Assessing the Effectiveness of an mHealth Intervention to Support Men Who Have Sex With Men Engaging in Chemsex (Budd): Single-Case and Pre-Post Experimental Design Study
Bibliographic record
Abstract
BACKGROUND: This study focuses on the Budd app, a mobile health intervention designed for gay, bisexual, and other men who have sex with men who participate in chemsex. Chemsex, the use of psychoactive drugs in a sexual context, presents substantial health risks including increased HIV transmission and mental health issues. Addressing these risks requires innovative interventions tailored to the unique needs of this population. OBJECTIVE: This study aims to evaluate the effectiveness of the Budd app in promoting drug harm reduction practices among its users, focusing on knowledge, behavioral intention, risk behavior awareness, and self-efficacy. METHODS: The study used a mixed methods approach, combining a single-case experimental design and a pre-post study. A total of 10 participants from an outpatient clinic were recruited, and each attended the clinic 3 times. During the first visit, participants installed a restricted version of the Budd app, which allowed them to report daily mood and risk behavior after chemsex sessions. Phase A (baseline) lasted at least 2 weeks depending on chemsex participation. In the second visit, participants gained full access to the Budd app, initiating phase B (intervention). Phase B lasted at least 6 weeks, depending on chemsex participation, with identical data input as phase A. Participants completed pre- and postintervention surveys assessing behavioral determinants during the first and third visit. RESULTS: The study observed an increased knowledge about chemsex substances postintervention, with a mean percentage improvement in knowledge scores of 20.59% (SD 13.3%) among participants. Behavioral intention and self-efficacy showed mixed results, with some participants improving while others experienced a decrease. There was also a variable impact on awareness of risk behavior, with half of the participants reporting a decrease postintervention. Despite these mixed results, the app was generally well-received, with participants engaging with the app's features an average of 50 times during the study. CONCLUSIONS: The Budd app showed effectiveness in enhancing knowledge about chemsex substances among gay, bisexual, and other men who have sex with men. However, its impact on safe dosing behavior, behavioral intention, self-efficacy, and risk behavior awareness was inconsistent. These findings suggest that while educational interventions can increase knowledge, translating this into behavioral change is more complex and may require more participants, a longer follow-up period, and additional strategies and support mechanisms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".