AI-Supported Gamified Training for Sharps Injury Prevention in Nurses
The Effect of An Artifıcial Intelligence-Supported Gamifıed Training Program on Nurses' Knowledge and Attitudes Toward The Prevention of Sharps Injuries
1 other identifier
interventional
36
0 countries
N/A
Brief Summary
This study aims to evaluate the effect of a Health Belief Model (HBM)-based, artificial intelligence (AI)-supported gamified training program on nurses' knowledge and attitudes toward the prevention of sharps injuries. Sharps injuries remain a significant occupational risk for healthcare workers, particularly nurses, despite existing standard precautions. The study will be conducted in two phases. In the first phase, the validity and reliability of the Sharps Injury Prediction Scale will be tested in a nurse population. In the second phase, a quasi-experimental pretest-posttest control group design will be used to assess the effectiveness of the intervention. The study will be carried out in two hospitals from the same healthcare group located in different cities to prevent interaction between groups. A total of 36 nurses will be included, with 18 participants in the intervention group and 18 in the control group. The intervention group will receive a structured, HBM-based training program consisting of seven sessions incorporating AI-supported content, gamified scenarios, interactive materials, and feedback mechanisms to enhance engagement and promote behavior change. The control group will receive routine institutional training on sharps injury prevention. Data will be collected at baseline, immediately after the intervention, and two months later. Outcome measures include nurses' knowledge, attitudes toward safe sharps use, and sharps injury risk perception. It is expected that the AI-supported gamified training program will significantly improve knowledge, attitudes, and risk awareness compared to routine training. The findings may support the integration of innovative, theory-based educational interventions into institutional training programs to enhance occupational safety.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for not_applicable
Started Jun 2026
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
April 26, 2026
CompletedFirst Posted
Study publicly available on registry
May 1, 2026
CompletedStudy Start
First participant enrolled
June 1, 2026
ExpectedPrimary Completion
Last participant's last visit for primary outcome
September 1, 2026
Study Completion
Last participant's last visit for all outcomes
June 1, 2027
May 1, 2026
April 1, 2026
3 months
April 26, 2026
April 26, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Nurses' knowledge level regarding sharps injury prevention
Knowledge level will be assessed using a structured knowledge questionnaire consisting of 25 items developed based on the literature. Higher scores indicate greater knowledge regarding sharps injury prevention.
Baseline (pretest), immediately after the intervention (posttest), and 2 months after the intervention
Secondary Outcomes (2)
Attitudes toward safe use of sharps
Baseline, immediately after the intervention, and 2 months after the intervention
Sharps injury risk perception and prediction score
Baseline, immediately after the intervention, and 2 months after the intervention
Study Arms (2)
Intervention Group
EXPERIMENTALNurses in the intervention group will receive a Health Belief Model-based, AI-supported gamified training program designed to improve knowledge and attitudes toward sharps injury prevention.
Control Group
ACTIVE COMPARATORStandard training provided by the institution, including lectures and question-answer sessions on sharps injury prevention.
Interventions
A structured training program consisting of seven sessions, incorporating artificial intelligence-supported educational content, gamified scenarios, interactive videos, and digital feedback mechanisms to enhance learning and promote behavior change.
Standard training provided by the institution, including lectures and question-answer sessions on sharps injury prevention.
Eligibility Criteria
You may qualify if:
- Registered nurses working in the participating hospitals
- Working in the institution for at least 2 months (Phase 1) or newly employed within the last 1 month (Phase 2)
- Willing to participate in the study
- Able to use smartphone-based technologies
- Providing written informed consent
You may not qualify if:
- Having received prior training based on the Health Belief Model for sharps injury prevention
- Previous professional nursing experience before current employment (for newly recruited nurses in Phase 2)
- Being on leave during the data collection period
- Failure to attend training sessions (intervention group)
- Incomplete data collection forms
- Withdrawal from the study at any stage
Contact the study team to confirm eligibility.
Sponsors & Collaborators
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Ziyafet Uğurlu, Professor
Baskent University
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- NON RANDOMIZED
- Masking
- NONE
- Purpose
- PREVENTION
- Intervention Model
- PARALLEL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Researcher, PhD student
Study Record Dates
First Submitted
April 26, 2026
First Posted
May 1, 2026
Study Start (Estimated)
June 1, 2026
Primary Completion (Estimated)
September 1, 2026
Study Completion (Estimated)
June 1, 2027
Last Updated
May 1, 2026
Record last verified: 2026-04
Data Sharing
- IPD Sharing
- Will not share
Individual participant data (IPD) will not be shared due to ethical and privacy considerations. The study involves human participants, and sharing individual-level data may pose a risk to confidentiality despite anonymization. Data will be used solely for research purposes in accordance with ethical approval and institutional regulations.