An Exposure-Based Video Game (Dr. Zoo) to Reduce Needle Phobia in Children Aged 3 to 6 Years: Development and Mixed Methods Pilot Study
Bibliographic record
Abstract
BACKGROUND: Needle phobia, which affects 19% of children aged 4 to 6 years, prevents many children from receiving necessary or preventive medical treatments. Digital interventions have been made to target needle phobia but currently rely on distraction rather than evidence-based exposure. OBJECTIVE: We designed and evaluated a serious exposure-based mobile game called Dr. Zoo to reduce the fear of needles in children aged 3 to 6 years, where players administered shots to cartoon animals. METHODS: We conducted a mixed methods study with 30 parents (mean age 35.87, SD 4.39 years) and their 36 children (mean age 4.44, SD 1.11 years) who played the game for 5 days leading to a scheduled appointment that included an injection (eg, influenza vaccination). After the study, parents completed exit surveys and participated in semistructured interviews to evaluate ease of use, acceptability, and preliminary effectiveness of the game and to provide insights on their experience with the game to inform future developments. Interview transcripts were analyzed by 3 independent coders following an open coding process and subsequently coded and discussed to reach consensus. RESULTS: Parents rated their child's difficulty in completing the game as very low on average (scale 1-5; mean 1.76, SD 0.82) and were highly likely to recommend Dr. Zoo to other parents (scale 1-5; mean 4.41, SD 0.87), suggesting Dr. Zoo's strong ease of use and high acceptability. In the exit survey, parents rated their child's fear as significantly lower after participating in the study (scale 1-5; mean 3.09, SD 1.17) compared with that before participating (scale 1-5; mean 4.37, SD 0.81; z score=-4.638; P<.001). Furthermore, 74% (26/35) of the parents reported that the game had a positive impact on their child's fear or perception of needles (only 2 parents reported a negative impact). Qualitative analysis of the interview transcripts revealed potentially important features of the game in this positive impact, such as the game's interactive design, as observed in 69% (24/35) of our participants. CONCLUSIONS: The results suggest that an evidence-based serious mobile game can be an easy-to-use, acceptable, and potentially effective intervention for changing young children's fear and perceptions of needles. Leveraging digital interventions may be a potential solution to needle anxiety as a public health concern.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".