Development of a Mobile Phone App to Promote Safe Sex Practice Among Youth in Stockholm, Sweden: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) has been shown to be effective in increasing knowledge of sexual health among youth. To date, evaluations mostly refer to interventions delivered via computer, email, and text messages. The possibility of downloading apps on mobile devices has opened up opportunities to develop engaging interventions on safe sexual health promotion. To attract young users and have them engage with a sexual health app, it is important to involve youth in intervention development. OBJECTIVE: This study aimed to obtain input from youth on the content of a mobile phone app intended to promote safe sex and increase condom use among youth in Stockholm. METHODS: This study was conducted at the Youth Health Clinics (YHC) in Stockholm County, Sweden. A total of 15 individual in-depth interviews and 2 focus group discussions (with youth aged 18-23 years) were conducted at the YHC in Stockholm. Areas explored were: (1) youth perceptions of condom use (advantages and obstacles), (2) perceptions of mHealth to promote safe sexual practices, and (3) content development for a mobile phone app to promote safe sex. RESULTS: The smartphone app was developed based on the categories that emerged from the data. With regard to content, youth requested sex education, including information on sexually transmitted infections. In addition, condom-specific information, including practical usage technique, advice on how to have the condom talk, and how to decrease shame related to condom use, was requested. Youth suggested different modes to deliver the content, including text messages, movie clips, and push notifications. It was suggested that the tone of the messages delivered should be fun, entertaining, and supportive. The inputs from youth influenced the development of the following sections of the app: Condom Obstacles and Solutions; Quiz; Games; Self-Refection; Challenges; Stories by Peers (stories from peers and information from a doctor); Condom Tips, Pep Talk, and Boosting; and Random Facts. CONCLUSIONS: It is important to use input from youth when developing a smartphone intervention since the success of the intervention largely depends on the level of engagement and usage by youth. Furthermore, if proven efficient in increasing condom use, it is important that the development, including content and mode, is thoroughly described so that the intervention can be replicated. Likewise, if proven inefficient, it is important to learn from mistakes to improve and adjust the intervention. The effect of this smartphone app on safe sexual practices among youth is being evaluated in a pragmatic randomized controlled trial in Stockholm (ISRCTN13212899) and will be reported separately.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".