Integrating Patient-Generated Digital Data Into Mental Health Therapy: Mixed Methods Analysis of User Experience.

Background: Therapists and their patients increasingly discuss digital data from social media, smartphone sensors, and other online engagements within the context of psychotherapy.

Objective: We examined patients' and mental health therapists' experiences and perceptions following a randomized controlled trial in which they both received regular summaries of patients' digital data (eg, dashboard) to review and discuss in session. The dashboard included data that patients consented to share from their social media posts, phone usage, and online searches.

Disparities by Race and Urbanicity in Online Health Care Facility Reviews.

Importance: Online review platforms offer valuable insights into patient satisfaction and the quality of health care services, capturing content and trends that traditional metrics might miss. The COVID-19 pandemic has disrupted health care services, influencing patient experiences.

Objective: To examine health care facility numerical ratings and patient experience reported on an online platform by facility type and area demographic characteristics after the COVID-19 pandemic (ie, post-COVID).

Detecting and monitoring concerns against HPV vaccination on social media using large language models.

Health risks due to preventable infections such as human papillomavirus (HPV) are exacerbated by persistent vaccine hesitancy. Due to limited sample sizes and the time needed to roll out, traditional methodologies like surveys and interviews offer restricted insights into quickly evolving vaccine concerns. Social media platforms can serve as fertile ground for monitoring vaccine-related conversations and detecting emerging concerns in a scalable and dynamic manner.

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.

Mental health conversational agents (a.k.a.

Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g.

A cross-cultural examination of temporal orientation through everyday language on social media.

Past research has shown that culture can form and shape our temporal orientation-the relative emphasis on the past, present, or future. However, there are mixed findings on how temporal orientations vary between North American and East Asian cultures due to the limitations of survey methodology and sampling. In this study, we applied an inductive approach and leveraged big data and natural language processing between two popular social media platforms-Twitter and Weibo-to assess the similarities and differences in temporal orientation in the United States of America and China, respectively.

The online language of work-personal conflict.

With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals' language use on social media.

Generative adversarial networks based skin lesion segmentation.

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks.

Investigating Social Media to Evaluate Emergency Medicine Physicians' Emotional Well-being During COVID-19.

Importance: Emergency medicine (EM) physicians experience tremendous emotional health strain, which has been exacerbated during COVID-19, and many have taken to social media to express themselves.

Objective: To analyze social media content from academic EM physicians and resident physicians to investigate changes in content and language as indicators of their emotional well-being.

Design, Setting, And Participants: This cross-sectional study used machine learning and natural language processing of Twitter posts from self-described academic EM physicians and resident physicians between March 2018 and March 2022.

Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.

Background: The COVID-19 pandemic was accompanied by an "infodemic"-an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic's readers by identifying themes and longitudinal trends among question box submissions.

Reddit language indicates changes associated with diet, physical activity, substance use, and smoking during COVID-19.

COVID-19 has adversely impacted the health behaviors of billions of people across the globe, modifying their former trends in health and lifestyle. In this paper, we compare the psychosocial language markers associated with diet, physical activity, substance use, and smoking before and after the onset of COVID-19 pandemic. We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.

Effect of Integrating Patient-Generated Digital Data Into Mental Health Therapy: A Randomized Controlled Trial.

Objective: The authors sought to determine whether providing summaries of patients' social media and other digital data to patients and their clinicians improves patients' health-related quality of life (HRQoL) measured by the RAND 36-Item Short Form Health Survey (SF-36).

Methods: The authors randomly assigned 115 adults receiving outpatient mental health therapy to usual care or to periodic sharing of summaries of their digital data with their clinician providing psychosocial therapy. The study was conducted October 2020-December 2021.

Head versus heart: social media reveals differential language of loneliness from depression.

We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors.

Understanding the expression of loneliness on Twitter across age groups and genders.

Some individuals seek support around loneliness on social media forums. In this work, we aim to determine differences in the use of language by users-in different age groups and genders (female, male), who publish posts on Twitter expressing loneliness. We hypothesize that these differences in the use of language will reflect how these users express themselves and some of their support needs.

COVID-19 contact tracing app reviews reveal concerns and motivations around adoption.

Background: Google and Apple's Exposure Notifications System (ENS) was developed early in the COVID-19 pandemic to complement existing contact tracing efforts while protecting user privacy. An analysis by the Associated Press released in December 2020 estimated approximately 1 in 14 people had downloaded apps in states one was available. In this study, we assessed the motivation and experience of individuals who downloaded ENS apps from the Google Play and Apple App Stores.

Patient Experience and Satisfaction in Online Reviews of Obstetric Care: Observational Study.

Background: The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public.

Objective: The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp.

Methods: We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017.

Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images.

The emotional and mental health impact of the murder of George Floyd on the US population.

On May 25, 2020, George Floyd, an unarmed Black American male, was killed by a White police officer. Footage of the murder was widely shared. We examined the psychological impact of Floyd's death using two population surveys that collected data before and after his death; one from Gallup (117,568 responses from = 47,355) and one from the US Census (409,652 responses from = 319,471).

Characterizing COVID-19 Content Posted to TikTok: Public Sentiment and Response During the First Phase of the COVID-19 Pandemic.

Purpose: The purpose of this study was to characterize COVID-19 content posted by users and disseminated via TikTok, a social media platform that has become known largely as an entertainment platform for viral video-sharing. We sought to capture how TikTok videos posted during the initial months of the COVID pandemic changed over time as cases accelerated.

Methods: This study is an observational analysis of sequential TikTok videos with #coronavirus from January to March 2020.

Twitter discourse reveals geographical and temporal variation in concerns about COVID-19 vaccines in the United States.

The speed at which social media is propagating COVID-19 misinformation and its potential reach and impact is growing, yet little work has focused on the potential applications of these data for informing public health communication about COVID-19 vaccines. We used Twitter to access a random sample of over 78 million vaccine-related tweets posted between December 1, 2020 and February 28, 2021 to describe the geographical and temporal variation in COVID-19 vaccine discourse. Urban suburbs posted about equitable distribution in communities, college towns talked about in-clinic vaccinations near universities, evangelical hubs posted about operation warp speed and thanking God, exurbs posted about the 2020 election, Hispanic centers posted about concerns around food and water, and counties in the ACP African American South posted about issues of trust, hesitancy, and history.

Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling.

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views.

Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study.

Background: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems.

Objective: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared.

Methods: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020.