SD 6.15]); Mann-Whitney P=.03 and with a moderate effect size of 0.63. Moreover, 67.7% of user-provided feedback responses found the app experience helpful and encouraging. CONCLUSIONS The real-world data evaluation findings on the effectiveness and engagement levels of Wysa app on users with self-reported symptoms of depression show promise. However, further work is required to validate these initial findings in much larger samples and across longer periods.

scholarly journals Companion Animal Type and Level of Engagement Matter: A Mixed-Methods Study Examining Links between Companion Animal Guardianship, Loneliness and Well-Being during the COVID-19 Pandemic

Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2349
Author(s):  
Heather Clements ◽  
Stephanie Valentin ◽  
Nicholas Jenkins ◽  
Jean Rankin ◽  
Nancy R. Gee ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Quantitative Data ◽  
Online Survey ◽  
Qualitative Data ◽  
Companion Animals ◽  
Well Being ◽  
Companion Animal ◽  
Mixed Methods Study ◽  
The Impact ◽  
Mental Well Being

To reduce the spread of COVID-19, countries worldwide placed limitations on social interaction, which is anticipated to have severe psychological consequences. Although findings are inconsistent, prior research has suggested that companion animals may positively influence human well-being and reduce loneliness. In the context of COVID-19, this has important implications, as companion animal guardians may be less negatively affected by the pandemic. The primary aim of this research was to investigate the influence of companion animals on mental well-being and loneliness during the pandemic, with specific interest in the role of ornamental fishes. A mixed-methods study was conducted, using an international sample. Quantitative data were collected via an online survey (n = 1199) and analysed using robust hierarchical multiple regression analyses; the influence of level of engagement with companion animals was examined for dogs, cats and ornamental fishes. There was no evidence that companion animal guardianship was associated with loneliness and mental well-being during the pandemic but spending more time engaging physically or socially with dogs (and to a lesser extent cats) was generally associated with poorer outcomes. Qualitative data were collected through open-ended survey responses (n = 757) and semi-structured interviews (n = 25) and analysed using reflexive thematic analysis. Two themes were developed—one related to companion animals as providers of social and emotional support, and the other to companion animals as providers of purpose and perspective. Concerns regarding the impact of the pandemic on animal welfare were also identified. Compared to other animal types, more participants expressed indifference regarding the impact of their fishes on their well-being during the pandemic, possibly because fishes cannot provide comfort via physical touch. The findings of this study reflect the wider field of human–animal interaction; although qualitative data suggest guardians believe their companion animals are a positive influence in their lives, there is little convincing quantitative data to support these beliefs. This highlights the need to refine theories regarding which aspects of companion animal guardianship may influence human well-being; the findings from this research may be useful in the refinement of such theories.

scholarly journals Ondansetron use is associated with lower COVID-19 mortality in a Real-World Data network-based analysis

2021 ◽  
Author(s):  
Gregory M Miller ◽  
Austin J Ellis ◽  
Rangaprasad Sarangarajan ◽  
Amay Parikh ◽  
Leonardo O Rodrigues ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Long Term Effects ◽  
Real World Data ◽  
World Data ◽  
Antiemetic Agent ◽  
Future Loss ◽  
Selection Operator

Objective: The COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19 morbidity, mortality, long term effects, and therapeutic solutions. The objective of this study was to generate insights on COVID-19 mortality-associated factors and identify potential new therapeutic options for COVID-19 patients by employing artificial intelligence analytics on real-world data. Materials and Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within Interrogative Biology® platform was used for network learning, inference causality and hypothesis generation to analyze 16,277 PCR positive patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of mortality for specific COVID-19 patient populations. These findings were validated by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the SARS-CoV-2 PCR positive patient cohort, early use of the antiemetic agent ondansetron was associated with increased survival in mechanically ventilated patients. Conclusions: The results demonstrate how real world COVID-19 focused data analysis using artificial intelligence can generate valid insights that could possibly support clinical decision-making and minimize the future loss of lives and resources.

