Ellie Fan Yang

Background: Online communities such as Reddit can reveal critical information needs (CINs) among people who discuss opioid use for chronic or acute pain under conditions of stigma and uncertainty. Methods: We used a mixed-method design to analyze 3428 unique Reddit posts from 1 January to 31 December 2022. Sentence-BERT embeddings and K-means clustering identified eight dominant themes, followed by qualitative analysis of a random subsample of 400 posts. Results: The most prevalent theme was seeking alternatives to opioids for pain relief (28.00%), followed by medications for chronic pain (19.84%) and kratom use experiences (14.64%). Qualitative findings showed recurrent concerns about addiction, stigma in clinical encounters, withdrawal, treatment access, and peer-to-peer discussion of kratom, tianeptine, cannabis, and other substitute substances. Conclusions: The study suggests an unmet need for accessible, nonstigmatizing, and clinically grounded information on pain management, tapering, and lower-risk alternatives to opioid use. These results have implications for addiction prevention, harm reduction, and patient-facing communication strategies for clinical practice and community health.

Aligned with the United Nations' Sustainable Development Goals, creating inclusive, safe, and sustainable cities has become a key objective in urban planning and environmental management. While prior studies have examined built environment characteristics and social disparities, few have explored how neighborhood visual environments-shaped by actual built features-differ across social and vulnerable groups at a large scale. This study addresses this gap by developing a multi-modal machine learning framework to assess visual environmental inequity in Los Angeles County, a region notable for its ethnic diversity and socioeconomic contrasts (Figure 1). Using over 500,000 street view images from Mapillary and actual built environment data from OpenStreetMap and Google Earth Engine, we quantify neighborhoods in terms of 5D dimensions (density, diversity, design, distance, destination) and measure visual perceptions (beautiful, safe, wealthy, livable, boring, depressing) using a ResNet-based image classification model trained on crowd-sourced perception scores. Semantic segmentation is conducted using DeepLabV3+, and a gradient boosting ensemble model identifies the built features most associated with perceived soundness.

Background: Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders. Objective: This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network). Methods: Tailored for people with HIV living with substance use disorders, the mHealth app was distributed among 208 participants aged >18 years from 2 US health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Using the weekly check-in record provided by participants as the unit of analysis (N=681), linear regression and structure equation models with cluster-robust SEs were used for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the structure equation models. Results: We found that (1) intensity, not initiation, of system use; (2) dyadic message expression and reception; and (3) network expression positively predicted medication adherence through joint mediators (substance use and confidence in HIV management). However, intraindividual reception (ie, rereading saved entries for personal motivation) negatively predicts medication adherence through joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup. Conclusions: These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for people with HIV and tailoring these systems to Black communities.

Verbal authenticity is the degree to which a person is monitoring self-expression that reflects one’s motivation. Though benefits of authentic expression are studied in interpersonal, social, and entrepreneurial success, few have explored authenticity within online health communities for social support and self-disclosure, especially when the health issues are stigmatized offline, such as living with chronic pain and opioid use. Given the health benefits authentic expression provides, the present study explored the discourse of pain management prevalent on Reddit.com and investigated the authentic expression patterns across its labeled communities: subreddits. Relevant messages (N = 57, 564) from posts and replies were collected and evaluated using the authenticity category developed by the Linguistic Inquiry and Word Count (LIWC) software. The analysis suggests that subreddits built for inviting inquiries such as “r/AskReddit” “r/AskDocs” had higher scores in terms of authentic expression, while others like “r/politics” “r/marvelstudios” had lower authenticity. Also, the more frequently a user posts and replies in a community, the more likely they expressed authentically for the post construction, but not for the reply authenticity. This study explicates the concept of authenticity by exploring what authentic expression means for health benefits in pain management. It also contributes to a growing body of studies focusing on the therapeutic effect of authentic expression in online health communities.

BACKGROUND Despite the increasing popularity of mobile health (mHealth) technologies, little is known about which types of mHealth system engagement might affect the maintenance of antiretroviral therapy among people with HIV and substance use disorders. OBJECTIVE This study aimed to use longitudinal and detailed system logs and weekly survey data to test a mediation model, where mHealth engagement indicators were treated as predictors, substance use and confidence in HIV management were treated as joint mediators, and antiretroviral therapy adherence was treated as the outcome. We further distinguished the initiation and intensity of system engagement by mode (expression vs reception) and by communication levels (intraindividual vs dyadic vs network). METHODS Tailored for people with HIV living with substance use disorders, the mHealth app was distributed among 208 participants aged >18 years from 2 US health clinics. Supervised by medical professionals, participants received weekly surveys through the app to report their health status and medication adherence data. System use was passively collected through the app, operationalized as transformed click-level data, aggregated weekly, and connected to survey responses with a 7-day lagged window. Using the weekly check-in record provided by participants as the unit of analysis (N=681), linear regression and structure equation models with cluster-robust SEs were used for analyses, controlling within-person autocorrelation and group-level error correlations. Racial groups were examined as moderators in the structure equation models. RESULTS We found that (1) intensity, not initiation, of system use; (2) dyadic message expression and reception; and (3) network expression positively predicted medication adherence through joint mediators (substance use and confidence in HIV management). However, intraindividual reception (ie, rereading saved entries for personal motivation) negatively predicts medication adherence through joint mediators. We also found Black participants have distinct usage patterns, suggesting the need to tailor mHealth interventions for this subgroup. CONCLUSIONS These findings highlight the importance of considering the intensity of system engagement, rather than initiation alone, when designing mHealth interventions for people with HIV and tailoring these systems to Black communities.

This study examined what kinds of TikTok video and message features are associated with user engagement in the context of COVID-19 vaccination. Content analysis was applied to study a sample of 223 COVID-19 vaccination-related videos from creators with at least 10,000 followers. The content analysis involved coding themes, video formats, the valence of attitude toward vaccination, and emotional expressions from the influencers. A majority of videos showcased personal vaccination experiences, followed by fictitious dramas and instructional information. More fictitious dramas expressed unclear attitudes, neither explicitly supporting nor opposing the COVID-19 vaccine, compared to personal vaccination stories and instructional videos. Some imaginative and dramatic scenes, such as zombie transformation or dramatic spasms after taking the vaccines, were widely imitated across influencers, perhaps humorously, and raised concerns about diminishing positive images of vaccine uptake. Videos with simultaneous expression of humor and frustration significantly predicted engagement when the video content opposed or was uncertain about taking the vaccine, implying the effectiveness of mixed emotional attributes within a message. This study provides insight into how social context and message choices by creators interact to influence audience engagement.

Background: Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient. Objective: The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers’ substance use and well-being outcomes. Methods: Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes. Results: Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app’s support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood. Conclusions: This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals’ expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.