Development of a Mobile App for Self-Care Against COVID-19 Using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) Model: Methodological Study
Bibliographic record
Abstract
BACKGROUND: Mobile apps have been shown to play an important role in the management, care, and prevention of infectious diseases. Thus, skills for self-care-one of the most effective ways to prevent illness-can be improved through mobile health apps. OBJECTIVE: This study aimed to design, develop, and evaluate an educational mobile-based self-care app in order to help the self-prevention of COVID-19 in underdeveloped countries. We intended the app to be easy to use, quick, and inexpensive. METHODS: In 2020 and 2021, we conducted a methodological study. Using the ADDIE (analysis, design, development, implementation, and evaluation) educational model, we developed a self-care management mobile app. According to the ADDIE model, an effective training and performance support tool is built through the 5 phases that comprise its name. There were 27 participants who conducted 2 evaluations of the mobile app's usability and impact using the mobile health app usability and self-care inventory scales. The study design included pre- and posttesting. RESULTS: An Android app called MyShield was developed. The results of pre- and posttests showed that on a scale from 0 to 5, MyShield scored a performance average of 4.17 in the physical health dimension and an average of 3.88 in the mental well-being dimension, thereby showing positive effects on self-care skills. MyShield scored highly on the "interface and satisfaction," "ease of use," and "usefulness" components. CONCLUSIONS: MyShield facilitates learning self-care skills at home, even during quarantine, increasing acquisition of information. Given its low development cost and the ADDIE educational design on which it is based, the app can be helpful in underdeveloped countries. Thus, low-income countries-often lacking other tools-can use the app as an effective tool for fighting COVID-19, if it becomes a standard mobile app recommended by the government.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".