The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence

Blood Reviews ◽  
2021 ◽  
pp. 100914
Author(s):  
Francesco Passamonti ◽  
Giovanni Corrao ◽  
Gastone Castellani ◽  
Barbara Mora ◽  
Giulia Maggioni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
The Future

scholarly journals Continuous Glucose Monitoring in the Real World Using Photosurveillance of #Dexcom on Instagram: Exploratory Mixed Methods Study (Preprint)

2018 ◽  
Author(s):  
Michelle L Litchman ◽  
Sarah E Wawrzynski ◽  
Whitney S Woodruff ◽  
Joseph B Arrington ◽  
Quynh C Nguyen ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Continuous Glucose Monitoring ◽  
Real World ◽  
Methodological Approach ◽  
Glucose Monitoring ◽  
Well Being ◽  
The Body ◽  
Mixed Methods Study ◽  
World Health ◽  
Off Label

BACKGROUND Individuals with diabetes are using social media as a method to share and gather information about their health via the diabetes online community. Infoveillance is one methodological approach to examine health care trends. However, infoveillance, while very effective in identifying many real-world health trends, may miss opportunities that use photographs as primary sources for data. We propose a new methodology, photosurveillance, in which photographs are analyzed to examine real-world trends. OBJECTIVE The purpose of this research is to (1) assess the use of photosurveillance as a research method to examine real-world trends in diabetes and (2) report on real-world use of continuous glucose monitoring (CGM) on Instagram. METHODS This exploratory mixed methods study examined all photographs posted on Instagram that were identified with the hashtag #dexcom over a 3-month period—December 2016 to February 2017. Photographs were coded by CGM location on the body. Original posts and corresponding comments were textually coded for length of CGM device wear and CGM failure and were analyzed for emerging themes. RESULTS A total of 2923 photographs were manually screened; 12.08% (353/2923) depicted a photograph with a CGM site location. The majority (225/353, 63.7%) of the photographs showed a CGM site in an off-label location, while 26.2% (92/353) were in an FDA-approved location (ie, abdomen) and 10.2% (36/353) were in an unidentifiable location. There were no significant differences in the number of likes or comments based on US Food and Drug Administration (FDA) approval. Five themes emerged from the analysis of original posts (N=353) and corresponding comments (N=2364): (1) endorsement of CGM as providing a sense of well-being; (2) reciprocating information, encouragement, and support; (3) reciprocating CGM-related frustrations; (4) life hacks to optimize CGM use; and (5) sharing and learning about off-label CGM activity. CONCLUSIONS Our results indicate that individuals successfully used CGM in off-label locations, posting photos of these areas with greater frequency than of the abdomen, with no indication of sensor failure. While these photographs only capture a snapshot in time, these posts can be used to inform providers and industry leaders of real-world trends in CGM use. Additionally, there were instances in which sensors were worn beyond the FDA-approved 7-day period; however, they represented the minority in this study.

P460 NASAL POLYPOSIS IN RUSSIA: REAL-WORLD DATA FROM OPEN INTERNET SOURCES ASSESSED BY ARTIFICIAL INTELLIGENCE

2019 ◽  
Vol 123 (5) ◽  
pp. S61
Author(s):  
O. Kurbacheva ◽  
V. Kozlov ◽  
Y. Solovkina ◽  
O. Ostankova ◽  
V. Bulatov
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Nasal Polyposis ◽  
Real World Data ◽  
World Data

scholarly journals Cross cultural evaluation of the Warwick-Edinburgh mental well-being scale (WEMWBS) -a mixed methods study

2013 ◽  
Vol 11 (1) ◽  
pp. 27 ◽  
Author(s):  
Frances Taggart ◽  
Tim Friede ◽  
Scott Weich ◽  
Aileen Clarke ◽  
Mark Johnson ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Well Being ◽  
Cross Cultural ◽  
Mixed Methods Study ◽  
Mental Well Being

scholarly journals Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

2021 ◽  
Vol 3 ◽  
Author(s):  
A. Damiani ◽  
C. Masciocchi ◽  
J. Lenkowicz ◽  
N. D. Capocchiano ◽  
L. Boldrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Warehouse ◽  
Real World ◽  
Digital Health ◽  
Hospital Data ◽  
Real World Data ◽  
World Data ◽  
Real World Evidence ◽  
Artificial Intelligence Laboratory ◽  
New Research

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

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