Transgender and gender-expansive (TGE) youth endure stark disparities in health and wellbeing compared to their cisgender peers. A key social determinant of health for TGE adolescents and emerging adults is gender affirmation, which encompasses multidimensional validations of an individual's lived gender. Lacking available resources for one's gender affirmation, TGE young people may engage in highrisk maladaptive coping behaviors, linked to their disproportionately high HIV-acquisition risk. A range of innovative mobile technologies are guided by the Gender-Affirmative Framework to promote the health of TGE communities, including through HIV prevention and care continuum outcomes. The aim of this review was to examine key features of existing mobile technologies that can be leveraged to advance the field of TGE-responsive mHealth. We systematically searched scientific records, gray literature, and the iOS and Android app distribution services. To be eligible, platforms and interventions needed to be tailored exclusively to a TGE user base, incorporate gender-affirming features, and be optimized for or adaptive to mobile technologies. Eligible interventions (N=24) were compared on evidence of utility, core functionalities, and dimensions of gender affirmation. Smartphone applications (apps) and webapps (n=16) were the most common delivery modality. Many interventions (n=9) aimed to address HIV-related outcomes and integrated gender-affirmative features. The most common gender-affirmative features originated in fields of human-computer interactions and informatics, or were crowdfunded by TGE developers. HIV-focused interventions incorporated evidence-based health behavior change strategies and utilized rigorous evaluation methods. Across modalities and disciplines, behavioral self-monitoring and access to HIV prevention services were the most frequent features. Over two-thirds of the interventions reviewed aimed to provide medical gender affirmation (e.g, provided guidance on obtaining medically sanctioned hormone therapies, or safely practicing non-medical options such as chest-binding) or psychological gender affirmation (e.g, provided linkage to mental health counseling). Our results show that mHealth and other technology-mediated interventions offer a diverse range of both evidence-based and innovative features; however, many have not been rigorously evaluated in a randomized controlled trial to support TGE users. A continuing commitment to evidence-based health behavior change strategies, exemplified by the HIV-focused interventions included in this review, is essential to advancing gender-affirmative mHealth. The unique and highly innovative features of platforms originating outside the fields of HIV prevention and care suggest new directions for TGE-responsive mHealth, and the need for more conscientious models of knowledge exchange with investigators across scientific disciplines, private-sector developers, and potential users. mHealth, 2021
Background HIV mobile health (mHealth) interventions often incorporate interactive peer-to-peer features. The user-generated content (UGC) created by these features can offer valuable design insights by revealing what topics and life events are most salient for participants, which can serve as targets for subsequent interventions. However, unstructured, textual UGC can be difficult to analyze. Interpretive thematic analyses can preserve rich narratives and latent themes but are labor-intensive and therefore scale poorly. Natural language processing (NLP) methods scale more readily but often produce only coarse descriptive results. Recent calls to advance the field have emphasized the untapped potential of combined NLP and qualitative analyses toward advancing user attunement in next-generation mHealth. Objective In this proof-of-concept analysis, we gain human-centered design insights by applying hybrid consecutive NLP-qualitative methods to UGC from an HIV mHealth forum. Methods UGC was extracted from Thrive With Me, a web app intervention for men living with HIV that includes an unstructured peer-to-peer support forum. In Python, topics were modeled by latent Dirichlet allocation. Rule-based sentiment analysis scored interactions by emotional valence. Using a novel ranking standard, the experientially richest and most emotionally polarized segments of UGC were condensed and then analyzed thematically in Dedoose. Design insights were then distilled from these themes. Results The refined topic model detected K=3 topics: A: disease coping; B: social adversities; C: salutations and check-ins. Strong intratopic themes included HIV medication adherence, survivorship, and relationship challenges. Negative UGC often involved strong negative reactions to external media events. Positive UGC often focused on gratitude for survival, well-being, and fellow users’ support. Conclusions With routinization, hybrid NLP-qualitative methods may be viable to rapidly characterize UGC in mHealth environments. Design principles point toward opportunities to align mHealth intervention features with the organically occurring uses captured in these analyses, for example, by foregrounding inspiring personal narratives and expressions of gratitude, or de-emphasizing anger-inducing media.
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