The design and implementation of a randomized controlled trial of a risk reduction and human immunodeficiency virus prevention videogame intervention in minority adolescents: <i>PlayForward: Elm City Stories</i>
Bibliographic record
Abstract
BACKGROUND: To address the need for risk behavior reduction and human immunodeficiency virus prevention interventions that capture adolescents "where they live," we created a tablet-based videogame to teach skills and knowledge and influence psychosocial antecedents for decreasing risk and preventing human immunodeficiency virus infection in minority youth in schools, after-school programs, and summer camps. METHODS: We developed PlayForward: Elm City Stories over a 2-year period, working with researchers, commercial game designers, and staff and teens from community programs. The videogame PlayForward provides an interactive world where players, using an avatar, "travel" through time, facing challenges such as peer pressure to drink alcohol or engage in risky sexual behaviors. Players experience how their choices affect their future and then are able to go back in time and change their choices, creating different outcomes. A randomized controlled trial was designed to evaluate the efficacy of PlayForward. Participants were randomly assigned to play PlayForward or a set of attention/time control games on a tablet at their community-based program. Assessment data were collected during face-to-face study visits and entered into a web-based platform and unique real-time "in-game" PlayForward data were collected as players engaged in the game. The innovative methods of this randomized controlled trial are described. We highlight the logistical issues of conducting a large-scale trial using mobile technology such as the iPad(®), and collecting, transferring, and storing large amounts of in-game data. We outline the methods used to analyze the in-game data alone and in conjunction with standardized assessment data to establish correlations between behaviors during gameplay and those reported in real life. We also describe the use of the in-game data as a measure of fidelity to the intervention. RESULTS: In total, 333 boys and girls, aged 11-14 years, were randomized over a 14-month period: 166 were assigned to play PlayForward and 167 to play the control games. To date (as of 1 March 2016), 18 have withdrawn from the study; the following have completed the protocol-defined assessments: 6 weeks: 271 (83%), 3 months: 269 (84%), 6 months: 254 (79%), 12 months: 259 (82%), and 24 months: is ongoing with 152 having completed out of the 199 participants (76%) who were eligible to date (assessment windows were still open). CONCLUSION: Videogames can be developed to address complex behaviors and can be subject to empiric testing using community-based randomized controlled trials. Although mobile technologies pose challenges in their use as interventions and in the collection and storage of data they produce, they provide unique opportunities as new sources of potentially valid data and novel methods to measure the fidelity of digitally delivered behavioral 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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".