A Digital Sexual Health Intervention for Urban Adolescent and Young Adult Male Emergency Department Patients: User-Centered Design Approach
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults frequently present to the emergency department (ED) for medical care and continue to have many unmet sexual health needs. Digital interventions show promise to improve adolescent and young adult sexual health; yet, few interventions focus on male ED patients, despite their infrequent use of contraceptives and rising rates of sexually transmitted infections. OBJECTIVE: This paper describes the design and development of Dr. Eric (Emergency Room Interventions to Improve Care), a digital app focused on promoting condom use among sexually active adolescent and young adult male ED patients. METHODS: This study followed 4 phases of app development, which were based on user-centered design and the software development lifecycle. In phase 1, define, we explored our target population and target health problem (infrequent condom use among male ED patients) by collecting key stakeholder input and conducting in-depth interviews with male patients and urban ED medical providers. In phase 2, discover, we partnered with a digital product agency to explore user experience and digital strategy. In phase 3, design, we refined Dr. Eric's content, a 5-part sexual health educational module and a 10-week SMS text messaging program that focuses on condom use and partner communication about effective contraceptives. We conducted semistructured interviews with male adolescent and young adults to gather feedback on the app and perform usability testing, editing the app after each interview. We also interviewed informatics experts to assess the usability of a high-fidelity prototype. Interviews were recorded and analyzed via descriptive thematic analysis; informatic expert feedback was categorized by Nielsen's heuristic principles. In phase 4, develop, we created the technical architecture and built a responsive web app. These findings were gathered leading to the final version of the digital Dr. Eric program. RESULTS: Using data and key stakeholder input from phases 1 and 2, we iteratively created the Dr. Eric prototype for implementation in the ED setting. Interviews with 8 adolescent and young adult male ED patients suggested that users preferred (1) straightforward information, (2) a clear vision of the purpose of Dr. Eric, (3) open-ended opportunities to explore family planning goals, (4) detailed birth control method information, and (5) games presenting novel information with rewards. Five usability experts provided heuristic feedback aiming to improve the ease of use of the app. These findings led to the final version of Dr. Eric. CONCLUSIONS: Following these mobile health development phases, we created a digital sexual health mobile health intervention incorporating the principles of user experience and interface design. Dr. Eric needs further evaluation to assess its efficacy in increasing condom use among adolescent and young adult male ED patients. Researchers can use this framework to form future digital health ED-based digital interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".