User Input in the Development of Digital Sexual Health Tools: A Scoping Review and Guidance for Tool Developers
Bibliographic record
Abstract
BACKGROUND OR CONTEXT: Studies reporting the use of digital tools to promote the prevention and treatment of sexually transmitted and blood borne infections (STBBIs) have proliferated in recent years. Previous reviews highlight variability in the input sought from users in tool development, and its contribution to impact. OBJECTIVE: This scoping review sought to describe approaches to seeking and utilising user input, with the goal of providing guidance for developers. SEARCH STRATEGY: Searches were conducted in MEDLINE, PsycInfo, and the Social Science Citation Index and results screened by two reviewers. The reference lists of included studies and review papers were also checked. INCLUSION CRITERIA: Peer reviewed qualitative and mixed methods studies seeking user input on digital tools promoting the prevention and treatment of STBBIs, from prototyping onwards, published from after 2014 in English, were included. DATA EXTRACTION AND SYNTHESIS: Reported methods and findings were charted in Excel and synthesised using content analysis to provide an overview of methods and domains of user input and utilisation of this input. MAIN RESULTS: A total of 1838 unique titles and abstracts and the full text of 50 publications were screened. Data was charted from 37 eligible studies reporting findings from 34 projects developing digital health tools, including smartphone/tablet applications, websites/web-based applications, chatbots, interactive automated SMS, and purpose-built tools within dating and social media applications. Studies reported on tools developed for use by diverse target populations. The most common domain of input reported was usability (n = 31), while others-namely, satisfaction (n = 27), acceptability (n = 25), formative (n = 24), impact (n = 22), accessibility (n = 17), and engagement (n = 11)-were reported less consistently. User views were sought using qualitative methods such as interviews, focus groups and open-ended survey questions, more often in combination with quantitative measures such as participant-rated measures and engagement analytics. User suggestions for changes were reported in relation to three in four projects studied but incorporation of changes in less than half of projects. DISCUSSION AND CONCLUSIONS: This review demonstrates considerable homogeneity in reported user input in the development of digital health tools. Input from users as co-designers may improve the impact of tools on their intended outcomes. PATIENT AND PUBLIC CONTRIBUTION: This literature review brought together a group of researchers who have sought user input in the development of digital sexual health tools, but, due to resource limitations, did not involve potential users themselves, who are of diverse and disparate groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".