Mobile Health Apps That Help With COVID-19 Management: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps have played an important role in mitigating the coronavirus disease (COVID-19) response. However, there is no resource that provides a holistic picture of the available mHealth apps that have been developed to combat this pandemic. OBJECTIVE: Our aim is to scope the evidence base on apps that were developed in response to COVID-19. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, literature searches were conducted on Google Search, Google Scholar, and PubMed using the country's name as keywords and "coronavirus," "COVID-19," "nCOV19," "contact tracing," "information providing apps," "symptom tracking," "mobile apps," "mobile applications," "smartphone," "mobile phone," and "mHealth." Countries most affected by COVID-19 and those that first rolled out COVID-19-related apps were included. RESULTS: A total of 46 articles were reviewed from 19 countries, resulting in a total of 29 apps. Among them, 15 (52%) apps were on contact tracing, 7 (24%) apps on quarantine, 7 (24%) on symptom monitoring, and 1 (3%) on information provision. More than half (n=20, 69%) were from governmental sources, only 3 (10%) were from private organizations, and 3 (10%) from universities. There were 6 (21%) apps available on either Android or iOS, and 10 (34%) were available on both platforms. Bluetooth was used in 10 (34%) apps for collecting data, 12 (41%) apps used GPS, and 12 (41%) used other forms of data collection. CONCLUSIONS: This review identifies that the majority of COVID-19 apps were for contact tracing and symptom monitoring. However, these apps are effective only if taken up by the community. The sharing of good practices across different countries can enable governments to learn from each other and develop effective strategies to combat and manage this pandemic.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".