Implementation and use of mHealth home telemonitoring in adults with acute COVID-19 infection: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: mHealth refers to digital technologies that, via smartphones, mobile apps and specialised digital sensors, yield real-time assessments of patient's health status. In the context of the COVID-19 pandemic, these technologies enable remote patient monitoring, with the benefit of timely recognition of disease progression to convalescence, deterioration or postacute sequelae. This should enable appropriate medical interventions and facilitate recovery. Various barriers, both at patient and technology levels, have been reported, hindering implementation and use of mHealth telemonitoring. As systematised and synthesised evidence in this area is lacking, we developed this protocol for a scoping review on mHealth home telemonitoring of acute COVID-19. METHODS AND ANALYSIS: We compiled a search strategy following the PICO (Population, Intervention, Comparator, Outcome) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendation for Scoping Reviews) guidelines. MEDLINE, Embase and Web of Science will be searched from 1 March 2020 to 31 August 2021. Following the title and abstract screening, we will identify, systematise and synthesise the available knowledge. Based on pilot searches, we preview three themes for descriptive evidence synthesis. The first theme relates to implementation and use of mHealth telemonitoring, including reported barriers. The second theme covers the interactions of the telemonitoring team within and between different levels of the healthcare system. The third theme addresses how this telemonitoring warrants the continuity of care, also during disease transition into deterioration or postacute sequelae. ETHICS AND DISSEMINATION: The studied evidence is in the public domain, therefore, no specific ethics approval is required. Evidence dissemination will be via peer-reviewed publications, conference presentations and reports to the policy makers.
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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 | 0.083 | 0.073 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.011 |
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".