Information and communication technology-based health interventions for transgender people: A scoping review

Community involvement and engagement in designing a social-media based educational intervention for oral and sexual health promotion in transgender women in Malaysia: a narrative summary

Patient and public involvement and engagement (PPIE) in health care research has been advocated to ensure that the research is relevant, acceptable and beneficial to the communities that it serves. However, there remains a significant gap in research projects incorporating PPIE among the transgender communities, particularly from the global South. In this narrative summary the authors describe the methodology of PPIE through a novel collaboration with community members for the design and development of a bilingual and culturally sensitive educational intervention that was tailor made for transgender women in Malaysia. The educational intervention named Ms Radiance was aimed at creating oral health awareness, improving oral health care utilization and promoting safe oral sex practices among transgender women. The PPIE team included local transgender community advocates and artists who played a pivotal role in making the educational content accessible, engaging and suitable for delivery through a popular social media platform, Instagram®. PPIE enhanced the intervention's relevance and effectiveness through community friendly, culturally appropriate content creation, delivery and trust-building. Instructional designs such as the ADDIE (analyse, design, develop, implement and evaluate) ensure that educational interventions are well-planned and effective. A structed PPIE approach in the research project was seamlessly integrated with the ADDIE instructional design that guided the effective planning and delivery of oral health and sexual health education The research highlights the impact of PPIE on the project and offers insights for future interventions in marginalized communities from the global South.

Comparative- and Cost-Effectiveness Research Determining the Optimal Intervention for Advancing Transgender Women With HIV to Full Viral Suppression (Text Me, Alexis!): Protocol for a Randomized Controlled Trial

BACKGROUND

Many transgender women with HIV achieve suboptimal advancement through the HIV Care Continuum, including poor HIV health care usage, retention in HIV medical care, and rates of viral suppression. These issues are exacerbated by comorbid conditions, such as substance use disorder, which is also associated with reduced quality of life, increased overdose deaths, usage of high-cost health care services, engagement in a street economy, and cycles of incarceration. Thus, it is critical that efforts to End the HIV Epidemic include effective interventions to link and retain transgender women in HIV care through full viral suppression.

OBJECTIVE

This study builds on the promising findings from our two Health Resources and Services Administration-funded demonstration projects, The Alexis Project and Text Me, Girl!, which used peer health navigation (PHN) and SMS text messaging, respectively, for advancing transgender women with HIV to full viral suppression. Though the effectiveness of both interventions has been established, their comparative effectiveness, required resources or costs, cost-effectiveness, and heterogeneous effects on subgroups, including those with substance use disorder, have not been evaluated. Given the many negative personal and public health consequences of untreated or undertreated HIV, and that HIV services for transgender women are frequently delivered in resource-limited, community-based settings, a comprehensive economic evaluation is critical to inform decisions of stakeholders, such as providers, insurers, and policy makers.

METHODS

Text Me, Alexis! is a 3-arm randomized controlled trial. Participants (N=195) will be randomized (1:1:1) into: PHN alone (n=65), SMS text messaging alone (n=65), or PHN+SMS text messaging (n=65). Using the same time points as the Health Resources and Services Administration demonstration projects, the repeated-measures design will assess participants at baseline, 3, 6, 12, and 18 months post randomization. Over the course of the 90 days, participants in the PHN arm will receive unlimited navigation sessions; participants in the SMS text messaging arm will receive 270 theory-based SMS text messages (3 messages daily) that are targeted, tailored, and personalized specifically for transgender women with HIV; and participants in the PHN+SMS text messaging arm will receive a combined PHN and SMS text message intervention. The desired outcome of Text Me, Alexis! is viral suppression and cost-effectiveness.

RESULTS

Recruitment began on April 10, 2024, and the first participant was enrolled on April 11, 2024. Data collection is expected to be completed in July 2027. Primary outcome analyses will begin immediately following the conclusion of the follow-up evaluations.

CONCLUSIONS

Transgender women are a high-priority population for reaching End the HIV Epidemic goals. Findings have the potential to improve individual and population health outcomes by generating significant improvements in viral suppression among transgender women and guiding service provision and public policy.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/65313.

Technology-Based Interventions, with a Stepped Care Approach, for Reducing Sexual Risk Behaviors and Increasing PrEP Initiation Among Transgender and Gender Expansive Youth and Young Adults

TechStep was a technology-based trial, with a stepped care approach, to reduce sexual risks and increase PrEP uptake among transgender and gender expansive youth and young adults (15-24 years old). From October 2019 to September 2021, 254 participants were randomized into: 1) Text (n = 82), or 2) Webapp (n = 87), or 3) Control (n = 85). At the 3-month follow-up assessment, those randomized to Text and Webapp and did not demonstrate improvement on primary outcomes were re-randomized to receive virtual eCoaching (Text + or Webapp +), or to remain in their initial condition without eCoaching. Results showed no effect on condomless encounters at 6-month, the primary endpoint, when comparing the Webapp + (0.33 decrease; 95%CI: -0.01, 0.67, p-value = 0.057) or the Text + (0.27 decrease; 95%CI: -0.13, 0.68, p-value = 0.181) conditions to the Control condition. However, in secondary analyses, condomless encounters were significantly reduced for Text compared to Control. The rate of PrEP uptake was low for all study arms.Trial registration: Clinical Trials # NCT04000724 (registered June 26, 2019).

Technology for transgender healthcare: Access, precarity & community care

While much of the transgender health literature has focused on poor health outcomes, less research has examined how trans people find reliable information on, and actually go about accessing, gender-affirming healthcare. Through qualitative interviews with creators of trans technologies, that is, technologies designed to address problems that trans people face, we found that digital technologies have become important tools for proliferating access to gender-affirming care and related health information. We found that technologists often employed different processes for creating their technologies, but they coalesced around the goal of enabling and increasing access to gender-affirming care. Creators of trans health technologies also encountered precarious conditions for creating and maintaining their technologies, including regional gaps left by national resources focused on the US east and west coasts. Findings demonstrated that trans tech creators were motivated to create and maintain these technologies as a means of caring for one another and forming trans communities in spite of the precarious conditions trans people face living under systemic oppression.

Health literacy of trans and gender diverse individuals -a cross sectional survey in Germany

INTRODUCTION

To date, there has been little research on the general health literacy of trans and gender diverse individuals, even though previous research undermines the importance of good health literacy in this sample. The aim of the article is therefore to describe the general health literacy of trans and gender diverse individuals based on a German survey.

METHODS

In September 2022, a survey study was conducted in which health literacy was recorded using HLS-EU-16. Data will be presented descriptively; gender differences will be explored using a Χ2- test and a univariate analysis of variance (ANOVA).

RESULTS

Out of N = 223 participants, n = 129 individuals (57.8%) identified as non-binary; n = 49 (22.0%) identified themselves as male, while n = 45 (20.2%) identified as female. Mean age was 28.03 years. Overall, 26.4% of all the participants showed an inadequate health literacy, as proposed by the HLS-EU-16. In trend, health-related task related to media use were more often perceived as easy compared to the German general population.

CONCLUSION

Individuals, who identify as trans and gender diverse may have a general health literacy below average compared to the German general population. However, tasks related to media use were perceived as easy, which might be a good starting point for health literacy related interventions.

TRIAL REGISTRATION

DRKS00026249, Date of registration: 15/03/2022.

Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study

BACKGROUND

The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs.

OBJECTIVE

This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data.

METHODS

Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria.

RESULTS

Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image.

CONCLUSIONS

Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.