Increasing Awareness and Early Detection of Common Skin Diseases in Indonesia Through an mHealth App: Protocol for an Awareness and Acceptability Study and Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Various media are used to enhance public understanding about diseases. While mobile health apps are widely used, there is little proof for using such apps to raise awareness of skin diseases. OBJECTIVE: We intend to develop an app, called DEDIKASI-app, to raise awareness of skin diseases, including leprosy. The study will explore baseline awareness, assess the app's acceptance by community members and health care workers, and evaluate its effectiveness in enhancing awareness about skin diseases. METHODS: The study will be conducted in four phases: (1) development of DEDIKASI-app, (2) questionnaire development for an awareness study, (3) acceptability testing, and (4) effect measurement of DEDIKASI-app. We will adopt design thinking methodology to develop the app, involving systematic reviews, expert consultations, focus group discussions, and validation of the questionnaire on skin disease awareness. We will recruit 50 members of the community for the awareness and acceptability study and 1 health care worker per community health center to assess their perception of the app. A pilot study will assess the acceptability of DEDIKASI-app among community members and health care workers based on various constructs, with responses categorized as positive, negative, or undecided. The validity and reliability of a newly developed questionnaire on skin disease awareness will be tested, with validity results analyzed qualitatively and reliability measured using Cronbach α. The effectiveness of DEDIKASI-app in improving community awareness will be evaluated through a randomized controlled trial, using total scores, means, and SDs for control and intervention groups. Statistical significance of awareness level changes will be determined by delta change (P value), with P<.01 considered significant. RESULTS: This study received ethical approval from the Ethics Review Board of the Faculty of Public Health, Universitas Airlangga (160/EA/KEPK/2023) and was registered in the Indonesia Clinical Research Registry (INA-O8EX278). Funding for the field research was secured in the period of May 2022-December 2024 from NLR Indonesia and Erasmus University Medical Center. As of manuscript submission, phase 1 (app development) and phase 2 (questionnaire development) have been completed. Data collection for the randomized controlled trial has just finished and data analysis is ongoing, with publication of the study results expected in late 2025. CONCLUSIONS: Innovative approaches are required to enhance awareness in the community. This study will introduce new tools and insights to address the limited knowledge about detecting skin problems. TRIAL REGISTRATION: INA-CRR Indonesia Clinical Research Registry INA-O8EX278; https://tinyurl.com/ms3k5yvn. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64057.
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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 | 0.042 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.060 | 0.009 |
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".